School of Engineering
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Adam de la Zerda
Associate Professor of Structural Biology and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsMolecular imaging technologies for studying cancer biology in vivo
Professor of Radiology (Canary Cancer Center) and, by courtesy, of Electrical Engineering
BioDr. Demirci is currently a Professor with tenure at Stanford University School of Medicine and Principal Investigator of the Demirci Bio-Acoustic MEMS in Medicine (BAMM) Lab at the Canary Center at Stanford for Cancer Early Detection. He received his B.S. degree in Electrical Engineering in 1999 as a James B. Angell Scholar (summa cum laude) from University of Michigan, Ann Arbor. He received his M.S. degree in 2001 in Electrical Engineering, M.S. degree in Management Science and Engineering in 2005, and Ph.D. in Electrical Engineering in 2005, all from Stanford University.
BAMM Lab specializes in applying micro- and nanoscale technologies to problems in medicine and early cancer detection at the interface between micro/nanoscale engineering and medicine. Our goal is to apply innovative technologies to clinical problems. Our major research theme focuses on creating new microfluidic technology platforms targeting broad applications in medicine. In this interdisciplinary space at the convergence of engineering, biology and materials science, we create novel technologies for disposable point-of-care (POC) diagnostics and monitoring of infectious diseases, cancer and controlling cellular microenvironment in nanoliter droplets for biopreservation and microscale tissue engineering applications. These applications are unified around our expertise to test the limits of cell manipulation by establishing microfluidic platforms to provide solutions to real world problems at the clinic.
Our lab creates technologies to manipulate cells in nanoliter volumes to enable solutions for real world problems in medicine including applications in infectious disease diagnostics and monitoring for global health, cancer early detection, cell encapsulation in nanoliter droplets for cryobiology, and bottom-up tissue engineering. Dr. Demirci has published over 120 peer reviewed publications in journals including PNAS, Nature Communications, Advanced Materials, Small, Trends in Biotechnology, Chemical Society Reviews and Lab-chip, over 150 conference abstracts and proceedings, 10+ book chapters, and an edited book. His work was highlighted in Wired Magazine, Nature Photonics, Nature Medicine, MIT Technology Review, Reuters Health News, Science Daily, AIP News, BioTechniques, and Biophotonics. He is fellow-elect of the American Institute of Biological and Medical Engineering (AIMBE, 2017). His scientific work has been recognized by numerous national and international awards including the NSF Faculty Early Career Development (CAREER) Award (2012), the IEEE-EMBS Early Career Achievement Award (2012), Scientist of the year award from Stanford radiology Department (2017). He was selected as one of the world’s top 35 young innovators under the age of 35 (TR-35) by the MIT Technology Review at the age of 28. In 2004, he led a team that won the Stanford University Entrepreneur’s Challenge Competition and Global Start-up Competition in Singapore. His work has been translated to start-up companies including DxNow, KOEK Biotechnology and LEVITAS. There has been over 10,000 live births in the US, Europe, Asia, and Middle East using the sperm selection technology that came out of Dr. Demirci's lab.
Assistant Professor of Statistics, of Electrical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsMy work spans statistical learning, optimization, information theory, and computation, with a few driving goals: 1. To discover statistical learning procedures that optimally trade between real-world resources while maintaining statistical efficiency. 2. To build efficient large-scale optimization methods that move beyond bespoke solutions to methods that robustly work. 3. To develop tools to assess and guarantee the validity of---and confidence we should have in---machine-learned systems.
Robert and Barbara Kleist Professor in the School of Engineering, Emeritus
BioDutton's group develops and applies computer aids to process modeling and device analysis. His circuit design activities emphasize layout-related issues of parameter extraction and electrical behavior for devices that affect system performance. Activities include primarily silicon technology modeling both for digital and analog circuits, including OE/RF applications. New emerging area now includes bio-sensors and the development of computer-aided bio-sensor design.