Nesar Ramachandra

Nesar Ramachandra

Computational Scientist, Argonne National Laboratory

Publications

Up-to-date publication list and citations can be found here: Google Scholar

2025

AstroMLab 1: Who wins astronomy jeopardy!?

Astronomy and Computing, 2025

[Google Scholar](https://scholar.google.com/scholar?q=AstroMLab+1:+Who+wins+astronomy+jeopardy!?)

Y-S Ting and T Dung Nguyen and Tirthankar Ghosal and Rui Pan and Hardik Arora and Zechang Sun and Tijmen de Haan and Nesar Ramachandra and Azton Wells and Sandeep Madireddy and Alberto Accomazzi (2025). "AstroMLab 1: Who wins astronomy jeopardy!?". Astronomy and Computing. https://www.sciencedirect.com/science/article/pii/S2213133724001082

Constraining Early Dark Energy Models with Power Spectra Emulators

Bulletin of the American Physical Society, 2025

[Google Scholar](https://scholar.google.com/scholar?q=Constraining+Early+Dark+Energy+Models+with+Power+Spectra+Emulators)

Niyantri Krishnan and Mary Gerhardinger and Salman Habib and Katrin Heitmann and Nesar Ramachandra (2025). "Constraining Early Dark Energy Models with Power Spectra Emulators". Bulletin of the American Physical Society. https://meetings.aps.org/Meeting/PSS25/Session/H01.2

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Journal of Mechanical Design, 2025

[Google Scholar](https://scholar.google.com/scholar?q=Data-Efficient+Dimensionality+Reduction+and+Surrogate+Modeling+of+High-Dimensional+Stress+Fields)

Anirban Samaddar and Sandipp Krishnan Ravi and Nesar Ramachandra and Lele Luan and Sandeep Madireddy and Anindya Bhaduri and Piyush Pandita and Changjie Sun and Liping Wang (2025). "Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields". Journal of Mechanical Design. https://asmedigitalcollection.asme.org/mechanicaldesign/article/147/3/031701/1202979

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

arXiv preprint arXiv:2502.20309, 2025

[Google Scholar](https://scholar.google.com/scholar?q=EAIRA:+Establishing+a+Methodology+for+Evaluating+AI+Models+as+Scientific+Research+Assistants)

Franck Cappello and Sandeep Madireddy and Robert Underwood and Neil Getty and Nicholas Lee-Ping Chia and Nesar Ramachandra and Josh Nguyen and Murat Keceli and Tanwi Mallick and Zilinghan Li and Marieme Ngom and Chenhui Zhang and Angel Yanguas-Gil and Evan Antoniuk and Bhavya Kailkhura and Minyang Tian and Yufeng Du and Yuan-Sen Ting and Azton Wells and Bogdan Nicolae and Avinash Maurya and M Mustafa Rafique and Eliu Huerta and Bo Li and Ian Foster and Rick Stevens (2025). "EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants". arXiv preprint arXiv:2502.20309. https://arxiv.org/abs/2502.20309

GAN-based Event-level Inverse Mapper (GEIM)-An Application on Quantum Chromodynamics Global Analysis

Preprint, 2025

[Google Scholar](https://scholar.google.com/scholar?q=GAN-based+Event-level+Inverse+Mapper+(GEIM)-An+Application+on+Quantum+Chromodynamics+Global+Analysis)

Tareq Alghamdi and Yaohang Li and Kishansingh Rajput and Nesar Ramachandra (2025). "GAN-based Event-level Inverse Mapper (GEIM)-An Application on Quantum Chromodynamics Global Analysis". Preprint. https://digitalcommons.odu.edu/gradresearch_achievementday/2024/sciences/12/

Snowmass2021-Letter of Interest Scientific AI Approaches in Computational Cosmology

Preprint, 2025

[Google Scholar](https://scholar.google.com/scholar?q=Snowmass2021-Letter+of+Interest+Scientific+AI+Approaches+in+Computational+Cosmology)

