About the Program
The Designated Emphasis (DE) in Communication, Computation and Statistics provides an academic structure for an interdisciplinary exchange of ideas. Many of the most significant developments in modern information technology—including communication and data networks, multimedia information processing, and large-scale, distributed data analysis in science, engineering and commerce—are no longer the province of a single academic field, but draw on ideas from diverse sources in computer science, electrical engineering and statistics. The DE in Communication, Computation and Statistics enables specialized, multi-disciplinary training and research opportunities in various emerging areas of information technology. Admitted students not only participate in a cutting-edge program and receive explicit recognition of specialization in the Designated Emphasis, but are also well positioned to compete for the most desirable jobs in information technology, both in academia and in industry.
To be admitted to the Designated Emphasis in Communication, Computation and Statistics, an applicant must already be accepted into a PhD program at the University of California, Berkeley. The candidate must also submit a petition for admission prior to taking the Qualifying Examination, after one year of study in his or her home department, and preferably in the second year of his or her graduate training. The petition for admission must be accompanied by a letter of recommendation from a sponsoring faculty member (e.g., the student's adviser), a personal statement, a Curriculum Vitae, and copies of transcripts. The petition for admission must be signed by the sponsoring faculty member before submission to the graduate student services adviser.
For further information regarding admission to graduate programs at UC Berkeley, please see the Graduate Division's Admissions website.
Designated Emphasis Requirements
A student in EECS must choose two breadth courses from the following list, and a student in a department other than EECS or Statistics must choose one breadth courses from the following list:
|STAT 204||Probability for Applications||4|
|STAT C205A||Probability Theory||4|
|STAT C205B||Probability Theory||4|
|STAT C206A||Advanced Topics in Probability and Stochastic Process||3|
|STAT C206B||Advanced Topics in Probability and Stochastic Processes||3|
|STAT 210A||Theoretical Statistics||4|
|STAT 210B||Theoretical Statistics||4|
|STAT 215A||Statistical Models: Theory and Application||4|
|STAT 215B||Statistical Models: Theory and Application||4|
|STAT 230A||Linear Models||4|
|STAT 232||Experimental Design||4|
|STAT 240||Nonparametric and Robust Methods||4|
|STAT C241A||Statistical Learning Theory||3|
|STAT C241B||Advanced Topics in Learning and Decision Making||3|
|STAT 248||Analysis of Time Series||4|
|STAT 260||Topics in Probability and Statistics||3|
A student in Statistics must choose two breadth courses from the following list, and a student in a department other than EECS or Statistics must choose one breadth courses from the following list:
|EL ENG 221A||Linear System Theory||4|
|EL ENG 222||Nonlinear Systems--Analysis, Stability and Control||3|
|EL ENG 223||Stochastic Systems: Estimation and Control||3|
|EL ENG 224A||Digital Communications||4|
|EL ENG 225A||Digital Signal Processing||3|
|EL ENG 225B||Digital Image Processing||3|
|EL ENG 225D||Audio Signal Processing in Humans and Machines||3|
|EL ENG 226A||Random Processes in Systems||4|
|EL ENG 227AT||Course Not Available||4|
|EL ENG 227BT||Convex Optimization||4|
|EL ENG 228A||High Speed Communications Networks||3|
|EL ENG 229A||Information Theory and Coding||3|
|EL ENG 229B||Error Control Coding||3|
|EL ENG C291||Control and Optimization of Distributed Parameters Systems||3|
|EL ENG C291E||Hybrid Systems and Intelligent Control||3|
|COMPSCI 270||Combinatorial Algorithms and Data Structures||3|
|COMPSCI 271||Randomness and Computation||3|
|COMPSCI C280||Computer Vision||3|
|COMPSCI C281A||Statistical Learning Theory||3|
|COMPSCI C281B||Course Not Available||3|
|COMPSCI 288||Natural Language Processing||4|
|COMPSCI 289A||Introduction to Machine Learning||4|
The Designated Emphasis will be satisfied in the qualifying examination by the designation of communication, computation and statistics as substantive areas of interrogation. The qualifying examination committee must include a member of the Designated Emphasis Group who may represent either the home department of the student or another discipline. The qualifying examination committee must include an Academic Senate member from outside the student's home department. Satisfactory performance on the qualifying examination for the doctorate will be judged independently from performance in the Designated Emphasis.
One member of the Designated Emphasis Group must serve on the dissertation committee and insure that the thesis contributes to the interdisciplinary study of communication, computation and statistics in a significant way. The dissertation committee must include an Academic Senate member from outside the student's home department.
