Graduate Course Catalog - Biomedical Engineering - College of Engineering - Carnegie Mellon University (2023)

42610 | Introduction to Biomaterials | Teacher Rosalyn Abbott | Spring
An important goal will be the understanding of the fundamentals of structure-function relationships in biomaterials in relation to material functions and cell and tissue environments. The course consists of lectures, readings, projects and technical writing assignments. The synthesis, characterization and functional properties of organic and inorganic biomaterials and the processes involved in their application in tissue engineering and regenerative medicine are discussed. Key issues related to the usefulness of biomaterials will be addressed, including biomechanics, transport, degradability, biointerfaces and biocompatibility, stability, fate in the body, and some of the basic characterization approaches. Clinical applications of biomaterials and new directions in design and synthesis to achieve better biocompatibility will be emphasized.

42612 | Tissue Engineering | Professor Adam Feinberg fall
This course trains students in advanced methods of cell and tissue engineering that apply the physical, mechanical, and chemical manipulation of materials to control cell and tissue function. Students will learn about benchtop research techniques and equipment including cell culture, immunofluorescence imaging, soft lithography, substrates of varying stiffness, force application/measurement, and other methods. Students will integrate reading and lab skills, using the scientific method to develop a unique project while working in a team environment, maintaining a detailed lab notebook, and completing required milestones. The focus is on developing the written and oral communication skills necessary for the professional scientist. The class culminates in a poster session based on class projects. Prerequisite: knowledge of cell biology and biomaterials or permission of the lecturer

42613 | Polymeric Biomaterials | Professor Adam Feinberg Spring
This course covers aspects of polymeric biomaterials in medicine, from molecular principles to device design and scale-up manufacturing. Topics include the chemistry, characterization, and processing of synthetic polymer materials; cell-biomaterial interactions, including interfacial phenomena, tissue reactions and biodegradation mechanisms; Aspects of design and fabrication of polymeric microsystems for medical device applications. Recent advances on these issues are also discussed.

42614 | Special Topics: Stem Cell Engineering | Professor Adam Feinberg every other year
Special Topics - This course provides an overview of stem cell research and introduces students to current issues at the frontiers of this field. It will introduce students to the different types of stem cells, as well as the environmental factors and signals involved in regulating the fate of the stem cells. The course will highlight stem cell engineering techniques and their microenvironment. They evaluate the use of stem cells for tissue engineering and therapies. Emphasis will be placed on discussions of current research areas and articles in this rapidly evolving field. Students select an interesting topic related to the classroom, conduct an extensive literature review, and present their findings in the form of a written report, discussion, and lecture. Lectures and discussions will be complemented by hands-on laboratory sessions including: stem cell harvest and culture, neural stem cell transfection, differentiation and immunostaining assays, polymeric microcapsules as advanced culture systems, and integration of stem cells into mouse brain tissue. The course is designed for graduate and college students with a strong interest in stem cell biology and a desire to actively participate in class discussions.

42616 | Bio-Nanotechnology: Principles and Applications | Prof. Tzahi Cohen-Karni | fall
Have you ever wondered what nanoscience and nanotechnology are and what impact they have on our lives? In this class we will discuss the key concepts related to the synthesis (including growth methods and characterization techniques) and chemical/physical properties of nanomaterials from zero-dimensional (0D) materials such as nanoparticles or one-dimensional quantum dots (QD) materials. such as nanowires and nanotubes to two-dimensional materials such as graphene. In problem-oriented discussions, students will explore a variety of biological applications of nanomaterials with the aim of developing design strategies based on a fundamental understanding of nanoscience. Examples include biomedical applications such as nanosensors for protein and DNA detection, nanodevices for bioelectrical interfaces, nanomaterials as building blocks in tissue engineering and drug delivery, and nanomaterials in cancer therapy, among others. This class is open to undergraduate (junior/senior) and graduate students.

42620 | molecular engineering cell biology| Professor Charlie Ren | fall
Cells are not only the basic units of living organisms, but also fascinating technical systems that exhibit incredible functionality, adaptability and complexity. The application of engineering perspectives and approaches to study the molecular mechanisms of cellular processes plays a key role in the development of modern biology. At the same time, an understanding of the principles that govern biological systems provides fundamental insights for the development of technical systems. The purpose of this course is to provide engineering students with little or no background in cell biology with fundamental molecular cell biology, with a particular emphasis on integrating technical concepts into the overall modern cell and molecular biology learning process. This course prepares advanced undergraduate or graduate students with the essential knowledge and mindset for future research projects dealing with the development of biological systems at the molecular and cellular level. In addition to introducing fundamental biological knowledge, this course aims to increase students' understanding and appreciation of (1) how engineering approaches have led to our current understanding of molecular and cellular biology; (2) what technical approaches are available that allow the manipulation and even creation of biological systems at the molecular, cellular, and tissue levels; (3) What are the current challenges in molecular and cell biology that could one day be solved by technical innovations? Course topics include the engineering of cellular components (DNA, RNA, proteins, cell membrane, mitochondria, extracellular matrix) and cellular processes (metabolism, proliferation, cell death, tissue formation). Requirements: none. Pre-completion of 03-121 Modern Biology is recommended but not required.

42624 | Biological drug transport and distribution | Professor Newell Washburn | Spring
Analysis of transport phenomena in vital processes at the molecular, cellular, organ and organism level and their application to the modeling and design of direct or delayed drug release technologies. The coupling of mass transport and reaction processes will be an ongoing topic as they relate to the receptor-mediated rates of solute uptake into cells, drug transport and biodistribution, and drug release from cells. The design concepts underlying advances in nanomedicine are described.

42630 | Introduction to Neurotechnology | Professor Matt Smith | Spring
Neural engineering is at the interface between neuroscience and engineering and applies classic engineering approaches and principles to understand the nervous system and its function. Modern neural engineering techniques have been used to measure neural activity with tools based on light, electricity and magnetism. The same measurement tools can be redirected to modulate neural activity and manipulate the way an organism perceives, thinks, and acts. The aim of the course is to introduce students to a variety of neural engineering approaches to study and intervention in the nervous system, with an emphasis on quantitative understanding and fundamental engineering concepts. The course will combine lectures and discussions with projects involving real neural data (Matlab-based exercises). Example projects could include searching for visual responses in EEG data or determining how groups of individual neurons interact based on spike data. Overall, the student should gain an in-depth understanding of selected topics in the neurosciences and the application of quantitative neuronal engineering approaches to these topics. This course is aimed at advanced students and recent graduates. It helps to become familiar with linear algebra, signal processing and introductory programming in Matlab. This course is appropriate for students from a variety of backgrounds: (1) students from non-technical backgrounds who are seeking quantitative skills and want to learn an engineering approach to neuroscience problems, and (2) engineering students or other quantitative students seeking opportunities to develop their skills applied to scientific questions in the neurosciences.

42631 | Neural Data Analysis| teacher open |
The vast majority of behavioral information is carried through the brain via neurons as trains of action potentials. How can we understand the transmitted information? This course covers basic engineering and statistical tools commonly used to analyze peak neural train data, with an emphasis on practical application. Topics can include neural peak-train statistics (Poisson processes, peak intervals, Fano factor analysis), estimation (MLE, MAP), signal detection theory (d-prime, ROC analysis, psychometric curve fitting), information theory, discrete classification, be continuous decoding (PVA, OLE) and white noise analysis. Each topic covered is linked to the central ideas of graduation probability, and each task involves real-world analysis of neural data, real or simulated, using Matlab. Aimed at sixth form students or beginning graduate students, this course is designed for engineers who want to learn the neurophysiologist's toolbox and neurophysiologists who want to learn new tools. Those seeking broader neuroscience application (e.g. fMRI) or a greater focus on regression analysis are encouraged to do 36-746. Those looking for more advanced techniques are encouraged to do 18-699. Prerequisites: Probability of completion (36-225/227, or equivalent), some familiarity with linear algebra and Matlab programming

42632 | Neural Signal Processing | Professor Byron Yu | Spring
The brain is one of the most complex systems ever studied. Behind the brain's ability to process sensory information and direct motor actions is a network of about 10^11 neurons, each of which makes 10^3 connections to other neurons. Modern statistical and machine learning tools are required to interpret the wealth of neural data collected, both to (1) improve our understanding of how the brain works and (2) to develop biomedical devices that interact with the brain. This course covers a variety of statistical methods and their application to neural data analysis. Statistical topics include latent variable models, dynamical systems, point processes, dimensionality reduction, Bayesian inference, and spectral analysis. Neuroscience applications include neural decoding, firing rate estimation, characterization of neural systems, sensorimotor control, peak classification, and field potential analysis. Requirements: 18-290; 36-217 or equivalent Introductory Probability and Random Variables course; an introductory course in linear algebra; senior or graduate position. No prior neuroscientific knowledge is required.

42637 | Nano-Bio-Photonik | Prof.. Maysam Chamanzar | Frühling
Light can penetrate biological tissue non-invasively. Most available bio-optical tools are bulky. With the advent of new nanotechnologies, building chip-integrated photonic devices for applications such as sensing, imaging, neural stimulation, and monitoring is now possible. These devices can be integrated with portable electronic devices such as cell phones for point-of-care diagnostics. This course is designed to teach nano-bio-photonics concepts in a practical way to prepare students for exposure to new photonics technologies. The course begins with a review of the electrodynamics of light waves. The right choice of wavelength and material platform is the next topic. Next, waveguides and optical resonators will be discussed. Resonance-based detection is introduced, followed by a discussion of the figure of merit (FOM) used to design on-chip sensors. Silicon Photonics is presented as an example of a CMOS-compatible platform. On-chip spectroscopy is the next topic. The second part deals with nanoplasmonics for biosensing and therapy. Design methods are discussed, followed by an overview of nanofabrication and chemical synthesis, and then a discussion of applications. The last part of this course is devoted to an overview of newer applications such as optogenetic neural stimulation, calcium imaging, imaging and cancer therapy. High school diploma or college degree required. This course is related to 18416. Although students in 18-616 and 18-416 share the same lectures and recitations, students in 18-616 receive different course projects. Students in 18-416 and 18-616 are graded on separate curves.

42640 | Image-based computer-aided modeling and analysis | Teacher Jessica Zhang | fall
Biomedical modeling and visualization play an important role in mathematical modeling and computer simulation of real/artificial life to improve medical diagnosis and treatment. This course integrates mechanical engineering, biomedical engineering, computer science and mathematics. Topics to be studied include medical imaging, image processing, geometric modelling, visualization, computational mechanics and biomedical applications. The techniques presented are applied to examples of multiscale biomodeling and simulations at the molecular, cellular, tissue and organ levels.

