I am a third year PhD student in the UCI department of Statistics supervised by Professor Pierre Baldi. My research interests are in machine learning and deep learning. My focus is on deep learning applied to high energy particle physics. I received my Bachelor of Science in Physics from Queen Mary, University of London and a Master of Science in Statistics from Imperial College London. I wrote my Masters thesis on MRI segmentation using stacked auto-encoders. For the first two years of my PhD I have studied the PhD Statistics curriculum. This includes theory and methods series, measure and large sample theory and two sets of comprehensive exams. In between I did an internship on ConvNet Pedestrian detection at Honda Research Institute. I now work on convolutional and recurrent neural networks for event reconstruction in the ATLAS and NOvA experiments. Concurrently, I pursue projects in theoretical understanding of neural networks and variational inference. This summer I will be joining Carlos Guestrin's team at Apple in Seattle. Resume.


Approximate Inference for Deep Latent Gaussian Mixtures

Paper Summaries

Understanding Deep Learning Requires Rethinking Generalization
Deep Rl Through Policy Optimization (tutorial)
Hyperband: Bandit Based Configuration Evaluation For Hyperparameter Optimization
Label Free Supervision Of Neural Networks With Physics And Domain Knowledge


I am currently the teaching assistant for STATS 7 (Introductory Statistics). Other classes that I have TA'ed before are STATS 240 (Multivariate Analysis - Master level) with Dr. Zhaoxia Yu and STATS 111/202 (Categorical Data Analysis - Master level) with Dr. Stacey Hancock. See below for materials.