Stanford University
Showing 1-100 of 536 Results
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Eric Abdulaziz
Masters Student in Mechanical Engineering, admitted Autumn 2023
Bio→ HCP Graduate Mechanical Engineering part time student. Full time Mechanical Engineer at Intuitive Surgical.
→ Bachelors in Mechanical Engineering at the University of California, Irvine.
→ Grew an interest in the medical device field through self led research in developing a prosthetic for a user with a congenital limb deficiency of the hand.
→ Later grew passionate about Minimally Invasive Surgery through industry experience in Neuroendovascular Surgery.
→ Strongly believe that Minimally Invasive Surgical Robotics is an imperative step to catalyzing a paradigm shift in significantly improving patient outcomes and broadening scope of impact. -
Thomas P. Andriacchi
Professor of Mechanical Engineering and of Orthopaedic Surgery, Emeritus
Current Research and Scholarly InterestsProfessor Andriacchi's research focuses on the biomechanics of human locomotion and applications to medical devices, sports injury, osteoarthritis, the anterior cruciate ligament and low cost prosthetic limbs
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Andres Arriagada Romero
Postdoctoral Scholar, Mechanical Engineering
BioPorous media, Heterogeneous combustion, Packed-bed reactors.
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Spencer Carlton Barnes
Ph.D. Student in Mechanical Engineering, admitted Autumn 2022
Masters Student in Mechanical Engineering, admitted Spring 2024BioI am currently a Mechanical Engineering graduate student at Stanford University pursuing a PhD. At the university, I work as a research assistant in the high-temperature gas dynamics laboratory. My current work involves novel concepts in laser spectroscopy. I pride myself in being self-motivated, detail oriented, and a team player.
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David Beach
Professor (Teaching) of Mechanical Engineering, Emeritus
BioBeach teaches courses in the areas of design and manufacturing. Beach and Craig Milroy co-direct the Product Realization Laboratory which provides 1700 students annually with hands on experiences in product definition, conceptual design, detail design, and prototype creation. The PRL offers courses, mentors and tools in support of integrated designing and making. Pedagogically, Beach believes that creation of experience from which students (and teams of students) can interpret and internalize their own conclusions provides an excellent complement to content based teaching. His goal is to add strength in tacit knowledge which derives from the hands-on synthesis of design, prototype building, presentation and criticism.. The resulting judgment and instinct regarding materials, devices, materials transformation processes, and design process complement classical analytical engineering education to create superior engineers.
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Adam Boies
Associate Professor of Mechanical Engineering and Center Fellow at the Precourt Institute for Energy
Current Research and Scholarly InterestsSee www.ANEEStanford.com/research
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Emanuele Bombardi
Affiliate, Mechanical Engineering - Flow Physics and Computation
BioEmanuele Bombardi received his BSc (2020) and MSc (2022) degrees in Mathematical and Aerospace Engineering from the Politecnico di Torino. He subsequently began his doctoral studies at the Aéro-Thermo-Mécanique Laboratory at the Université libre de Bruxelles, under the supervision of Prof. Alessandro Parente and co-supervised by Prof. Luca Magri at Imperial College London, within the MODELAIR project funded by a Horizon Europe Marie Skłodowska-Curie Actions grant, with an expected graduation in 2027. He is a participant in the 2026 Summer Program at the Center for Turbulence Research at Stanford University.
Bombardi's research focuses on data assimilation methods for turbulence modelling and pollutant dispersion in urban atmospheric flows. Specific topics of interest include the development of ensemble Kalman filter frameworks for the calibration of turbulence closures, uncertainty quantification for low-fidelity simulations using high-fidelity data, and the application of these methods to complex urban environments. -
Mourad Bouache
Lecturer
BioMourad Bouache, Ph.D. is a pioneering computer scientist and tech leader operating at the critical intersection of hardware-software co-design, high-performance computing, and generative artificial intelligence.
Currently serving as the TPU and Models SW-HW Co-Design Tech Lead at Google, Mourad guides elite engineering teams tasked with architecting the next generation of accelerated AI infrastructure. His career spans over a decade of systemic evolution in AI, including serving as the Generative AI Engineering Tech Lead at Meta, driving core AI initiatives at Intel, and spending ten foundational years at Yahoo (through its Oath and Verizon chapters) scaling intelligent solutions across massive global ecosystems.
Anchored by deep academic roots, Mourad earned his Ph.D. in Computer Science from the University of Perpignan UPVD in France, followed by three prestigious postdoctoral fellowships focused on AI and Performance Optimization across the United States and Canada.
Committed to bridge-building between industry frontiers and academic excellence, he also serves as a lecturer at Stanford University, translating the complex realities of modern machine learning to inspire and equip the next generation of elite AI engineers. -
Tom Bowman
Professor of Mechanical Engineering, Emeritus
BioProfessor Bowman studies reacting flows, primarily through experimental means, and the processes by which pollutants are formed and destroyed in flames. In addition, he is interested in the environmental impact of energy use, specifically greenhouse gas emissions from use of fossil fuels.
