Find Academic Supervisors

David W. JacobsView profile

Professor

• Computer Vision
• Machine Learning
• Object Recognition
• +11 more

David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He also served as the interim Director of the University of Maryland Center for Machine Learning starting in 2018. University: University of Maryland School: College of Computer, Mathematical, and Natural Sciences Department: Department of Computer Science Academic Rank: Professor Education: He received his B.A. from Yale University, and M.S. and Ph.D. in Computer Science from MIT. Research Interests: His research primarily focuses on computer vision and machine learning, particularly visual object recognition, lighting variation modeling, 3D reconstruction, perceptual organization, motion understanding, and the integration of vision with graphics and human-computer interaction. A major applied contribution is the development of Leafsnap , an electronic field guide app for plant identification, which has been downloaded over 1.5 million times and used in biodiversity and educational contexts. Publication Trends: His recent scholarly output centers on deep learning, convolutional networks, residual architectures, generative models (especially GANs), and interpretability. His work often bridges theoretical insights with practical applications in vision and AI. Scientific Awards: Honorable Mention, Best Paper Award, CVPR 2000 Best Student Paper Award, UIST 2003 Best Paper Award, Eurographics 2016 2011 Edward O. Wilson Biodiversity Technology Pioneer Award for Leafsnap Teaching and Advising: He has taught advanced courses such as CMSC 422 (Introduction to Machine Learning) and CMSC 828L (Deep Learning). He mentors students through course projects and research, though specific advisees are not listed. He has collaborated with institutions like Columbia University and the Smithsonian on impactful interdisciplinary projects. Labs and Teams: He is affiliated with UMIACS and leads research efforts in vision and learning, contributing to the University of Maryland Center for Machine Learning. His team has developed several mobile applications including Leafsnap, Birdsnap, and Dogsnap, demonstrating a strong focus on real-world deployment of vision technology.

John MacFarlaneView profile

Professor

• Philosophy of Language
• Philosophical Logic
• Metaphysics
• +8 more

John MacFarlane is a Professor of Philosophy at the University of California, Berkeley , with affiliations to the Group in Logic and the Methodology of Science and co-organizer roles in the Meaning Sciences Club and Townsend Center Working Group . His research spans philosophy of language, philosophical logic, metaphysics, epistemology, philosophy of mathematics, history of logic , and ancient philosophy , particularly Aristotle . He has held visiting positions at institutions like École des Hautes Études en Sciences Sociales (EHESS), Paris . Education : Ph.D. in Philosophy (2000, University of Pittsburgh), M.A. in Classics (1997) and Philosophy (1994), A.B. summa cum laude in Philosophy (1991, Harvard). Research Interests focus on semantic relativism , particularly assessment sensitivity as explored in his book Assessment Sensitivity: Relative Truth and Its Applications (Oxford, 2014). He investigates how epistemic modals and vagueness interact with truth evaluation. His work in philosophical logic includes a textbook Philosophical Logic: A Contemporary Introduction (Routledge, 2021). Secondary interests include ancient philosophy (Aristotle, Plato), formal logic , and fiddlosophy . Article Trends reveal contributions to relativism , epistemic modals , vagueness , and logicism . His recent work (2025) examines Plato’s Protagoras and belief utility , while 2022–2020 articles address semantic indecision and probabilistic knowledge . Earlier works (2008–2000) include foundational studies on Frege, Kant, and Aristotle . Scientific Awards include a 2023 Residential Fellowship at Wissenschaftskolleg, Berlin (declined), 2016–17 Fellow at IEA Paris , and 2015 American Academy of Arts and Sciences membership . He has received multiple Humanities Research Fellowships at Berkeley (2016, 2008, 2003) and Research Enabling Grants (2000–2009). Advising includes supervising 15+ PhD students in areas like logic, epistemology, and philosophy of language . Notable advisees include Richard Lawrence (2017) and Sophie Dandelet (2021). He also guided honors theses at Berkeley, such as Benjamin Logan’s work on epistemic modals (2018). Labs and Teams involve co-organizing the Townsend Center Working Group in History and Philosophy of Logic, Mathematics, and Science and contributing to the Group in Logic and Methodology of Science . His interdisciplinary approach integrates philosophy, logic, and computational tools , including creating Pandoc for document conversion.

