Michael R. Kellman

I am a PhD graduate student at UC Berkeley in the EECS department, where I work on signal processing, computational sensing, compressive sensing, and inverse problems. I am advised by Prof. Laura Waller and Prof. Michael Lustig and am funded by NSF GRFP. I am an enthusiast of engineering, science, food, and art.

I did my undergrad at Carnegie Mellon University in the ECE Department, where I was advised by Prof. Richard Stern. I have also spent time on Fitbit’s R&D team and at the Labratory of Biomedical Stochastic Physics at NIH working with Prof. Jana Kainerstorfer.

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Featured Research

I’m interested in signal processing, computational imaging, compressed sensing, inverse problems, and optimization. I work predominantly in the fields of optics, microscopy, and impedance cytometry. Much of my research is about jointly designing physical sensors and signal processing algorithms to solve problems. I have also worked in the area of biologically-inspired speech processing.

Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution
Michael Kellman, Francios Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig

Using communication theory and detection theory, we create a Barker-coded high-throughput microfluidic channel. We jointly design the channel’s system response and cell detection heuristic to efficiently resolve coincidence events by way of an inverse problem formulation.

This work is done in collaboration with the Sohn Lab.



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