Find UC Berkeley Faculty

Research Expertise and Interest

Search by keyword or research area. Use quotes for exact phrases — e.g., "computational biology".

Name

Department

Jacob Steinhardt

Jacob Steinhardt

Dept of Statistics

artificial intelligence, machine learning

Jason Lee

Dept of Statistics

Division of Computer Science (EECS)

machine learning theory, optimization, statistics, deep learning

Song Mei

Dept of Statistics

data science, statistics, machine learning

NikitaZhivotovskiy

Nikita Zhivotovskiy

Dept of Statistics

mathematical statistics, applied probability, statistical learning theory

Dept of Statistics

statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology

Dept of Statistics

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

Mark van der Laan

Dept of Statistics

School of Public Health

targeted learning, real-world data integration in RCTs, sequential adaptive designs, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies

Adityanand Guntuboyina

Adityanand Guntuboyina

Dept of Statistics

nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory

Dept of Statistics

Division of Computer Science (EECS)

machine learning, statistical learning theory, adaptive control

Jennifer Listgarten

Division of Computer Science (EECS)

artificial intelligence, machine learning, computational biology, protein engineering, statistical design of experiments, statistical machine learning, experimental design

Michael DeWeese

Dept of Neuroscience

Dept of Physics

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

Ying Cui

Dept of Industrial Engineering & Operations Research

continuous optimization, optimization under uncertainty, statistical estimations, machine learning, artificial intelligence

Ryan Tibshirani giving a whiteboard talk

Ryan Tibshirani

Dept of Statistics

statistical computing, applications in public health, high-dimensional data analysis, nonparametric inference, artificial intelligence, machine learning

Sandrine Dudoit

Sandrine Dudoit

Dept of Statistics

School of Public Health

statistics, machine learning, data science, applied statistics, statistical computing, computational biology, computational genomics, precision medicine, precision health

Nilah Ioannidis

Nilah Ioannidis

Division of Computer Science (EECS)

computational biology, machine learning, artificial intelligence, genomics, personal genome interpretation, precision health, rare diseases, statistical genetics, molecular biology, biophysics

Christian H. Borgs

Christian Borgs

Division of Computer Science (EECS)

theoretical computer science, probability theory, combinatorics, complex networks, statistical physics, artificial intelligence, applications of artificial intelligence and machine learning in engineering and sciences

Lexin Li

School of Public Health

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

Kannan Ramchandran

Kannan Ramchandran

Division of Computer Science (EECS)

Division of Electrical Engineering (EECS)

statistical and sparse signal processing, adversarial and distributed machine learning, coded computing, privacy and security, scalable distributed video-on-demand delivery, coding theory, communications, information theory, peer-to-peer networking, blockchains

Ahmed Alaa

Computational Precision Health Program

machine learning and artificial intelligence, health care, statistics

Fei Xu

Dept of Psychology

cognitive development, language development, learning mechanisms, statistical learning and statistical inference, conceptual development, developmental psychology, word learning, physical reasoning, social cognition in infants and children, learning in infants and young children, computational models of cognitive development, Bayesian probabilistic models, cognitive development and AI models, psychology and philosophy

Dept of Statistics

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

Person placeholder image

Division of Computer Science (EECS)

data science, machine learning, inequality, Healthcare

Bin Yu

Dept of Statistics

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

Dept of Materials Science and Engineering

energy storage, computational modeling, machine learning, autonomous laboratories

School of Public Health

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