AI for Decisions That Shape Communities

What's Happening at SCOPE Lab

Use-Inspired Projects

Recent Publications

1. V. Nath, F. Liu, G. He, D. Rogers, A. Chhokra, J. P. Talusan, M. Ma, A. Mukhopadhyay, and A. Dubey, SchoolRide: A Data-Driven Platform for School Bus Disruption Management, in Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026.

   Summary BibTeX Abstract PDF

   @inproceedings{iccps2026_schoolride,
     author = {Nath, Vakul and Liu, Fangqi and He, Guocheng and Rogers, David and Chhokra, Ajay and Talusan, Jose Paolo and Ma, Meiyi and Mukhopadhyay, Ayan and Dubey, Abhishek},
     title = {SchoolRide: A Data-Driven Platform for School Bus Disruption Management},
     year = {2026},
     booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
     location = {Saint Malo, France},
     keywords = {school transportation, disruption management, vehicle routing, optimization, cyber-physical systems, transit operations, real-time decision-making},
     note = {Acceptance rate: 28\%; Short Paper; Track: Systems and Applications},
     series = {HSCC/ICCPS '26},
     what = {SchoolRide is a comprehensive cyber-physical system platform designed for school bus disruption management and operational resilience. The system integrates live telemetry, real-time status collection, and dynamic bus status monitoring to detect and respond to disruptions such as vehicle breakdowns, traffic congestion, and driver absences. Using an integrated pipeline that combines baseline routing with travel-time prediction and constrained optimization, SchoolRide automatically recomputes routing plans when disruptions occur. The platform serves as a testbed for evaluating data-driven optimization strategies for real-world school transportation systems with practical constraints.},
     why = {School transportation is a societal-scale transportation cyber-physical system serving 26 million students daily, yet it remains vulnerable to operational disruptions despite strict schedules and regulations. Most existing disruption management relies on manual coordination, while SchoolRide advances the state-of-the-art by demonstrating that systematic, data-driven optimization can enhance operational resilience at realistic scale. This work is innovative because it balances competing objectives—student service quality (waiting time, delays, schedule adherence) with operational efficiency—while respecting institutional constraints and preserving privacy through synthetic data generation.},
     results = {Experiments on synthetic benchmarks and real district data demonstrate strong performance and scalability of the SchoolRide optimization approach. The AdVIns insertion heuristic consistently outperforms baseline human-intuition policies on student-centered metrics, achieving substantially lower average stop and school delays. Across large-scale synthetic instances and real district scenarios, the system effectively handles realistic disruption patterns while generating high-quality rerouting solutions that balance feasibility with optimality.},
     project_tags = {transit, CPS, planning}
   }
   

   As a societal-scale transportation Cyber-Physical System (CPS), school transportation integrates large-scale physical operations with cyber components for planning and control under uncertainty. Despite its scale and societal importance, the system remains vulnerable to operational disruptions such as vehicle breakdowns, road closures, traffic congestion, and driver absences. This work demonstrates how data-driven optimization can enhance operational resilience in a real-world school transit context. To advance research in this domain, we introduce SchoolRide, a platform developed in close collaboration with a school district in the southern United States. SchoolRide serves as a comprehensive testbed for studying and evaluating robust operational policies for disruption management, enabling systematic investigation of strategies under realistic data and operational constraints. We design an integrated pipeline for dynamic bus status collection and formulate the School Bus Disruption Management (SBDM) problem as a combinatorial optimization task that replans routes based on predefined schedules, real-time status, and disruption events. The framework balances student service quality (e.g., waiting time and school delays) with operational efficiency (e.g., route adjustments and driver workload). We explore heuristic and optimization-based approaches that leverage historical disruption logs from the partner district to proactively replan routes and evaluate their performance using synthetic data generated from real-world operational records to protect privacy. The generated synthetic datasets will be released to facilitate future research in this domain. Our approach outperforms current operational policies, effectively preserving service quality while reducing disruptions and workload.

2. H. Hu, R. Sen, J. P. Talusan, A. Dubey, A. Laszka, and S. Samaranayake, Column Generation for the Micro-Transit Zoning Problem. 2026.

