Yonghan Jung
Graduate Student
Graduate Teaching Assistant
Joined department: Fall 2018
⭐️ 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!
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.
More detailed information can be found in my website: https://www.yonghanjung.me/
Selected Publications
[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. [Forthcoming]
[Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning] Y.Jung, J. Tian, E. Bareinboim. In Proceedings of the 38th International Conference on Machine Learning, 2021. [pdf]
[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] spotlight presentation (one of 3% of 9122 submissions))