Salman Habib and Nesar Ramachandra and Xiaofeng Dong and Sandeep Madireddy (2025). "Snowmass2021-Letter of Interest Scientific AI Approaches in Computational Cosmology". Preprint. https://www.academia.edu/download/77182246/SNOWMASS21-CompF3_CompF2_Ramachandra-109.pdf

2024

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model

arXiv preprint arXiv:2411.09012, 2024

[Google Scholar](https://scholar.google.com/scholar?q=AstroMLab+3:+Achieving+GPT-4o+Level+Performance+in+Astronomy+with+a+Specialized+8B-Parameter+Large+Language+Model)

Tijmen de Haan and Yuan-Sen Ting and Tirthankar Ghosal and Tuan Dung Nguyen and Alberto Accomazzi and Azton Wells and Nesar Ramachandra and Rui Pan and Zechang Sun (2024). "AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model". arXiv preprint arXiv:2411.09012. https://arxiv.org/abs/2411.09012

Benchmarking AI-evolved cosmological structure formation and expanding dimensions through parallelization frameworks

APS April Meeting Abstracts, 2024

[Google Scholar](https://scholar.google.com/scholar?q=Benchmarking+AI-evolved+cosmological+structure+formation+and+expanding+dimensions+through+parallelization+frameworks)

Xiaofeng Dong and Nesar Ramachandra and Azton Wells and Michael Buehlmann and Salman Habib and Katrin Heitmann (2024). "Benchmarking AI-evolved cosmological structure formation and expanding dimensions through parallelization frameworks". APS April Meeting Abstracts. https://ui.adsabs.harvard.edu/abs/2024APS..APRKK3006D/abstract

Diffusion model based emulator for synthetic cosmological structure formation

APS April Meeting Abstracts, 2024

[Google Scholar](https://scholar.google.com/scholar?q=Diffusion+model+based+emulator+for+synthetic+cosmological+structure+formation)

Junbo Peng and Zhaodi Pan and Xiaofeng Dong and Nesar Ramachandra and Salman Habib and Katrin Heitmann (2024). "Diffusion model based emulator for synthetic cosmological structure formation". APS April Meeting Abstracts. https://ui.adsabs.harvard.edu/abs/2024APS..APRBB0005P/abstract

Efficient mapping between void shapes and stress fields using deep convolutional neural networks with sparse data

Journal of Computing and Information Science in Engineering, 2024

[Google Scholar](https://scholar.google.com/scholar?q=Efficient+mapping+between+void+shapes+and+stress+fields+using+deep+convolutional+neural+networks+with+sparse+data)

Anindya Bhaduri and Nesar Ramachandra and Sandipp Krishnan Ravi and Lele Luan and Piyush Pandita and Prasanna Balaprakash and Mihai Anitescu and Changjie Sun and Liping Wang (2024). "Efficient mapping between void shapes and stress fields using deep convolutional neural networks with sparse data". Journal of Computing and Information Science in Engineering. https://asmedigitalcollection.asme.org/computingengineering/article/24/5/051008/1195153

Enhancing Interpretability in Generative Modeling: Disentangled Latent Spaces in Scientific Datasets

Authorea Preprints, 2024

[Google Scholar](https://scholar.google.com/scholar?q=Enhancing+Interpretability+in+Generative+Modeling:+Disentangled+Latent+Spaces+in+Scientific+Datasets)

Arkaprabha Ganguli and Nesar Ramachandra and Julie Bessac and Emil Constantinescu (2024). "Enhancing Interpretability in Generative Modeling: Disentangled Latent Spaces in Scientific Datasets". Authorea Preprints. https://advance.sagepub.com/doi/full/10.22541/essoar.172926700.08399445

High-dimensional Surrogate Modeling for Image Data with Nonlinear Dimension Reduction

Preprint, 2024

[Google Scholar](https://scholar.google.com/scholar?q=High-dimensional+Surrogate+Modeling+for+Image+Data+with+Nonlinear+Dimension+Reduction)

Lele Luan and Sandipp Krishnan Ravi and Anindya Bhaduri and Piyush Pandita and Liping Wang and Nesar Ramachandra and Sandeep Madireddy (2024). "High-dimensional Surrogate Modeling for Image Data with Nonlinear Dimension Reduction". Preprint. https://arc.aiaa.org/doi/abs/10.2514/6.2024-0388

