About || News || Projects || Publications || Softwares || Blogs
Photo of saad

Saaduddin Mahmud

I am a third-year PhD student working under the guidance of Professor Shlomo Zilberstein at UMass Amherst. My doctoral research is centered around enhancing the safety, compliance, and explainability of sequential decision-making systems. Over the course of my academic journey, I have developed a diverse skill set in relevant areas such as Reinforcement Learning (RL), Learning from Demonstrations (LfD), Explanation Generation, Shared Autonomy, and Continual Learning. Currently, my focus lies on two intriguing topics: learning discrete representations such as Automata, Binary Decision Diagrams, and Tree structures for transparent and incremental learning, and developing sequential perturbation-based explanations for RL agents. I am actively seeking a research internship opportunity in the aforementioned domains for the summer of 2024. For more detailed information about my qualifications and experiences, you can access my CV here: [CV].

News:

PhD Research:

  1. Saaduddin Mahmud, Marcell VazquezChanlatte, Stefan Witwicki and Shlomo Zilberstein. Explaining the Behavior of POMDP-based Agents Through the Impact of Counterfactual Information. (AAMAS 2024).
  2. Connor Basich*,Saaduddin Mahmud*, and Shlomo Zilberstein. Learning Constraints on Autonomous Behavior from Proactive Feedback (IROS 2023).
  3. Saaduddin Mahmud, Sandhya Saisubramanian, and Shlomo Zilberstein. Explanation-Guided Reward Alignment.(IJCAI 2023). [IJCAI]
  4. Saaduddin Mahmud, Samer B. Nashed, Claudia V. Goldman, and Shlomo Zilberstein. Estimating Causal Responsibility for Explaining Autonomous Behavior(EXTRAAMAS-2023 @ AAMAS-2023). [SPRINGER]
  5. Saaduddin Mahmud*, Connor Basich*, and Shlomo Zilberstein. Semi-Autonomous Systems with Contextual Competence Awareness(AAMAS 2023). [ACM]
  6. Saaduddin Mahmud, Sandhya Saisubramanian, and Shlomo Zilberstein. REVEALE: A Framework for Reward Verification and Learning.(SafeAI @ AAAI-23, Best Paper Award Nomination (Top 4)). [CEUR-WS]
  7. Samer B. Nashed, Saaduddin Mahmud, Claudia V. Goldman, and Shlomo Zilberstein. Causal Explanations for Sequential Decision Making Under Uncertainty(AAMAS 2023 (Ext. Abs.) and Under Review for JAIR). [ARXIV]

Undergrad Research:

  1. Saaduddin Mahmud, Md. Mosaddek Khan, Moumita Choudhury, Long Tran-Thanh, and Nicholas R. Jennings. Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs. In the proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020). [ARXIV] [IJCAI]
  2. Saaduddin Mahmud, Moumita Choudhury, Md. Mosaddek Khan, Long Tran-Thanh, and Nicholas R. Jennings. AED: An Anytime Evolutionary DCOP Algorithm. In the proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2020). [ARXIV] [IFAAMAS] [ACM]
  3. Moumita Choudhury, Saaduddin Mahmud, and Md. Mosaddek Khan. A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems. In the proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). [AAAI]
  4. Saaduddin Mahmud and Moumita Choudhury (Equal Contribution). Applying Population-Based Algorithms to Solve Large (F)DCOPs. Undergraduate Thesis, Department of Computer Science and Engineering, University Of Dhaka, 2020.
  5. Rafid Amir Mahmud, Fahim Faisal, Saaduddin Mahmud, and Md. Mosaddek Khan. A Simulation-Based Online Planning Algorithm for Multi-Agent Cooperative Environments. [AAMAS 2022 (Ext. Abs.)] [ARXIV]

Softwares:

  1. AL.GO: A project written in Java to visualize well-known algorithms and help students learn them faster. [Youtube, 2017]
    • Step-by-step algorithm visualizer.
    • Contains codes, and problem links on specific topics to help students learn faster.
  2. MuSyc: An android application for music synchronization across mobile devices. [Github, 2017]
    • Music synchronization across different mobiles.
    • Social-network for sharing music.
  3. EasyML: A web application written in Python for automated data visualization and classification. [Github, 2018]
    • Automated data visualization and classification.
    • Fast hyper-parameter optimization for different classifiers.

Blogs: