Nika Haghtalab Division of Computer Science (EECS) artificial intelligence, machine learning, theoretical computer science, game theory and mechanism design
Alan Hubbard School of Public Health causal inference, targeted learning, statistical issues in epidemiology, precision medicine and public health
Bin Yu Dept of Statistics machine learning, trustworthy data science and AI, interdisciplinary research in biomedicine, neuroscience, climate science
Alexei Efros Division of Computer Science (EECS) computer vision, computer graphics, computational photography, machine learning, artificial intelligence
Alexandre Bayen Division of Electrical Engineering (EECS) control, optimization, machine learning, applications: transportation; mobile sensing; connected health
Maya L. Petersen School of Public Health Computational Precision Health Program causal inference, precision health, targeted learning, HIV, global health, pandemics
Ryan Giordano Dept of Statistics machine learning, variational inference, Bayesian methods, robustness quantification, applied statistics
Xin Guo Dept of Industrial Engineering & Operations Research stochastic control and games, theory of machine learning and applications to medical and financial data analysis, applied probability and stochastic models
Heather Gray Dept of Physics particle physics, machine learning, algorithms, quantum algorithms, general purpose computation on the GPU (GPGPU)
Armando Fox Division of Computer Science (EECS) online education, MOOC, blended learning, parallel programming, software engineering, Web development
Irene Chen Computational Precision Health Program Division of Computer Science (EECS) machine learning, personalized medicine and healthcare systems, global health equity, precision health
Avi Feller Dept of Statistics Goldman School of Public Policy applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences
Dor Abrahamson School of Education mathematical cognition, design-based research, mixed-media design for mathematics learning environments, embodied interaction
Daniel Siefman Dept of Nuclear Engineering neutronics, criticality safety, nuclear data validation, machine learning, neutron noise, reactor dosimetry
Stuart J. Russell Division of Computer Science (EECS) artificial intelligence, computational biology, algorithms, machine learning, real-time decision-making, probabilistic reasoning
Jelani Nelson Division of Computer Science (EECS) theory, algorithms, streaming algorithms, dimensionality reduction, randomized algorithms, machine learning, privacy
Joseph J. Campos Dept of Psychology social-emotional development in infancy, emotional communication, perception of emotion, relation of motor development to cognitive and social and emotional development
David Bamman School of Information natural language processing, machine learning, digital humanities, computational social science, data science
Joshua Bloom Dept of Astronomy machine learning, gamma-ray bursts, supernovae, time-domain astronomy, data-driven discovery
Khalid M. Mosalam Dept of Civil and Environmental Engineering bridges, concrete and masonry structures, damage mechanics, earthquake engineering, fracture mechanics, machine learning
James W. Rector Dept of Civil and Environmental Engineering geoarchaeology, geophysics, acoustics, Seismology, machine learning, energy, carbon capture and storage, oil and gas
Adrian Aguilera Computational Precision Health Program School of Social Welfare digital health, depression interventions, cognitive behavioral therapy, health equity, machine learning, implementation science
Bethany L. Goldblum Dept of Nuclear Engineering applied nuclear physics, neutron detection, scintillation physics, machine learning applications, nuclear weapon policy
Alastair Iles Dept of Environmental Science, Policy & Management chemicals policy and politics, sustainable food systems, environmental STS, sustainability transitions, sustainability learning and societal change
Peter Bickel Dept of Statistics statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology
Nika Haghtalab
Nika Haghtalab
Nika Haghtalab
