AI and ML for Applications across Physical Sciences
FOCUS · MAR-W42 · ID: 4001070
Presentations
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Beyond AI: Using information theory to extract information from data without fitting
ORAL · Invited
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Publication: Jackson Kubal, Vincent R. Ragusa, Christoph Adami, Beyond AI: Predicting drug response from transcriptomics using information theory. Manuscript in preparation
Vincent R. Ragusa and C. Adami, Automatic Generation of Highly Functional Sequences. Manuscript in preparationPresenters
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Christoph Adami
- Michigan State University
Authors
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Christoph Adami
- Michigan State University
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Impact of Training Set Sampling on Parameter Estimation Neural Networks for Monoexponential, Biexponential, and mcDESPOT Models with applications in Myelin Water Imaging
ORAL
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Presenters
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Rajib Chowdhury
- National Institutes of Health
Authors
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Rajib Chowdhury
- National Institutes of Health
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Jeffrey Han
- National Institutes of Health
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Aaron Jon Lee
- National Institutes of Health
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Wojtek Czaja
- University of Maryland
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Richard Spencer
- National Institutes of Health
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Quantum Gas Analysis and Inference (Q-GAIN): A Python Package for Machine Learning and Physically Informed Analysis Applications
ORAL
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Publication: S. Guo, A. R. Fritsch, C. Greenberg, I. B. Spielman and J. P. Zwolak, Machine-learning enhanced dark soliton detection in Bose–Einstein condensates, Mach. Learn.: Sci. Technol. 2(3), 035020 (2021), doi:10.1088/2632-2153/abed1e, Publisher: IOP Publishing.
S. Guo, S. M. Koh, A. R. Fritsch, I. B. Spielman and J. P. Zwolak, Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons, Phys. Rev. Res. 4(2), 023163 (2022), doi:10.1103/PhysRevResearch.4.023163.
A. R. Fritsch, S. Guo, S. M. Koh, I. B. Spielman and J. P. Zwolak, Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research, Mach. Learn.: Sci. Technol. 3, 047001 (2022), doi:10.1088/2632-2153/ac9454
M. Doris, S. Guo, S. M. Koh, L. Ritter, A. R. Fritsch, S. Mukherjee, I. B. Spielman, J. P. Zwolak, Quantum gas analysis and inference (Q-GAIN): A python package for machine learning and physically informed analysis applications, (2025), (In progress)Presenters
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Michael J Doris
- University of Maryland College Park
Authors
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Michael J Doris
- University of Maryland College Park
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Shangjie Guo
- University of Maryland College Park
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Sophia Koh
- Amherst College
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Lisa Ritter
- National Institute of Standards and Technology (NIST)
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Amilson Rogelso Fritsch
- Institute of Physics - University of Sao Paulo
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Shouvik Mukherjee
- University of Maryland College Park
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Ian B Spielman
- University of Maryland College Park
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Justyna P Zwolak
- National Institute of Standards and Technology (NIST)
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Abstract Withdrawn
ORAL · Withdrawn
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Robust Neutron Lifetime Extraction in UCNτ Measurements using Deep Learning
ORAL
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Presenters
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Shanny Lin
- Los Alamos National Laboratory (LANL)
Authors
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Shanny Lin
- Los Alamos National Laboratory (LANL)
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Looking into the black box: probing internal activations in a data-driven weather model reveals interpretable physical features
ORAL
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Presenters
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Theodore MacMillan
- Stanford University
Authors
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Theodore MacMillan
- Stanford University
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Nicholas T Ouellette
- Stanford University
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Abstract Withdrawn
ORAL · Withdrawn
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Extracting Meaningful Physical Signals from Mixures of Sources and Noise: A Machine Learning Framework for Experimental Data Analysis
ORAL
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Publication: Ni, Y., Chen, Z., Petsch, A. N., Xu, E., Peng, C., Kolesnikov, A. I., Chowdhury, S., Bansil, A., Thayer, J. B., & Turner, J. J. (2025). Physics-guided dual implicit neural representations for source separation. Machine Learning: Science and Technology. Advance online publication. https://doi.org/10.1088/2632-2153/ae14ac
Presenters
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Yuan Ni
- SLAC National Accelerator Laboratory
Authors
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Yuan Ni
- SLAC National Accelerator Laboratory
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Zhantao Chen
- Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, Texas 78712, USA.
- The University of Texas at Austin
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Alexander Nicolas Dominik Petsch
- SLAC National Accelerator Laboratory
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Edmund Xu
- Computer Science and Engineering Department. University of California Santa Cruz, Santa Cruz, CA 95064
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Cheng Peng
- SLAC National Accelerator Laboratory
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Alexander I Kolesnikov
- Oak Ridge National Laboratory
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Sugata Chowdhury
- Howard University
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Arun Bansil
- Department of Physics, Northeastern University, Boston, MA, USA
- Northeastern University
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Jana B Thayer
- SLAC National Accelerator Laboratory
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Joshua J Turner
- SLAC National Accelerator Laboratory
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Abstract Withdrawn
ORAL · Withdrawn
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Maximum Entropy Inference of Human Decision Policies in Spatial Exploration Tasks
ORAL
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Presenters
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Iulia Rusu
- University of California, San Diego
Authors
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Iulia Rusu
- University of California, San Diego
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Tatyana Olegivna Sharpee
- Salk Institute
- Salk Institute for Biological Studies
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Sergei Gepshtein
- Salk Institute
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Poisson Log-Normal (PoLoN) Process for non-parametric prediction of count data
ORAL
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Presenters
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Anushka Saha
- Rutgers, State University of NJ
Authors
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Anushka Saha
- Rutgers, State University of NJ
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Abhijith Gandrakota
- Rutgers University, New Brunswick
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Alex V Morozov
- Rutgers University
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Quantum Reservoir Computing for Noisy, High-Dimensional Magnetic Navigation
ORAL
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Presenters
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Lili Ye
- Arizona State University
Authors
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Lili Ye
- Arizona State University
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