News

  • 09/2023 TAMU News article about our new NSF grant for the project “Combining Deep Reinforcement Learning Control with Novel Vertical Flight Concepts for Robust Ship based Operation” (Link).

  • 09/2023 TAMU News article about our new ONR grant for the project “EdgeRIC: Empowering Real-time Intelligent Control and Optimization for NextG Cellular Radio Access Networks” (Link).

  • 08/2023 Kishan Panaganti has graduated with a PhD dissertation on “Robust Reinforcement Learning: Theory and Algorithms”! He will join as a Postdoc in Caltech.

  • 08/2023 Archana Bura has graduated with a PhD dissertation on “Constrained Reinforcement Learning for Wireless Networks”! She will join as a Postdoc in University of California, San Diego (UCSD).

  • 08/2023 Desik Rengarajan has graduated with a PhD dissertation on “Enhancing Reinforcement Learning using Data and Structure”! He will join as a Research Scientist in the HP Labs.

  • 04/2023 My research is featured in the TAMU Research Bulletin (Link) and in the College of Engineering News (Link)

  • 05/ 2023: Co-organizer of the workshop “Foundations of Systems and Control for the 21st Century: In Honor and Memory of Pravin Varaiya” at the American Control Conference (ACC), San Diego, May, 2023 (Link).

  • 10/ 2022: Co-organizer of the workshop “ReStoq: Reinforcement learning and stochastic control in queues and networks” in WiOpt 2022 (Link).

  • 10/ 2021: Kishan, a PhD stduent in my group, was awarded the Nokia Bell Labs summer intern award for outstanding innovation (News Link). Congratulations Kishan!

  • 05/2021: Career Initiation Fellow Award from Texas A&M Institute of Data Science (TAMIDS)! (Link)

  • 01 /2021: Delighted and honored to receive NSF CAREER Award! (Link)

  • 10 /2020: Elevated to the IEEE Senior Member!

  • 09/2020: Serving as the Publication Chair for WiOpt 2020.

  • 04/2020: Tutorial on the topic “Reinforcement Learning - Algorithms and Applications”, organized by TAMU Institute of Data Science. (Link)

  • 04/2019: Co-organizer of the Texas Systems Day (Link)

  • 04/2019: Outreach lecture at the Physics and Engineering Festival at Texas A&M University on the topic of “A Path to Artificial Intelligence Through Reinforcement Learning”. (Link)

  • 03/2019: Delighted and honored to receive the NSF CRII Award!

  • Sping, 2018: Research Fellow at the Simons Institute for the Theory of Computing in the Real-Time Decision Making program (Link)

Research

My main research area is reinforcement learning (RL) theory and algorithms, and their applications in real-world systems such as mobile robotics, power systems, and communication networks. The overarching theme of my research is to develop a principled approach for the RL-based design of control algorithms that are robust, safe and adaptive, with provable guarantees and scalable performance.

Robust Reinforcement Learning

Since training RL algorithms directly on the real-world systems is expensive and potentially dangerous, they are typically trained on a simulator. The real-world system, however, can be different from that of the simulator. For example, in robotics applications, the simulator parameter settings (mass, friction, wind conditions, sensor noise, action delays etc.) can be different from that of the robot in the real-world. This mismatch, known as simulation- to-reality gap, can significantly degrade the real-world performance of the RL algorithms trained on a simulator. In my research, I address this fundamental challenge by developing robust RL algorithms.

Representative publications: [Robust RL 1], [Robust RL 2], [Robust RL 3]

Safe Reinforcement Learning

The control policy of any real-world system should always maintain the necessary safety constraints to avoid undesirable outcomes. For example, in power systems, the phase angle and frequency should be kept within a tight band to avoid a blackout. A mobile robot should reach the desired target quickly without any collisions. Off-the-shelf RL algorithms do not consider such safety requirements, which can lead to disastrous consequences. In my research, I develop a class of safe RL algorithms to overcome this important challenge.

