P.R. Kumar

University Distinguished Professor | Regents Professor | O’Donnell Foundation Chair I
Contact
  • Room 331E   Tel: (979) 862-1645    Email: prk.tamu@gmail.com

Biography

Regents Professor

O’Donnell Foundation Chair I

Member National Academy of Engineering, Member World Academy of Science, Fellow ACM, IEEE and IFAC, Franklin Woeltge Professor Emeritus at the University of Illinois, Urbana-Champaign, Honorary Professor at IIT Hyderabad

Research Interests

Machine Learning, Reinforcement Learning, Communication Networks, Cyber Physical systems, AI in Health care and Manufacturing.

Cyber-Physical Systems Lab

Research Interests

Books

Publications

Past Conference Presentations

Awards and Honors

Students and Post-docs

Presentations

Table Tennis

Miscellaneous

Short Biography

Mailing Address: 3259 TAMU, College Station, TX 77843-3259

Administrative Coordinator: Carolyn Warzon, carolyn@ece.tamu.edu

Research Interests

 

 

Books

 

View books (6 entries)

In reverse chronological order:

Past Conference Presentations

 

View past conference presentations (183 entries)

Awards and Honors

 

View awards and honors (7 entries)

Brief Bio

P. R. Kumar obtained his B.Tech. degree in Electrical Engineering (Electronics) from I.I.T. Madras in 1973, and the M.S. and D.Sc. degrees in Systems Science and Mathematics from Washington University, St. Louis in 1975 and 1977, respectively. From 1977-84, he was a faculty member in the Department of Mathematics at the University of Maryland Baltimore County. From 1985-2011, he was a faculty member in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois, where he remains a Franklin W. Woeltge Emeritus Professor. Currently he is at Texas A&M University where he is a University Distinguished Professor, Regents Professor, and O’Donnell Foundation Chair I.

Kumar has worked on problems in game theory, adaptive control, simulated annealing, machine learning, queueing networks, manufacturing systems, scheduling wafer fabrication plants, wireless networks and network information theory. His current research focus includes renewable energy, power systems, security, privacy, automated transportation, unmanned aerial vehicle traffic management, millimeter wave 5G, and cyber-physical systems.

Kumar is a member of the National Academy of Engineering of the USA, the World Academy of Sciences, and the Indian National Academy of Engineering. He was awarded an honorary doctorate by ETH (the Swiss Federal Institute of Technology), Zurich. He received the IEEE Alexander Graham Bell Medal, the IEEE Field Award for Control Systems, the Donald P. Eckman Award of American Automatic Control Council, the Fred Ellersick Prize of IEEE Communications Society, the Outstanding Contribution Award of ACM SIGMOBILE, the Infocom Achievement Award, and SIGMOBILE Test-of-Time Paper Award. He is a Fellow of ACM, IEEE and IFAC. He is an Honorary Professor at IIT Hyderabad, and Franklin Woeltge Professor Emeritus at the University of Illinois, Urbana-Champaign. He was awarded the Distinguished Alumnus Award from IIT Madras, the Alumni Achievement Award from Washington University in St. Louis, and the Daniel C. Drucker Eminent Faculty Award from the College of Engineering at the University of Illinois.

Reinforcement Learning

Reward Biased Maximum Likelihood Estimation:

The exploration-exploitation trade-off remains a challenging issue in reinforcement learning. A novel class of model-based learning algorithms, RBMLE can be applied to various learning tasks such as Markov decision processes, stochastic bandits, linear quadratic systems, and contextual bandits. Theoretical analysis shows that RBMLE has comparable regret bounds to state-of-the-art methods. Empirical results show that RBMLE outperforms existing techniques, including Upper Confidence Bound (UCB) and Thompson Sampling.

