School of Engineering
Showing 301-350 of 498 Results
-
Paul Milgrom
Shirley R. and Leonard W. Ely, Jr. Professor in the School of Humanities and Sciences, Professor of Economics, Senior Fellow at SIEPR and Professor, by courtesy, of Economics at the GSB and of Management Science and Engineering
BioPaul Milgrom is the Shirley and Leonard Ely professor of Humanities and Sciences in the Department of Economics at Stanford University and professor, by courtesy, in the Stanford Graduate School of Business and in the Department of Management Sciences and Engineering. Born in Detroit, Michigan on April 20, 1948, he is a member of both the National Academy of Sciences and the American Academy of Arts and Sciences and a winner of the 2008 Nemmers Prize in Economics, the 2012 BBVA Frontiers of Knowledge award, the 2017 CME-MSRI prize for Innovative Quantitative Applications, and the 2018 Carty Award for the Advancement of Science.
Milgrom is known for his work on innovative resource allocation methods, particularly in radio spectrum. He is coinventor of the simultaneous multiple round auction and the combinatorial clock auction. He also led the design team for the FCC's 2017 incentive auction, which reallocated spectrum from television broadcast to mobile broadband.
According to his BBVA Award citation: “Paul Milgrom has made seminal contributions to an unusually wide range of fields of economics including auctions, market design, contracts and incentives, industrial economics, economics of organizations, finance, and game theory.” As counted by Google Scholar, Milgrom’s books and articles have received more than 80,000 citations.
Finally, Milgrom has been a successful adviser of graduate students, winning the 2017 H&S Dean's award for Excellence in Graduate Education. -
David Miller
W.M. Keck Foundation Professor of Electrical Engineering and Professor, by courtesy, of Applied Physics
Current Research and Scholarly InterestsDavid Miller’s research interests include the use of optics in switching, interconnection, communications, computing, and sensing systems, physics and applications of quantum well optics and optoelectronics, and fundamental features and limits for optics and nanophotonics in communications and information processing.
-
Tara Yasmin Mina
Postdoctoral Scholar, Aeronautics and Astronautics
BioTara Mina obtains her Ph.D. in Electrical Engineering at Stanford University. She received her Master of Science in Electrical and Computer Engineering from the University of Illinois at Urbana- Champaign in 2019 and her Bachelor of Science in Electrical Engineering from Iowa State University in 2017, with summa cum laude honors. For her doctoral thesis, Tara researches strategies to advance the next-generation Global Positioning System (GPS) signal capabilities. Her research focuses on secure, attack-resilient position, navigation, and timing as well as designing new spreading codes for the future GPS signals. She has also been involved with research for designing satellite-based navigation and timing to enable future lunar exploration missions.
As of August 2023, Tara has 22 research publications, including 7 published or accepted journal papers, and a coverpage magazine article. She has also won several awards for her graduate research, including the National Science Foundation (NSF) Graduate Research Fellowship, the National Defense Science and Engineering Graduate (NDSEG) Fellowship, the Amelia Earhart Fellowship, and 4 Best Presentation of the Session awards. Outside of her research work, Tara has won 2 student teaching awards, including the Centennial Teaching Assistant Award and the AIAA Best Course Assistant Award. She also currently serves as the co-president of Stanford’s Engineering Students for Diversity, Equity, and Inclusion (DEI), and has also won the Community Impact Award for her leadership, outreach, and volunteering efforts within the student group.
For the most up-to-date information, research work, and publications, please check out Tara's personal website: https://sites.google.com/view/tara-mina -
Lloyd B. Minor, MD
The Carl and Elizabeth Naumann Dean of the School of Medicine, Vice President for Medical Affairs, Stanford University, Professor of Otolaryngology - Head and Neck Surgery and Professor of Neurobiology and of Bioengineering, by courtesy
Current Research and Scholarly InterestsThrough neurophysiological investigations of eye movements and neuronal pathways, Dr. Minor has identified adaptive mechanisms responsible for compensation to vestibular injury in a model system for studies of motor learning. Following his discovery of superior canal dehiscence, he published a description of the disorder’s clinical manifestations and related its cause to an opening in the bone covering of the superior canal. He subsequently developed a surgical procedure to correct the problem.
