Nasik Muhammad Nafi is a Postdoctoral Research Associate in the Advanced Computing for Life Sciences & Engineering group at the National Center for Computational Sciences division. Prior to joining ORNL, Nafi obtained his Ph.D. in Computer Science from Kansas State University, Manhattan, KS in 2024. Previously, he received his Masters degree in Computer Science from Kansas State University and Bachelors degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology. During his graduate studies, he worked as an intern at DEKA Research and Development as a Machine Learning Intern and at C2FO as a Data Scientist Intern.
Nafi’s research focuses on improving generalization capability of machine learning models while learning from limited data, unlocking their potential to operate in complex, diverse, and dynamic real-world environments. His research interest broadly lies in the intersection of computer vision and deep reinforcement learning. His Ph.D. dissertation focused on developing algorithmic and architectural strategies to improve generalization in reinforcement learning. In particular, his research introduced efficient mechanisms that enables RL agent to transfer it’s learning to take optimal decisions in previously unseen variations of the tasks. He also worked on analyzing the capability of generative models and developing spatio-temporal feature extraction framework for video processing. His research works are applied to plant disease identification, bee species classification, and risky tackle detection. At ORNL, he is currently working on developing transferable model for medical image segmentation and generalizable model for turbulence modeling problems. He was the best paper award winner at IEEE IWSSIP 2020. He has published conference and workshop papers in multiple venues such as NeurIPS, AAAI, AAMAS, IJCNN, ICMLA.
