About Abrar Alam
Dr. Abrar Alam is a Postdoctoral Fellow at the O’Donnell Data Science and Research Computing Institute. His research focuses on the intersection of artificial intelligence, large language models (LLMs), machine learning, and high-performance computing (HPC), with particular emphasis on large-scale AI applications and computational efficiency. At ODSRCI, Dr. Alam investigates LLM-based lossless data compression using transformer architectures integrated with arithmetic coding. This implementation is available through the project’s repository. He also conducts performance and energy-efficiency benchmarking of AI/ML models for deployment on edge-computing devices.
Prior to joining the O’Donnell Institute, Dr. Alam’s research involved designing and building an Online Kernel Clustering framework combining sparse kernel factorization and convex optimization for adaptive clustering of streaming data. His work, published in the journal Pattern Recognition, was subsequently extended through reinforcement learning techniques, including Q-learning, with applications in human activity recognition and hyperspectral image analysis.
Dr. Alam earned his Ph.D. in Electrical Engineering from the University of Texas at Arlington, an M.S. in Electrical Engineering from Stevens Institute of Technology, and a B.S. in Electrical Engineering with a minor in Mathematics from Purdue University Fort Wayne. He also holds a Graduate Certificate in Data Engineering from Stevens Institute of Technology.
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