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Kristin Burns
Design Group Manager, Mechanical Engineering - Design
Current Role at StanfordME Design Group Manager
Manager, Industry Affiliate Program for Teaching Design Thinking -
Wei Cai
Professor of Mechanical Engineering and, by courtesy, of Materials Science and Engineering
BioPredicting mechanical strength of materials through theory and simulations of defect microstructures across atomic, mesoscopic and continuum scales. Developing new atomistic simulation methods for long time-scale processes, such as crystal growth and self-assembly. Applying machine learning techniques to materials research. Modeling and experiments on the metallurgical processes in metal 3D printing. Understanding microstructure-property relationship in materials for stretchable electronics, such as carbon nanotube networks and semiconducting elastomers.
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David Camarillo
Associate Professor of Bioengineering and, by courtesy, of Neurosurgery and of Mechanical Engineering
BioDavid B. Camarillo is Associate Professor of Bioengineering, (by courtesy) Mechanical Engineering and Neurosurgery at Stanford University. Dr. Camarillo holds a B.S.E in Mechanical and Aerospace Engineering from Princeton University, a Ph.D. in Mechanical Engineering from Stanford University and completed postdoctoral fellowships in Biophysics at the UCSF and Biodesign Innovation at Stanford. Dr. Camarillo worked in the surgical robotics industry at Intuitive Surgical and Hansen Medical, before launching his laboratory at Stanford in 2012. His current research focuses on precision human measurement for multiple clinical and physiological areas including the brain, heart, lungs, and reproductive system. Dr. Camarillo has been awarded the Hellman Fellowship, the Office of Naval Research Young Investigator Program award, among other honors including multiple best paper awards in brain injury and robotic surgery. His research has been funded by the NIH, NSF, DoD, as well as corporations and private philanthropy. His lab’s research has been featured on NPR, the New York Times, The Washington Post, Science News, ESPN, and TED.com as well as other media outlets aimed at education of the public.
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Mark A. Cappelli
Professor of Mechanical Engineering
BioProfessor Cappelli received his B.Sc. degree in Physics (McGill, 1980), and M.A.Sc and Ph.D. degrees in Aerospace Sciences (Toronto, 1983, 1987). He joined Stanford University in 1987 and is currently a Professor in the Department of Mechanical Engineering and Co-Director of the Engineering Physics Program. He carries out research in applied plasma physics with applications to a broad range of fields, including space propulsion, aerodynamics, medicine, materials synthesis, and fusion.
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Ovijit Chaudhuri
Professor of Mechanical Engineering and, by courtesy, of Bioengineering
Current Research and Scholarly InterestsWe study the physics of cell migration, division, and morphogenesis in 3D, as well cell-matrix mechanotransduction, or the process by which cells sense and respond to mechanical properties of the extracellular matrices. For both these areas, we use engineered biomaterials for 3D culture as artificial extracellular matrices.
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Helen L. Chen
Research Scientist
BioHelen L. Chen is a research scientist in the Designing Education Lab in the Department of Mechanical Engineering at Stanford University. She holds an undergraduate degree in communication from UCLA and a PhD in communication with a minor in psychology from Stanford. Helen is a board member for the Association for Authentic, Experiential and Evidence-Based Learning (AAEEBL) and is a co-author of Documenting Learning with ePortfolios: A Guide for College Instructors and co-executive editor of the International Journal of ePortfolio. She works closely with the Association of American Colleges and Universities and consults with institutions on general education redesign, authentic assessment approaches, design thinking, and personal branding and ePortfolios. Helen's current research and scholarship focus on engineering and entrepreneurship education; the pedagogy of portfolios and reflective practice in higher education; and redesigning how learning is recorded and recognized in traditional transcripts and academic credentials.
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Jianqing Chen
Affiliate, Mechanical Engineering - Design
Visiting Scholar, Mechanical Engineering - DesignBioMy work focuses on robotic remote control and manipulation systems utilizing reinforcement learning (RL). The research centers on developing RL-based algorithms that enable robots to learn optimal control strategies for tasks such as navigation, object manipulation, and interaction within dynamic environments. By training robots through iterative trial-and-error processes, these systems continuously improve performance, adapt to new situations, and enhance autonomous control. The broader goal is to achieve more efficient, precise, and scalable robotic behavior in real-world applications.
In parallel, I have over seven years of experience in investment and asset management. I lead 280 Capital, a family-backed investment office overseeing more than $1 billion across digital assets and emerging technologies.
