Hello! I’m Jamil Gafur, a Ph.D. candidate in Computer Science at the University of Iowa, with a strong interest in high-performance computing, scientific computing, and explainable machine learning. As a U.S. citizen, I’ve had the opportunity to explore my passion for these fields and develop skills in neural network optimization and development.
Recently, my research interests have shifted towards explainable ML using generative adversarial attacks and meta-heuristic algorithms. I’m fascinated by the challenges associated with current machine learning techniques, including biased models due to unequal data distribution and the lack of standards for developing neural models. As a result, I’m eager to uncover what’s behind the “black box” design of neural networks and apply this knowledge to other projects.
In terms of education, I obtained a BS in Computer Science with a minor in Business Administration from CUNY: Lehman College, Bronx, NY and an AS in Computer Science from SUNY: Dutchess Community College, Poughkeepsie, NY. Currently, I am pursuing my Ph.D. in Computer Science at the University of Iowa where I have taken several featured classes including Statistical Machine Learning, Scientific Computing and Machine Learning, Design and Implementation of Algorithms, and Optimization Techniques.
Throughout my academic journey, I have been fortunate enough to receive several awards, including the GEM Employer Fellowship Award in Fall 2022 and the U-Iowa Computer Science Grant Award in Fall 2021. These awards have allowed me to further my research and education in the field of computer science.
In terms of technical skills, I have experience programming in Python, Bash/Shell, Java, Fortran 90, and CUDA. I am skilled in using Git, Tensorflow/Keras, deep learning, data visualization, and graph processing.
Additionally, I have expertise in neural network pruning/optimization, parallel workflow processing, adversarial network development, graph convolutional design, and heuristic optimization.
In terms of research experience, I have had the privilege to work as a Graduate (Year-Round) Researcher Intern at the National Renewable Energy Lab in Golden, Colorado, where I applied machine learning techniques to renewable energy and energy efficiency for modular network validation and performance. Additionally, I worked as an intern at Cornell University, applying machine learning approaches to epigenomic datasets for research data generated by the Epigenomics Core Facility.