When: Jun 27 2025 @ 1:30 PM
Where: Zoom
Categories:
CS Faculty Research Panel. Labeled headshots of Uthsav Chitra, Anjalie Field, Liliana Florea, and Michael Oberst.

Faculty will present on their own research and areas of specialty and answer questions you might have on how to apply your degree to the broader world of computer science, at Hopkins and beyond.

Zoom link >>

Learn more about the faculty presenting:

Headshot of Uthsav Chitra.Uthsav Chitra

Uthsav Chitra is an incoming assistant professor in the Department of Computer Science and a member of the Johns Hopkins Data Science and AI Institute. His research broadly develops machine learning methods for analyzing high-dimensional and multimodal biological data, with a focus on understanding the organization and dynamics of genes, proteins, and cells in healthy and diseased settings.

Headshot of Anjalie Field.Anjalie Field

Anjalie Field is an assistant professor of computer science and a member of the Data Science and AI Institute and the Center for Language and Speech Processing whose research focuses on the ethics and social science aspects of natural language processing. Her current work includes developing computational models to address societal issues like discrimination and propaganda as well as critically assessing and improving privacy, transparency, and fairness in AI pipelines.

Headshot of Liliana Florea.Liliana Florea

Liliana Florea is an associate professor in the McKusick-Nathans Department of Genetic Medicine. Her research focuses on developing algorithms, computational models, and software tools for analyzing sequencing data to characterize genes and their variations and help infer molecular mechanisms and genetic determinants of diseases.

Headshot of Michael Oberst.Michael Oberst

Michael Oberst is an assistant professor of computer science and a member of the Malone Center for Engineering in Healthcare and Data Science and AI Institute. His research focuses on reliable machine learning for decision-making in health care, with the long-term goal of ensuring that ML systems are as reliable as any FDA-approved medication or diagnostic test.