-
Brando Miranda
Ph.D. Student in Computer Science, admitted Autumn 2022
BioBio
Brando Miranda is a current Ph.D. Student at Stanford University under the supervision of Professor Sanmi Koyejo in the department of Computer Science. Previously he has been a graduate student at University of Illinois Urbana-Champaign, Research Assistant at MIT’s Center for Brain Minds and Machines (CBMM), and graduate student at the Massachusetts Institute of Technology (MIT). Miranda’s research interests lie in the field of meta-learning, foundation models for theorem proving, and human & brain inspired Artificial Intelligence (AI). Miranda completed his Master of Engineering in Electrical Engineering and Computer Science under the supervision of Professor Tomaso Poggio – where he did research on Deep Learning Theory. Miranda has been the recipient of several awards, including Most Cited Paper Certificate awarded by International Journal of Automation & Computing (IJAC), two Honorable Mention with the Ford Foundation Fellowship, Computer Science Excellence Saburo Muroga Endowed Fellow, Stanford School of Engineering fellowship, and is currently an EDGE Scholar at Stanford University.
About me (Informal)
I am a scientist and an engineer that is interested in moving forward the powerful and beautiful field of A.I. closer to true Artificial General Intelligence (AGI). I believe an important direction is understanding how to combine cognitive and neuro-inspired models, specially investigating how reasoning and learning work together. In addition, I also believe being able to adapt to new tasks using prior experience and knowledge is crucial for AGI to occur. Consequently, I decided to pursue a Ph.D in AI and machine learning. I currently work on meta-learning and machine learning (ML) for Theorem Proving (TP) at Stanford University. -
Eduardo Miranda
Professor of Civil and Environmental Engineering
Current Research and Scholarly InterestsRegional seismic risk assessment, ground motion directionality
-
Azalia Mirhoseini
Assistant Professor of Computer Science
BioAzalia Mirhoseini is an Assistant Professor in the Computer Science Department at Stanford University. Professor Mirhoseini's research interest is in developing capable, reliable, and efficient AI systems for solving high-impact, real-world problems. Her work includes generalized learning-based methods for decision-making problems in systems and chip design, self-improving AI models through interactions with the world, and scalable deep learning optimization. Prior to Stanford, she spent several years in industry AI labs, including Anthropic and Google Brain. At Anthropic, she worked on advancing the capabilities and reliability of large language models. At Google Brain, she co-founded the ML for Systems team, with a focus on automating and optimizing computer systems and chip design. She received her BSc degree in Electrical Engineering from Sharif University of Technology and her PhD in Electrical and Computer Engineering from Rice University. Her work has been recognized through the MIT Technology Review’s 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including MIT Technology Review, IEEE Spectrum, The Verge, The Times, ZDNet, VentureBeat, and WIRED.
-
Shahab Mirjalili
Physical Science Research Scientist
Current Research and Scholarly InterestsBroadly, my research lies in the intersection of fluid mechanics, scientific computing, and machine learning. My work aims to develop and use computational methods to provide a predictive understanding of complex flow problems, including those involving multi-physics couplings and multiphase dynamics across a wide range of scales and Reynolds numbers. In this vein, I develop physically consistent models, robust numerical schemes, and high-performance computing (HPC) software that enable high-fidelity simulations of flows involving complex multi-physics effects. These developments build upon my novel work on modeling multiphase flows and my high-performance multiphase, multi-physics software. In addition to simulations, I use asymptotic analyses and machine learning (ML) to construct reduced-order models (ROMs) that can be used for engineering analysis, control, design, and especially optimization. I am interested in a wide range of applications involving impactful problems. In particular, I am passionate about improving the predictive understanding of multiphase flows in:
- Propulsion and energy conversion/storage
- Additive manufacturing processes
- Biophysical systems
- Environmental flows
