»Ph.D. in Computational and Data Sciences

Computational Science is the art of creating, developing, and validating models in order to gain a profound understanding of real-life complex problems. Data Science is the art of generating insight, knowledge, and predictions by applying modern methods to large datasets.

In Chapman University’s Ph.D. in Computational and Data Sciences program, you will collaborate on innovative research as you work closely with nationally and internationally renowned faculty mentors who will help prepare you to thrive in a variety of professional settings, from academia to private industry, scientific research labs to government agencies. You will learn to design and implement mathematical models and refine quantitative analysis techniques to solve complex scientific problems. Develop your dissertation with focus on advancement of theory and applications of statistical, machine learning and AI in diverse data science related fields such as medicine and epidemiology, climate and Earth hazards, big data and high-performance computing, drug design, genetics, natural language processing, bioinformatics and biotechnology, economics, and sports analytics.

Employment and Future Opportunities

In our tech-driven world, employers are increasingly recognizing the value of data science professionals. According to U.S. News and World Report, the Bureau of Labor Statistics projects 35.8% employment growth for data scientists between 2021 and 2031. In this period, an estimated 40,500 jobs should open up.

Graduates from the program have gone on to work in a variety of industries, such as:

Artificial Intelligence and Machine Learning
Higher Education Institutions
Healthcare
Entertainment Industry
Government Agencies
Large Tech Companies such as Amazon, Microsoft, Google, Yahoo

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The Computational and Data Sciences (CADS) program is focused on developing and implementing state-of-the-art statistical, machine learning and AI models to advance high accuracy medical diagnoses, drug discovery. survival and quality of life, reduce medical expenditures, predict extreme weather events and their impacts, sport analytics and player trading optimization.

The Ph.D. program offers an interdisciplinary and personalized graduate education that emphasizes innovative research and its applications. After having completed a set of core courses teaching the modern methodologies and techniques of computational science, students specialize in an area based on shared interests with one or more faculty mentors, culminating in a final dissertation and peer reviewed publications.

For the latest information on the current curriculum, please visit the Graduate Catalog.

Prerequisites

It is expected that students admitted to the CADS Ph.D. program will have completed substantial preparatory coursework as an undergraduate major or minor from a regionally accredited institution in one of the following disciplines, or the equivalent: Mathematics, Statistics, Computer Science, Data Science, Physics, Electrical Engineering, or Software Engineering.

Preparatory coursework must include the following courses, or the equivalent:

Linear Algebra
Multivariable Calculus
Differential Equations
Computer Programming: Data Structures preferred (R, Python, and SQL)
Probability and Statistics (Distributions, Confidence Intervals, Hypothesis Testing, Linear Models)

Core Courses (13 credits)

CS 510 Computing for Scientists (3)

CS 520 Mathematical Modeling (3)

CS 530 Data Mining (3)

CS 555 Multivariate Data Analysis (3)

CS 595 Computational Science Seminars (1)

Elective and Research Courses (45 credits)

Courses and descriptions can be found in the Graduate Catalog. A minimum of 15 credits must be at the 700 level (excluding the dissertation courses).

Dissertation (12 credits)

CS 798 Dissertation Research (1–6)

In order to advance to doctoral candidacy, a student must:

· Pass qualifying examinations on topics from the core courses.

· Pass a preliminary oral examination on topics from elective and research courses selected by the student’s Doctoral Committee.

Total Credits: 70

Admission Requirements

An undergraduate degree specifically in computational science is not required for admission. The program will consider applicants from a broad range of undergraduate and master’s level science disciplines (e.g. biology, chemistry, computer science, biochemistry and molecular biology, mathematics, physics). Admission will depend on the relationship between the student’s goals and the program’s objectives as well as the likelihood that the student will benefit from the program.

1. Prerequisite Courses

Preparatory coursework must include the following courses, or the equivalent:

1. Linear Algebra

2. Multivariable Calculus

3. Differential Equations

4. Computer Programming: Data Structures preferred (R, Python, and SQL)

5. Probability and Statistics (Distributions, Confidence Intervals, Hypothesis Testing, Linear Models)

2. Application Requirements

Admission to the program may be achieved by the completion of the following requirements:

Online application for admission (including $60 non-refundable application fee)

Official transcript from degree granting institution. If prerequisite courses have been taken at schools other than the degree granting institution, those transcripts must also be submitted. Applicants must have earned a minimum grade point average of 3.00.

Letters of recommendation - two letters of recommendation are required, including one from an academic source which describes your professional and academic abilities.

