phd computer science ucla

Artificial Intelligence

The computational study of intelligent behavior. This includes research in logical and probabilistic reasoning, machine learning, causality, heuristic search and combinatorial optimization, and natural language processing.

Kai-Wei Chang Adnan Darwiche Alyson Fletcher Saadia Gabriel Aditya Grover Quanquan Gu Cho-Jui Hsieh Richard Korf Baharan Mirzasoleiman Judea Pearl Nanyun Peng Guy Van den Broeck

Computer System Architecture & CAD

The study of the structure and behavior of computer systems; development of new algorithms and computing structures to be implemented in hardware, firmware, and software; development of tools to enable system designers to describe, model, fabricate and test highly complex computer systems.

Jason (Jinsheng) Cong Tony Nowatzki Miodrag Potkonjak Glenn Reinman Majid Sarrafzadeh Yuval Tamir Blaise-Pascal Tine

Computational Systems Biology

An integrative approach to understanding biological systems, with research areas that span systems biology, bioinformatics, genomics, computational biology, and biomedical engineering.

Joseph DiStefano Jason Ernst Eleazar Eskin Eran Halperin Sriram Sankararaman

Graphics & Vision

The synthesis and analysis of images by computer. Graphics — rendering, motion capture, and geometric, physics-based and artificial life modeling/animation for the movie and game industries. Vision — texture, shape, motion and illumination, 3D reconstruction from images, object recognition, real-time vision/control for autonomous vehicles, visual sensor networks and surveillance, and medical image analysis.

Achuta Kadambi Stefano Soatto Demetri Terzopoulos Bolei Zhou

Human Computer Interaction

A multi-disciplinary approach to study, design, and build interactive systems for diverse users (e.g., scientists, artists, journalists, students) working on a broad range of applications (e.g., research, policy, fabrication, software, AI). The goal of HCI is to empower more people to use technology to live according to their values.

Xiang Anthony Chen Eunice Jun

Information & Data Management

The development of models, techniques and tools to improve the functionality, performance and usability of database management and Web systems that provide enabling technology for our information society — including Web search engines, digital libraries, big data, data mining, distributed databases, data stream management systems, and information systems for medicine and science.

Junghoo (John) Cho Yizhou Sun Remy Wang Wei Wang

Network Systems

The study and design of distributed and often mobile systems — including computers, vehicles, people, and sensors interconnected by a communications network. This includes the development of novel applications that run on these systems, new protocols that enable various network components to work together, and techniques to optimize performance and to ensure reliability.

Omid Abari Leonard Kleinrock Songwu Lu Mani Srivastava George Varghese Lixia Zhang

Software Systems

A broad array of ongoing research that spans the entire spectrum of software systems — including programing language design and implementation, software engineering, software verification, operating systems, and embedded systems.

Miryung Kim Sam Kumar Todd Millstein Jens Palsberg Yuan Tian  Harry Xu

Computer Science Theory

The use of simple and concise mathematical models to investigate computational questions and issues — including research in centralized, parallel and distributed models of computation; optimal approximate and randomized online algorithms; complexity, cryptography, games, auctions and mechanism design theory.

Eliezer Gafni Raghu Meka Rafail Ostrovsky Amit Sahai Alexander Sherstov

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2021-2022 Computer Science Overview

Computer science is concerned with the design, modeling, analysis, and applications of computer systems. Its study at UCLA provides education at the undergraduate and graduate levels necessary to understand, design, implement, and use the software and hardware of digital computers and digital systems. The programs offer comprehensive and integrated studies of subjects in computer system architecture, computer networks, distributed computer systems, programming languages and software systems, information and data management, artificial intelligence, computer science theory, computational systems biology and bioinformatics, and computer vision and graphics.

The undergraduate and graduate studies and research projects in the Department of Computer Science are supported by significant computing resources. In addition to the departmental computing facility, there are over a dozen research laboratories specializing in areas such as distributed systems, multimedia computer communications, distributed sensor networks, VLSI systems, VLSI CAD, embedded and reconfigurable systems, computer graphics, bioinformatics, and artificial intelligence. Also, the Cognitive Systems Laboratory is engaged in studying computer systems that emulate or support human reasoning. The Biocybernetics Laboratory is devoted to multidisciplinary research involving the application of engineering and computer science methods to problems in biology and medicine.

The BS degree may be attained through the Computer Science and Engineering major, the Computer Science major, or the Computer Engineering major described below.