42641 | Remediation Technology | Professor Edmundo Lopresti | Fall 2024
Rehabilitation engineering is the systematic application of engineering to design, develop, adapt, test, evaluate, apply, and provide technological solutions to problems faced by people with disabilities. This course focuses on assistive technologies: technologies developed for use in the daily lives of people with disabilities to assist them in carrying out activities of daily living. The course covers assistive technologies developed for a variety of functional disabilities including mobility, communication, hearing, vision and cognition as applied to activities related to employment, independent living, education and community integration. This course considers not only the technical issues involved in device development, but also the psychosocial factors and market forces that influence individual and market acceptance of devices. Prerequisite: Junior or Senior position

42645 | Cellular Biomechanics | every other year
This course discusses how mechanical quantities and processes such as force, motion and deformation affect the behavior and function of cells, with a focus on the connection between mechanics and biochemistry. Specific topics include: (1) the role of stress in cytoskeletal dynamics related to cell growth, proliferation, motility and adhesion; (2) the generation of force and motion by molecules in question; (3) stretch-activated ion channels; (4) deformation of proteins and DNA; (5) Mechanochemical coupling in signal transduction. If time permits, we will also address protein transport and secretion and the effects of mechanical forces on gene expression. The focus is on biomechanics at the cellular and molecular level; its clinical and technical implications are clarified. 3 Hours of Reading Prerequisite: Instructor's Permission. Requirements: none. Requirements: None. Across Courses: 24-655 Notes: None. Reservations:

42648 | Cardiovascular Mechanics | Professor Calliope Roberts | Spring
The main goal of the course is to learn how to model blood flow and mechanical forces in the cardiovascular system. After a brief overview of cardiovascular physiology and fluid mechanics, students will progress from modeling blood flow in a.) small constant flow applications through b.) small pulsatile applications to c.) complex pulsatile system applications or large flows. Students also learn to calculate mechanical forces on cardiovascular tissues (blood vessels, heart) and cardiovascular cells (endothelial cells, platelets, red and white blood cells) and the effects of these forces. Finally, the students get to know different methods for modeling the heart function. If necessary, the students apply these concepts to the construction and operation of selected medical devices (heart valves, heart support systems, artificial lungs).

42649 | Introduction to Biomechanics | teachers to be appointed| Cair
The aim of this course is to provide a comprehensive overview of the application of mechanics to the human body. These include solid, fluid, and viscoelastic mechanisms applied to single cells, the cardiovascular system, lungs, muscles, bones, and human movement. The physiology of each system is reviewed as a basis before mechanical applications within that system are discussed. There are no hard and fast prerequisites, but statics, fluid mechanics and biology are helpful.

42650 | Introduction to Biomedical Imaging | Prof. Jana Kainerstorfer | fall
The field of medical imaging describes methods for visualizing the inside of the human body and for the visual representation of tissue and organ functions. The materials covered in this course provide an overview of existing medical imaging equipment used in clinical and pre-clinical settings. The course introduces the principles of medical imaging technologies, explains mathematical and physical principles, and describes the fundamentals of device design. Students will gain a deep understanding of how these methods are used to map morphological and physiological features. Imaging methods include ultrasound, X-ray, computed tomography (CT) and magnetic resonance imaging (MRI) as well as optical methods. For each method, the basic principles of imaging are discussed and examples of clinical applications are presented. Prior knowledge of imaging methods is not required.

42656 | Introduction to Machine Learning for Biomedical Engineers | Prof.. Parjanaporn Chalacheva | fall
This course introduces fundamental concepts, methods and applications of machine learning and data mining. We cover topics such as parametric and non-parametric learning algorithms, support vector machines, neural networks, clustering, clustering and principal component analysis. The focus is on learning high-level concepts behind machine learning algorithms and applying them to biomedical problems. This course is intended for advanced undergraduate and graduate students in biomedical engineering or related disciplines. Students should have experience with a high-level programming language such as Matlab, basic knowledge of probability, statistics and linear algebra, and be familiar with the manipulation of vectors and matrices.

42661 | Surgery for Engineers | Professor Howard Edington | Spring
This course explores the impact of technique on surgery. Students will interact with clinical professionals and examine the technological challenges faced by these professionals. Several visits to the medical center are planned to gain hands-on experience with different technologies used by surgeons to demonstrate the results of advances in biomedical engineering. These experiences are expected to include microvascular surgery, robotic surgery, laparoscopic and endoscopic techniques. Visits to the operating room and shock trauma unit will be arranged. If possible, the observation of an operation will be organized (if the schedule allows). Visiting surgeons will represent disciplines such as cardiovascular surgery, plastic and reconstructive surgery, surgical oncology, trauma surgery, minimally invasive surgery, oral and maxillofacial surgery, bariatric surgery, thoracic surgery, orthopedic surgery, and others. The lead instructor is Howard Edington, M.D., President, MBA System of Surgery, Allegheny Health Network. This course takes place once a week for 3 hours. Several sessions take place at the Medical Center, including transportation. Prerequisite: Physiology 42-202 and one of the introductory engineering courses, 42-101, 06-100, 12-100. 18-100, 19-101, 24-101 or 27-100 Preference is given to BME students with postgraduate studies and additional specializations, followed by BME minor students.

42665 | Brain-Computer Interface: Principles and Applications | Professor Bin He | Spring
This course provides a comprehensive introduction and review of the concepts, principles, and methods of Brain-Computer Interface (BCI) technology. BCIs emerged as a new technology that connects the brain to external devices. BCIs are designed to decipher human intent, resulting in direct brain control from a computer or device, bypassing the neuromuscular pathway. Bidirectional brain-computer interfaces not only enable device control, but also open the door to central nervous system modulation via the neural interface. Using various recorded brain signals that reflect the brain's "intent," BCI systems have demonstrated the ability to control external devices such as computers and robots. Neuronal stimulation with electrical, magnetic, optical, and acoustic energy has demonstrated the ability to better understand brain functions and manipulate the central nervous system. This course teaches the fundamentals of how a BCI system works and various basic BCI components ranging from signal acquisition, signal processing, feature extraction, feature translation, neurostimulation to device control and various applications. Examples of non-invasive BCI are discussed to provide a deep understanding of non-invasive BCI technology. Open to seniors or graduates of engineering or science programs, or with instructor approval (e.g. for juniors exceptional).

42666 | Neuroengineering-Praxis | Professor Bin He | Fallen
This course explores topics and issues related to ethics, professional conduct, conflict, plagiarism, copyright, authorship, research design considerations, IRB, IACUC, intellectual property, process review documents, FDA regulatory science and procedures, professional presentations, and technical writing studied in the field of neuroengineering. . Students will discuss the neuroethical implications of neural technologies and learn about the process of bringing these technologies to market, including FDA approval and intellectual property considerations. Students will also discuss the essential professional development skills for a career in neuroengineering research and development in academia and industry. Students also have the opportunity to visit clinical and/or research neuroengineering laboratories. An important part of the course is the development of students' communication skills, including the development of an effective research proposal and technical report, and effective oral presentations of the ideas developed in the proposal and technical report. Essential elements for writing successful applications are discussed in case studies. Each student is expected to develop a research proposal based on their own research or a new research topic in neuroengineering. Each student is also asked to complete a technical report on a neuroengineering topic. Students are expected to improve their writing skills for proposal/report development with case studies, group discussions and individual feedback on writing and presentation from the students themselves. This course helps students develop practical skills for dealing with real-world problems in neuroengineering.

42667 | Biofabrication and Bioprinting | Prof. Rachelle Palchesko | Spring
Description: This laboratory course is designed to provide students with hands-on experience of methods used to fabricate scaffolds commonly used in tissue engineering, drug delivery and some medical devices. Methods to be taught include plastic FDM (Filament Deposition Methods) for 3D printing thermoplastic materials and molding for forming soft hydrogel materials, and 3D bioprinting of soft hydrogel materials onto a substrate. This course includes a reading component to introduce students to the concepts required to design and fabricate scaffolding. Presentation topics include (but are not limited to): chemical and physical properties of biomaterials, CAD and post-processing methods. There are no prerequisite courses; However, prior knowledge of an introductory laboratory is recommended.

42668 | "Fun" Basics of MRI and Neuroimaging Analysis | Prof. Sossena Madera | fall
Description: Neuroimaging gives us many opportunities to learn how the brain works across different functions and disease states without the need for invasive surgery. This course deals with the methodology and analysis of structural magnetic resonance imaging (MRI) and functional magnetic resonance imaging in humans and animals. Through lectures, discussions, and analysis of sample data, students will understand (A) the history of MRI, (B) the physics of MRI, (C) its use with MRIs and other equipment used to interpret biological tissues, (D) how to design an fMRI experiment and (E) MRI analysis techniques. By the end of the course, students will have a solid basic knowledge of MRI and fMRI and will acquire programming skills in MATLAB and learn about other tools such as SPM to process MRI and fMRI datasets in appropriate software packages.

42669 | Applications of energy in biology and medicine | Professor Yoed Rabin | Spring

Description: This course covers a wide range of energy-based applications in biology and medicine, e.g. B. Cancer treatments using cryosurgery (freezing), thermal ablation (heating), photodynamic therapy (light-activated drugs), and irreversible electroporation (a thermoelectric application). ). . This course also covers thermoregulation in humans and other mammals, and cryopreservation (low-temperature preservation) of tissues and organs for the benefit of organ banks and transplant medicines. The course combines lectures and individual assignments on fundamental engineering principles with teamwork on open-ended projects on competitive challenges in the convergence of engineering and medical sciences. The curriculum assumes mastery of the fundamentals of heat transfer at the undergraduate level.

42670 | Special topics: Host interactions of biomaterials in regenerative medicine | Professor Phil Campbell | Spring
Specific Topics: This course provides students with hands-on experience studying host responses to synthetic and natural biomaterials used in regenerative medicine applications. The students gain experience in the analysis of host reactions to these biomaterials as well as strategies to control the host interaction. Biocompatibility of biomaterials, immunological interactions, wound healing and tissue regeneration are discussed. Students will integrate classroom lectures with laboratory skills to assess host-material interactions in a laboratory setting. Laboratory characterization techniques include cell culture, microscopic analysis, cytochemistry, immunocytochemistry, and histology. Prerequisite: Junior or Senior level in Biomedical Engineering or instructor approval.

