Yonghan Jung

Ph.D. student, Department of Computer Science, Purdue University

Additional Information

Email:

  1. jung222 at purdue dot edu

  2. yhansjung at gmail dot com

Twitter

Google Scholar

Curriculum Vitae (03/29/22)

Summary

I am an aficionado of Causal Data Science!

I am a Ph.D. student in the Department of Computer Science at Purdue University. I am also a member of the CausalAI lab led by my advisor Elias Bareinboim at Columbia University. My research centers around estimating the causal effect using modern machine learning methods. My interest includes semiparametric causal effect estimation, statistical learning theory, causal decision making and their application to interpretable ML or explainable AI, and healthcare science.

I am fortunate to collaborate with many awesome scholars, including Jin Tian, Shiva Kasiviswanathan, Dominik Janzing, Kyungwoo Song, Sanghyuk Chun, and many others.

+ I am an avid runner๐Ÿƒ and swimmer๐ŸŠโ€โ™‚๏ธ! Find me in Strava!

News

  • ๐Ÿ‘ July, 2022. I am nominated as a "Top 10% reviewers" in ICML-22!

  • ๐Ÿ‘ July, 2022. I gave a lecture/tutorial titled "Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI" at Korea Summer Session on Causal Inference.

  • ๐Ÿ‘ July, 2022. I gave a series of lectures on (1) Tutorials on the Structural Causal Model, (2) Estimating Any Identifiable Causal Effects, (3) Application of Causality for Human-Centered AI/ML at the University of Seoul, Korea.

  • ๐Ÿ‘ July, 2022. I gave a lecture/tutorial titled "Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI" at the Graduate School of Data Science in Seoul National University.

  • ๐Ÿ‘ July, 2022. I gave a lecture/tutorial titled "Double/Debiased Machine Laerning" at Naver Clova AI.

  • ๐Ÿ‘ July, 2022. I gave a lecture/tutorial titled "Tutorial on the Structural Causal Model" at Postech AI.

  • ๐Ÿ‘ June, 2022. I hosted a tutorial titled "Shortcut learning in Machine Learning: Challenges, Analysis, Solutions" in FAccT-2022 in Seoul, with Kyungwoo Song and Sanghyuk Chun. I will present on "Understanding Shortcut Learning through the Lens of Causality & Invariance".

  • ๐Ÿ‘ May, 2022. Our paper, "On Measuring Causal Contributions via do-interventions", is accepted in the ICML-22!

  • ๐Ÿ‘ Apr, 2022. I gave a lecture/tutorial titled "Estimating Identifiable Causal Effects" in Trustworthy-AI Reading Group. [link]

  • ๐Ÿ‘ Mar, 2022. I gave a lecture/tutorial titled "Double/Debiased Machine Laerning" to AWS Causality Lab, Mar. 2022

  • ๐Ÿ‘ Mar, 2022. I gave a 2.5 hrs of lecture/tutorial titled "Double/Debiased Machine Learning for Estimating Causal Effects" in a course Causal Inference II (COMS W4775/Spring 2022) in Columbia University, USA!

Publications

2022

  • ๐Ÿ”†(R-9) [Understanding Shortcut Learning through the Lens of Causality & Robustness] Y.Jung Technical Report (presented at the Tutorial Session in FAccT-22) [pdf]

  • ๐Ÿ”†(R-8) [On Measuring Causal Contributions via do-interventions] Y.Jung, S. Kasiviswanathan, J. Tian, D. Janzing, P. Bloebaum, E. Bareinboim. ICML-22. In Proceedings of the 39th International Conference on Machine Learning, 2022. (Acceptance rate: 19.9%) [forthcoming]

2021

  • (R-7) [Double Machine Learning Density Estimation for Local Treatment Effects with Instruments] Y.Jung, J. Tian, E. Bareinboim. NeurIPS-21. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems, 2021. (Acceptance rate: 26.0%) [pdf, poster] ๐ŸŽŠspotlight presentation๐ŸŽŠ (one of 3% of 9122 submissions))

  • (R-6) [Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning] Y.Jung, J. Tian, E. Bareinboim. ICML-21. In Proceedings of the 38th International Conference on Machine Learning, 2021. (Acceptance rate: 21.8%) [pdf, video]

  • (R-5) [Estimating Identifiable Causal Effects through Double Machine Learning] Y.Jung, J. Tian, E. Bareinboim. AAAI-21. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. (Acceptance rate: 21%) [pdf, media]

2020:

