{ "college": "uc-irvine", "edition": "2026-2027", "departmentName": "Interdisciplinary Studies", "id": "interdisciplinary-studies--computationalscience-phd", "name": "Computational Science", "award": "Ph.D.", "department": "interdisciplinary-studies", "sourceUrl": "https://catalogue.uci.edu/interdisciplinarystudies/computationalscience_phd/", "blocks": [ { "kind": "text", "markdown": "### Overview\n\n949-824-2164 \ncomputationalscience.uci.edu\n\n##### Joint Doctoral Program with UC Irvine and San Diego State University\n\nA 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.\n\nThe 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." }, { "kind": "text", "markdown": "### Admission\n\nStudents are admitted into the joint program via a Joint Admissions Committee. Applicants apply to UCI directly using the UCI graduate application.\n\nApplicants are expected to hold a Bachelor’s degree in one of the science, technology, engineering, and mathematics (STEM) fields.\n\nApplicants 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." }, { "kind": "text", "markdown": "### Requirements\n\n##### Program Requirements\n\nThe 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:\n\n- Minimum of 18 units of graduate-level coursework as SDSU.\n- Minimum of 24 units of graduate-level coursework at UCI.\n- Minimum of 24 units of combined research, practicum, and dissertation research units at either institution.\n\n##### Summer Research Survey\n\nStudents are required to attend the annual summer seminar series featuring participating faculty members describing their current research and possible projects.\n\n##### Course Requirements\n\n##### Core Courses at SDSU\n\nMATH 636 - Mathematical Modeling OR MATH 638 - Continuous Dynamical Systems and Chaos \nMATH 693B - Advanced Computational PDEs \nCOMP 605 - Scientific Computing\n\n##### Elective Courses at SDSU\n\n_Students select 9 units from the following list, or appropriate substitutions, with the approval of the program director and their research mentor_\n\nAE 601 - Computational Fluid Mechanics \nAE 641 - Structural Optimization \nAE 670 - Optimal Control \nBIO SCI 606 - Biological Data \nBIO SCI 668 - Advanced Biological Data Analysis \nBIO SCI 740 - Phylogenetic Systematics \nBIOMI 608 - Programming Problems in Bioinformatics \nCHEM 711 - Chemical Thermodynamics \nCHEM 712 - Chemical Kinetics \nCHEM 713 - Quantum Chemistry \nCIVE 620 - Traffic Flow and Control \nCIVE 697 - Traffic Signals Systems Operations and Control \nCOMP 526 - Computational Methods for Scientists \nCOMP 607 - Computational Database Fundamentals \nCOMP 670 - Seminar: Problems in Computational Science \nCOMPSCI 600 - Bioinformatics \nCOMPSCI 610 - Computational Genomics \nCOMPSCI 653 - Data Mining and Knowledge \nCOMPSCI 666 - Advanced Distributed Systems \nCOMPSCI 696 - Programming Problems in Bioinformatics \nEE 645 - Antennas and Wave Propagation \nEE 657 - Digital Signal Processing \nEE 658 - Advanced Digital Signal Processing \nEE 665 - Multimedia Wireless Networks \nEE 740 - Advanced Topics in Physical Electronics Antenna Design \nMATH 693A - Advanced Computational Optimization \nMATH 693B - Advanced Computational PDEs \nMB 610A-B - Advanced Topics in Molecular Biology \nME 610 - Finite Element Methods \nPHYSICS 604 - Electricity and Magnetism \nPHYSICS 606 - Statistical Mechanics \nPHYSICS 608 - Classical Mechanics \nPHYSICS 610 - Quantum Mechanics \nSTATS 657 - Statistical and Machine Learning Methods \nSTATS 658 - Advanced Data Analytics \nSTATS 676 - Bayesian Statistics \nSTATS 678 - Survival Analysis \nSTATS 700 - Data Analysis \nSTATS 701 - Monte Carlo Methods \nSTATS 702 - Data Mining" }, { "kind": "courselist", "title": "Core Courses at UCI", "rows": [ { "type": "course", "code": "COMPSCI 206", "title": "Principles of Scientific Computing", "indent": false }, { "type": "course", "code": "COMPSCI 271", "title": "Introduction to Artificial Intelligence", "indent": false }, { "type": "course", "code": "ENGRCEE 290A", "title": "Machine, Model, and Statistical Learning I", "indent": false }, { "type": "course", "code": "STATS 201", "title": "Statistical Methods for Data Analysis I", "indent": false } ] }, { "kind": "text", "markdown": "##### Elective Courses at UCI\n\n_Students select 8 units from the following list, or appropriate substitutions, with the approval of the program director and their research mentor_" }, { "kind": "courselist", "rows": [ { "type": "course", "code": "BME 232", "title": "Introduction to Computational Biology", "indent": false }, { "type": "course", "code": "BME 233", "title": "Dynamic Systems in Biology and Medicine", "indent": false }, { "type": "course", "code": "BME 238", "title": "Spectroscopy and Imaging of Biological Systems", "indent": false }, { "type": "course", "code": "CHEM 230", "title": "Classical Mechanics and Electromagnetic Theory", "indent": false }, { "type": "course", "code": "CHEM 231A", "title": "Fundamentals of Quantum Mechanics", "indent": false }, { "type": "course", "code": "CHEM 231B", "title": "Applications of Quantum Mechanics", "indent": false }, { "type": "course", "code": "CHEM 232A", "title": "Thermodynamics and Introduction to Statistical Mechanics", "indent": false }, { "type": "course", "code": "CHEM 232B", "title": "Advanced Topics in Statistical Mechanics", "indent": false }, { "type": "course", "code": "CHEM 250", "title": "Computational Chemistry", "indent": false }, { "type": "course", "code": "CHEM 250L", "title": "Computational Chemistry Laboratory", "indent": false }, { "type": "course", "code": "COMPSCI 211A", "title": "Visual Computing", "indent": false }, { "type": "course", "code": "COMPSCI 221", "title": "Information Retrieval, Filtering, and Classification", "indent": false }, { "type": "course", "code": "COMPSCI 242", "title": "Parallel Computing", "indent": false }, { "type": "course", "code": "COMPSCI 261", "title": "Data Structures", "indent": false }, { "type": "course", "code": "COMPSCI 263", "title": "Analysis of Algorithms", "indent": false }, { "type": "course", "code": "COMPSCI 265", "title": "Graph Algorithms", "indent": false }, { "type": "course", "code": "COMPSCI 266", "title": "Computational Geometry", "indent": false }, { "type": "course", "code": "COMPSCI 268", "title": "Introduction to Optimization", "indent": false }, { "type": "course", "code": "COMPSCI 273A", "title": "Machine Learning", "indent": false }, { "type": "course", "code": "COMPSCI 274A", "title": "Probabilistic Learning: Theory and Algorithms", "indent": false }, { "type": "course", "code": "COMPSCI 274B", "title": "Learning in Graphical Models", "indent": false }, { "type": "course", "code": "COMPSCI 276", "title": "Causal and Probabilistic Reasoning with Graphical Models", "indent": false }, { "type": "course", "code": "COMPSCI 278", "title": "Probability Models", "indent": false }, { "type": "course", "code": "COMPSCI 284A", "title": "Artificial Intelligence in Biology and Medicine", "indent": false }, { "type": "course", "code": "COMPSCI 284C", "title": "Computational Systems Biology", "indent": false }, { "type": "course", "code": "EECS 203A", "title": "Digital Image Processing", "indent": false }, { "type": "course", "code": "EECS 215", "title": "Design and Analysis of Algorithms", "indent": false }, { "type": "course", "code": "EECS 227", "title": "Cyber-Physical System Design", "indent": false }, { "type": "course", "code": "EECS 240", "title": "Random Processes", "indent": false }, { "type": "course", "code": "EECS 242", "title": "Information Theory", "indent": false }, { "type": "course", "code": "EECS 250", "title": "Digital Signal Processing I", "indent": false }, { "type": "course", "code": "EECS 280A", "title": "Advanced Engineering Electromagnetics I", "indent": false }, { "type": "course", "code": "EECS 280B", "title": "Advanced Engineering Electromagnetics II", "indent": false }, { "type": "course", "code": "ENGRCEE 250", "title": "Finite Element Method in Structural Engineering", "indent": false }, { "type": "course", "code": "ENGRCEE 270", "title": "Flood Risk and Modeling", "indent": false }, { "type": "course", "code": "ENGRCEE 273", "title": "Watershed Modeling", "indent": false }, { "type": "course", "code": "ENGRCEE 274", "title": "Climate Data Analysis", "indent": false }, { "type": "course", "code": "ENGRCEE 292", "title": "Wavelets in Hydrology, Engineering, and Geoscience", "indent": false }, { "type": "course", "code": "ENGRMAE 230A", "title": "Inviscid Incompressible Fluid Mechanics I", "indent": false }, { "type": "course", "code": "ENGRMAE 230B", "title": "Viscous Incompressible Fluid Mechanics II", "indent": false }, { "type": "course", "code": "ENGRMAE 270A", "title": "Linear Systems I", "indent": false }, { "type": "course", "code": "STATS 202", "title": "Statistical Methods for Data Analysis II", "indent": false }, { "type": "course", "code": "STATS 230", "title": "Statistical Computing Methods", "indent": false } ] }, { "kind": "text", "markdown": "##### Research Units at SDSU\n\nCOMP 897 - Doctoral Research \nCOMP 898 - Practicum \nCOMP 899 - Dissertation" }, { "kind": "courselist", "title": "Research Units at UCI", "rows": [ { "type": "course", "code": "COMPSCI 298", "title": "Thesis Supervision", "indent": false }, { "type": "course", "code": "COMPSCI 299", "title": "Individual Study", "indent": false }, { "type": "course", "code": "ENGR 299", "title": "Individual Research", "indent": false } ] }, { "kind": "text", "markdown": "##### Practicum and Doctoral Research\n\nDissertation 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.\n\n##### Research Report Exam\n\nThe 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.\n\n##### Dissertation Proposal\n\nStudents 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.\n\n##### Advancement to Doctoral Candidacy\n\nAfter 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.\n\n##### Dissertation and Final Oral Examination\n\nOn 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." } ] }