Learning Relationships Between Disparate Representations of Objects with Transformers and Contrastive Losses

Authorea Preprints, 2024

[Google Scholar](https://scholar.google.com/scholar?q=Learning+Relationships+Between+Disparate+Representations+of+Objects+with+Transformers+and+Contrastive+Losses)

Nesar Ramachandra and Azton Wells and Nick Frontiere and Salman Habib (2024). "Learning Relationships Between Disparate Representations of Objects with Transformers and Contrastive Losses". Authorea Preprints. https://www.authorea.com/doi/full/10.22541/essoar.172675995.55091022

Reducing Model Error Using Optimised Galaxy Selection: Weak Lensing Cluster Mass Estimation

arXiv preprint arXiv:2406.11950, 2024

[Google Scholar](https://scholar.google.com/scholar?q=Reducing+Model+Error+Using+Optimised+Galaxy+Selection:+Weak+Lensing+Cluster+Mass+Estimation)

Markus Michael Rau and Florian Kéruzoré and Nesar Ramachandra and Lindsey Bleem (2024). "Reducing Model Error Using Optimised Galaxy Selection: Weak Lensing Cluster Mass Estimation". arXiv preprint arXiv:2406.11950. https://arxiv.org/abs/2406.11950

2023

2023 AI Testbed Expeditions Report

Preprint, 2023

[Google Scholar](https://scholar.google.com/scholar?q=2023+AI+Testbed+Expeditions+Report)

Venkat Vishwanath and Murali Emani and Varuni Sastry and William Arnold and Rajeev Thakur and Valerie Taylor and Ian Foster and Salman Habib and Michael E Papka and Bryce Allen and Henry Chan and Rodrigo Ceccato de Freitas and Mathew J Cherukara and Miaoqi Chu and Jose M Monsalve Diaz and Neil Getty and Ross Harder and Kyle Hippe and Saugat Kandel and Antonino Miceli and Suresh Narayanan and Oleksandr Narykov and Alexander Partin and Nesar Ramachandra and Arvind Ramanathan and Esteban Rangel and Siddhisanket Raskar and Andrew Siegel and John Tramm and Thomas Uram and Azton Wells and Leighton Wilson and Fangfang Xia and Kazutomo Yoshii and Ruoxi Zhao and Tao Zhou (2023). "2023 AI Testbed Expeditions Report". Preprint. https://www.osti.gov/servlets/purl/2439992

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

arXiv preprint arXiv:2303.16869, 2023

[Google Scholar](https://scholar.google.com/scholar?q=Application+of+probabilistic+modeling+and+automated+machine+learning+framework+for+high-dimensional+stress+field)

Lele Luan and Nesar Ramachandra and Sandipp Krishnan Ravi and Anindya Bhaduri and Piyush Pandita and Prasanna Balaprakash and Mihai Anitescu and Changjie Sun and Liping Wang (2023). "Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field". arXiv preprint arXiv:2303.16869. https://arxiv.org/abs/2303.16869

Carbon-enhanced metal-poor star candidates from BP/RP spectra in Gaia DR3

Monthly Notices of the Royal Astronomical Society, 2023

[Google Scholar](https://scholar.google.com/scholar?q=Carbon-enhanced+metal-poor+star+candidates+from+BP/RP+spectra+in+Gaia+DR3)

Madeline Lucey and Nariman Al Kharusi and Keith Hawkins and Yuan-Sen Ting and Nesar Ramachandra and Adrian M Price-Whelan and Timothy C Beers and Young Sun Lee and Jinmi Yoon (2023). "Carbon-enhanced metal-poor star candidates from BP/RP spectra in Gaia DR3". Monthly Notices of the Royal Astronomical Society. https://academic.oup.com/mnras/article-abstract/523/3/4049/7191262

