Publications and Talks

Publications list on ADS

* My students

Journal Articles

  • Muthukrishna, D., *Baek, J., *Lupo, J., Vanderspek, R., Correcting Scattered Light and Systematic Effects in Astronomical Images with Diffusion Models. 2024, in prep.
  • *Gupta, R. & Muthukrishna, D. Transfer Learning and Contrastive Learning for Astronomical Transient Classification. 2024, in prep.
  • Muthukrishna, D., Haviland, J., Vanderburg, A., Shporer, A., Audenaert, J., Ricker, G., Identifying Exoplanets with Deep Learning. VII. Astronet Vetting Model, in prep.
  • Fang, M., Viana, J., Vanderburg, A., Muthukrishna, D., Identifying Exoplanets with Deep Learning VI: SCOOP: A Pipeline for Distinguishing On-Target and Off-Target Signals in TESS Data, in prep.
  • *Lupo, J., Muthukrishna, D., Vanderspek, R., Conditional Diffusion models for Correcting Systematic Effects in Astronomical Images from Space Telescopes. 2024, submitted to the Machine Learning and the Physical Sciences Workshop, Neural Information Processing Systems (NeurIPS) 2024.
  • *Gupta, R., Muthukrishna, D., Lochner, M., A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Time-Series. 2024, AI for Science, International Conference for Machine Learning (ICML) 2024. https://arxiv.org/abs/2408.08888
  • *Gupta, R., Muthukrishna, D., Lochner, M., A Classifier‑Based Approach to Multi‑Class Anomaly Detection for Astronomical Transients. 2024, in review. https://arxiv.org/abs/2403.14742
  • *Huang, H., Muthukrishna, D., *Nair, P., *Zhang, Z., Fausnaugh, M., *Majumder, T., Foley, R., Ricker, G. Predicting the Age of Astronomical Transients from Real‑Time Multivariate Time Series. 2023, Machine Learning and the Physical Sciences Workshop, Neural Information Processing Systems (NeurIPS) 2023. https://arxiv.org/abs/2311.17143
  • Tey, E., et al. (incl Muthukrishna, D.), Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations. 2022, AJ, 165, 95. https://arxiv.org/abs/2301.01371
  • Biswas, E., Ishida, E., et al. (incl Muthukrishna, D.), Enabling the discovery of fast transients: A science module for the Fink broker. 2022, A&A, 677, A77. https://arxiv.org/abs/2210.17433
  • D. Muthukrishna, K. Mandel, M. Lochner, S. Webb, G. Narayan, Real-time detection of anomalies in large-scale transient surveys, MNRAS. (2021). https://arxiv.org/abs/2111.00036
  • D. Muthukrishna, K. Mandel, M. Lochner, S. Webb, G. Narayan, Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data, Neurips workshop on Machine Learning and the Physical Sciences. (2021). https://arxiv.org/abs/2112.08415
  • D. Chatterjee, G. Narayan, P. Aleo, K. Malanchev, D. Muthukrishna, Electromagnetic Counterpart Identification of Gravitational-wave candidates using deep-learning, Neurips workshop on Machine Learning and the Physical Sciences. (2021). Link to paper
  • D. Chatterjee, G. Narayan, P. Aleo, K. Malanchev, D. Muthukrishna, El-CID: A filter for Gravitational-wave Electromagnetic Counterpart Identification, MNRAS. (2021). https://arxiv.org/abs/2108.04166
  • S. Webb, M. Lochner, D. Muthukrishna, et al., Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves, MNRAS. (2020). https://arxiv.org/abs/2008.04666
  • C. Stachie et al., Using machine learning for transient classification in searchers for gravitational-wave counterparts, MNRAS. (2020). https://arxiv.org/abs/1912.06383
  • D. Muthukrishna, G. Narayan, K. Mandel, R. Biswas, R. Hložek, RAPID: Early classification of explosive transients using Recurrent Neural Networks, PASP. (2019). https://arxiv.org/abs/1904.00014
  • D. Muthukrishna, D. Parkinson and B. Tucker, DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts, ApJ (2019). https://arxiv.org/abs/1903.02557
  • R. Kessler et al., Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC), PASP. (2019). https://arxiv.org/abs/1903.11756
  • DES Collaboration et al., First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters, ApJ. (2018). https://arxiv.org/abs/1811.02374.
  • D. Brout, et al., First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Analysis, Systematic Uncertainties, and Validation, ApJ. (2018). https://arxiv.org/abs/1811.02377.
  • R. Kessler, et al., First Cosmology Results using Type Ia Supernova from the Dark Energy Survey: Simulations to Correct Supernova Distance Biases, ApJ. (2018). https://arxiv.org/abs/1811.02379.
  • C. Andrea, et al., First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Survey Overview and Supernova Spectroscopy, ApJ. (2018). https://arxiv.org/abs/1811.09565.
  • A. Malz, et al., The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals, ApJ (submitted), https://arxiv.org/abs/1809.11145.
  • F. Campuzano, G. Bosch, G. Hägele, V.Firpo, D. Muthukrishna, M. Cardaci, Chemodynamics in Blue Compact Dwarf galaxies: II Zw 33 and Mrk 600, Boletín de la Asociación Argentina de Astronomía, 60, p.148-150 (2018). http://adsabs.harvard.edu/abs/2018BAAA…60..148C.
  • V. Firpo, D. Muthukrishna, F. Campuzano-Castro, S. Torres-Flores, G. Bosch and G. Hagele, Violent star-forming processes in interacting galaxies, MNRAS. (2018). (in preparation).

