Faculty and Fields of Interest
Ramprasad Balasubramanian (co-director) (CIS) computer vision, robotics, artificial intelligence
Paul Bergstein (CIS) software engineering, database systems
Gary Davis (co-director) (MTH) memory systems, DEs, mathematics education, data science education
Hua Fang (CIS) computational statistics, machine learning, pattern recognition, behavioral trajectory patterns, wireless health
Scott Field (MTH) Bayesian inference problems, gravitational wave data science, scientific and high performance computing
Sigal Gottlieb (MTH) strong stability preserving and positivity preserving time discretizations, spatial discretization for hyperbolic problems, spectral and pseudospectral methods, WENO and ENO methods, reduced basis methods, high performance parallel computing, data science
David Koop (CIS) visualization, data science environments, computational provenance
Ming Shao (CIS) transfer learning/domain adaptation, deep learning, large-scale graph approximation/clustering, social media analytics
Maoyuan Sun (CIS) visual analytics, information visualization, human-centered machine learning, human-computer interaction
Donghui Yan (MTH) statistics, machine learning, data science
The Data Science degree, jointly offered by Computer Science in Engineering and Mathematics in Arts & Sciences will provide undergraduates with education and training in the rapidly emerging fields of data analytics and discovery informatics, which integrates mathematics and computer science for the quantification and manipulation of information from a cognate area of application (e.g., science, engineering, business, sociology, healthcare, planning). Emphasis is placed on merging strong foundations in information theory, mathematics and computer science with current methodologies and tools to enable data-driven discovery and problem solving.
Students will be prepared for leadership positions in data analytics, information management, and knowledge engineering. Students will have opportunities to work on industry, agency or faculty sponsored research projects. Students may also participate in co-op and internship opportunities where they can gain valuable hands-on experience sought by employers locally, nationally, and globally. Upon completing the program, graduates will have skills in computer programming, statistics, data mining, machine learning, data analysis and visualization that enable solving challenging problems involving large, diverse data sets from different application domains.
The goals of the Bachelor’s degree program in Data Science are to:
- Expand education opportunities in rapidly growing areas of information technology and information systems;
- Offer state-of-the-art technology-based courses in data analysis, data mining, statistical modeling, and data visualization;
- Prepare graduates with entry-level skills for managing, understanding, interpreting and communicating database and information needs of a wide variety of producers and consumers;
- Increase enrollment in STEM disciplines;
- Stimulate and assist the development of computationally-focused options within existing departments;
- Educate and train students to work in industry or academia as data scientists; and,
- Broaden and deepen the basic data science education in computer science, mathematics, and statistics, with real-life data science projects in cognate disciplines, including Accounting, Biology, Chemistry, Decision & Information Sciences, Engineering, Finance, Marketing, Nursing, Physics, Political Science, and Sociology.
At the time of graduation, students will:
- be able to apply contemporary techniques for managing, mining, and analyzing big data across multiple disciplines;
- be able to apply computation and computational thinking to gain new knowledge and to solve real-world problems of high complexity;
- be able to communicate their ideas and findings persuasively in written, oral and visual form and to work in a diverse team environment;
- be prepared for graduate school or employment and have an appreciation for life-long learning;
- have an appreciation for the professional, societal and ethical considerations of data collection and use.