42675 | Fundamentals of biomedical computing | Prof. Yu-Li Wang | fall
The aim of this course is to enable students with little or no programming experience to use computational methods to solve fundamental biomedical engineering problems. Students will use MATLAB to solve systems of linear equations, fit models using least squares techniques (linear and nonlinear), interpolate data, perform numerical integration and differentiation, solve differential equations, and visualize data. Examples of specific themes are drawn from different areas of biomedical engineering, such as: B. bioimaging and signal processing, biomechanics, biomaterials and cellular and biomolecular technology.

(Video) Career Exploration Series: Biomedical Engineering

42678 | Innovation and realization of medical devices | Professor Denver Faulk | Spring
The accelerating pace of medical discovery and new technology represents a unique and exciting time for medical devices. Medical devices range from biomaterials that stimulate the body to repair itself to drug delivery devices and robotic surgical systems. Because they strive to improve and prolong human health, medical device development has unique requirements and challenges compared to most other industries. This course examines how innovation in medical devices is practiced today and the drivers driving it, such as: B. the FDA, intellectual property, reimbursement and financing. By the end of this course, students should be able to: (1) gain a broad understanding of medical devices; (2) identify new product opportunities; (3) understand drivers affecting medical device development; and (4) develop strategies to address these drivers within the overall medical device development plan.

42682 | Bioinstrumentation | Prof. Yu-Li Wang | Spring
This curriculum unit aims to develop concepts and competencies in electronics for the design and construction of instruments for biomedical applications. The course uses an inverted and accelerated format to cover a wide variety of electronic components and circuits, including resistors, capacitors, transistors, sensors, actuators, amplifiers, signal filters, and microcontrollers, through lectures, tutorials, weekly lab projects, and semesters. Projects Students, with or without experience in electronics, acquire practical skills in building functional instruments for physiological measurements such as temperature, gas concentration, blood pressure and ECG signals.

42684 | Principles of Immunoengineering and Drug Development for Immunotherapy | Professor Elizabeth Wayne | Spring
This course provides context for the application of technical principles of modulation of the immune system to solve problems in human health. Basic understanding of the components and function of the innate and adaptive immune system. Students leave the course with a basic understanding of immunology and the engineering techniques used to design and characterize immunotherapy systems. Where relevant, we will discuss how immune engineering fits into other engineering disciplines such as mechanics, chemistry and materials science. Since the goal of immune engineering is to treat disease, we will discuss the line of therapy, clinical trial development, and the FDA approval process. Immunotherapy is also being evaluated in various disease scenarios, including cancer, infectious diseases, allergies, prostheses and implants, neurological and musculoskeletal diseases.

42685 | Biostatistics | Prof.. Parjanaporn Chalacheva | Frühling
This course introduces statistical methods to draw inferences in engineering, biology and medicine. Students will learn how to choose the most appropriate methods, how to apply those methods to real-world data, and how to read and interpret the computer output of a commonly used statistical package. Topics covered are descriptive statistics; elementary probability; discrete and continuous random variables and their distributions; Hypothesis testing with interval (continuous and discrete) and categorical (nominal and ordinal) variables for two and three or more treatments; simple and multiple linear regression; time series analysis; group and sort; and time-to-event analysis (survival). Students will also learn how to write the statistical component of a "Results" section for an academic paper and learn the limitations of statistical analysis. Basic familiarity with probability and probability distribution is desirable but not required.

42687 | AI applications at the BME | Teacher. Parjanaporn Chalacheva Spring

Description: This course provides hands-on experience in applying the fundamentals of artificial intelligence/machine learning (AI/ML) to problems in a variety of biomedical research areas and applications. Students work in teams to design, develop, and evaluate an AI/ML system to achieve one or more of the following goals: identify patterns in data, model input and output relationships, and/or data into different classify categories. The datasets for this course come from a variety of areas related to BME, provided by biomedical researchers, clinicians, and other publicly available sources. In addition to the design work, the course covers topics specific to the development and implementation of AI algorithms in medical environments. These include FDA approval, human clinical trials, the Health Insurance Portability and Accountability Act, and medical ethics. This computer design-based course is available to all students who have completed the Introduction to Machine Learning course.

42691 | Biomechanics of Human Movement | Teacher. any omen halilaj
This course provides an overview of the mechanical principles underlying the biomechanics of human movement and the experimental and modeling techniques used to study them. Specific topics include locomotion, motion capture systems, force platforms, muscle mechanics, musculoskeletal modeling, three-dimensional kinematics, inverse dynamics, advanced dynamic simulations, and image-based biomechanics. Homework and theses will emphasize applications of movement biomechanics in orthopaedics, rehabilitation and sports.

42692 | Special Topics: Nanoscale Manufacturing Using Structural DNA Nanotechnology
This course provides an introduction to modern nanoscale manufacturing using structural DNA nanotechnology. This DNA-based fabrication approach has much in common with other micro- and nano-engineered fabrication methods: computational design tools are required for device design, and the resulting structures can only be seen with advanced microscopy. However, instead of processing larger materials into micro- and nanostructures, DNA origami is made using a “bottom-up” approach to self-assemble individual oligonucleotides into 2D and 3D nanostructures. The resulting structures can be engineered to exhibit novel mechanical and electrical properties and find applications as broad as medicine, biological computing, and energy systems. The course includes lectures, hands-on physical modeling, homework, 3D DNA origami modeling using cDNAno and CANDO software, and student team projects and presentations.

42693 | Special topics of integrated systems technology: biomedical micro/nano devices | teacher Siyang Zheng | fall
Biomedical devices require constant innovation. Micro/nano-fabrication not only miniaturizes devices and instruments, but also enables new biomedical devices and greatly increases device performance. This course introduces basic micro/nano manufacturing technologies and materials related to biomedical devices. The biomedical background and design principles of various biomedical devices are presented. Diagnostic and therapeutic devices will be discussed, including point-of-care diagnostic devices, biosensors, DNA sequencers, medical implants, prosthetic devices, drug delivery systems, medical robots, etc.

42694 | Basics of medical technology | teacher Siyang Zheng | Spring

Medical devices are devices that are widely used in the diagnosis, treatment and prevention of human diseases. The invention and adoption of medical devices is one of the key drivers behind the revolution in modern healthcare. This course takes a systematic, quantitative approach to the design and implementation of medical devices. We will mainly focus on three main categories of medical devices: bioelectrical devices, biomechanical devices and medical devices enabled by new technologies. For each category, domain knowledge and basic principles are introduced, and detailed design, implementation, and performance analysis are explored. Where appropriate, analytical equations and simulation tools are used. The course prepares students with a solid foundation for further study, research and work in medical technology fields. Prerequisite or additional requirement: 42-202 and (21-120 or 21-122 or 21-259) and (33-141 or 33-142) or instructor approval

42696 | Featured Topics: Wearable Health Technologies | Prof Eni Halilaj | Spring

This course provides an overview of emerging wearable healthcare technologies and provides students with hands-on experience in solving ongoing technical challenges. The field of wearable detection is experiencing explosive growth with exciting applications in medicine. The new lightweight devices will make it easier to monitor real-time health status, automatically import data into healthcare IT systems, and provide haptic feedback with people in the loop. We will look at various aspects of these technologies including hardware, software, user experience, communication networks, applications and big data analytics. Students are paired with a company for a semester-long project exploring contemporary computing challenges. Programming experience, no matter in which language, is a prerequisite.

42702 | Advanced Physiology | Professor Phil Campbell | fall
This course is an introduction to human physiology and includes units on all major organ systems. Particular attention is paid to the musculoskeletal, cardiovascular, respiratory, digestive, excretory and endocrine systems. Modules on Molecular Physiology, Tissue Engineering and Physiological Modeling are also included. Because of the close relationship between structure and function in biological systems, each functional topic is introduced through a brief exploration of the anatomical structure. Basic physical laws and principles of physiological function are explored. Prerequisite: 03-121 Modern Biology or teaching license.

42737 | Biomedical Optical Imaging | Professor Jana Kainerstorfer | Spring
Biophotonics or biomedical optics is a field concerned with the application of optical science and imaging technology to biomedical problems, including clinical applications. The course introduces basic concepts of electromagnetism and light-tissue interactions, including optical tissue properties, absorption, fluorescence and light scattering. Imaging methods are described including fluorescence imaging, Raman spectroscopy, optical coherence tomography, diffuse optical spectroscopy and photoacoustic tomography. The basic physics and engineering of each imaging technique is emphasized. Their relevance to the diagnosis of human diseases and clinical applications such as breast cancer imaging and monitoring, 3D imaging of the retina, non-invasive methods of tumor detection and brain imaging will be considered.

42744 | Medical Devices | Teacher Boyle Cheng | fall
This research course is an introduction to the technical, clinical, legal, regulatory and commercial aspects of medical device performance and failure. Topics covered will include a wide range of successful medical devices in clinical use, as well as historical case studies of devices that have been withdrawn from the market due to observed failures. The in-depth study of specific medical devices includes cardiovascular, orthopedic and neurological disciplines. We will examine the best practices used in the clinical setting, the principles that govern the design processes, and the modes of failure as a risk to the patient population. Other lectures will provide fundamental topics on biomaterials for implantable medical devices (metals, polymers, ceramics), biocompatibility, imaging, patient risks and failure mechanisms (wear, corrosion, fatigue, friction, etc.). The technical content level requires a junior position for MCS and CIT students, a science or engineering degree for non-MCS or CIT graduates, or a teaching license for all other students. 42-744 may only be used for degrees.

42781 | Career issues in biomedical engineering | Professor Keith Cook | Spring, Cair
This course familiarizes students with many of the problems faced by biomedical engineers. Provides an overview of professional topics such as bioethics, regulatory issues, communication skills, teamwork and other topical issues. External speakers and case studies describe professional and real problems in biotechnology and bioengineering and progress in solving them. A thesis describing how the lecture topics are applicable to a specific biomedical industry is also required.

42782 | Modeling and analysis of biomedical technical systems | Prof. Sossena-Holz | Spring; Prof Chalacheva | fall
This course prepares students to develop mathematical models for biological systems and for biomedical engineering systems, devices, components and processes and to use models for data reduction and analysis, prediction and optimization of system performance. The models considered come from a wide range of applications and are based on algebraic equations, ordinary differential equations and partial differential equations. Advanced engineering mathematics tools are introduced, encompassing analytical, computational, and statistical approaches, and used for model manipulation. There will be an additional project.