In addition, UCLA Samueli offers MS and PhD degrees in Computer Science, as well as minor fields for graduate students seeking engineering degrees. In cooperation with the John E. Anderson Graduate School of Management, the Computer Science Department offers a concurrent degree program that enables students to obtain the MS in Computer Science and the MBA (Master of Business Administration).

Department Mission

The Computer Science Department strives for excellence in creating, applying, and imparting knowledge in computer science and engineering through comprehensive educational programs, research in collaboration with industry and government, dissemination through scholarly publications, and service to professional societies, the community, state, and nation.

At an Upsilon Pi Epsilon industry event, Computer Science alumni talk about what it’s like to build technology for Google.

General Catalog

Computer science ms, phd.

Grad Development

Admissions Requirements for the Graduate Major in Computer Science

After exploring options and choosing a specific program, follow the steps on our University’s graduate application process:

• Director’s Welcome
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• Frontiers in Computational Biosciences Seminar Series
• Current Ph.D. Students
• Current M.S. Students
• Bioinformatics Department Handbook
• B.I.G. Summer Institute
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• Joint UCLA-USC Meeting
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Doctoral Dissertation

• Student Publications
• Capstone Project
• Undergraduate Courses
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• Bioinformatics Minor Course Requirements
• Bioinformatics Minor FAQs
• Bioinformatics Minor End-of-Year Celebration
• For Engineering Students
• Graduate Program

The UCLA Bioinformatics Ph.D. Program

UCLA offers an integrated doctoral training for students interested in working at the interface of computer science, biology, and mathematics to address the fundamental challenges of contemporary genomic-scale research. The interdisciplinary Ph.D. program consists of an integrated one-year core curriculum, research rotations, over 50 elective courses, and faculty mentors spanning biology, mathematics, engineering, and medicine.

Doctoral Degree Requirements & Time-To-Degree

The program provides a comprehensive system of advising throughout a student’s graduate study.  During orientation, the advising committee and the program chair meet with new students to review first-year requirements.  Throughout their first term, students are expected to meet individually with the chair or other members of the advising committee to identify faculty is closest to the student’s own interests and whose laboratory rotations would be most appropriate to join.  At the end of the fall quarter, the entire advising committee meets with the first-year students to answer questions that have arisen.  In subsequent quarters, each student’s enrollment and performance in core courses and laboratory rotations are closely monitored, and students are counseled individually by the advising chair.  At the end of spring quarter of the first year, students submit a Faculty Mentor Approval form, co-signed by the proposed mentor, to the advising committee, which considers the choice of mentor and the ability of faculty to serve in this capacity.

The advising program continues after a student has chosen a faculty research mentor.  Each year, students receive a memorandum outlining current requirements (e.g., course electives, the written and oral qualifying examinations and midstream seminar).  The advising committee also meets annually to discuss student progress and identify potential problems.  Students then receive a letter assessing their current progress and making specific recommendations as needed.  An overall assessment of student progress is also made annually to the neuroscience committee.

In addition to the formal advising procedures outlined above, students are encouraged to seek advice on career development from faculty members in the Bioinformatics community at UCLA.  An annual retreat serves to allow informal and organized contacts between faculty and students, providing further opportunity for advising.

Major Fields or Subdisciplines

These fields include computer science, genomics, molecular evolution/comparative genomics, mathematics, neuroinformatics, proteomics and statistics.

Foreign Language Requirement

Course Requirements

Students must take the core courses, Chemistry and Biochemistry 202 (six units), M252 (six units), 260A-260B, Statistics M254, 12 units of laboratory rotation courses, and at least six units of seminar courses.

Students who have gaps in their previous training may take, with their adviser’s approval, appropriate undergraduate courses. However, these courses may not be applied toward the required courses for the doctoral degree.

Teaching Experience

One quarter of teaching experience is required.

Written and Oral Qualifying Examinations

Academic Senate regulations require all doctoral students to complete and pass University written and oral qualifying examinations prior to doctoral advancement to candidacy. Also, under Senate regulations the University oral qualifying examination is open only to the student and appointed members of the doctoral committee. In addition to University requirements, some graduate programs have other pre-candidacy examination requirements. What follows in this section is how students are required to fulfill all of these requirements for this doctoral program.

Doctoral students must complete the core curriculum before they are permitted to take the written and oral qualifying examinations. Students are required to pass a written qualifying examination that consists of a research proposal outside of their dissertation topic and the University Oral Qualifying Examination in which they defend their dissertation research proposal before their doctoral committee. Students are expected to complete the written examination by the beginning of the second year and the oral examination by the end of Spring Quarter of the third year.

