Nesar Ramachandra

According to our database, Nesar Ramachandra authored at least 17 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models.

Arkaprabha Ganguli, Anirban Samaddar, Florian Kéruzoré, Nesar Ramachandra, Julie Bessac, Sandeep Madireddy, Emil M. Constantinescu

CoRR, February, 2026

Predicting New Concept-Object Associations in Astronomy by Mining the Literature.

Jinchu Li, Yuan-Sen Ting, Alberto Accomazzi, Tirthankar Ghosal, Nesar Ramachandra

CoRR, February, 2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration.

LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz, Matthew R. Becker, Biswajit Biswas, Rahul Biswas, Boris Bolliet, Adam S. Bolton, Clecio R. Bom, Raphaël Bonnet-Guerrini, Alexandre Boucaud, Jean-Eric Campagne, Chihway Chang, Aleksandra Ciprijanovic, Johann Cohen-Tanugi, Michael W. Coughlin, John Franklin Crenshaw, Juan C. Cuevas-Tello, Juan de Vicente, Seth W. Digel, Steven Dillmann, Mariano Javier de León Dominguez Romero, Alex Drlica-Wagner, Sydney Erickson, Alexander T. Gagliano, Christos Georgiou, Aritra Ghosh, Matthew Grayling, Kirill A. Grishin, Alan Heavens, Lindsay R. House, Mustapha Ishak, Wassim Kabalan, Arun Kannawadi, François Lanusse, C. Danielle Leonard, Pierre-François Léget, Michelle Lochner, Yao-Yuan Mao, Peter Melchior, Grant Merz, Martin Millon, Anais Möller, Gautham Narayan, Yuuki Omori, Hiranya Peiris, Laurence Perreault Levasseur, Andrés Alejandro Plazas Malagón, Nesar Ramachandra, Benjamin Remy, Cécile Roucelle, Jaime Ruiz-Zapatero, Stefan Schuldt, Ignacio Sevilla-Noarbe, Ved G. Shah, Tjitske Starkenburg, Stephen Thorp, Laura Toribio San Cipriano, Tilman Tröster, Roberto Trotta, Padma Venkatraman, Amanda Wasserman, Tim White, Justine Zeghal, Tianqing Zhang, Yuanyuan Zhang

CoRR, January, 2026

Multi-task Modeling for Engineering Applications with Sparse Data.

Yigitcan Comlek, R. Murali Krishnan, Sandipp Krishnan Ravi, Amin Moghaddas, Rafael Giorjao, Michael Eff, Anirban Samaddar, Nesar Ramachandra, Sandeep Madireddy, Liping Wang

CoRR, January, 2026

2025

Enhancing interpretability in generative modeling: statistically disentangled latent spaces guided by generative factors in scientific datasets.

Arkaprabha Ganguli, Nesar Ramachandra, Julie Bessac, Emil M. Constantinescu

Mach. Learn., September, 2025

AstroMLab 4: Benchmark-Topping Performance in Astronomy Q&A with a 70B-Parameter Domain-Specialized Reasoning Model.

Tijmen de Haan, Yuan-Sen Ting, Tirthankar Ghosal, Tuan Dung Nguyen, Alberto Accomazzi, Emily Herron, Vanessa Lama, Rui Pan, Azton Wells, Nesar Ramachandra

CoRR, May, 2025

EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants.

Franck Cappello, Sandeep Madireddy, Robert Underwood, Neil Getty, Nicholas Lee-Ping Chia, Nesar Ramachandra, Josh Nguyen, Murat Keçeli, Tanwi Mallick, Zilinghan Li, Marieme Ngom, Chenhui Zhang, Angel Yanguas-Gil, Evan R. Antoniuk, Bhavya Kailkhura, Minyang Tian, Yufeng Du, Yuan-Sen Ting, Azton Wells, Bogdan Nicolae, Avinash Maurya, M. Mustafa Rafique, Eliu A. Huerta, Bo Li, Ian T. Foster, Rick Stevens

CoRR, February, 2025

AstroMLab 1: Who wins astronomy jeopardy!?

Yuan-Sen Ting, Tuan Dung Nguyen, Tirthankar Ghosal, Rui Pan, Hardik Arora, Zechang Sun, Tijmen de Haan, Nesar Ramachandra, Azton Wells, Sandeep Madireddy, Alberto Accomazzi

Astron. Comput., 2025

InferA: A Smart Assistant for Cosmological Ensemble Data.

Justin Z. Tam, Pascal Grosset, Divya Banesh, Nesar Ramachandra, Terece L. Turton, James P. Ahrens

Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, 2025

Towards an Event-Level Analysis in Hadronic Physics Using Generative AI-Based Surrogates.

Tareq Alghamdi, Jitao Xu, Nesar Ramachandra, Nobuo Sato, Yaohang Li

Proceedings of the 37th IEEE International Conference on Tools with Artificial Intelligence, 2025

2024

Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data.

Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang

J. Comput. Inf. Sci. Eng., April, 2024

2023

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers.

Daniela Huppenkothen, Michelle Ntampaka, M. Ho, M. Fouesneau, Brian Nord, Joshua E. G. Peek, Mike Walmsley, John F. Wu, Camille Avestruz, Tobias Buck, M. Brescia, D. P. Finkbeiner, Andy D. Goulding, Tomasz Kacprzak, Peter Melchior, M. Pasquato, Nesar Ramachandra, Yuan-Sen Ting, G. van de Ven, S. Villar, V. A. Villar, Elad Zinger

CoRR, 2023

Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field.

Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang

CoRR, 2023

2022

Interpretable Uncertainty Quantification in AI for HEP.

Thomas Y. Chen, Biprateep Dey, Aishik Ghosh, Michael Kagan, Brian Nord, Nesar Ramachandra

CoRR, 2022

2021

Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning.

Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira

Nat. Mach. Intell., 2021

2020

Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation.

Romit Maulik, Themistoklis Botsas, Nesar Ramachandra, Lachlan Robert Mason, Indranil Pan

CoRR, 2020

2019

Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images.

Sandeep Madireddy, Nan Li, Nesar Ramachandra, Prasanna Balaprakash, Salman Habib

CoRR, 2019