United States
Kamal is a recent Ph.D. graduate from Carnegie Mellon University, where he worked under the supervision of Prof. Olexandr Isayev. His primary interests lie in computational materials science/chemistry problems, with a focus on algorithm development and machine learning. His PhD research centers on building Machine-learned Interatomic Potentials (MLIPs) and accurately modeling long-range intermolecular interactions in complex molecular systems. He is a self-motivated and spirited person who sees his goals and aspirations with a clear vision and strives hard to complete any task to the best of his capability. He believes in highly support collaborative and synergistic work culture and is driven by challenges. He is actively seeking full-time roles where he can contribute as a Research or Applied Scientist in accelerated materials discovery, data-driven materials design, and high-throughput automated workflows.
Building Machine-learned Interatomic Potentials (MLIPs) and accurately modeling long-range intermolecular interactions in complex molecular systems.
Applied Machine Learning (AML) Summer Research Fellowship '22 Project Supervisor: Dr. Benjamin Nebgen Active learning for rapid interatomic potential development.
Crowdmapping rural Tanzania to fight Female Genital Mutilation through crowdsourcing, open data, and sustainable development efforts.
Project Supervisor: Prof. Dr. Jeremy Richardson Improving Ab initio instanton rate theory using Gaussian process & Kernel ridge regression.
Project Supervisor: Prof. Dage Sundholm Performing Configuration interaction (CI) in locally defined orthonormal orbitals obtained from a numerical finite-element basis, to make low-order CI reach the limit of full.