Massimiliano Lupo Pasini

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 32

Top Topics

Iterative Methods

Scientific Computing

Top Venues

Mach. Learn. Sci. Technol.

IEEE Comput. Intell. Mag.

Publications

Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Pei Zhang, David Rogers, Jonghyun Bae, Khaled Z. Ibrahim, Ashwin M. Aji, Karl W. Schulz, Jorda Polo, Prasanna Balaprakash
Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN. CoRR (2024)
Massimiliano Lupo Pasini, Mariia Karabin, Markus Eisenbach
Transferring predictions of formation energy across lattices of increasing size. Mach. Learn. Sci. Technol. 5 (2) (2024)
Jonghyun Bae, Jong Youl Choi, Massimiliano Lupo Pasini, Kshitij Mehta, Khaled Z. Ibrahim
MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training. IPDPS (Workshops) (2024)
Massimiliano Lupo Pasini
A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities]. IEEE Comput. Intell. Mag. 19 (3) (2024)
Massimiliano Lupo Pasini
AI for Materials Design and Discovery Using Atomistic Scale Information [Industrial and Governmental Activities]. IEEE Comput. Intell. Mag. 19 (2) (2024)
Kshitij Mehta, Massimiliano Lupo Pasini, Stephan Irle, Pilsun Yoo, Frédéric Suter, Dmitry Ganyushin, Scott Klasky
Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules. IPDPS (Workshops) (2024)
Jong Youl Choi, Massimiliano Lupo Pasini, Pei Zhang, Kshitij Mehta, Frank Liu, Jonghyun Bae, Khaled Ibrahim
DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets. SC Workshops (2023)
Massimiliano Lupo Pasini, Simona Perotto
Hierarchical Model Reduction Driven by Machine Learning for Parametric Advection-Diffusion-Reaction Problems in the Presence of Noisy Data. J. Sci. Comput. 94 (2) (2023)
Massimiliano Lupo Pasini, Junqi Yin
Stable parallel training of Wasserstein conditional generative adversarial neural networks. J. Supercomput. 79 (2) (2023)
Massimiliano Lupo Pasini, Luka Malenica, Kwitae Chong, Stuart R. Slattery
A deep learning approach for adaptive zoning. CoRR (2023)
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR (2023)
Massimiliano Lupo Pasini, Simona Perotto
Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data. CoRR (2022)
Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew Blanchard, Massimiliano Lupo Pasini
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. J. Cheminformatics 14 (1) (2022)
Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve, Jong Youl Choi
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems. CoRR (2022)
Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve, Jong Youl Choi
. Mach. Learn. Sci. Technol. 3 (2) (2022)
Yuanyuan Zhao, Massimiliano Lupo Pasini
A deep learning approach to solve forward differential problems on graphs. CoRR (2022)
Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew Blanchard, Massimiliano Lupo Pasini
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. CoRR (2022)
Massimiliano Lupo Pasini, Junqi Yin
Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks. CoRR (2022)
Andrew E. Blanchard, Pei Zhang, Debsindhu Bhowmik, Kshitij Mehta, John Gounley, Samuel Temple Reeve, Stephan Irle, Massimiliano Lupo Pasini
Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models. SMC (2022)
Markus Eisenbach, Mariia Karabin, Massimiliano Lupo Pasini, Junqi Yin
Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials. SMC (2022)
Davide Calabrò, Massimiliano Lupo Pasini, Nicola Ferro, Simona Perotto
A deep learning approach for detection and localization of leaf anomalies. CoRR (2022)
Massimiliano Lupo Pasini, M. Paul Laiu
Anderson acceleration with approximate calculations: applications to scientific computing. CoRR (2022)
Massimiliano Lupo Pasini, Junqi Yin, Ying Wai Li, Markus Eisenbach
A scalable algorithm for the optimization of neural network architectures. Parallel Comput. (2021)
Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin, Simona Perotto, Nouamane Laanait
Scalable balanced training of conditional generative adversarial neural networks on image data. J. Supercomput. 77 (11) (2021)
Massimiliano Lupo Pasini, Junqi Yin
Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence. CSCI (2021)
Massimiliano Lupo Pasini, Marko Burcul, Samuel Temple Reeve, Markus Eisenbach, Simona Perotto
Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks. SMC (2021)
Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin, Simona Perotto, Nouamane Laanait
Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data. CoRR (2021)
Massimiliano Lupo Pasini, Junqi Yin, Viktor Reshniak, Miroslav Stoyanov
Stable Anderson Acceleration for Deep Learning. CoRR (2021)
Massimiliano Lupo Pasini, Bruno Turcksin, Wenjun Ge, Jean-Luc Fattebert
A parallel strategy for density functional theory computations on accelerated nodes. Parallel Comput. 100 (2020)
Massimiliano Lupo Pasini
Convergence analysis of Anderson-type acceleration of Richardson's iteration. Numer. Linear Algebra Appl. 26 (4) (2019)
Massimiliano Lupo Pasini, Junqi Yin, Ying Wai Li, Markus Eisenbach
A greedy constructive algorithm for the optimization of neural network architectures. CoRR (2019)
Michele Benzi, Thomas M. Evans, Steven P. Hamilton, Massimiliano Lupo Pasini, Stuart R. Slattery
Analysis of Monte Carlo accelerated iterative methods for sparse linear systems. Numer. Linear Algebra Appl. 24 (3) (2017)