42783 | Laboratory for Neurotechnology | Professor Matt Smith | fall
Neural Engineering applies classical engineering approaches and principles to understand the nervous system and its function. Measuring neuronal activity comprises a set of basic tools that have evolved over decades to detect neuron activity (individual neurons, neuron populations and nerve fibers) or neuron-related activities (e.g. blood oxygenation in the brain). . In order to intervene in the nervous system, a comparable set of tools has been developed to change neuronal activity locally or globally, on short or long time scales. The successful application of these methods to measure and manipulate neural activity requires a basic scientific and technical understanding of the principles behind their action, as well as practical experience in their application in real-world settings. This laboratory course combines lectures with laboratory exercises to gain an in-depth understanding of the tools we use to measure and manipulate neural activity and analytical approaches to this data. This includes building and diagnosing recording hardware, experimental data acquisition, data analysis in Matlab or Python, and scientific writing. Overall, the aim is to provide students with a comprehensive understanding of the methods used to obtain experimental data in neuroscience. Knowledge of signal processing and introductory programming in Matlab or Python is helpful. This course is suitable for students from different backgrounds: (1) students with an experimental background who are looking for diverse hands-on experience in different experimental settings and a deeper understanding of different experimental methods, and (2) students with an engineering background and other quantitative background who are interested would like to deal with experimental data collection methods and practices.

42790 | Internship in Biomedical Engineering | Prof. Patyana Porno Chalacheva | autumn, spring, summer
Students work with a faculty member, a local biomedical company, or a local clinical researcher on a technical research, development, or expansion project. A faculty member affiliated with the Department of Biomedical Engineering acts as a consultant for an internal project or as a liaison for an external industrial/clinical project. The project concludes with an oral presentation and an internally filed written report that documents the project and its results. The presentation and the report are reviewed by the faculty advisor/trusted person; this assessment serves as the basis for awarding the course grade. Prerequisite: permanent university degree and approval of the advisor / faculty contact. variable units.

42792 | Internship in Biomedical Engineering | Prof. Patyana Porno Chalacheva | Summer

A summer internship in an industrial or clinical setting offers our master's students a unique opportunity to acquire the knowledge and skills to practice biomedical engineering in a real-world environment. The internship will improve teamwork, communication skills and problem-solving skills. These skills are practiced in a real-world setting to prepare students seeking industrial employment after a master's degree. After the internship, the student will take time to reflect on the internship and summarize his/her experiences in the form of a written report that will be submitted to the department. Prerequisite: Permanent graduate degree, requires special agreement by advisor and department approval, and Office of International Education approval for international students.

42799 | Directing Studies | autumn spring summer

This course is intended for directed study only with the approval of the Deputy Head of Department.

42801/42701 | Biomedical Engineering Seminar | Teacher Rosalyn Abbott | autumn Spring
The Biomedical Engineering Seminar is required each semester for all BME residency students. It provides opportunities to learn about research in many related fields being conducted at other universities, industries and hospitals. All graduate students are required to enroll in this course each semester of full-time study. Attendance: Mandatory. Students can register for 0 units as the 42-701 Biomedical Engineering Seminar or for 3 units as the Self-Study Biomedical Engineering Seminar. Students enrolling at 42-701 receive a pass/fail grade based on submission of notes taken in seminars. Students who register at 42-801 will receive a grade based on seminar grades and 2-hour self-study reports after each seminar.

(Video) BME Graduate Student Spotlight: Lucas Gil Nadolskis

Courses listed below are offered by other departments but have been pre-approved to meet BME course requirements. Descriptions of these courses can be found in the university's course catalogue. Students are encouraged to contact their teacher if they are unsure of the required background. Other courses can be recognized as compulsory electives upon application, which must be submitted before the course begins. Regardless of individual course admission, all course selection should reflect a clear theme in biomedical engineering.

02601 | Programming for Scientists
It provides a hands-on introduction to programming for students with little programming experience who are interested in science. Basic scientific algorithms are introduced and extensive programming tasks are based on analytical tasks that scientists can handle, such as analysis, simulation and optimization. The principles of good software engineering are also emphasized. The course introduces students to the Go programming language, a modern, industry-supported programming language, the syntax of which is covered in detail. Other tasks can be assigned in other programming languages ​​to emphasize similarities and differences between languages. Knowledge of biology is not required. Analytical skills, understanding of basic programming concepts and mathematical maturity are required.

02604 | Basics of bioinformatics
How do we find potentially harmful mutations in your genome? How can we rebuild the tree of life? How do we compare similar genes from different species? These are just three of the many central questions in modern biology that can only be answered by computational approaches. This 12-unit course will delve deeper into some of the basic computational ideas used in biology and allow students to apply existing resources that thousands of biologists use in the field every day. The course offers students with an introductory programming background the opportunity to become more experienced programmers in a biological environment. As such, it represents a natural next course for students who have completed 02-601.

02680 | Basic mathematics and statistics for scientists
This course thoroughly introduces first-year MSc students to fundamental topics in mathematics and statistics to prepare them for more advanced computer courses. Samples are drawn from topics in information theory, graph theory, proof techniques, phylogenetics, combinatorics, set theory, linear algebra, neural networks, probability distributions and densities, multivariate probability distributions, maximum likelihood estimation, statistical inference, hypothesis testing, Bayesian and stochastic inference. Legal action. Students who complete this course will acquire a wide range of mathematical techniques and statistical inference, as well as an in-depth understanding of mathematical proofs. You have the quantitative foundations to immediately start an introductory course in automation or machine learning at master's level. This training is also useful for students taking advanced courses that apply machine learning concepts to scientific datasets, such as 02-710 (Computational Genomics) or 02-750 (Automation of Biological Research). The course grade is calculated from homework, midterms and class attendance.

02710 | Computational Genomics
Dramatic advances in experimental technology and computational analysis are fundamentally changing the fundamental nature and purpose of biological research. The emergence of new frontiers in biology, such as B. evolutionary genomics and systems biology, requires new methods that can tackle quantitative problems with considerable computational and mathematical sophistication. From a computational perspective, this course focuses on modern machine learning methods for computational problems in molecular biology and genetics, including probabilistic models, inference and learning algorithms, data integration, time series analysis, active learning, etc. This course counts as a CSD Applications elective.

02718 | Computer-aided medicine
Modern medical research increasingly relies on the analysis of large patient datasets to improve our understanding of human diseases. This course focuses on computational problems arising from the study of human disease and the translation of bedside research to improve human health. Topics covered include computational strategies to advance personalized medicine, pharmacogenomics to predict individual drug responses, metagenomics to explore the role of the microbiome in human health, electronic medical record searches to identify disease phenotypes, and case studies on complex human diseases such as cancer and asthma. We will discuss how machine learning methods such as regression, classification, clustering, semi-supervised learning, probabilistic modeling, and time series modeling are used to analyze a variety of datasets collected from clinicians. The sessions consist of lectures, discussions of literature articles, and guest lectures by physicians and other experts in the field. Grading is based on homework and a project. 02-250 is a suggested requirement.

02730 | Modeling of cells and systems
This course introduces students to the theory and practice of modeling biological systems from the molecular to the organismic level, with a focus on intracellular processes. Topics covered include kinetic and equilibrium descriptions of biological processes, systematic approaches to modeling and parameter estimation, analysis of biochemical cycles modeled as differential equations, modeling of noise effects using stochastic methods, modeling of spatial effects, and modeling of higher levels of abstraction or scalability through logical or agent-based approaches. A variety of biological models and applications are considered, including gene regulatory networks, cell signaling, and cell cycle regulation. Weekly lab sessions give students hands-on experience with the methods and models presented in the classroom. Course requirements include regular class attendance, bi-weekly homework, a homework test, and a final project. The course is designed for high school and graduate students from a variety of backgrounds. The course is intended to be self-contained, but students may need to do additional work to become fluent in the fundamentals. Students should have a basic understanding of calculus, differential equations and chemistry, as well as some prior knowledge of molecular biology and biochemistry. Programming and numeracy experience is helpful but not required. The lab exercises use MATLAB as the main calculation and modeling tool, supplemented with additional software as needed. *THIS COURSE WILL TAKE PLACE AT PITT

02750 | Automation of scientific research
Automated scientific instruments are widely used in research and engineering. Robots dramatically increase the reproducibility of scientific experiments and are generally cheaper and faster than humans, but are most commonly used to perform brute force sweeps under experimental conditions. The result is that many experiments are "wasted" under conditions where the effect could have been expected. Therefore, there is a need for computational techniques capable of selecting the most informative experiments. This course introduces students to artificial intelligence and machine learning techniques to automatically select experiments to accelerate the discovery rate and reduce the overall cost of research. Real applications of biology, biotechnology and medicine are examined. Grading is based on homework and two tests. The course must be self-contained, but students must have a basic understanding of biology, programming, statistics, and machine learning.

03534 | Biological imaging and fluorescence spectroscopy
This laboratory serves to convey experimental concepts and methods in cell and developmental biology. Students work with a variety of organisms to study how cells progress from rapidly dividing undifferentiated cells to cellular attachment and the establishment of spatial and temporal patterns of gene expression to the specific characteristics and responses of differentiated cells. The course makes intensive use of phase contrast video microscopy, DIC and fluorescence microscopy. Biochemical, immunological and molecular biology techniques are used to study the molecules and processes of developing cells. Experimentation with living organisms and/or their tissues, cells or molecules is an essential part of this course.

03620 | Techniques of Electron Microscopy
This internship aims to provide students with the ability to perform measurements and interpret data from living systems. Experimental modules reinforce the concepts from 42-101 Introduction to Biomedical Engineering and introduce students to four areas of biomedical engineering: biomedical imaging and signal processing, biomaterials, biomechanics, and cellular and molecular biotechnology. Several cross modules are also included. The course includes weekly theoretical classes to complement the experimental component. Students who have declared the additional profession Biomedical Engineering are given preference. Notes: This course number is reserved for CIT students enrolled in the HPP program.

03712 | Computational methods for biological modeling and simulation
This course covers a variety of computational methods that are important for modeling and simulating biological systems. It is intended for intermediate and advanced students with a background in biology or computer science who are interested in the development of computer models and simulations of biological systems. The course focuses on practical algorithms and algorithm design methods from various disciplines of computer science and applied mathematics that are useful in biological applications. The general topics covered are models for optimization, simulation, and sampling and parameter matching problems. Coursework includes problem solving with significant programming components and independent or group completion projects.

