Stanford HAI Welcomes 2024-25 Graduate and Postdoc Fellows
The Stanford Institute for Human-Centered AI is pleased to announce 29 scholars will join as graduate fellows and postdoctoral fellows for the upcoming academic year 2024-25. These scholars represent a diverse range of research, from education data science to digital health innovation, AI safety and ethical development, energy science, psychology, geophysics, AI literacy, and precision medicine.
The Stanford HAI fellowship programs seek to support scholars who are working at intersections often overlooked by traditional academic departments. Fellows will work with Stanford faculty and research teams on interesting challenges in human-centered AI.
“Each year, our fellows have a unique opportunity to learn from HAI’s leading faculty, as well as to collaborate with each other,” says HAI Director of Research Programs Vanessa Parli, who oversees both fellowship programs. “The primary goal is to develop a vibrant community of researchers who are passionate about keeping humans at the center of AI exploration and development."
Graduate Fellows
Khaulat Abdulhakeem: Khaulat, currently pursuing her master’s in education data science, is committed to empowering young professionals in the workforce. Prior to coming to Stanford, she founded a company focused on helping people navigate technology careers.
Joel Adu-Brimpong: Joel is a third-year MD/MBA student interested in developing learning health systems and digital health innovation. Prior to medical school, he served as a research fellow at the National Institutes of Health, working with policy advisors to inform the development of the All of Us Precision Medicine Initiative and researching the social and biological determinants of obesity and cardiovascular disease.
Malika Aubakirova: Malika is pursuing a joint MBA/master’s in public policy. Previously, she worked in the cybersecurity space as a senior software engineer, who helped launch the Google Chronicle Detect product across its Cloud Platform. Malika aims to explore the safety, security, and broader societal impacts of increasingly autonomous AI entities, with a keen interest in ensuring AI safety and ethical development.
Parnian Azizian: Parnian is a PhD student in mechanical engineering who is deeply committed to advancing human-centered AI for neurodevelopmental health care. Her current research focuses on developing human-in-the-loop, movement-based behavioral phenotyping AI models for scalable, expert-free diagnostics of neurodevelopmental conditions, such as autism and ADHD.
Nicholas Broadbent: Nicholas is pursuing a PhD in mechanical engineering at the Dynamic Design Lab and Center for Automotive Research. His research focuses on the development of AI and machine learning algorithms for automated racing and drifting applications to enhance the safety and performance of autonomous vehicle control. He is interested in exploring new paradigms for ensuring the reliability and robustness of AI deployed in safety-critical systems, at the interface of control theory, social philosophy, and legal principles.
Martin Juan José Bucher: Martin is a PhD student of civil and environmental engineering, who works in the Gradient Spaces Lab. His research interests lie at the intersection of deep generative modeling, representation learning, and computer vision — primarily centered on leveraging generative models and scene synthesis for a circular built environment. This endeavor is shaping a new frontier that he envisions as computational circularity.
Derek Chong: Derek is pursuing a master’s in computer science in the Natural Language Processing Group. His research focuses on the technical advancement and societal impact of large language models (LLMs). Derek is committed to driving positive change through the thoughtful application of technology, a passion he has pursued throughout a diverse career in technology consulting and entrepreneurship.
Samin Khan: Samin is passionate about equitable AI research and development in education and social science research. He is a graduate student researcher in the EduNLP Lab within the Graduate School of Education and affiliated with Stanford SPARQ.
Tasha Kim: Tasha’s research focuses on developing algorithms and computational frameworks that can enhance purposeful, human-centered cooperation with AI agents. She is currently a graduate student in the Department of Computational and Mathematical Engineering.
Ivan Lopez: Ivan is an MD/PhD student in biomedical data science. With a foundation in physiological sciences, he actively pursues research at the intersection of data science and medicine. Ivan's research interests are focused on the application of deep learning techniques to develop innovative algorithms and tools that enhance health care delivery. Currently, Ivan is collaborating with health care leaders to develop a novel AI tool that empowers crisis counselors to provide more effective, timely, and consistent support to patients in crisis.