Constructing impactful machine learning research for astronomy: Best practices for researchers and reviewers

arXiv preprint arXiv:2310.12528, 2023

[Google Scholar](https://scholar.google.com/scholar?q=Constructing+impactful+machine+learning+research+for+astronomy:+Best+practices+for+researchers+and+reviewers)

Daniela Huppenkothen and Michelle Ntampaka and Matthew Ho and Morgan Fouesneau and Brian Nord and Joshua EG Peek and Mike Walmsley and John F Wu and Camille Avestruz and Tobias Buck and Massimo Brescia and Douglas P Finkbeiner and Andy D Goulding and Tomasz Kacprzak and Peter Melchior and Mario Pasquato and Nesar Ramachandra and Y-S Ting and Glenn van de Ven and Soledad Villar and VA Villar and Elad Zinger (2023). "Constructing impactful machine learning research for astronomy: Best practices for researchers and reviewers". arXiv preprint arXiv:2310.12528. https://arxiv.org/abs/2310.12528

Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems

Preprint, 2023

[Google Scholar](https://scholar.google.com/scholar?q=Scalable+Probabilistic+Modeling+and+Machine+Learning+With+Dimensionality+Reduction+for+Expensive+High-Dimensional+Problems)

Lele Luan and Nesar Ramachandra and Sandipp Krishnan Ravi and Anindya Bhaduri and Piyush Pandita and Prasanna Balaprakash and Mihai Anitescu and Changjie Sun and Liping Wang (2023). "Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems". Preprint. https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-abstract/IDETC-CIE2023/87295/1170365

2022

AI for High Energy Physics: Interpretable Uncertainty Quantification

Bulletin of the American Physical Society, 2022

[Google Scholar](https://scholar.google.com/scholar?q=AI+for+High+Energy+Physics:+Interpretable+Uncertainty+Quantification)

Thomas Chen and Biprateep Dey and Aishik Ghosh and Michael Kagan and Brian Nord and Nesar Ramachandra (2022). "AI for High Energy Physics: Interpretable Uncertainty Quantification". Bulletin of the American Physical Society. https://meetings.aps.org/Meeting/MAS22/Session/F01.29

Interpretable uncertainty quantification in AI for HEP

arXiv preprint arXiv:2208.03284, 2022

[Google Scholar](https://scholar.google.com/scholar?q=Interpretable+uncertainty+quantification+in+AI+for+HEP)

Thomas Y Chen and Biprateep Dey and Aishik Ghosh and Michael Kagan and Brian Nord and Nesar Ramachandra (2022). "Interpretable uncertainty quantification in AI for HEP". arXiv preprint arXiv:2208.03284. https://arxiv.org/abs/2208.03284

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

Monthly Notices of the Royal Astronomical Society, 2022

[Google Scholar](https://scholar.google.com/scholar?q=Machine+learning+synthetic+spectra+for+probabilistic+redshift+estimation:+SYTH-Z)

Nesar Ramachandra and Jonás Chaves-Montero and Alex Alarcon and Arindam Fadikar and Salman Habib and Katrin Heitmann (2022). "Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z". Monthly Notices of the Royal Astronomical Society. https://academic.oup.com/mnras/article-abstract/515/2/1927/6623680

Neural network based point spread function deconvolution for astronomical applications

arXiv preprint arXiv:2210.01666, 2022

[Google Scholar](https://scholar.google.com/scholar?q=Neural+network+based+point+spread+function+deconvolution+for+astronomical+applications)

Hong Wang and Sreevarsha Sreejith and Yuewei Lin and Nesar Ramachandra and Anže Slosar and Shinjae Yoo (2022). "Neural network based point spread function deconvolution for astronomical applications". arXiv preprint arXiv:2210.01666. https://arxiv.org/abs/2210.01666

Over 2.7 Million Carbon-Enhanced Metal-Poor stars from BP/RP Spectra in $ Gaia $ DR3

arXiv e-prints, 2022

[Google Scholar](https://scholar.google.com/scholar?q=Over+2.7+Million+Carbon-Enhanced+Metal-Poor+stars+from+BP/RP+Spectra+in+$+Gaia+$+DR3)