   Summary BibTeX Abstract PDF

   @misc{hu2026columngenerationmicrotransitzoning,
     author = {Hu, Hins and Sen, Rishav and Talusan, Jose Paolo and Dubey, Abhishek and Laszka, Aron and Samaranayake, Samitha},
     title = {Column Generation for the Micro-Transit Zoning Problem},
     year = {2026},
     eprint = {2603.07821},
     archiveprefix = {arXiv},
     primaryclass = {math.OC},
     url = {https://arxiv.org/abs/2603.07821},
     keywords = {micro-transit, zoning, column generation, combinatorial optimization, urban mobility, demand-responsive transit, public transportation},
     what = {This paper generalizes the Micro-Transit Zoning Problem to incorporate a global budget constraint on operational costs rather than a fixed limit on the number of zones. The work reformulates the problem into a Column Generation framework where candidate zones are generated iteratively through a pricing subproblem, and develops a scalable pricing heuristic that replaces exact integer programming with a greedy node-addition strategy. The approach is validated on real-world mobility data from five major U.S. cities including Chattanooga, where CARTA provided origin-destination trip data.},
     why = {Micro-transit services require carefully designed geo-fenced zones to operate effectively, but existing computational methods impose unrealistic constraints like fixed zone counts and suffer from scalability issues in larger cities. The innovation is applying Column Generation — a decomposition technique from operations research — to the zoning problem, which naturally handles the exponentially large space of candidate zones by generating only promising candidates guided by dual variables. This also enables a more realistic global budget formulation that reflects how transit agencies actually plan service areas.},
     results = {Experiments across Miami, Boston, Atlanta, Chattanooga, and Nashville demonstrate that the CG framework produces higher-quality solutions than the state-of-the-art two-phase enumeration approach while scaling more efficiently to larger cities. The pricing heuristic achieves near-optimal solution quality with dramatically reduced computation time, making the approach practical for real-world deployment. Additional analysis provides parameter tuning guidance for transit agencies adopting the method.},
     project_tags = {transit, planning}
   }
   

   Along with the rapid development of new urban mobility options like ride-sharing over the past decade, on-demand micro-transit services stand out as a middle ground, bridging the gap between fixed-line mass transit and single-request ride-hailing, balancing ridership maximization and travel time minimization. However, effective operation of micro-transit services requires planning geo-fenced zones in advance, which involves solving a challenging combinatorial optimization problem. Existing approaches enumerate candidate zones first and select a fixed number of optimal zones in the second step. In this paper, we generalize the Micro-Transit Zoning Problem (MZP) to allow a global budget rather than imposing a size limit for candidate zones. We also design a Column Generation (CG) framework to solve the problem and several pricing heuristics to accelerate computation. Extensive numerical experiments across major U.S. cities demonstrate that our approach produces higher-quality solutions more efficiently and scales better in the generalized setting.

3. A. Dubey, T. T. Johnson, X. Koutsoukos, B. Luo, D. M. Lopez, M. Maroti, A. Mukhopadhyay, N. Potteiger, S. Serbinowska, D. Stojcsics, Y. Zhang, and G. Karsai, Toward Assured Autonomy Using Neurosymbolic Components and Systems, in Neurosymbolic AI, John Wiley & Sons, Ltd, 2026, pp. 89–118.

   Summary BibTeX Abstract DOI

   @inbook{dubey2026neurosymbolic,
     author = {Dubey, Abhishek and Johnson, Taylor T. and Koutsoukos, Xenofon and Luo, Baiting and Lopez, Diego Manzanas and Maroti, Miklos and Mukhopadhyay, Ayan and Potteiger, Nicholas and Serbinowska, Serena and Stojcsics, Daniel and Zhang, Yunuo and Karsai, Gabor},
     title = {Toward Assured Autonomy Using Neurosymbolic Components and Systems},
     booktitle = {Neurosymbolic AI},
     publisher = {John Wiley \& Sons, Ltd},
     year = {2026},
     chapter = {4},
     pages = {89-118},
     isbn = {9781394302406},
     doi = {https://doi.org/10.1002/9781394302406.ch04},
     url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781394302406.ch04},
     keywords = {neurosymbolic AI, assured autonomy, UAV, world model, planning, trajectory control, model checking, hybrid systems},
     what = {This book chapter presents how neurosymbolic techniques can implement three core functions of an autonomous UAV system: world model maintenance (updating an internal representation of the environment from sensory inputs), planning (generating waypoints for the vehicle), and trajectory control (producing fine-grain control commands). The components are developed for a UAV mission — localizing a specific object in an urban area — and evaluated in a virtual environment. An assurance technique based on model checking is also presented for verifying neurosymbolic components that combine finite-state control with neural modules.},
     why = {Autonomous systems increasingly rely on neural components for perception and decision-making, but assuring the safety of these components remains a fundamental challenge. Pure neural approaches lack formal guarantees, while pure symbolic approaches cannot handle the complexity of real-world perception. The innovation is decomposing the autonomy stack into neurosymbolic components where each combines learned perception or prediction with symbolic reasoning and constraints, and then applying model checking to verify properties of the resulting hybrid system — providing a principled path toward assured autonomy.},
     results = {The neurosymbolic components successfully implement world model maintenance, subgoal-based planning, and trajectory control for a UAV target localization mission. Evaluation in a virtual urban environment demonstrates that the neurosymbolic architecture achieves mission objectives while enabling formal verification of safety properties through model checking. The chapter documents lessons learned from integrating neural and symbolic components, including the importance of safety constraints in the planning loop and the role of landmark selection in maintaining accurate world models.},
     project_tags = {planning, CPS, scalable AI}
   }
   