Alice Nuz: Alice is a PhD student in energy science and engineering, who works on inverse modeling for carbon capture, utilization, and storage (CCUS). In this role, she simulates subsurface injection to understand how a CO2 plume travels under various geologic conditions with limited data. By leveraging machine learning models, she aims to enhance the safety, efficiency, and economics of monitoring carbon storage projects.
Carlota Parés-Morlans: Carlota is a computer science PhD student whose research interests lie at the intersection of robotics, computer vision, and machine learning. She previously earned her master’s in electrical engineering from Stanford in 2023, supported by a fellowship from La Caixa Foundation. Carlota received the Dean's Graduate Student Advisory Council Exceptional Master's Student Award for her outstanding contributions. Her work has been published in respected venues, and she actively promotes STEM education through mentoring programs.
Joshua Rines: Josh is a student in the geophysics department, where his research focuses on leveraging machine learning to advance our understanding of the cryosphere under a warming climate. In particular, he employs deep learning methods to enhance datasets of meltwater features across the Greenland Ice Sheet, with the goal of providing higher-fidelity data to forward models to reduce uncertainties in predicting glacial mass loss.
Alvin Tan: Alvin is a PhD student in psychology. He is interested in the role of environmental input on language learning in young children and the ways in which child language learning differs from machine language learning.
Merve Tekgürler: Merve is a PhD candidate in history and an MS student in symbolic systems. Merve's dissertation, tentatively titled “Crucible of Empire: Danubian Borderlands and the Making of Ottoman Administrative Mentalities,” focuses on the Ottoman-Polish borderlands from the mid-18th century to the early 19th century, examining the changes and continuities north of the Danube River in relation to Russian and Austrian expansions. Merve, who studies the Ottoman news and information networks in this region and their impact on production and mobilization of imperial knowledge, is currently working on training a neural machine translation model for translating Ottoman Turkish into English.
Sam Young: Sam is a second-year physics PhD student, specializing in experimental particle physics. He graduated summa cum laude and Phi Beta Kappa with bachelor's and master's degrees in physics from the University of Pennsylvania in 2023. His current research, conducted at SLAC National Accelerator Laboratory, focuses on developing novel artificial intelligence techniques to model and analyze peta-scale datasets in neutrino experiments. By combining particle physics with artificial intelligence techniques, Sam hopes to uncover truths about the fundamental building blocks of our universe.
Harrison Zhang: Harrison is an MD/PhD trainee in the School of Medicine, where he is building a future in which artificial intelligence and biotechnologies standardize more personalized, accessible, and equitable health care. He is especially interested in developing machine learning approaches that advance genome science and precision medicine.
Postdoctoral Fellows
Ibrahim (Joba) Adisa: Ibrahim is working on the CRAFT AI project, a teaching resource for K-12 teachers, at the Graduate School of Education. His postdoctoral research lies at the intersection of learning sciences, computing education, and AI literacy. He is focused on creating pedagogical tools and resources that enhance data literacy and promote creativity, computational thinking, and collaborative problem-solving with AI in K-12 education. Ibrahim received his PhD in learning sciences from Clemson University, where he supported several NSF-funded projects on STEM, data science, and AI literacy. Before graduate school, he worked as a digital learning specialist at Tek Experts, a global digital tech talent corporation.
Vasiliki (Vicky) Bikia: Vicky received her advanced diploma in electrical and computer engineering from the Aristotle University of Thessaloniki (AUTH) and her PhD in biomedical engineering from Swiss Federal Institute of Technology of Lausanne (EPFL). Her doctoral research focused on addressing the clinical need for non-invasive cardiovascular monitoring tools, utilizing machine learning and physics-based numerical modeling. Currently, Vicky's work centers on developing large multimodal models to enhance biomarker identification and predict patient outcomes. She is also passionate about developing patient-facing chatbots to help individuals better understand imaging results and hospital discharge instructions. Her overarching goal is to improve patient outcomes while making health care more accessible and effective for everyone.
