Michael Munje

Hi there! I am a PhD student at UT Austin and am a receipient of the GEM PhD fellowship. I am broadly interested reinforcement learning and its applications to robotics.

I finished my M.S. in Computer Science at Georgia Tech with a specialization in Machine Learning where I had the pleasure of collaborating with Matthew Gombolay working on Human-AI collaboration and was a recipient of the GEM MS fellowship.

I interned previously for the ML group at NASA JPL where I worked on the automatic detection of Martian impact craters. I also interned for Microsoft twice where I optimized advertisement systems. I interned for the ML group at Riverside Research twice where I worked on robot manipulation tasks and RL. I also interned for IBM Research where I worked on quantization for large language models.

I completed my B.S. in Computer Science at California State University Northridge. During this time, I collaborated with the following groups: Theory of Matter and Interfaces Group, Adriano Zambom, Computational Geometry group at Tufts University, the Systems Engineering Research Laboratory, and the TaV lab.

See my CV for more info.

Email  /  CV  /  Bio  /  Google Scholar  /  Twitter  /  Github

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I am interested in human-centric autonomous agents and robots that can solve complex sequential decision-making tasks. As such, my work draws in elements from machine learning (ML), reinforcement learning (RL), computer vision, and robotics. Some areas of focus include imitation learning, leveraging prior knowledge, and improving collaboration between humans and robots. My long-term research goal is to enable human-centric robot autonomy in everyday human life.

Using machine learning to reduce observational biases when detecting new impacts on Mars
Kiri L. Wagstaff, Ingrid J. Daubar, Gary Doran, Michael J. Munje, Valentin T. Bickel, Annabelle Gao, Joe Pate, Daniel Wexler
Icarus, 2022

Trained a model for detecting new impact craters and deployed across the nearly the entire surface of Mars. Resulted in the finding of many previously unknown new impact craters.

TEAM3 Challenge: Tasks for Multi-Human and Multi-Robot Collaboration with Voice and Gestures
Michael J. Munje, Lylybell K. Teran, Bradon Thymes, Joseph P. Salisbury
HRI Late-Breaking Reports, 2023

Proposed new challenge for multi-agent collaboration between humans and robots.

Providing predictions of adversary movements in a gridworld environment to a human-machine team improves teaming performance
Jeffry A. Coady, Paul Dysart, Aidan Schumann, Stephan A. Koehler, Michael J. Munje, William D. Casebeer, David M. Huberdeau
SPIE Defense + Commercial Sensing, 2023

Theory of mind predictive modeling in an adversary-avoidance game improves human-machine fluency.

This template was forked from Jon Barron's website.