School of Engineering
Showing 401-500 of 2,228 Results
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Srivatsava Daruru
Affiliate, Program-Koyejo, O.
BioSrivatsava Daruru is a researcher and machine learning leader whose work spans natural language processing, neuro-symbolic AI, and large-scale learning systems. He is currently Chief AI Officer at Exlens AI and was formerly Senior Manager of Machine Learning at ServiceNow, where he led research in retrieval-augmented generation (RAG), question answering, post-training optimization of large language models, and agentic workflows for conversational AI. His contributions shaped ServiceNow’s generative AI strategy, including the company’s first production-grade generative application, Genius Q&A.
Daruru’s research interests focus on self-improving large language models, reasoning, and mathematical verification. He is currently workin on VeriBench, an end-to-end benchmark for translating Python into Lean 4, and VeriCI, a continuous verification framework for CI/CD pipelines, as part of neuro-symbolic software reliability.
He has published at leading venues such as ACM SIGKDD and IEEE ICDM, with research spanning scalable clustering for terascale astronomy, parallel data mining, and large-scale telecom analytics. His Google Scholar profile reflects a consistent track record of contributions to data mining, NLP, and applied machine learning. In addition, he is the inventor on multiple patents in NLP, fact validation, and semi-automated data labeling.
Daruru holds an M.S. in Computer Science from the University of Texas at Austin and a B.Tech. (Hons) in Computer Science from IIIT Hyderabad.
About Me (Informal)
I am a scientist and engineer working at the intersection of large language models, reasoning, and verification. My long-term vision is to build AI systems that are not only powerful but also trustworthy, capable of explaining themselves and proving their correctness. I’m especially excited about self-improving LLMs, agentic workflows, and neuro-symbolic methods that combine data-driven learning with formal verification. Currently, I’m working on VeriBench and VeriCI, projects that push AI systems toward rigorous mathematical guarantees while remaining practical for real-world development pipelines. -
Mateus Gheorghe De Castro Ribeiro
Ph.D. Student in Civil and Environmental Engineering, admitted Autumn 2022
Ph.D. Minor, Computer ScienceBioMateus Gheorghe de Castro Ribeiro is a PhD candidate in the Stanford Sustainable Systems Lab. He has worked on various topics at the intersection of engineering applications and artificial intelligence (AI). His main area of research focuses on AI applied to sustainable energy systems, specifically using data-driven methods to accelerate the electrification of bus fleets, ensure reliable operations with minimal costs, and achieve 24/7 carbon-free operations. Mateus obtained his bachelor's and master's degrees in mechanical engineering from the Federal University of Juiz de Fora and the Pontifical Catholic University of Rio de Janeiro, respectively. In 2022, he was awarded the CAPES/Fulbright Scholarship to pursue his PhD in the Department of Civil and Environmental Engineering at Stanford University.
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Gabriela de Sá
Undergraduate, Computer Science
Bioweb.stanford.edu/~gabisa/
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Nurullah Demir
Visiting Postdoc, Computer Science
Affiliate, Program-Durumeric, Z.BioMy research focuses on building automated, agentic systems to identify and mitigate structural security and privacy risks at internet scale.
I hold a PhD from KIT. Previously, I was a Visiting Scholar at UC Davis. I also work closely with the if(is) and Intellisec research groups and I am a core maintainer of the open-source project HTTP Archive and the Editor-in-Chief of the Web Almanac.
I actively translate my research into operational tools. I am the founder of the global threat intelligence platform SecuSeek. -
Yegor Denisov-Blanch
Research Scientist, Program-Koyejo, O.
BioResearch Scientist
Stanford Artificial Intelligence Laboratory (SAIL)
Department of Computer Science, Stanford School of Engineering
Yegor Denisov-Blanch studies how artificial intelligence is changing software engineering. His research focuses on measuring real-world engineering productivity, AI adoption, code quality, and organizational outcomes across large populations of repositories and teams. He designs empirical methods and metrics that move beyond simple proxies to accurately quantify software output, rework, and AI-assisted development at scale.
His work has been covered by the World Bank, the United Nations, and The Washington Post, and has been reshared by Elon Musk.
Yegor graduated with highest honors from Indiana University, where he studied operations research. He also earned an MBA from Stanford Graduate School of Business on full-tuition scholarships. He left school after the eighth grade, founded a company, and later entered university skipping 5 grades. He is a Master of Sport of Russia in Olympic weightlifting, a national champion-equivalent distinction awarded in 2013. -
David Dill
Donald E. Knuth Professor in the School of Engineering, Emeritus
Current Research and Scholarly InterestsSecure and reliable blockchain technology at Facebook.
