Field 1: Biomedical Instrumentation (BMI)
This field of emphasis is designed to train bioengineers interested in the applications and development of instrumentation used in medicine and biotechnology. Examples include the use of lasers in surgery and diagnostics, new micro electrical machines for surgery, sensors for detecting and monitoring of disease, microfluidic systems for cell-based diagnostics, new tool development for basic and applied life science research, and controlled drug delivery devices. The principles underlying each instrument and specific clinical or biological needs will be emphasized. Graduates of this program will be targeted principally for employment in academia, government research laboratories, and the biotechnology, medical devices, and biomedical industries.
Faculty: Marvin Bergsneider, Louis Bouchard, Rob Candler, Greg Carman, Yong Chen, Pei-Yu Chiou, Chi On Chui, Mark Cohen, Joseph Demer, Dino Di Carlo, Katrina Dipple, Jeff Eldredge, Robin Garrell, Warren Grundfest, Thomas Mason, Chih-Ming Ho, Jean-Pierre Hubschman, Yongho Sungtaek Ju, Chang Jin Kim, Daniel Levi, Aydogan Ozcan, Laurent Pilon, Nader Pouratian, Amy Rowat, Jacob Schmidt, Michael Teitel, Hsian-Rong Tseng, R. Michael van Dam, Danny Wang, Tom Zangle, Hong Zhou
Field 2: Molecular Cellular Tissue Therapeutics (MCTT)
This field of emphasis covers novel therapeutic development across all biological length scales from molecules to cells to tissues. At the molecular and cellular levels, this area of research encompasses the engineering of biomaterials, ligands, enzymes, protein-protein interactions, intracellular trafficking, biological signal transduction, genetic regulation, cellular metabolism, drug delivery vehicles, and cell-cell interactions, as well as the development of chemical/biological tools to achieve this. At the tissue level, this field encompasses two sub-fields which include biomaterials and tissue engineering. The properties of bone, muscles and tissues, the replacement of natural materials with artificial compatible and functional materials such as polymers, composites, ceramics and metals, and the complex interactions between implants and the body are studied at the tissue level. The emphasis of research is on the fundamental basis for diagnosis, disease treatment, and re-design of molecular, cellular, and tissue functions. In addition to quantitative experiments required to obtain spatial and temporal information, quantitative and integrative modeling approaches at the molecular, cellular, and tissue levels are also included within this field. Although some of the research will remain exclusively at one length scale, research that bridges any two or all three length scales are also an integral part of this field. Graduates of this program will be targeted principally for employment in academia, government research laboratories, and the biotechnology, pharmaceutical, and biomedical industries.
Faculty: Douglas Black, Greg Carman, Yong Chen, Samson Chow, Linda Demer, Timothy Deming, Dino Di Carlo, Katrina Dipple, Joseph DiStefano III, Bruce Dunn, James Dunn, Daniel Ennis, Robin Garrell, Robert Gunsalu, Vijay Gupta, Dean Ho, Daniel Kamei, Andrea Kasko, William Klug, H. Phillip Koeffler, Min Lee,James Liao, Karen Lyons, Heather Maynard, Harry McKellop, Samuel Murray, Kayvan Niazi, Ichiro Nishimura, Zhilin Qu, Amy Rowat, Tatiana Segura, Stephanie Seidlits, Desmond Smith, Michael Sofroniew, Igor Spigelman, Ren Sun, Yi Tang, Bill Tawil, Michael Teitell, James Tidball, Kang Ting, Hsian-Rong Tseng, R. Michael van Dam, Cun-Yu Wang, Gerard Wong, Benjamin Wu, Lily Wu, Xinshu Xiao, Tom Zangle, Hong Zhou
Field 3: Imaging, Informatics and Systems Engineering (IIS)
This field consists of the following four subfields: Biomedical Signal and Image Processing (BSIP), Biosystem Science and Engineering (BSSE), Medical Imaging Informatics (MII), and NeuroEngineering (NE).
Subfield 1: Biosystem Science and Engineering (BSSE)
Graduate study in Biosystem Science and Engineering (BSSE) emphasizes the systems aspects of living processes, as well as their component parts. It is intended for science and engineering students interested in understanding biocontrol, regulation, communication, measurement or visualization of biomedical systems (of aggregate parts – whole systems), for basic or clinical applications. Dynamic systems engineering, mathematical, statistical and multiscale computational modeling and optimization methods – applicable at all biosystem levels – form the theoretical underpinnings of the field. They are the paradigms for exploring the integrative and hierarchical dynamical properties of biomedical systems quantitatively – at molecular, cellular, organ, whole organism or societal levels – and leveraging them in applications. The academic program provides directed interdisciplinary biosystem studies in these areas – as well as quantitative dynamic systems biomodeling methods – integrated with the biology for specialized life science domain studies of interest to the student. Typical research areas include molecular and cellular systems physiology, organ systems physiology, medical, pharmacological and pharmacogenomic system studies; neurosystems, imaging and remote sensing systems, robotics, learning and knowledge-based systems, visualization and virtual clinical environments. The program fosters careers in research and teaching in systems biology/physiology, engineering, medicine, and/or the biomedical sciences, or research and development in the biomedical or pharmaceutical industry.
