About

Subhonmesh Bose is an Associate Professor at The Grainger College of Engineering, University of Illinois Urbana-Champaign. He holds a Ph.D. and M.S. in Electrical Engineering from the California Institute of Technology, obtained in 2014 and 2012 respectively, and a B.Tech. in Electrical Engineering from the Indian Institute of Technology Kanpur, completed in 2009. His academic career includes a postdoctoral fellowship at Cornell University in the Electrical and Computer Engineering department from August 2014 to December 2015 before joining UIUC as an Assistant Professor in 2016. His research interests encompass transportation electrification, coordination of distributed energy resources, electricity market design with renewables, nonstationary reinforcement learning, and data-driven analysis and control of dynamical systems. He focuses on the dynamics and stability of power systems, energy system economics and public policy, networked control systems, and the operation and control of power systems. His work also involves the study of stochastic systems and control, with a particular emphasis on AI theory applied to electric transportation and energy markets.

Research topics

• Computer Science
• Economics
• Microeconomics
• Business
• Mathematics
• Pathology
• Engineering
• Medicine
• Industrial organization
• Mathematical optimization
• Environmental economics
• Geometry
• Virology
• Operations research
• Statistics
• Data science
• Econometrics

Selected publications

• Learning Where to Look: UCB-Driven Controlled Sensing for Quickest Change Detection

  arXiv (Cornell University) · 2026-03-30

  preprintOpen access

  We study the multichannel quickest change detection problem with bandit feedback and controlled sensing, in which an agent sequentially selects one of the data streams to observe at each time-step and aims to detect an unknown change as quickly as possible while controlling false alarms. Assuming known pre- and post-change distributions and allowing an arbitrary subset of streams to be affected by the change, we propose two novel and computationally efficient detection procedures inspired by the Upper Confidence Bound (UCB) multi-armed bandit algorithm. Our methods adaptively concentrate sensing on the most informative streams while preserving false-alarm guarantees. We show that both procedures achieve first-order asymptotic optimality in detection delay under standard false-alarm constraints. We also extend the UCB-driven controlled sensing approach to the setting where the pre- and post-change distributions are unknown, except for a mean-shift in at least one of the channels at the change-point. This setting is particularly relevant to the problem of learning in piecewise stationary environments. Finally, extensive simulations on synthetic benchmarks show that our methods consistently outperform existing state-of-the-art approaches while offering substantial computational savings.

  PublisherDOI

• Learning Where to Look: UCB-Driven Controlled Sensing for Quickest Change Detection

  arXiv (Cornell University) · 2026-03-30

  articleOpen access

  We study the multichannel quickest change detection problem with bandit feedback and controlled sensing, in which an agent sequentially selects one of the data streams to observe at each time-step and aims to detect an unknown change as quickly as possible while controlling false alarms. Assuming known pre- and post-change distributions and allowing an arbitrary subset of streams to be affected by the change, we propose two novel and computationally efficient detection procedures inspired by the Upper Confidence Bound (UCB) multi-armed bandit algorithm. Our methods adaptively concentrate sensing on the most informative streams while preserving false-alarm guarantees. We show that both procedures achieve first-order asymptotic optimality in detection delay under standard false-alarm constraints. We also extend the UCB-driven controlled sensing approach to the setting where the pre- and post-change distributions are unknown, except for a mean-shift in at least one of the channels at the change-point. This setting is particularly relevant to the problem of learning in piecewise stationary environments. Finally, extensive simulations on synthetic benchmarks show that our methods consistently outperform existing state-of-the-art approaches while offering substantial computational savings.

  PublisherOA PDF

• Harnessing Information in Incentive Design

  2025-12-09 · 1 citations

  article

  Incentive design deals with interaction between a principal and an agent where the former can shape the latter’s utility through a policy commitment. It is well known that the principal faces an information rent when dealing with an agent that has informational advantage. In this work, we embark on a systematic study of the effect of information asymmetry in incentive design games. Specifically, we first demonstrate that it is in principal’s interest to decrease this information asymmetry. To mitigate this uncertainty, we let the principal gather information either by letting the agent shape her belief (aka Information Design), or by paying to acquire it. Providing solutions to all these cases we show that while introduction of uncertainty increases the principal’s cost, letting the agent shape its belief can be advantageous. We study information asymmetry and information acquisition in both matrix games and quadratic Gaussian game setups.