   Neurosymbolic techniques are expected to deliver more functionalities and better performance in autonomous systems, but their assurance remains a challenge. There are various roles such components can play in an autonomous vehicle, for instance, world model maintenance, planning, and trajectory control. The world model is an internal representation of the external environment of the vehicle that is continuously updated based on new sensory inputs; the planning component generates waypoints for the vehicle to reach, while the trajectory controller produces the fine-grain control commands for the vehicle’s path. This chapter presents how these three functions can be implemented using neurosymbolic techniques, and presents results and the lessons learned. The components were developed in the context of a UAV executing a specific mission: localization of a specific object in an urban area, and evaluated in a virtual environment. An assurance technique based on model checking is presented that can be applied to a class of neurosymbolic components that include finite-state control with neural components.

4. A. Sivagnanam, A. Mukhopadhyay, S. Samaranayake, A. Dubey, and A. Laszka, Dynamic Vehicle Routing with Prompt Confirmation and Continual Optimization, in Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026.

   Summary BibTeX Abstract PDF

   @inproceedings{iccps2026_prompt_confirmation,
     author = {Sivagnanam, Amutheezan and Mukhopadhyay, Ayan and Samaranayake, Samitha and Dubey, Abhishek and Laszka, Aron},
     title = {Dynamic Vehicle Routing with Prompt Confirmation and Continual Optimization},
     year = {2026},
     booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
     location = {Saint Malo, France},
     keywords = {dynamic vehicle routing, on-demand transportation, prompt confirmation, optimization, stochastic requests, anytime algorithms, reinforcement learning},
     note = {Acceptance rate: 28\%; Regular Paper; Track: Systems and Applications},
     series = {HSCC/ICCPS '26},
     what = {This paper introduces a novel computational approach for dynamic vehicle routing with prompt confirmation of advance requests. The work addresses the problem of on-demand transportation services that must make real-time decisions about accepting or rejecting trip requests while continuously optimizing vehicle manifests and routes. The research formulates this as a two-stage optimization problem: first deciding whether to accept or reject incoming requests with immediate response requirements, then continuously improving route plans to accommodate future requests between arrival of consecutive requests.},
     why = {Real-world on-demand transit services face a fundamental challenge: agencies must provide prompt confirmation of whether requests can be accepted, yet future requests are unknown and will influence optimal route plans. Most prior work either provides immediate confirmation without optimizing or continuously optimizes without addressing the confirmation timing problem. This work is innovative because it bridges this gap by combining quick insertion search for rapid decision-making with continuous optimization, enabling both high service rates and operational efficiency while managing computational constraints.},
     results = {The proposed computational approach demonstrates significantly better trade-offs between confirmation timeliness and service rate compared to existing methods on real-world and synthetic problem instances from a public transit agency. The anytime algorithm with continuous optimization provides prompt confirmation while also improving subsequent route plans, achieving higher service rates than approaches that simply optimize without considering confirmation requirements.},
     project_tags = {transit, planning}
   }
   

   Transit agencies that operate on-demand transportation services have to respond to trip requests from passengers in real time, which involves solving dynamic vehicle routing problems with pick-up and drop-off constraints. Based on discussions with public transit agencies, we observe a real-world problem that is not addressed by prior work: when trips are booked in advance (e.g., trip requests arrive a few hours in advance of their requested pick-up times), the agency needs to promptly confirm whether a request can be accepted or not, and ensure that accepted requests are served as promised. State-of-the-art computational approaches either provide prompt confirmation but lack the ability to continually optimize and improve routes for accepted requests, or they provide continual optimization but cannot guarantee serving all accepted requests. To address this gap, we introduce a novel problem formulation of dynamic vehicle routing with prompt confirmation and continual optimization. We propose a novel computational approach for this vehicle routing problem, which integrates a quick insertion search for prompt confirmation with an anytime algorithm for continual optimization. To maximize the number requests served, we train a non-myopic objective function using reinforcement learning, which guides both the insertion and the anytime algorithms towards optimal, non-myopic solutions. We evaluate our computational approach on a real-world microtransit dataset from a public transit agency in the U.S., demonstrating that our proposed approach provides prompt confirmation while significantly increasing the number of requests served compared to existing approaches.

