Projects
Smart and Equitable Public Transit
I am one of the lead investigators of SmartTransit.AI, a multidisciplinary research team comprising computer scientists, civil engineers, social scientists, urban planners, and public transportation experts dedicated to designing innovative solutions for enhancing public transportation operations. Our focus is on improving availability, reliability, effectiveness, and efficiency. Funded through federal grants and in collaboration with partner agencies such as the Chattanooga Area Regional Transportation Authority (CARTA) and Nashville WeGo, we design AI-based algorithms to address integrated multi-modal logistics challenges at scale, incorporating both same-day and long-term future trends. A key aspect of our work is the design of models for real-time energy consumption of mixed-vehicle fleets, including electric, hybrid, and diesel vehicles.
Funded by the National Science Foundation (PI), US Department of Energy (Co-PI), and the state of Tennessee TNGo (Co-PI).
Key Publications
Sophie Pavia, David Rogers, Amutheezan Sivagnanam, Michael Wilbur, Danushka Edirimanna, Youngseo Kim, Philip Pugliese, Samitha Samaranayake, Aron Laszka, Ayan Mukhopadhyay, and Abhishek Dubey. “Deploying Mobility-On-Demand for All by Optimizing Paratransit Services” International Joint Conference on Artificial Intelligence (IJCAI 2024).
Sophie Pavia, David Rogers, Amutheezan Sivagnanam, Michael Wilbur, Danushka Edirimanna, Youngseo Kim, Ayan Mukhopadhyay, Philip Pugliese, Samitha Samaranayake, Aron Laszka, and Abhishek Dubey. “SmartTransit.AI: A Dynamic Paratransit and Microtransit Application.” International Joint Conference on Artificial Intelligence (IJCAI 2024 Demonstration Track)
Jose Paolo Talusan, Chaeeun Han, Ayan Mukhopadhyay, Aron Laszka, Dan Freudberg and Abhishek Dubey. “An Online Approach to Solving Public Transit Stationing and Dispatch Problem.” International Conference in Cyber-Physical Systems (ICCPS 2024) (Acceptance rate: 28.2%) [Best Paper Award]
Amutheezan Sivagnanam, Salah Kadir, Ayan Mukhopadhyay, Philip Pugliese, Abhishek Dubey, Samitha Samaranayake, Aron Laszka, “Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit,” International Joint Conference on Artificial Intelligence (IJCAI 2022).
Sophie Pavia, J. Carlos Martinez Mori, Aryaman Sharma, Philip Pugliese, Abhishek Dubey, Samitha Samaranayake, and Ayan Mukhopadhyay, “Designing Equitable Transit Networks”, ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 2023) [INFORMS Diversity, Equity, and Inclusion (DEI) Student Paper Finalist]
Michael Wilbur, Ayan Mukhopadhyay, Sayyed Vazirizade, Philip Pugliese, Aron Laszka, Abhishek Dubey, “Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-Task and Inductive Transfer Learning”, European Conference on Machine Learning (ECML 2021).
ADVISER: Resource Allocation for Improving Maternal and Infant Health
I was the PI for the "ADVISER: AI-driven Vaccination Intervention Optimizer." Over the last three years, I have collaborated with HelpMum, a non-profit agency based in Nigeria, and Google AI for Social Good to optimize the allocation of health interventions under limited resources. ADVISER is based on designing machine learning models to forecast immunization outcomes and algorithmic approaches for solving large-scale mathematical programs for resource allocation. ADVISER is currently deployed in thirteen local governments in Nigeria, improving immunization outcomes by over 30%.
Funded by Google AI for Social Good (PI) and the Patrick J McGovern Foundation (PI).
Key Publications
Kehinde Opadele, Abdul Ruth, Afolabi Bose, Vir Parminder, Namblard Corinne, Mukhopadhyay Ayan, and Adereni Abiodun. “Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence for Child Vaccination Uptake in Nigeria,” AAAI Conference on Artificial Intelligence (AAAI 2024).
Vineet Nair, Kritika Prakash, Michael Wilbur, Aparna Taneja, Corrine Namblard, Oyindamola Adeyemo, Abhishek Dubey, Abiodun Adereni, Milind Tambe, Ayan Mukhopadhyay, “ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria,” International Joint Conference on Artificial Intelligence (IJCAI 2022). [Best Paper Award]
Robust and Adaptive Decision-Making for Non-Stationary CPS
A major thrust of my research involves designing adaptive algorithms for decision-making under uncertainty. In many real-world applications, agents must make sequential decisions in environments where conditions are subject to change due to various exogenous factors. These non-stationary environments pose significant challenges to traditional decision-making models, which typically assume stationary dynamics. Non-stationary Markov decision processes (NS-MDPs) offer a framework to model and solve decision problems under such changing conditions. I work on designing approaches to detect such changes and then adapt to them online.
