Michael Kellman, Ph.D.
About Michael Kellman, Ph.D.
Michael Kellman, Ph.D., is a Lead Machine Learning Research Engineer at Zendar, where he has worked since 2021. He specializes in solving large-scale inverse problems in medical imaging and microscopy, and has a strong background in electrical engineering and computer science.
Work at Zendar
Michael Kellman, Ph.D., serves as the Lead Machine Learning Research Engineer at Zendar. He has been with the company since 2023, following a prior role as Senior Research Engineer in Machine Learning, which he held for three years starting in 2021. In his current position, Kellman focuses on advancing machine learning techniques and enjoys collaborating with diverse teams to enhance project outcomes.
Education and Expertise
Kellman earned his Doctor of Philosophy (Ph.D.) in Electrical Engineering and Computer Science from the University of California, Berkeley, where he studied from 2015 to 2020. He also holds a Bachelor of Science (BS) in Electrical and Computer Engineering from Carnegie Mellon University, completed from 2011 to 2015. His expertise includes solving large-scale inverse problems for reconstructing high-dimensional image data, particularly in the fields of medical imaging and microscopy.
Background
Michael Kellman's professional background includes various research and internship roles. He worked as a Postdoctoral Researcher at the University of California, San Francisco from 2020 to 2021. Prior to that, he held positions at Carnegie Mellon University as a Laboratory Teaching Assistant and as a Graduate Student Researcher at UC Berkeley. His internship experiences include roles at Google, Fitbit, and the National Institutes of Health.
Achievements
Throughout his career, Kellman has developed data-driven methods aimed at optimizing experimental design and signal priors in computational imaging systems. His work has contributed to advancements in the field of machine learning and imaging technology, showcasing his ability to apply theoretical knowledge to practical challenges.