Candidates for the multiple biomedical and data science faculty position openings in the College of Computing will be visiting campus this semester, including Neerav Kaushal.
Bio
Dr. Neerav Kaushal is currently a Deep Learning Scientist II at the Flagship Pioneering ecosystem of companies in Boston, where he applies machine and deep learning algorithms to discover and design novel small molecule, RNA, and nanoparticles-based drugs for enhanced delivery.
He earned his Master in Physics from Panjab University and later his PhD in Engineering Physics from Michigan Technological University, where he worked at the intersection of machine learning and cosmology to study and simulate the evolution of large-scale structure of the Universe.
In addition to drug discovery and cosmology, his interdisciplinary research extends across multiple fields, including astroparticle physics, where he worked on relativistic image doubling; geoinformation sciences, where he developed software pipelines to streamline NASA satellite data products; and structural biology, where he worked on the optimization of novel RNA components for functional enhancement.
Candidate: Neerav Kaushal
Dates of visit: January 20-21, 2025
Abstract
Accelerating Drug Discovery with Machine Learning
The design space of drug-like small molecules is of the order of 10^63. Optimizing this parameter space is a computationally intensive and near impossible challenge because it is large, discrete, and unstructured. However, recent advancements in machine learning, particularly deep learning, have enabled the development of continuous, data-driven molecular representations, transforming complex molecular structures into machine-readable formats. In this talk, I will discuss how I conceptualize and build machine and deep learning frameworks capable of understanding, navigating, and optimizing this immense design space to discover and design novel drug candidates. I will also discuss how I leverage generative machine learning to generate novel RNAs optimized for specific functions. The foundational models grasp the chemical language of drugs and the biological language of RNAs, leading to the development of fine-tuned models that accelerate drug discovery and delivery. The recent application of machine learning to drug discovery benefits pharmaceutical researchers, chemists, and computational biologists by speeding up and optimizing the process, ultimately benefiting patients through the development of innovative and targeted therapies. In addition, I will discuss my research background and future research plans.
About the College of Computing
The Michigan Tech College of Computing, established in 2019, is the first academic unit in Michigan dedicated solely to computing, and one of only a handful such academic units in the United States. The college is composed of two academic departments. The Computer Science department offers four bachelor of science programs in computer science, cybersecurity, data science, and software engineering; four master of science programs in applied computer science, computer science, cybersecurity, and data science; and a doctoral program in computer science. The Applied Computing department offers four bachelor of science programs in cybersecurity, electrical engineering technology, information technology, and mechatronics; two master of science programs in health informatics and mechatronics; and a doctoral program is in computational science and engineering.
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