  PublisherDOI

• DAL: A Practical Prior-Free Black-Box Framework for Non-Stationary Bandits

  ArXiv.org · 2025-01-31

  preprintOpen access

  We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of non-stationary bandits without prior knowledge of the underlying non-stationarity. DAL accepts any stationary bandit algorithm as input and augments it with a change detector, enabling applicability to all common bandit variants. Extensive experimentation demonstrates that DAL consistently surpasses current state-of-the-art methods across diverse non-stationary scenarios, including synthetic benchmarks and real-world datasets, underscoring its versatility and scalability. We provide theoretical insights into DAL's strong empirical performance, complemented by thorough experimental validation.

  PublisherOA PDFDOI

• Detection Augmented Bandit Procedures for Piecewise Stationary MABs: A Modular Approach

  arXiv (Cornell University) · 2025-01-02

  preprintOpen access

  Conventional Multi-Armed Bandit (MAB) algorithms are designed for stationary environments, where the reward distributions associated with the arms do not change with time. In many applications, however, the environment is more accurately modeled as being non-stationary. In this work, piecewise stationary MAB (PS-MAB) environments are investigated, in which the reward distributions associated with a subset of the arms change at some change-points and remain stationary between change-points. Our focus is on the asymptotic analysis of PS-MABs, for which practical algorithms based on change detection have been previously proposed. Our goal is to modularize the design and analysis of such Detection Augmented Bandit (DAB) procedures. To this end, we first provide novel, improved performance lower bounds for PS-MABs. Then, we identify the requirements for stationary bandit algorithms and change detectors in a DAB procedure that are needed for the modularization. We assume that the rewards are sub-Gaussian. Under this assumption and a condition on the separation of the change-points, we show that the analysis of DAB procedures can indeed be modularized, so that the regret bounds can be obtained in a unified manner for various combinations of change detectors and bandit algorithms. Through this analysis, we develop new modular DAB procedures that are order-optimal. Finally, we showcase the practical effectiveness of our modular DAB approach in our experiments, studying its regret performance compared to other methods and investigating its detection capabilities.

  PublisherOA PDFDOI

• Harnessing Information in Incentive Design

  ArXiv.org · 2025-09-02

  preprintOpen access

  Incentive design deals with interaction between a principal and an agent where the former can shape the latter's utility through a policy commitment. It is well known that the principal faces an information rent when dealing with an agent that has informational advantage. In this work, we embark on a systematic study of the effect of information asymmetry in incentive design games. Specifically, we first demonstrate that it is in principal's interest to decrease this information asymmetry. To mitigate this uncertainty, we let the principal gather information either by letting the agent shape her belief (aka Information Design), or by paying to acquire it. Providing solutions to all these cases we show that while introduction of uncertainty increases the principal's cost, letting the agent shape its belief can be advantageous. We study information asymmetry and information acquisition in both matrix games and quadratic Gaussian game setups.

  PublisherOA PDFDOI

• Risk-Sensitive Security-Constrained Economic Dispatch: Pricing and Algorithm Design

  IEEE Transactions on Power Systems · 2025-07-16

  article

  We propose a risk-sensitive security-constrained economic dispatch (R-SCED) formulation capturing the tradeoff between dispatch cost and resilience against potential line failures, where risk is modeled via the conditional value at risk (CVaR). In the context of our formulation, we analyze revenue adequacy and side payments of two pricing models, one based on nominal generation costs, and another based on total marginal cost including contingencies. In particular, we prove that the system operator's (SO) merchandising surplus (MS) and total revenue are nonnegative under the latter, while under the former the same does not hold in general. We demonstrate that the proposed R-SCED formulation is amenable to decomposition and describe a Benders' decomposition algorithm to solve it. In numerical examples, we illustrate the differences in MS and total revenue under the considered pricing schemes, and the computational efficiency of our decomposition approach.