Faculty and Instructors
Pieter Abbeel, Associate Professor. Artificial Intelligence (AI); Control, Intelligent Systems, and Robotics (CIR); Machine Learning.
David Aldous, Professor. Mathematical probability, applied probability, analysis of algorithms, phylogenetic trees, complex networks, random networks, entropy, spatial networks.
Venkat Anantharam, Professor. Communications & Networking (COMNET); Artificial Intelligence (AI); Control, Intelligent Systems, and Robotics (CIR); Security (SEC); Signal Processing (SP).
Peter L. Bartlett, Professor. Statistics, machine learning, statistical learning theory, adaptive control.
Alexandre M. Bayen, Professor. Transportation, modelling and control of distributed parameters systems, large scale infrastructure systems, water distribution.
John Canny, Professor. Computer science, activity-based computing, livenotes, mechatronic devices, flexonics.
Laurent El Ghaoui, Professor. Decision-making under uncertainty, convex optimization, robust solutions, semidefinite programming, exhaustive simulation.
Noureddine El Karoui, Associate Professor. Applied statistics, theory and applications of random matrices, large dimensional covariance estimation and properties of covariance matrices, connections with mathematical finance.
Adityanand Guntuboyina, Assistant Professor.
Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization.
Daniel Klein, Professor. Artificial Intelligence (AI); Natural Language Processing, Computational Linguistics, Machine Learning.
Robert C. Leachman, Professor. Logistics, manufacturing, semiconductors, scheduling, supply chain systems, dynamic production models, production planning and scheduling.
Edward A. Lee, Professor. Embedded Software, Real-Time Systems, Cyber-Physical Systems, Concurrency; Design, Modeling and Analysis (DMA); Programming Systems (PS);Signal Processing (SP).
Jitendra Malik, Professor. Artificial Intelligence (AI); Biosystems & Computational Biology (BIO); Control, Intelligent Systems, and Robotics (CIR); Graphics (GR); Human-Computer Interaction (HCI); Signal Processing (SP);.
Kannan Ramchandran, Professor. Communications & Networking (COMNET); Signal Processing (SP); Control, Intelligent Systems, and Robotics (CIR).
Benjamin Recht, Associate Professor. Control, Intelligent Systems, and Robotics (CIR); Signal Processing (SP); Machine Learning (ML); Optimization (OPT).
Stuart Russell, Professor. Artificial intelligence, computational biology, algorithms, machine learning, real-time decision-making, probabilistic reasoning.
Anant Sahai, Associate Professor. Communications & Networking (COMNET), Information Theory, Cognitive Radio and Spectrum Sharing; Control, Intelligent Systems, and Robotics (CIR), Distributed and Networked Control; Signal Processing (SP); Theory (THY), Information Theory.
S. Shankar Sastry, Professor. Computer science, robotics, arial robots, cybersecurity, cyber defense, homeland defense, nonholonomic systems, control of hybrid systems, sensor networks, interactive visualization, robotic telesurgery, rapid prototyping.
Alistair Sinclair, Professor. Theory (THY); Randomized algorithms; applied probability; statistical physics.
Claire Tomlin, Professor. Control, Intelligent Systems, and Robotics (CIR); Biosystems & Computational Biology (BIO); Control theory; hybrid and embedded systems; biological cell networks.
Martin Wainwright, Professor. Statistical machine learning, High-dimensional statistics, information theory, Optimization and algorithmss.
Bin Yu, Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization.
Avideh Zakhor, Professor. Signal Processing (SP); Artificial Intelligence (AI); Control, Intelligent Systems, and Robotics (CIR); Graphics (GR).
Peter J. Bickel, Professor Emeritus. Statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology.
Jerome A. Feldman, Professor Emeritus. Artificial Intelligence (AI); Biosystems & Computational Biology (BIO); Security (SEC); cognitive science.
Nelson Morgan, Professor Emeritus. Signal Processing (SP).
John A. Rice, Professor Emeritus. Transportation, astronomy, statistics, functional data analysis, time series analysis.
Pravin Varaiya, Professor Emeritus. Communications & Networking (COMNET); Control, Intelligent Systems, and Robotics (CIR); Energy (ENE); Control; Networks; Power systems; Transportation.
Graduate Group in Communication, Computation, and Statistics
Director of Graduate Matters, EECS
217 Cory Hall
Director of Student Services, School of Information
Siu Yung Wong
104 South Hall
Graduate Student Services Adviser, Statistics
La Shana Polaris
373 Evans Hall