Joshua Zhanson

Mount Washington Trail, WA

Email: joshspam at jzhanson dot com

Resume, CV (under construction)

I'm a senior (4th-year undergraduate) at Carnegie Mellon University studying computer science with a minor in machine learning.

I'm broadly interested in leveraging structure to tackle tough problems, such as robotic control in deep reinforcement learning.

I'm passionate about teaching and undergraduate research. I have fun organizing events. I also occasionally write instructive blog posts.

In my spare time, I love hiking, biking, running, and the great outdoors.

Research

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Proprioceptive Spatial Representations for Generalized Locomotion

Joshua Zhanson, Emilio Parisotto, Ruslan Salakhutdinov

pdf, video, website, code (coming soon)

Workshop on Structure and Priors in Reinforcement Learning (SPiRL) at ICLR 2019

A body-space state representation that implicitly encodes relative positional information by mapping sensor readings onto a spatial grid over the robot’s body facilitates learning of a general locomotion policy transferable across many randomized body configurations with different balances and requiring different walking gaits.

Teaching

https://pbs.twimg.com/media/Dp3-bvGXQAAa2Kz.jpg

10-703 Deep Reinforcement Learning and Control

TA, Fall 2019

https://image.slidesharecdn.com/writingforcomputerscience3rdeditionspringer-161111045634/95/writing-for-computer-science-3rd-edition-springer-1-638.jpg?cb=1478840240

15-300 Research and Innovation in Computer Science

TA, Fall 2019

I help equip students with the tools needed to get involved in and effectively carry out undergraduate research. I also provide mentorship and support during undergraduate research projects and coaching on topics such as academic writing and presenting research to an academic and non-academic audience.

http://csapp.cs.cmu.edu/3e/images/csapp3e-cover.jpg

15-213 Introduction to Computer Systems

TA, Summer 2019

I developed active learning lecture activities on system-level I/O, network protocols, and participated in development for activities on bit-level representations, machine programming, and exceptional control flow (processes and signals). I also scaled and benchmarked memory access traces to evaluate student dynamic memory allocator submissions.

Projects

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Parallel Monte Carlo Tree Search for Tak

With Eric Nie.

Final project for 15-418 Parallel Computer Architecture and Programming, Spring 2019

We implemented and benchmarked a leaf-parallelized, root-parallelized, and tree-parallelized Monte Carlo Tree Search agent to play the fantasy board game Tak.

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breakout-demo

I implemented and trained Advantage Actor-Critic agent to reach 200+ average reward per episode over 100 episodes and max reward of 428 on OpenAI Gym Breakout-v0 environment. I also wrote a blog post about it.

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Slither-703

With Shreyan Bakshi and Eric Nie.

Final project for 10-703 Deep Reinforcement Learning and Control, Spring 2018

We implemented Snake in Python as a single-agent and multi-agent OpenAI Gym environment and successfully trained DDQN and A2C on both environments. Then, we patched up the abandoned OpenAI Universe and trained A2C on the online multiplayer game slither.io against real humans.

Involvement

https://scottylabs.org/img/shareable-brand.png

ScottyLabs

Former Director of Events

It's my fourth year organizing CMU's largest annual hackathon TartanHacks. I gave a tech talk on Git and using the Bash terminal at tech talk event Crash Course. Later, I organized and mentored other speakers for tech talk events Web Dev Weekend and Crash Course.

https://pbs.twimg.com/profile_images/876904401192878080/H9sV0WNl_400x400.jpg

Salmon Days

Previously, I volunteered at Issaquah, WA's annual Salmon Days festival celebrating the return of almost two tons of salmon as a general-purpose can-do problem solver on the legendary "Ofishal XStream Team."

Coursework