Welcome

Dr. Daniel Muthukrishna is a postdoctoral researcher at the Massachusetts Institute of Technology (MIT), specializing in the application of machine learning to astrophysical time-series data. He received his PhD in Astrophysics from the University of Cambridge in 2021. Daniel grew up in Brisbane, Australia, and earned a Bachelor of Engineering (Electrical & Aerospace) and a Bachelor of Science (Physics) from the University of Queensland, Australia.

Daniel’s research primarily focuses on leveraging advanced machine-learning techniques to analyze and interpret astronomical data. His work ranges from modeling supernovae to classifying exoplanets using deep learning and Bayesian methods. At MIT, he is a key member of the Transiting Exoplanet Survey Satellite (TESS) team, where he leads the effort to classify exoplanets using neural networks. His current research involves applying state-of-the-art machine learning approaches, including diffusion models, transformers, and recurrent neural networks, to better understand the universe.

Daniel has developed several widely-used software packages designed for classification, modeling, and anomaly detection in large datasets. These tools have contributed significantly to the field, enabling more efficient and accurate analysis of complex astrophysical phenomena.

In addition to his research, Daniel lectures a public course on “Data-Driven Astronomy: Machine Learning and Statistics for Modern Astrophysics” and supervises both graduate and undergraduate students. Daniel regularly presents his work at academic conferences and public science events, contributing to the dissemination of knowledge in the rapidly evolving intersection of astrophysics and machine learning.

Daniel’s work aims to advance our understanding of the cosmos through innovative applications of artificial intelligence, positioning him at the forefront of modern astrophysical and machine learning research.