Representative publications: [Safe RL 1], [Safe RL 2], [Safe RL 3], [Safe RL 4]

Adaptive and Scalable Reinforcement Learning

Standard RL algorithm often follow an environment agnostic ‘learning from scratch’ approach, which is highly data inefficient. However, we often have access to additional data or structural information about the problem we intend to solve. For example, the current task may be similar to the ones seen before, we may have additional information about the dynamics, or we may have demonstration data from a human expert. In my work, I develop adaptive and scalable RL algorithms that take advantage of data and structure to aid the learning process for real-world problems.

Representative publications: [Meta RL 1], [Meta RL 2], [Sparse RL 1], [Federated RL 1], [Multi-agent RL 1]

RL in Mobile Robotics

Designing control algorithms for autonomous air/ground vehicles is a challenging task due to their complex real-world operating conditions, inevitable modeling errors, and the adversarial disturbances. In my work, I develop domain specific RL algorithms that can control mobile robots in adversarial enviornments.

We developed a robust RL algorithm for autonomous landing of UAVs in adversarial wind gusts and demonstrated its superior performance in real-world setting using a Parrot drone. See the videos [UAV - Video 1], [UAV - Video 2], and the respective publications [UAV - Paper 1], [UAV - Paper 2].

My ongoing effort is to bridge the gap between the theory and practice in RL research by demonstrating the performance of our RL algorithms on mobile robots platform. We have already implemented some of our recent RL algorithms on TurtleBot as part of this effort, see the demonstration videos. [Sparse RL - Video] [Meta RL - Video], [Federated RL - Video]

RL in Power Systems

The rapid increase of wind/solar based renewable energy generation and the proliferation of distributed energy resources such as electric vehicles make the efficient operation and control the next generation power systems a challenging task. In my work, I develop domain specific RL algorithms to overcome some the challenging control problems that arise in this setting. I have also developed algorithms for the next generation power systems by explicitly considering its multi-agent and human-in-the-loop nature.

Representative publications: [RL for Voltage Regulation], [RL for System Protection], [Sharing Economy for Smart Grid], [Mechanism Design for Demand Response].

RL in Communication Networks

The rapid growth of wireless modalities, applications, data volume, high bandwidth, and user mobility demand a data-driven and machine learning based approach for the next generation communication systems. In my work, I develop domain specific RL algorithms to overcome some the challenging control problems that arise in this setting

Representative publications: [RL for Caching], [RL for Media Streaming].

Multi-Armed Bandits

In many applications, we may have to make a sequence of decisions with incomplete information. A good model of the system may not be available and more information can be obtained only as a noisy feedback of these sequential decisions. Adaptive routing in networks, click-through rate maximizing online ad-placement, and clinical drug trials are a few examples. In my work, I have developed algorithms for decentralized learning in multi-player multi-armed bandits for addressing this important class of problems. I have also worked structured multi-armed bandits problems.

Representative works: [Decentralized MAB], [Structred MAB]

Research Presentation Videos

  • Research presentation on the topic of robust and safe RL



  • Research presentation (given by my PhD student Desik Rengarajan) on the topic of sparse, meta, and federated RL



Publications

My Google Scholar Page

Submitted Paper and Pre-Prints

[P11]. Chandra Shekhara Kaushik Valmeekam, Krishna Narayanan, Dileep Kalathil, Jean-Francois Chamberland, Srinivas Shakkottai,“LLMZip: Lossless Text Compression using Large Language Models”, arXiv Link.

[P10]. Desik Rengarajan, Nitin Ragothaman, Dileep Kalathil, Srinivas Shakkottai,“Federated Ensemble- Directed Offline Reinforcement Learning”, arXiv Link.

[P9]. Ting-Jui Chang, Sapana Chaudhary, Dileep Kalathil, Shahin Shahrampour,“Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems”, arXiv Link.

[P8]. Deepan Muthirayan, Dileep Kalathil, Pramod Khargonekar, “Meta-Learning Online Control for Linear Dynamical Systems”. arXiv Link.