Publications:

Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems,
Akshay Mete, R. Singh & P. R. Kumar , NeurIPS 2022
Link: https://arxiv.org/abs/2201.10542

Reward Biased Maximum Likelihood Estimation ,
Akshay Mete, R. Singh & P. R. Kumar, CSS 2022 (Invited Paper)
Link: https://ieeexplore.ieee.org/abstract/document/9751189

Reward Biased Maximum Likelihood Estimation for Reinforcement Learning,
Akshay Mete, R. Singh & P. R. Kumar, L4DC 2021
Link: http://proceedings.mlr.press/v144/mete21a.html

Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits
Yu-Heng Hung, Ping-Chun Hsieh, Xi Liu, P. R. Kumar, AAAI 2021
Link:https://ojs.aaai.org/index.php/AAAI/article/view/16961/16768

Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
Xi Liu, Ping-Chun Hsieh, Yu Heng Hung, A. Bhattacharya, P. R. Kumar, ICML 2020.
Link: https://proceedings.mlr.press/v119/liu20g.html

Provable Learning with Real-World Constraints:

(Reinforcement) learning methods have been playing important roles in many fields, e.g., manipulating robotics, playing computer games, chip placement design, and recommendation systems. However, it is challenging to extend such success to real-world applications by directly applying existing approaches. Real-world systems are always subject to several constraints, including safety, multiple objectives, simulation-to-reality gap, multi-agent cooperation/competition, small sample size, etc. To deal with these challenges, we have done a series of works about safe RL, multi-objective RL, robust RL, multi-agent RL, and learning from few samples.

Safe and Multi-Objective Reinforcement Learning:

The goal of safe RL is to learn a policy to maximize the objective while satisfying the safety constraints. We solve several open problems under this topic.

1. Previous OFU (optimism in the face of uncertainty) style algorithms can learn the safe policy, but naïve OFU algorithms violate the constraints during learning. We propose two new algorithms that can achieve an O(√K) regret with respect to the performance objective, while guaranteeing zero or bounded safety constraint violation with arbitrarily high probability.

2. Previous policy gradient-based algorithms for constraint MDPs suffer from an O(1/ √T) convergence rate. We propose a new policy mirror descent-primal dual approach that can provably achieve a faster O(log(T)/T) convergence rate for both the optimality gap and the constraint violation and enjoy superior empirical performance compared to previous methods.

The goal of multi-objective RL is to optimize a policy for multiple objectives simultaneously. Based on the system requirements, the agent can employ different criteria on value vectors, e.g., proportional fairness, hard constraints, max-min trade-off. Previously, there was no systematic way to design policy gradient-based algorithms for multi-objective RL and no systematic theory to analyze convergence rate under all scenarios. We propose an Anchor-changing Regularized Natural Policy Gradient (ARNPG) framework, which achieves O(log(T)/T) global convergence with exact gradients and enjoys superior empirical performance compared to some existing policy gradient-based approaches in both exact gradients and sample-based deep RL scenarios.

Publications:

Tao Liu, Ruida Zhou, Dileep Kalathil, P. R. Kumar, and Chao Tian. “Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs.” Advances in Neural Information Processing Systems 34 (2021): 17183-17193. [link]

Tao Liu, Ruida Zhou, Dileep Kalathil, P. R. Kumar, and Chao Tian. “Policy Optimization for Constrained MDPs with Provable Fast Global Convergence.” arXiv preprint arXiv:2111.00552 (2021). [link] [code]

Ruida Zhou, Tao Liu, Dileep Kalathil, P. R. Kumar, and Chao Tian. “Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning.” Advances in Neural Information Processing Systems 35 (2022): 13584-13596. [link] [code]

Robust Reinforcement Learning:

The goal of robust RL is to determine a well-performing policy that is robust against model mismatch between the training simulator and the testing environment. Previous policy-based robust RL algorithms mainly focus on the tabular setting under uncertainty sets that facilitate robust policy evaluation, but are no longer tractable when the number of states scales up. To this end, we propose two novel uncertainty set formulations, one based on double sampling and the other on an integral probability metric. Both make large-scale robust RL tractable even when one only has access to a simulator. We propose a robust natural actor-critic (RNAC) approach that incorporates the new uncertainty sets and employs function approximation. We provide finite-time convergence guarantees for the proposed RNAC algorithm to the optimal robust policy within the function approximation error. Finally, we demonstrate the robust performance of the policy learned by our proposed RNAC approach in multiple MuJoCo environments and a real-world TurtleBot navigation task.

