School of Engineering
Showing 3,901-4,000 of 6,463 Results
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Priya Nair
Ph.D. Student in Bioengineering, admitted Autumn 2020
BioI received my Bachelor's degree in Biomedical Engineering with a minor in Industrial Design from Georgia Institute of Technology in 2020. During my time at Georgia Tech, I worked as an undergraduate researcher in Dr. Ajit Yoganathan's Cardiovascular Fluid Mechanics Lab. My project was focused on studying the contribution of foreign materials to thrombosis in transcatheter aortic valves using an in vitro flow loop. Beyond my research interests, I was also actively involved in the Society of Women Engineers, promoting outreach activities and creating mentorship opportunities for women in STEM.
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Sanjiv Narayan
Professor of Medicine (Cardiovascular Medicine)
Current Research and Scholarly InterestsDr. Narayan directs the Computational Arrhythmia Research Laboratory, whose goal is to define the mechanisms underlying complex human heart rhythm disorders, to develop bioengineering-focused solutions to improve therapy that will be tested in clinical trials. The laboratory has been funded continuously since 2001 by the National Institutes of Health, AHA and ACC, and interlinks a disease-focused group of clinicians, computational physicists, bioengineers and trialists.
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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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Koosha Nassiri Nazif
Affiliate, Program-Pop, E.
BioDr. Koosha Nassiri Nazif received his Ph.D. in Electrical Engineering (Jan 2022) and his M.S. in Mechanical Engineering (2016) from Stanford University. Along the way, he worked at Apple (2019) on OLED/LCD displays and at HP Labs (2017) on 3D electronics thermal management. He is currently a post-doctoral scholar at Stanford developing novel flexible optoelectronic devices, including solar cells and wearable sensors, based on 2D transition metal dichalcogenides. https://www.arinna.xyz.
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Luke Neal
Masters Student in Chemical Engineering, admitted Autumn 2025
BioI'm currently a process engineer at Merck working at the Formulation and Laboratory Experimentation Facility with a focus on oral solid dosage production. I recently graduated from Yale University with a Bachelor’s of Science in Chemical Engineering and an Energy Studies certificate. At Yale, I was on the Varsity Men's Tennis team. My internship experiences during undergraduate studies included working as a Process Engineering Intern in ExxonMobil’s Technology and Engineering division. I was focused on modeling the extraction of battery grade lithium from brine. I also gained experience in the renewable energy and green engineering fields though my internships at Tesla and West Environmental.
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Drew Nelson
Professor of Mechanical Engineering, Emeritus
BioResearch involves development of improved methods for predicting the fatigue life of engineering materials, incuding the effects of manufacturing processes, and investigation of new approaches in the field of experimental mechanics, such as determination of residual stresses using optical methods.
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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. -
Brett Newman
Lecturer
BioAcademic
2013 - 2018 : Stanford : Lecturer : Visual Thinking, ME115C: Design and Business Factors
2018 - Present : Stanford : Lead Lecturer : Design 161 Capstone
Professional
2004 - 2007 : Azud : VP Product
2007 - Present : Daylight Design : Partner -
Jonas Ngnawe
Graduate, Computer Science
BioJonas Ngnawé is a visiting student researcher at the Stanford Trustworthy AI Research (STAIR) lab, led by Prof. Sanmi Koyejo. He is currently a Ph.D. candidate in Computer Science at Mila – Quebec AI Institute and Université Laval. With a background in Computer Engineering from Ecole Polytechnique Yaoundé (2016), he also holds master’s degrees in Mathematical Sciences from AIMS-African Institute for Mathematical Sciences (2017) and Machine Learning from AMMI-African Master's in Machine Intelligence funded by Meta and Google (2019). His research focuses on developing safe, efficient and trustworthy AI for high-stakes applications—such as transportation, finance and healthcare—with a particular focus on adversarial robustness and uncertainty estimation in deep learning models. Before beginning his Ph.D., Jonas was an AI Resident at Google.
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Andrew Khoa Nguyen
Masters Student in Management Science and Engineering, admitted Autumn 2023
Stanford Stdnt Employee-Summer, GSB Research HubBioUndergraduate at Stanford University pursuing BA Economics, BS Mathematics and MS Computer Science with an interest in financial engineering and quantitative finance, specifically high frequency and/or algorithmic trading. Co-Founder and President of crowdfunding platform (Innovation Crowds) which helps startups find the right investors and collaborators who can help achieve the mission and form an army of innovators fueling growth. Founder and CEO of 501c3 nonprofit organization dedicated towards improving the lives of orphans across the globe (Orphan Assistance Fund).
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Liem M. Nguyen
Masters Student in Management Science and Engineering, admitted Autumn 2019
Current Research and Scholarly InterestsDevelopment of machine learning methods to identify structures and processes that promote high quality health care using large databases of electronic health record metadata.
