Scholar
Daniel Muthukrishna
Google Scholar ID: LXiEIVcAAAAJ
Massachusetts Institute of Technology, University of Cambridge, University of Queensland, Australian
Citations & Impact
Citations
2,384
H-index
18
i10-index
21
Publications
20
Co-authors
1
list available
Publications
Resume (English only)
Academic Achievements
- Developed several widely-used software packages designed for classification, modeling, and anomaly detection in large datasets. These tools have significantly contributed to the field, enabling more efficient and accurate analysis of complex astrophysical phenomena. He also lectures a public course on “Data-Driven Astronomy: Machine Learning and Statistics for Modern Astrophysics” and supervises both graduate and undergraduate students.
Research Experience
- A research scientist at the Massachusetts Institute of Technology (MIT), and concurrently an AstroAI Fellow at the Harvard University & Smithsonian’s Center for Astrophysics. At MIT, he is a key member of the TESS team, leading the effort to classify exoplanets using neural networks. His current research involves applying state-of-the-art machine learning approaches, such as diffusion models, transformers, and recurrent neural networks, to better understand the universe.
Education
- Received his PhD in Astrophysics from the University of Cambridge in 2021; earned dual degrees in Bachelor of Engineering (Electrical & Aerospace) and a Bachelor of Science (Physics) from the University of Queensland, Australia.
Background
- An astrophysicist and machine learning scientist. His primary research focus is on leveraging advanced machine learning techniques to analyze and interpret astronomical data, including modeling supernovae and classifying exoplanets using deep learning and Bayesian methods.
Miscellany
- He 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.