[P7]. Vishnu Saj, Bochan Lee, Dileep Kalathil, Moble Benedict, “Robust Reinforcement Learning Algorithm for Vision-based Ship Landing of UAVs”. arXiv Link.

[P6]. Amit Jena, Tong Huang, S Sivaranjani, Dileep Kalathil, Le Xie,“Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems”. arXiv Link.

[P5]. Tao Liu, Ruida Zhou, Dileep Kalathil, PR Kumar, Chao Tian, “Policy Optimization for Constrained MDPs with Provable Fast Global Convergence”. arXiv Link.

[P4]. Deepan Muthirayan, Dileep Kalathil, Pramod Khargonekar, “Online Robust Control of Linear Dynamical Systems with Prediction”. arXiv Link.

[P3]. Deepan Muthirayan, Jianjun Yuan, Dileep Kalathil, Pramod Khargonekar, “Online Learning for Predictive Control with Provable Regret Guarantees”. arXiv Link.

[P2]. Sutanoy Dasgupta, Yabo Niu, Kishan Panaganti, Dileep Kalathil, Debdeep Pati, Bani Mallick, “Off- Policy Evaluation Using Information Borrowing and Context-Based Switching”. arXiv Link.

[P1]. Akhil Nagariya, Dileep Kalathil, Srikanth Saripalli, “OTTR: Off-Road Trajectory Tracking using Reinforcement Learning”. arXiv Link.


Machine Learning Conference Publications

[M14]. Ruida Zhou, Tao Liu, Dileep Kalathil, PR Kumar, Chao Tian, “Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation”, Neural Information Processing Systems (NeurIPS), December, 2023. arXiv Link.

[M13]. Zaiyan Xu, Kishan Panaganti, Dileep Kalathil, “Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning”, International Conference on Artificial Intelligence and Statistics (AISTATS), April, 2023. Publication Link.

[M12]. Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh, “Robust Reinforcement Learning using Offline Data”, Neural Information Processing Systems (NeurIPS), December, 2022. Publication Link.

[M11]. Tao Liu, Ruida Zhou, Dileep Kalathil, PR Kumar, Chao Tian, “Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning”, Neural Information Processing Systems (NeurIPS), December, 2022. Publication Link.

[M10]. Desik Rengarajan, Sapana Chaudhary, Jaewon Kim, Dileep Kalathil, Srinivas Shakkottai, “Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments”, Neural Information Processing Systems (NeurIPS), December, 2022. Publication Link.

[M9]. Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, Jean-Francois Chamberland, “DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning”, Neural Information Processing Systems (NeurIPS), December, 2022. Publication Link.

[M8]. Desik Rengarajan, Gargi Vaidya, Akshay Sarvesh, Dileep Kalathil, Srinivas Shakkottai, “Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration”, International Conference on Learning Representations (ICLR), Spotlight Presentation (5.1% accepetance rate), April, 2022, Publication Link.

[M7]. Kishan Panaganti, Dileep Kalathil, “Sample Complexity of Robust Reinforcement Learning with a Generative Model”, International Conference on Artificial Intelligence and Statistics (AISTATS), March, 2022, Publication Link.

[M6]. Sapana Chaudhary, Dileep Kalathil, “Safe Online Convex Optimization with Unknown Linear Safety Constraints”, AAAI Conference on Artificial Intelligence, February, 2022. arXiv Link.

[M5]. Tao Liu, Ruida Zhou, Dileep Kalathil, PR Kumar, Chao Tian, “Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs”, Neural Information Processing Systems (NeurIPS), December, 2021. Publication Link.

[M4]. Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, “Model-Based Reinforcement Learning for Infinite Horizon Discounted Constrained Markov Decision Processes”, International Joint Conference on Artificial Intelligence (IJCAI), August, 2021. Publication Link.

[M3]. Kishan Panaganti, Dileep Kalathil, “Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees”, International Conference on Machine Learning (ICML), July, 2021, Publication Link.

[M2]. Kiyeob Lee, Desik Rengarajan, Dileep Kalathil, Srinivas Shakkottai, “Reinforcement Learning for Mean Field Games with Strategic Complementarities”, International Conference on Artificial Intelligence and Statistics (AISTATS), February, 2021. Publication Link.

