| 2025 | AAAI | DK-BEHRT: Teaching Language Models International Classification of Disease (ICD) Codes using Known Disease Descriptions. | Ulzee An, Simon A. Lee, Moonseong Jeong, Aditya Gorla, Jeffrey N. Chiang, Sriram Sankararaman |
| 2025 | ICML | Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models. | Ulzee An, Moonseong Jeong, Simon A. Lee, Aditya Gorla, Yuzhe Yang, Sriram Sankararaman |
| 2025 | ICML | CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation. | Aditya Gorla, Ryan Wang, Zhengtong Liu, Ulzee An, Sriram Sankararaman |
| 2024 | RECOMB | A Scalable Adaptive Quadratic Kernel Method for Interpretable Epistasis Analysis in Complex Traits. | Boyang Fu, Prateek Anand, Aakarsh Anand, Joel Mefford, Sriram Sankararaman |
| 2024 | RECOMB | Scalable Summary Statistics-Based Heritability Estimation Method with Individual Genotype Level Accuracy. | Moonseong Jeong, Ali Pazokitoroudi, Zhengtong Liu, Sriram Sankararaman |
| 2022 | RECOMB | AutoComplete: Deep Learning-Based Phenotype Imputation for Large-Scale Biomedical Data. | Ulzee An, Na Cai, Andy Dahl, Sriram Sankararaman |
| 2022 | RECOMB | Tractable and Expressive Generative Models of Genetic Variation Data. | Meihua Dang, Anji Liu, Xinzhu Wei, Sriram Sankararaman, Guy Van den Broeck |
| 2021 | AISTATS | CONTRA: Contrarian statistics for controlled variable selection. | Mukund Sudarshan, Aahlad Manas Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath |
| 2021 | ICML | Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data. | Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Weiss-Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach |
| 2021 | WABI | An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts. | Brandon Jew, Jiajin Li, Sriram Sankararaman, Jae Hoon Sul |
| 2020 | ICML | Explaining Groups of Points in Low-Dimensional Representations. | Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar |
| 2020 | RECOMB | A Scalable Method for Estimating the Regional Polygenicity of Complex Traits. | Ruth Johnson, Kathryn S. Burch, Kangcheng Hou, Mario Paciuc, Bogdan Pasaniuc, Sriram Sankararaman |
| 2019 | RECOMB | Scalable Multi-component Linear Mixed Models with Application to SNP Heritability Estimation. | Ali Pazokitoroudi, Yue Wu, Kathryn S. Burch, Kangcheng Hou, Bogdan Pasaniuc, Sriram Sankararaman |
| 2019 | RECOMB | Fast Estimation of Genetic Correlation for Biobank-Scale Data. | Yue Wu, Anna Yaschenko, Mohammadreza Hajy Heydary, Sriram Sankararaman |
| 2018 | RECOMB | Tensor Composition Analysis Detects Cell-Type Specific Associations in Epigenetic Studies. | Elior Rahmani, Regev Schweiger, Saharon Rosset, Sriram Sankararaman, Eran Halperin |
| 2018 | RECOMB | A Unifying Framework for Summary Statistic Imputation. | Yue Wu, Eleazar Eskin, Sriram Sankararaman |
| 2012 | COMSNETS | TrickleDNS: Bootstrapping DNS security using social trust. | Sriram Sankararaman, Jay Chen, Lakshminarayanan Subramanian, Venugopalan Ramasubramanian |
| 2008 | RECOMB | On the Inference of Ancestries in Admixed Populations. | Sriram Sankararaman, Gad Kimmel, Eran Halperin, Michael I. Jordan |