Anocha AribargView profile

Professor

• Consumer Behavior
• Marketing Research
• Econometric Modeling
• +3 more

Anocha Aribarg is a Professor of Marketing and Area Chair of Marketing at the Ross School of Business, University of Michigan, with additional faculty affiliation at the Center for Southeast Asian Studies (CSEAS). Her interdisciplinary research bridges psychological theory, consumer behavior, and advanced statistical modeling to address complex marketing challenges. Education: PhD in Marketing, University of Wisconsin, 2004 MBA, University of Wisconsin-Milwaukee, 1998 BS in Statistics, Chulalongkorn University, Thailand, 1994 Research Focus: Prof. Aribarg investigates cognitive processes in consumer decision-making, specializing in individual/joint choice dynamics, product search behaviors, and responses to marketing stimuli. Her methodology integrates Bayesian econometrics with physiological measures (eye tracking, skin conductance) and multi-method experimental designs to uncover hidden decision mechanisms. Publication Trends: Her 14 recent publications (2009-2024) reveal consistent innovation in choice modeling, with increasing emphasis on attention dynamics (2020), moral psychology in aesthetics (2022), and high-frequency service data integration (2023). Work appears predominantly in Marketing Science Journal of Marketing Research, and Psychological Science, demonstrating methodological rigor across consumer behavior, advertising, and service contexts. Academic Service: She serves as Associate Editor for Marketing Science, Journal of Marketing, and Journal of Marketing Research, while contributing to the Journal of Retailing editorial board. Teaching: Prof. Aribarg delivers graduate-level courses in Marketing Research and Analytics and Predictive Analytics at Ross, emphasizing data-driven decision frameworks.

John P. O'DohertyView profile

Professor

• Decision Neuroscience
• Reward-related Learning
• Computational Modeling
• +3 more

John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.

Katerina MarcoulidesView profile

Associate Professor

• Data Mining
• Structural Equation Modeling
• Longitudinal Data Analysis
• +3 more

Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.

James GrossView profile

Professor

• Emotion Regulation
• Psychology
• Neuroscience
• +6 more

James J. Gross is the Ernest R. Hilgard Professor of Psychology at Stanford University, directing the Stanford Psychophysiology Laboratory. He specializes in emotion regulation, with affiliations in Philosophy (courtesy). His education includes a Ph.D. in Clinical Psychology from UC Berkeley (1993), a B.A. in Philosophy and Psychology from Yale (1987), and a visiting graduate year at Oxford (1988). Research focuses on emotion regulation mechanisms, psychopathology, neuroimaging, and cultural influences. His work bridges clinical, cognitive, and social psychology, examining how individuals manage emotions to influence mental health and behavior. Notable contributions include the Process Model of Emotion Regulation and interventions targeting emotion regulation deficits. Publications (~650, 250k citations) highlight studies on emotion regulation strategies, clinical applications, and neural underpinnings. Recent work explores transdiagnostic treatments, digital health interventions, and the interplay between beliefs and emotion regulation. Awards: Stanford Dean’s Teaching Award, Walter J. Gores Award (highest teaching honor), multiple mentoring awards, and honorary doctorates from UC Louvain and Tilburg. Professional Roles: Co-founding President of the Society for Affective Science, Founding Co-Editor-in-Chief of Affective Science , and Fellow of major psychological and scientific societies. Labs/Teams: Directs the Stanford Psychophysiology Lab, collaborating on projects involving neuroimaging, psychophysiological assessment, and computational modeling of emotion processes.

Alan StockerView profile

Professor

• Behavioral Neuroscience
• Cognitive Neuroscience
• Sensation and Perception
• +4 more

Alan Stocker is a Professor in the Department of Psychology at the University of Pennsylvania, affiliated with the Neuroscience Graduate Group, Bioengineering Graduate Group, and Computational Neuroscience Initiative. His research focuses on understanding how prior beliefs and expectations shape visual perception, combining theoretical models with psychophysical and physiological experiments. Key areas include optimal inference frameworks, neural coding efficiency, and decision-making under uncertainty. Education: PhD in Physics (ETH Zurich, 2002); MSc in Biomedical Engineering and Materials Science (ETH Zurich, 1995). Prior appointments include postdoctoral work at New York University and ETH Zurich. Research Interests: Behavioral Neuroscience, Cognitive Neuroscience, Sensation and Perception. Specific projects explore statistical properties of visual environments, subjective expectations from past decisions, and neural computations underlying perception. Advising: Current advisees include Cheng Qiu (Postdoc), Ansh Soni, and Mengting Fang (Graduate Students). Courses taught: Perception, Visual Neuroscience, and Probabilistic Models in Perception and Cognition. Labs/Teams: Leads the Computational Perception and Cognition (CPC) Laboratory, emphasizing theory-driven experimentation. Collaborates widely across disciplines including bioengineering and electrical systems engineering.

