Search Results for author: Shantenu Jha

Found 24 papers, 10 papers with code

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

1 code implementation6 Jun 2025 Tianle Wang, Jorge Ramirez, Cristina Garcia-Cardona, Thomas Proffen, Shantenu Jha, Sudip K. Seal

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns.

Active Learning

Improved Dimensionality Reduction for Inverse Problems in Nuclear Fusion and High-Energy Astrophysics

no code implementations5 May 2025 Jonathan Gorard, Ammar Hakim, Hong Qin, Kyle Parfrey, Shantenu Jha

Many inverse problems in nuclear fusion and high-energy astrophysics research, such as the optimization of tokamak reactor geometries or the inference of black hole parameters from interferometric images, necessitate high-dimensional parameter scans and large ensembles of simulations to be performed.

Dimensionality Reduction valid

Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications

no code implementations17 Mar 2025 Andre Merzky, Mikhail Titov, Matteo Turilli, Ozgur Kilic, Tianle Wang, Shantenu Jha

Hybrid workflows combining traditional HPC and novel ML methodologies are transforming scientific computing.

Management

Envisioning National Resources for Artificial Intelligence Research: NSF Workshop Report

no code implementations13 Dec 2024 Shantenu Jha, Yolanda Gil

This is a report of an NSF workshop titled "Envisioning National Resources for Artificial Intelligence Research" held in Alexandria, Virginia, in May 2024.

The need to implement FAIR principles in biomolecular simulations

no code implementations23 Jul 2024 Rommie Amaro, Johan Åqvist, Ivet Bahar, Federica Battistini, Adam Bellaiche, Daniel Beltran, Philip C. Biggin, Massimiliano Bonomi, Gregory R. Bowman, Richard Bryce, Giovanni Bussi, Paolo Carloni, David Case, Andrea Cavalli, Chie-En A. Chang, Thomas E. Cheatham III, Margaret S. Cheung, Cris Chipot, Lillian T. Chong, Preeti Choudhary, Gerardo Andres Cisneros, Cecilia Clementi, Rosana Collepardo-Guevara, Peter Coveney, Roberto Covino, T. Daniel Crawford, Matteo Dal Peraro, Bert de Groot, Lucie Delemotte, Marco De Vivo, Jonathan Essex, Franca Fraternali, Jiali Gao, Josep Lluís Gelpí, Francesco Luigi Gervasio, Fernando Danilo Gonzalez-Nilo, Helmut Grubmüller, Marina Guenza, Horacio V. Guzman, Sarah Harris, Teresa Head-Gordon, Rigoberto Hernandez, Adam Hospital, Niu Huang, Xuhui Huang, Gerhard Hummer, Javier Iglesias-Fernández, Jan H. Jensen, Shantenu Jha, Wanting Jiao, William L. Jorgensen, Shina Caroline Lynn Kamerlin, Syma Khalid, Charles Laughton, Michael Levitt, Vittorio Limongelli, Erik Lindahl, Kresten Lindorff-Larsen, Sharon Loverde, Magnus Lundborg, Yun Lyna Luo, Francisco Javier Luque, Charlotte I. Lynch, Alexander MacKerell, Alessandra Magistrato, Siewert J. Marrink, Hugh Martin, J. Andrew McCammon, Kenneth Merz, Vicent Moliner, Adrian Mulholland, Sohail Murad, Athi N. Naganathan, Shikha Nangia, Frank Noe, Agnes Noy, Julianna Oláh, Megan O'Mara, Mary Jo Ondrechen, José N. Onuchic, Alexey Onufriev, Silvia Osuna, Anna R. Panchenko, Sergio Pantano, Carol Parish, Michele Parrinello, Alberto Perez, Tomas Perez-Acle, Juan R. Perilla, B. Montgomery Pettitt, Adriana Pietropalo, Jean-Philip Piquemal, Adolfo Poma, Matej Praprotnik, Maria J. Ramos, Pengyu Ren, Nathalie Reuter, Adrian Roitberg, Edina Rosta, Carme Rovira, Benoit Roux, Ursula Röthlisberger, Karissa Y. Sanbonmatsu, Tamar Schlick, Alexey K. Shaytan, Carlos Simmerling, Jeremy C. Smith, Yuji Sugita, Katarzyna Świderek, Makoto Taiji, Peng Tao, D. Peter Tieleman, Irina G. Tikhonova, Julian Tirado-Rives, Inaki Tunón, Marc W. Van Der Kamp, David van der Spoel, Sameer Velankar, Gregory A. Voth, Rebecca Wade, Ariel Warshel, Valerie Vaissier Welborn, Stacey Wetmore, Travis J. Wheeler, Chung F. Wong, Lee-Wei Yang, Martin Zacharias, Modesto Orozco

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations.

Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations

no code implementations10 Jul 2024 Pradeep Bajracharya, Javier Quetzalcóatl Toledo-Marín, Geoffrey Fox, Shantenu Jha, Linwei Wang

High-performance scientific simulations, important for comprehension of complex systems, encounter computational challenges especially when exploring extensive parameter spaces.

Active Learning Diversity

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

1 code implementation17 Jul 2023 Gilchan Park, Byung-Jun Yoon, Xihaier Luo, Vanessa López-Marrero, Shinjae Yoo, Shantenu Jha

Understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems and investigating the underlying mechanisms of biological functions and complex diseases.

