Part of my research is in astrophysical data analysis and I am often applying statistical methods, including likelihoods, Bayesian analysis, and MCMC to physical models and data sets. For example I am searching for ways to reduce the computational complexity of a difficult analysis (see for example this paper), or for methods to combine different data sources to test a hypothesis (this paper).
A long term interest of mine is Machine Learning and Artificial Intelligence (I also minored in robotics). I have co-founded Wolution, an online image analysis platform that offers deep learning for scientific image analysis. One of my contributions is a CNN based pixel classifier implemented with tensorflow that now runs in production on www.wolution.com. Wolution also runs a number of modern image analysis algorithms including Deeplab, R-CNN and Mask R-CNN with different neural network architectures, and part of my job at Wolution is to include new algorithms in our computing engine.
I am also working on applying Machine Learning to cosmology and have several ongoing projects in this domain. Cosmological data analysis is becoming increasingly sophisticated and I believe that Machine Learning will be a crucial component in fully exploiting the huge amounts of data from upcoming cosmology experiments like CMB S4 and LSST.
We have also recently been discussing deep reinforcement learning, generative adversarial networks, the information bottleneck method and other deep learning research in the PI machine learning reading group and I’m very interested in connections of these to theoretical physics.