Madeline Lucey and Nariman Al Kharusi and Keith Hawkins and Yuan-Sen Ting and Nesar Ramachandra and Timothy C Beers and Young Sun Lee and Adrian M Price-Whelan and Jinmi Yoon (2022). "Over 2.7 Million Carbon-Enhanced Metal-Poor stars from BP/RP Spectra in $ Gaia $ DR3". arXiv e-prints. https://scholar.google.com/scholar?cluster=6329097045594348&hl=en&oi=scholarr

2021

Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks

Monthly Notices of the Royal Astronomical Society, 2021

[Google Scholar](https://scholar.google.com/scholar?q=Anomaly+detection+in+Hyper+Suprime-Cam+galaxy+images+with+generative+adversarial+networks)

Kate Storey-Fisher and Marc Huertas-Company and Nesar Ramachandra and Francois Lanusse and Alexie Leauthaud and Yifei Luo and Song Huang and J Xavier Prochaska (2021). "Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks". Monthly Notices of the Royal Astronomical Society. https://academic.oup.com/mnras/article-abstract/508/2/2946/6369368

Beyond the hubble sequence–exploring galaxy morphology with unsupervised machine learning

Monthly Notices of the Royal Astronomical Society, 2021

[Google Scholar](https://scholar.google.com/scholar?q=Beyond+the+hubble+sequence–exploring+galaxy+morphology+with+unsupervised+machine+learning)

Ting-Yun Cheng and Marc Huertas-Company and Christopher J Conselice and Alfonso Aragon-Salamanca and Brant E Robertson and Nesar Ramachandra (2021). "Beyond the hubble sequence–exploring galaxy morphology with unsupervised machine learning". Monthly Notices of the Royal Astronomical Society. https://academic.oup.com/mnras/article-abstract/503/3/4446/6168393

Constraining gravity with a -cut cosmic shear analysis of the Hyper Suprime-Cam first-year data

Physical Review D, 2021

[Google Scholar](https://scholar.google.com/scholar?q=Constraining++gravity+with+a+-cut+cosmic+shear+analysis+of+the+Hyper+Suprime-Cam+first-year+data)

Leah Vazsonyi and Peter L Taylor and Georgios Valogiannis and Nesar S Ramachandra and Agnès Ferté and Jason Rhodes (2021). "Constraining gravity with a -cut cosmic shear analysis of the Hyper Suprime-Cam first-year data". Physical Review D. https://journals.aps.org/prd/abstract/10.1103/PhysRevD.104.083527

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

Nature Machine Intelligence, 2021

[Google Scholar](https://scholar.google.com/scholar?q=Global+field+reconstruction+from+sparse+sensors+with+Voronoi+tessellation-assisted+deep+learning)

Kai Fukami and Romit Maulik and Nesar Ramachandra and Koji Fukagata and Kunihiko Taira (2021). "Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning". Nature Machine Intelligence. https://www.nature.com/articles/s42256-021-00402-2

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

Physica D: Nonlinear Phenomena, 2021

[Google Scholar](https://scholar.google.com/scholar?q=Latent-space+time+evolution+of+non-intrusive+reduced-order+models+using+Gaussian+process+emulation)

Romit Maulik and Themistoklis Botsas and Nesar Ramachandra and Lachlan R Mason and Indranil Pan (2021). "Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation". Physica D: Nonlinear Phenomena. https://www.sciencedirect.com/science/article/pii/S0167278920305467

Peculiar velocity estimation from kinetic SZ effect using deep neural networks

Monthly Notices of the Royal Astronomical Society, 2021

[Google Scholar](https://scholar.google.com/scholar?q=Peculiar+velocity+estimation+from+kinetic+SZ+effect+using+deep+neural+networks)

Yuyu Wang and Nesar Ramachandra and Edgar M Salazar-Canizales and Hume A Feldman and Richard Watkins and Klaus Dolag (2021). "Peculiar velocity estimation from kinetic SZ effect using deep neural networks". Monthly Notices of the Royal Astronomical Society. https://academic.oup.com/mnras/article-abstract/506/1/1427/6307032

