The mission of the Data Sciences Ph.D. program is to create scientifically minded and technically proficient professionals with a comprehensive background in the methodological diversity of data sciences and the intellectual depth to offer influential perspectives to analytical teams across disciplines.
Our Data Science Ph.D. program is a research-focused degree geared towards guiding students to independent research and practical problem solving by applying principles of Data Science to diverse domain applications including machine learning, deep learning, computer vision, natural language processing, forecasting, Bayesian modeling, and AI applications as well as domain expertise in economics, marketing, psychology, biology, politics, and healthcare.
Program Goals
The Data Sciences Program will produce Ph.D. graduates who:
- Assess the objectives, scope, and methodological limitations for domain-specific problems.
- Conduct appropriate and insightful research.
- Connect domain-specific applications to contributions to the data science field.
- Analyze ethical issues in research related to intellectual property (IP), data security, integrity, and privacy.
- Communicate effectively to a variety of audiences and stakeholders.
Within a few years after graduation, our graduates will have:
- Applied diverse data science methodologies using a scientific process to expand their body of knowledge.
- Grown professionally through self-study, continuing education, and professional development.
- Used effective communications to explain insights from research based on analytical processes on data to diverse audiences.
Affiliated Faculty
Background and Expertise:
- Ph.D. in Social Psychology
- Natural Language Processing, Research Methodology and Statistics, Psychology, Social Processes, Language and Communication
Research Interests:
- Understanding and predicting social processes and attitudes from text data
- Analyzing and modeling political language and communication
- Exploring social media data for public opinion and insights
- Understanding the social and ethical implications of AI
Background and Expertise:
- PhD in Computational Linguistics
- Natural Language Processing, Statistical Modeling, Semantics
Research Interests:
- Modeling semantic memory and the underlying language network
- Understanding and improving statistical practices and education
- Open-science and meta-science
Background and Expertise:
- PhD in Computational Biology
- Bayesian modeling, causal inference, epidemiology
Research Interests:
- Bayesian analysis ecological, serological, and epidemiological data
- Intersection of vertebrate ecology and disease surveillance
Background and Expertise:
- PhD in Data Science
- Machine learning, deep learning, graph neural networks, mRNA-seq analysis
Research Interests:
- Machine learning and deep learning applications in pharmacology, genomics, multiomics, and metabolomics.
- Developing models to study drug interactions, uncover disease mechanisms, and identify biomarkers for personalized medicine
Background and Expertise:
- PhD in Computational Social Science
- Consumer behavior, machine learning, marketing, network & spatial analysis
Research Interests:
- Cultural markets (e.g., entertainment, religion) as complex systems where technology shapes how we organize and behave
- Applications of social simulation, machine learning, network analysis, and geostatistics to study patterns of a social system’s behavior
- How culture and innovation evolve together in a changing world
E. André L’Huillier holds a Ph.D. in Computational Social Science from George Mason University. His training spans quantitative and computational methodologies applied to market and social behavior. Before joining academia, he worked as a strategic planner in advertising and as a market-research consultant. His main research interest is the diffusion of innovations, particularly how adoption of technology affects culture.
Background and Expertise:
- Ph.D. in Economics
- Risk modeling, finance, time series forecasting
Research Interests:
- Optimal design of public support for private product research and development
- Applications of economic and entrepreneurial expertise to practice
Background and Expertise:- Ph.D. in Social Psychology
- Social Cognition, Quantitative Methods, Statistical Modeling
- Intergroup relations and interpersonal evaluation, with a focus on the origins, characteristics, and consequences of implicit bias
- Metascience, big team science, and reproducibility in psychology
Background and Expertise:-
Ph.D. in Instructional Design and Technology (Analytics), M.S. in Computer Science, M.S. in Computer Engineering, Harvard Business Analytics Program
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Artificial Intelligence, Machine Learning, Learning Analytics, Structural Equation Modeling, Meta-Analysis, and Reproducible Analytics
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Applying artificial intelligence and machine learning to improve decision-making in education, health, and business
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Developing learning analytics and predictive models to improve student success and academic outcomes
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Evaluating the effectiveness and responsible adoption of generative AI in education and organizations
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Advancing reproducible research, meta-analysis, and quantitative methods for transparent and evidence-based decision-making
Doctorate Program Admissions Process
Applications for program admission are accepted for fall, spring, and summer terms. Deadlines for applications are typically eight (8) weeks before the beginning of the semester, with admissions decisions made four (4) weeks prior to the beginning of the semester. Additional information or interview may be requested before application decision is made.
Applicants of the program should have the following qualifications:
- Master’s degree or equivalent in a relevant field (preferably data science or analytics).
- Relevant skills and professional experience in data science, such as statistical modeling, programming, and basic research.
- Proficiency in scientific writing.
- Research interest in data-science-related topic (preferably aligning with faculty interests and expertise).
General admissions requirements can be found on the Graduate Student Admissions Page. For the Data Science Ph.D., additional requirements include:
- A research statement identifying the specific research areas which you are interested in pursuing and how their research interests would contribute to the field of data science. This should include evidence of achievements in research as well as highlighting evidence of knowledge/skills in statistics, computer science, programming, and other relevant domains.
- Evidence of research potential such as research papers, master’s thesis, and/or other relevant academic or practical experience.
- Two letters of recommendation from academic or industry professionals with at least one letter highlighting your potential for independent research. At least one letter from an academic professional is strongly recommended.
Admissions questions should be directed to PhD@HarrisburgU.edu.
Doctorate program applicants are encouraged to apply at least six months prior to the start of any semester. This application process allows ample time for an admissions decision and development of an academic schedule. The Admissions Committee reviews all documents and will request an interview with the applicant prior to making an admission decision for a limited number of applicants to become resident or non-resident candidates for the degree.
Learn More: Graduate Admissions
Industry Highlights
Program Courses
The program requires a minimum of 36 semester hours. In the first year, students complete 18 semester hours of upper-level courses; each semester, students take two courses (six semester hours). Students take four foundational courses focused on statistical thinking and research design, one topic-based seminar course, and one elective course.
Upon completion of the first year, students take a 6-semester-hour doctoral seminar, during which a comprehensive exam is administered. Students select a dissertation topic and chair who will serve as their primary advisor for their dissertation research.
Once a student has passed the comprehensive exam, they will complete at least 2 semesters (12 semester hours) of Doctoral Studies. During the first two semesters, students will complete and defend a dissertation proposal. Then, students must complete and defend their final dissertation, which typically takes one to four semesters.
