I will soon be starting my new computational data science and neuroscience lab as part of the Biomedical Engineering Department at Johns Hopkins Univerity! I will be hiring, starting July 2020, students interested in pushing the boundaries of neuroscience using statistical, algorithmic and signal processing techniques.

Now announcing the "Conference on the Mathematical Theory of Deep Learning" to take place on Oct. 31 - Nov. 1 2019. For information on attending and submitting contributions (1-page abstracts), please see the conference website here. Videos from last years symposium on this topic are now online!


I'm currently a post-doc working with Dr. Jonathan Pillow at the Princeton Neuroscience Institute (PNI) and an Assistant Research Professor at the Biomedical Engineering (BME) department at The Johns Hopkins University. I graduated with a Ph.D. in Electrical and Computer Engineering at the Georgia Institute of Technology working with Dr. Christopher Rozell at the center for Signal and Information Processing (CSIP) and with close ties to the Georgia Tech Neurolaboratory. I work primarily in the areas of signal processing for neural imaging, data analysis, machine learning and other applications (including remote sensing and theoretical/compuational neuroscience), using tools from probabilistic and low-dimensional modeling. I believe that new methods for recording and interpreting larger neural populations than what is currently achievable is key to understanding the complex computations that the brain performs and furthering our understanding of how we as humans experience the world.

I have had the opportunity to have worked on a mixture of projects spanning from the theoretical to the applied. As an undergraduate researcher at The Cooper Union's center for Signal Processing, Communications and Computer Engineering Research (S*ProCom2), I worked with Electroencephalography (EEG) data from the language center of the brain. For my master's thesis I worked extensively with adaptive filtering, developing an adaptive method for adjusting sub-band widths for sub-band adaptive filters. In my graduate studies at Georgia Tech I worked on a number of projects, including characterizing the short term-memory (STM) of random neural networks, developing stochastic filtering methods for correlated sparse signals, and developing methods to further hyperspectral imaging information extraction. Currently I am also working on a number of signal processing techniques for two-photon calcium imaging, as well as probabilistic models for better modeling super-Poisson variability in neural spike trains.

Aside from research, I also run the Computational Neuroscience Journal Club at PNI.

My full CV is here: pdf

My Google scholar profile is here.