Funded by the National Science Foundation (Co-Investigator) and DARPA.
Key Publications
Luo, Baiting, Zhang, Yunuo, Dubey, Abhishek, and Mukhopadhyay, Ayan. “Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes.” International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2024).
Pettet, Ava, Zhang, Yunuo, Luo, Baiting, Wray, Kyle, Baier, Hendrik, Laszka, Aron, Dubey, Abhishek, and Mukhopadhyay, Ayan. “Decision Making in Non-Stationary Environments with Policy-Augmented Search.” International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2024).
Baiting Luo, Shreyas Ramakrishna, Ava Pettet, Christopher Kuhn, Gabor Karsai, Ayan Mukhopadhyay, “Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber-Physical Systems,” International Conference on Cyber-Physical Systems (ICCPS 2022).
Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel Kochenderfer, Milind Tambe, Yevgeniy Vorobeychik, “Robust Spatio-Temporal Incident Prediction,” Conference on Uncertainty in Artificial Intelligence (UAI 2020).
Multi-Agent Systems for Emergency Response
Planning and preparation in anticipation of urban emergency incidents are critical because of the alarming extent of the damage such incidents cause and the sheer frequency of their occurrence. From the perspective of stochastic control processes, proactive incident stationing and response are semi-Markovian decision processes that are computationally difficult to solve. Over the past six years, my research on algorithmic approaches to ERS has developed proactive stationing and principled dispatch strategies to reduce overall response times.
Funded by the National Science Foundation (lead student during PhD) and Tennessee Department of Transportation (Co-PI).
Key Publications
Amutheezan Sivagnanam, Ava Pettet, Hunter Lee, Ayan Mukhopadhyay, Abhishek Dubey, and Aron Laszka. “Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing.” International Conference on Machine Learning (ICML 2024)
Geoffrey Pettet, Ayan Mukhopadhyay, Mykel Kochenderfer, Abhishek Dubey, “Hierarchical Planning for Resource Allocation in Emergency Response Systems,” ACM/IEEE Conference on Cyber-Physical Systems (ICCPS 2021). [One of the best papers, TCPS Special Issue Invite]
Yasas Senarath, Ayan Mukhopadhyay, Sayyed Mohsen Vazirizade, Hemant Purohit, Saideep Nannapaneni, Abhishek Dubey, “Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services,” International Conference on Data Mining (ICDM 2021).
Geoffrey Pettet, Ayan Mukhopadhyay, Mykel Kochenderfer, Yevgeniy Vorobeychik, Abhishek Dubey, “On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities,” Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2020).
Ayan Mukhopadhyay, Zilin Wang, Yevgeniy Vorobeychik, “A Decision-Theoretic Framework for Emergency Responder Dispatch,” Conference on Autonomous Agents and MultiAgent Systems. (AAMAS 2018).
Ayan Mukhopadhyay, Yevgeniy Vorobeychik, Abhishek Dubey, Gautam Biswas, “Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model,” Conference on Autonomous Agents and MultiAgent Systems. (AAMAS 2017).
Chao Zhang, Victor Bucarey, Ayan Mukhopadhyay, Arunesh Sinha, Yundi Qian, Yevgeniy Vorobeychik, Milind Tambe, “Using abstractions to solve opportunistic crime security games at scale.” Conference on Autonomous Agents & Multiagent Systems (AAMAS 2016).
Fair and Interpretable Decision-Making
As decision-making approaches increasingly use large volumes of data and complex neural networks, we must design equitable and fair approaches. At the same time, we must design interpretable algorithms to ensure that domain experts and end-users can trust and understand how algorithms optimize decisions. While much prior work has focused on exploring the fairness and explainability of learning-based approaches, decision-making has remained largely unexplored. I work to design algorithmic approaches for sequential decision-making and combinatorial optimization that are fair and explainable.
Key Publications
Ziyan An, Hendrik Baier, Abhishek Dubey, Ayan Mukhopadhyay, Meiyi Ma. “Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic” European Conference on Artificial Intelligence (ECAI 2024)
Sophie Pavia, J. Carlos Martinez Mori, Aryaman Sharma, Philip Pugliese, Abhishek Dubey, Samitha Samaranayake, and Ayan Mukhopadhyay, “Designing Equitable Transit Networks”, ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 2023)[INFORMS Diversity, Equity, and Inclusion (DEI) Student Paper Finalist]
Yiqi Zhao, Ziyan An, Xuqing Gao, Ayan Mukhopadhyay, Meiyi Ma, “Fairguard: Harness Logic-based Fairness Rules in Smart Cities,” ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI 2023)