  • (R-4) [Learning Causal Effects via Weighted Empirical Risk Minimization] Y.Jung, J. Tian, E. Bareinboim. NeurIPS-20. In Proceedings of the 34th Annual Conference on Neural Information Processing Systems, 2020. (Acceptance rate: 20.1%) [pdf, poster, presentation, media, Video]

  • (R-3) [Estimating Causal Effects Using Weighting-Based Estimators] Y.Jung, J. Tian, E. Bareinboim. AAAI-20. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020. (Acceptance rate: 20.6%) [pdf, bib, poster, 1-page summary]

2018:

  • (R-2) [Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost]. Y. Chen, X. Wang, Y.Jung, V. Abedi, R. Zand, M. Bikak, M. Adibuzzaman. Physiological measurement, 2018 [pdf]

2017:

  • (R-1) [Detection of PVC by using a wavelet-based statistical ECG monitoring procedure] Y.Jung, H. Kim. Biomedical Signal Processing and Control, 2017 [pdf]

Talks/Seminars

  • Tutorial on Estimating Identifiable Causal Effects, Trustworthy-AI Reading Group, Apr. 2022

  • Tutorial on Double/Debiased Machine Laerning, AWS Causality Lab, Mar. 2022

  • "Double/Debiased Machine Learning for Estimating Causal Effects", Causal Inference II (COMS W4775/Spring 2022) in Columbia University, USA, Mar. 2022

  • "Estimating Identifiable Causal Effects through Double Machine Learning - Graph-based & Data-driven Approaches", Online Causal Inference Seminar (OCIS), Nov. 2021. Presenter: Jin Tian (Iowa State University), Q&A: Yonghan Jung, Discussant: Ilya Shpitser (Johns Hopkins University) [slide, discussion, video]

  • "Causal Inference under the rubric of Structural Causal Model", Korea Summer Session on Causal Inference, Aug. 2021. [video (in Korean)]

  • "Causal effect estimation for arbitrary functional", Causal Inference II (COMS W4775/Spring 2021) in Columbia University, USA, Mar. 2021. [pdf]

  • Estimating Identifiable Causal Effects through Double Machine Learning, AAAI-21 Poster session, Feb. 2021.

  • "Learning Causal Effects via Weighted Empirical Risk Minimization", NeurIPS-20 Poster session, Dec. 2020 [poster]

  • "Estimating Causal Effects Using Weighting-Based Estimators", AAAI-20 Poster session, Feb. 2020 [poster]

  • "Regenerating Evidence from Landmark Trials in ARDS Using Structural Causal Models on Electronic Health Record", American Thoracic Society International Conference, 2018 [link, abstract, poster]

  • "Tutorial: Causal Inference", Industrial Statistics Lab, KAIST, South Korea, Jul. 2018

  • "Structural Causal Model (SCM) to Identify Causation from Observational Data", Regenstrief Center for Healthcare Engineering, Purdue University, USA, Jun. 2017 [info]

Education

Purdue University

[Sep. 2018 - Present] Ph.D. student in Department of Computer Science

Purdue University

[Sep. 2016 - Sep.2018] Ph.D. Student in School of Industrial Engineering (Transferred)

KAIST

[2016] M.S. in Department of Industrial and Systems Engineering (in Industrial Statistics Lab).

  • Advisor: Heeyoung Kim

  • Thesis: Detection of premature ventricular contraction using wavelet-based statistical ECG monitoring

KAIST

[2014] (Double majors) B.S. in Mathematical Sciences and B.A. in Business and Technology Management

Academic Services

  • Reviewer:

    • Journal: Statistics in Medicine, Statistical Science, European Journal of Operation Research, Journal of Causal Inference, Epidemiology, Biostatistics

    • Conference:, ICML, AAAI, NeurIPS, ICLR, IJCAI, etc.

  • Software: Contributing to CausalFusion and Causal101

  • External Advisor & Visiting Scholar: Serving as an external advisor to the lab led by Kyungwoo Song.

Teaching

  • Graduate Teaching Assistant: CS490 Data Science Capstone

  • Graduate Teaching Assistant: CS573 Data Mining (Fall 2021)

  • Invited Lecturer: Causal Inference II (COMS W4775/Spring 2021) in Columbia University, USA (Spring 2021) [pdf]

  • Graduate Teaching Assistant: CS471 Introduction to Artificial Intelligence (Spring 2021)

  • Graduate Teaching Assistant: CS573 Data Mining (Fall 2020)

  • Graduate Teaching Assistant: IE383 Integrated Production Systems (Spring 2017)

  • Graduate Teaching Assistant: IE383 Integrated Production Systems (Fall 2016)