Yonghan Jung

News

• ☀️ 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.

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]

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