[M1]. Aria HasanzadeZonuzy, Archana Bura, Dileep Kalathil, Srinivas Shakkottai, “Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs”, AAAI Conference on Artificial Intelligence, February, 2021. Publication Link.


Journals Publications

[J22]. Vasudev Gohil, Satwik Patnaik, Hao Guo, Dileep Kalathil,J eyavijayan Rajendran,“DETERRENT:Detecting Trojans using Reinforcement Learning”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, August, 2023. Publication Link.

[J21]. Bochan Lee, Vishnu Saj, Moble Benedict, Dileep Kalathil, “Intelligent Vision-based Autonomous Ship Landing of VTOL UAVs”, Journal of the American Helicopter Society, 68(2):113-126, April, 2023. arXiv Link.

[J20]. Rayan El Helou, S. Sivaranjani, Dileep Kalathil, Andrew Schaper, LeX ie, “The impact of heavy-duty vehicle electrification on large power grids: A synthetic Texas case study”, Advances in Applied Energy, (6):100093, June, 2022. Publication Link.

[J19]. Dongqi Wu, Dileep Kalathil, Le Xie, Miroslav Begovic, Kevin Ding, “Deep Reinforcement Learning-Based Robust Protection in DER-Rich Distribution Grids”, IEEE Open Access Journal of Power and Energy, March, 2022. Publication Link.

[J18]. Ran Wang, Karthikeya S. Parunandi, Dan Yu, Dileep Kalathil, Suman Chakravorty, “Decoupled Data Based Approach for Learning to Control Nonlinear Dynamical Systems”, IEEE Transactions on Automatic Control, 67(7):3582-3589, July, 2022. Publication Link.

[J17]. Rajarshi Bhattacharyya, Archana Bura, Desik Rengarajan, Mason Rumuly, Bainan Xia, Srinivas Shakkottai, Dileep Kalathil, Ricky K. P. Mok, Amogh Dhamdhere, “QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge”, IEEE Transactions on Networking, 30(1): 32-46, February, 2022. Publication Link.

[J16]. Archana Bura, Desik Rengarajan, Dileep Kalathil, Srinivas Shakkottai, Jean-Francois Chamberland, “Learning to Cache and Caching to Learn: Regret Analysis of Caching Algorithms”, IEEE Transactions on Networking, 30(1): 18-31, February, 2022. Publication Link.

[J15]. Rodrigo Henriquez-Auba, Patricia Pauli, Dileep Kalathil, Duncan S. Callaway, Kameshwar Poolla, “Sharing Economy and Optimal Investment Decisions for Distributed Solar Generation”, Applied Energy, (294):117029, July, 2021. Publication Link.

[J14]. Rayan El Helou, Dileep Kalathil, Le Xie, “Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power”, IEEE Open Access Journal of Power and Energy, (8):175 - 185, May, 2021. Publication Link.

[J13]. Xiangtian Zheng, Bin Wang, Dileep Kalathil, Le Xie, “Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification”, IEEE Open Access Journal of Power and Energy, (8):68-76, February, 2021. Publication Link.

[J12]. Kishan Panaganti, Dileep Kalathil, “Bounded Regret for Finitely Parametrized Multi-Armed Bandits”, IEEE Control Systems Letters, July, 2020. arXiv Link.

[J11]. Dileep Kalathil, Vivek Borkar, Rahul Jain, “Empirical Q-Value Iteration”, INFORMS Journal on Stochastic Systems, 11(1):1-18, March, 2021. Publication Link.

[J10]. Deepan Muthirayan, Dileep Kalathil, Kameshwar Poolla, Pravin Varaiya, “Selling Demand Response Using Options”, IEEE Transactions on Smart Grid, 12(1): 279-288, January, 2021. Publication Link.

[J9]. Deepan Muthirayan, Dileep Kalathil, Kameshwar Poolla, Pravin Varaiya, “Mechanism Design for Demand Response Programs”, IEEE Transactions on Smart Grid, 11(1):61-73, January, 2020. Publication Link.

