PI: Alhassan S. Yasin
Dr. Yasin is a Senior Research Scientist at Johns Hopkins University Applied Physics Laboratory and Lecture in Computer Science in the Whiting School of Engineering at Johns Hopkins University. He is also actively consulting and advising industry, government, and academic partners in science and technology. He is a multidisciplinary scientist with expertise in physics, computer science, and engineering. His experience and background are in applied mathematics, theoretical & computational physics, machine learning (ML), artificial intelligence (AI), mechanical and aerospace engineering. Dr. Yasin’s key areas of expertise are in multidisciplinary system design and analysis.
His current research concentration is in theoretical and computational development of new approaches to investigate adaptive machine learning techniques for high-throughput computing and hybrid physics/ ML framework. Also, develop hybrid computational techniques to aid in the exploration of novel approaches in the areas of mathematics, physics, and engineering that implement state-of-the-art computational modeling and programming tools. As well as, investigate thermal and electrical transport from a multi-scale perspective using first-principle calculation and ML approach to aid in the exploration of large design structure studies. Currently researching and developing non-classical approaches to resolve complex systems, biologically inspired, quantum computers, artificial intelligence, etc.
His research involves developing algorithms that allow computer programs to automatically improve through experience. He also has interest in developing the science and engineering of making computer systems behave in ways that until recently, were thought to require human intelligence. His research aims to leverage and understand how multidisciplinary perspectives can further advance novel approaches to system design.
His classes and work deal with ethics, human and machine interactions, and the philosophy and sociology of science in our evolving society. As a lecturer he aims to impart his experience and research associated with the theoretical and computational development of novel approaches to investigate adaptive physics and machine learning techniques to students. Currently, he teaches two sections of Foundations of Algorithms, one section of Applied Machine Learning, and an Independent Study in Bioinformatics. He works with students to mentor, advise, and foster their curiosity in academia and throghout their career.