Jagdish Ramakrishnan
Research Scientist, Meta
Welcome to my homepage!
I am a research scientist in the Central Applied Science team at Meta (formerly, Facebook), where I work on improving products or systems backed by research. I’m a tech lead working on efficient human review allocation, e.g., labels for ML models and integrity enforcement (see papers section for our recently published work).
Before Meta, I was a staff data scientist at Walmart Labs, where I worked on ML approaches for automated pricing of millions of products on Walmart’s online catalog. I received my Ph.D. in EECS at MIT, advised by Prof. John N. Tsitsiklis, Prof. David Craft, and Prof. Thomas Bortfeld, where I worked on optimization approaches for radiotherapy cancer treatment.
Recent Papers (full list here)
- From Labels to Decisions: A Mapping-Aware Annotator Model
E. Yao, J. Ramakrishnan, X. Chen, V. Nguyen, U. Weinsberg
KDD 2023, Applied Data Science Track, oral presentation
[code] [Acceptance rate = 25% (184/725)] - Crowdsourcing with Contextual Uncertainty
V. Nguyen, P. Shi, J. Ramakrishnan, N. Torabi, N. S. Arora, U. Weinsberg, M. Tingley
KDD 2022, Applied Data Science Track, oral presentation
[Meta research blog] [Acceptance rate = 26% (196/753)] - CLARA: Confidence of Labels and Raters
V. Nguyen, P. Shi, J. Ramakrishnan, U. Weinsberg, H. C. Lin, S. Metz, N. Chandra, J. Jing, D. Kalimeris
KDD 2020, Applied Data Science Track, oral presentation
[code] [FB research blog ] [Acceptance rate = 5.8% (44/756)] - Anomaly Detection for an E-commerce Pricing System
J. Ramakrishnan, E. Shaabani, C. Li, and M. A. Sustik
KDD 2019, Applied Data Science Track, oral presentation, Honorable Mention for Audience Appreciation Award
[code] [video] [Acceptance rate = 6.4% (45/700)] .