03730 | advanced genetics
This course covers selected current topics in molecular genetics at an advanced level. The focus is on discussing research papers in the classroom. The themes may change each year. Examples of previous topics include: nucleocytoplasmic RNA trafficking in yeast, genomic imprinting in mammals, molecular genetics of learning and memory in Drosophila, viral genomics, using yeast as a model system to study the molecular basis of human neurodegenerative diseases, and CRISPR/Cas9 genome editing.

03741 | Advanced Cell Biology
This course covers fourteen topics on which major advances or current controversies have been reported. Each topic has a faculty-led background lecture, student presentations of important relevant research, and a general class discussion. Example topics include: extracellular matrix control of normal and cancerous cell cycles, force-generating mechanisms in transmembrane protein translocation, signal transduction control of cell motility, and a molecular mechanism for membrane fusion.

03742 | Advanced Molecular Biology
The structure and expression of eukaryotic genes are discussed, with an emphasis on model systems from a variety of organisms, including yeast, flies, worms, mice, humans, and plants. Topics discussed include (1) genomics, proteomics, and functional proteomics, and (2) control of gene expression at the level of DNA-mRNA transcription, pre-mRNA splicing, export of spliced ​​mRNA from the nucleus to the cytoplasm, and mRNA translation .

03751 | Advanced developmental biology and human health
This course examines current research in developmental biology, with a focus on areas that have important biomedical implications. The course will examine stem cell biology, cell reprogramming, cell signaling pathways, tissue morphogenesis, and the genetic/developmental mechanisms of human diseases and birth defects. Emphasis is placed on critical reading of original and recent research and classroom discussions with supporting faculty lectures.

03762 | Advanced Cell Neuroscience
This course is an introductory graduate course in cellular neuroscience. As such, you will undertake little or no groundwork, but will quickly move on to discussing articles from the mainstream literature. The structure of the course is approximately half lecture and half discussion of recent and classic articles from the primary literature. These discussions are mainly led by the students. Topics covered include ion channels and excitability, synaptic transmission and plasticity, molecular understanding of brain diseases, and cell biology of neurons. Assessment is based on classroom attendance, including performance in class presentations and a writing assignment.

03763 | Advanced systems neuroscience
This course is a graded version of 03-363. Students attend the same conferences as students in 03-363, plus an additional meeting once a week. At this meeting, the topics covered in the conferences are deepened, often through discussions of articles from the primary literature. Students will read and should have a deep understanding of several classic articles from the literature as well as current articles that illustrate innovative approaches in systems neuroscience or important new concepts. The use of animals as research model systems is also discussed. Performance in this part of the lesson will be assessed using additional test questions as well as additional homework.

03871 | Structural Biophysics
This course (MB-1) is the foundation course of the first semester for the joint CMU-Pitt graduate program in Molecular Biophysics and Structural Biology (MBSB). The physical properties of biological macromolecules and the methods for analyzing their structure and function are discussed in detailed lectures. Topics covered include: protein folding and architecture; Structures and energetics of nucleic acids; structure determination by X-ray crystallography and NMR; optical spectroscopy with a focus on absorption and fluorescence, NMR spectroscopic methods; other methods for characterizing proteins and protein-ligand interactions, such as mass spectrometry, calorimetry, single molecule measurements and manipulation, and surface plasmon resonance. Enough detail is provided to enable students to make a critical assessment of the current literature.

06462 | Modeling and optimization algorithms
Formulation and solution of mathematical optimization problems with and without constraints. Target functions are based on economic or functional specifications. Discrete and continuous variables are considered.

06607 | Physical chemistry of colloids and surfaces
surface thermodynamics; Adsorption at gaseous, liquid and solid interfaces; Capillarity; wetting, distribution, lubrication and adhesion; monolayer and thin film properties; Preparation and characterization of colloids; colloidal stability, flocculation kinetics, micelles, electrokinetic phenomena and emulsions.

06609 | Physical chemistry of macromolecules
This course develops the fundamental principles of polymer science. Physico-chemical concepts related to the macromolecular nature of polymeric materials are emphasized. Technical aspects of the physical, mechanical and chemical properties of these materials are discussed in relation to the molecular structure. Topics include an introduction to polymer science and a general discussion of commercially important polymers; molecular weight; Condensation and Addition Synthesis Mechanisms with Emphasis on Molecular Weight Distribution; solution thermodynamics and molecular conformation; rubber elasticity; and the rheological and mechanical properties of polymer systems. Students who do not meet the listed requirements can ask their teacher for permission.

06610 | Rheology and structure of complex liquids
This course covers the fundamental concepts of rheology and mechanical behavior of fluid systems. Both experimental and theoretical aspects of rheology are discussed. The fundamental forces affecting rheology and the rheology of complex fluids are described and discussed; including excluded volume, van der Waals, electrostatics and other interactions. Structure characterization methods are addressed, including scattering techniques, optical polarimetry and microscopy. The examples focus on different types of complex liquids, including polymer melts and solutions, gel systems, suspensions, and self-assembling liquids.

06663 | Analysis and modeling of transport phenomena
The students get to know basic differential equations and boundary conditions that determine momentum, heat and mass transport. The students learn to think of these equations as dimensionless and to apply them to the modeling of physical and chemical processes. The main way to solve them will be numerically. Analytical results of classical highly symmetric problems are also presented to serve as a basis for comparison and validation. Software: A finite element and computational transport tool.

06804 | drug delivery systems
The body is remarkable in its ability to sequester and eliminate foreign objects, whether they are "bad" (e.g., pathogens) or "good" (e.g., therapeutic drugs). This course examines the design principles used to design modern drug delivery systems capable of overcoming the body's defenses to achieve a therapeutic effect. Specifically, we will study the chemistry, formulation, and mechanisms of systems designed to deliver DNA, siRNA, chemotherapeutic agents, and proteins across a variety of physiological barriers. This is a postgraduate course also open to final year students.

09707 | nanoparticles
This course covers the chemical, physical, and biological aspects of several major types of nanoparticles, including metal, semiconductor, magnetic, carbon, and polymer nanostructures. For each type of nanoparticles, we select educational examples (e.g. Au, Ag, CdSe, etc.) and present their synthesis methods, physical and chemical properties, self-assembly, and various applications. Besides nanoparticle materials, other topics that will be briefly covered will include microscopy and spectroscopy techniques to characterize nanoparticles and nanolithography techniques to fabricate nanoarrays. The course is primarily descriptive and focuses on understanding key concepts (such as plasmon, exciton, polaron, etc.). Lectures are PowerPoint presentation style with enough graphical materials to help students better understand the course materials. Overall, this course aims to provide an introduction to the new frontiers of nanoscience and nanotechnology. Students gain an understanding of important concepts and research topics in nanoscience and nanotechnology and develop their skills to conduct highly disciplinary nanoscience research. read 3 hours

09719 | Bioorganic Chemistry: Peptides, Proteins, and Combinatorial Chemistry
This course introduces students to new developments in chemistry and biology, with a focus on synthesis, structural and functional aspects of peptides, proteins and small molecules. The basic concepts of bioorganic chemistry are presented in the context of current literature and the students have the opportunity to get to know experimental methods in various research laboratories. An introduction to combinatorial chemistry related to drug discovery and design will also be presented. The students have to inform themselves about the current literature. Homework and team projects are assigned regularly. Homework requires data interpretation and experimental design; and team projects give students the opportunity to work in teams to address current problems at the intersection of chemistry and biology. At the end of their studies, students of the Graduate School (09-719) must submit an original research project in addition to the designated homework and the partial and final examinations required for the bachelor's degree.

(Video) Day in the Life of an Engineering Student at Carnegie Mellon

09741 | Organic Polymer Chemistry
A study of the synthesis and reactions of high polymers. The focus is on the practical preparation of polymers and the basic kinetics and mechanisms of polymerization reactions. Topics include: synthesis and structure relationship, step growth polymerization, chain growth polymerization through radical, ionic and coordinative intermediates, copolymerization, analysis of specialty polymers and polymer reactions. 09-741 students complete the same courses and exams as those enrolled in 09-502, but also complete a thesis on advanced polymer materials, which requires instructor approval. 09-509 or 09-715, Physical Chemistry of Macromolecules is excellent preparation for this course but is not required. 3-6 hours reading time

09801 | Special Topics in Physical Chemistry: Computational Tools for Molecular Science
The aim of this course is to provide students with modern computing tools essential for productive and creative activities in the field of molecular and nanosciences. This goal is achieved through a series of hands-on computational exploration segments covering key areas such as: Data Visualization and Manipulation, Elements of Linear Algebra and their Practical Applications, Fourier Analysis, Partial and Ordinary Differential Equations, Elements of Computational Quantum Chemistry, and Hands-on Introduction to Machinery Learn. The main calculation tool used in the course is Mathematica, which enables a seamless combination of numerical and symbolic calculations using extensive libraries of high-performance algorithms. The direct link between Mathematica and MATLAB is also covered. Choosing these tools makes it possible to shift the emphasis from computer programming to computer-aided exploration, problem-solving, and discovery. It also makes the course accessible to students with no prior technical computing experience.

10701 | Introduction to Machine Learning for PhD
Machine learning examines the question "How can we create computer programs that automatically improve their performance through experience?" This includes learning to perform many types of tasks based on many types of experiences. These include, for example, robots that learn to navigate better based on experience they have gained navigating their environment, medical decision-making aids that learn to predict which therapies for which diseases on the basis of data from mining records and historical health systems work best. Understand his speech based on the experience of listening to him. This course is designed to provide PhD students with a solid foundation in the methods, mathematics, and algorithms required to study and apply machine learning. Students who enter the course with prior knowledge of probability, statistics, and algorithms have an advantage, but the course is designed so that anyone with a strong background in math and computer science can catch up and participate fully. If you're interested in this topic but aren't a graduate student or graduate student who doesn't specialize in machine learning, you might want to consider the Machine Learning Master's Course, 10-601 The Theory and Algorithms Behind ML ML Course Comparison :

10702 | Statistical Machine Learning
Machine learning examines the question "How can we create computer programs that automatically improve their performance through experience?" This includes learning to perform many types of tasks based on many types of experiences. These include, for example, robots that learn to navigate better based on experience they have gained navigating their environment, medical decision-making aids that learn to predict which therapies for which diseases on the basis of data from mining records and historical health systems work best. Understand his speech based on the experience of listening to him. This course is designed to provide PhD students with a solid foundation in the methods, mathematics, and algorithms required to study and apply machine learning. Students who enter the course with prior knowledge of probability, statistics, and algorithms have an advantage, but the course is designed so that anyone with a strong background in math and computer science can catch up and participate fully. If you're interested in this topic but aren't a graduate student or graduate student who doesn't specialize in machine learning, you might want to consider the Machine Learning Master's Course, 10-601 The Theory and Algorithms Behind ML ML Course Comparison :

10708 | Probabilistic graphical models
Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The framework of probabilistic graphical models provides a unified view for this wide range of problems, enabling efficient inference, decision making, and learning for problems with a large number of attributes and large data sets. This postgraduate course provides a solid foundation both for applying graphical models to complex problems and for addressing fundamental research topics in graphical models. The course covers classical families of directed and undirected graph models (i.e. Markov random fields and Bayesian networks), modern deep generative models as well as causal inference topics. It will also cover the necessary algorithmic tools including variational inference and Markov chain Monte Carlo methods. Students taking the course are expected to have a prior knowledge of probability, statistics and algorithms, although the course is designed to allow students with a strong mathematical background to catch up and participate fully. Students must have successfully completed an introductory ML course (e.g. 10715, 10701 or 10601) or equivalent.