Publication:

Ruida Zhou, Tao Liu, Min Cheng, Dileep Kalathil, P. R. Kumar, and Chao Tian. “Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation.” Advances in Neural Information Processing Systems 36 (2023). [link] [code]

Multi-Agent Reinforcement Learning:

The goal of multi-agent RL is to learn a policy that cooperates or competes among several agents. We study an independent natural policy gradient (NPG) algorithm for the multi-agent reinforcement learning problem in Markov potential games. It is shown that, under mild technical assumptions and the introduction of the suboptimality gap, the independent NPG method with an oracle providing exact policy evaluation asymptotically reaches an \epsilon-Nash Equilibrium (NE) within O(1/\epsilon) iterations. This improves upon the previous best result of O(1/\epsilon^2) iterations and is of the same order, O(1/\epsilon), that is achievable for the single-agent case. Empirical results for a synthetic potential game and a congestion game are presented to verify the theoretical bounds.

Publication:

Youbang Sun, Tao Liu, Ruida Zhou, P. R. Kumar, Shahin Shahrampour, “Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games.” Advances in Neural Information Processing Systems 36 (2023). [link] [code]

Learning from Few Samples:

Motivated by the problem of learning with small sample sizes, we show how to incorporate into support-vector machines (SVMs) those properties that have made convolutional neural networks (CNNs) successful. Particularly important is the ability to incorporate domain knowledge of invariances, e.g., translational invariance of images. Kernels based on the maximum similarity over a group of transformations are not generally positive definite. Perhaps it is for this reason that they have not been studied theoretically. We address this lacuna and show that positive definiteness indeed holds with high probability for kernels based on the maximum similarity in the small training sample set regime of interest, and that they do yield the best results in that regime. We also show how additional properties such as their ability to incorporate local features at multiple spatial scales, e.g., as done in CNNs through max pooling, and to provide the benefits of composition through the architecture of multiple layers, can also be embedded into SVMs. We verify through experiments on widely available image sets that the resulting SVMs do provide superior accuracy in comparison to well-established deep neural network benchmarks for small sample sizes.

Publication:

Tao Liu, P. R. Kumar, Ruida Zhou, and Xi Liu. “Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales.” Advances in Neural Information Processing Systems 35 (2022): 9151-9163. [link] [code]

Cyber Physical Systems

Cyber-Security for Networked Cyber-Physical Systems (CPS)

Networked Cyber-Physical Systems (CPS) refer to control systems that involve multiple computing nodes and diverse agents interacting with the physical world. These agents can consist of intelligent systems or robotic agents deployed in real-world applications. CPS are composed of a combination of physical components, such as plants, processes, or systems, and cyber components, such as software, code, and computation that are interconnected through a network, which can be either wired or wireless. The integration and cooperation of physical and cyber components are beneficial for CPS; however, the presence of networked cyber and physical components renders them vulnerable to potential cyber-attacks and other disruptive events.

In light of the fact that numerous networked CPS are utilized to support critical infrastructures or are classified as safety-critical systems, it is crucial to examine the feasibility of securing these systems against cyber threats and ensuring their adaptability to potential disruptions. The deployment of networked CPS, including Autonomous Vehicles, Chemical Process Control Systems, Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Smart Energy Systems, etc. are typically reliant on sensor measurements to enable closed-loop control. As a result, these control systems are susceptible to cyber-attacks, whereby malicious agents may compromise the sensors or the networks that transmit sensor measurements.

We developed a general-purpose cyber-attack defense methodology for defending networked CPS against arbitrary cyber-attacks, which is called “Dynamic Watermarking (DW)”. Our DW method has been tested on prototype chemical process control system (coupled water tanks), mechanical system (helicopter), the power system with power electronics (grid-tied inverter), industrial adjustable speed drive system, and vehicular system (autonomous car).

Publications:

Jaewon Kim, Hasan Ibrahim, Shaozhe Wang, Akshay Mete, Le Xie, Prasad Enjeti, and P. R. Kumar, “Cyber-Secure and Safe Operation of Solar Photovoltaic Power Distribution Systems”. will be appear IEEE APEC (Applied Power Electronics Conference and Exposition) 2024.