5. R. Sen, F. Liu, J. P. Talusan, A. Pettet, Y. Suzue, A. Mukhopadhyay, and A. Dubey, Persistent Vehicle-to-Building Integration via Neuro-Symbolic Control, in Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026.

   Summary BibTeX Abstract PDF

   @inproceedings{iccps2026_pv2b,
     author = {Sen, Rishav and Liu, Fangqi and Talusan, Jose Paolo and Pettet, Ava and Suzue, Yoshinori and Mukhopadhyay, Ayan and Dubey, Abhishek},
     title = {Persistent Vehicle-to-Building Integration via Neuro-Symbolic Control},
     year = {2026},
     booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
     location = {Saint Malo, France},
     keywords = {vehicle-to-building, EV charging, demand charge management, user persistence, neuro-symbolic control, Monte Carlo tree search, model predictive control},
     note = {Acceptance rate: 28\%; Regular Paper; Track: Systems and Applications},
     series = {HSCC/ICCPS '26},
     what = {P-V2B introduces a neuro-symbolic framework for vehicle-to-building charging that incorporates user persistence information alongside technical optimization. The work addresses the persistent user problem where electric vehicles exhibit recurring arrival patterns over time at buildings, enabling buildings to anticipate charging demand and schedule charging strategically. The approach combines a neuro-symbolic control framework integrating Monte Carlo Model Predictive Control with a learned value function to handle both short-horizon feasibility and long-horizon demand-charge prediction, accounting for user behavior patterns while managing real-time constraints.},
     why = {Vehicle-to-building systems present a complex control challenge combining real-time physical constraints with long-horizon stochastic effects of user behavior, where traditional decomposition approaches fail to capture crucial dependencies. The innovation lies in explicitly leveraging user persistence—the observation that EV users exhibit recurring patterns—as a key input alongside technical constraints, enabling more intelligent demand charge management. This bridges control theory and behavioral modeling, providing a principled way to incorporate user behavioral patterns into cyber-physical system optimization.},
     results = {Evaluation on real EV fleet data from a major California manufacturer demonstrates substantial improvements in demand charge reduction and total operating costs compared to both heuristic baselines and prior work that ignore user persistence. The neuro-symbolic framework achieves significant cost savings while ensuring feasibility and full compliance with user charging requirements, validating the effectiveness of persistence-aware control strategies.},
     project_tags = {energy, CPS, planning}
   }
   

   Vehicle-to-Building (V2B) integration is a cyber–physical system (CPS) where Electric Vehicles (EVs) enhance building resilience by serving as mobile storage for peak shaving, reducing monthly peak-power demand charges, supporting grid stability, and lowering electricity costs. We introduce the Persistent Vehicle-to-Building (P-V2B) problem, a long-horizon formulation that incorporates user-level persistence, where each EV corresponds to a consistent user identity across days. This structure captures recurring arrival patterns and travel-related external energy use, common in employee-based facilities with regular commuting behavior. Persistence enables multi-day strategies that are unattainable in single-day formulations, such as over-charging on low-demand days to support discharging during future high-demand periods. Real-time decision making in this CPS setting presents three key challenges: (i) uncertainty in long-term EV behavior and building load forecasts, which causes traditional control and heuristic methods to degrade under stochastic conditions; (ii) inter-day coupling of decisions and rewards, where early actions affect downstream feasible charging and discharging opportunities, complicating long-horizon optimization; and (iii) high-dimensional continuous action spaces, which exacerbate the curse of dimensionality in reinforcement learning (RL) and search-based approaches. To address these challenges, we propose a neuro-symbolic framework that integrates a constraint-based Monte Carlo Model Predictive Control (MC-MPC) layer with a learned Value Function (VF). The MC–MPC enforces physical feasibility and manages environmental uncertainty, while the VF provides long-term strategic foresight. Evaluations using real building and EV fleet data from an EV manufacturer in California demonstrate that the hybrid framework substantially outperforms state-of-the-art baselines, significantly reducing demand charge and total energy costs, while ensuring feasibility and full compliance with user charging requirements.

