| 2024 | COMAD | A Learning Approach for Discovering Cost-Efficient Integrated Sourcing and Routing Strategies in E-Commerce. | Omkar Shelke, Pranavi Pathakota, Anandsingh Chauhan, Hardik Meisheri, Harshad Khadilkar, Balaraman Ravindran |
| 2023 | COMAD | Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning. | Harshad Khadilkar, Hardik Meisheri |
| 2023 | COMAD | Learning to Minimize Cost to Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce. | Pranavi Pathakota, Kunwar Zaid, Anulekha Dhara, Hardik Meisheri, Shaun D'Souza, Dheeraj Shah, Harshad Khadilkar |
| 2022 | COMAD | Identifying efficient curricula for reinforcement learning in complex environments with a fixed computational budget. | Omkar Shelke, Hardik Meisheri, Harshad Khadilkar |
| 2021 | IJCNN | FoLaR: Foggy Latent Representations for Reinforcement Learning with Partial Observability. | Hardik Meisheri, Harshad Khadilkar |
| 2019 | MABS | Reinforcement Learning of Supply Chain Control Policy Using Closed Loop Multi-agent Simulation. | Souvik Barat, Prashant Kumar, Monika Gajrani, Harshad Khadilkar, Hardik Meisheri, Vinita Baniwal, Vinay Kulkarni |
| 2017 | ICDM | Sentiment Extraction from Consumer-Generated Noisy Short Texts. | Hardik Meisheri, Kunal Ranjan, Lipika Dey |