Sangdon Park

Short Bio

My research interest focuses on designing safe and secure learning-enabled autonomous systems by understanding from theory to implementation.

I’m exicted to join Prof. Taesoo Kim’s group at GaTech as a postdoc researcher. Previously, I obtained a PhD degree in Computer and Information Science from University of Pennsylvania, where I was fortunate to work with Prof. Insup Lee and Prof. Osbert Bastani. I received a 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 was a research intern at Google Cloud AI, hosted by Kihyuk Sohn.



Sequential Covariate Shift Detection Using Classifier Two-Sample Tests
Sooyong Jang, Sangdon Park, Insup Lee, and Osbert Bastani
International Conference on Machine Learning (ICML) 2022

PAC Prediction Sets Under Covariate Shift
Sangdon Park, Edgar Dobriban, Insup Lee, and Osbert Bastani
International Conference on Learning Representations (ICLR) 2022
[arXiv] [Paper] [Code] [Video]

iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Ramneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Edgar Dobriban, Oleg Sokolsky, and Insup Lee
Association for the Advancement of Artificial Intelligence (AAAI) 2022

PAC Confidence Predictions for Deep Neural Network Classifiers
Sangdon Park, Shuo Li, Insup Lee, and Osbert Bastani
International Conference on Learning Representations (ICLR) 2021
[arXiv] [Paper] [Code] [Video]

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] [Video]

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
[Paper] [DOI]

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]


Uncertainty Estimation Toward Safe AI
PhD thesis, UPenn, Aug. 2021.

Abnormal Object Detection by Transformed-Canonical Scene Generation
MS thesis, Seoul National University, Aug. 2012.
Distinguished Dissertation Award

Behavioral Intelligence for Crowd Avatar in 3D Virtual Worlds
BS thesis, Seoul National University, Feb. 2010.