Yonghan Jung
Ph.D. student, Department of Computer Science, Purdue University
Additional Information
Email:
jung222 at purdue dot edu
yhansjung at gmail dot com
Curriculum Vitae (10/19/2022)
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 fortunate to be a member of the CausalAI lab led by my advisor Elias Bareinboim at Columbia University. My research focuses on estimating the causal effect using modern machine-learning methods. I am particularly interested in semiparametric causal effect estimation, double/debiased machine learning, causal decision-making and their application to explainable AI and healthcare science.
I am fortunate to collaborate with many awesome scholars, including Elias Bareinboim, Jin Tian, Shiva Kasiviswanathan, Dominik Janzing, Kyungwoo Song, Sanghyuk Chun, and many others.
+ I am an avid runner🏃 and swimmer🏊♂️! Find me in Strava!
News
July, 2022. I am nominated as a "Top 10% reviewers" in ICML-22!
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.
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.
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.
July, 2022. I gave a lecture/tutorial titled "Double/Debiased Machine Learning" at Naver Clova AI.
July, 2022. I gave a lecture/tutorial titled "Tutorial on the Structural Causal Model" at Postech AI.
June, 2022. I hosted a tutorial labeled "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".
May, 2022. Our paper, "On Measuring Causal Contributions via do-interventions", is accepted in the ICML-22!
Apr, 2022. I gave a lecture/tutorial titled "Estimating Identifiable Causal Effects" in Trustworthy-AI Reading Group. [link]
Mar, 2022. I gave a lecture/tutorial titled "Double/Debiased Machine Laerning" to AWS Causality Lab, Mar. 2022
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!
Nov, 2021. My collaborator, Jin Tian (Iowa State University), presented "Estimating Identifiable Causal Effects through Double Machine Learning - Graph-based & Data-driven Approaches", which summarized our works R-5 and R-6, in Online Causal Inference Seminar (OCIS)! I helped in the Q&A session. [slide, discussion, video]
Oct, 2021. Our paper, "do-Shapley: Towards Causal Interpretation of Model Prediction", is on! This is based on the results of my Summer internship at Amazon.
Sep, 2021. Our paper, "Double Machine Learning Density Estimation for Local Treatment Effects with Instruments", is accepted in NeurIPS-21! This paper is selected as a 🎊spotlight presentation🎊 (one of 3% of 9122 submissions)!
Aug, 2021. I presented a tutorial "Causal Inference under the rubric of Structural Causal Model" in Korea Summer Session on Causal Inference 2021! [video (in Korean)]
July, 2021. I presented our work "Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning" in ICML-21!
June, 2021. I will be an applied scientist intern at Amazon AWS Causality Lab from Jun-Sep 2021, working with Shiva Kasiviswanathan and Dominik Janzing!
May, 2021. Our paper, "Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning", is accepted in ICML-21!
Mar, 2021. I will give a 2.5 hrs of lecture/tutorial titled "Causal effect estimation for arbitrary functional" in a course Causal Inference II (COMS W4775/Spring 2021) in Columbia University, USA!
Feb, 2021. I presented our work "Estimating Identifiable Causal Effects through Double Machine Learning", is in AAAI-21!
Dec, 2020. Our paper, "Estimating Identifiable Causal Effects through Double Machine Learning", is accepted in AAAI-21!
Dec, 2020. I presented our work "Learning Causal Effects via Weighted Empirical Risk Minimization in NeurIPS-20!
Sep, 2020. Our paper, "Learning Causal Effects via Weighted Empirical Risk Minimization", is accepted in NeurIPS-20!
May, 2020. I completed the course requirements and qualifications of Purdue Computer Science.
Feb, 2020. I presented our work (poster) on estimating causal effect using weighting-based estimator in AAAI-20.
Publications
2022
(R-9) [Understanding Shortcut Learning through the Lens of Causality & Robustness] Y.Jung Technical Report (presented at the Tutorial Session in FAccT-22) [pdf]
(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%) [link]
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] 🎊spotlight presentation🎊 (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 and its Application toward Interpretable ML/AI, Korea Summer Session on Causal Inference.
Lecture Series on (1) Tutorial on Structural Causal Model, (2) Estimating Any Identifiable Causal Effects, (3) Application of Causality for Human-Centered AI/ML, University of Seoul, Korea, July. 2022.
Tutorial on Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI, Graduate School of Data Science, Seoul National University, Korea, July. 2022
Tutorial on Double/Debiased Machine Laerning, Naver Clova AI, July. 2022
Tutorial on Structural Causal Model, Postech AI, Korea, July. 2022.
Tutorial on Shortcut learning in Machine Learning: Challenges, Analysis, Solutions - Understanding Shortcut Learning through the Lens of Causality & Invariance, FAccT-2022
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
Advisor: Elias Bareinboim
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, Transactions on Machine Learning Research
Conference:, ICML, AAAI, NeurIPS, ICLR, IJCAI, AISTAT, UAI, CLeaR, 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: CS408 Software Testing
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)