Physical Benchmarking for AI-Generated Cosmic Web

arXiv preprint arXiv:2112.05681, 2021

[Google Scholar](https://scholar.google.com/scholar?q=Physical+Benchmarking+for+AI-Generated+Cosmic+Web)

Xiaofeng Dong and Nesar Ramachandra and Salman Habib and Katrin Heitmann and Michael Buehlmann and Sandeep Madireddy (2021). "Physical Benchmarking for AI-Generated Cosmic Web". arXiv preprint arXiv:2112.05681. https://arxiv.org/abs/2112.05681

Physical Benchmarking for AI-Generated Cosmic Web

Neural Information Processing Systems (NeurIPS) 2021 AI for Science Workshop (2021), 2021

Xiaofeng Dong, Nesar Ramachandra , Salman Habib, Katrin Heitmann, Michael Buehlmann, Sandeep Madireddy; Physical Benchmarking for AI-Generated Cosmic Web, Neural Information Processing Systems (NeurIPS) 2021 AI for Science Workshop (2021) https://arxiv.org/abs/2112.05681

2020

Anomaly detection in astronomical images with generative adversarial networks

arXiv preprint arXiv:2012.08082, 2020

[Google Scholar](https://scholar.google.com/scholar?q=Anomaly+detection+in+astronomical+images+with+generative+adversarial+networks)

Kate Storey-Fisher and Marc Huertas-Company and Nesar Ramachandra and Francois Lanusse and Alexie Leauthaud and Yifei Luo and Song Huang (2020). "Anomaly detection in astronomical images with generative adversarial networks". arXiv preprint arXiv:2012.08082. https://arxiv.org/abs/2012.08082

From the inner to outer Milky Way: a photometric sample of 2.6 million red clump stars

Monthly Notices of the Royal Astronomical Society, 2020

[Google Scholar](https://scholar.google.com/scholar?q=From+the+inner+to+outer+Milky+Way:+a+photometric+sample+of+2.6+million+red+clump+stars)

Madeline Lucey and Yuan-Sen Ting and Nesar S Ramachandra and Keith Hawkins (2020). "From the inner to outer Milky Way: a photometric sample of 2.6 million red clump stars". Monthly Notices of the Royal Astronomical Society. https://academic.oup.com/mnras/article-abstract/495/3/3087/5838046

Probabilistic neural networks for fluid flow model-order reduction and data recovery

arXiv preprint arXiv:2005.04271, 2020

[Google Scholar](https://scholar.google.com/scholar?q=Probabilistic+neural+networks+for+fluid+flow+model-order+reduction+and+data+recovery)

Romit Maulik and Kai Fukami and Nesar Ramachandra and Koji Fukagata and Kunihiko Taira (2020). "Probabilistic neural networks for fluid flow model-order reduction and data recovery". arXiv preprint arXiv:2005.04271. https://scholar.google.com/scholar?cluster=1353454629629408925&hl=en&oi=scholarr

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

Physical Review Fluids, 2020

[Google Scholar](https://scholar.google.com/scholar?q=Probabilistic+neural+networks+for+fluid+flow+surrogate+modeling+and+data+recovery)

Romit Maulik and Kai Fukami and Nesar Ramachandra and Koji Fukagata and Kunihiko Taira (2020). "Probabilistic neural networks for fluid flow surrogate modeling and data recovery". Physical Review Fluids. https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.5.104401

The SPTPoL extended cluster survey

The Astrophysical Journal Supplement Series, 2020

[Google Scholar](https://scholar.google.com/scholar?q=The+SPTPoL+extended+cluster+survey)

