School of Engineering
Showing 151-200 of 644 Results
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Samuel Seidel
Adjunct Professor
BioSam is the K12 Lab Director of Strategy + Research at the Stanford d.school, and co-author of Creative Hustle (Ten Speed Press, 2022), Changing the Conversation About School Safety (Stanford d.school, 2022), Hip Hop Genius 2.0 (Rowman & Littlefield, 2022), and Hip Hop Genius: Remixing High School Education (Rowman & Littlefield, 2011).
He speaks internationally about education, race, culture, systems, and design.
Sam graduated from Brown University with a degree in Education and a teaching certification, was a Visiting Practitioner at Harvard Graduate School of Education, a Scholar-in-Residence at Columbia University's Institute for Urban and Minority Education, and a Community Fellow at the Rhode Island School of Design. -
Serdar Selamet
Adjunct Lecturer, Civil and Environmental Engineering
BioAssoc. Professor with focus on Fire Engineering, Steel Structures and Numerical Modeling. Dr. Selamet specializes in fire protection engineering with focus on buildings and structures. His areas of expertise include heat transfer analysis, stability and critical temperature assessment of structural members, passive fire protection, building envelope (i.e. façade) fires, performance-based structural analysis under fire conditions and house fires in wildland urban interface. He specializes in providing origin and cause determinations for fires and explosions. Dr. Selamet has expertise in thermo-mechanical modeling and response using finite element software Abaqus and OpenSees Fire. In addition, he has gained experience using computer zone models such as OZone and Consolidated Model of Fire and Smoke Transport (CFAST) to simulate compartment fire dynamics.
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Austin Sendek
Adjunct Professor, Materials Science and Engineering
BioAustin Sendek is Adjunct Professor of Materials Science & Engineering at Stanford University. His research and teaching focuses broadly on harnessing the power of machine learning and A.I. to accelerate the design and discovery of new materials for decarbonizing the global economy. He serves as an advisor and collaborator on several initiatives at Stanford, spanning from fundamental materials science research to technology entrepreneurship mentoring. He is also the Founder and Chief Executive Officer of Aionics, Inc., a technology company dedicated to designing high performance batteries with A.I. and high performance compute (HPC)-based quantum mechanical simulation. He was included on the 2019 list of Forbes 30 Under 30 in Energy, and served as a Guest Lecturer in Mechanical Engineering at Columbia University in 2019 and 2020. He holds a B.S. in Applied Physics from UC Davis and a Ph.D. in Applied Physics from Stanford University.
Upcoming courses:
FALL 2023: Materials Science and Engineering 331: Computational materials science at the atomic scale. Introduction to computational materials science methods at the atomistic level, with an emphasis on quantum methods. A brief history of computational approaches is presented, with deep dives into the most impactful methods: density functional theory, tight-binding, empirical potentials, and machine learning-based property prediction. Computation of optical, electronic, phonon properties. Bulk materials, interfaces, nanostructures. Molecular dynamics. Prerequisites - undergraduate quantum mechanics. Experience writing code is preferred but not required.
Select publications:
AD Sendek, B Ransom, ED Cubuk, LA Pellouchoud, J Nanda, EJ Reed. Machine learning modeling for accelerated battery materials design in the small data regime. ACS Energy Materials 12, 2200553 (2022).
AD Sendek, Q Yang, ED Cubuk, KAN Duerloo, Y Cui, EJ Reed. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy & Environmental Science 10 (1), 306-320 (2017).
AD Sendek, ED Cubuk, ER Antoniuk, G Cheon, Y Cui, EJ Reed. Machine learning-assisted discovery of solid Li-ion conducting materials. Chemistry of Materials 31 (2), 342-352 (2018).
AD Sendek, G Cheon, M Pasta, EJ Reed. Quantifying the search for solid Li-ion electrolyte materials by anion: a data-driven perspective. The Journal of Physical Chemistry C 124 (15), 8067-8079 (2020).
AD Sendek, ER Antoniuk, ED Cubuk, B Ransom, BE Francisco, J Buettner-Garrett, Y Cui, EJ Reed. Combining Superionic Conduction and Favorable Decomposition Products in the Crystalline Lithium–Boron–Sulfur System: A New Mechanism for Stabilizing Solid Li-Ion Electrolytes. ACS Applied Materials & Interfaces 12 (34), 37957-37966 (2020).
J Xie, AD Sendek, ED Cubuk, X Zhang, Z Lu, Y Gong, T Wu, F Shi, W Liu, EJ Reed, Y Cui. Atomic Layer Deposition of Stable LiAlF4 Lithium Ion Conductive Interfacial Layer for Stable Cathode Cycling. ACS Nano 11 (7), 7019-7027 (2017).
B Ransom, N Zhao, AD Sendek, ED Cubuk, W Chueh, EJ Reed. Two low-expansion Li-ion cathode materials with promising multi-property performance. MRS Bulletin (2021).
