Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity. - Sandeep Madireddy; Prasanna Balaprakash; Philip H. Carns; Robert Latham; Robert B. Ross; Shane Snyder; Stefan M. Wild (NAS, 2017)

Modeling I/O Performance Variability Using Conditional Variational Autoencoders. - Sandeep Madireddy; Prasanna Balaprakash; Philip H. Carns; Robert Latham; Robert B. Ross; Shane Snyder; Stefan M. Wild (CLUSTER, 2018)

Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems. - Sandeep Madireddy; Prasanna Balaprakash; Philip H. Carns; Robert Latham; Robert B. Ross; Shane Snyder; Stefan M. Wild (ISC, 2018)

Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time. - Sunwoo Lee 001; Qiao Kang; Sandeep Madireddy; Prasanna Balaprakash; Ankit Agrawal 001; Alok N. Choudhary; Richard Archibald; Wei-keng Liao (IEEE BigData, 2019)

Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning. - Sandeep Madireddy; Angel Yanguas-Gil; Prasanna Balaprakash (ICONS, 2019)

Adaptive Learning for Concept Drift in Application Performance Modeling. - Sandeep Madireddy; Prasanna Balaprakash; Philip H. Carns; Robert Latham; Glenn K. Lockwood; Robert B. Ross; Shane Snyder; Stefan M. Wild (ICPP, 2019)

Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models. - Romit Maulik; Vishwas Rao; Sandeep Madireddy; Bethany Lusch; Prasanna Balaprakash (CoRR, 2019)

Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images. - Sandeep Madireddy; Nan Li 022; Nesar Ramachandra; Prasanna Balaprakash; Salman Habib 002 (CoRR, 2019)

Value-Added Chemical Discovery Using Reinforcement Learning. - Peihong Jiang; Hieu Doan; Sandeep Madireddy; Rajeev Surendran Assary; Prasanna Balaprakash (CoRR, 2019)

Toward Generalizable Models of I/O Throughput. - Mihailo Isakov; Eliakin Del Rosario; Sandeep Madireddy; Prasanna Balaprakash; Philip H. Carns; Robert B. Ross; Michel A. Kinsy (ROSS@SC, 2020)

HPC I/O throughput bottleneck analysis with explainable local models. - Mihailo Isakov; Eliakin Del Rosario; Sandeep Madireddy; Prasanna Balaprakash; Philip H. Carns; Robert B. Ross; Michel A. Kinsy (SC, 2020)

Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis. - Eliakin Del Rosario; Mikaela Currier; Mihailo Isakov; Sandeep Madireddy; Prasanna Balaprakash; Philip H. Carns; Robert B. Ross; Kevin Harms; Shane Snyder; Michel A. Kinsy (PDSW@SC, 2020)

Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning. - Sandeep Madireddy; Angel Yanguas-Gil; Prasanna Balaprakash (CoRR, 2020)

Domain adaptation techniques for improved cross-domain study of galaxy mergers. - Aleksandra Ciprijanovic; Diana Kafkes; S. Jenkins; K. Downey; Gabriel N. Perdue; Sandeep Madireddy; T. Johnston; Brian Nord (CoRR, 2020)

DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains. - Aleksandra Ciprijanovic; Diana Kafkes; K. Downey; S. Jenkins; Gabriel N. Perdue; Sandeep Madireddy; T. Johnston; Gregory F. Snyder; Brian Nord (CoRR, 2021)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (CoRR, 2021)

Robustness of deep learning algorithms in astronomy - galaxy morphology studies. - Aleksandra Ciprijanovic; Diana Kafkes; Gabriel N. Perdue; Kevin Pedro; Gregory F. Snyder; F. Javier Sánchez; Sandeep Madireddy; Stefan M. Wild; Brian Nord (CoRR, 2021)

In situ compression artifact removal in scientific data using deep transfer learning and experience replay. - Sandeep Madireddy; Ji Hwan Park; Sunwoo Lee 001; Prasanna Balaprakash; Shinjae Yoo; Wei-keng Liao; Cory D. Hauck; M. Paul Laiu; Richard Archibald (Mach. Learn. Sci. Technol., 2021)

HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization. - Matthieu Dorier; Romain Egele; Prasanna Balaprakash; Jaehoon Koo; Sandeep Madireddy; Srinivasan Ramesh; Allen D. Malony; Robert B. Ross (CLUSTER, 2022)

AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures. - Angel Yanguas-Gil; Sandeep Madireddy (ICRC, 2022)

A Taxonomy of Error Sources in HPC I/O Machine Learning Models. - Mihailo Isakov; Mikaela Currier; Eliakin Del Rosario; Sandeep Madireddy; Prasanna Balaprakash; Philip H. Carns; Robert B. Ross; Glenn K. Lockwood; Michel A. Kinsy (SC, 2022)

Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. - Anirban Samaddar; Sandeep Madireddy; Prasanna Balaprakash (CoRR, 2022)

Sequential Bayesian Neural Subnetwork Ensembles. - Sanket R. Jantre; Sandeep Madireddy; Shrijita Bhattacharya; Tapabrata Maiti; Prasanna Balaprakash (CoRR, 2022)

Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness. - Sumegha Premchandar; Sandeep Madireddy; Sanket R. Jantre; Prasanna Balaprakash (CoRR, 2022)

Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. - Aleksandra Ciprijanovic; Ashia Lewis; Kevin Pedro; Sandeep Madireddy; Brian Nord; Gabriel N. Perdue; Stefan M. Wild (CoRR, 2022)

General policy mapping: online continual reinforcement learning inspired on the insect brain. - Angel Yanguas-Gil; Sandeep Madireddy (CoRR, 2022)