Huang, Peng-Hao, Jaewon Kim, P. R. Kumar, Jeyavijayan JV Rajendran, and Prasad Enjeti. “Enhancing Cybersecurity for Industrial Control Systems: Innovations in Protecting PLC-dependent Industrial Infrastructures.” (2023).
(Submitted and Under Review now)

Ibrahim, Hasan, Jaewon Kim, Jorge Ramos-Ruiz, Woo Hyun Ko, Tong Huang, Prasad Enjeti, P. R. Kumar, and Le Xie. “Detection of cyber attacks in grid-tied pv systems using dynamic watermarking.” IEEE Transactions on Industry Applications (2023).
Link: https://ieeexplore.ieee.org/abstract/document/10268968

Ko, Woo-Hyun, Jorge A. Ramos-Ruiz, Tong Huang, Jaewon Kim, Hasan Ibrahim, Prasad N. Enjeti, P. R. Kumar, and Le Xie. “Robust Dynamic Watermarking for Cyber-Physical Security of Inverter-Based Resources in Power Distribution Systems.” IEEE Transactions on Industrial Electronics (2023).
Link: https://ieeexplore.ieee.org/abstract/document/10242131

Alotaibi, Faris H., Hasan Ibrahim, Jaewon Kim, P. R. Kumar, and Prasad Enjeti. “Designing an Intrusion Detection for an Adjustable Speed Drive System Controlling a Critical Process.” IEEE Access (2023).
Link: https://ieeexplore.ieee.org/abstract/document/10188841

Alotaibi, Faris H., Hasan Ibrahim, Jaewon Kim, and Prasad Enjeti. “A Method to Detect Man in the Middle Attack (MiTM) on a Grid Following PV Inverter.” In 2023 IEEE 24th Workshop on Control and Modeling for Power Electronics (COMPEL), pp. 1-6. IEEE, 2023.
Link: https://ieeexplore.ieee.org/abstract/document/10221187

Lantian Shangguan, Kenny Chour, Woo Hyun Ko, Jaewon Kim, Gopal Krishna Kamath, Bharadwaj Satchidanandan, Swaminathan Gopalswamy, and P. R. Kumar. “Dynamic watermarking for cybersecurity of autonomous vehicles.” IEEE Transactions on Industrial Electronics (2022).
Link: https://ieeexplore.ieee.org/abstract/document/9994719

Jaewon Kim, Akshay Mete, and P. R. Kumar. “Safe Control of Networked Chemical Process Plants Under Cyber-Attacks.” In 2022 AIChE Annual Meeting. AIChE, 2022.
Link: https://aiche.confex.com/aiche/2022/meetingapp.cgi/Paper/644720

Faris Alotaibi, Hasan Ibrahim, Jaewon Kim, and Prasad Enjeti. “Designing an Intrusion Proof Adjustable Speed Drive System Controlling a Critical Process.” In 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), pp. 1-7. IEEE, 2022.
Link: https://ieeexplore.ieee.org/abstract/document/9923160

Hasan Ibrahim, Jaewon Kim, Prasad Enjeti, P. R. Kumar, and Le Xie. “Detection of cyber attacks in grid- tied pv systems using dynamic watermarking.” In 2022 IEEE Green Technologies Conference (GreenTech), pp. 57-61. IEEE, 2022.
Link: https://ieeexplore.ieee.org/abstract/document/9772036

Hasan Ibrahim, Jorge Ramos-Ruiz, Jaewon Kim, Woo Hyun Ko, Tong Huang, Prasad Enjeti, P. R. Kumar, and Le Xie. “An active detection scheme for sensor spoofing in grid-tied pv systems.” In 2021 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 1433-1439. IEEE, 2021.
Link: https://ieeexplore.ieee.org/abstract/document/9595733

