I am an Assistant Professor in the Graduate School of Data Science at Seoul National University, directing Causality Lab . Prior to joining Seoul National University, I was an Associate Research Scientist at Columbia University and Postdoctoral Research Associate at Computer Science, Purdue University working with Prof. Elias Bareinboim. I got my Ph.D. in College of Information Sciences and Technology, Pennsylvania State University, University Park, under the supervision of Prof. Vasant Honavar.

Currently, I am working on developing methods for applying causality in sequential decision-making and developing theory of causal effect identifiability and transportability. During my Ph.D. study, I focused on causal discovery in a relational domain.
[Curriculum Vitae]

Research Interests

• Causal Inference (how can we identify the effect of an intervention?), Causal Decision Making (how can we utilize causal information in decision making?), Causal Discovery (how can we establish causal relationships from complex data?)
• (Future) Developing theories and applications for health and social domains with causality as a first principle by bringing ideas from economics. Developing robust machine learning algorithms utilizing causal knowledge.

Employment & Education

News

• (Jan 2024) Soheun’s paper on cyclic causal discovery is accepted at AISTATS. Congrats!
• (Nov 2023) Two NeurIPS workshop papers are accepted.
• (Jun 2023) Causal dynamics learning paper by Inwoo is accepted to a workshop at ICML 2023.
• (Jan 2023) Inwoo’s paper on local causal discovery using neural network is accepted at CLeaR 2023!
• (May 2022) A co-authored paper on the intersection between transportability and counterfactual identification will be presented at ICML.
• (Dec 2021) Two papers on identification accepted to NeurIPS 2021 and a paper to a sequential decision making workshop at NeurIPS.
• (Jul 2021) A paper on ‘counterfactual identification’ (joint work with Correa and Bareinboim) is online.
• (Mar 2021) I joined Graduate School of Data Science at Seoul National University as an Assistant Professor.

Academic Activities

Program Committee/Reviewed for

• 2024 NeurIPS (Area Chair), CLeaR, AISTATS, ICML, UAI, JMLR
• 2023 UAI, NeurIPS (top reviewer), Journal of Causal Inference
• 2022 ICLR (highlighted reviewer), AAAI, AISTATS, CLeaR, ICML, UAI (top reviewer), JCI, NeurIPS
• 2021 ICLR, AAAI, AISTATS, UAI, ICML, NeurIPS, Journal of Artificial Intelligence Research, NeurIPS Workshop on Causal Inference and Machine Learning: Why now?
• 2020 NeurIPS, UAI, ICML (top reviewer), AAAI, AISTATS, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Journal of Artificial Intelligence (AIJ), Journal of Causal Inference (JCI), NeurIPS Workshop on Causal Discovery and Causality-Inspired Machine Learning (CDML, Area Chair)
• 2019 NeurIPS (Best Reviewer Award), WHY conference, Journal of Machine Learning Research (JMLR), 2017 Causality Workshop at UAI, 2016 ACM CHI, 2014 ACM Transactions on Intelligent Systems and Technology

Research Projects

• Causal Machine Learning (NRF, PI, 2023~27 with Innovative Research Lab Initiation Grant)
• Self-Motivated AI: Developing self-directed AI agents that can solve new problems (IITP, co-PI, 2022~26, PI: Byoung-Tak Zhang)
• Center for Optimizing Hyperscale AI Models and Platforms (NRF, co-PI, 2023.06~, PI: Jaejin Lee)
• Metabolomic Big Data Analysis (MFDS, co-PI, 2023~25)
• An algorithmic aspect of proxy-based causal inference (SNU, PI, 2021~24)
• Causal Discovery for Time Series (LG AI Research, PI, 2023.04~24.04)

Causality Lab.

Ph.D. students

• Inwoo Hwang
• Yeahoon Kwon, causal imitation learning
• Yesong Choe, efficient causal inference
• Yeha Kim, deep counterfactual inference
• Juhyeon Kim, causal NLP
• Min Woo Park, causal decision making
• Yeo Dong Youn

Master students

• (2021) Taehan Kim, causal feature selection
• (2022) Jung Soo Kim, representation learning for instrumental variables
• (2022) Dahhee Yim, counterfactual inference
• (2022) Jonghwan Kim, robust and efficient causal discovery
• (2023) Heejin Choi, interpretable causal inference
• (2023) Kyung A Song, bias-mitigated recommender system
• (2023) Jaeho Jeong, ML-based IV estimation under misspecification
• (2023) Kwon Ho Kim, causal discovery with score matching, causally disentangled representation learning
• (2023) Jin A Choi
• (2023) Soungmin Park, causal RL, counterfactual Identification
• (2023) Oh Yoon Kwon
• (2023) Hyunwoo Park
• (2024) Hyeonji Kim
• (2024) Jihae Chung

Undergraduate Researchers

• Sangyeon Cho, causal data science for economic analysis
• Min Young Cho
• HyeJin Kwon

Alumni

• Jeong Ha Moon (master 2022)
• Jeongsup Park (master 2022)
• Chaeyoung Chung (master 2021)
• Dong Kyu Cho (master 2021, LG AI Research (intern), a soon-to-be NYU Ph.D. student)
• Jewon Kang (master 2021~2023, KDB)
• Soheun Yi (CMU Ph.D. program)

Past Projects

• Semantic Search for Korean Medical and Legal Documents (SNU, co-PI, 2022~23, PI: Hyopil Shin)
• Association and Causality in Metabolomic data (MFDS, co-PI, 2022)
• Supply Chain based Financial Keyword Analysis (NH Investment, 2021)