Statement of Intent - a 750 word essay; applicants are expected to address science topics they are interested in and how they envision applying computational science in those areas.

Resume - a resume or curriculum vitae is required

International student application requirements

Chapman's language of instruction is English. If you have not received a bachelor's degree (or higher) at an institute where English was the language of instruction, you must demonstrate English proficiency by submitting official scores from an English language exam. You can find additional information here.

Official Transcripts and Diploma

Your application requires official transcripts in both the native language, and in English. If your university does not provide translations of your transcript, you will need to have your transcript translated, line-by-line and word-for-word exactly. You will need to submit both the official transcript and the official translation.
If your university only provides one official transcript, you will need to submit a notarized copy. You will need to take your official transcript and have certified copies made, and translated into English if needed. These documents should be stamped by the legal notary who made the copy and/or translation. We do not accept uncertified copies directly from students. Please note that official documents will be required upon acceptance.
While your diploma will not be required with your application, your enrollment into Chapman University will be dependent upon submission of your official diploma. Should you be admitted, your diploma will need to be submitted in both the native language, and in English. You will need to submit both the official diploma and the official translation. If your university only provides one official diploma, you may send a notarized copy, or bring the original documents into our office at the time classes begin.

GPA Evaluation - Once your transcripts are received, Chapman University will conduct an in-house evaluation of your credentials to determine your U.S. equivalent GPA.

Supplemental Application

The International Supplemental Application is the financial certification form that provides comprehensive information about your passport, I-20 requirements, and financial support for your studies. This form is required for F-1 student visa applicants.
Should you be admitted into our program, you will be sent information on how to access the Supplemental Application.
If you hold a U.S. passport, or are a permanent resident, you do not need to submit this document. You will apply as a domestic student.

See the Academic Calendar for semester start and other dates

Optional: Graduate Admission Test Scores (School Code: 4047); the Graduate Record Examination (GRE) general test scores are optional and must have been taken within the last five years.

Tuition Information

Financial assistance is available in the form of federal loans, department scholarships, teaching assistantships, and research assistantships.

More information can be found on the Financial Aid website or by contacting Graduate Financial Aid at gradfinaid@chapman.edu or (714) 628-2730.

Admission – Please contact Melissa Liberman, Senior Graduate Counselor, liberman@chapman.edu / (714) 628-2847, regarding your application, to schedule a campus visit or for other non-program specific questions.

Application: How to Apply

International Students – View our international student admissions page for additional information regarding applying to Chapman.

Tuition - Contact Student Business Services at (714) 997-6617 for information regarding tuition, fees, billing & payments. Please note that program staff are prohibited from discussing financial information.

Federal Financial Aid - For more information, email gradfinaid@chapman.edu or call (714) 628-2730.

Housing - For graduate student housing options, contact Housing and Residence Life at (714) 997-6603.

	Cyril Rakovski, Ph.D. Co-Program Director and Associate Professor Areas of Research: Statistical modeling, Time Series Analysis, Bayesian
	Mohamed Allali, Ph.D. Associate Professor Areas of Research: Mathematical Modeling, Image Processing, Signal Processing
	Daniel Alpay, Ph.D. Professor Areas of Research: Schur Analysis; Slice-Hyperholomorphic functions; Signal Processing; Linear Systems; Wavelet Filters; White Noise Space
	Vincent Berardi, Ph.D. Assistant Professor Areas of Research: Computational Health Psychology, Behavioral Science, Mathematics, and Computational Science.
	Peter Jipsen, Ph.D. Professor Areas of Research: Universal Algebra; Lattice Theory; Residuated Lattices; Algebraic Logic; Substructural logics; enumerative combinatorics
	Erik Linstead, Ph.D. Associate Professor Areas of Research: Machine Learning; GPU Programming; Autism Spectrum Disorder; Assistive Technologies; Predictive Analytics; Virtual Reality
	Uri Maoz, Ph.D. Assistant Professor Areas of Research: Computational Neuroscience, Brain and Behavioral Sciences, Neural computation
	Andrew Moshier, Ph.D. Professor Areas of Research: Computation, Algebra & Topology
	David Porter, Ph.D. Professor Areas of Research: Economics and Mathematics, Testing and implementing new and complex market systems
	Stephen Rassenti, Ph.D. Professor Areas of Research: Economic Systems Design, Experimental Economics, Organizational Design
	Amir Raz, Ph.D. Professor Areas of Research: Brain and Behavioral Sciences
	Ahmed Sebbar, Ph.D. Professor Areas of Research: Green’s functions, Bergman kernel, Heat equation, Modular forms, infinite order differential operators, Frobenius determinant
	Aaron Schurger, Ph.D. Assistant Professor Areas of Research: Brain Science, Behavioral Sciences, Computational Psychology
	Gennady Verkhivker, Ph.D. Professor Areas of Research: Computational Cancer Biology, Translational Bioinformatics, and Computational Pharmacology

Q: What is required for admission to the program?

