| 2025 | AAAI | Tab-Shapley: Identifying Top-k Tabular Data Quality Insights. | Manisha Padala, Lokesh Nagalapatti, Atharv Tyagi, Ramasuri Narayanam, Shiv Kumar Saini |
| 2024 | COMAD | Tutorial on Fair and Private Deep Learning. | Manisha Padala, Sankarshan Damle, Sujit Gujar |
| 2023 | AAAI | Combinatorial Civic Crowdfunding with Budgeted Agents: Welfare Optimality at Equilibrium and Optimal Deviation. | Sankarshan Damle, Manisha Padala, Sujit Gujar |
| 2023 | PAKDD | F3: Fair and Federated Face Attribute Classification with Heterogeneous Data. | Samhita Kanaparthy, Manisha Padala, Sankarshan Damle, Ravi Kiran Sarvadevabhatla, Sujit Gujar |
| 2023 | SAGT | Coordinating Monetary Contributions in Participatory Budgeting. | Haris Aziz, Sujit Gujar, Manisha Padala, Mashbat Suzuki, Jeremy Vollen |
| 2022 | COMAD | Fair Federated Learning for Heterogeneous Data. | Samhita Kanaparthy, Manisha Padala, Sankarshan Damle, Sujit Gujar |
| 2022 | PRICAI | Fair Allocation with Special Externalities. | Shaily Mishra, Manisha Padala, Sujit Gujar |
| 2022 | PRICAI | EEF1-NN: Efficient and EF1 Allocations Through Neural Networks. | Shaily Mishra, Manisha Padala, Sujit Gujar |
| 2021 | ICONIP | Effect of Input Noise Dimension in GANs. | Manisha Padala, Debojit Das, Sujit Gujar |
| 2021 | ICONIP | Federated Learning Meets Fairness and Differential Privacy. | Manisha Padala, Sankarshan Damle, Sujit Gujar |