Summary
Michael Kellman is a Staff Machine Learning Research Engineer with 11 years of experience at the intersection of electrical engineering, signal processing, and data-driven machine learning. He holds a PhD from UC Berkeley and has applied large-scale inverse problem techniques to reconstruct high-dimensional image data in medical imaging and microscopy, then translated that expertise into industry roles at Zendar. Michael designs optimization- and statistics-driven methods to improve experimental design and signal priors for computational imaging systems, combining theory with practical engineering to deliver robust, production-ready models. A collaborative communicator based in Berkeley, he has moved between academia (UCSF, UC Berkeley) and industry (Google, Fitbit) while mentoring teams and elevating cross-disciplinary projects. Less obvious: he repeatedly bridges hands-on systems work and foundational research, from mentorship and lab teaching to leading ML research engineering at a startup focused on real-world environmental sensing. His personal research portfolio is available at mrkellman.com.
11 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (PhD) Electrical Engineering and Computer Science, Doctor of Philosophy (PhD) Electrical Engineering and Computer Science at University of California, Berkeley
Bachelor of Science (BS) Electrical and Computer Engineering, Bachelor of Science (BS) Electrical and Computer Engineering at Carnegie Mellon University
English