Daniel Muthukrishna
Research Scientist at Massachusetts Institute of Technology
Cambridge, England, United States
Summary
👤
Senior🎓
Top SchoolDaniel Muthukrishna is a research scientist and astrophysicist who applies deep learning and Bayesian modeling to large-scale astrophysical time-series, currently working with the TESS team at MIT. He combines a PhD from Cambridge with dual undergraduate degrees in Electrical & Aerospace Engineering and Physics, bringing a rare mix of hardware, signal-processing and advanced ML expertise. Over a decade he has built widely used open-source tools for classification, modeling and anomaly detection in transient surveys—most notably the DASH supernova spectral classifier—deployed by major collaborations. His background in RF engineering and observatory internships gives him practical instrumentation and data-acquisition insight that informs his algorithm design. He lectures on data-driven astrophysics and maintains active roles across MIT and the Center for Astrophysics, bridging research, software engineering and teaching. Colleagues describe him as a problem-solver who turns complex astronomical datasets into robust, production-ready ML pipelines.
10 years of coding experience
8 years of employment as a software developer
Bachelor of Science (B.Sc.) Physics, Bachelor of Science (B.Sc.) Physics at The University of QueenslandDoctor of Philosophy - PhD Astronomy and Astrophysics, Doctor of Philosophy - PhD Astronomy and Astrophysics at University of Cambridge
English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (17)
classification10
spectral10
python9
machine-learning9
ssa9
deep-learning9
rapid9
computer-vision8
crystallography8
tensorflow8
data-science7
neural-network7
kinematics6
astronomy6
deep-neural-networks5
Programming languages (3)
Github contributions (5)
daniel-muthukrishna/topics-python-in-research
Jun 2018 - Jun 2019
Contributions:7 commits, 7 pushes in 1 year
daniel-muthukrishna/astrorapid
Jan 2019 - Jul 2021
Real-time Automated Photometric IDentification (RAPID) of astronomical transients using deep learning
Contributions:252 commits, 5 PRs, 208 pushes in 2 years 6 months
transientsdeep-learningcomputer-visionrapididentification
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