I am an Assistant Professor of Teaching in the Computer Science and Engineering Department at University at Buffalo. My current research focuses on optimal control in complex systems. My interests include social behavior modeling, deep reinforcement learning, multi-agent settings, resource allocation, deep learning, imitation learning, adversarial machine learning, transportation and large-scale social system dynamics.

Teaching

CSE4/546: Reinforcement Learning (senior/graduate)

• Spring 2023
• Fall 2022
• Spring 2022
• Fall 2021
• Spring 2021
• Fall 2020
• Spring 2020
• Fall 2019
• Summer 2019

CSE368: Introduction to Artificial Intelligence (undergraduate)

• Fall 2023 (scheduled)
• Fall 2022
• Summer 2022
• Summer 2020
• Summer 2019

CSE4/574: Introduction to Machine Learing (senior/graduate)

• Fall 2023 (scheduled)
• Spring 2023 Total: 486 students (three sections)
• Spring 2022

CSE705: Recent Advances in Deep Learning & Reinforcement Learning Seminar (graduate)

• Summer 2023
• Summer 2022

CSE616: Multi-agnet Systmes (graduate)

• Fall 2022

EAS595: Fundamentals of AI (graduate)

• Fall 2021

CSE 4/510: Applied Deep Learning (senior/graduate)

• Summer 2020

Selected Publications

• Nitin Kulkarni, Chunming Qiao and Alina Vereshchaka. Optimizing Pharmaceutical and Non-pharmaceutical Interventions during Epidemics. 2022 SBP-BRiMS
• Nitin Kulkarni, Jake Sanders, Sargur Srihari, Chunming Qiao and Alina Vereshchaka. Modeling Priorities in Multi-agent Multi-objective Systems. 2022 SBP-BRiMS
• Alina Vereshchaka. Human-machine Interactions in Multi-agent Reinforcement Learning. AAAI Spring Symposium 2022
• Alina Vereshchaka and Nitin Kulkarni. “Optimization of Mitigation Strategies during Epidemics using Offline Reinforcement Learning.” International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, 2021 (oral presentation)
• Fan Yang, Alina Vereshchaka, Changyou Chen and Wen Dong. “Bayesian Multi-type Mean Field Multi-agent Imitation Learning.” In Advances in Neural Information Processing Systems (NIPS 2020, spotlight presentation)
• Alina Vereshchaka and Wen Dong. "Dynamic Resource Allocation During Natural Disasters Using Multi-agent Environment." International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, 2019 (oral presentation)
• Fan Yang, Alina Vereshchaka and Wen Dong. "Predicting and Optimizing City-Scale Road Traffic Dynamics Using Trajectories of Individual Vehicles", IEEE International Conference on IEEE in Big Data, 2018 (oral presentation)

Research Team

• Nitin Kulkarni, PhD (co-advise with Dr. Chunming Qiao)
• Ankith Bala, MS, Project: Chatbot for course logistics
• Aditya Srinevas Muralidharan, MS, Project: Disaster Classification: Identifying the Causes of Structural Damage through Image Analysis
• Shraddha Bajarang Shekhar, MS, Project: Fairness in Multi-Agent Reinforcement Learning
• Nandini Chinta, MS, Project: Fairness in Multi-Agent Reinforcement Learning

Professional Activities

• Program commitee: Computational and Mathematical Organization Theory (CMOT)
• Program commitee: International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation
• Organizer of Practical Workshops at CSE, University at Buffalo. Topics: Git and Github, presenters, Matplotlib for RL, PyTorch, Project Management, Graph Machine Learning
• Committee member of Student Engagement and Experiential Learning (SEEL)
• Committee member of Teaching Effectiveness Committee (TEC)
• Judge for AI Track for the UB Hackathon and CSE PhD Posters at UB
• Panel Moderator, SBP-BRiMS, 2021
• Organizer, Applications in RL Workshop, University at Buffalo, Spring 2020
• Organizer, Ethics in AI Workshop, University at Buffalo, Fall 2019
• Tutorial on Deep Reinforcement Learning, Alina Vereshchaka and Wen Dong, SBP-BRiMS 2019
• Organizer, UB Reinfrocement Learning Challenge 2019