Hello! I'm Yuan-Sen, an Associate Professor of Astronomy at Ohio State University and an Adjunct Scientist at the Max Planck Institute for Astronomy. Previously, I held positions as an Associate Professor of Astronomy and Computer Science at the Australian National University, and have held Visiting Professor position at Tsinghua University and Universiti Malaya.

I received my Ph.D. in Astrophysics and Astronomy from Harvard University. As a Malaysian native, I've had the privilege of living in seven countries and picking up six languages along the way, truly embracing the role of a global citizen. I completed my concurrent Bachelor's and Master's degrees at the National University of Singapore and École Polytechnique in France on an Eiffel scholarship. During my studies, I was honored with the Institute for Physics Medal and the National Academy of Science Award in Singapore, as well as a NASA Earth and Space Science Fellowship for my studies.

Post-Ph.D., I was awarded a NASA Hubble Fellowship, Carnegie-Princeton Fellowship, and IAS Fellowship, enabling me to conduct postdoctoral research at Princeton's Institute for Advanced Study. I then joined the Australian National University as a tenured faculty member before moving back to the U.S. to join OSU. My career has been further enriched by recognitions such as the Humboldt Fellowship, the CCAPP Price Prize, and the ARC DECRA Fellowship.

My research harnesses deep learning and artificial intelligence to advance data modeling and statistical inference in astrophysics. I specialize in extracting weak signals from massive datasets to tackle some of the field's most challenging problems. This work has resulted in over 240 publications, garnering more than 11,000 citations, including a Nature cover story. As the leader of AstroMLab, a collaborative effort with Oak Ridge and Argonne National Laboratories, I guide a team of computer scientists and astrophysicists in developing specialized large language models and research agents for astrophysical applications.

I'm deeply committed to promoting science in my home country, Malaysia. I've organized two major astronomy conferences and a summer school there, including an IAU Symposium, the first in the region since 1990s, and I regularly write columns for Sin Chew Jit Poh, a leading Chinese newspaper with over a million readers. I've also given a TEDx talk in Malaysia and created two TED-Ed videos that have amassed over four million views combined. I was also the first Malaysian astronomer to join the Malaysian Olympiad on Astronomy & Astrophysics Council, lecturing and selecting a team representing Malaysia.

Beyond academia, I've served as a chief science officer on a project detecting art forgeries. And during my younger years, I was a top Night-Elf player in Warcraft 3 in Malaysia!

I have taught courses in both computer science and astronomy, developing pedagogical materials that bridge theoretical foundations with computational practice. This work has resulted in two open-access textbooks that reflect my teaching philosophy: building understanding while incorporating AI tools into the learning process.

Statistical Machine Learning for Astronomy (2025) - This ~700-page textbook demonstrates that machine learning techniques are natural extensions of the statistical methods astronomers have used. Rather than presenting machine learning as a collection of disconnected algorithms or "black boxes," the textbook builds understanding from probability theory and Bayesian inference through to neural networks. The emphasis on classical statistical foundations—alongside deep learning—provides the theoretical grounding needed for scientific application of these methods in astronomy. Topics span supervised and unsupervised learning, computational inference methods, Gaussian Processes, and neural networks, unified through a Bayesian framework. The textbook addresses the challenges of astronomical data: irregular sampling, heteroscedastic uncertainties, physical constraints, and error propagation and uncertainty quantification. [arXiv:2506.12230]

Coding Essentials for Astronomers (2025) - This textbook approaches coding education through the lens of human-AI collaboration, which has become increasingly relevant to scientific computing. The 22-lecture series covers Python and scientific computing libraries (NumPy, Pandas, Matplotlib, SciPy, Astropy) while teaching students how to leverage large language models throughout their workflow—from code generation and debugging to building tools like Streamlit interfaces and implementing the Model Context Protocol for research automation. The curriculum reflects that researchers now need to understand both the fundamentals of scientific computing and how to work with AI systems. This approach prepares students for astronomical research where coding increasingly involves interaction between human reasoning and AI tools. [Open Textbook] [GitHub] [DOI:10.5281/zenodo.17850426]