Title: Transcending the Limits of Astrostatistics with Machine Learning Methods

Speaker: Ting Yuan Sen

Institute:  The Australian National University/ The Ohio State University

Host: Feng Siyi

Time: 14:30-16:30, Friday, July 07

Location: Physics Building 117

Abstract: Astronomy has undergone a profound transformation in recent years, as the acquisition of ever-growing amounts of data through increasingly powerful instruments has opened up a wealth of new avenues of exploration. However, this boon is not without its own set of challenges, as astronomical observations are often multi-dimensional in nature, encompassing the most meticulous imaging of weak lensing, reionization, and protoplanetary disks at their finest details, as well as the comprehensive characterization of complex galaxy mergers throughout cosmic history. In this realm, conventional astrostatistical methods falter.

To address this challenge, I will expound upon two different machine learning approaches for characterizing these complex astronomical systems. Firstly, the Mathematics of Information: I will explore how machine learning can refine the compression of information and extract higher-order moments in stochastic processes. Secondly, a Generative Paradigm: I will delve into how generative models, such as normalizing flows and diffusion models, permit us to model astronomical data sets with exactitude, furthering the study of complicated astronomical systems within their observational domain.

Bio：Yuan-Sen is an Associate Professor of astronomy and computer science at the Australian National University and an Associate Professor of astronomy at the Ohio State University. His research endeavours revolve around the utilization of machine learning in advancing statistical inferences, specifically utilizing vast astronomical survey data. Hailing from Malaysia, Yuan-Sen was awarded a PhD in astronomy and astrophysics from Harvard University in 2017 and was subsequently granted a four-way fellowship from Princeton University, Carnegie Institute for Sciences, NASA Hubble, and the Institute for Advanced Study at Princeton. In recognition of his pioneering work in AI x Science, he was acknowledged as a Future Leader by the Association of Universities for Research in Astronomy, in addition to being honored as a NASA Earth and Space Science Fellow. Recently, Yuan-Sen received the ARC DECRA fellowship following his induction to ANU.

In parallel to his academic pursuits, Yuan-Sen assumes the role of co-chair for the NASA Cosmic Programs Stars Science Interest Group, spearheading multiple future spectroscopic surveys as a science group leader. Beyond academia, Yuan-Sen is fervent about public outreach and has authored monthly columns in the largest Chinese newspaper in Malaysia and produced two TED education videos with over 4 million views. Moreover, he has served as a chief science officer, developing machine learning tools to detect art forgery in paintings. Yuan-Sen's past endeavours include semi-professional gaming, where he was once recognized as a top Night-Elf player in Warcraft 3 in Malaysia.