A: Please review our admissions requirements for more information. You may also contact our graduate admissions team at (714) 997-6711, or gradadmit@chapman.edu

Q: Am I required to take the TOEFL (or equivalent)?

A: Applicants who have completed their bachelor’s degree or higher at an institution where English was not the primary language of instruction must submit scores for an English Proficiency exam. Chapman University's institution code for the TOEFL is 4047.

Q: Who should my letters of recommendation come from? May I submit additional letters?

A: Letters of recommendation should come from former faculty members or those you've worked with in industry who can attest to your academic and professional abilities. Two letters is recommended, but you can submit more if you wish.

Q: Can I send in transcripts to show coursework from non-degree granting institutions?

A: Yes, all courses you have completed will be taken into account by the admission committee.

Q: Can I submit my application before I have all the necessary documents?

A: Yes, although some sections are required before submitting. Admissions will hold your application and notify us as your documents become available. You will not receive an admissions decision until all documents have been received.

Q: How many students are accepted each year?

A: The Ph.D. program accepts an average of 8 applicants each fall.

Q: Do you accept admissions on a rolling basis?

A: No, students are admitted once a year – for the following fall semester.

Q: What is the cost of the program?

A: The 25/26 cost of the Ph.D. program is $1,965 per credit. However, most students receive funding and TA opportunities.

Q: How long does the program take to complete?

A: Normative completion to the doctoral degree is 4-6 years, depending on the student’s level of preparation, research topic, and rate of publication.

Q: Am I allowed to attend part-time?

A: Yes, although part-time Ph.D. students are expected to provide their own funding.

Q: Is this program online?

A: No, this program is not online and does not offer any hybrid courses.

Q: When are classes offered?

A: Most courses are offered in the afternoons and evenings.

Q: Can I transfer courses?

A: Up to 18 credits may be accepted as transfer credit. We accept both standard and online courses that meet all transfer requirements and are from regionally accredited schools.

Q: Is there financial support available?

A: Yes, highly qualified Ph.D. applicants will be offered financial packages upon admission.

Q: Do I find out about available assistantships?

A: Students who would like to be considered for assistantships should send their CV and evaluations from any previous teaching assignments to the Program Coordinator prior to the application deadline. Please specify level of knowledge in each of the following undergraduate areas: math, physics, statistics, and/or computer science.

Q: What scholarships are available?

A: Students are encouraged to apply for external scholarships sponsored by government agencies, corporations, and foundations. Some scholarship search options are found on the Financial Aid - Outside Scholarships page.

Q: What are the housing options?

A: On-campus housing is extremely limited and graduate students are encouraged to research alternative living arrangements off-campus by visiting our Introduction to Off-Campus Living page. After being accepted to the program, you can connect to the community through Facebook Off Campus Housing and Roommate Corner and Off-Campus Housing Listings. International students should also check with International Student & Scholar Services.

Additional Information for International Students:

Q: Are Chapman's Computational and Data Sciences degrees STEM (Science, Technology, Engineering, Mathematics) programs?

A: Yes, students in our program are eligible to apply for STEM benefits. See the International Student & Scholar Services for more information. You can also contact the International Student Services Office at iss@chapman.edu or (714) 744-2110, with any questions.

Q: What is OPT?

A: Optional Practical Training or OPT allows you to work for one year, following graduation, in a job related to your major or field of study. See the International Student & Scholar Services for more information. You can also contact the International Student Services Office at iss@chapman.edu or (714) 744-2110, with any questions.

Q: What is CPT?

A: Curricular Practical Training or CPT allows you to participate in an off-campus paid internship that is related to your major or field of study. See the International Student & Scholar Services for more information. You can also contact the International Student Services Office at iss@chapman.edu or (714) 744-2110, with any questions.

Our Graduate Students

Eric Adams

eradams@chapman.edu

Eric is a Ph.D. candidate in Computational and Data Science, with a focus on clinical AI and multilingual NLP, including large language models. He utilizes MIMIC-IV data and causal inference to investigate the clinical outcomes associated with various ADHD treatment strategies over time.

Dimitri Bredikhin

dbredikhin@chapman.edu

Research interests: Computational models of visual perception, neuroscience of consciousness, decoding of sensory inputs and motor intentions, new approaches to EEG/MEG/fMRI data analysis.

Christian Carnahan

carnahan@chapman.edu

Research interests: Causal inference, public policy, economics.

Alejandro De Miguel

demiguelgmez@chapman.edu

Research interests: Brain-computer interfaces and machine learning.

Shahryar Fazli

sfazli@chapman.edu

Shahryar is a Ph.D. candidate with a focus on AI, data science, machine learning, and their applications in environmental studies. His expertise lies in advanced climate change research, agriculture, and hydrology. He utilizes state-of-the-art data science methodologies to tackle challenges such as extreme weather prediction, crop yield forecasting, and water resource management. His work integrates remote sensing, geospatial analysis, and advanced statistical modeling to understand the impacts of climate variability on agricultural sustainability. In addition, he has a growing interest in AI applications in healthcare, where he has recently begun gaining experience in applying machine learning techniques to enhance healthcare outcomes. He aims to leverage AI to drive innovations across multiple domains, from environmental sustainability to improving healthcare systems.

Google Scholar | LinkedIn

Lianlei Fu

lfu@chapman.edu

Mr. Lianlei Fu is a Ph.D. student in Computational and Data Sciences at Chapman University. Lianlei is also a modeler interested in the relationships between carbon, water, and plants under climate change through an ecosystem model, Community Land Model (CLM). Lianlei completed his undergraduate degree at Nanjing Forestry University, China, and his master’s degree from the University of Illinois at Urbana-Champaign. He would like to answer: How does climate change impact carbon cycling, and what is the feedback of carbon to climate change?

Wenxuan (Oliver) Gu

wgu@chapman.edu

Research interests: Artificial general intelligence, Computational precision health, digital immortality.

Wenxuan Gu received his B.A. in Econometrics from the University of California, Davis, and M.S. in Data Science from DePaul University, Chicago. His research integrates synthetic intelligence and computational biomedical intelligence, applying causal inference to model complex disease dynamics with Dr. Rakovski. His vision advances digital consciousness toward the concept of "perpetual life," the virtual preservation of selfhood through computational embodiment.

Howard Huang

minhuang@chapman.edu

Research interests: Diabetes data and targeted learning.

MS Computational and Data Science, Chapman University,
MS Thesis: A Novel Correction to the Multivariate Ljung-Box Test
BS Electrical Engineering, Cal State Fullerton

LinkedIn

Lucas Jeay-Bizot

jeaybizot@chapman.edu

Research interests: Volition, self-initiated movements, agency.

Ryan Joshi

rjoshi@chapman.edu

Research interests: geographic information systems, satellite data, earth systems science.

Recent publication:
Ryan C. Joshi, Annalise Jensen, Madeleine Pascolini-Campbell, Joshua B. Fisher, Coupling between evapotranspiration, water use efficiency, and evaporative stress index strengthens after wildfires in New Mexico, USA, International Journal of Applied Earth Observation and Geoinformation, 135 (2024). https://doi.org/10.1016/j.jag.2024.104238.

Jeomoan Francis Kurian

kurian@chapman.edu

Research interests: Large language models, synthetic data, data observability.

M. Phil in Planning from Indian Institute of Technology (Bombay, India)
MS in Computational and Data Sciences from Chapman University (Orange, CA)

Recent publication:
Kurian, J.F., Allali, M. Detecting drifts in data streams using Kullback-Leibler (KL) divergence measure for data engineering applications. J. of Data, Inf. and Manag. 6, 207–216 (2024). https://doi.org/10.1007/s42488-024-00119-y

Olin Li

shuli@chapman.edu

Olin's research focuses on applying survival analysis and machine learning to understand how targeted breast cancer therapies influence long-term patient outcomes. In practice, this means he works with complex genomic and clinical data, using statistical models to uncover what truly drives survival differences. Olin likes to describe his role as a data-driven investigator — carefully guiding high-dimensional data, predictive algorithms, and hazard ratios into producing insights that can meaningfully support personalized treatment decisions.

LinkedIn

Surendra Maharjan

maharjan@chapman.edu

Research interests: Deep learning, generative AI, machine learning, hydrological modeling, climate extremes and risk assessment, remote sensing for water and environmental monitoring, geospatial daa science, uncertainty quantification in climate and hydrological models.