10725 | convex optimization
Almost all machine learning problems can be formulated to optimize a function, possibly under certain constraints. This universal reduction might indicate that such optimization problems are unsolvable. Fortunately, many real-world problems have a special structure, such as B. convexity, smoothness, separability, etc., which allow us to formulate optimization problems that can often be solved efficiently. This course aims to provide the PhD student with a solid foundation for formulating optimization problems that exploit such a structure and for efficient solution methods for these problems. The main focus is on the formulation and solution of convex optimization problems, although we will discuss some recent advances in non-convex optimization. These general concepts are also illustrated through applications in machine learning and statistics. Students taking the course are expected to have some prior knowledge of algorithms, although the course is designed to allow students with a solid background in arithmetic to become familiar with it and participate fully. Although not required, it is highly recommended that you have taken 10-701 or equivalent course in Machine Learning or Statistical Modeling as we will be using applications in Machine Learning and Statistics to demonstrate the concepts we will cover in class . During the entire semester, students work on an extensive optimization-based project.

11785 | Introduction to Deep Learning
Neural networks have increasingly taken over many AI tasks and are currently producing the latest in many AI tasks, from computer vision and self-driving car design to computer games. Basic knowledge of NN, currently known as “deep learning” in popular literature, familiarity with various formalisms and knowledge of tools, is now an essential requirement for any researcher or developer in most areas of AI and NLP. This course is a broad introduction to the field of neural networks and their "deep" learning formalisms. The course traces part of the evolution of neural network theory and design over time, which quickly leads to a discussion of various network formalisms, including simple forward, convolutional, recursive, and probabilistic formalisms, the reasons for their development, and the challenges behind learning such networks. and several suggested solutions. Then we look at different extensions and models that allow their application to different tasks, such as: B. computer vision, speech recognition, machine translation and games. Instruction Unlike previous issues of 11-785, instruction will be provided primarily through lecturers and occasional guest speakers. Assessment Students are assessed based on weekly continuous assessment tests and their performance on homework and a thesis. There will be six hands-on tasks requiring low-level coding and toolkit-based implementation of neural networks, covering basic MLP, convolution and recursive formalisms, as well as one or more advanced tasks and final design.

12659 | Special Topics: Matlab
This short course is designed as a hands-on introduction to Engineering Scientific Computing. Topics in this course include basic matrix calculus, solving ordinary and partial differential equations, solving systems of linear equations, computing eigenvalues ​​and eigenvectors, and basic signal processing and neural network techniques. Throughout the course, these scientific computing tools are demonstrated using interactive scientific software called MATLAB.

15686 | neural computing
Computational neuroscience is an interdisciplinary science that attempts to understand how the brain calculates in order to achieve natural intelligence. It seeks to understand the principles and computational mechanisms of intelligent behavior and mental abilities such as perception, language, motor control, and learning, and to build artificial systems and computational models using the same capabilities. This course examines how neurons encode and process information, adapt and learn, communicate, cooperate, compete, and compute at the individual, network, and system levels. It will introduce fundamental concepts in computer modelling, information theory, signal processing, systems analysis, statistical and probabilistic inference. Concrete examples of the visual and motor systems are drawn and examined from computational, psychological and biological perspectives. Students learn to conduct computer experiments with Matlab and quantitative investigations of neurons and neural networks.

15853 | Algorithms not real world
Without description. Please contact the department.

15883 | Computer models of neural systems
This course is an in-depth study of information processing in real neural systems from a computational perspective. We will examine several areas of the brain, such as the hippocampus and cerebellum, where processing is well understood in terms of specific representations and algorithms. We will mainly focus on computer models of these systems after establishing the necessary anatomical, physiological and psychophysical context. There will be some neuroscience tutorial lectures for those who have no prior knowledge in the field. See for the latest schedule updates.

16711 | Kinematics, dynamic systems and controls
Kinematics, Dynamic Systems, and Control is a graduate-level introduction to robotics. The course covers fundamental concepts and methods for analyzing, modeling, and controlling robotic mechanisms that move and manipulate the physical world. Main topics are the basics of kinematics, dynamics and control applied to the kinematics, dynamics and control of rigid body chains. Other topics are state estimation and dynamic parameter identification.

16720 | Computer Vision
This course introduces the basic techniques of computer vision, i. H. the analysis of patterns in visual images to reconstruct and understand the objects and scenes that produced them. Topics covered include image generation and rendering, camera geometry and calibration, computational imaging, multiview geometry, stereo, 3D image reconstruction, motion analysis, physically based vision, image segmentation, and object recognition. The material is based on graduate texts, supplemented with research where appropriate. Assessment is based on assignments and a final project. The tasks include extensive Matlab programming exercises. Recommended but not required texts: Title: "Computer Vision Algorithms and Applications" Author: Richard Szeliski Series: Computer Science Texts Publisher: Springer ISBN: 978-1-84882-934-3 Title: "Computer Vision: a modern approach" Authors : David Forsyth and Jean Ponce Editors: Prentice Hall ISBN: 0-13-085198-1

16722 | detection and sensors
The principles and practices of quantitative cognition (sensing), exemplified by the devices and algorithms (sensors) that implement them. Learn to critically question the sensor requirements of robotic applications, to specify the required sensor properties, to analyze whether these specifications are feasible at all, to compare what is theoretically feasible with what can actually be bought or built, to understand the technical factors that that explain discrepancies, and design transduction, digitization, and computer systems that come fairly close to realizing the actual capabilities of available sensors. Grading is based on homework, class attendance and a final exam. Three to four of the homework will be hands-on, take-home exercises that will be done using an Arduino kit that students will buy instead of buying a textbook. Higher course modules include (1) Sensors, Signals and Measurements, (2) Sources, Nature and Noise Reduction, (3) End-to-End Sensor Systems, (4) Cameras and other Image Sensors and Systems, (5) Perception and Distance Imaging, (6) sensors and navigation systems, (7) other topics of interest to the class (time permitting).

16725 | Analysis of (bio)medical images
Students acquire theoretical and practical skills in the analysis of 2D, 3D and 4D biomedical images, including skills relevant to image analysis in general. The fundamentals of computational analysis of medical images are explored, leading to current research in the application of geometry and statistics to image segmentation, registration, visualization and understanding. Additional and related topics covered include noise removal/restoration, morphology, level sets, and shape/feature analysis. Students gain hands-on experience through projects using the latest version of the National Library of Medicine Insight Toolkit (ITK) and SimpleITK, a popular open-source software library developed by a consortium of institutions including Carnegie Mellon University and the University of Pittsburgh . In addition to image analysis, the course includes interaction with radiologists and pathologists. *** The lectures take place at the CMU and the students visit the doctors at the UPMC. Some or all lectures may also be videotaped for public distribution, but students may request that the distributed video be removed. 16-725 is a graduate class and 16-425 is a cross-class section. 16-425 is new this year and has significantly reduced thesis and major homework requirements, nor does it require medical follow-up. Prerequisites: Knowledge of vector calculus, simple probability, and C++ or Python, including basic command line familiarity and passing arguments to your own command line programs. Extensive experience with C++ and templates is not required, but some students may find it helpful.

16824 | Visual Learning and Recognition
A graduate seminar in computer vision focusing on the representation and justification of large amounts of data (images, videos and associated labels, text, GPS locations, etc.) with the aim of understanding images. We read an eclectic mix of classic and recent articles on topics such as: theories of perception, intermediate level vision (grouping, segmentation, poselets), object and scene recognition, understanding 3D scenes, action recognition, contextualization, image, articulation models of language and vision etc. We will cover a wide range of supervised, semi-supervised and unsupervised approaches to each of the above topics.

16868 | biomechanics and motor skills
The course provides an introduction to the mechanics and control of leg movement with a focus on the human system. Key topics covered include fundamental concepts, skeletal muscle mechanics and neural control. Examples of bio-inspiration in robots and rehabilitation devices are highlighted. By the end of the course you will have the basic knowledge to create your own dynamic control models for human and animal motion. The course develops the material in parallel with an introduction to Matlab's Simulink and SimMechanics environments for modeling nonlinear dynamical systems. Team assignments and projects allow you to apply your knowledge in theory and computer simulations to movement problems in animals and humans.

16879 | medical robotics
This course provides an overview of medical robotics intended for graduate and advanced students. Topics include robot kinematics, registration, navigation, tracking, treatment planning, and technical and medical aspects of specific applications. The course includes guest lectures by robotics researchers and surgeons, as well as observation of surgical cases. The course is open to people without a specialization who have the necessary training.

18491 | Basics of signal processing
This course covers the mathematics, implementation, design, and application of digital signal processing algorithms that are widely used in fields such as multimedia telecommunications and speech and image processing. Topics include discrete-time signals and systems, discrete-time Fourier transforms and Z-transforms, discrete and fast Fourier transforms, digital filter design and implementation, and multirate signal processing. The course includes introductory discussions on 2D signal processing, linear prediction, adaptive filtering and selected application areas. The theoretical lessons are supplemented by implementation exercises in MATLAB. The 18491 and 18691 students will share the same lectures and recitations. However, students who have credited 18691 must complete an additional final project at the end of the semester. Students in 18691 may have additional homework problems weekly.

18612 | Neural Technology: Perception and Stimulation
This course provides an overview of the concept of sustainability, including changing attitudes and values ​​towards technology and the environment in the late 20th and early 21st centuries. Relevant topics of sustainable technology will be discussed, including population growth, urbanization, energy, water, food and material resources. Tools for sustainable engineering are presented, including sustainability metrics, environmental design principles, and the use of material and energy balances in sustainable systems.

