I’m a research scientist at the Nvidia Research AI/ML team. Before joining Nvidia, I received a Ph.D. from Stanford University. I had a great pleasure working with great minds at Stanford on navigation, 2D feature learning, 2D scene graph, 3D perception, 3D reconstruction, building 3D datasets, and 4D perception. Before joining the lab, I also briefly worked on convex optimization, information theory, and graphics and the experience helped me shape my research. Also, I am interested in unsupervised learning, ML algorithms, robotics, and reinforcement learning. Feel free to send an email to firstname.lastname@example.org or email@example.com if you have a question regarding my research.
- 2020-10-25: I will give an invited talk at the IROS 2020 Deep Representation and Estimation of State for Robotics workshop
- 2020-07-14: Two papers got accepted for ECCV 2020.
- 2020-06-10: I will give an invited talk at the ScanNet workshop, CVPR 2020. slides
- 2020-05-07: I will give an invited talk at the Stanford Vision and Learning Lab quarterly talk series on June 11.
- 2020-05-07: I will give an invited talk at AIBee seminar on May 20.
- 2020-04-06: Joined NVIDIA Research as a Research Scientist.
- 2020-03-23: Two papers got accepted for oral presentations at CVPR2020.
- 2020-02-12: I passed the PhD oral exam slides, thesis.
- 2020-01-14: I will give an invited talk at KAIST.
- 2020-01-13: I will give an invited talk at Pohang University of Science and Technology (POSTECH).
- 2019-10-30: Open-sourced the code for Open Universal Correspondence Network.
- 2019-09-28: Open-sourced the code for Fully Convolutional Geometric Features, ICCV'19.
- 2019-07-12: I will be visiting the Technical University of Munich (TUM) Visual Computing Group led by Prof. Matthias Nießner from mid August for about a month.
- 2019-06-19: I will give an invited talk at the CVPR'19 ScanNet workshop.
- 2019-04-19: I will give an invited talk at Pohang University of Science and Technology (POSTECH).
- 2019-01-09: I will start my internship at the Intel Intelligent Systems Lab led by Vladlen Koltun.
- 2018-11-16: I won the 2019 ScanNet 3D Semantic Segmentation Challenge by a large margin.
Computer Vision and Pattern Recognition (CVPR), 2020, Oral
Computer Vision and Pattern Recognition (CVPR), 2019
Computer Vision and Pattern Recognition (CVPR), 2017
Computer Vision and Pattern Recognition (CVPR), 2015
Stanford Library (2020)
- Universal correspondence network, US Patent App. 10/115,032
- Systems and Methods for Semantic Segmentation of 3D Point Clouds, US Patent App. 16/155,843
- Minkowski Engine, an auto-diff library for sparse tensors code
- Fully Convolutional Geometric Features, fast and accurate 3D feature code
- 3D Recurrent Reconstruction Neural Network code
- Fully Differentiable Deep Neural Decision Forest code
- Python GPU Accelerated K-Nearest Neighbor code
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- Optimization in Neural Networks, Stanford Deep Learning Reading Group, CA, USA, July 14th 2017, slides.
- Toyota Research Institute, Weakly Supervised Generative Adversarial Network for 3D Reconstruction, CA, USA, June 14th 2017.
- Stanford Faculty Lunch, Learning to Perceive the 3D World, April 18th 2017, CA, USA, slides.
- Apple Deep Learning Reading Group, Weakly Supervised Generative Adversarial Network for 3D Reconstruction, CA, USA, Feb 26th 2017, slides.
- Stanford Vision Reading Group, Wasserstein GAN and Bidirectional GAN, CA, USA, January 2017, slides.
- Neural Information Processing Systems, Oral, Universal Correspondence Networks, Barcelona, Spain, December 2016, slides.
- European Conference on Computer Vision, Amsterdam, Netherlands, ObjectNet3D, Spotlight Oral, October 2016.
- Korea Advanced Institute of Science and Technology, Universal Correspondence Networks and 3D Recurrent Reconstruction Neural Networks, Daejeon, Korea, June 2016.
- NVIDIA GTC Hangout: Deep Learning in Image and Video 2016, 3D Recurrent Reconstruction Neural Networks, CA, USA, April 6th 2016.
- Stanford Deep Learning Reading Group, Decision Forest and Deep Neural Decision Forest, CA, USA, Feb 2016, slides.
- CS231A Teaching Assistant, 2015
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- Computer Vision
- Machine Learning
- Information Theory
- Stochastic Control
- Stochastic Processes
- Mathematical Analysis
- Convex Optimization I
- Convex Optimization II
- Mining Massive Data Sets
- Statistical Signal Processing
- Interactive Computer Graphics
- Probabilistic Graphical Models
- Modern Applied Statistics: Learning
- Modern Applied Statistics: Data Mining
- Convolutional Neural Networks For Visual Recognition
- Ph.D. in EE at Stanford University
- M.S. in EE at Stanford University
- B.S. in EE at Korea Advanced Institute of Science and Technology
- Summa Cum Laude
Honors and Awards
- Winner of the 2019 ScanNet Semantic Segmentation Challenge
- Stanford SystemX FMA Fellowship
- The Korea Foundation for Advanced Studied, Ph.D. Fellowship
- The 5th Presidential Science Fellowship for Undergraduate Study
- Korea Advanced Institute of Science and Technology EECS Merit Scholarship for Best Performance
- Korea Advanced Institute of Science and Technology Alumni Chairman’s Award
- Korea Science Olympiad, Junior High, Gold Medal