[05/26/2020] I have implemented (using PyMC3) a Bayesian inference procedure of COVID-19 antibody tests with unknown specificity and sensitivity following Gelman & Carpenter proposal. Data can be found in the famous but (potentially) wrongly analyzed study by Bendavid et al. titled COVID-19 Antibody Seroprevalence in Santa Clara County, California. For source code, see my repo.
[05/13/2019] New manuscript titled “Constrained optimization as ecological dynamics with applications to random quadratic programming in high dimensions” is officially published at Physical Review E. See also my blogpost for an intro/preivew.
[04/15/2019] New paper titled “Pinned, locked, pushed, and pulled traveling waves in structured environments” is published now at Theoretical Population Biology!
[04/01/2019] The published verision of our machine leanring review titled “A high-bias, low-variance introduction to Machine Learning for physicists” is out (still open-access) at Physics Reports. Also check out the Python Jupyter notebooks that come with it.
[11/13/2018] New manuscript titled “The strength of protein-protein interactions controls the information capacity and dynamical response of signaling networks” is on arXiv:1811.05371 and bioRxiv:10.1101/469197 now!
Generally speaking, I’m interested in applying statistical physics to understand how collective behavior of matters emerges. I also have a running interest in machine learning, optimization, and statistics. In the past, I did some condensed matter physics (ranging from the strongly correlated physics and nonlinear dynamics of ultra-cold atoms to the Ginzburg-Landu theory of liquid-solid interfacial dynamics), but now I focus more on biological systems. For details, check out my Research page.
The best way to reach me is through e-mail: