Find UC Berkeley Faculty

Research Expertise and Interest

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Name

Department

Patrick Bradshaw

School of Public Health

epidemiologic methods, Bayesian methods, cancer epidemiology

Alper Atamturk

Dept of Industrial Engineering & Operations Research

sparse learning, integer programming, computational optimization, robust optimization, logistics

Uros Seljak

Dept of Physics

astrophysics, cosmology, machine learning, Bayesian statistics

Xueyin (Snow) Zhang

Dept of Philosophy

Bayesian epistemology, decision theory, philosophy of probability, Chinese philosophy

Ying Cui

Dept of Industrial Engineering & Operations Research

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

Max Willsey

Division of Computer Science (EECS)

programming optimization, programming languages

Peng Ding

Dept of Statistics

Statistical causal inference, missing data, Bayesian statistics, applied statistics

Javad Lavaei

Dept of Industrial Engineering & Operations Research

control theory, optimization, power systems

Ryan Giordano

Dept of Statistics

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

Thibaut Mastrolia

Dept of Industrial Engineering & Operations Research

stochastic control, game theory, finance, optimization

Satish Rao

Division of Computer Science (EECS)

combinatorial optimization, design and analysis of algorithms

Paul Grigas

Dept of Industrial Engineering & Operations Research

optimization, machine learning, data-driven decision-making

Joseph Lewnard

School of Public Health

infectious diseases, antimicrobial resistance, public health surveillance, mathematical modeling, Bayesian inference

Avi Feller

Dept of Statistics

Goldman School of Public Policy

applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences

Jason Lee

Dept of Statistics

Division of Computer Science (EECS)

machine learning theory, optimization, statistics, deep learning

Prasad Raghavendra

Division of Computer Science (EECS)

theory(THY), optimization, complexity theory, approximation algorithms

Ming Gu

Dept of Mathematics

mathematics, scientific computing, numerical linear algebra, convex optimization

Boubacar Kante

Division of Electrical Engineering (EECS)

optoelectronics, quantum optics, optimization, Antennas, Biophysics and Sensing

Sam Pimentel

Dept of Statistics

causal inference, health services & policy analysis, biostatistics, discrete optimization

Anil Aswani

Dept of Industrial Engineering & Operations Research

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

Rajan Udwani

Dept of Industrial Engineering & Operations Research

algorithms, optimization under uncertainty, revenue management, pricing, online platforms

Scott Moura

Dept of Civil and Environmental Engineering

control systems, optimization, data science, batteries, electric vehicles, energy systems

Michael J. Lindsey

Dept of Mathematics

applied mathematics, mathematical analysis, probability, numerical analysis, optimization, Monte Carlo methods

Duncan Callaway

Dept of Energy & Resources Group

Division of Electrical Engineering (EECS)

energy systems analysis, grid decarbonization, modeling and control and optimization for electric power systems

Alexandre Bayen

Division of Electrical Engineering (EECS)

control, optimization, machine learning, applications: transportation; mobile sensing; connected health

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