• Cognitive Robotics
• Developmental Robotics
• Human-Robot Interaction
• +5 more

Tetsuya Ogata is a Professor at the Faculty of Science and Engineering, School of Fundamental Science and Engineering at Waseda University. He also holds joint appointments as a Fellow at the Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), and as a visiting professor at the National Institute of Informatics (NII). Professor Ogata serves as director of the Institute of AI and Robotics at Waseda University, chairperson of the AI Robot Association (AIRoA), and research supervisor of JST CREST "Fundamentals and Core Technologies for Embodied AI" since 2025. Professor Ogata received his B.S., M.S., and D.E. degrees in mechanical engineering from Waseda University in 1993, 1995, and 2000, respectively. He began his academic career as a Research Associate at Waseda University (1999-2001), followed by positions as a Research Scientist at RIKEN Brain Science Institute (2001-2003), Lecturer (2003-2005) and Associate Professor (2005-2012) at Kyoto University's Graduate School of Informatics, before becoming a Professor at Waseda University in 2012. He was also a JST PRESTO Researcher from 2009 to 2015. Professor Ogata's research focuses on cognitive developmental robotics, deep predictive learning, and the integration of vision, language, and action (VLA) for human-robot interaction. His work explores how robots can learn through experience and interaction with humans and environments, developing models that allow for flexible behavior generation and adaptation. His research has significant implications for creating robots that can understand human intentions, collaborate effectively in real-world settings, and continuously learn from their experiences. The integration of multimodal information processing with predictive models forms the core of his approach to embodied artificial intelligence. Analysis of Professor Ogata's recent publications reveals a strong focus on deep predictive learning frameworks that enable robots to anticipate future states and generate appropriate actions. His research spans multiple domains including soft object manipulation, bimanual coordination, transparent object grasping, and integration of large language models with robotic systems. A notable trend is the increasing emphasis on multimodal integration, combining visual, tactile, and linguistic information to create more robust and adaptable robotic systems. His recent work also shows growing interest in the application of foundation models to robotics, aiming to create more general-purpose robotic capabilities. Best Paper Award, Advanced Robotics, The Robotics Society of Japan (2024) Best paper award Nomination Finalist, IEEE/SICE International Symposium on System Integration (2024) Frontiers of Science Awards, The International Congress for Basic Science (2023) Minister of Education, Culture, Sports, Science and Technology Award (2023) Fellow, Japan Society of Mechanical Engineering (2023) Fellow, The Society of Instrument and Control Engineers (2022) Fellow, The Robotics Society of Japan (2022) IBM Academic Awards (2017) Professor Ogata has been instrumental in establishing collaborative research frameworks between academia and industry, serving in leadership roles for multiple professional organizations including the Robotics Society of Japan, Japanese Society for Artificial Intelligence, and Japan Deep Learning Association. His research has been supported by significant grants including JST CREST funding for "Fundamentals and Core Technologies for Embodied AI." He has mentored numerous researchers who have gone on to contribute to the field of robotics and AI, with many of his former students now holding positions in leading academic and industrial research institutions. Professor Ogata directs the Ogata Laboratory at Waseda University, which focuses on cognitive developmental robotics and deep predictive learning. The lab maintains strong collaborations with industry partners including major robotics companies and technology firms. In 2025, he played a key role in establishing the AI Robot Association (AIRoA), which brings together companies like Toyota, Nissan, and KDDI to advance the development of practical AI robotics technologies. His research group is known for its interdisciplinary approach, combining insights from neuroscience, cognitive science, and engineering to create more human-like robotic capabilities.

Ilker YildirimView profile

Assistant Professor

• Computational Modeling
• Visual Perception
• Cognitive Neuroscience
• +5 more

Ilker Yildirim is an Assistant Professor of Psychology at Yale University, where he leads the Cognitive and Neural Computation Lab (CNCL). He received his Ph.D. from the University of Rochester in 2014. His research focuses on understanding how perception transforms raw sensory signals into meaningful representations of objects and scenes through computational modeling approaches. His research interests include: Computational modeling of visual perception and cognition Intuitive physics and physical reasoning Bayesian inference and probabilistic models in cognition Integration of graphics and physics engines in cognitive modeling Neural mechanisms of object representation Attention and resource allocation in dynamic scenes Yildirim's lab develops computational frameworks that bridge cognitive processes with neural mechanisms, using tools including probabilistic models, simulation engines, and deep learning. His work often involves testing these models through behavioral and neural experiments to create unified accounts of perception and cognition. His recent publications reveal a strong focus on how humans perceive physical properties of objects, particularly soft materials and liquids, and how attention dynamically allocates resources during scene perception. Among his notable achievements is receiving an NSF CAREER award for his project "CAREER: CompCog: Reverse-engineering neural mechanisms of object cognition with multilevel computational modeling," which will run from 2025 to 2030. His Nature Human Behaviour paper by Qi Lin was featured on CBS News, Germany's NZZ, and Wired Italy. Yildirim mentors a diverse group of graduate students and postdocs working on various aspects of perception, cognition, and computational modeling. His students have received prestigious awards including the Jane Olejarczyk Award and the Leonard J. Savage Prize.

Matthew B. BlaschkoView profile

Professor

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +5 more

Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.