My technical background spans artificial intelligence, enterprise storage systems, encryption, and large-scale computing infrastructure. I contributed to the design of next-generation enterprise SSD controller and storage system chips ranging from 16nm to 7nm at Broadcom and SK Hynix. At Roche, I led the construction and optimization of large-scale biodata software and hardware architectures supporting advanced DNA sequencing and scalable computational biology workloads. I also hold multiple U.S. patents related to improving storage and computing efficiency through deep learning, AI, and advanced systems design. -
Woongbi Cho
Postdoctoral Scholar, Mechanical Engineering
BioWoongbi Cho is a postdoctoral scholar in the Department of Mechanical Engineering at Stanford University. He received his B.S. in Polymer Science and Engineering from Inha University in February 2019 , and his Ph.D. in Organic and Nano Engineering from Hanyang University in February 2025. His doctoral research focused on developing next-generation polymer composites, emphasizing processing-structure-property-performance (PSPP) relationships in liquid crystalline and sulfur-rich polymers. Currently, Woongbi's research interests center on adaptive materials, electromagnetically (EM)-driven soft robotics, polymer assembly mechanisms, and active metamaterials for applications in soft robotics, optoelectronics, and energy harvesting.
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Savannah Cofer
Ph.D. Student in Mechanical Engineering, admitted Autumn 2020
BioReconfigurable Origami Robotics, Stanford SHAPE Lab
PhD Mechanical Engineering
Stanford Knight-Hennessy Scholars
NSF GRFP Fellowship -
Steven Hartley Collins
Associate Professor of Mechanical Engineering and, by courtesy, of Bioengineering
BioSteve Collins is an Associate Professor of Mechanical Engineering at Stanford University, where he teaches courses on design and robotics and directs the Stanford Biomechatronics Laboratory. His primary focus is to speed and systematize the design and prescription of prostheses and exoskeletons using versatile device emulator hardware and human-in-the-loop optimization algorithms (Zhang et al. 2017, Science). Another interest is efficient autonomous devices, such as highly energy-efficient walking robots (Collins et al. 2005, Science) and exoskeletons that use no energy yet reduce the metabolic energy cost of human walking (Collins et al. 2015, Nature).
Prof. Collins received his B.S. in Mechanical Engineering in 2002 from Cornell University, where he performed undergraduate research on passive dynamic walking robots. He received his Ph.D. in Mechanical Engineering in 2008 from the University of Michigan, where he performed research on the dynamics and control of human walking. He performed postdoctoral research on humanoid robots at T. U. Delft in the Netherlands. He was a professor of Mechanical Engineering and Robotics at Carnegie Mellon University for seven years. In 2017, he joined the faculty of Mechanical Engineering at Stanford University.
Prof. Collins is a member of the Scientific Board of Dynamic Walking and the Editorial Board of Science Robotics. He has received the Young Scientist Award from the American Society of Biomechanics, the Best Medical Devices Paper from the International Conference on Robotics and Automation, and the student-voted Professor of the Year in his department. -
Murray Connelly Cutforth
Affiliate, Mechanical Engineering - Mechanics and Computation
BioMurray Cutforth is a research scientist on the PSAAP III project at the Center for Turbulence Research. He works with Professor Eric Darve on uncertainty quantification of laser-ignited turbulent combustion. During his PhD at the University of Cambridge, Murray studied sharp interface methods for multi-material flow, and subsequently has worked on applications of machine learning in medical image and text analysis in industry.
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Mark Cutkosky
Fletcher Jones Professor in the School of Engineering
BioCutkosky applies analyses, simulations, and experiments to the design and control of robotic hands, tactile sensors, and devices for human/computer interaction. In manufacturing, his work focuses on design tools for rapid prototyping.
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Rafaela Da Silva Presa
Graduate Visiting Researcher Student, Mechanical Engineering
BioRafaela is a master in Bioengineering (Molecular Biotechnology branch) since 2019 by Faculty of Engineering of University of Porto, Portugal (FEUP)/ School of Medicine and Biomedical Sciences of University of Porto (ICBAS) with her MSc thesis done in the field of biomaterials for bone cancer treatment and regeneration simultaneously.
Rafaela is performing her PhD project in Biofabrication Group in the Institute for Innovation and Research in Health from University of Porto (i3s). The project is focused on the development of mechano-modulatory 3D in vitro model of human skin fibrosis. Currently, Rafaela is doing a period abroad at Chaudhuri Lab, Mechanical Engineering department at Stanford University, to study the impact of viscoelasticity in fibroblast fate and activation.
Moreover, she is enrolled in the International Doctoral Program in Cellular and Molecular Biotechnology Applied to Health Sciences (BiotechHealth) in ICBAS from University of Porto.
Her multidisciplinary background in bioengineering and biomaterials allowed her to develop strong skills in complementary research areas ranging from materials synthesis and biofabrication to cellular biology.
Rafaela has excellent communication skills and experience in presenting scientific data in national and international meetings. -
Eric Darve
Director, Institute for Computational and Mathematical Engineering (ICME) and Professor of Mechanical Engineering
Current Research and Scholarly InterestsThe research interests of Professor Darve span across several domains, including machine learning for science and engineering, large-language models, transformer models, surrogate and reduced order modeling, stochastic inversing, anomaly detection, numerical linear algebra, high-performance, parallel, and GPU computing.
