Three CS faculty members and their collaborative research teams have been chosen to receive 2020 Johns Hopkins Discovery Awards. Chosen from a record 274 proposals, Mark Dredze, Tom Lippincott, and Ilya Shpitser are among 138 individuals on the 41 multidisciplinary endeavors that have been selected to receive support this year.
The Discovery Awards program was announced in early 2015, as was the Catalyst Awards program for early-career researchers. Together the two programs represent a $30 million university commitment by university leadership, along with the deans and directors of JHU’s divisions, to faculty-led research.
The Discovery Awards are intended to spark new interactions among investigators across the university rather than to support established projects. Teams can apply for up to $100,000 to explore a new area of collaborative work with special emphasis on preparing for an externally funded large-scale grant or cooperative agreement.
Dredze’s research centers around statistical models of language with applications to social media analysis, public health, and clinical informatics. He will work with Mary Catherine Beach, a professor in the School of Medicine with joint appointments in the Center for Health Equity and the Berman Institute of Bioethics, on “Hiding in Plain Sight ‘Stigmatizing Language in Patients’ Medical Records.”
Lippincott’s research focuses on how machine learning can support and facilitate traditional scholarship in the humanities, particularly the use of unsupervised models structured for interpretability and isomorphism with respect to a domain of interest. He is involved in two projects that received Discovery Awards this year:
- “Data-Driven AI models for Document Analysis in Medicine, Social Sciences, and the Humanities,” which he will work on with Margaret Chisolm, a professor of psychiatry and behavioral sciences at the School of Medicine; Francois Furstenberg, a professor of history in the Krieger School of Arts and Sciences; and Tara Sell, an associate professor of environmental health and engineering in the Bloomberg School of Public Health; and
- “Emulating How Experts Think… Under Unknown Objectives and Constraints: Augmenting Machine Learning Through Inverse Optimization to Automatically Generate Personalized Treatment Plans,” on which he is joined by Kimia Ghobadi, the John C. Malone Assistant Professor of Civil and Systems Engineering; Tinglong Dai, the Bernard T. Ferrari Professor at the Johns Hopkins Carey Business School; and Todd McNutt, an associate professor of radiation oncology and molecular radiation sciences in the School of Medicine.
Shpitser works on causal and semi-parametric inference, missing data, and algorithmic fairness—ubiquitous data complications that may arise in datasets of all types, such as those obtained from social networks, electronic medical records, criminal justice databases, or longitudinal studies. Along with Paul Yi, an adjunct assistant research scientist in the Malone Center for Engineering in Healthcare; Jeremias Sulam, an assistant professor of biomedical engineering; and School of Medicine faculty Cheng Ting Lin, Tin Yan Alvin Liu, and Elise Ng, he will research “Evaluating and Overcoming Performance Biases Against Underrepresented Populations in Deep Learning for Diagnosis of Disease on Medical Images.”