[J8]. Shahana Ibrahim, Dileep Kalathil, Rene O. Sanches, Pravin Varaiya, “Estimating Phase Duration for SPaT Messages”, IEEE Transactions on Intelligent Transportation systems, 20(7):2668 - 2676, July, 2019. Publication Link.

[J7]. Dileep Kalathil, Chenye Wu, Kameshwar Poolla, Pravin Varaiya, “The Sharing Economy for the Electricity Storage”, IEEE Transactions on Smart Grids, 10(4):556 - 567, January, 2019. Publication Link.

[J6]. Naumaan Nayyar, Dileep Kalathil, Rahul Jain, “Optimal Decentralized Control with Asymmetric One-Step Delayed Information Sharing”, IEEE Transactions on Control of Network Systems, 5(1):653 - 663, March, 2018. Publication Link.

[J5]. Naumaan Nayyar, Dileep Kalathil, Rahul Jain, “On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits”, IEEE Transactions on Control of Network Systems, 5(1):597 - 606, March, 2018. Publication Link

[J4]. Dileep Kalathil, Vivek Borkar, Rahul Jain, “Approachability in Stackelberg Stochastic Games with Vector Costs”, Dynamic Games and Applications, 7(3):422-42, September, 2017. Publication Link, arXiv Link.

[J3]. (Authors are in the alphabetical order) William Haskel, Rahul Jain, Dileep Kalathil, “Empirical Dynamic Programming”, Mathematics of Operations Research, 41(2):402 - 429, January, 2016. Publication Link.

[J2]. Dileep Kalathil, Naumaan Nayyar, Rahul Jain, “Decentralized Learning for Multi-Player Multi-Armed Bandits”, IEEE Transactions on Information Theory, 60(4):2331-2345, April, 2014. Publication Link.

[J1]. Dileep Kalathil, Rahul Jain, “Spectrum Sharing through Contracts for Cognitive Radios”, IEEE Transactions on Mobile Computing, 12(10):1999-2011, October, 2013. Publication Link.

Unpublished Manuscripts

[U2]. Dileep Kalathil, Ram Rajagopal, “Learning Demand Response with Bandits”, August, 2017. A shorter version appeared in Allerton Conference on Communications, Control and Computing, October, 2015. PDF

[U1]. Jonathan Mather, Enrique Baeyens, Dileep Kalathil, Kameshwar Poolla, “The Geometry of Locational Marginal Prices”, PDF

Conference Proceedings

[C33]. Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh, “Distributionally Robust Behavioral Cloning for Robust Imitation Learning”, IEEE Conference on Decision and Control (CDC), December, 2023.

[C32]. Deepan Muthirayan, Dileep Kalathil, Pramod Khargonekar,“Meta-Learning Online Control for Linear Dynamical Systems”, IEEE Conference on Decision and Control (CDC), December, 2022.

[C31]. Deepan Muthirayan,Jianjun Yuan,Dileep Kalathil,Pramod Khargonekar,“Online Learning for Predictive Control with Provable Regret Guarantees”, IEEE Conference on Decision and Control (CDC), December, 2022.

[C30]. Dheeraj Narasimha,Kiyeob Lee,Dileep Kalathil,Srinivas Shakkottai, “Multi-Agent Learning via Markov Potential Games in Marketplaces for Distributed Energy Resources”, IEEE Conference on Decision and Control (CDC), December, 2022.

[C29]. Vasudev Gohil, Satwik Patnaik, Hao Guo, Dileep Kalathil, Jeyavijayan Rajendran, “DETERRENT:Detecting Trojans using Reinforcement Learning”, Design Automation Conference (DAC), July, 2022.

[C28]. Dongqi Wu, Dileep Kalathil, Miroslav Begovic, Le Xie, “PyProD: A Machine Learning- Friendly Platform for Protection Analytics in Distribution Systems”, Hawaii International Conference on System Sciences (HICSS), January, 2022.