Sarah H. Cen: Sarah is working with Professor Daniel Ho and Professor Percy Liang in Stanford's RegLab and HAI. She received her PhD from MIT EECS, where she was advised by Professor Aleksander Mądry and Professor Devavrat Shah. Sarah's work uses methods from machine learning, statistics, causal inference, and game theory to study the design of responsible AI and AI policy. Previously, she has written about social media, trustworthy algorithms, algorithmic fairness, and more. She is currently interested in AI auditing, AI supply chains, and the impact of generative AI on IP.
Yiwen Dong: Yiwen’s research is pioneering the development of health-aware environments through ambient vibration sensing and physics-informed machine learning. Her innovative approach fuses interdisciplinary knowledge from engineering and medicine, making real-world impacts by providing accessible gait health monitoring for children with muscular dystrophy and cerebral palsy. In addition to improving human health, Yiwen’s research has enabled pig health monitoring in collaboration with the USDA. Her work has garnered recognition through publications in top-tier conferences and journals across engineering and medicine, earning her multiple best paper and presentation awards.
Jane E: Jane will be an incoming assistant professor at the National University of Singapore in fall 2025. Previously, she was a postdoc at The Design Lab at UCSD, and she earned her PhD in computer science from Stanford. Jane’s research lies at the intersection of human-computer interaction, computer graphics, and AI, with a focus on designing computational guidance to support novices in developing their own creative expertise. Her work takes inspiration from cognitive science and education theory to design computational tools that scaffold novices’ creative processes.
Basil Halperin: Basil is a postdoc in the Stanford Digital Economy Lab. In fall 2025, he will join the University of Virginia as an assistant professor of economics. Basil's research focuses on topics in monetary economics, macroeconomic growth, and AI. Basil received his PhD in economics from MIT in 2024. In past lives, he has worked as a data scientist at Uber and as a quant at AQR Capital Management. Basil did his undergrad at the University of Chicago.
Andreas Haupt: Andreas Haupt is a Postdoctoral Fellow at the Digital Economy Lab and the Institute for Human-Centered AI. He earned his Ph.D. from MIT’s CSAIL and the College of Computing, with a thesis focusing on the economics of personalization. Andy also holds master’s degrees in Economics and Mathematics. Previously, he served at the U.S. Federal Trade Commission, contributed to the EU’s antitrust team for platform regulation, and taught high school mathematics. His research seeks to make the use of large language models more transparent and legible.
Wanheng Hu: Wanheng is an Embedded Ethics fellow, jointly appointed by the McCoy Family Center for Ethics in Society, the Institute for Human-Centered Artificial Intelligence (HAI), and the Computer Science Department. He is currently an affiliate at the Data & Society Research Institute. Wanheng received his PhD in science and technology studies from Cornell University, where he also completed a minor in media studies and remains an active member of the Artificial Intelligence, Policy, and Practice (AIPP) initiative. His research lies at the intersection of social studies of science, medicine, and technology; critical data/algorithm studies; media studies; and public engagement with science.
Julia Irwin: Julia researches the history and philosophy of artificial intelligence as a lens for understanding contemporary issues of AI safety, values alignment, and human-computer interaction. Her scholarship uncovers the ways twentieth-century theories of human perception and reason have shaped the development of AI and how machine capabilities have likewise informed our theories of human intelligence and social organization. She holds a PhD in film and media from UC Berkeley and a master’s from NYU Tisch’s Interactive Telecommunications Program.
Jonas Kloeckner: Jonas is a postdoctoral fellow at the Stanford Doerr School of Sustainability. His research focuses on forecasting and exploring Earth’s resources to support sustainable energy production while examining environmental and societal impacts. Jonas specializes in developing data-driven methodologies for uncertainty quantification, prediction, and decision analysis in subsurface systems. He earned his PhD and master’s in mineral resources engineering and a bachelor’s in mining engineering. Currently, he is focused on leveraging AI to enable smarter, more responsible exploration practices that prioritize sustainability and minimize critical mineral extraction's environmental and social impacts.
