Biosystems

Overview

Biosystems research in UW’s Department of Electrical Engineering is a highly collaborative endeavor. Our faculty focus on four areas of Biosystems research: synthetic & systems biology, neural engineering, biomedical devices, and mobile health. Many of our faculty hold secondary appointments and work closely with collaborators from other departments including Bioengineering, Computer Science and Engineering, Biology, Genome Sciences, Applied Mathematics, and the UW Medical Center. Our Biosystems faculty work with many cross-disciplinary institutes such as the eScience Institute, the NSF Engineering Research Center for Sensorimotor Neural Engineering, the Institute for Protein Design, the Bloedel Hearing Research Center and the University of Washington Institute for Neuroengineering.

Topics

Synthetic Biology

Biotechnology, macromolecular engineering tools, advanced materials, genetic engineering, computer aided design, laboratory automation, DNA/RNA sequence assembly, information theory and machine learning for genomics applications.

Faculty: Eric Klavins, Georg Seelig, Sreeram Kannan, Jeff Bilmes

Neural Engineering

Neural Control, Brain-Computer Interfaces, Neural Security, Device control, spinal cord rehabilitation, neural signaling, neuromechanics and computational neuroscience.

Faculty: Blake Hannaford, Howard Jay Chizeck, Sam Burden, Eli Shlizerman, Joshua R. Smith, Visvesh Sathe

Medical Devices

Design of biomedical devices including research and clinical neural interfaces, diagnostic devices, wearable sensors, and embedded processing and wireless communication links for biomedical devices.

Faculty: Babak Parviz, Shwetak N. Patel, Joshua R. Smith, Matt Reynolds, Visvesh Sathe, Jacques Christophe Rudell, Blake Hannaford, Howard Jay Chizeck, Les Atlas

Mobile Health

Development of new health monitoring, diagnostics, and health management applications and tools using emerging mobile devices and sensors. Research in this area applies advances in imaging, app development, physiological modeling, statistical algorithms, and machine learning. This work has implications for home health monitoring and low-resource environments.

Faculty: Shwetak N. Patel, Joshua R. Smith, Matt Reynolds, Linda G. Shapiro