Computational Science
Ph.D. Interdisciplinary Studies · UC Irvine · 2026-2027 Catalog
Overview
949-824-2164
computationalscience.uci.edu
Joint Doctoral Program with UC Irvine and San Diego State University
A joint offering with San Diego State University (SDSU), the Ph.D. program in Computational Science trains professionals capable of developing novel computational approaches to solve complex problems in both fundamental sciences and applied sciences and engineering. A program of study combining applied mathematics, computing, and a solid training in basic science culminates in doctoral research focused on an unsolved scientific problem.
The Ph.D. in Computational Science produces broadly educated, research-capable scientists that are well prepared for diverse careers in academia, industry, business, and government research laboratories.
Admission
Students are admitted into the joint program via a Joint Admissions Committee. Applicants apply to UCI directly using the UCI graduate application.
Applicants are expected to hold a Bachelor’s degree in one of the science, technology, engineering, and mathematics (STEM) fields.
Applicants are evaluated on the basis of their prior academic record and their potential for creative research and teaching, as demonstrated in submitted materials. These materials include official university transcripts, three letters of recommendation, a Statement of Purpose, and a Personal History statement.
Requirements
Program Requirements
The normative time to completion is five years. The maximum time to completion is seven years. A total minimum of 66 units of course work, independent study, and research must be completed. These units must be distributed as follows:
- Minimum of 18 units of graduate-level coursework as SDSU.
- Minimum of 24 units of graduate-level coursework at UCI.
- Minimum of 24 units of combined research, practicum, and dissertation research units at either institution.
Summer Research Survey
Students are required to attend the annual summer seminar series featuring participating faculty members describing their current research and possible projects.
Course Requirements
Core Courses at SDSU
MATH 636 - Mathematical Modeling OR MATH 638 - Continuous Dynamical Systems and Chaos
MATH 693B - Advanced Computational PDEs
COMP 605 - Scientific Computing
Elective Courses at SDSU
Students select 9 units from the following list, or appropriate substitutions, with the approval of the program director and their research mentor
AE 601 - Computational Fluid Mechanics
AE 641 - Structural Optimization
AE 670 - Optimal Control
BIO SCI 606 - Biological Data
BIO SCI 668 - Advanced Biological Data Analysis
BIO SCI 740 - Phylogenetic Systematics
BIOMI 608 - Programming Problems in Bioinformatics
CHEM 711 - Chemical Thermodynamics
CHEM 712 - Chemical Kinetics
CHEM 713 - Quantum Chemistry
CIVE 620 - Traffic Flow and Control
CIVE 697 - Traffic Signals Systems Operations and Control
COMP 526 - Computational Methods for Scientists
COMP 607 - Computational Database Fundamentals
COMP 670 - Seminar: Problems in Computational Science
COMPSCI 600 - Bioinformatics
COMPSCI 610 - Computational Genomics
COMPSCI 653 - Data Mining and Knowledge
COMPSCI 666 - Advanced Distributed Systems
COMPSCI 696 - Programming Problems in Bioinformatics
EE 645 - Antennas and Wave Propagation
EE 657 - Digital Signal Processing
EE 658 - Advanced Digital Signal Processing
EE 665 - Multimedia Wireless Networks
EE 740 - Advanced Topics in Physical Electronics Antenna Design
MATH 693A - Advanced Computational Optimization
MATH 693B - Advanced Computational PDEs
MB 610A-B - Advanced Topics in Molecular Biology
ME 610 - Finite Element Methods
PHYSICS 604 - Electricity and Magnetism
PHYSICS 606 - Statistical Mechanics
PHYSICS 608 - Classical Mechanics
PHYSICS 610 - Quantum Mechanics
STATS 657 - Statistical and Machine Learning Methods
STATS 658 - Advanced Data Analytics
STATS 676 - Bayesian Statistics
STATS 678 - Survival Analysis
STATS 700 - Data Analysis
STATS 701 - Monte Carlo Methods
STATS 702 - Data Mining
Core Courses at UCI
| Course | Title | Units |
|---|---|---|
| COMPSCI 206 | Principles of Scientific Computing | |
| COMPSCI 271 | Introduction to Artificial Intelligence | |
| ENGRCEE 290A | Machine, Model, and Statistical Learning I | |
| STATS 201 | Statistical Methods for Data Analysis I |
Elective Courses at UCI
Students select 8 units from the following list, or appropriate substitutions, with the approval of the program director and their research mentor
| Course | Title | Units |
|---|---|---|
| BME 232 | Introduction to Computational Biology | |
| BME 233 | Dynamic Systems in Biology and Medicine | |
| BME 238 | Spectroscopy and Imaging of Biological Systems | |
| CHEM 230 | Classical Mechanics and Electromagnetic Theory | |
| CHEM 231A | Fundamentals of Quantum Mechanics | |
| CHEM 231B | Applications of Quantum Mechanics | |
