Preprints/In Preparation

• Nonparametric Sparse Online Learning of the Koopman Operator with Boya Hou, Sina Sanjari, Alec Koppel, and Subhonmesh Bose, submitted to the Journal of Machine Learning Research. 

• Risk-Sensitive Security-Constrained Economic Dispatch: Pricing and Algorithm Design with Avinash N. Madavan, Subhonmesh Bose, and Lang Tong, submitted to the IEEE Transactions on Power Systems. 

• Equivalent and Compact Representations of Neural Network Controllers With Decision Trees with Kevin Chang, Rahul Jain, and Pierluigi Nuzzo, submitted to the IEEE Transactions on Automatic Control. 

Journals

• Controlling a Markov Decision Process with an Abrupt Change in the Transition Kernel with Subhonmesh Bose and Venugopal V. Veeravalli, Special Issue on Control, Teams, and Games of Applied and Computational Mathematics, V23, N3, 2024. 

• Conditional Value at Risk-Sensitive Solar Hosting Capacity Analysis in Distribution Networks with Avinash N. Madavan, Subhonmesh Bose, and Lang Tong, IEEE Transactions on Power Systems, 2023. 

• Two-Stage Electricity Markets with Renewable Energy Integration: Market Mechanisms and Equilibrium Analysis with Rahul Jain, IEEE Transactions on Control of Network Systems, 2022. 

• Scheduling Flexible Non-Preemptive Loads in Smart-Grid Networks with Rahul Jain, IEEE Transactions on Control of Network Systems, 2022.

Conferences

• Conditional Kernel Imitation Learning for Continuous State Environments with Rishabh Agrawal, Rahul Jain, and Ashutosh Nayyar, accepted to the 7th Annual Learning for Dynamics & Control Conference (L4DC 2025). 

• Sustainable Grid through Distributed Data Centers: Spinning AI Demand for Grid Stabilization and Optimization with Scott Evans, Ibrahima Ndiaye, Sachini Piyoni Ekanayake, Alexander Duncan, Blake Rose, and Hao Huang, accepted to the 12th IEEE Conference on Technologies for Sustainability (IEEE Sustech 2025). 

• Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning with Rishabh Agrawal, Rahul Jain, and Ashutosh Nayyar, accepted to 39th AAAI Conference on Artificial Intelligence (AAAI 2025).

• Exact and Cost-Effective Automated Transformation of Neural Network Controllers to Decision Tree Controllers with Kevin Chang, Rahul Jain, and Pierluigi Nuzzo, at the 62nd IEEE Conference on Decision and Control (CDC 2023). 

• Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach with Boya Hou, Sina Sanjari, Subhonmesh Bose and Umesh Vaidya, at the Fortieth International Conference on Machine Learning (ICML 2023).

• Controlling a Markov Decision Process with an Abrupt Change in the Transition Kernel with Subhonmesh Bose and Venugopal V. Veeravalli, at the American Control Conference (ACC 2023).

• Compressed Decentralized Learning of Conditional Mean Embedding Operators in Reproducing Kernel Hilbert Spaces with Boya Hou, Sina Sanjari, and Subhonmesh Bose, 37th AAAI Conference on Artificial Intelligence (AAAI 2023).

• Practical Control Design for the Deep Learning Age: Distillation of Deep RL-Based Controllers with Kevin Chang, Krishna Chaitanya Kalagarla, Rahul Jain, and Pierluigi Nuzzo, at the 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton 2022).

• Scheduling of Flexible Non-Preemptive Loads with Rahul Jain, at the 59th IEEE Conference on Decision and Control (CDC 2020). 

• A Risk Aware Two-Stage Market Mechanism for Electricity with Renewable Generation with Rahul Jain, at the American Control Conference (ACC 2020). 

• A Two-Stage Market Mechanism for Electricity with Renewable Generation with Rahul Jain, at the IEEE 58th Conference on Decision and Control (CDC 2019). 

• A Two Stage Stochastic Mechanism for Selling Random Power with Rahul Jain, at the American Control Conference (ACC 2019).

Workshops

• Policy Optimization for Strictly Batch Imitation Learning with Rishabh Agrawal, Rahul Jain, and Ashutosh Nayyar, at the 16th International OPT Workshop on Optimization for Machine Learning (OPT 2024) at NeurIPS 2024. 

• Designing Interpretable Approximations to Deep Reinforcement Learning with Krishna Chaitanya Kalagarla, Nikhil Naik, Rahul Jain, and Pierluigi Nuzzo, at the Reinforcement Learning for Real Life Workshop at ICML 2021.