- Ph.D. Department of Computer Science, New York University
- M.S. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India
- B.Tech. Department of Computer Science and Engineering, University of Kalyani, India
Sunandan Chakraborty focuses on data science for social good. Building computational models that leverage vast data sets, he applies them to a broad spectrum of problems in social and environmental science, agriculture, health, and other fields. He draws on diverse data sets (news, social media, images, etc.) and uses tools such as big data analytics, machine learning, information extraction, and time series analysis to compile information and discover knowledge that can lead to solutions.
Before becoming an assistant professor of data science at our school, Chakraborty worked with Jennifer Jacquet as a Moore-Sloan postdoctoral researcher at the NYU Center for Data Science. Their award-winning research explored the problem of illegal online wildlife trading, utilizing complex digital text analyses.
Chakraborty was part of the Big Data Group and the Center for Technology and Economic Development while earning his doctorate. He did his research under the supervision of Lakshminarayanan Subramanian at the Courant Institute of Mathematical Sciences of New York University.
- Text and data mining
- Machine learning
- Big data analytics
- Computational sustainability
- Information and Computation for Development (ICTD)
- Computational social science
INFO H516 Applied Cloud Computing for Data Intensive Sciences
INFO H518 Deep Learning Neural Networks
INFO H611 Mathematical and Logical Foundations of Informatics
- November 8, 2023
AI chatbot to increase cultural relevancy of STEM lessons, engage marginalized students
- May 3, 2023
AI applications everywhere, all the time: Luddy IUPUI researchers leading the way
- November 2, 2022
Data science faculty receives $216K NSF grant to address wildlife trafficking
- October 2, 2020
Chakraborty awarded NSF grant to develop AI approaches for identifying dynamic clinical data patterns, causal links in text