  PublisherDOI

• Nonparametric Sparse Online Learning of the Koopman Operator

  ArXiv.org · 2025-01-27

  preprintOpen accessSenior author

  The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. Data-driven techniques to learn the Koopman operator typically assume that the chosen function space is closed under system dynamics. In this paper, we study the Koopman operator via its action on the reproducing kernel Hilbert space (RKHS), and explore the mis-specified scenario where the dynamics may escape the chosen function space. We relate the Koopman operator to the conditional mean embeddings (CME) operator and then present an operator stochastic approximation algorithm to learn the Koopman operator iteratively with control over the complexity of the representation. We provide both asymptotic and finite-time last-iterate guarantees of the online sparse learning algorithm with trajectory-based sampling with an analysis that is substantially more involved than that for finite-dimensional stochastic approximation. Numerical examples confirm the effectiveness of the proposed algorithm.

  PublisherOA PDFDOI

• Risk-Sensitive Security-Constrained Economic Dispatch: Pricing and Algorithm Design

  ArXiv.org · 2025-02-19

  preprintOpen access

  We propose a risk-sensitive security-constrained economic dispatch (R-SCED) formulation capturing the tradeoff between dispatch cost and resilience against potential line failures, where risk is modeled via the conditional value at risk (CVaR). In the context of our formulation, we analyze revenue adequacy and side payments of two pricing models, one based on nominal generation costs, and another based on total marginal cost including contingencies. In particular, we prove that the system operator's (SO) merchandising surplus (MS) and total revenue are nonnegative under the latter, while under the former the same does not hold in general. We demonstrate that the proposed R-SCED formulation is amenable to decomposition and describe a Benders' decomposition algorithm to solve it. In numerical examples, we illustrate the differences in MS and total revenue under the considered pricing schemes, and the computational efficiency of our decomposition approach.

  PublisherOA PDFDOI

• Wholesale Market Participation of DERAs: DSO-DERA-ISO Coordination

  IEEE Transactions on Power Systems · 2024-01-12 · 16 citations

  article

  Distributed energy resource aggregators (DERAs) must share the distribution network together with the distribution utility in order to participate in the wholesale electricity markets that are operated by independent system operators (ISOs). We propose a forward auction that a distribution system operator (DSO) can utilize to allocate distribution network access limits to DERAs. As long as the DERAs operate within their acquired limits, these limits define operating envelopes that guarantee distribution network security, thus defining a mechanism that requires no real-time intervention from the DSOs for DERAs to participate in the wholesale markets. Our auctions take the form of robust and risk-sensitive markets with bids/offers from DERAs and utility's operational costs. Properties of the proposed auction, e.g., resulting surpluses of DSO and the DERAs, and the auction prices, along with empirical performance studies, are presented.

  PublisherDOI

Recent grants

• CAREER: Risk-Sensitive Market Design for Power Systems: Scalable Learning and Pricing

  NSF · $500k · 2021–2027

• Collaborative Research: CPS: Medium: Empowering Prosumers in Electricity Markets Through Market Design and Learning

  NSF · $300k · 2020–2024

• Collaborative Research: Dynamic Data Analytics for the Power Grid via Koopman and Perron-Frobenius Operators

  NSF · $204k · 2020–2024

Frequent coauthors

• J. Andreä

  Institut Pluridisciplinaire Hubert Curien

  59 shared

• U. Goerlach

  Institut Pluridisciplinaire Hubert Curien

  55 shared

• D. Blöch

  Institut Pluridisciplinaire Hubert Curien

  55 shared

• E. C. Chabert

  Institut Pluridisciplinaire Hubert Curien

  53 shared

• C. Collard

  Institut Pluridisciplinaire Hubert Curien

  53 shared

• E. Conte

  Institut Pluridisciplinaire Hubert Curien

  52 shared

• P. Verdier

  Institute of High Energy Physics

  47 shared

• S. Perriès

  Institute of Nuclear Physics of Lyon

  46 shared

Labs

• Subhonmesh Bose LabPI