My research interest focuses on designing safe and secure learning-enabled autonomous systems by understanding from theory to implementation. I collaborate with Insup Lee and Osbert Bastani at University of Pennsylvania.
I received Bachelor’s degree in Computer Science from Seoul National University in 2010 under the mentorship of Prof. Byoung-Tak Zhang and Prof. Jehee Lee. I continued my graduate study and received a Master’s degree in Electrical and Computer Engineering in 2012 under the supervision of Prof. Kyoung Mu Lee. I had a research internship at Google Cloud AI, hosted by Kihyuk Sohn.
PAC Prediction Sets Under Covariate Shift
Sangdon Park, Edgar Dobriban, Insup Lee, and Osbert Bastani
Calibrated Predictions with Covariate Shift via Unsupervised Domain Adaptation
Sangdon Park, Osbert Bastani, James Weimer, and Insup Lee
International Conference on Artificial Intelligence and Statistics (AISTATS) 2020
[arXiv] [Paper] [Code]
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
Sangdon Park, Osbert Bastani, Nikolai Matni, and Insup Lee
International Conference on Learning Representations (ICLR) 2020
[arXiv] [Paper] [Code] [Video] [Short, ICML UDL20]
From Verification to Learning for Defense against Adversarial Examples in Neural Networks
Sangdon Park, Radoslav Ivanov, James Weimer, and Insup Lee
Korea Cyber-security Competition 2018
Best paper award
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data
Sangdon Park, James Weimer, and Insup Lee
International Conference on Cyber-Physical Systems (ICCPS) 2017
[Paper] [arXiv] [BibTex] [DOI]
Integrated Intelligence for Human Robot Teams
Jean Oh, Thomas Howard, Matthew Walter, Daniel Barber, Menglong Zhu, Sangdon Park, Arne Suppe, Luis NavarroSerment, Felix Duvallet, Abdeslam Boularias, Oscar Romero, Jerry Vinokurov, Terence Keegan, Robert Dean, Craig Lennon, Barry Bodt, Marshal Childers, Jianbo Shi, Kostas Daniilidis, Nicholas Roy, Christian Lebiere, Martial Hebert, and Anthony Stentz
International Symposium on Experimental Robotics (ISER) 2016
Abnormal Object Detection by Canonical Scene-based Contextual Model
Sangdon Park, Wonsik Kim, and Kyoung Mu Lee
European Conference on Computer Vision (ECCV) 2012
[Project Page] [Paper] [BibTex] [Code] [Dataset]
Sangdon Park, “Abnormal Object Detection by Transformed-Canonical Scene Generation,” M.S. thesis, Seoul National University, Aug. 2012.
Distinguished Dissertation Award
Sangdon Park, “Behavioral Intelligence for Crowd Avatar in 3D Virtual Worlds,” B.S. thesis, Seoul National University, Feb. 2010.