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NSF awards $1.5 million TRIPODS institute to Texas A&M to bolster data-driven discovery / agsandrew

A cross-disciplinary team of Texas A&M University researchers led by statistician Bani K. Mallick has been awarded a three-year, $1.5 million Transdisciplinary Research In Principles of Data Science (TRIPODS) grant from the National Science Foundation to establish a new institute, the Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS).

Texas A&M’s new TRIPODS institute, funded through the Division of Computing and Communications Foundations (CCF) from October 2019 through September 2022, brings together nearly three dozen researchers from six disciplinary areas: statistics, mathematics, electrical engineering, computer science, industrial engineering and information and operations management. The institute will conduct research on the foundations of data science motivated by problems arising in bioinformatics, the energy arena and both power and transportation systems.

Mallick, distinguished professor and holder of the Susan M. Arseven ’75 Chair in Data Science and Computational Statistics in the Department of Statistics, serves as principal investigator for the project. He is joined by co-principal investigators Dilma Da Silva, professor and holder of the Ford Motor Company Design Professorship II in the Department of Computer Science and Engineering; Ronald DeVore, distinguished professor and holder of the Dr. Walter E. Koss Professorship in Mathematics in the Department of Mathematics; Nicholas Duffield, TEES Research Professor in the Department of Electrical and Computer Engineering and director of the Texas A&M Institute of Data Science; and Panganamala Kumar, distinguished professor and holder of the College of Engineering Chair in Computer Engineering in the Department of Electrical and Computer Engineering.

Much like its five principal investigators, the institute’s larger team features an impressive array of skill sets and scholarly knowledge representing six different departments within Texas A&M’s College of Engineering, College of Science and Mays Business School:

  • Computer Science and Engineering:  Xia Ben Hu
  • Electrical and Computer Engineering:  Dileep Kalathil, Krishna Narayana, Le Xie, Xiaoning Qian
  • Industrial and Systems Engineering:  Yu Ding, Shahin Shahrampour, Rui Tuo
  • Information and Operations Management:  Ravi Sen
  • Mathematics:  Yalchin Efendiev, Simon Foucart, Boris Hanin, Guergana Petrova
  • Statistics:  Anirban Bhattacharya, Raymond Carroll, Irina Gaynanova, Jianhua Huang, Mikyoung Jun, Matthias Katzfuss, Debdeep Pati, Huiyan Sang, Raymond Wong

Given the broad expertise, experience and expectations of both the institute’s leadership and overall team, Mallick says it will be well-positioned to develop rigorous theories, novel methodologies and efficient computational techniques to solve data challenges in many application domains.

“Data science is rapidly evolving as an essential interdisciplinary field where advances often result from a combination of ideas from several disciplines,” Mallick added. “New types of data have emerged and present tremendous complexities and challenges that require a novel way of interdisciplinary thinking.”

TRIPODS awards seek to enable data-driven discovery through major investments in state-of-the-art mathematical and statistical tools, better data mining and machine learning approaches, enhanced visualization capabilities and more. These awards build upon the NSF’s long history of investments in foundational research, contributing key advances to the emerging data science discipline, and supporting researchers to develop innovative educational pathways to train the next generation of data scientists.

“The TRIPODS award signals an important step forward for the advancement of data sciences on the Texas A&M University campus,” said Dr. Valen E. Johnson, distinguished professor of statistics and dean of the College of Science. “It will provide a mechanism for researchers from several fields, including computer science, mathematics and statistics, to advance the theory of data science and extend it to a host of important real-world applications.”