AI-coupled HPC Workflows

no code implementations24 Aug 2022 Shantenu Jha, Vincent R. Pascuzzi, Matteo Turilli

Increasingly, scientific discovery requires sophisticated and scalable workflows.

scientific discovery

Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflows

no code implementations23 Aug 2022 Vincent R. Pascuzzi, Ozgur O. Kilic, Matteo Turilli, Shantenu Jha

Heterogeneous scientific workflows consist of numerous types of tasks that require executing on heterogeneous resources.

Scheduling

Optimal Decision Making in High-Throughput Virtual Screening Pipelines

no code implementations23 Sep 2021 Hyun-Myung Woo, Xiaoning Qian, Li Tan, Shantenu Jha, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon

The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design.

Decision Making Drug Discovery +2

Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening

1 code implementation13 Jun 2021 Austin Clyde, Thomas Brettin, Alexander Partin, Hyunseung Yoo, Yadu Babuji, Ben Blaiszik, Andre Merzky, Matteo Turilli, Shantenu Jha, Arvind Ramanathan, Rick Stevens

Our analysis of the speedup explains that to screen more molecules under a docking paradigm, another order of magnitude speedup must come from model accuracy rather than computing speed (which, if increased, will not anymore alter our throughput to screen molecules).

Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations

no code implementations10 Apr 2021 Alexander Brace, Igor Yakushin, Heng Ma, Anda Trifan, Todd Munson, Ian Foster, Arvind Ramanathan, Hyungro Lee, Matteo Turilli, Shantenu Jha

The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.

Protein Folding

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

1 code implementation28 May 2020 Yadu Babuji, Ben Blaiszik, Tom Brettin, Kyle Chard, Ryan Chard, Austin Clyde, Ian Foster, Zhi Hong, Shantenu Jha, Zhuozhao Li, Xuefeng Liu, Arvind Ramanathan, Yi Ren, Nicholaus Saint, Marcus Schwarting, Rick Stevens, Hubertus van Dam, Rick Wagner

Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

BIG-bench Machine Learning

Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations

no code implementations29 Sep 2019 Geoffrey Fox, Shantenu Jha

We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities.

BIG-bench Machine Learning

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

1 code implementation17 Sep 2019 Hyungro Lee, Heng Ma, Matteo Turilli, Debsindhu Bhowmik, Shantenu Jha, Arvind Ramanathan

Our study provides a quantitative basis to understand how DL driven MD simulations, can lead to effective performance gains and reduced times to solution on supercomputing resources.

Deep Learning Protein Folding

Understanding ML driven HPC: Applications and Infrastructure

no code implementations5 Sep 2019 Geoffrey Fox, Shantenu Jha

We recently outlined the vision of "Learning Everywhere" which captures the possibility and impact of how learning methods and traditional HPC methods can be coupled together.

Parallel Performance of Molecular Dynamics Trajectory Analysis

1 code implementation28 Jun 2019 Mahzad Khoshlessan, Ioannis Paraskevakos, Geoffrey C. Fox, Shantenu Jha, Oliver Beckstein

The performance of biomolecular molecular dynamics simulations has steadily increased on modern high performance computing resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is becoming a bottleneck.

Distributed, Parallel, and Cluster Computing Quantitative Methods D.1.3; J.2

Task-parallel Analysis of Molecular Dynamics Trajectories

1 code implementation23 Jan 2018 Ioannis Paraskevakos, Andre Luckow, Mahzad Khoshlessan, George Chantzialexiou, Thomas E. Cheatham, Oliver Beckstein, Geoffrey C. Fox, Shantenu Jha

We also provide a quantitative performance analysis of the different algorithms across the three frameworks.

Distributed, Parallel, and Cluster Computing

Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations

1 code implementation3 Jan 2018 Jumana Dakka, Kristof Farkas-Pall, Matteo Turilli, David W Wright, Peter V Coveney, Shantenu Jha

This paper makes three main contributions: (1) shows the importance of adaptive execution for ensemble-based free energy protocols to improve binding affinity accuracy; (2) presents and characterizes HTBAC -- a software system that enables the scalable and adaptive execution of binding affinity protocols at scale; and (3) for a widely used free-energy protocol (TIES), shows improvements in the accuracy of simulations for a fixed amount of resource, or reduced resource consumption for a fixed accuracy as a consequence of adaptive execution.

Distributed, Parallel, and Cluster Computing

Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks

no code implementations1 Dec 2017 Jumana Dakka, Pouya Bashivan, Mina Gheiratmand, Irina Rish, Shantenu Jha, Russell Greiner

Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention.

Functional Connectivity

Ensemble Toolkit: Scalable and Flexible Execution of Ensembles of Tasks

1 code implementation1 Feb 2016 Vivekanandan Balasubramanian, Antons Treikalis, Ole Weidner, Shantenu Jha

Motivated by the missing capabilities of these computing systems and the increasing importance of task-level parallelism, we introduce the Ensemble toolkit which has the following application development features: (i) abstractions that enable the expression of ensembles as primary entities, and (ii) support for ensemble-based execution patterns that capture the majority of application scenarios.

Distributed, Parallel, and Cluster Computing

Hadoop on HPC: Integrating Hadoop and Pilot-based Dynamic Resource Management

no code implementations31 Jan 2016 Andre Luckow, Ioannis Paraskevakos, George Chantzialexiou, Shantenu Jha

High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications.

Distributed, Parallel, and Cluster Computing

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