MACHINE LEARNING RESEARCHER & COMPUTER
I am a graduate student working in the field of Machine Learning and Reinforcement Learning at UC Berkeley, where I am advised by Dr. Ram Akella. I spent two years at Facebook and at MIT Media Labs working with Dr. Ramesh Raskar on problems in computer vision and deep learning.
My research interests are in Reinforcement Learning and Statistics. I am most excited about Decision Making under Uncertainty or problems that involve partial observability.
Most of my current work is around Bayesian Inference, Hidden Markov Models, Probabilistic Graphical Models, Variational Inference, Particle Filters, POMDPs, Model-based and Model-free learning, Inverse Reinforcement Learning, etc.
Relevant Courses I have taken at UC Berkeley: EECS227A-Optimization Models, CS281A/STAT241A-Statistical Learning Theory, CS294-Machine Learning for Systems, CS294-Deep Reinforcement Learning, STAT248-Time Series Analysis, INFO271B-Quantitative Research
Other Relevant MOOCs: Stanford CS231n Convolutional Neural Network for Visual Recognition, Stanford CS224N NLP with Deep Learning, Stanford CS288 Probabilistic Graphical Models, Berkeley CS287 Advanced Robotics, MIT18.065 Signal Processing for Machine Learning, MIT6.262 Discrete Stochastic Processes
Theory and practice
Technical Interest and Expertise
++C / C
MATLAB / R
MongoDB / NoSQL
AWS/ GCP/ Kubernetes
Scikit Learn / Numpy / Pandas
KEYNOTES & TALKS
I'm graduating in May 2020 and open to full-time opportunities in research. Get in touch if my profile looks like a match!