NSF Awards Nearly $3 Million for Graduate Research Training Project in Data Science and A.I.

Data science and artificial intelligence (AI) have become powerful tools for generating new knowledge, fueling innovation, and dealing with some of society’s most pressing problems. However, “big data” and machine learning tools can perpetuate biases that advantage some people, while disadvantaging others.

Now, a new award of nearly $3 million will enable Stony Brook University to provide interdisciplinary cross-training to PhD students in the data sciences alongside PhD students in the human-centered sciences to detect and address biases in data, models, people, and institutions.

This innovative five-year training project spans eight departments in Stony Brook’s (Departments of Psychology, Linguistics, Economics, Sociology, Political Science, and Neurobiology and Behavior) and the (Departments of and ). Human-centered scientists will master cutting-edge computational methods that empower their research; they will take bridge courses in computer science as well as data science graduate courses and will earn a graduate certificate in AI. Data science students will earn a certificate in the human-centered sciences; they will learn theories and practices within these disciplines including study designs, associated limitations, and ethical issues.

Together, trainees from both tracks will collaborate on convergent research projects that unite deep disciplinary knowledge in human-centered sciences with techniques in data science and AI. They will develop strong identities as computational human-centered scientists capable of addressing the impacts of data-driven technology upon human beings and society.

The project will fund qualifying trainees with NSF-level stipends of $34K and will also train non-funded trainees, including international students. It will be led by principal investigator Susan Brennan, Department of Psychology, with co-PIs C.R. Ramakrishnan, Department of Computer Science; Wei Zhu, Department of Applied Math and Statistics; Bonita London, Department of Psychology; and Jeffrey Heinz, Department of Linguistics.

“Our science students tend to gather in distinct siloes, with much higher proportions of women in the human-centered sciences than in the data sciences,” said Brennan. “The lack of diversity in today’s AI workforce is concerning because addressing bias and solving hard societal problems demands bringing together different perspectives. We want our trainees to learn how to recognize and deal with bias where it can do harm.”

Other SBU faculty participants and senior personnel (listed alphabetically by department) include Adryan Wallace (Departments of Africana Studies, Women’s, Gender and Sexuality Studies, and Political Science); H. Andrew Schwarz, Klaus Mueller, Niranjan Balasubramanian, and Steven Skiena of Computer Science; Marina Azzimonti (Economics); Mónica Bugallo (Electrical & Computer Engineering); Owen Rambow (Linguistics); Il Memming Park (Neurobiology and Behavior); Reuben Kline (Political Science); Christian Luhmann (Psychology); and Jason Jones (Sociology). Psychologist Catherine Good of Baruch College, CUNY will serve as Evaluator.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.