Recent publications:

Maharjan, S., Li, W., Bolten, J.D. et al. The future intensification of hydrological extremes and whiplashes in the contiguous United States increase community vulnerability. Commun Earth Environ 6, 668 (2025). https://doi.org/10.1038/s43247-025-02672-9

Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Aqil Tariq, Rejoice Thomas, Cyril Rakovski, Hesham El-Askary, Enhancing water scarcity resilience in Egypt through machine learning-driven phenological crop mapping and water use efficiency analysis, International Journal of Applied Earth Observation and Geoinformation, 141 (2025). https://doi.org/10.1016/j.jag.2025.104668

Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Arshad Ansari, Suraj Tiwari, Roma Thakurathi, Rejoice Thomas, Hesham El-Askary, Unfolding cascading impacts of changing South Asia monsoon on a Hindu Kush Himalayas basin, Journal of Hydrology: Regional Studies, 57 (2025). https://doi.org/10.1016/j.ejrh.2024.102155

Hesham Morgan

hmorgan@chapman.edu

Hesham Morgan is a Ph.D. candidate in Computational and Data Sciences at Chapman University, where he also serves as a Research Assistant in the Earth Systems Science & Data Solutions Lab. His research integrates remote sensing, hydrology, earth science, and artificial intelligence (AI) to study water resources and environmental change in arid regions.

Google Scholar | LinkedIn

Omer Odabas

odabas@chapman.edu

Omer Odabas is a Ph.D. candidate in Computational and Data Sciences at Chapman University. His primary research interests lie in the fields of causal inference and targeted learning. He is focused on developing and applying novel statistical methods to analyze complex, high-dimensional data, such as electronic health records (EHR), to evaluate causal treatment effects and improve health outcomes. In addition to his doctoral studies, Omer is a Senior Cybersecurity Data Scientist at USAA , where he leads the development of enterprise-wide MLOps architectures and designs advanced machine learning models for anomaly detection. His professional experience in applied AI and large-scale model deployment complements his academic research in computational statistics. Omer also is a Lecturer at Cal Poly Pomona and Irvine Valley College. He holds a Master of Liberal Arts in Information Management Systems from Harvard University and a Master of Science in Chemistry from the University of Utah.

Ben Perry

beperry@chapman.edu

Research interests: Top-down processes affecting perception, consciousness, and meta-experimental approaches to studying anomalous phenomena.

Maksim Popov

mpopov@chapman.edu

Research interests: AI-driven process automation and systems integration, documentation processing and generation (NLP), performance metrics and efficiency analysis, data science for insight and communication

Mohammadreza Rezai

mrezaei@chapman.edu

Mohammadreza's research interests lie at the intersection of data science, health informatics, and applied machine learning, with a focus on using large scale real world healthcare data to understand disease patterns, outcomes, and disparities. He is particularly interested in causal inference, predictive modeling, and reinforcement learning using structured data such as claims, diagnoses, procedures, and socioeconomic indicators, as well as unstructured clinical text, medical notes, images, and videos. His work explores longitudinal and cross-sectional analyses to capture disease progression, multimorbidity, and survival outcomes, with applications in cancer research and chronic disease modeling. Mohammadreza is also interested in leveraging ensemble methods, modern machine learning models, large language models, and vision language models to extract clinically meaningful features that are not well captured by traditional coding systems, with the broader goal of improving risk stratification, decision support, and population level health insights.

Google Scholar | LinkedIn | GitHub

Iti Shrivas

shrivas@chapman.edu

Research interests: Remote sensing for water and environmental monitoring, machine learning, geospatial data science, glaciology, climate and hydrological models.

Niko Todorov

todorov@chapman.edu

Research focus: AI-based predictive models for hurricane genesis

GitHub

CONTACT US

Cyril Rakovski, Ph.D.
Program Co-Director
rakovski@chapman.edu

Adrian Vajiac, Ph.D.
Program Co-Director
avajiac@chapman.edu

Darla Welty
Graduate Program Coordinator
dwelty@chapman.edu

Melissa Liberman, MA
Senior Graduate Admission Counselor
melissal@chapman.edu

Graduate Financial Aid
gradfinaid@chapman.edu
(714) 628-2730

Application Deadlines: 2025 Academic Year

Early Admission Deadline: December 1, 2025
Regular Deadline: January 15, 2026

Applications submitted after the deadline will be reviewed on a space-available basis.

Featured Faculty

Joshua Fisher

Associate Professor Joshua Fisher is a Climate Scientist focusing on terrestrial ecosystems, water, carbon, and nutrient cycling using a combination of remote sensing, supercomputer models, and field campaigns from the Amazon to the Arctic.

Read about Dr. Fisher