Hello, my name is Norman.
I am a PhD student in Canada. My research focuses on reinforcement learning. The primary objectives of my work are to improve agent performance, data efficiency, and task transferability in complex environments. My research applies to financial markets (algorithmic trading), control systems, and robotics.
In my spare time, I enjoy creating and maintaining profitable trading algorithms. Currently, I am applying reinforcement learning and statistical models to cryptocurrency markets.
Dynamic Planning Networks: Learning to dynamically create state-actions trees to improve planning performance https://arxiv.org/abs/1812.11240.
I worked fulltime at Scaled Inference in Palo Alto, CA. My work focused on distributed systems and machine learning.
I am the author of PLE a reinforcement learning environment for python.
Interned at Scaled Inference in Palo Alto, CA. My internship focused on the combination of bayesian models and deep neural networks. Additionally, I spent time improving modeling speed by porting code to run on GPUs.
Interned at Flipboard where I created a method for Image Super Resolution. While there I had to create a way to optimize model parameters and did so with bayesian optimization techniques over clusters of GPUs.
Performed research at the University of Western Ontario focusing on anomaly detection with electrical stream data using machine learning methods.
@normantasfi or email (n plus tasfi at google email)