18614 | Microelectromechanical Systems
This course introduces the fundamentals of fabrication and design of microelectromechanical systems (MEMS): systems of sensors and actuators on a chip with dimensions in the micron range. Basic principles covered include microstructure fabrication, mechanics of silicon and thin film materials, electrostatic force, capacitive motion detection, fluid damping, piezoelectricity, piezoresistivity, and thermal micromechanics. Applications covered include pressure sensors, micromirror displays, accelerometers and gas microsensors. Grades are based on tests and homework. read 4 hours

18751 | Applied stochastic process
Basic concepts of probability: probability space, simple and compound events, statistical independence and Bayes' rule. overall probability concept; Bernoulli processes; poison law. De Moivre-Laplace theorem. definition of the random variable (RV); Probability distribution of an RV: cumulative distribution function (CDF) and probability density function (PDF). two random variables; several random variables. VR features? Yes; conditional distributions; conditional expectations; common distributions. Moments, generating functions and characteristic functions of RVs. Chebyshev inequality. I guessed; linear estimation; least mean squares estimation; and principle of orthogonality. limit theorems; Central Limit Theorem; Law of large numbers (both strong LLN and weak LLN). Definition of Random Process (RP). Different notions of stationarity. Poisson and Gaussian processes. Autocorrelation and Power Spectral Density (PSD) of a PR. Processing of random processes (stochastic) by linear systems. ergodicity. spectral analysis. Appropriate filtering. Selected telecom applications, data networks (queuing), Kalman filtering.

18752 | Estimation, Recognition and Learning
This course covers estimation, detection, identification, and machine learning, covering a range of methods from classic to modern. In recognition, topics covered include hypothesis testing, Neyman-Pearson recognition, Bayesian classification, and methods for combining classifiers. For estimation, topics include maximum likelihood and Bayesian estimation, regression, prediction and filtering, Monte Carlo methods, and packed detection. Machine learning and identification topics include low-dimensional and Gaussian models, kernel learning, support vector machines, neural networks, deep learning, Markov models, and graph models.

18771 | linear systems
A modern approach to the analysis and design applications of linear systems. Modeling and linearization of dynamic physical systems with multiple inputs and multiple outputs. State variables and transfer function matrices. Emphasis on linear and matrix algebra. Numerical matrix algebra and computational problems in solving systems of linear algebraic equations, singular value decomposition, eigenvalue-eigenvector problems and least squares problems. Analytical and numerical solutions of differential and difference equation systems. Structural properties of linear dynamic physical systems, including controllability, observability, and stability. Canonical realizations, linear state variable feedback controller and asymptotic observer design. Computational design and applications for electronic circuits, control engineering, dynamics and signal processing. 4 hours reading Prerequisites: 18-470 or 18-474 and degree in CIT or MCS.

18792 | Advanced digital signal processing
This course covers a range of advanced topics and applications in one-dimensional digital signal processing, with a focus on optimal signal processing techniques. Topics include modern spectral estimation, linear prediction, short-term Fourier analysis, adaptive filtering, and selected topics in matrix processing and homomorphic signal processing with applications in speech and music processing. read 4 hours

18793 | Image and video editing
This course covers specialized signal processing techniques for processing 2D (images) and 3D (video) signals. It builds on 1D signal processing techniques developed in 18-290 and 18-491 and specializes them in the case of images and video. In this course, you will learn basic image and video processing tools and techniques and learn how to apply them to a variety of practical applications. This course teaches the fundamentals of studying images and videos. We will develop specific signal models for images and videos, and develop associated optimization techniques to solve restoration problems such as denoising, repainting, and investigating specialized compression algorithms. Special focus will be in the field of transformation, PDE and scarcity-based models and associated optimization techniques. These formal techniques are enriched by applications on mobile devices, medical imaging and comprehensive detection.

18794 | pattern recognition theory
Decision theory, parameter estimation, density estimation, nonparametric techniques, supervised learning, linear discriminant functions, clustering, unsupervised learning, artificial neural networks, feature extraction, support vector machines, and pattern recognition applications (e.g. face recognition, fingerprint recognition, target recognition). , etc.). 4 hours of reading Prerequisites: 36-217 or equivalent Introductory course in Probability and Random Variables and an introductory course in Linear Algebra and Bachelor's or Master's level.

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18799 | K Special Topics in Signal Processing: Advanced Machine Learning
Visit the ECE website for the Special Topics in Signal Processing course descriptions.

21690 | optimization methods
An introduction to the theory and algorithms of linear and nonlinear programming with an emphasis on modern computational considerations. The simplex method and its variants, duality theory and sensitivity analysis. Extensive linear programming. Optimization conditions for unconstrained nonlinear optimization. Newton's method, linear search, confidence intervals and convergence rates. Constraint problems, legal point methods, penalty and barrier methods, interior point methods. (Three 50 minute lessons)

24614 | Microelectromechanical Systems
This course introduces the fundamentals of fabrication and design of microelectromechanical systems (MEMS): systems of sensors and actuators on a chip with dimensions in the micron range. Basic principles covered include microstructure fabrication, mechanics of silicon and thin film materials, electrostatic force, capacitive motion detection, fluid damping, piezoelectricity, piezoresistivity, and thermal micromechanics. Applications covered include pressure sensors, micromirror displays, accelerometers and gas microsensors. Grades are based on tests and homework.

24618 | Computer-aided analysis of transport phenomena
In this course, students will develop fundamental knowledge and skills to perform simulations of transport phenomena (mass, momentum and energy transport) for engineering applications using a CAE tool, they will learn to analyze simulation results and compare them with theory or available data and will become the Develop the ability to relate numerical predictions to the behavior of the governing equations and the underlying physical system. The first 8 weeks of the course include simulation-based lectures and homework. During the last 7 weeks, student teams will work on self-proposed projects related to the computational analysis of transport phenomena. In the project, students learn to tackle loosely defined problems by designing an appropriate computational network, choosing the appropriate numerical scheme and boundary conditions, choosing appropriate physical models, using available computational resources efficiently, etc. Each team communicates the results of their project. through several oral presentations and a final written report. For the detailed syllabus, please refer to the URL below.

24623 | Molecular simulation of materials
The aim of this course is to introduce engineering students to the theory and implementation of numerical techniques for modeling behavior at the atomic level. The focus is on molecular dynamics and Monte Carlo simulations. Students will write their own simulation computer code and learn how to perform calculations on various thermodynamic sets. Applications in heat transfer, mass transfer, fluid mechanics, mechanics and materials science are considered. The course assumes some knowledge of thermodynamics and computer programming. read 4 hours

24673 | Soft robots: mechanics, design and modelling
Soft, elastically deformable machines and electronics will dramatically improve the functionality, versatility and biocompatibility of future robotic systems. In contrast to conventional robots and machines, these ?soft robots? It consists of elastomers, gels, liquids, gases and other non-rigid materials. We will study emerging paradigms in lightweight robotics and explore their design principles using classical theories in solid mechanics, thermodynamics and electrostatics. Specific topics include artificial muscles, peristaltic robotics, soft pneumatic robotics, liquid-impregnated elastomers, and particle locking. This course includes a basic project that students can work on individually or in teams. For the project, the students should design, simulate and/or build a flexible robot in whole or in part (e.g. sensors, actuators, grippers, etc.). Prerequisites: Statics and stress analysis or equivalent.

24674 | Design of biomechatronic systems for humans
This course introduces the fundamentals of fabrication and design of microelectromechanical systems (MEMS): systems of sensors and actuators on a chip with dimensions in the micron range. Basic principles covered include microstructure fabrication, mechanics of silicon and thin film materials, electrostatic force, capacitive motion detection, fluid damping, piezoelectricity, piezoresistivity, and thermal micromechanics. Applications covered include pressure sensors, micromirror displays, accelerometers and gas microsensors. Grades are based on tests and homework.

24688 | Introduction to CAD and CAE tools
This course provides hands-on training in applying modern CAD and CAE software tools to engineering design, analysis and manufacturing. In the first section, through 7 hands-on projects, students will learn how to model complex free-form 3D objects using commercial CAD tools. In the second section, through 7 hands-on projects, students will learn how to simulate complex multiphysics phenomena using commercial CAE tools. Units: 12 Format: 2 hours. Reading, 2 hours. computer lab

24703 | Numerical methods in engineering
This course focuses on numerical methods for solving differential equations, which are important in engineering. Methods for solving systems of ordinary differential equations and boundary value problems in partial differential equations are presented. Students must develop computer algorithms and use them in a variety of technical applications. Wherever possible, a comparison is made with analysis results from 24-701. 4 hours of reading time Prerequisite: Some programming experience is required.

24718 | Computational Fluid Dynamics
This course focuses on numerical techniques for solving partial differential equations, including the complete incompressible Navier-Stokes equations. Various space-time discretization methods are taught, namely the finite difference method, the finite volume method and briefly the finite element method. Explicit and implicit approaches as well as methods for solving linear equations are used to study fluid flow. An overview of various finite difference methods for analyzing elliptic, hyperbolic and parabolic partial differential equations and the concepts of stability, consistency and convergence are presented at the beginning of the course to familiarize students with general numerical methods. For the detailed syllabus, please refer to the URL below. 4 hours of lessons

24755 | Finite elements in mechanics I
The basic theory and applications of the finite element method in mechanics are presented. Development of the FEM as a Galerkin method for the numerical solution of boundary value problems. Applications to second order stationary problems including heat conduction, elasticity, convective transport, viscous flow, among others. Introduction to advanced topics, including fourth-order equations, time dependence, and nonlinear problems. 12 units Prerequisite(s): Instructor or graduate approval

24778 | Mechatronisches Design
Mechatronics is the synergetic integration of mechanical, electronic and computer-aided control mechanisms into a functional system. Because of the emphasis on integration, this course focuses on laboratory projects in which small teams of students configure, design, and implement mechatronic systems. Lectures complement the laboratory experience with operating principles and system design issues related to the spectrum of mechanical, electrical and microcontroller components. Lectures cover selected topics including mechatronic design methods, system modeling, mechanical components, sensors and I/O interfaces, motor control and microcontroller fundamentals.

24780 | technical computing
This course covers the practical programming and computing skills that engineers need. This includes: (1) C++ programming, (2) visualization with OpenGL, (3) basic data structures, and (4) basic algorithms. The course covers computational techniques required to solve common engineering problems, as well as background algorithms and data structures used in modern computer-aided design, computer-aided manufacturing processes, and computer-aided design tools. The course also features intensive computer labs to practice common applications.

24783 | Advanced Engineering Computing
This course covers advanced programming and computing skills needed to solve technical problems. These include (1) efficient data structures and algorithms for modeling and processing real data sets such as trees, hash tables, lookup books, priority queues, etc. (2) simulation and visualization techniques, such as B. numerical resolution of ODEs and PDEs, control visualization, programmable shader, etc., (4) tools for version control, scripting and code creation, including Subversions, Git and cmake . Students will receive hands-on training in the above programming knowledge and skills through bi-weekly assignments and a core team project. Prerequisites: Computer Engineering 24-780 or equivalent programming experience in C++ and OpenGL

24787 | Machine learning and artificial intelligence for engineers
This course introduces fundamental machine learning and artificial intelligence techniques useful for engineers working on data-intensive problems. Topics include: probabilistic and Bayesian learning, generative and discriminative classification methods, supervised and unsupervised learning, neural networks, support vector machines, clustering, dimensionality reduction, regression, optimization, evolutionary computing, and research. The lectures emphasize the theoretical foundations and mathematical modeling of the introduced techniques, while the bi-weekly homework focuses on the implementation and testing of the techniques learned in the software. Tasks require knowledge of Python, including text and image input/output, vector and raster operations, simple loops, and data visualization. The students should have experience at bachelor level with linear algebra and vector calculus.

27410 | computer techniques in engineering
This course develops methods for formulating fundamental technical problems in a way that makes them amenable to computational/numerical analysis. The course consists of three main modules: basic programming skills, discretization of ordinary and partial differential equations, and numerical methods. These modules are followed by two modules from a larger list: Monte Carlo-based methods, molecular dynamics methods, image analysis methods, etc. Students will learn how to work with numerical libraries and how to compile and compile scientific code written in Fortran-90 and C++ executes Students are required to work on a course project that integrates aspects of at least two course modules.

27565 | Nanostructured Materials
This course is an introduction to nanostructured materials or nanomaterials. Nanomaterials are objects with sizes larger than the atomic or molecular length scale but smaller than microstructures with at least one dimension in the range of 1 to 100 nm. The physical and chemical properties of these materials often differ significantly from those of bulk materials. For example, gold nanoparticles with diameters of ~15 nm are red and gold nanoparticles with ~40 nm are purple, while gold in bulk is golden in color. The course begins with a discussion of top-down and bottom-up fabrication methods for fabricating nanostructures and imaging and characterizing nanomaterials, including scanning probe microscopy. Emerging nanomaterials such as fullerenes, graphene, carbon nanotubes, quantum dots, and nanocomposites are also discussed. The course focuses on applications of nanomaterials in microelectronics, particularly in nanoscale devices, and in the emerging field of molecular-scale electronics. The miniaturization of integrated systems that detect mechanical or chemical changes and generate an electrical signal will be presented. The principles and applications of quantum confinement effects on optical properties, mainly as sensors, are discussed. The last part of the course is a discussion on nanoscale mechanisms in biomimetic systems and how these phenomena are applied in new technologies, including molecular motors.

27734 | Computational Materials Science Methoden
This course introduces students to the theory and practice of computational materials science from the electronic to the microstructural scale. Both the underlying physical models and their implementation as computational algorithms are discussed. Topics include: Density Functional Theory Molecular Dynamics Monte Carlo Methods Phase Field Models Cellular Automata Data Science Examples and homework are drawn from all areas of materials science. The course uses specially developed software packages and computer code. Students should be able to write, compile, and run simple computer programs in MatLab, Python, or a similar environment.

33441 | Introduction to biophysics
Biological physics, or the physics of living systems, is an exciting interdisciplinary frontier field of physics that aims to understand the phenomenon of life using physical concepts and tools. This advanced course introduces you to the general concepts and principles underlying the physical behavior of living systems, from the dynamics of proteins and molecules to the collective behavior of living cells and organisms. The course develops key physical concepts most relevant to biological processes, including energy conversion, information transfer, mechanics of motion, statistical phenomena, and fluid flow. We will apply these physical concepts to demonstrate how biological systems work, build simplified mathematical models to predict behavior, and use experimental data to inform and test models. The integration of biological phenomena, physical concepts, mathematical models and the analysis of experimental data represents a completely new way of learning based on research strategies. These strategies will overcome the traditional disciplinary barriers between physics and biology. Students are expected to gain an intuitive understanding of ways to: formulate the physical problem, identify appropriate theoretical frameworks, analyze experimental data, and develop ways of generalizing and understanding the dependence of biophysical phenomena on length and time scales develop. No previous knowledge of biology is expected. This course is offered in the fall of even years (e.g. fall 22, 24, etc.)

33767 | Biophysics: from fundamental concepts to current research
This course combines lectures and student presentations on advanced topics in biological physics. In the course, the students gain a deep understanding of the fact that very basic physical and chemical principles underlie many core processes in life. Not only is life compatible with the laws of physics and chemistry, it exploits them in subtle ways. After completing the course, the students should be able to name examples of such situations for which they can provide a coherent line of argument that describes these relationships. You will be able to explain the large-scale experiments by which these compounds were found or are routinely made today, and outline simple back-cover estimates used to convince oneself of the validity or inapplicability of certain popular models and ideas can. Enough with the key terminologies often encountered in biology to be able to further educate yourself using the biological and biophysical literature. The course uses the Physical Biology of the Cell by Rob Phillips et al. (Garland Science, New York, NY, 2013, ISBN 978-0-8153-4450-6).

36700 | Probability and Mathematical Statistics
This is a one semester course covering the fundamentals of statistics. We will first provide a brief introduction to probability theory, and then cover basic mathematical statistics topics such as point estimation, hypothesis testing, asymptotic theory, and Bayesian inference. When time permits, we also cover more advanced and useful topics, including nonparametric inference, regression, and classification. Prerequisites: One and two variable calculus and matrix algebra. Doctoral students in degree-seeking programs are given preferential consideration.

36759 | Statistical models of the brain
This new course is intended for CNBC students as an additional option to meet the requirements of the Computational Foundation course, but is also open to Statistics and Machine Learning students. It should be of interest to anyone interested in seeing how statistical ideas develop in the brain sciences and will provide a series of case studies on the role of stochastic models in scientific inquiry. Statistical ideas have been part of neurophysiology and brain research since the first stochastic description of spike trains and the quantum hypothesis of neurotransmitter release more than 50 years ago. Many contemporary theories of the behavior of neural systems are based on statistical models. For example, it is commonly believed that integrating and firing neurons are driven in part by stochastic noise; the role of peak synchronization involves distinguishing between Poisson and non-Poisson neurons; and oscillations are characterized in that the variation is broken down into frequency-based components. In the visual system, individual V1 cells are often described using both linear and nonlinear Poisson models; in the motor system, neural response may involve directional adaptation; and the plasticity of the receptive field of the CA1 hippocampus was characterized using dynamic site models. It has also been suggested that perceptions, decisions, and actions are the result of an optimal (Bayesian) combination of sensory information with previously learned regularities; and some researchers report new insights from considering whole-brain pattern responses as analogous to statistical classifiers. Throughout the field of statistics, models containing random "noise" components are used as an effective means of data analysis. However, in neuroscience, models also help to form a conceptual framework for understanding neuronal function. This course examines some of the key methods and claims that have emerged from the application of statistical reasoning.

45906 | The business of health innovation
Entrepreneurial Alternatives will explore entrepreneurial avenues outside of founding high growth startups. The course specifically focuses on the tactical elements of business acquisition and franchise buying, including target assessment, target financial analysis, business valuation, business structuring, procurement and operational finance, and post-sales integration. In addition to its focus on business acquisition and franchising, this course will explore alternative entrepreneurial avenues, including social entrepreneurship and corporate entrepreneurship.

76795 | academic writing
You will learn to write clear, well-organized, and engaging articles on science, technology, and health topics for the general public. You will learn to research scientific topics using primary and secondary sources, conduct interviews and organize this information logically for presentation. For writing students, the course will improve your understanding of scientific inquiry and how to accurately and persuasively describe it to the general public. For science students, this course teaches you how to write powerful, flowing prose so you can bring your topics to life. The course is not just for those who want to become science writers, but for anyone who needs to explain science, medicine or technology to a general audience, whether you are an engineer describing a green building project to a target audience. or a computer programmer describing new software to a company's marketing team. Scientists and educators today are increasingly concerned about the public's lack of understanding of scientific principles and practices, and this course is a step in addressing that deficit. You will have the opportunity to read numerous examples of high-quality academic writing and to interview researchers, but the focus is on writing a series of articles and revising them after editing. Their tasks range from profiling scientists to explaining how something works. This year's course will specifically focus on how science and society interact, be it the way science writers write about public health and the COVID pandemic or climate change. The course is held partly as a writing workshop, in which students form teams to brainstorm, edit and critique each other's work in class, in a process similar to what journalists routinely go through.

85765 | Cognitive Neuroscience
This course covers fundamental insights and approaches in cognitive neuroscience with the aim of providing an overview of the field at an advanced level. Topics include high-level vision, spatial perception, working memory, long-term memory, learning, language, executive control, and emotions. Each topic is approached from a variety of methodological directions, for example computer modelling, cognitive assessment in humans with brain damage, non-invasive brain monitoring in humans and recording of single neurons in animals. Lectures alternate with sessions in seminar format. Requirements: University degree or two advanced psychology courses in the fields of developmental psychology, cognitive psychology, computational intelligence modelling, neuropsychology or neurosciences.

86675 | Computational Perception
In this course, we will first explore the basic biological and psychological understanding of biological perception systems, and then apply computational reasoning to examine the principles and mechanisms underlying natural perception. The course this year will focus on vision but will also address other sensory modalities. You will learn to reason scientifically and computationally about problems and perceptual problems, to extract essential computational properties from these abstract ideas, and finally to transform them into explicit mathematical models and computational algorithms. Topics include perceptual representation and inference, perceptual organization, perceptual constancy, object recognition, learning, and scene analysis. Prerequisites: Basic knowledge of linear algebra and probability theory as well as some programming experience are desirable.

Pitt BIOE 2330 | Biomedical Imaging
Biomedical Imaging introduces the main imaging modalities (X-ray, CT, MRI, Ultrasound) used in clinical medicine and biomedical research, as well as the fundamentals of imaging from a signal and systems perspective.

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