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portfolio

publications

Tracing the cosmic web

Published in Monthly Notices of the Royal Astronomical Society, 2018

arXiv link

Recommended citation: Noam I. Libeskind, Rien van de Weygaert, Marius Cautun, Bridget Falck, Elmo Tempel, Tom Abel, Mehmet Alpaslan, Miguel A. Aragón-Calvo, Jaime E. Forero-Romero, Roberto Gonzalez, Stefan Gottlöber, Oliver Hahn, Wojciech A. Hellwing, Yehuda Hoffman, Bernard J. T. Jones, Francisco Kitaura, Alexander Knebe, Serena Manti, Mark Neyrinck, Sebastián E. Nuza, Nelson Padilla, Erwin Platen, Nesar Ramachandra , Aaron Robotham, Enn Saar, Sergei Shandarin, Matthias Steinmetz, Radu S. Stoica, Thierry Sousbie, Gustavo Yepes; Tracing the cosmic web, Monthly Notices of the Royal Astronomical Society , Volume 473, Issue 1, 1 January 2018, Pages 1195–1217 https://doi.org/10.1093/mnras/stx1976

A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling

Published in Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (2019), 2020

arXiv link

Recommended citation: Sandeep Madireddy, Nan Li, Nesar Ramachandra , James Butler, Prasanna Balaprakash, Salman Habib, Katrin Heitmann; A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling, Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (2019) https://arxiv.org/abs/1911.03867

Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z

Published in Monthly Notices of the Royal Astronomical Society, 2022

arXiv link

Recommended citation: Nesar Ramachandra , Jonás Chaves-Montero, Alex Alarcon, Arindam Fadikar, Salman Habib, Katrin Heitmann; Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z , Monthly Notices of the Royal Astronomical Society, Volume 515, Issue 2, September 2022, Pages 1927–1941 https://doi.org/10.1093/mnras/stac1790

Interpretable Uncertainty Quantification in AI for HEP

Published in Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021, Submitted), 2022

arXiv link

Recommended citation: Thomas Y. Chen, Biprateep Dey, Aishik Ghosh, Michael Kagan, Brian Nord, Nesar Ramachandra ; Interpretable Uncertainty Quantification in AI for HEP, Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021) https://doi.org/10.48550/arXiv.2208.03284

talks

teaching

Graduate Teaching Assistant

Department of Physics and Astronomy, University of Kansas, 2014

  • Fall 2017 - PHSX 214 (General Physics II Honors)
  • Fall 2016 - PHSX 214 (General Physics II Honors)
  • Spring 2015 - PHSX 216 (General Physics I)
  • Fall 2014 - PHSX 114 (College Physics I)

Head Teaching Assistant

Department of Physics and Astronomy, University of Kansas, 2015

Coordinated Teaching Assistants in the Department. Managed performance analysis on labs and quizzes of nearly 4000 students in 12 courses.

  • Summer 2016
  • Spring​ ​2016
  • Fall​ ​2015​