School of Engineering
Showing 651-700 of 1,134 Results
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Pedram Mokrian
Lecturer
Instructor, Stanford Engineering Center for Global and Online EducationBioPedram Mokrian is Adjunct Professor at Stanford University and a lecturer at the Haas School of business at UC Berkeley where he teaches and advises entrepreneurs and global 1000 companies alike on entrepreneurship, business model disruption, and technology innovation strategy. He was previously a Principal at Mayfield, one of Silicon Valley’s most storied venture capital firms, where he was part of the investment team with over $3.5B assets under management. Mokrian is a founding Partner of the Ratio Academy, New Line Ventures. He also serves as a mentor or advisor to a number of start-ups, innovation incubators, including Global Innovation Catalyst, the Texas Medical Center Innovation Center, Innovation Labs, MISO, and Moog, and serves on the advisory board of Phillips66.
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Suzanne Morze
Associate Director, Leadership Giving, School of Engineering - External Relations
Current Role at StanfordAssociate Director of Annual Giving, School of Engineering
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Rachelle Mozeleski
Web Content Manager, Electrical Engineering
Current Role at StanfordWeb Content Manager for the Department of Electrical Engineering
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Fernando Mujica
Adjunct Professor, Electrical Engineering
BioFernando Mujica is an Adjunct Professor in the Department of Electrical Engineering at Stanford University. He received the Ph.D. degree in electrical engineering from the Georgia Institute of Technology in 1999 and B.S. and M.S. degrees from Universidad Simón Bolivar in 1993 and 1995, respectively. Prof. Mujica's research interests are in the area of signal processing. He has been granted more than 25 US patents over a wide range of applications. Prof. Mujica was elected to the Tau Beta Pi Teaching Honor Roll in 2022.
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Tiffany Murray
Executive Associate to Russ B. Altman, MD, PhD & Teri E. Klein, PhD, Bioengineering
Current Role at StanfordExecutive Associate to
Russ B. Altman, MD, PhD
The Kenneth Fong Professor of Engineering
Professor, Bioengineering, Genetics, Medicine, Biomedical Data Science and, by courtesy, of Computer Science
Teri E. Klein, PhD
Professor, Biomedical Data Science & Medicine and, by courtesy, of Genetics -
Ronjon Nag
Summer CSP Instructor
Instructor, Stanford Engineering Center for Global and Online EducationBioRonjon Nag is an inventor, teacher and entrepreneur. He is an Adjunct Professor in Genetics at the Stanford School of Medicine, becoming a Stanford Distinguished Careers Institute Fellow in 2016. He teaches AI, Genes, Ethics, Longevity Science and Venture Capital. He is a founder and advisor/board member of multiple start-ups and President of the R42 Group, a venture capital firm which invests in, and creates, AI and Longevity companies. As an AI pioneer of smartphones and app stores, his companies have been sold to Apple, BlackBerry, and Motorola. More recently he has worked on the intersection of AI and Biology. He has been awarded the IET Mountbatten Medal by the Institution of Engineering and Technology, the 2021 IEEE-SCV Outstanding Engineer Award, the $1m Verizon Powerful Answers Award, the 2023 COX AI Lifetime Achievement Award, the 2023 MIT Great Dome Award, and is the 2024 Inductee in the Silicon Valley Engineering Hall of Fame. Professor Nag has a Ph.D from Cambridge, an M.S from Massachusetts Institute of Technology and a B.Sc. from Birmingham in the UK. He has numerous interests in the intersection of AI and Healthcare including being CEO of Agemica.ai working on creating a vaccine for aging.