[C27]. Amit Jena, Tong Huang, S, Sivaranjani, Dileep Kalathil, Le Xie, “Distributed Learning-Based Stability Assessment for Large Scale Networks of Dissipative Systems”, IEEE Conference on Decision and Control (CDC), December, 2021.

[C26]. Kishan Panaganti, Dileep Kalathil, “Sample Complexity of Model-Based Robust Reinforcement Learning”, IEEE Conference on Decision and Control (CDC), December, 2021.

[C25]. Bochan Lee, Vishnu Saj, Moble Benedict and Dileep Kalathil, “A Deep Reinforcement Learning Control Strategy for Vision-based Ship Landing of Vertical Flight Aircraft”, AIAA Aviation Forum, August, 2021.

[C24]. Kishan Panaganti, Dileep Kalathil, “Bounded Regret for Finitely Parametrized Multi-Armed Bandits”, IEEE Conference on Decision and Control (CDC), December, 2020.

[C23]. Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, “Reinforcement Learning for Multi-Hop Scheduling and Routing of Real-Time Flows”, International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT), June, 2020.

[C22]. Rayan El Helou, Dileep Kalathil, Le Xie, “Communication-free Voltage Regulation in Distribution Networks with Deep PV Penetration”, 53rd Hawaii International Conference on System Sciences (HICSS), January, 2020.

[C21]. Dongqi Wu, Xiangtian Zheng, Dileep Kalathil, Le Xie, “Nested Reinforcement Learning Based Control for Protective Relays in Power Distribution Systems”, IEEE Conference on Decision and Control (CDC), December, 2019.

[C20]. Bainan Xia, Kiyeob Lee, Srinivas Shakkottai, Dileep Kalathil, “A Market for Retail Electric Provider Based Demand Response”, IEEE Conference on Decision and Control (CDC), December, 2019.

[C19]. R. Bhattacharyya, A. Bura, D. Rengarajan, M. Rumuly, S. Shakkottai, D. Kalathil, R. Mok, A. Dhamdhere, “QFlow: A Reinforcement Learning Approach to High QoE Video Streaming over Wireless Networks”, International Symposium on Mobile Ad Hoc Networking (MobiHoc), July, 2019.

[C18]. Vamsi Krishna Vegamoor, Dileep Kalathil, Sivakumar Rathinam, Swaroop Darbha, “Reducing Time Headway in Homogeneous CACC Vehicle Platoons in the Presence of Packet Drops”, European Control Conference (ECC), May, 2019.

[C17]. Deepan Muthirayan, Dileep Kalathil, Kameshwar Poolla, Pravin Varaiya, “Baseline Estimation and Scheduling for Demand Response”, IEEE Conference on Decision and Control (CDC), December, 2018.

[C16]. Rodrigo Henriquez-Auba, Patricia Pauli, Dileep Kalathil, Duncan S. Callaway, Kameshwar Poolla, “The Sharing Economy for Residential Solar Generation”, IEEE Conference on Decision and Control (CDC), December, 2018.

[C15]. Deepan Muthirayan, Dileep Kalathil, Kameshwar Poolla, Pravin Varaiya, “Mechanism Design for Self-Reporting Baselines in Demand Response”, American Control Conference (ACC), July, 2016.

[C14]. Dileep Kalathil, Ram Rajagopal, “Online Learning for Demand Response”, Allerton Conference on Communications, Control and Computing, October, 2015.

[C13]. Dileep Kalathil, Vivek Borkar, Rahul Jain, “Blackwell’s Approachability in Stackelberg Stochastic Games: A Learning Version”, IEEE Conference on Decision and Control (CDC), December, 2014.

[C12]. (Authors are in the alphabetical order) William Haskel, Rahul Jain, Dileep Kalathil, “Empirical Policy Iteration for Approximate Dynamic Programming”, IEEE Conference on Decision and Control (CDC), December, 2014.

[C11]. Naumaan Nayyar, Dileep Kalathil, Rahul Jain, “Optimal Decentralized Control in Unidirectional One-Step Delayed Sharing Pattern with Partial Output Feedback”, American Control Conference, June, 2014.