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. - Aleksandra Ciprijanovic; Diana Kafkes; Gregory F. Snyder; F. Javier Sánchez; Gabriel Nathan Perdue; Kevin Pedro; Brian Nord; Sandeep Madireddy; Stefan M. Wild (Mach. Learn. Sci. Technol., 2022)

Biological underpinnings for lifelong learning machines. - Dhireesha Kudithipudi; Mario Aguilar-Simon; Jonathan Babb; Maxim Bazhenov; Douglas Blackiston; Josh C. Bongard; Andrew P. Brna; Suraj Chakravarthi Raja; Nick Cheney; Jeff Clune; Anurag Reddy Daram; Stefano Fusi; et al. (Nat. Mach. Intell., 2022)

Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. - Anirban Samaddar; Sandeep Madireddy; Prasanna Balaprakash; Taps Maiti; Gustavo de los Campos; Ian Fischer (AISTATS, 2023)

Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. - Sandeep Madireddy; Angel Yanguas-Gil; Prasanna Balaprakash (CoLLAs, 2023)

Memristor-Spikelearn: A Spiking Neural Network Simulator for Studying Synaptic Plasticity under Realistic Device and Circuit Behaviors. - Yuming Liu; Angel Yanguas-Gil; Sandeep Madireddy; Yanjing Li (DATE, 2023)

Towards Continually Learning Application Performance Models. - Ray A. O. Sinurat; Anurag Reddy Daram; Haryadi S. Gunawi; Robert B. Ross; Sandeep Madireddy (CoRR, 2023)

Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models. - Yixuan Sun; Elizabeth Cucuzzella; Steven Brus; Sri Hari Krishna Narayanan; Balu Nadiga; Luke Van Roekel; Jan Hückelheim; Sandeep Madireddy (CoRR, 2023)

Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. - Tung Nguyen; Rohan Shah; Hritik Bansal; Troy Arcomano; Sandeep Madireddy; Romit Maulik; Veerabhadra Kotamarthi; Ian T. Foster; Aditya Grover (CoRR, 2023)

A domain-agnostic approach for characterization of lifelong learning systems. - Megan M. Baker; Alexander New; Mario Aguilar-Simon; Ziad Al-Halah; Sébastien M. R. Arnold; Eseoghene Ben-Iwhiwhu; Andrew P. Brna; Ethan Brooks; Ryan C. Brown; Zachary Daniels; Anurag Reddy Daram; Fabien Delattre; et al. (Neural Networks, 2023)

DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection. - Aleksandra Ciprijanovic; Ashia Lewis; Kevin Pedro; Sandeep Madireddy; Brian Nord; Gabriel N. Perdue; Stefan M. Wild (Mach. Learn. Sci. Technol., 2023)

REMEDI: Corrective Transformations for Improved Neural Entropy Estimation. - Viktor Nilsson; Anirban Samaddar; Sandeep Madireddy; Pierre Nyquist (ICML, 2024)

Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. - Tung Nguyen; Rohan Shah; Hritik Bansal; Troy Arcomano; Romit Maulik; Rao Kotamarthi; Ian T. Foster; Sandeep Madireddy; Aditya Grover (NeurIPS, 2024)

Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates. - Yixuan Sun; Elizabeth Cucuzzella; Steven Brus; Sri Hari Krishna Narayanan; Balasubramanya T. Nadiga; Luke Van Roekel; Jan Hückelheim; Sandeep Madireddy; Patrick Heimbach (PASC, 2024)

Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks. - Zizhang Chen; Pengyu Hong; Sandeep Madireddy (CoRR, 2024)

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry. - Yoel Zimmermann; Adib Bazgir; Zartashia Afzal; Fariha Agbere; Qianxiang Ai; Nawaf Alampara; Alexander Al-Feghali; Mehrad Ansari; Dmytro Antypov; Amro Aswad; Jiaru Bai; Viktoriia Baibakova; et al. (CoRR, 2024)

Heimdall: Optimizing Storage I/O Admission with Extensive Machine Learning Pipeline. - Daniar Heri Kurniawan; Rani Ayu Putri; Peiran Qin; Kahfi S. Zulkifli; Ray A. O. Sinurat; Janki Bhimani; Sandeep Madireddy; Achmad Imam Kistijantoro; Haryadi S. Gunawi (EuroSys, 2025)

Can LLMs Model the Environmental Impact on SSD? - Mayur Akewar; Gang Quan; Sandeep Madireddy; Janki Bhimani (HotStorage, 2025)

AstroMLab 1: Who wins astronomy jeopardy!? - Yuan-Sen Ting; Tuan Dung Nguyen; Tirthankar Ghosal; Rui Pan 002; Hardik Arora; Zechang Sun; Tijmen de Haan; Nesar Ramachandra; Azton Wells; Sandeep Madireddy; Alberto Accomazzi (Astron. Comput., 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; et al. (CoRR, 2025)

AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons. - Shaona Ghosh; Heather Frase; Adina Williams; Sarah Luger; Paul Röttger; Fazl Barez; Sean McGregor; Kenneth Fricklas; Mala Kumar; Quentin Feuillade-Montixi; Kurt Bollacker; Felix Friedrich; et al. (CoRR, 2025)

Efficient Flow Matching using Latent Variables. - Anirban Samaddar; Yixuan Sun; Viktor Nilsson; Sandeep Madireddy (CoRR, 2025)

UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making. - Jinhao Duan; James Diffenderfer; Sandeep Madireddy; Tianlong Chen 001; Bhavya Kailkhura; Kaidi Xu (CoRR, 2025)