6. R. Sen, F. Liu, J. P. Talusan, A. Pettet, Y. Suzue, M. Bailey, A. Mukhopadhyay, and A. Dubey, CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty, in Proceedings of the 24th Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2026), 2026.

   Summary BibTeX Abstract PDF

   @inproceedings{sen2026negotiations,
     author = {Sen, Rishav and Liu, Fangqi and Talusan, Jose Paolo and Pettet, Ava and Suzue, Yoshinori and Bailey, Mark and Mukhopadhyay, Ayan and Dubey, Abhishek},
     title = {CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty},
     booktitle = {Proceedings of the 24th Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2026)},
     year = {2026},
     note = {Acceptance rate: 25\%},
     location = {Paphos, Cyprus},
     publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
     series = {AAMAS '26},
     keywords = {vehicle-to-building, energy management, negotiation, demand response, incentive design, semi-Markov decision processes, user flexibility},
     what = {CONSENT is a negotiation framework that enables coordination between EV owners and smart buildings under uncertainty in vehicle-to-building charging systems. The work formulates the V2B charging problem as a semi-Markov decision process with negotiation between buildings and users. The system offers personalized charging options based on user flexibility constraints, building energy efficiency goals, and uncertainty in EV arrival patterns, allowing users to express preferences through bounded SoC and departure time adjustments while buildings optimize charging schedules.},
     why = {Vehicle-to-building energy coordination creates a fundamental conflict: buildings want to minimize peak demand costs while users want convenient, low-cost charging. Existing approaches either assume full system control or fail to capture real-world incentive-based coordination where users voluntarily participate. CONSENT is innovative because it explicitly bridges technical control with behavioral negotiation, using formal constraint handling and incentive design to enable mutually beneficial cooperation without requiring users to fully comply with building preferences.},
     results = {Simulation and user study evaluation demonstrates that CONSENT generates mutually beneficial outcomes: buildings achieve 23% cost reductions compared to baseline approaches while users maintain satisfaction with their charging requirements through negotiated flexibility options. The framework proves effective at aligning disparate objectives through structured negotiation, significantly reducing operational costs while ensuring user voluntary participation.},
     project_tags = {energy, CPS, planning}
   }
   

   The growth of Electric Vehicles (EVs) creates a conflict in vehicle-to-building (V2B) settings between building operators, who face high energy costs from uncoordinated charging, and drivers, who prioritize convenience and a full charge. To resolve this, we propose a negotiation-based framework that, by design, guarantees voluntary participation, strategy-proofness, and budget feasibility. It transforms EV charging into a strategic resource by offering drivers a range of incentive-backed options for modest flexibility in their departure time or requested state of charge (SoC). Our framework is calibrated with user survey data and validated using real operational data from a commercial building and an EV manufacturer. Simulations show that our negotiation protocol creates a mutually beneficial outcome: lowering the building operator’s costs by over 3.5% compared to an optimized, non-negotiating smart charging policy, while simultaneously reducing user charging expenses by 22% below the utility’s retail energy rate. By aligning operator and EV user objectives, our framework provides a strategic bridge between energy and mobility systems, transforming EV charging from a source of operational friction into a platform for collaboration and shared savings.

7. J. Buckelew, J. P. Talusan, V. Sivaramakrishnan, A. Mukhopadhyay, A. Srivastava, and A. Dubey, WENFlow: Wavelet-Enhanced Normalizing Flows for Real-Time Anomaly Detection in CPS, in Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026.

