Yonghan Jung
Ph.D. candidate, Department of Computer Science, Purdue University
⭐️ I am actively on the 2024-2025 job market, seeking tenure-track faculty positions in academia and research scientist roles in industry. If you're interested, please feel free to reach out!
Introduction
I am an aficionado of Causal Data Science!
I am a Ph.D. candidate in the Department of Computer Science at Purdue University. I am fortunate to be a member of the CausalAI lab led by Professor Elias Bareinboim at Columbia University.
My research focuses on developing estimation frameworks for causal effects using modern machine learning methods. I am interested in various areas of causal inference, with a particular emphasis on semiparametric causal effect estimation, debiased machine learning, and their application in explainable AI and healthcare.
I have collaborated with many awesome scholars, including Elias Bareinboim, Jin Tian, Iván Díaz, Shiva Kasiviswanathan, Patrick Blöbaum, Dominik Janzing, Kyungwoo Song, Sanghyuk Chun, Sanghack Lee, Alexis Bellot, and many others.
Here are other facts about me that I want to share!
I am an avid runner🏃 (My Strava)!
I won the 5km running competition in my age group in Run for Justice 2024 🎉
I won Top 3 in my age group in 2024 Purdue Boilermaker Half-Marathon & 5k 🎉
I am a potential bone marrow doner since 2024. More information on the bone marrow donation is here.
News
☀️ Oct 2024. I won Top 3 in my age group in 2024 Purdue Boilermaker Half-Marathon & 5k 🎉
☀️ Sep 2024. Three papers that I particiated as the first author are accepted in NeurIPS-24!
☀️ June-Aug 2024. I will be a visiting student of Causal AI Lab led by Elias Barenboim at Columbia University.
☀️ Apr 2024. I won the 5km running competition in my age-group in Run for Justice 2024! 🎉
☀️ Apr 2024. I received the Graduate Teaching Award 🏆 from Purdue Computer Science. Thanks!
☀️ Feb 2024. I passed the preliminary exam (thesis proposal)!
Dec 2023. I presented our work Estimating Causal Effects Identifiable from a Combination of Observations and Experiments in NeurIPS-23 at the Poster session [poster]
Oct 2023. I posted a technical report "A Short Note on Finite Sample Analysis on Double/Debiased Machine Learning".
Sep 2023. Our paper, "Estimating Causal Effects Identifiable from a Combination of Observations and Experiments" is accepted at NeurIPS-23!
July 2023. I presented our work Estimating Joint Treatment Effects by Combining Multiple Experiments in ICML-23 Poster session [Poster]
June 2023. Our paper, Estimating Causal Effects Identifiable from a Combination of Observations and Experiments, is online!
June 2023. Our paper accepted in ICML-23, Estimating Joint Treatment Effects by Combining Multiple Experiments, is online!
May 2023. I won the Best UAI Reviewers Award of UAI-23!
May 2023. I am selected as a recipient of the Purdue University Graduate School Summer Research Grant!
May 2023. My collaborator, Shiva Kasiviswanathan, will present our work "On Measuring Causal Contributions via Do-Interventions" (presented in ICML-22) in the Causality Reunion workshop at Simon Institute [Link].
Apr 2023. Our paper, "Estimating Joint Treatment Effects by Combining Multiple Experiments" is accepted in ICML-23!
July 2022. I am nominated as a "Top 10% reviewer" 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 at 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 Learning" 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!
Ma, 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) iatn 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 effects using a weighting-based estimator in AAAI-20.
Publications
An asterisk * means the equal contributions.