| CHEM 232A | Thermodynamics and Introduction to Statistical Mechanics | |
| CHEM 232B | Advanced Topics in Statistical Mechanics | |
| CHEM 250 | Computational Chemistry | |
| CHEM 250L | Computational Chemistry Laboratory | |
| COMPSCI 211A | Visual Computing | |
| COMPSCI 221 | Information Retrieval, Filtering, and Classification | |
| COMPSCI 242 | Parallel Computing | |
| COMPSCI 261 | Data Structures | |
| COMPSCI 263 | Analysis of Algorithms | |
| COMPSCI 265 | Graph Algorithms | |
| COMPSCI 266 | Computational Geometry | |
| COMPSCI 268 | Introduction to Optimization | |
| COMPSCI 273A | Machine Learning | |
| COMPSCI 274A | Probabilistic Learning: Theory and Algorithms | |
| COMPSCI 274B | Learning in Graphical Models | |
| COMPSCI 276 | Causal and Probabilistic Reasoning with Graphical Models | |
| COMPSCI 278 | Probability Models | |
| COMPSCI 284A | Artificial Intelligence in Biology and Medicine | |
| COMPSCI 284C | Computational Systems Biology | |
| EECS 203A | Digital Image Processing | |
| EECS 215 | Design and Analysis of Algorithms | |
| EECS 227 | Cyber-Physical System Design | |
| EECS 240 | Random Processes | |
| EECS 242 | Information Theory | |
| EECS 250 | Digital Signal Processing I | |
| EECS 280A | Advanced Engineering Electromagnetics I | |
| EECS 280B | Advanced Engineering Electromagnetics II | |
| ENGRCEE 250 | Finite Element Method in Structural Engineering | |
| ENGRCEE 270 | Flood Risk and Modeling | |
| ENGRCEE 273 | Watershed Modeling | |
| ENGRCEE 274 | Climate Data Analysis | |
| ENGRCEE 292 | Wavelets in Hydrology, Engineering, and Geoscience | |
| ENGRMAE 230A | Inviscid Incompressible Fluid Mechanics I | |
| ENGRMAE 230B | Viscous Incompressible Fluid Mechanics II | |
| ENGRMAE 270A | Linear Systems I | |
| STATS 202 | Statistical Methods for Data Analysis II | |
| STATS 230 | Statistical Computing Methods |
Research Units at SDSU
COMP 897 - Doctoral Research
COMP 898 - Practicum
COMP 899 - Dissertation
Research Units at UCI
| Course | Title | Units |
|---|---|---|
| COMPSCI 298 | Thesis Supervision | |
| COMPSCI 299 | Individual Study | |
| ENGR 299 | Individual Research |
Practicum and Doctoral Research
Dissertation research is carried out at either UCI or SDSU, or at an industry or national laboratory under the supervision of the Doctoral Advisor. While conducting dissertation research, students must enroll in the appropriate research units at the campus of the Doctoral Advisor. If research is done outside of UCI or SDSU, students should register in-absentia if appropriate.
Research Report Exam
The student is expected to pass the Research Report Exam within three years of admittance. This examination consists of a term research project supervised by a faculty mentor. The student is required to prepare a written account of research work performed and its results, and offer an oral presentation before the members of the Doctoral Committee. The student must submit a paper based on their research report before giving the oral presentation to the Doctoral Committee. Should a student fail the Research Report Exam, one retake is allowed.
Dissertation Proposal
Students must submit a dissertation proposal to the doctoral committee by the end of their third year in the program. This proposal should take the form of a scientific grant proposal to a major funding agency. It should describe the research project that the student intends to carry out and upon which their doctoral dissertation will be based. The student must also offer an oral presentation of the proposal before the Computational Science faculty. Upon successful completion of this presentation, the student will be recommended for advancement to candidacy for the doctoral degree.
Advancement to Doctoral Candidacy
After successful completion of the dissertation proposal and certification that all other requirements are fulfilled, the student is advanced to candidacy at both campuses. Students not registered at UCI will need to formally advance to candidacy in the summer term. Advancement to candidacy for the Ph.D. must occur at least one term prior to dissertation defense.
Dissertation and Final Oral Examination
On completion of the research, the student prepares the dissertation in accordance with UCI regulations. A final draft of the dissertation is presented to each member of the doctoral committee at least three weeks prior to the final oral examination. The oral defense is held on the campus of the primary faculty advisor. Students must follow UCI filing deadlines. Students are required to be registered for Dissertation Research (3 units) at SDSU and Dissertation Research (4 units) simultaneously at UCI during the semester in which they present their doctoral defense. Alternatively, students can request filing fee status at UCI in the quarter in which they present their doctoral defense.