Dylan Hadfield-MenellView profile

Assistant Professor

• AI Alignment
• Human-AI Interaction
• Reinforcement Learning
• +3 more

Dylan Hadfield-Menell is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, holding the Bonnie and Marty (1964) Tenenbaum Career Development Professorship. His research focuses on AI alignment and human-AI interaction within MIT's School of Engineering. His research interests center on agent alignment problems in AI systems, particularly examining uncertainty in objective optimization for human-robot teams and societal oversight of machine learning systems. Key areas include the principal-agent alignment problem , assistance games frameworks , and robust preference learning that accounts for hidden contextual factors in reinforcement learning from human feedback. His recent publications reveal strong trends toward multi-agent cooperation , formal contract mechanisms for resolving social dilemmas, and advanced evaluation methodologies for AI safety. The research spans theoretical frameworks like open-universe assistance games while addressing practical challenges in language model alignment and cultural bias assessment. Scientific awards include: AI2050 Early Career Fellowship from Schmidt Futures Berkeley Fellowship NSF Graduate Research Fellowship C.V. Ramamoorthy Distinguished Research Award His work bridges theoretical computer science with real-world AI governance challenges, as demonstrated through MIT's participation in AI policy white papers. Current research directions include developing frameworks for transparent AI systems and addressing fundamental limitations in aligning recommender systems with human values through interdisciplinary synthesis.

• Information Discovery
• Bayesian Networks
• Information Retrieval
• +5 more

Petri Myllymäki serves as Director of multiple prominent research entities at Aalto University's School of Science, including the Helsinki Institute for Information Technology (HIIT), the Department of Computer Science, the Finnish Center for Artificial Intelligence (FCAI), and his own research group. His leadership positions demonstrate significant influence in the Finnish and international AI research community. Dr. Myllymäki's research focuses on information discovery, Bayesian networks, information retrieval, and machine learning. His work bridges theoretical computer science with practical applications, particularly in developing systems that enhance how humans interact with information spaces. His research fingerprint prominently features Information Discovery (100%), Bayesian Networks (50%), Hashing (50%), Approximation Algorithms (50%), Information Retrieval (50%), structure learning (50%), Search Engine technology (37%), and Task Performance (33%). His recent publications (2015-2025) reveal a consistent trajectory in interactive information discovery systems, with increasing applications in diverse domains including decision science, public health, and social media analysis. Notably, his 2024-2025 work shows expansion into cognitive burden in decision-making, health outcomes research using machine learning, and political social media analysis – demonstrating how his core AI methodologies are being applied to address complex societal challenges. Recognized at AI Finland Gala 2024 for contribution reaching over 1.4 million users Featured in multiple media outlets including X (formerly Twitter) and Bluesky Work referenced in patents and Wikipedia pages Dr. Myllymäki actively hosts academic visitors and participates in collaborative research activities. His work contributes to UN Sustainable Development Goals through applications in information access and AI development. The Myllymäki Petri group (HIIT) serves as a hub for interdisciplinary research at the intersection of artificial intelligence, human-computer interaction, and information systems.

• Bayesian methodology
• Variable selection and model averaging
• Nonparametric regression models
• +3 more

Scientia Professor Robert Kohn is a distinguished academic at the University of New South Wales, holding a position in the School of Economics within the UNSW Business School. With a career spanning several decades, Professor Kohn has established himself as a leading expert in statistical methodology and econometric modeling. His research has significantly contributed to Bayesian statistics and computational methods for complex data analysis. Professor Kohn's research focuses on advanced statistical methodologies including Bayesian methodology, variable selection and model averaging, nonparametric regression models, time series modeling, multivariate Gaussian and non-Gaussian regression, and Markov chain Monte Carlo simulation algorithms. His work bridges theoretical statistics with practical applications across economics, finance, and cognitive science. His research demonstrates a consistent trajectory toward developing more efficient computational methods for complex statistical models, with recent work emphasizing variational Bayesian methods, particle filtering techniques, and applications to time series analysis. Analysis of his recent publications (2022-2025) reveals a strong focus on advancing computational statistical methods, particularly in Bayesian inference for complex models. His work shows increasing integration of machine learning techniques with traditional statistical methods, especially in handling high-dimensional data and complex time series structures. Professor Kohn has made significant contributions to variational inference methods, particle-based computational techniques, and applications to financial time series and cognitive modeling. Professor Kohn has maintained an exceptionally productive research career with continuous publication output since the 1970s, demonstrating remarkable longevity and adaptability in his research focus as statistical methodologies have evolved. His work shows strong international collaboration, particularly with researchers in Australia, the United States, and Europe, reflecting his standing in the global statistical community.

Padhraic SmythView profile

Professor

• Machine Learning
• Artificial Intelligence
• Pattern Recognition
• +3 more

Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.

• Language Models
• Diffusion Models
• Theory of Deep Learning
• +6 more

Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.