Open-source software for collision detection in external beam radiation therapy. - Vinith M. Suriyakumar; Renee Xu; Csaba Pinter; Gabor Fichtinger (Image-Guided Procedures, 2017)

Using Generative Models for Pediatric wbMRI. - Alex Chang; Vinith M. Suriyakumar; Abhishek Moturu; Nipaporn Tewattanarat; Andrea Doria 001; Anna Goldenberg (CoRR, 2020)

Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness. - Victoria Cheng; Vinith M. Suriyakumar; Natalie Dullerud; Shalmali Joshi; Marzyeh Ghassemi (FAccT, 2021)

Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. - Vinith M. Suriyakumar; Nicolas Papernot; Anna Goldenberg; Marzyeh Ghassemi (FAccT, 2021)

3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI. - Alex Chang; Vinith M. Suriyakumar; Abhishek Moturu; James Tu; Nipaporn Tewattanarat; Sayali Joshi; Andrea Doria 001; Anna Goldenberg (CoRR, 2021)

Public Data-Assisted Mirror Descent for Private Model Training. - Ehsan Amid; Arun Ganesh; Rajiv Mathews; Swaroop Ramaswamy; Shuang Song 001; Thomas Steinke 002; Vinith M. Suriyakumar; Om Thakkar 001; Abhradeep Thakurta (ICML, 2022)

Algorithms that Approximate Data Removal: New Results and Limitations. - Vinith M. Suriyakumar; Ashia C. Wilson (NeurIPS, 2022)

When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction. - Vinith M. Suriyakumar; Marzyeh Ghassemi; Berk Ustun (CoRR, 2022)

When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction. - Vinith Menon Suriyakumar; Marzyeh Ghassemi; Berk Ustun (ICML, 2023)

Algorithmic Pluralism: A Structural Approach Towards Equal Opportunity. - Shomik Jain; Vinith M. Suriyakumar; Ashia Wilson (CoRR, 2023)

Private Multi-Winner Voting for Machine Learning. - Adam Dziedzic; Christopher A. Choquette-Choo; Natalie Dullerud; Vinith M. Suriyakumar; Ali Shahin Shamsabadi; Muhammad Ahmad Kaleem; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (Proc. Priv. Enhancing Technol., 2023)

Algorithmic Pluralism: A Structural Approach To Equal Opportunity. - Shomik Jain; Vinith M. Suriyakumar; Kathleen Creel; Ashia Wilson (FAccT, 2024)

One-shot Empirical Privacy Estimation for Federated Learning. - Galen Andrew; Peter Kairouz; Sewoong Oh; Alina Oprea; Hugh Brendan McMahan; Vinith Menon Suriyakumar (ICLR, 2024)

Architecture-Level Modeling of Photonic Deep Neural Network Accelerators. - Tanner Andrulis; Gohar Irfan Chaudhry; Vinith M. Suriyakumar; Joel S. Emer; Vivienne Sze (ISPASS, 2024)

Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models. - Vinith M. Suriyakumar; Rohan Alur; Ayush Sekhari; Manish Raghavan; Ashia C. Wilson (CoRR, 2024)

When Style Breaks Safety: Defending Language Models Against Superficial Style Alignment. - Yuxin Xiao; Sana Tonekaboni; Walter Gerych; Vinith M. Suriyakumar; Marzyeh Ghassemi (CoRR, 2025)

UCD: Unlearning in LLMs via Contrastive Decoding. - Vinith M. Suriyakumar; Ayush Sekhari; Ashia Wilson (CoRR, 2025)