Pradeep Ramesh
About Pradeep Ramesh
Pradeep Ramesh is an Associate Director of ML/AI at Tessera Therapeutics and has a strong background in machine learning and biomedical engineering. He has held various positions in prominent institutions, contributing to advancements in therapeutics through deep-learning techniques.
Work at Tessera Therapeutics
Pradeep Ramesh serves as the Associate Director of ML/AI at Tessera Therapeutics, a position he has held since 2023. His role involves leading initiatives that integrate machine learning and artificial intelligence into therapeutic development processes. This position is based in Boston, Massachusetts, and allows for remote work, reflecting the company's flexible work environment.
Current Roles and Responsibilities
In addition to his role at Tessera Therapeutics, Ramesh works as a Machine Learning Consultant at Thermo Fisher Scientific. He has been in this position since 2022, operating remotely from Los Angeles, California. He also holds a position as a Visiting Scholar at Caltech, where he has been since 2022, contributing to ongoing research and academic projects on-site in Pasadena, California.
Previous Experience at Sherlock Biosciences
Ramesh previously worked at Sherlock Biosciences, where he held the position of Principal Machine Learning Scientist from 2022 to 2023. His tenure there followed a role as Senior Machine Learning Scientist from 2021 to 2022. His experience at Sherlock focused on applying machine learning techniques to enhance bioscience applications.
Education and Expertise
Pradeep Ramesh earned his Doctor of Philosophy (PhD) in Bioengineering and Biomedical Engineering from Caltech, where he studied from 2013 to 2019. He also obtained a PhD in Biophysics from the University of California, Berkeley in 2013. His educational background includes a Bachelor of Science (BS) in Engineering Physics/Applied Physics from Caltech. His diverse academic training supports his expertise in molecular and cellular biology, biomedical imaging, and computational modeling.
Research Contributions and Interdisciplinary Approaches
Ramesh has contributed to the advancement of novel therapeutics through the application of deep-learning techniques. His interdisciplinary background enhances his approach to AI/ML projects, allowing him to integrate concepts from molecular biology and engineering. His work emphasizes the importance of computational modeling in the biomedical field.