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Research Expertise and Interest

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Name

Department

neuroimaging data analysis, deep brain stimulation, brain-computer-interface, statistical machine learning, Deep Learning, reinforcement learning, networks data analysis, functional data analysis, tensor analysis, ordinary differential equations, dimension reduction, high dimensional inference

Gašper Beguš

Dept of Linguistics

computational linguistics, machine learning, artificial intelligence, Generative AI, Deep Learning, speech processing, NLP, phonology, articulatory and acoustic phonetics, historical linguistics, Indo-European linguistics

Biwen Zhang

Haas School of Business

capital markets, conflicts of interest, financial intermediation, machine learning

Anil Aswani

Dept of Industrial Engineering & Operations Research

machine learning and artificial intelligence, precision health, optimization, statistics

Amanda Coston

Dept of Statistics

causal inference, machine learning, nonparametric statistics, responsible AI, algorithmic fairness

Joshua Blumenstock

School of Information

machine learning, development economics, poverty and public policy, inequality and crisis

John Wright

Division of Computer Science (EECS)

quantum state learning, quantum complexity theory, property testing, approximation algorithms

Mohammad Reza Kaazempur Mofrad

Dept of Bioengineering

Dept of Mechanical Engineering

molecular cell biomechanics, bacterial communities / microbiomes, deep learning for biology and medicine

Eileen D. Gambrill

School of Social Welfare

social welfare, professional ethics and education, social learning theory, behavioral methods

Jose M. Carmena

Division of Electrical Engineering (EECS)

brain-machine interfaces, neural ensemble computation, neuroprosthetics, sensorimotor learning and control

Deirdre Mulligan

School of Information

information policy, law, privacy, security, machine learning and artificial intelligence

Lin Lin

Dept of Mathematics

applied mathematics, Quantum Chemistry, quantum computation, Scientific machine learning

Alan Hubbard

School of Public Health

causal inference, targeted learning, statistical issues in epidemiology, precision medicine and public health

Alane Suhr

Division of Computer Science (EECS)

artificial intelligence, natural language processing, machine learning, computer vision, computational linguistics

Jingshen Wang

School of Public Health

precision medicine, causal inference, adaptive experiment, machine learning and artificial intelligence

Andy Shanken

Dept of Architecture

memory, visionary architecture, the unbuilt, paper architecture, heritage conservation, architectural representation, urban representation, diagrams, history of professions, historiography, world's fairs, expositions, California architecture, themed environments

James W. Rector

Dept of Civil and Environmental Engineering

geoarchaeology, geophysics, acoustics, Seismology, machine learning, energy, carbon capture and storage, oil and gas

Michael DeWeese

Dept of Neuroscience

Dept of Physics

machine learning, computation, systems neuroscience, auditory cortex, neural coding, statistical mechanics

Nika Haghtalab

Division of Computer Science (EECS)

artificial intelligence, machine learning, theoretical computer science, game theory and mechanism design

Bin Yu

Dept of Statistics

machine learning, trustworthy data science and AI, interdisciplinary research in biomedicine, neuroscience, climate science

Khalid M. Mosalam

Dept of Civil and Environmental Engineering

bridges, concrete and masonry structures, damage mechanics, earthquake engineering, fracture mechanics, machine learning

Maya L. Petersen

School of Public Health

Computational Precision Health Program

causal inference, precision health, targeted learning, HIV, global health, pandemics

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

Ryan Giordano

Dept of Statistics

machine learning, variational inference, Bayesian methods, robustness quantification, applied statistics

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