Michael R. Kellman
kellman at eecs dot berkeley dot edu

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 the areas signal processing, computational imaging, compressed sensing, inverse problems, and learning. My work is predominantly in the areas of optics and microscopy. Specifically, I focus on joint design of hardware systems and signal processing algorithms to beat traditional imaging limits.

Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging
Michael Kellman, Emrah Bostan, Nicole Repina, Michael Lustig, Laura Waller

We present a novel data-driven design method to optimize the end-to-end performance of the LED array microscope and a non-linear quantitative phase reconstruction. By unrolling the iterations of a phase reconstruction we build a network that incorporates both the system physics of measurement formation and the non-linearities of iterative reconstruction. Our Unrolled Network is effiently parameterized by only a few design variables which can be learned from just a few simulated training examples and still generalize well in the experimental setting.



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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