   Summary BibTeX Abstract PDF

   @inproceedings{iccps2026_wenflow,
     author = {Buckelew, Jacob and Talusan, Jose Paolo and Sivaramakrishnan, Vasavi and Mukhopadhyay, Ayan and Srivastava, Anurag and Dubey, Abhishek},
     title = {WENFlow: Wavelet-Enhanced Normalizing Flows for Real-Time Anomaly Detection in CPS},
     year = {2026},
     booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
     location = {Saint Malo, France},
     keywords = {anomaly detection, cyber-physical systems, wavelet transforms, normalizing flows, spatiotemporal analysis, unsupervised learning, interpretability},
     note = {Acceptance rate: 28\%; Regular Paper; Track: Foundations},
     series = {HSCC/ICCPS '26},
     what = {WENFlow proposes a wavelet-enabled normalizing flow framework for unsupervised anomaly detection in high-dimensional cyber-physical systems. The work addresses the challenge of detecting subtle anomalies in systems like power grids and water networks that exhibit complex spatiotemporal patterns. WENFlow combines discrete wavelet transform for multi-scale temporal feature extraction with gated selective self-attention to identify critical sensors, conditional density estimation for likelihood-based anomaly scoring, and interpretable analysis through log-density and feature importance.},
     why = {Real-time anomaly detection in complex infrastructure systems requires capturing both slow operational trends and fast localized disruptions, with scalable robustness to contaminated training data and high dimensionality. Existing methods struggle with spatiotemporal dependencies and contamination from unlogged maintenance events. WENFlow is innovative because it achieves linear complexity scaling with sensor dimensionality through wavelet decomposition and feature-wise attention, providing both accurate anomaly detection and interpretable explanations of which sensors and temporal patterns indicate anomalies.},
     results = {Extensive evaluation on power grid and water treatment benchmarks demonstrates WENFlow achieves superior anomaly detection performance compared to state-of-the-art methods including transformers and density-based approaches, while maintaining linear scaling with system dimensionality and robustness to contaminated training data. The framework provides interpretable analysis through feature importance scores and temporal pattern visualization.},
     project_tags = {CPS, ML for CPS, Explainable AI}
   }
   

   Real-time anomaly detection in high-dimensional data is crucial for ensuring the security of cyber-physical systems (CPS) such as power grids and water distribution networks. Such data commonly take the form of multivariate time series, often unlabeled and necessitating the need for unsupervised detection methods. However, many unsupervised deep learning methods make assumptions about the normality of training data, which is unrealistic in real-world CPS where training data often contain anomalies or rare patterns. Furthermore, these methods rely on inefficient mechanisms to learn spatiotemporal dependencies in the data and scale quadratically with the number of system features. To address these problems, we propose Wavelet-Enhanced Normalizing Flows (WENFlow), an unsupervised deep learning model that identifies anomalies in low-density regions of the data distribution and does not assume access to anomaly-free training data. Notably, WENFlow leverages a scalable Gated Selective Self-Attention mechanism for capturing the most critical spatial dependencies between features. Compared to existing models, WENFlow scales linearly with respect to the number of system features and meets real-time inference requirements for anomaly detection. In our experiments, WENFlow achieves superior AUC scores against baseline methods across datasets with varying anomaly ratios, showcasing its robustness against contaminated training data. We evaluate WENFlow on 2 real-world benchmark datasets and a simulated phasor measurement unit dataset collected from a power grid testbed.

8. Z. An, X. Wang, H. Baier, Z. Chen, A. Dubey, A. Mukhopadhyay, T. T. Johnson, J. Sprinkle, and M. Ma, LogiEx: Logic-Integrated Explanations for Stochastic Planning in Cyber-Physical Systems, in Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026.

   Summary BibTeX Abstract PDF

   @inproceedings{iccps2026_logiex,
     author = {An, Ziyan and Wang, Xia and Baier, Hendrik and Chen, Zirong and Dubey, Abhishek and Mukhopadhyay, Ayan and Johnson, Taylor T. and Sprinkle, Jonathan and Ma, Meiyi},
     title = {LogiEx: Logic-Integrated Explanations for Stochastic Planning in Cyber-Physical Systems},
     year = {2026},
     booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
     location = {Saint Malo, France},
     keywords = {explainable AI, transit planning, formal logic, large language models, Monte Carlo tree search, knowledge graphs, human-AI interaction},
     note = {Acceptance rate: 28\%; Regular Paper; Track: Systems and Applications},
     series = {HSCC/ICCPS '26},
     what = {LogiEx integrates formal logic and large language models to provide explainable sequential planning for human-centered cyber-physical systems like intelligent transportation. The system combines Monte Carlo Tree Search planning with logical reasoning to generate trustworthy explanations for planning decisions. LogiEx categorizes user queries into three types: those answerable from existing search trees, those requiring human-guided search, and those requiring background knowledge. The framework generates logical evidence supporting planning decisions and translates this into natural language explanations that users can verify.},
     why = {Traditional planning algorithms like Monte Carlo Tree Search achieve strong performance but lack transparency, making their outputs unsuitable for high-stakes CPS applications where users need to understand and trust system decisions. LogiEx is innovative because it bridges the transparency gap by combining stochastic search with formal logic verification, allowing the system to explain not just what actions it recommends but why those actions are justified given domain constraints and objectives. This addresses a critical safety concern in AI-driven CPS.},
     results = {Quantitative evaluation demonstrates LogiEx achieves up to 7.9x higher semantic similarity and 1.7x higher factual consistency compared to LLM-only baselines on explaining transportation planning decisions. User studies validate that the framework provides faithful, consistent explanations that help users understand the planning process while maintaining the ability to ask follow-up questions for deeper reasoning.},
     project_tags = {transit, planning, Explainable AI}
   }
   

