About Me

I am a Ph.D. student in the Information Systems Department at University of Maryland, Baltimore County (UMBC), advised by Dr. James Foulds. I am also working as a research assistant in the Foulds Research Group - The Latent Lab.

I was an intern in the wavelet development team at MathWorks Inc., Natick, MA for Summer, 2019. Prior to joining UMBC as a graduate student, I worked in Huawei Technologies (Bangladesh) Ltd. as a Core Network Engineer. Before that, I received my Master’s Degree in Electrical Engineering at University of Dhaka, Bangladesh, supervised by Prof. Zahid H. Mahmood.

Research Interests

My research interests lie in the general area of machine learning and artificial intelligence, particularly in the area of AI Fairness and Ethics. My work is focused on building socially conscious and fair algorithms for machine learning, deep learning, recommender systems and natural language processing.

Recent News

• Our paper Debiasing Career Recommendations with Neural Fair Collaborative Filtering has been accepted to The Web Conference 2021 (formerly known as WWW).
• Our paper was accepted at SIAM International Conference on Data Mining (SDM) 2021. The paper is called Equitable Allocation of Healthcare Resources with Fair Survival Models. Almost all of the reviewers rated the paper as “top 80% of accepted papers at the conference.” Congrats to Kamrun Keya.
• December 2020: Awarded Master of Science in Information Systems from UMBC.
• Our paper on equitable allocation of healthcare resources with fair Cox models was accepted at AAAI/FSS-2020 Artificial Intelligence in Government and Public Sector track. Congrats to Kamrun Keya.
• Our paper, Fair Heterogeneous Network Embeddings, was accepted at 15th International Conference on Web and Social Media (ICWSM 2021).
• Our extended abstract paper on the user study for fair recommender systems was accepted at 8th Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL 2020).
• Our differential fairness paper titled An Intersectional Definition of Fairness was accepted at 36th IEEE International Conference on Data Engineering (ICDE 2020). Here is the link to the Dr. Foulds’ Talk Video and Slides.
• Our paper titled Bayesian Modeling of Intersectional Fairness: The Variance of Bias was accepted at SIAM International Conference on Data Mining (SDM 2020).
• Attended The Promise and the Risk of the AI Revolution Conference, U.S. Naval Academy, MD on October, 2019.
• Attended 37th Annual NAJIS Conference 2019 on October, 2019.
• Code for calculating our differential fairness metric is now available in the AI Fairness 360 toolkit from IBM Research! [Github page]
• Our submission to the NeurIPS 2019 Workshop on Machine Learning with Guarantees was accepted!
• Our submission to the KDD 2019 Social Impact Track, on mitigating bias in social-media based recommender systems, was accepted for oral presentation!
• Attended NAACL 2019, Minneapolis MN on June 3 – June 5, 2019.
• I have accepted an intern position at MathWorks Inc., beginning Summer 2019.
• My paper, “Scalable collapsed inference for high-dimensional topic models” to scale up topic models to 10,000 topics with a single machine, was accepted at 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)!
• My extended abstact paper on stochastic topic model was accepted at the Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL 2018).
• Joined in the Foulds Research Group on Spring 2018 as a graduate research assistant

Academic Services

• Reviewer: ICML 2020, ICTAI 2020.

Honors and Awards

• GSA Professional Development Grant and IS Dept. Travel Grant to participate NAACL 2019.
• NST Fellowship 2014 for M.S. thesis from Ministry of Science and Technology, Bangladesh