White Papers and Notes

Astronomical Telegrams

Invited Talks & Conference Presentations

  • Korea Astronomy and Space Science Institute, Invited speaker at the Early Career Researcher Cosmology Seminar Series, (March 2022).
  • Rubin Observatory’s Dark Energy Science Collaboration, Invited speaker at the Machine Learning Topical Team Seminar Series, (June 2021).
  • Telstra (top Australian Telecommunications company), Invited speaker at the AI ML Forum, (March 2021).
  • University of Sheffield, UK, Invited speaker at the  Astronomy Seminar (December 2020).
  • SLAC National Accelerator Laboratory at Stanford University, Guest speaker at the AI Seminar Series (February 2020).
  • MIT, Cambridge, MA, USA, Guest speaker at the MIT Kavli Institute’s Brown Bach Lunch Talk Series (February 2020).
  • University of Berkeley, San Francisco, California, USA, Guest speaker at the weekly SETI (Search of Extraterrestrial Intelligence) Meeting (February 2020).
  • Texas A&M University, College Station, Texas USA, Invited speaker at the Astronomical Data Science Workshop (February 2020).
  • DESY Zeuthen, Germany, Invited Astroparticle seminar speaker (November 2019).
  • ESO Garching, Germany, Contributed talk to the ESO conference on The extragalactic explosive Universe: the new era of transient surveys and data-driven discovery, (September 2019), “Classifying transients and detecting anomalies in upcoming large-scale surveys”, Garching near Munich, Germany.
  • Northwestern University, Illinois, USA, Invited speaker at the Hotwiring the transient universe workshop, (August 2019), “RAPID: Early Classification of Explosive Transients using Deep Learning”, Evanston, Illinois, USA.
  • Joint Statistical Meeting (JSM), Colorado, USA, Invited to the conference for being nominated for the Best Student Paper in Astrostatistics Award, (July 2019), “Time-series classification with Recurrent Neural Networks”, Denver, Colorado.
  • EWASS, Lyon, France, Contributed talk to the Time-domain symposium at the annual European Week of Astronomy and Space Science, (June 2019), “Early classification of transients”, Lyon, France.
  • Institute of Astronomy, University of Cambridge, Wednesday seminar, (June 2019), “Real-Time Classification of Explosive Transients Using Deep “, Cambridge, UK.
  • Space Telescope Science Institute, Baltimore, USA, Contributed talk at the Enabling Multi-messenger Astrophysics in the Big Data Era, (April 2019), “RAPID: Early Classification of Explosive Transients using Deep Learning”, Baltimore, USA.
  • Royal Astronomical Society, London, UK, Contributed talk at the Machine learning and Artificial Intelligence applied to astronomy workshop (March 2019), “RAPID: Real-time classification of explosive transients using Deep learning”, London, UK.
  • University of California, Santa Cruz, Transient Talk (March 2019), “RAPID: Real-time classification of explosive transients using Deep learning”, UC Santa Cruz, USA.
  • Harvard University (January 2019), “Real-time classification of explosive transients using deep recurrent neural networks”, Talk to CHASC Topics in Astrostatistics group.
  • Clare College, Cambridge, Invited talk to non-scientific audience (October 2018), “What is “Dark Energy, and how can exploding stars and machine learning help us find out if Einstein was right?”, Cambridge, United Kingdom.
  • University of Oxford, Contributed talk at the Wetton Workshop: Planning for Surprises in the era of Data-Driven Astronomy (June 2018), “Transient Classification with machine learning”, Oxford, United Kingdom.

Conference Posters

  • D. Muthukrishna, K. Mandel, and G. Narayan, Deep Learning for Real-Time Classification of Transient Time Series from Massive Astronomical Data Streams, Poster presented at the Cantab Capital Institute for the Mathematics of Information – connecting with industry workshop in Cambridge, UK (Nov 2018).
  • N. Lowson, D. Muthukrishna, B. Tucker, Determining the progenitor of the peculiar transient DES16X3bdj, Poster presented at the Astronomical Society of Australia Annual Meeting in Canberra, Australia (Jul 2017).
  • V. Firpo, D. Muthukrishna, F. Campuzano-Castro, S. Torres-Flores, G. Bosch and G. Hägele, Violent star-forming processes in interacting galaxies, Poster presented at the European Week of Astronomy and Space Science (EWASS) in Prague, Czech Republic (Jun 2017).

Theses

Follow me on:
Daniel Muthukrishna 2024
Vega Wordpress Theme by LyraThemes