Faculty: Alex Bui, Tom Chou, Dino Di Carlo, Joseph DiStefano III, Alan Garfinkel, Tom Graeber, Daniel Kamei, Elliot Landlaw, Dejan Markovic, Heather Maynard, Thomas Mason, Harold Monbouquette, Peter Narins, Matteo Pellegrini, Amy Rowat, Stephanie Seidlits, Desmond Smith, Zhilin Qu, Cun-Yu Wang, Howard Winet, Xinshu Xiao, Zhaoyan Zhang, Hong Zhou
Subfield 2: Biomedical Signal and Image Processing (BSIP)
The Biomedical Signal and Image Processing (BSIP) graduate program prepares students for a career in the acquisition and analysis of biomedical signals; and enables students to apply quantitative methods applied to extract meaningful information for both clinical and research
applications. The BSIP program is premised on the fact that a core set of mathematical and statistical methods are held in common across signal acquisition and imaging modalities and across data analyses regardless of their dimensionality. These include signal transduction, characterization and analysis of noise, transform analysis, feature extraction from time series or images, quantitative image processing and imaging physics. Students in the BSIP program have the opportunity to focus their work over a broad range of modalities including electrophysiology, optical imaging methods, MRI, CT, PET and other tomographic devices and/or on the extraction of image features such as organ morphometry or neurofunctional signals, and detailed anatomic/functional feature extraction. The career opportunities for BSIP trainees include medical instrumentation, engineering positions in medical imaging, and research in the application of advanced engineering skills to the study of anatomy and function.
Faculty: Marvin Bergsneider, Louis Bouchard, Tom Chou, Mark Cohen, Joseph Demer, Joseph DiStefano III, Benjamin Ellingson, Daniel Ennis, Alan Garfinkel, Peng Hu, Jody Kreiman, , Zili Liu, Dejan Markovic, Thomas Mason, Aydogan Ozcan, Nader Pouratian, Dario Ringach, Ladan Shams, Ren Sun, , Albert Thomas, Hsian-Rong Tseng, Dan Ruan, Danny Wang, Hong Zhou, Howard Winet, Holden Wu, Tom Zangle
Subfield 3: Medical Imaging Informatics (MII)
Medical imaging informatics (MII) is the rapidly evolving field that combines biomedical informatics and imaging, developing and adapting core methods in informatics to improve the usage and application of imaging in healthcare. Graduate study in this field encompasses principles from across engineering, computer science, information sciences, and biomedicine. Imaging informatics research concerns itself with the full spectrum of low-level concepts (e.g., image standardization and processing; image feature extraction) to higher-level abstractions (e.g., associating semantic meaning to a region in an image; visualization and fusion of images with other biomedical data) and ultimately, applications and the derivation of new knowledge from imaging. Notably, medical imaging informatics addresses not only the images themselves, but encompasses the associated (clinical) data to understand the context of the imaging study; to document observations; and to correlate and reach new conclusions about a disease and the course of a medical problem. Research foci include distributed medical information architectures and systems; medical image understanding and applications of image processing; medical natural language processing; knowledge engineering and medical decision-support; and medical data visualization. Coursework is geared towards students with science and engineering backgrounds, introducing them to these areas in addition to providing exposure to fundamental biomedical informatics, imaging, and clinical issues. This area encourages interdisciplinary training, with faculty from multiple departments; and emphasizes the practical, translational development and evaluation of tools/applications to support clinical research and care.
Subfield 4: NeuroEngineering (NE)
The NeuroEngineering (NE) subfield is designed to enable students with a background in biological science to develop and execute projects that make use of state-of-the-art technology, including microelectromechanical systems (MEMS), signal processing, and photonics. Students with a background in engineering will develop and execute projects that address problems that have a neuroscientific base, including locomotion and pattern generation, central control of movement, and the processing of sensory information. Trainees will develop the capacity for the multidisciplinary teamwork, in intellectually and socially diverse settings, that will be necessary for new scientific insights and dramatic technological progress in the 21st century. NE students take a curriculum designed to encourage cross-fertilization of neuroscience and engineering. Our goal is for neuroscientists and engineers to speak each others’ language and move comfortably among the intellectual domains of the two fields.
Faculty: Marvin Bergsneider, James Bisley, Chi On Chui, Mark Cohen, Joseph DiStefano III, Bruce Dunn, Alan Garfinkel, Christopher Giza, Warren Grundfest, Chih-Ming Ho, Yongho Sungtaek Ju, Daniel Kamei, Chang-Jin Kim, Zili Liu, Wentai Liu, Dejan Markovic, Sotiris Masmanidis, Istvan Mody, Harold Monbouquette, Peter Narins, Ichiro Nishimura, Nader Pouratian, Dario Ringach, Jacob Schmidt, Stephanie Seidlits, Laden Shams, Michael Sofroniew, Igor Spigelman, Benjamin Wu