Research Interest

"Two things fill the mind with ever new and increasing admiration and awe, the more often and steadily we reflect upon them: the starry heavens above me and the moral law within me." - Immanuel Kant

My research group tackles the most challenging aspects of astrophysics in light of large data sets. My work draws heavily on a combination of theoretical modeling, statistical inferences, and machine learning. I use these tools to provide new innovative angles and shed light on the most fundamental questions of star formation, galactic evolution, the formation of black holes, and cosmology.

I primarily work on the Milky Way, capitalizing on a wide range of on-going large-scale surveys and most key future surveys in the next decade, including spectroscopy (SDSS-V, DESI, GALAH, APOGEE, LAMOST, JWST), astrometry (Gaia), photometry (DES, LSST, Euclid, WFIRST) and asteroseismology (TESS, PLATO). I am an "end-to-end" large survey-oriented scientist -- I develop novel machine learning methods to maximally harness information in the data, build theoretical models, and confront them with observation via statistical inference.


Galaxy Evolution


Stellar Astrophysics




Simulation-Based Inference


Theoretical Machine Learning


Natural Language Processing

International Astronomical Union Symposium in Malaysia

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Why machine learning

"One of the principal objects of theoretical research is to find the point of view from which the subject appears in the greatest simplicity." - Josiah Willard Gibbs

The new era of big data

Astronomy today is a fundamentally different field than it was just a decade ago. The order of magnitude of change has been powered by our ability to gather extraordinarily large sets of data from ever more powerful instruments. But with big data, astronomy has also taken a quantum leap forward in its relationship with data science. The quantity of data matters, but our ability to interpret this big data in meaningful ways is paramount.

Impasses in astronomy

The evolution of the Universe and its constituents are inherently stochastic. The Universe’s inherent complexity exceeds the limits of analytic calculation and therefore, in many cases, understanding the cosmos requires cutting-edge simulations. This leads invariably to a question at the heart of most astronomical impasses: How can we design robust statistical descriptors to compare simulations with observations?

A vision

Surpassing this impasse will require us to fully harness the power of machine learning, while retaining our ability to interpret. In order to “​see what the machine sees​,” and use this to advance our understanding of the universe, requires an open, multi-dimensional academy, where conversations among astronomy, machine learning, and statistics can flow freely. I believe that by cross-pollinating astronomy with the data analytics of machine learning and classical statistics, a hybrid vigor in astronomical research may result.


"The limits of my language mean the limits of my world." - Ludwig Wittgenstein

Research Group

"We are all in the gutter, but some of us are looking at the stars." - Oscar Wilde

Public Outreach

"The reward of the young scientist is the emotional thrill of being the first person in the history of the world to see something or to understand something." - Cecilia Payne-Gaposchkin

TED: How do we study the stars [0.6M views]

TED: How to measure distances [ 2.6M views ]



Interactive Modules: Interstellar Absorption and the Lyman Alpha Forest (full screen)

Mainstream Media Columns

"As we look out into the Universe and identify the many accidents of physics and astronomy that have worked together to our benefit, it almost seems as if the Universe must in some sense have known that we were coming." - Freeman Dyson


"The struggle itself towards the heights is enough to fill a man's heart. One must imagine Sisyphus happy." - Albert Camus

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Professional Appointment


Australian National University — Associate Professor (tenured)


Australian National University — Assistant Professor (tenured)


Institute for Advanced Study, Princeton — NASA Hubble Fellow


Princeton University — Carnegie-Princeton Fellow



Harvard University — Ph.D., Astrophysics

Short Bio

When I imagine my background I see one of those Russian nesting dolls: I was born and grew up near Kuala Lumpur, the capital of Malaysia, and my hometown is a beautiful riverside village known as Sibu. Since high school, I have been pursuing my education on different continents and in various cultures, adding another self to the growing body of my work. The process started when I undertook a concurrent, double-degree program from the National University of Singapore, and École Polytechnique, in France.

Goethe pointed out that, “They who know no languages know nothing of their own,” and so, having acquired a couple of dialects of Chinese from my parents in Malaysia, and French from immersion courses in Singapore, I used my best sitcom-derived American English that I learned in France to apply for graduate school. Fortune smiled on me, and I moved to the United States to pursue a Ph.D. in astrophysics at Harvard University, funded by a NASA Earth and Space Science Fellowship. After earning my doctorate in 2017, I was honored to be the first scholar awarded a joint-fellowship from the Institute for Advanced Study at Princeton, Princeton University, and Carnegie Observatory (and a NASA Hubble Fellowship).

Two years after doctorate, I was appointed a permanent faculty position at the Australian National University in Canberra. After the havoc due to the pandemic, I joined the school in 2021, was promoted to an Associate Professor the same year and have been there since.

National U. Singapore
École Polytechnique
Harvard University
Australian National U.


Australian Research Council DECRA Fellowship

AURA Future Leader

NASA Hubble Fellowship

Carnegie-Princeton Fellowship

Institute for Advanced Study Fellowship

CCAPP Price Prize in Cosmology and AstroParticle Physics

NASA Earth and Space Science Fellowship

Research Milestones


Journal publications


First/Supervising Author


Second/Third Author






Seminars/conference presentations


"We cannot work without hoping that others will advance further than we have. In principle, this progress goes on ad infinitum. " - Max Weber

Astro-machine-learning - 2022

Australian National Institute for Theoretical Astrophysics (ANITA) - Summer School

International Olympiad - 2021

Malaysian Academic Council

Astro-statistics - 2020

Tsinghua University - Guest Lecturer

Algebra - 2017

Princeton Prison Teaching Innitiative

Stellar astrophysics - 2014

Harvard University

Classical Mechanics

National University of Singapore


National University of Singapore


National University of Singapore

Linear Algebra

National University of Singapore


National University of Singapore

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