During their first year, doctoral students perform laboratory rotations with program faculty whose research is of interest to them and select a dissertation adviser from the program faculty inside list by the end of their third quarter of enrollment. By the end of their second spring quarter, students must select a doctoral committee that consists of three program faculty and one outside member and is approved by the program chair and the Graduate Division. By the end of their second year, students must submit a written dissertation proposal.  All members of the doctoral committee must receive a copy of the proposal before the oral qualifying examination is scheduled.

Approximately one year after the successful completion of the qualifying examinations and advancement to doctoral candidacy, students must present, within an oral seminar format, a summary of their research to date and proposed future research.

Advancement to Candidacy

Students are advanced to candidacy upon successful completion of the written and oral qualifying examinations.

Every doctoral degree program requires the completion of an approved dissertation that demonstrates the student’s ability to perform original, independent research and constitutes a distinct contribution to knowledge in the principal field of study.

Final Oral Examination (Defense of the Dissertation)

Required for all students in the program.

Time-to-Degree

Students are expected to complete the written qualifying examination by the beginning of the second year of study and the University Oral Qualifying Examination by the end of Spring Quarter of the third year. The normative time-to-degree is 16 quarters.

Termination of Graduate Study and Appeal of Termination

University Policy

A student who fails to meet the above requirements may be recommended for termination of graduate study. A graduate student may be disqualified from continuing in the graduate program for a variety of reasons. The most common is failure to maintain the minimum cumulative grade point average (3.00) required by the Academic Senate to remain in good standing (some programs require a higher grade point average). Other examples include failure of examinations, lack of timely progress toward the degree and poor performance in core courses. Probationary students (those with cumulative grade point averages below 3.00) are subject to immediate dismissal upon the recommendation of their department. University guidelines governing termination of graduate students, including the appeal procedure, are outlined in Standards and Procedures for Graduate Study at UCLA .

Special Departmental or Program Policy

Students must receive at least a grade of B- in core courses or repeat the course. Students who received three grades of B- in core courses, who fail all or part of the written or oral qualifying examinations twice (the examination committee determines the form of re-examination for students who fail all or part of the written examination), or who fail to maintain minimum progress may be recommended for termination by vote of the entire interdepartmental program committee. Students may appeal a recommendation for termination in writing to the interdepartmental program committee and may personally present additional or mitigating information to the committee, in person or in writing.

Featured News

Researchers awarded $4.7 million to study genomic variation in stem cell production, dr. nandita garud recognized for her research on gut microbiome, ucla study reveals how immune cells can be trained to fight infections, ucla scientists decode the ‘language’ of immune cells, dr. eran halperin elected as fellow of international society for computational biology, upcoming events, omolola (lola) ogunyemi seminar, angela brooks seminar, ghayda mirzaa seminar, jana lipkova seminar, memorial day holiday, recent student publications.

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Integra FEC

Data analytics and engineering intern.

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Job description Integra is a boutique economic consulting and forensic analytics firm that works with government agencies and private entities to identify financial fraud. Integra is hiring quantitatively-skilled students/graduates with data science, data analytics, or software development experience who are interested in translating their academic interests into the real-world applications. Our team leverages a variety of data science/analytics techniques to extract meaningful insights from large datasets. As an intern you will develop tools used to collect large data sets, automate data pipelines, and perform cloud-based analytics. You will additionally gain experience at all stages of the data lifecycle in a professional environment.

Responsibilities

• Design, develop, and maintain existing tools/scripts for retrieving, ingesting, and processing data in a cloud environment
• Build data pipelines and transform data to support usage in econometric analysis
• Work with our team to identify needs, design and implement solutions, and test results
• Apply appropriate data mining, analytics, or NLP techniques to verify integrity of and glean information from large datasets
• Document processes and present results to our team

Required Qualifications

• Currently enrolled in a PhD or Master’s degree program in Data Science, Data Analytics, Computer Science, Software Engineering, Applied Math, or other quantitative fields
• Experience scripting/programming in Python (3.1x)
• Experience designing, implementing, or interfacing with SQL databases
• Experience with data cleaning and preparation
• Experience interfacing with REST APIs
• Basic knowledge of cloud computing / storage technologies
• Knowledge of statistical methods and machine learning techniques

Preferred Qualifications

• Strong background in SQL and Python as applied to data science, analysis, engineering, or warehousing tasks
• Experience with cloud based data warehousing tools (e.g., Google BigQuery, AlloyDB, CloudSQL, Amazon Redshift / Aurora, Azure Data Warehouse/Synapse, Snowflake)
• Experience using Github for version control
• At least one Python based project available for our team to review