ED Cubuk, AD Sendek, EJ Reed. Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data. The Journal of Chemical Physics 150 (21), 214701 (2019). -
Debbie Senesky
Associate Professor of Aeronautics and Astronautics, of Electrical Engineering and Senior Fellow at the Precourt Institute for Energy
BioDebbie G. Senesky is an Associate Professor at Stanford University in the Aeronautics and Astronautics Department and the Electrical Engineering Department. In addition, she is the Principal Investigator of the EXtreme Environment Microsystems Laboratory (XLab). Her research interests include the development of nanomaterials for extreme harsh environments, high-temperature electronics for Venus exploration, and microgravity synthesis of nanomaterials. In the past, she has held positions at GE Sensing (formerly known as NovaSensor), GE Global Research Center, and Hewlett Packard. She received the B.S. degree (2001) in mechanical engineering from the University of Southern California. She received the M.S. degree (2004) and Ph.D. degree (2007) in mechanical engineering from the University of California, Berkeley. Prof. Senesky is the Site Director of nano@stanford. She is currently the co-editor of two technical journals: IEEE Journal of Microelectromechanical Systems and Sensors. In recognition of her research, she received the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2025, Emerging Leader Abie Award from AnitaB.org in 2018, Early Faculty Career Award from the National Aeronautics and Space Administration (NASA) in 2012, Gabilan Faculty Fellowship Award in 2012, and Sloan Ph.D. Fellowship from the Alfred P. Sloan Foundation in 2004.
Prof. Senesky's career path and research has been featured by Scientific American, Seeker, People Behind the Science podcast, The Future of Everything radio show, Space.com, and NPR's Tell Me More program. More information about Prof. Senesky can be found at https://xlab.stanford.edu and on Instagram (@astrodebs). -
Rajsekhar Setaluri
Ph.D. Student in Computer Science, admitted Autumn 2015
BioI am a PhD candidate in Computer Science working with Prof. Pat Hanrahan. I am broadly interested in hardware tools, compilers and languages and improving hardware design productivity.
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Kawin Setsompop
Professor of Radiology (Radiological Sciences Laboratory) and, by courtesy, of Electrical Engineering
BioKawin Setsompop is a Professor of Radiology and, by courtesy, of Electrical Engineering. His research focuses on the development of novel MRI acquisition methods, with the goal of creating imaging technologies that can be used to help better understand brain structure and function for applications in Healthcare and Health sciences. He received his Master’s degree in Engineering Science from Oxford University and his PhD in Electrical Engineering and Computer Science from MIT. Prior to joining Stanford, he was a postdoctoral fellow and subsequently a faculty at the A.A. Martinos center for biomedical imaging, MGH, as well as part of the Harvard and MIT faculty. His group has pioneered several widely-used MRI acquisition technologies, a number of which have been successfully translated into FDA-approved clinical products on Siemens, GE, Phillips, United Imaging and Bruker MRI scanners worldwide. These technologies are being used daily to study the brain in both clinical and neuroscientific fields.
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Ross Shachter
Associate Professor of Management Science and Engineering
Current Research and Scholarly InterestsProf. Shachter's research has focused on the representation, manipulation, and analysis of uncertainty and probabilistic reasoning in decision systems. As part of this work, he developed the DAVID influence diagram processing system for the Macintosh. He has developed models scheduling patients for cancer follow-up, and analyzing vaccination strategies for HIV and Helobacter pylori.
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Alay Shah
Masters Student in Chemical Engineering, admitted Spring 2024
Bio→ Graduate Chemical Engineering student.
→ Previously, Process Engineer at Kite, a Gilead Company.
→ Bachelors in Biomedical Engineering at the University of Texas, Austin.
→ 5 years of experience working in cGMP pharmaceutical manufacturing and upstream process development. Working knowledge of cell and gene therapy, lean manufacturing, risk assessment &mitigation, IOPQ Validation, quality systems, eQRMS, asset lifecycle management, SAP ERP, Syncade MES, Oracle EBS, LIMS, ISO standards and FDA regulations.
→ Through Stanford's MS program, I aim to build upon my biomanufacturing experience, further developing my skillsets in bioreactor design and data analytics to model and improve standardized development of therapeutics for patients -
Nigam H. Shah, MBBS, PhD
Professor of Medicine (Computational Medicine), of Biomedical Data Science and, by courtesy, of Computer Science
Current Research and Scholarly InterestsWe answer clinical questions using aggregate patient data at the bedside. The Informatics Consult Service (https://greenbutton.stanford.edu/) put this idea in action and led to the creation of Atropos Health. We build predictive models that allow taking mitigating actions, keeping the human in the loop.
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Yash Shah
Ph.D. Student in Computer Science, admitted Autumn 2025
Current Research and Scholarly InterestsMy research interests lie in developing neuroconnectionist mechanistic models of the brain that deepen our understanding of neural computation and representations. I aim to explore how physiological and anatomical constraints shape cortical topography and, in turn, scaffold development. I am particularly intrigued by observing certain behaviors emerge from mechanistic models, even when the model was not optimized to do so.
