About me

I received my Ph.D. from University of Maryland (UMD), College Park, under the supervision of Prof. Sanghamitra Dutta. My research focuses on Trustworthy and Reliable Machine Learning, with recent work on uncertainty quantification, knowledge distillation, and long-context understanding in LLMs. I’ve also studied AI safety related topics, including alignment, explainability, robustness, and fairness. I previously interned as an Applied AI Researcher at Capital One and an AI Research Intern at JPMorgan AI Research, where I focused on improving the reliability of LLMs in high-stakes domains.

At UMD, I'm a recipient ECE Ph.D. Distinguished Dissertation Fellowship Award (2025), the Graduate School Ann G. Wylie Dissertation Fellowship (2025), and the George Corcoran Memorial Award (2024) for Excellence in Teaching. Additionally, I have received the Outstanding Teaching Assistant (TA) Award (twice) and serve as a TA Training and Development Fellow (2022–2024),  a program aimed at creating a cohort of leaders in the TA community.

Prior to joining UMD ECE, I graduated summa cum laude (High Honors) with a B.Sc. in Electrical and Electronics Engineering from Isik University, Istanbul, Turkey, where I was the overall best-graduating student and Valedictorian for the class of 2020 (GPA: 3.98/4.00).

Research Interests

• Trustworthy and Reliable AI/ML

• Large Language Models

• Reinforcement Learning  

• Explainability/Interpretability  

• Privacy/Fairness 

• Information Theory

Publications

Updated list of my publications on Google Scholar.  

New Papers

• F. Hamman, P. Dissanayake, Yanjun Fu, S. Dutta, "Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations,” Accepted at Neurips 2025.

• F. Hamman, P. Dissanayake, S. Mishra, F. Lecue, S. Dutta, "Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs”, Accepted at the International Conference on Machine Learning (ICML 2025).

• F. Hamman, C. Zhu, A. Kumar, X. Peng, S. Dutta, D. Liu, A. Samuel, “Improving Consistency in Retrieval-Augmented Systems with Group Similarity Rewards,” NeurIPS 2025 Workshop on Reliable ML from Unreliable Data.

• Yanjun Fu, F. Hamman, S. Dutta, "T-SHIRT: Token-Selective Hierarchical Data Selection for Instruction Tuning,”  Accepted at Neurips, 2025.

Conference Publications

• F. Hamman, P. Dissanayake, S. Mishra, F. Lecue, S. Dutta, "Quantifying Prediction Consistency Under Model Multiplicity in Tabular LLMs”, Accepted at the International Conference on Machine Learning (ICML 2025).

• F. Hamman,  S. Dutta,  "Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition," International Conference on Learning Representations (ICLR 2024).          (Also Spotlight Talk ✨ at ISIT 2024 Workshop on Information-Theoretic Methods for Trustworthy Machine Learning)         (Also Invited to feature in Montreal AI Ethics Brief)

•  F. Hamman, E. Noorani, S. Mishra, D. Magazzeni, S. Dutta, "Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees," In: International Conference on Machine Learning (ICML 2023).

• F. Hamman,  S. Dutta,  "A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition,"  IEEE International Symposium on Information Theory (ISIT 2024). 

• F. Hamman, J. Chen, S. Dutta, “Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity,” In: ACM Conference on Fairness, Accountability, and Transparency (FAccT 2023) | Previously in NeurIPS 2022 Algorithmic Fairness through the Lens of Causality and Privacy Workshop. 

• P. Dissanayake,  F. Hamman, B. Halder, Q. Zhang, I. Sucholutsky, S. Dutta, “Quantifying Knowledge Distillation using Partial Information Decomposition,” Accepted at the International Conference on Artificial Intelligence and Statistics (AISTATS 2025).

• E. Noorani, P. Dissanayake, F. Hamman, S. Dutta. “Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures.”Accepted at International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025).

Journal Publications 

• F. Hamman, E. Noorani, S. Mishra, D. Magazzeni, and S. Dutta, "Robust Algorithmic Recourse Under Model Multiplicity with Probabilistic Guarantees,” Journal on Selected Areas in Information Theory: Information-Theoretic Methods for Trustworthy Machine Learning (JSAIT 2024).

• B. Halder, F. Hamman, P. Dissanayake, Q. Zhang, I. Sucholutsky, S. Dutta, "Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition,"  Transactions on Machine Learning Research (TMLR), 2025.

• S. Dutta,  F. Hamman, "A Review of Partial Information Decomposition in Algorithmic Fairness and Explainability, "  Entropy Journal: Information Theory, Probability, and Statistics. Special Issue: Fairness in Machine Learning, 2023.

Other Papers (Peer-Reviewed Workshops/Preprints)

• F. Hamman,  S. Dutta,  "Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Information Theory," In: Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities Workshop at ICML 2023. (Also Invited to feature in Montreal AI Ethics Brief)

• B. Halder, F. Hamman, P. Dissanayake, Q. Zhang, I. Sucholutsky, S. Dutta, "Quantifying Spuriousness of Biased Datasets Using Partial Information Decomposition,"  Workshop on Data-centric ML Research: Datasets for Foundation Models, ICML 2024. 

• P. Dissanayake, F. Hamman, B. Halder, Q. Zhang, I. Sucholutsky, S. Dutta, "Formalizing Limits of Knowledge Distillation Using Partial Information Decomposition,"  Machine Learning and Compression Workshop, Neurips 2024.

Honors & Awards

• ECE Ph.D. Distinguished Dissertation Fellowship (2025)

• Graduate School Ann G. Wylie Dissertation Fellowship (2025)

• ECE George Corcoran Memorial Award in Teaching (2023)

• Outstanding Teaching Assistant (TA) Award (Fall 2022)

• Outstanding Teaching Assistant (TA) Award (Spring 2023)

• TA Training & Development Fellowship (2022-2024)

Services & Outreach

• Reviewer ICML 2023, AAAI 2025, ICLR 2024 & 2025, ISIT 2024 & 2025, NeurIPS 2023 & 2025, FAccT 2023, NeurIPS 2022 (Fairness Workshop), ICAIF 2023 & 2024 (Explainability Workshops)

• Co-organizer for AI Alignment Workshop at UMD

• Conference Volunteer ICML 2023, FAccT 2022

Education

• Ph.D/MS. In Electrical and Computer Engineering at the University of Maryland, College Park, USA (2020 - 2025)

• B.Sc.  in Electrical and Electronics Engineering at Isik University, Istanbul, Turkey. 

GPA: 3.98/4.00 (2016 - 2020)