Radhika Dua

(राधिका दुआ / 라디카 두아)

Hello, I'm a Ph.D. student at the Center for Data Science, New York University, where I am very fortunate to be advised by Prof. Eric Oermann and Prof. Kyunghyun Cho. My research focuses on reliable and clinically grounded medical AI, spanning three threads: interpretable evaluation of medical generative models, uncertainty quantification for safe decision-making, and cross-modal learning to extend access to advanced imaging.

Before NYU, I was a Predoctoral Researcher at Google DeepMind / Google Research, where I had the opportunity to work with Dr. Gaurav Aggarwal, Alok Talekar, Ishan Deshpande, and Dr. Sujoy Paul on Agricultural Landscape Understanding and related segmentation problems, including a country-scale system now serving 11 countries across the Global South (agri.withgoogle.com). I earned my M.S. from the KAIST Graduate School of AI under Prof. Edward Choi, where I worked on making models robust to distribution shifts. Earlier, I was a summer intern at Brown University with Prof. Srinath Sridhar, and a visiting researcher at IIT Hyderabad with Prof. Vineeth N. Balasubramanian.

Email / CV / Google Scholar / LinkedIn / Github / Twitter

Publications

* denotes equal contribution. See Google Scholar for the entire list.
Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation Radhika Dua, Young Joon (Fred) Kwon, Siddhant Dogra, Daniel Freedman, Diana Ruan, Motaz Nashawaty, Danielle Rigau, Daniel Alexander Alber, Kang Zhang, Kyunghyun Cho, Eric Karl Oermann Under review, Nature Communications NeurIPS 2025 GenAI4Health Workshop NeurIPS 2025 WiML Workshop paper / bibtex
Generalist Foundation Models Are Not Clinical Enough for Hospital Operations Lavender Y. Jiang, Angelica Chen, Xu Han, Xujin Chris Liu, Radhika Dua, Kevin Eaton, Frederick Wolff, Robert Steele, Jeff Zhang, Anton Alyakin, Qingkai Pan, Yanbing Chen, Karl L. Sangwon, Daniel A. Alber, Jaden Stryker, Jin Vivian Lee, Yindalon Aphinyanaphongs, Kyunghyun Cho, Eric Karl Oermann Under review, Nature Medicine paper / bibtex
Agricultural Landscape Understanding At Country-Scale Radhika Dua, Nikita Saxena, Aditi Agarwal, Alex Wilson, Gaurav Singh, Hoang Tran, Ishan Deshpande, Amandeep Kaur, Gaurav Aggarwal, et al., Alok Talekar Under review, KDD 2026 NeurIPS 2025 Workshop on Tackling Climate Change with Machine Learning (Climate Change AI) paper / bibtex
TAPUDD: Task Agnostic and Post-hoc Unseen Distribution Detection Radhika Dua, Seongjun Yang, Yixuan Li, Edward Choi WACV, 2023 ECCV 2022 workshop (short paper) paper / bibtex
Towards a Practical Utility of Federated Learning in the Medical Domain Seongjun Yang, Hyeonji Hwang, Daeyoung Kim, Radhika Dua, Jongyeup Kim, Eunho Yang, Edward Choi CHIL, 2023 paper / bibtex
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes Rahul Sajnani, Adrien Poulenard, Jivitesh Jain, Radhika Dua, Leonidas J. Guibas, Srinath Sridhar CVPR, 2022 paper / project / video / code / bibtex
Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity Comparison Taehee Kim, ChaeHun Park, Jimin Hong, Radhika Dua, Edward Choi, Jaegul Choo COLING, 2022 paper / bibtex
Automatic Detection of Noisy Electrocardiogram Signals Radhika Dua, Jiyoung Lee, Joon-myoung Kwon, Edward Choi International Workshop on Pattern Recognition in Healthcare Analytics, ICPR 2022 paper / bibtex
Natural Attribute-based Shift Detection Jeonghoon Park^, Jimin Hong^, Radhika Dua^*, Daehoon Gwak, Yixuan Li, Jaegul Choo, Edward Choi Under review paper / bibtex
Beyond VQA: Generating Multi-word Answers and Rationales to Visual Questions Radhika Dua^, Sai Srinivas Kancheti^, Vineeth N. Balasubramanian MULA Workshop, CVPR 2021 paper / slides / video / poster / bibtex
VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting Radhika Dua^, Divyam Madaan^, Prerana Mukherjee, Brejesh Lall IEEE GlobalSIP, 2019 paper / code / bibtex

Teaching

New York University

Fundamentals of Machine Learning (CSCI-UA 473, Courant Institute), Section Leader & Grader, Fall 2026, Prof. Kyunghyun Cho
Responsible Data Science (DS-UA 202, Center for Data Science), Teaching Assistant, Spring 2025, Prof. Umang Bhatt

KAIST

CC500: Scientific Writing, Teaching Assistant, Prof. Mik Fanguy
HSS583: Graduate English Presentations, Teaching Assistant, Prof. Mik Fanguy

NPTEL (with IIT Hyderabad)

Deep Learning for Computer Vision, Teaching Assistant, Prof. Vineeth N. Balasubramanian

Indian Institute of Technology Hyderabad

CS6360: Advanced Topics in Machine Learning (Spring 2020), Teaching Assistant, Prof. Vineeth N. Balasubramanian
CS5370: Deep Learning for Computer Vision (Fall 2019), Teaching Assistant, Prof. Vineeth N. Balasubramanian

Awards and Fellowships

NYU Data Science Graduate Fellowship, 2024–2029
NYU Center for Data Science Medical Fellowship, 2024–Present
Travel Award, NeurIPS (Women in Machine Learning), 2025
KAIST International Students Scholarship, 2020–2022
Grace Hopper Celebration India (GHCI) Student Scholarship, 2018
Celestini Project India (Marconi Society and Google), second prize, 2018
Economic Times Campus Stars (India's brightest engineering students), 2018–2019

Last updated May 3, 2026.

Design and source code from Jon Barron's website.