The Data Science in High-Energy Physics program at Drew University bridges two of the most exciting and impactful fields in modern science: data science and particle physics. As experiments in high-energy physics — such as those at CERN and other major research facilities — generate extraordinarily large and complex datasets, advanced computational methods have become essential for extracting scientific insight and making new discoveries. These data challenges involve everything from event reconstruction and particle classification to precision measurement and anomaly detection, often requiring artificial intelligence (AI), machine learning (ML), statistical modeling, and modern computing frameworks.
This program is designed to equip students with a robust foundation in data collection and management, statistical inference, artificial intelligence and machine learning, and scientific computing, all within the context of real high-energy physics research problems. Students will engage with state-of-the-art analytical tools and develop skills that are highly sought after in both research and data-centric industries.
Through coursework, collaborative projects, and exposure to real data from particle physics experiments, participants will gain experience in tackling data-intensive scientific questions. This prepares them not only for advanced academic research but also for careers in technology, data analytics, computational science, artificial intelligence, and machine learning — fields where the ability to interpret large, complex datasets is critically important.
Through Drew’s Master of Science in Data Science Applications in High Energy Physics, you will:
- Demonstrate advanced proficiency in data science, including data preparation and management, statistical modeling, artificial intelligence and machine learning, programming in Python/R and SQL, and data visualization and communication.
- Understand the language and methods of particle physics, including detectors, triggers, simulations, and analysis workflows.
- Draw on hands-on research experience within a major international experiment (Mu2e, DUNE or ProtoDUNE), working as part of an international collaboration.
- Build and present a portfolio of projects (internship plus capstone) that demonstrates the application of data science to complex, real-world scientific data.
Programs
The Master of Science program requires 30–36 credits and is designed to be completed in approximately one and a half years of full-time study
Core Data Science Curriculum
The MS in Data Science curriculum is built around applied coursework, internships, and experiential learning. Core courses typically include:
- Introductory Statistics (or equivalent) (prerequisite)
- Introduction to Programming (or equivalent) (prerequisite)
- Statistics Using R
- Computational Thinking / Programming in Python
- Data Science Using SQL and Relational Databases
- Applied Regression Analysis
- Statistical Machine Learning
- Data Visualization and Communication
Note: Course titles and requirements may change; consult the official MS in Data Science catalog for the most current information.
Particle Physics–Specific Requirements
- PHYS 306/606 – Introduction to Particle Physics: foundational concepts of particle physics, including the Standard Model, particle interactions, detectors, and basic analysis methods.
- Big Data Analysis Using ROOT: hands-on course using the ROOT framework common in high-energy physics to process large datasets, perform statistical analyses, create histograms and fits, and analyze Monte Carlo and experimental data.
- Research Internship in Mu2e, DUNE or ProtoDUNE: a research-based internship embedded in either the Mu2e experiment at Fermilab, the DUNE collaboration or the ProtoDUNE experiment at CERN. Students apply data science skills to real analysis tasks, software development, and data-quality or simulation studies within a major international experiment.
Research-Based Capstone
As part of the Data Science capstone, students in this specialization complete a research project connected to particle physics. Example topics include:
- Analyzing Monte Carlo and/or experimental data from Mu2e, DUNE or ProtoDUNE.
- Developing artificial intelligence and machine-learning tools for signal/background discrimination and data quality monitoring
- Creating visualization or monitoring tools for large data streams.
- Exploring novel statistical methods for rare-event searches.
The capstone culminates in a research-grade written report, a documented code repository, and a presentation suitable for PhD applications or industry roles.
Why Choose Drew University?
- Strong Data Science Foundation: Drew’s MS in Data Science emphasizes real-world projects, internships, and applied analytics, with a curriculum that integrates statistics, computing, and modern technologies.
- Active Particle Physics Group: The Drew Particle Physics Group is a member of the Mu2e collaboration at Fermilab and participates in the DUNE international neutrino experiment, offering research opportunities comparable to those at larger research universities.
- Expert Faculty Mentors: Work closely with faculty such as Prof. Kamal Benslama (experimental particle physics, Mu2e, DUNE) and Prof. Alex Rudniy (AI, machine learning, and applied data science).
- Research Opportunities in a Liberal-Arts Environment: Benefit from small classes, individualized mentoring, and a collaborative campus community while engaging in high-level scientific research.
- Location & Community: Study on a beautiful, wooded campus in Madison, New Jersey, with easy train access to New York City and a vibrant regional STEM ecosystem.
How to Apply
- Visit the Drew University Graduate Admissions website and start an application for the Master of Science in Data Science.
- In your application and personal statement, clearly indicate that you are applying for the “Data Science with Application in Particle Physics” specialization.
- Upload your transcripts, CV/résumé, personal statement, and any additional required materials listed on the MS in Data Science program page.
Learn More About MS in Data Science
Apply Through Graduate Admissions
For detailed deadlines, tuition, and financial aid information, please refer to the official Drew Graduate Admissions and MS in Data Science pages.
Contact
For academic questions about the specialization, research opportunities, or fit with your background, please contact:
- Prof. Kamal Benslama – Program Co-Director (Physics) – [email protected]
- Prof. Alex Rudniy – Program Co-Director (Data Science) – [email protected]
After Graduation
- PhD programs in physics, data science, AI/ML, or related fields.
- Research positions at national laboratories and universities.
- Industry roles where high-dimensional data, modeling, and analytics are central (e.g., tech, finance, healthcare, engineering).