He has many firsts including:
Firsts:
• First laptop with speech recognition built-in (with Apricot, 1984)
• First selling cursive handwriting recognition (with Lexicus, 1991)
• First speech recognition phones (with Lexicus/Motorola, 1996)
• First large-vocabulary Chinese speech recognition (with Lexicus/Motorola, 1996)
• First Chinese predictive text system on a phone (Lexicus/Motorola, 1997)
• First predictive text systems in 40 languages on Motorola phones, (Lexicus/Motorola, 1997)
• First touch screen mobile phone with handwriting recognition (Lexicus/Motorola, 1999)
• First combined mobile search engine and directory (with Cellmania, 2000)
• First private label downloadable operator billable apps store (Cellmania, 2000)
• First BlackBerry Operator Billing apps store (Cellmania,2010)
• First Neural Network Artificial Intelligence System in the Cloud (Ersatz Labs, 2014)
• First Throwable 360 Ball Camera (Bounce Imaging, 2015)
• First Android powered smart light switch (Brightswitch 2017)
• First blood pressure watch with temperature and pulse oximetry add-ons for Back to Work Covid Kit (GTCardio 2019)
• First no code AI life sciences app store (Superbio.ai 2022)
• First proposal for an aging vaccine (Agemica 2023) -
Suresh Nambi
Course Asst-Graduate, Electrical Engineering - Student Services
BioI am an AI/ML Compute Architect specializing in cross-stack optimization for LLMs and deep learning systems. Currently I am focused on datacenter-scale performance from application analysis to hardware design :
- Full-stack workload analysis and optimization from model architecture to writing hardware kernels
- Multi-Node Performance Simulation, Roofline modeling and architecture-specific optimizations
- Achieve efficiency at scale by delivering improvements in training/inference through novel microarchitectures
I am a master’s student pursuing MS EE (Software and Hardware Systems) at Stanford University. Before starting my MS, I worked at Nvidia as an ASIC Design Engineer in the GPU Hardware Security team, where I contributed to the development of computer chips used in datacenters. Prior to that I worked at Ceremorphic a stealth startup on their first energy-efficient AI supercomputing test chip and gained insight into their heterogenous computing model.
I completed my undergraduate studies at BITS Pilani, India. During this period, I was associated with Prof. Akash Kumar at the Chair for Processor Design, TU Dresden for my year-long undergraduate thesis. I also had the opportunity to work under Prof. Gerd Grau during my MITACS Globalink Summer Internship. -
Reza Nasiri Mahalati
Adjunct Professor, Electrical Engineering
BioReza Nasiri Mahalati is an Adjunct Professor in the department of Electrical Engineering at Stanford University and a senior hardware design engineer at Apple Inc. His current work focuses on the development of new hardware technologies that enable more fluid human computer interactions. He received the B.S. degree in Electrical Engineering from the Sharif University of Technology, Tehran, Iran in 2008, and the M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 2010 and 2013, respectively. While at Stanford, his research focused on mode-division multiplexing in multi-mode optical fibers, fiber-based imaging, optimization and digital signal processing.
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Dale Nesbitt
Adjunct Lecturer, Management Science and Engineering
BioDr. Nesbitt has been teaching MSE 252 (Decision Analysis), MSE 352 (Professional Decision Analysis), MSE 353 (Advanced Decision Analysis), MSE 299 (Coercion Free Social Systems), and MSE 254 (The Ethical Analyst) in the department. He has practiced and taught in these fields, and economic modeling, for several decades.
Dr. Nesbitt has been researching Bayesian statistical analysis, ethics, and ethical theories in a general setting (i.e., personal ethics not necessarily associated with any particular field or discipline). His research focuses on ethics per se, not ethics related to a specific technology, commodity, discipline, area, or practice. He is currently focused on ethics from a socio-personal perspective, one in which coercion is minimized or sanctioned, one that blends the utilitarian approach of Harsanyi, Mill, Bentham, and others with the uncoerced game theory approach of Nash and Harsanyi. The objective of this research is to give a roadmap for people (and groups) to behave ethically and do good and also to be able to consider ethical decision making under uncertainty.
Dr. Nesbitt is completing a monograph on Bayesian Linear Regression intended to unify key dimensions of the field around a pure Bayesian probabilistic viewpoint, what he calls “unabashed Bayes.” The monograph is scheduled for completion in 2022. Dr. Nesbitt continues to research and practice Bayesian regression and probabilistic analysis, recently applying it to disciplines such as automobile selection, jet technology and fuel projection, and petrochemicals demand.
Dr. Nesbitt has focused for many years on building economic-environmental models of the key energy commodities—oil and refined products, natural gas, petrochemicals, automobiles, electric power generation, natural gas and electricity storage, renewable energy, environmental emissions and remediation, and demand/emission. His models and work in the field are well known, extending the classical economic equilibrium approach.
Dr. Nesbitt has worked and published in the field of semi-Markovian Decision Problems (the area of his thesis at Stanford), energy economics, cartels and monopolies, methods for modeling markets, Bayesian statistics, and free (meaning uncoerced) social systems.
