안녕하세요, 데이터사이언스대학원에서 아래의 내용과 같이 BK21 x ERC 세미나를 개최하오니 여러분의 많은 관심과 참여를 부탁드립니다.
연사 Julius von Kügelgen 박사님은 ETH Zürich에서 Postdoctoral Researcher로 재직하고 있습니다.
고차원데이터의 생성을 담당하는 잠재변수들의 발견과 해당 변수들 간의 인과관계를 연구하는 Causal Representation Learning에서
세계적인 연구 결과를 발표하고 있으며, 해당 주제에 대한 강연을 진행할 예정입니다.
일시: 2025년 4월 17일 오후 2:00 - 3:30
장소: 서울대학교 942동 302호
Speaker: Dr. Julius von Kügelgen (ETH Zürich)
Title: Causal Representation Learning: Overview and Applications to Single-Cell Biology
Abstract: Many scientific questions are fundamentally causal in nature. Yet, existing causal inference methods cannot easily handle complex, high-dimensional data. Causal representation learning (CRL) seeks to fill this gap by embedding causal models in the latent space of a machine learning model. In this talk, I will provide an overview of CRL across a variety of settings. I will then present ongoing work on leveraging CRL methods for problems in bioinformatics, specifically for predicting the effects of unseen drug or gene perturbations from omics measurements. CRL requires rich experimental data and single-cell biology offers unique opportunities for gaining new scientific insights by leveraging such methods.
Bio: Julius von Kügelgen is a postdoc at the Seminar for Statistics at ETH Zürich. His research lies at the intersection of causal inference and machine learning. He obtained his PhD in Machine Learning from the University of Cambridge and the Max Planck Institute for Intelligent Systems. His work has been recognized with the Google PhD Fellowship, a Best Paper Award at the Conference on Causal Learning and Reasoning, and the Cambridge PhD Prize in Quantitative Research. Prior to his PhD, Julius studied Mathematics (BSc, MSci) at Imperial College London and Artificial Intelligence (MSc) at UPC Barcelona and TU Delft.