2024
[R-16] Taero Kim, Subeen Park, Sungjun Lim, Yonghan Jung, Krikamol Muandet, Kyungwoo Song. "Sufficient Invariant Learning for Distribution Shift." 2024, Technical Report
[R-15] Yonghan Jung, Min Woo Park, and Sanghack Lee. "Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference." Advances in Neural Information Processing Systems 38 (2024) (NeurIPS-24) [poster]
[R-14] Yonghan Jung*, Alexis Bellot*. "Efficient Policy Evaluation Across Multiple Different Experimental Datasets." Advances in Neural Information Processing Systems 38 (2024) (NeurIPS-24) [poster]
[R-13] Yonghan Jung, Jin Tian, and Elias Bareinboim. "Unified Covariate Adjustment for Causal Inference." Advances in Neural Information Processing Systems 38 (2024) (NeurIPS-24) [poster]
2023
[R-12] Yonghan Jung. "A Short Note on Finite Sample Analysis on Double/Debiased Machine Learning". Technical Report (2023)
[R-11] Yonghan Jung, Iván Díaz, Jin Tian, and Elias Bareinboim. "Estimating Causal Effects Identifiable from a Combination of Observations and Experiments." Advances in Neural Information Processing Systems 37 (2023) (NeurIPS-23) [poster]
[R-10] Yonghan Jung, Jin Tian, and Elias Bareinboim. "Estimating Joint Treatment Effects by Combining Multiple Experiments." In International Conference on Machine Learning, 2023 (ICML-23) [poster]
2022
[R-9] Sanghuck Chun*, Yonghan Jung*, Kyungwoo Song* (alphabetic order). "Shortcut Learning in Machine Learning: Challenges, Analysis, Solutions - Understanding Shortcut Learning through the Lens of Causality & Invariance." In Tutorial Session in ACM Conference on Fairness, Accountability, and Transparency, 2022 (FAccT-22) [video, technical report, slide]
[R-8] Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, and Elias Bareinboim. "On Measuring Causal Contributions via do-interventions.", In International Conference on Machine Learning: pp. 10476-10501. PMLR, 2022. (ICML-22) [link]
2021
[R-7] Yonghan Jung, Jin Tian, and Elias Bareinboim. "Double Machine Learning Density Estimation for Local Treatment Effects with Instruments." Advances in Neural Information Processing Systems 34 (2021): 21821-21833 (NeurIPS-21) [poster] spotlight presentation (one of 3% of 9122 submissions)
[R-6] Yonghan Jung, Jin Tian, and Elias Bareinboim. "Estimating Identifiable Causal Effects on Markov Equivalence class through Double Machine Learning." In International Conference on Machine Learning, pp. 5168-5179. PMLR, 2021. (ICML-21) [video]
[R-5] Yonghan Jung, Jin Tian, and Elias Bareinboim. "Estimating Identifiable Causal Effects through Double Machine Learning." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 13, pp. 12113-12122. 2021. (AAAI-21) [media]
2020:
[R-4] Yonghan Jung, Jin Tian, and Elias Bareinboim. "Learning Causal Effects via Weighted Empirical Risk Minimization." Advances in neural information processing systems 33 (2020): 12697-12709. (NeurIPS-20) [poster, presentation, media, Video]
[R-3] Yonghan Jung, Jin Tian, and Elias Bareinboim. "Estimating Causal Effects using Weighting-based Estimators." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 06, pp. 10186-10193. 2020. (AAAI-20) [poster, 1-page summary]
2018:
[R-2] Chen, Yao, Xiao Wang, Yonghan Jung, Vida Abedi, Ramin Zand, Marvi Bikak, and Mohammad Adibuzzaman. "Classification of Short Single-lead Electrocardiograms (ECGs) for Atrial Fibrillation Detection using Piecewise Linear Spline and XGBoost." Physiological measurement 39, no. 10 (2018): 104006.
2017:
[R-1] Yonghan Jung, and Heeyoung Kim. "Detection of PVC by using a Wavelet-based Statistical ECG Monitoring Procedure." Biomedical Signal Processing and Control 36 (2017): 176-182.
Talks/Seminars
Seminar on “On Measuring Causal Contributions via do-interventions”, AI Seminar, Samsung Electronics. May 2024
Seminar on ``Estimating Joint Treatment Effects from Marginal Experiments'', Quantitative Methods Research Seminars, Purdue Business Department. Nov. 2023
Tutorial on Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI, Korea Summer Session on Causal Inference, July. 2022.
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
[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 (TMLR), Journal of Machine Learning (JMLR)
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
Visiting student of Sanghack Lee's lab in Seoul National University in 2022 summer
Visiting student of Causal AI lab in Columbia University in 2024 summer
Teaching
Graduate Teaching Assistant: CS243 AI-Basics (Fall 2024)
Graduate Teaching Assistant: CS254 Data Structure (Spring 2024)
Graduate Teaching Assistant: CS448 Introduction to Database System (Fall 2023)
Graduate Teaching Assistant: CS490 Data Science Capstone (Spring 2023)
Graduate Teaching Assistant: CS408 Software Testing (Fall 2022)
Graduate Teaching Assistant: CS490 Data Science Capstone (Spring 2022)
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)