Subigya Nepal: Subigya received his PhD in computer science from Dartmouth. His research integrates ubiquitous computing, AI, and computational social science to gain insights into human behavior. Subigya leverages AI and passive sensing to assess well-being, develop just-in-time interventions, and enhance productivity across diverse settings. He has conducted extensive longitudinal studies, including a groundbreaking four-year study tracking over 200 Dartmouth students — the longest continuous mobile sensing study to date. His research spans clinical contexts, supporting patients with serious mental illness, and non-clinical environments like college campuses and workplaces, aiming to positively influence individual and societal well-being.
Jiaxin Pei: Jiaxin is affiliated with the Digital Economy Lab and the NLP group, working with Alex "Sandy" Pentland, Diyi Yang, and Erik Brynjolfsson. He obtained his PhD from The Blablablab, UMSI (University of Michigan School of Information) advised by David Jurgens. Jiaxin's work has won a Best Student Paper Award at the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), an Honorable Mention Award at the International Conference on Computational Social Science (IC2S2), and a Best Paper Award at the Workshop on Social Influence in Conversations.
Veronica Rivera: Veronica is an Embedded Ethics postdoctoral scholar, working with the Empirical Security Research Group, the Institute for Human-Centered AI, and the Center for Ethics in Society. Her research sits at the intersection of human-computer interaction and computer security. She studies tech-facilitated abuse, focusing on how technology-enabled harms occur between and beyond digital platforms and how to protect users. As part of Stanford's Embedded Ethics program, Veronica also develops and teaches ethics curriculum to prepare engineering students to consider the social impact of their work.
Stefan Stojanov: Stefan is a postdoctoral researcher interested in building computer vision systems guided by our knowledge about the generalization, adaptability, and efficiency of human perception and its development. He is also interested in applying advanced computer vision techniques to automate and scale analyses in developmental psychology. Stefan completed his PhD at the Georgia Institute of Technology, where he worked on self-supervised and data-efficient computer vision algorithms.
Philip Trammell: Philip is an incoming postdoc at the Stanford Digital Economy Lab. He is completing his Doctor of Philosophy in economics at Oxford and is a research affiliate at GPI, an Oxford research institute. His research touches decision theory, game theory, and growth theory. He graduated with distinction from the MPhil, where he won the prize for best thesis, and he has undergraduate degrees in economics and mathematics from Brown, where he also won the prize for best economics thesis.
Alan Wang: Alan is affiliated with the departments of computer science and psychiatry and behavioral sciences. Previously, he completed his PhD at Cornell University and Cornell Tech. Before that, he studied computer engineering at the University of Illinois at Urbana-Champaign (UIUC). His research interests are at the intersection of machine learning and medical imaging. In particular, he is interested in developing deep learning algorithms for medical imaging and health care, with an emphasis on improving interpretability, robustness, and fairness of deep learning models in these contexts.
Angelina Wang: Angela’s research focuses on machine learning fairness and algorithmic bias. She has been recognized by NSF GRFP, EECS Rising Stars, Siebel Scholarship, and Microsoft AI & Society Fellowship. She earned her PhD in computer science from Princeton University and a BS in electrical engineering and computer science from UC Berkeley.
Ge (Tiffany) Wang: Tiffany's research focuses on the intersection of human-computer interaction (HCI), human-centered artificial intelligence (HAI), and usable security and privacy, with a special emphasis on vulnerable populations including children, teenagers, and other marginalized communities. Before joining Stanford, Tiffany received a PhD in computer science from the University of Oxford, where she also completed a bachelor’s degree in physics.
Peter West: Peter’s research focuses on the interaction between AI, language, and scale. His recent research has explored hidden capabilities and limits in large language models (LLMs), developing methods to unlock abilities in compact models and characterizing challenges that even the largest models continue to face. Peter completed his PhD at the University of Washington and his bachelor’s degree at the University of British Columbia (UBC), both in computer science. He has conducted research at the Allen Institute for AI and Microsoft Research and has published work in optical physics and machine learning systems. Peter will begin a faculty position at UBC in 2025.
Lio Wong: Lio received a PhD in brain and cognitive sciences from MIT and will affiliate with the psychology department at Stanford. Lio’s research seeks to computationally model the astonishing breadth and versatility with which language informs what we know and believe as well as to answer key questions by integrating empirical evidence from how people use and understand language with computational tools from cognitive science and AI.