   Human-centered cyber-physical systems (CPS), such as intelligent transportation services, warehouse robotics operated by human supervisors, and healthcare infrastructures involving clinicians and medical staff, increasingly rely on Artificial Intelligence (AI)-driven sequential decision-making under uncertainty. However, the lack of transparent reasoning in these systems limits trust, verifiability, and human oversight. This challenge is particularly acute for planning algorithms like Monte Carlo Tree Search (MCTS), whose stochastic search processes are opaque to engineers and operators. To address this gap, we introduce LogiEx, a logic-integrated framework that combines large language models (LLMs) with formal methods to generate trustworthy explanations for planning behavior. LogiEx transforms free-form user queries into logical statements with templated variables, then verifies whether evidence extracted from the decision process aligns with both the environment state and the constraints of the stochastic planning model. This enables grounded explanations across a wide range of user questions—from factual retrieval to comparative reasoning. LogiEx also supports Human-Guided Search (HuGS), allowing users to pose conditional ‘what-if” queries that trigger new, scenario-specific searches, ensuring that humans are not passive observers but active participants who can steer and refine the planning process. We evaluate LogiEx through both quantitative assessments and user studies, finding that it consistently outperforms baselines, achieving up to 7.9 higher semantic similarity (BERTScore) and 1.6 higher factual consistency (FactCC) compared to baseline LLMs, and is the most preferred form of explanation among CPS practitioners.

9. A. B. Zulqarnain, J. P. Talusan, K. Napier, C. Gens, J. Higgs, C. Herndon, A. Mukhopadhyay, and A. Dubey, RESPOND: An Incident-Level Simulation Platform for Fire and EMS Operations, in Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026.

   Summary BibTeX Abstract PDF

   @inproceedings{iccps2026_respond,
     author = {Zulqarnain, Ammar Bin and Talusan, Jose Paolo and Napier, Kelly and Gens, Corey and Higgs, Jennifer and Herndon, Colleen and Mukhopadhyay, Ayan and Dubey, Abhishek},
     title = {RESPOND: An Incident-Level Simulation Platform for Fire and EMS Operations},
     year = {2026},
     booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
     location = {Saint Malo, France},
     keywords = {emergency response, dispatch optimization, facility location, simulation, policy evaluation, urban computing, resource allocation},
     note = {Acceptance rate: 28\%; Short Paper; Track: Systems and Applications},
     series = {HSCC/ICCPS '26},
     what = {RESPOND is a modular cyber-physical system platform for urban emergency response that integrates strategic planning and operational dispatch. The platform provides unified simulation infrastructure for evaluating fire/EMS dispatching policies and station placement strategies under realistic constraints. RESPOND enables scenario-driven evaluation by combining real station operations with hypothetical alternatives, incorporating actual incident data, travel times, and service metrics. The system allows researchers to evaluate counterintuitive policies and trade-offs between competing operational objectives like coverage and response time.},
     why = {Urban emergency response is a complex societal-scale CPS involving coordination between multiple agencies, tight operational constraints, and high consequences of failures. Most research remains fragmented and simulation-based, lacking integrated platforms that seamlessly combine strategic planning with operational dispatch evaluation. RESPOND is innovative because it provides a unified testbed for evaluating coupled planning and dispatch decisions at scale, enabling scenario-based exploration of policy alternatives that would be infeasible to test on real systems.},
     results = {Simulation fidelity assessment demonstrates that RESPOND accurately reproduces historical incident distributions when using real data, with MAE of approximately 6 incidents per station validating model accuracy. Counterintuitive scenario analysis shows that adding optimally-placed stations near downtown improves coverage by 10-25 seconds while reducing spatial imbalance, demonstrating the platform's ability to reveal non-obvious policy effects.},
     project_tags = {emergency, transit, planning, CPS}
   }
   

   Growing urban populations strain fire/Emergency Medical Services (EMS) systems, creating societal-scale concerns where decisions about station siting (strategy) and dispatch policies (operations) unfold in a tightly coupled cyber-physical loop. The core challenge lies in validating different approaches since direct experimentation on real populations is infeasible. Prior efforts address isolated components, treating strategic siting heatmaps and operational dispatch heuristics as separate problems. They lack a unified, incident-level simulator to expose the critical cross-policy trade-offs between siting and dispatch. We present RESPOND (REsponse Simulation Platform for Operations, Navigation, and Dispatch), a modular, incident-level, Operational Decision Support System. RESPOND holistically integrates these previously siloed functions, including: (i) optimal station placement, (ii) apparatus allocation, (iii) dispatch policies, (iv) travel time and service time models, and (v) survival modeling for incident prediction. The platform’s engine replays historical incidents at unit resolution and stress-tests counterfactual futures (e.g., station moves, demand surges). A planner-facing interface surfaces key metrics (SLA compliance, 90th Percentile (P90) response time) for deliberation. Evaluations demonstrate reproduction of observed response patterns and reveal policy trade-offs. The result is a unifying platform that transforms fragmented analysis into an operational decision environment, enabling safe and rigorous evaluation of coupled station placement and dispatch policies through simulation.

10. R. Sen, J. P. Talusan, A. Dubey, A. Mukhopadhyay, S. Samaranayake, and A. Laszka, MoveOD: Synthesizing Fine-Grained Origin–Destination Data for Transportation CPS, in Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026.

    Summary BibTeX Abstract PDF

    @inproceedings{iccps2026_moveod,
      author = {Sen, Rishav and Talusan, Jose Paolo and Dubey, Abhishek and Mukhopadhyay, Ayan and Samaranayake, Samitha and Laszka, Aron},
      title = {MoveOD: Synthesizing Fine-Grained Origin--Destination Data for Transportation CPS},
      year = {2026},
      booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
      location = {Saint Malo, France},
      keywords = {origin-destination synthesis, travel demand, transportation planning, data fusion, Bayesian methods, public datasets, traffic simulation},
      note = {Acceptance rate: 28\%; Short Paper; Track: Systems and Applications},
      series = {HSCC/ICCPS '26},
      what = {MoveOD presents a framework for synthesizing fine-grained origin-destination commute patterns from publicly available datasets by integrating census data, employment records, and road networks. The approach uses Bayesian decomposition to generate minute-level commute trip distributions while preserving spatial and temporal coherence with observed commuting patterns. The framework leverages public data sources including US Census Community Survey, Longitudinal Employer-Household Dynamics, and OpenStreetMap to generate realistic synthetic commute data.},
      why = {High-resolution origin-destination data is essential for transportation planning and traffic management, yet collecting such data through surveys or GPS tracking is expensive and privacy-invasive. Existing synthetic approaches fail to capture temporal and spatial granularity needed for realistic simulation. MoveOD is innovative because it demonstrates how publicly available marginal data can be combined through principled statistical methods to generate detailed, temporally-resolved commute patterns that preserve observed macro-level statistics while enabling microscopic simulations.},
      results = {Validation on Hamilton County, Tennessee data demonstrates that the calibrated MoveOD approach accurately reproduces observed census commute patterns while generating realistic minute-level departure time distributions. The framework achieves alignment with ACS travel time margins through careful calibration, enabling fast synthetic data generation suitable for any US county and providing a reusable tool for transportation research.},
      project_tags = {transit, planning}
    }
    

    High-resolution origin–destination (OD) tables are critical to cyber-physical transportation systems, enabling realistic digital twins, adaptive routing strategies, signal timing optimization, and demand-responsive mobility services. However, such OD data is rarely available outside a small number of data-rich metropolitan regions. We introduce MoveOD, an open-source pipeline that synthesizes publicly available datasets to generate fine-grained commuter OD flows with spatial and temporal departure distributions for any U.S. county. MoveOD fuses American Community Survey travel-time and departure distributions, Longitudinal Employer–Household Dynamics (LODES) residence–workplace flows, OpenStreetMap (OSM) road networks, and building footprint data. Our approach ensures consistency with observed commuter totals, workplace employment distributions, and reported travel durations. MoveOD is integrated with a transportation digital twin, enabling end-to-end CPS experimentation. We demonstrate the system in Hamilton County, Tennessee, generating approximately 150,000 synthetic daily trips and evaluating routing algorithms in a live dashboard.