Computational Data-enabled Science and Engineering

Description: The Computational Data-enabled Science and Engineering program at the University at Buffalo offers an innovative interdisciplinary education designed to equip students with the skills necessary to tackle complex scientific and engineering problems through advanced computational and data analysis techniques. This program integrates principles from computer science, applied mathematics, and domain-specific sciences to prepare graduates for the rapidly evolving landscape of scientific research and industry applications. Students will engage in coursework that covers high-performance computing, data visualization, machine learning, statistical analysis, and simulation methods, enabling them to process and interpret large-scale datasets effectively. The curriculum emphasizes hands-on experience through research projects, computational labs, and collaborations with industry and academic partners, fostering practical skills alongside theoretical knowledge. Graduates of this program will be well-prepared for careers in research institutions, technology companies, and government agencies, where data-driven decision-making and innovative computational solutions are in high demand. The program also offers pathways for pursuing further academic research or advanced professional certification, supporting lifelong learning and career advancement. By combining cutting-edge computational techniques with domain-specific applications, the Computational Data-enabled Science and Engineering program aims to cultivate innovative thinkers capable of addressing the global scientific and technological challenges of the future.

The PhD degree requires a minimum of 72 credit hours. Additional requirements include passing qualifying exams and preparing your dissertation.

Core required courses

Our curriculum is designed around three core subjects:

• Data Science
• Applied Mathematics and Numerical Methods
• High Performance and Data Intensive Computing

A minimum of 9 credits hours from the approved list of core classes must be taken in each area. The core course requirements total to 30 credits, with a minimum GPA of 3.2. Courses taken during your Master's program may be transferred and used toward this requirement, with the approval of your dissertation committee and the Graduate Director. We strongly suggest you finish this course work within the first 2 years of the program.

You will need to take courses to support your dissertation research. These courses, totalling 12 credits, must be completed by the end of your fifth semester. Depending on your dissertation topic, you may be advised to take elective courses that inform your research topic, or you may be advised to take other courses to broaden your CDSE-related knowledge.

• Master's degree in related field (including, but not limited to, engineering, mathematics, business, marketing, pharmacy)
• letters of recommendation
• GRE scores
• a GPA of 3.0 or better from your Bachelor's degree courses
• for International applicants, a test of English language proficiency
  • TOEFL (IBT): minimum score 79
  • TOEFL (PBT): minimum score 550
  • IELTS: minimum score 6.5 (with no sub-score below 6.0)
  • PTE Academic: minimum score 55 (with no subsection score below 50)
  • Scores must be less than 2 years old

The financial aid process for graduate and professional degree students is similar to that of an undergraduate student; however post-baccalaureate students also need to be aware of a few additional factors that may influence your financial aid awards.

Types of Aid Available

Federal financial aid options that you may be eligible for include:

• Federal Direct Subsidized & Unsubsidized Loans
• Federal Graduate/Professional PLUS Loans
• Health Profession Student Loans
• Federal Nursing Student Loans
• Federal TEACH Grant

To be eligible for federal financial aid awards you must complete a FAFSA application and indicate that you are seeking a graduate or professional degree.

New York State also offers the following programs for graduate and professional study:

• Economically Disadvantaged First Professional Study (EDPS) Program
• Veterans Tuition Award
• Senator Patricia K. McGee Nursing Faculty Scholarships
• SUNY Graduate Diversity Fellowship
• Graduate Opportunity Program
• Masters-in-Education Teacher Incentive Scholarship

The Computational Data-enabled Science and Engineering program at the University at Buffalo, The State University of New York, is designed to prepare students for the rapidly evolving field of data-driven scientific and engineering disciplines. This interdisciplinary program combines fundamental principles from computer science, applied mathematics, and domain-specific sciences to equip graduates with the skills needed to analyze and interpret large-scale data sets, develop computational models, and apply innovative techniques to solve complex scientific and engineering problems. Students in this program gain hands-on experience with advanced computational tools, programming languages, and data analysis techniques, enabling them to contribute to research and industry applications across various fields such as physics, biology, engineering, and environmental sciences.

The curriculum emphasizes a strong foundation in programming, algorithms, data structures, and statistical methods, along with coursework tailored to scientific computing, machine learning, data visualization, and high-performance computing. Throughout the program, students have opportunities to engage in research projects, internships, and collaborations with faculty and industry partners, fostering practical skills and professional development. The program prepares graduates for careers in academia, research institutions, government agencies, and private sector companies involved in data-intensive science and engineering projects.

Students are encouraged to develop interdisciplinary expertise, combining domain knowledge with computational and data skills to drive innovative solutions. The program also emphasizes ethical considerations and responsible data management, ensuring students are prepared to handle the societal impacts of data-enabled technologies. With the growing importance of big data and computational approaches in scientific discovery and engineering innovation, this program aims to produce versatile professionals capable of leading advancements in their respective fields. Overall, the Computational Data-enabled Science and Engineering program at the University at Buffalo offers a comprehensive education at the intersection of computing, data science, and scientific applications, fostering the next generation of innovative researchers and industry leaders.