Tong Huang, Jorge Ramos-Ruiz, Woo-Hyun Ko, Jaewon Kim, Prasad Enjeti, P. R. Kumar, and Le Xie. “Enabling secure peer-to-peer energy transactions through dynamic watermarking in electric distribution grids: Defending the distribution system against sophisticated cyberattacks with a provable guarantee.” IEEE Electrification Magazine 9, no. 3 (2021): 55-64.
Link: https://ieeexplore.ieee.org/abstract/document/9528341

Jorge Ramos-Ruiz, Hasan Ibrahim, Jaewon Kim, Woo Hyun Ko, Tong Huang, Prasad Enjeti, P. R. Kumar, and Le Xie. “Validation of a Robust Cyber Shield for a Grid Connected PV Inverter System via Digital Watermarking Principle.” In 2021 IEEE 12th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), pp. 1-6. IEEE, 2021.
Link: https://ieeexplore.ieee.org/abstract/document/9494227

Jaewon Kim, Woo-Hyun Ko, and P. R. Kumar. “Cyber-Security through Dynamic Watermarking for 2-rotor Aerial Vehicle Flight Control Systems.” In 2021 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1277-1283. IEEE, 2021.
Link: https://ieeexplore.ieee.org/abstract/document/9476684

Jorge Ramos-Ruiz, Jaewon Kim, Woo-Hyun Ko, Tong Huang, Prasad Enjeti, P. R. Kumar, and Le Xie. “An active detection scheme for cyber attacks on grid-tied PV systems.” In 2020 IEEE CyberPELS (CyberPELS), pp. 1-6. IEEE, 2020.
Link: https://ieeexplore.ieee.org/abstract/document/9311539

Jaewon Kim, and P. R. Kumar. “Security of Control Systems with Erroneous Observations.” IFAC- PapersOnLine 53, no. 2 (2020): 2225-2230.
Link: https://www.sciencedirect.com/science/article/pii/S2405896320302433

Jaewon Kim, Woo-Hyun Ko, and P. R. Kumar. “Cyber-security with dynamic watermarking for process control systems.” In 2019 AIChE Annual Meeting. AIChE, 2019.
Link: https://aiche.confex.com/aiche/2019/meetingapp.cgi/Paper/577246

Power Systems

A Multi-Harmonic Method for Real Time Capacitor Condition Monitoring in Adjustable Speed Drive Systems for Industry 4.0

Capacitor age monitoring is essential in power electronics since the age of the capacitor significantly affects the performance of the overall system. The life of a capacitor is measured using the ESR resistance associated with the capacitor. The capacitor needs to be replaced once the ESR resistor’s resistance doubles. Now typical Adjustable Speed Drives have capacitors in the DC link. Commonly ASD failures are attributed to wear and tear of the DC link capacitors. The proposed multi-harmonic method estimates the ESR resistance of the DC-link capacitor and its capacitance value using DC-link voltage and current measurements as obtained from the ASD. This implies that no additional sensors or signals are required for capacitor condition monitoring. This method has shown to be operational in both balanced and unbalanced voltage operating conditions and we envision its deployment as a cloud based algorithm to help monitor several ASD systems in an industrial setting leading to the Industry 4.0 standard.

mmWave and Cellular Networks

We work on Cellular Standards, and an academic member of 3GPP, and maintain strong relations with the Industry.

Our students played significant role in the development of research 5G network at TAMU Rellis.

mmWave Networks

Our work addressed the most important problem: Beam Management. mmWave networks communicate use narrow beams, therefore user mobility and blockage impacts the link

mmWave Testbed

User mobility requires continually switching of beams and to avoid outage during blockage,  Non-Line of Sight path communication is a must

We developed minimalist beam management algorithms for mmWave Systems.  Our protocols are implemented on 60 GHz Software defined radios of National Instruments

Demonstration Videos: Non Line of Sight Link Operation LoS Link Operation: Walking User LoS Link Operation: Rotational Motion

Man-in-the-Middle Attacks in Cellular Networks

Want to detect Cell-site simulators ?! Please look at our arxiv paper, it’s in the media too!

