Aug. 2016 - Dec. 2022
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University of Texas at Austin
Ph.D. in Computer Science
Dissertation: Imitation Learning with Auxiliary, Suboptimal, and Task-Agnostic Data
Advisor: Prof. Scott Niekum
Mar. 2008 - Feb. 2016
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Seoul National University
B.S. in Computer Science and Engineering (Summa Cum Laude, 3rd/56)
GPA 4.03/4.3, GPA in major 4.07/4.3
Sep. 2014 - Dec. 2014
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University of Toronto
Exchange Student in Computer Science Dept.
GPA in major 4.0/4.0
Jan. 2023 - Apr. 2024
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Senior Machine Learning Engineer
Tesla@Palo Alto, California
Topic: Autonomous driving via neural network; imitation learning and offline RL
May. 2021 - Aug. 2021
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Research Scientist Intern
Advisor : Prof. Joseph Lim and Dr. Minsuk Chang
Research Topic: Offline Reinforcement Learning
Sep. 2019 - Dec. 2019
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Teaching Assistant, CS394R: Reinforcement Learning: Theory and Practice
University of Texas at Austin
May. 2018 - Aug. 2018
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Research Scientist Intern
Advisor : Dr. Tommi Kerola and Toru Ogawa
Research Topic: Video object segmentation (VOS)
I achieved 4th place on the 1st Large-scale Video Object Segmentation Challenge
Sep. 2016 - Dec. 2016
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Teaching Assistant, CS313E: Elements of Software Design
University of Texas at Austin
Apr. 2016 - Jun. 2016
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Lead Web Programmer
Art 247@Paju, Gyeonggi-Do, Korea
I developed a commercial website selling baby clothes with Django web framework.
[site link] (currently unavailable)
Mar. 2015 - Apr. 2016
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Undergraduate Research Intern
Advisor : Prof. Gunhee Kim
I started my research career here, and this is the place where my broad research interest on computer vision and robotics grew up, while collaborating with Prof. Sung Ju Hwang and Prof. Jehee Lee.
Dec. 2010- Jan. 2014
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Lead Game Server Programmer(worked as alternative military service agent)
It was an great opportunity since I was able to get an industrial field experience during my undergraduate.
The three year experience taught me various programming skills from traditional, widely used skill-set, like C++, RDBMS to trending technologies, such as Node.js, NoSQL(mongoDB).
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Know Your Boundaries: The Necessity of Explicit Behavior Cloning in Offline RL
Wonjoon Goo and Scott Niekum
arXiv:2206.00695, June 2022.
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A Ranking Game for Imitation Learning
Harshit Sikchi, Akanksha Saran, Wonjoon Goo, and Scott Niekum
arXiv:2202.03481, February 2022.
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You Only Evaluate Once — a Simple Baseline Algorithm for Offline RL
Wonjoon Goo and Scott Niekum
Conference on Robot Learning (CoRL)
London, UK, 2021
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Self-Supervised Online Reward Shaping in Sparse-Reward Environments
Farzan Memarian*, Wonjoon Goo*, Rudolf Lioutikov, Ufuk Topcu, and Scott Niekum (*equal contribution)
International Conference on Intelligent Robots and Systems (IROS)
Online, 2021
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Local Nonparametric Meta-Learning
Wonjoon Goo and Scott Niekum
arXiv:2002.03272, February 2020.
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Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations
Daniel Brown, Wonjoon Goo, and Scott Niekum
Conference on Robot Learning (CoRL)
Osaka, Japan, 2019
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Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
Daniel Brown*, Wonjoon Goo*, Prabhat Nagarajan, and Scott Niekum (*equal contribution)
International Conference on Machine Learning (ICML)
Long Beach, CA, 2019
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One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video
Wonjoon Goo and Scott Niekum
International Conference on Robotics and Automation (ICRA)
Montreal, Canada, 2019
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Taxonomy-Regularized Semantic Deep Convolutional Neural Networks
Wonjoon Goo, Juyong Kim, Gunhee Kim, Sung Ju Hwang
European Conference on Computer Vision (ECCV)
Amsterdam, The Neterlands, 2016
If you feel stressed out, see this lazy picture!