eWEAR Symposium 2021: Session 2

Thursday, February 25, 12:00 to 2:00 pm PST

Affiliate Registration – eWEAR Affiliate member companies, VIPs, and the Stanford University community with SUNetID; Non-affiliate Registration – Prospective members and other paying attendees; Questions? Ask angela.mcintyre@stanford.edu

12:00 Professor Leanne Williams, “Personalized Neuroscience for Developing Mental Health Applications”

12:30 Dr. Yasser Khan, “Design Considerations of a Wearable for Mental Health and Wellness”

1:00 Professor Utkan Demirci, “Medical Micro/Nanorobots in Biofabrication and Precision Medicine”

1:30 Professor Boris Murmann, “Machine Learning in Resource Constrained Hardware”

Leanne Williams

Professor Leanne Williams

Professor of Psychiatry and Behavioral Sciences at Stanford University School of Medicine
Stanford University

Dr. Williams is the founding director of the Stanford Center for Precision Mental Health and Wellness and of the Stanford PanLab for Precision Psychiatry and Translational Neuroscience, Associate Chair of Translational Neuroscience in the Department of Psychiatry and Behavioral Sciences, and Director of Education and Precision Medicine at the Palo Alto VA Mental Illness Research, Education and Clinical Center.

Prior to joining the Stanford community, Dr. Williams was the founding chair of Cognitive Neuropsychiatry and directed the Brain Dynamics Center at Sydney Medical School.

Her PhD was completed with a British Council Scholarship for study at Oxford University.

Dr. Williams’ Center and translational programs integrate advanced neuroimaging, technology and digital innovation to transform the way we detect mental disorders, tailor interventions and promote wellness. She has developed the first taxonomy for depression and anxiety that quantifies brain circuits for diagnostic precision and prediction. Dr. Williams’ research programs are supported by funding from the National Institutes of Health, spanning priority Research Domain Criteria, Human Connectome and Science of Behavior Change initiatives. She has contributed over 325 scientific papers to the field.

We know that depression manifests physically in altered brain circuitry as well as in physical biomarkers, but no large-scale studies have been conducted to ascertain the relationship between symptoms and these physical indicators. Precise ways to measure physical manifestations of depression will bust the stigmatizing myth that depression is a character weakness and allow us to detect and target depression before it is has caused chronic disability.

Breakthroughs in Williams’ PanLab show that the physical manifestations of depression present as distinct combinations called ‘biotypes’. We are privileged to collaborate with leaders in Engineering to address an interdisciplinary objective: to leverage our breakthroughs in biotypes with advanced wearable sensors developed by our collaborates. We seek to accomplish for mental health what the Framingham Heart Study has for cardiovascular disease. Much of the common knowledge about heart disease is due to this study, and this foundation was the platform for scalable sensors for heart health.

Our approach will lead to a platform for quantifying how depression risk manifests in physical indicators of neural circuit function and peripheral physiology. This approach allows for a precise and personalized neuroscience approach to mental health that ultimately is scalable.

Yasser Khan

Yasser Khan, Ph.D.

Postdoctoral Scholar
Stanford University

Yasser Khan is a postdoctoral scholar at Stanford University, advised by Professor Zhenan Bao in Chemical Engineering and Professor Boris Murmann in Electrical Engineering. Yasser completed his Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley, in Professor Ana Claudia Arias’ Group. He received his B.S. and M.S. in Electrical Engineering from the University of Texas at Dallas and King Abdullah University of Science and Technology, respectively. Yasser’s research focuses on additive manufacturing and hardware AI to produce skin-like wearables, implantables, and ingestibles. These medical devices are being used for precision health and psychiatry.  Yasser received the EECS departmental fellowship at UC Berkeley, discovery scholarship and graduate fellowship at KAUST, and academic excellence scholarship at UT Dallas. Yasser published over 40 research publications and presentations in the most reputed platforms in the field, which were highlighted by BBC News, Wall Street Journal, NSF News, and many more.
Chronic stress has been associated with a variety of pathophysiological risks including developing mental illness. Yet, there is no existing method that accurately and objectively monitors stress. In this talk, I will outline the strategies and design considerations of a wearable electronic-skin that can continuously measure physiological parameters linked to chronic stress and other mental health and wellness. I will present a multi-part study that combines user-centered design with engineering-centered data collection to inform future design efforts for mental health wearables.

Utkan Demirci

Professor Utkan Demirci

Tenured Professor in the Radiology Department in the School of Medicine
Stanford University

Utkan Demirci is a tenured professor in the Radiology Department in the School of Medicine at Stanford University. He is Co-Director of the Stanford Canary Center for Cancer Early Detection. Before moving to Stanford, Dr. Demirci was an associate professor of Medicine and Health Sciences and Technology at Brigham and Women’s Hospital, Harvard Medical School (HMS) and at Harvard- Massachusetts Institute of Technology (MIT) Health Sciences and Technology.  Dr. Demirci is the recipient of many prestigious awards, including the Academy for Radiology & Biomedical Imaging Research (ARBIR) Distinguished Investigator Award, MIT TR-35 Award, Harvard Medical School-Young Investigator Award, Stanford Basic Scientist of the Year Award, Brigham and Women’s Hospital-Bright Future Award, IEEE EMBS Early Career Award, IEEE EMBS Translational Science Award, NSF CAREER Award, Coulter Foundation Early Career Award and Chinese International Young Scientist Award.  He is also a fellow-elect of the American Institute of Medical and Biological Engineering.  Dr. Demirci holds 25 issued or pending patents, provisional applications and invention disclosures that have been licensed to numerous companies, and he is the founder of such startups as DxNow, LevitasBio, and Koek Biotechnology.Ph.D. in Chemistry in 1990 from Virginia Tech.

Boris Murmann

Professor Boris Murmann

Professor of Electrical Engineering
Stanford University

Boris Murmann is a Professor of Electrical Engineering at Stanford University. He joined Stanford in 2004 after completing his Ph.D. degree in electrical engineering at the University of California, Berkeley. Earlier in his career, Dr. Murmann was with Neutron Microelectronics, Germany, where he developed low-power and smart-power ASICs in automotive CMOS technology. He has worked as a consultant with numerous Silicon Valley companies and received numerous awards.  Dr. Murmann was a co-recipient of the Best Student Paper Award at the VLSI Circuits Symposium and a recipient of the Best Invited Paper Award at the IEEE Custom Integrated Circuits Conference in 2008.  He received the Agilent Early Career Professor Award in 2009 and the Friedrich Wilhelm Bessel Research Award in 2012.  Dr. Murmann is an IEEE Fellow. He has served as Associate Editor of the IEEE Journal of Solid-State Circuits, an AdCom member and Distinguished Lecturer of the IEEE Solid-State Circuits Society, as well as the Data Converter Subcommittee Chair and the Technical Program Chair of the IEEE International Solid-State Circuits Conference. He is the founding faculty co-director of the Stanford SystemX Alliance and the faculty director of Stanford’s System Prototyping Facility.
Over the past decade, machine learning algorithms have been deployed in many cloud-centric applications. However, as the application space continues to grow, various algorithms are now being embedded “closer to the sensor” and in wearable devices, eliminating the latency, privacy and energy penalties associated with cloud access. In this talk, I will review mixed-signal circuit techniques that can improve the energy efficiency of moderate-complexity, low-power machine learning inference algorithms. Specific examples include analog feature extraction for image and audio processing, as well as mixed-signal compute circuits for convolutional neural networks.