Please write to santosh@tamu.edu for demonstrations and further details

Full Duplex and Double Full Duplex Man-in-the-Middle Attack on 4G/5G Networks

AI in Health Care

Suppressing Epileptic Seizures using the power of Neural Networks

An Epileptic seizure is defined as a transient synchronous neuronal activity which may temporarily lead to loss of motor functions. About 50 Million people worldwide suffer from Epilepsy and about 1.9% deaths in Epileptic patients are due to prolonged seizures. Currently medication and surgery are dominant solutions to this problem however both have severe downsides in terms of loss in quality of life. A new approach is based on stimulating the brain using a chip (electroceutical device) inserted in the epicenter of seizure activity. Thus, the problem statement is designing a detection and prediction algorithm to be used in this closed-loop electroceutical device which will be used to treat epilepsy and potentially also treat other brain disorders.

Cyberphysical Systems Lab

Overview of the system

An introduction and overview description of the system as well as a demo of some of the capabilities on the testbed. In particular, it focuses on the concept of ‘local temporal autonomy’ and demonstrates how the vehicles deal with communication, localization or other failures and how it recovers from them.

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Tackling Autonomous Vehicle Cybersecurity Issues

This video illustrates that the dynamic watermarking method successfully detects and responds to attacks on the position sensors which can otherwise cause collisions on a laboratory autonomous transportation system

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Seven Cars Scheduled in a City Traffic Scenario

This is a simulation of traffic flow through a grid traffic network. Individual cars are controlled by their own controller, however they get information on routes from a central traffic scheduler. The routes and schedules are updated periodically in order to account for new cars joining the system, cars behaving erratically or new goals. The result is a successful demonstration of a network of sensors and actuators controlled over a network of computation nodes and communication links. It does not illustrate good parking ability though and motivated the development of a collision avoidance system. (A map of the city grid is available here).
Watch on YouTube

Eight Cars Scheduled in a City Traffic Scenario

A similar demonstration as the one above except with 8 cars.
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Automatic Parking

Shows a car moving from one bin, through the traffic system to another bin and successfully parking. (A map of the city grid is available here).

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Cars Moving in a Figure Eight

A nice illustration system operation and performance.

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Principle of Safety Preserving Security Overrides

This demonstration illustrates the principle of safety preserving security overrides. This principle states that, in control systems, low-level safety features such as collision avoidance should not be overridden by higher level security overrides in general.

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Collision Avoidance Implementation

This is an overview of the performance of the collision avoidance algorithm that we have implemented on the testbed. An illustration of the original problem as well as several different scenarios are presented. (This effectively eliminates situations like the one on the left!)

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Component Restart Using Middleware

Illustrates the ability of the system to handle the restart of a component. During normal operation a controller for an individual car is halted and then restarted with no noticeable effect on the system performance.

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Component Migration Using Middleware

Component failure of communication errors may require a component to be moved from one node to another. This video is an example of how migration has been developed and implemented in the middleware.

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Component Upgrade Using Middleware

Since components can be halted and restarted without affecting the system performance there is no reason that we could not halt a particular component and start a different (perhaps better) one in its place. This video illustrates the replacement of car controllers with no adverse affects on the system.

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Car Going on a Gun Shot

In collaboration with some other CSL researchers, the testbed was used to illustrate a target localization problem. Several small ‘motes’ were used to identify the location of a small gunshot. This information was passed to a controller on the testbed which computes a route to get a car to the location.

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Crash

Things don’t always go as planned – and here is an example from before collision avoidance was implemented correctly…just to keep us honest!

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Experimental Results Involving Delay

This video illustrates how performance is degraded in the presence of delays. The car attempting to follow a rectangular trajectory. However, the feedback is delayed by 700ms and the video illustrates the effect of known vs. unknown delay.

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Recent Publications

 

View recent publications (105 entries)

Articles in Journals (In reverse chronological order):

Articles in Conference Proceedings (In reverse chronological order):

Students and Post-Docs

Current Members

Post-Doctoral Fellows
PhD Students

Past Members

Table Tennis

TAMU Table Tennis Club meets in the Rec Center.

Facilities are excellent!

Times vary each semester, but in the past it has generally been on weekends and Fridays.

Rec Center Information: https://recsports.tamu.edu/facilities/