LE Bleem and S Bocquet and B Stalder and Michael D Gladders and PAR Ade and SW Allen and AJ Anderson and James Annis and MLN Ashby and JE Austermann and S Avila and JS Avva and M Bayliss and JA Beall and K Bechtol and AN Bender and BA Benson and E Bertin and F Bianchini and C Blake and M Brodwin and D Brooks and E Buckley-Geer and DL Burke and JE Carlstrom and A Carnero Rosell and M Carrasco Kind and J Carretero and CL Chang and HC Chiang and R Citron and C Corbett Moran and M Costanzi and TM Crawford and LN da Costa and T de Haan and J De Vicente and S Desai and HT Diehl and JP Dietrich and MA Dobbs and TF Eifler and W Everett and B Flaugher and B Floyd and J Frieman and J Gallicchio and J García-Bellido and EM George and DW Gerdes and A Gilbert and D Gruen and RA Gruendl and J Gschwend and N Gupta and G Gutierrez and NW Halverson and N Harrington and JW Henning and C Heymans and GP Holder and DL Hollowood and WL Holzapfel and K Honscheid and JD Hrubes and N Huang and J Hubmayr and KD Irwin and DJ James and T Jeltema and S Joudaki and G Khullar and M Klein and L Knox and N Kuropatkin and AT Lee and D Li and C Lidman and A Lowitz and N MacCrann and G Mahler and MAG Maia and JL Marshall and M McDonald and JJ McMahon and P Melchior and F Menanteau and SS Meyer and R Miquel and LM Mocanu and JJ Mohr and J Montgomery and A Nadolski and T Natoli and JP Nibarger and G Noble and V Novosad and S Padin and A Palmese and D Parkinson and S Patil and F Paz-Chinchón and AA Plazas and C Pryke and NS Ramachandra and CL Reichardt and JD Remolina González and AK Romer and A Roodman and JE Ruhl and ES Rykoff and BR Saliwanchik and E Sanchez and A Saro and JT Sayre and KK Schaffer and T Schrabback and S Serrano and K Sharon and C Sievers and G Smecher and M Smith and M Soares-Santos and AA Stark and KT Story and E Suchyta and G Tarle and C Tucker and K Vanderlinde and T Veach and JD Vieira and G Wang and Jochen Weller and N Whitehorn and WLK Wu and V Yefremenko and Y Zhang (2020). "The SPTPoL extended cluster survey". The Astrophysical Journal Supplement Series. https://iopscience.iop.org/article/10.3847/1538-4365/ab6993/meta

Unstructured fluid flow data recovery using machine learning and Voronoi diagrams

APS Division of Fluid Dynamics Meeting Abstracts, 2020

[Google Scholar](https://scholar.google.com/scholar?q=Unstructured+fluid+flow+data+recovery+using+machine+learning+and+Voronoi+diagrams)

Kai Fukami and Romit Maulik and Nesar Ramachandra and Kunihiko Taira and Koji Fukagata (2020). "Unstructured fluid flow data recovery using machine learning and Voronoi diagrams". APS Division of Fluid Dynamics Meeting Abstracts. https://ui.adsabs.harvard.edu/abs/2020APS..DFDR01002F/abstract

VizieR Online Data Catalog: Photometric sample of 2.6 million red clump stars (Lucey+, 2020)

VizieR Online Data Catalog, 2020

[Google Scholar](https://scholar.google.com/scholar?q=VizieR+Online+Data+Catalog:+Photometric+sample+of+2.6+million+red+clump+stars+(Lucey+,+2020))

M Lucey and Y-S Ting and NS Ramachandra and K Hawkins (2020). "VizieR Online Data Catalog: Photometric sample of 2.6 million red clump stars (Lucey+, 2020)". VizieR Online Data Catalog. https://ui.adsabs.harvard.edu/abs/2020yCat..74953087L/abstract

VizieR Online Data Catalog: The SPTpol Extended Cluster Survey (Bleem+, 2020)

VizieR Online Data Catalog, 2020

[Google Scholar](https://scholar.google.com/scholar?q=VizieR+Online+Data+Catalog:+The+SPTpol+Extended+Cluster+Survey+(Bleem+,+2020))

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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), 2020

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

2019

A modular deep learning pipeline for galaxy-scale strong gravitational lens detection and modeling

arXiv preprint arXiv:1911.03867, 2019

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2018

Tracing the cosmic web

Monthly Notices of the Royal Astronomical Society, 2018

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2017

2015

2014

Многопотоковый портрет космической паутины Н. Рамачандра и С. Шандарин (США)

arXiv preprint arXiv:1412.7768, 2014

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