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Ron Dror
Cheriton Family Professor and Professor, by courtesy, of Structural Biology and of Molecular & Cellular Physiology
Current Research and Scholarly InterestsMy lab’s research focuses on computational biology, with an emphasis on 3D molecular structure. We combine two approaches: (1) Bottom-up: given the basic physics governing atomic interactions, use simulations to predict molecular behavior; (2) Top-down: given experimental data, use machine learning to predict molecular structures and properties. We collaborate closely with experimentalists and apply our methods to the discovery of safer, more effective drugs.
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John Duchi
Associate Professor of Statistics, of Electrical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsMy work spans statistical learning, optimization, information theory, and computation, with a few driving goals: 1. To discover statistical learning procedures that optimally trade between real-world resources while maintaining statistical efficiency. 2. To build efficient large-scale optimization methods that move beyond bespoke solutions to methods that robustly work. 3. To develop tools to assess and guarantee the validity of---and confidence we should have in---machine-learned systems.
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Zakir Durumeric
Assistant Professor of Computer Science
On Partial Leave from 10/01/2025 To 06/30/2026BioI am an Assistant Professor of Computer Science. My research brings a large-scale, empirical approach to the study of Internet security, trust, and safety. I am interested in how to protect people against attacks on the Internet ranging from cybercrime and harassment to censorship and disinformation. I am broadly an empiricist: I build systems to measure complex networked ecosystems at scale, which I use to understand real-world behavior, uncover weaknesses and attacks, architect more resilient defenses, and guide public policy.
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Mohamed Elmoghany
Researcher, Computer Science
BioMohamed has over 10 years of research and industry experience. He is currently working with Prof. Jiajun Wu, Mengdi Xu, and Weiyu Liu on robotics perception and learning. Previously, he interned at Adobe Research with Franck Dernoncourt (MIT PhD), submitting a CVPR main conference paper and publishing in the ICCV Long Video Foundations Workshop. He also published a NeurIPS’25 paper while interning at KAUST with Prof. Mohamed Elhoseiny. His research interests span Embodied AI, robot learning and manipulation, robotic perception, image & video diffusion models, video understanding, and AI for healthcare.
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Barbara Elizabeth Engelhardt
Professor (Research) of Biomedical Data Science and, by courtesy, of Statistics and of Computer Science
BioBarbara E Engelhardt is a Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science. She received her B.S. (Symbolic Systems) and M.S. (Computer Science) from Stanford University and her PhD from UC Berkeley (EECS) advised my Prof. Michael I Jordan. She was a postdoctoral fellow with Prof. Matthew Stephens at the University of Chicago. She was an Assistant Professor at Duke University from 2011-2014, and an Assistant, Associate, and then Full Professor at Princeton University in Computer Science from 2014-2022. She has worked at Jet Propulsion Labs, Google Research, 23andMe, and Genomics plc. In her career, she received an NSF GRFP, the Google Anita Borg Scholarship, the SMBE Walter M. Fitch Prize (2004), a Sloan Faculty Fellowship, an NSF CAREER, and the ISCB Overton Prize (2021). Her research is focused on developing and applying models for structured biomedical data that capture patterns in the data, predict results of interventions to the system, assist with decision-making support, and prioritize experiments for design and engineering of biological systems.
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Dawson Engler
Associate Professor of Computer Science and of Electrical Engineering
BioEngler's research focuses both on building interesting software systems and on discovering and exploring the underlying principles of all systems.
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Stefano Ermon
Associate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment
On Partial Leave from 10/01/2025 To 06/30/2026BioI am an Assistant Professor in the Department of Computer Science at Stanford University, where I am affiliated with the Artificial Intelligence Laboratory and a fellow of the Woods Institute for the Environment.
My research is centered on techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability. -
Chaofei Fan
Ph.D. Student in Computer Science, admitted Autumn 2020
BioI’m a Ph.D. student at Stanford unraveling the future of brain-computer interfaces to revolutionize communication.
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Judith Ellen Fan
Assistant Professor of Psychology, by courtesy, of Education and of Computer Science
BioI direct the Cognitive Tools Lab (https://cogtoolslab.github.io/) at Stanford University. Our lab aims to reverse engineer the human cognitive toolkit—in particular, how people use physical representations of thought to learn, communicate, and solve problems. Toward this end, we use a combination of approaches from cognitive science, computational neuroscience, and artificial intelligence to achieve deeper understanding of quintessentially human ways of thinking and imagining. Our broader goal is to leverage such scientific understanding of human cognition to guide the development of technologies that augment human agency and creativity.