[C10]. (Authors are in the alphabetical order) William Haskel, Rahul Jain, Dileep Kalathil, “Empirical Value Iteration for Approximate Dynamic Programming”, American Control Conference, June, 2014.

[C9]. Naumaan Nayyar, Dileep Kalathil, Rahul Jain, “Optimal Decentralized Control in Unidirectional One-Step Delayed Sharing Pattern”, Allerton Conference on Communications, Control and Computing, October, 2013.

[C8]. Dileep Kalathil, Naumaan Nayyar, Rahul Jain, “Decentralized Learning for Multi-Player Multi-Armed Bandits”, IEEE Conference on Decision and Control (CDC), December, 2012.

[C7]. Dileep Kalathil, Rahul Jain, “Incentives for Cooperative Relaying in a Simple Information Theoretic Model”, IEEE International Symposium on Information Theory (ISIT), July, 2012.

[C6]. Dileep Kalathil, Rahul Jain, “Communication Games on the Generalized Gaussian Relay Channel”, Allerton Conference on Communications, Control and Computing, October, 2010.

[C5]. Dileep Kalathil, Rahul Jain, “Spectrum Sharing Through Contracts”, IEEE Symposium on Dynamic Spectrum Access Networks (DySPAN), April, 2010.

[C4]. Dileep Kalathil, Rahul Jain, “A Contract Based Approach to Spectrum Sharing in Cognitive Networks”, IEEE WiOpt Conference, June, 2010.

[C3]. K.Kuchi, Vinod R., Dileep Kalathil, M.S.Padmanabhan, Dhivagar R., “Interference Mitigation Using Conjugate Data Repetition”, IEEE International Conference on Communication (ICC), 2009.

[C2]. Dileep Kalathil, A.Iyengar, A.Thangaraj, S.Bhashyam, “Low Density Parity Check Codes in OFDM Systems”, National Conference on Communication (NCC), 2009.

[C1]. A.Iyengar, Dileep Kalathil, A.Thangaraj, S.Bhashyam, “Thresholds for LDPC codes over OFDM”, IEEE International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE), 2008.

Teaching

  • ECEN 689: Reinforcement Learning
    • Fall 2018, Fall 2019, Spring 2021, Spring 2023
  • ECEN 303: Random Signals and Systems
    • Fall 2017, Spring 2022
  • ECEN 489: Artificial Intelligence and Applications
    • Spring 2019, Spring 2020
  • ECEN 605: Linear Systems
    • Fall 2020

Students

PhD Students

  • Zaiyan Xu (Webpage). (Fall 2020 - Present)
  • Sapana Chaudhary (Webpage). (Fall 2019 - Present)
  • Kishan Panaganti (Webpage). (Fall 2018 - Summer 2023).
    • Next position: Postdoc, Caltech.
  • Archana Bura (Webpage) (Fall 2017 - Summer 2023). (co-advised with Prof. Srinivas Shakkottai).
    • Next position: Postdoc, UCSD.
  • Desik Rengarajan (Webpage) (Fall 2017 - Summer 2023) (co-advised with Prof. Srinivas Shakkottai).
    • Next position: Research Scientist, HP Labs.

Masters Students

  • Gargi Vaidya (First position: Rivian) (Fall 2019 - Fall 2021)
  • Vishnu Saj (First position: PhD student, TAMU) (Fall 2019 - Fall 2021)
  • Shahana Ibrahim (First position: PhD student, Oregon State University) (Fall 2017 - Fall 2018)
  • Prabhasa Kalkur (First position: SAP) (Fall 2018 - Fall 2020)

Undergraduate Students

  • Amogh Pandey (First position: PhD student, UIUC) (Spring 2020 - Summer 2021)
  • Rebeca Reyes (First position: PhD student, Microsoft) (Fall 2020 - Summer 2021)

Contact

  • dileep [dot] kalathil [at] tamu [dot] edu
  • Room 334G, WEB, Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128