Innovative Algorithms
Algorithms to perform the real-time processing in the trigger and the reconstruction of both real and simulated detector data are critical components of HEP’s computing challenge. University personnel, including graduate students and post-docs working on physics research grants, frequently develop and maintain innovative algorithms and implementations. These algorithms face a number of new challenges in the next decade due to new and upgraded accelerator facilities, detector upgrades and new detector technologies, increases in anticipated event rates, and emerging computing architectures. Tracking for the HL-LHC is an area in particular need of novel approaches, though the Institute will pursue other high-impact applications. The Institute will employ a wide range of strategies for the development of Innovative Algorithms.
Contact us: ia-team@iris-hep.org
IA Projects
ACTS
Development of experiment-independent, inherently parallel track reconstruction.More information
IA Presentations
- 17 Jul 2020 - "Graphs, Trees, and Sets: structured data in physics", Kyle Cranmer, ICML Workshop on Graph Representation Learning
- 17 Jul 2020 - "Likelihood-based models for Simulation-based Inference", Kyle Cranmer, ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models
- 10 Jun 2020 - "Reusable Workflows, active learning, and simulation-based inference", Kyle Cranmer, UCI Symposium on Reproducibility in Machine Learning
- 18 May 2020 - "Graph Neural Network Tracking using the Endcaps", Markus Atkinson, Exa.Trkx Weekly Meeting
- 7 May 2020 - "How machine learning can help us get the most out of our highest fidelity physical models", Kyle Cranmer, MIT Physics Department Colloquium
- 4 May 2020 - "Pixel Detector Tracklet Finding", Markus Atkinson, Exa.Trkx Weekly Meeting
- 26 Apr 2020 - "Autoencoders for Compression and Simulation in Particle Physics", Daniel Craik, International Conference on Learning Representations 2020
- 20 Apr 2020 - "An updated hybrid deep learning algorithm for identifying and locating primary vertices", Marian Stahl, Connecting The Dots 2020
- 5 Mar 2020 - "Artifical Intelligence for High-Energy Physics", Mark Neubauer, Artificial Intelligence for Nuclear Physics Workshop
- 5 Mar 2020 - "Machine Learning and Physics", Kyle Cranmer, Special QU-PCD Colloquium at DESY (CANCELED due to COVID19)
- 3 Mar 2020 - "Machine Learning for Effective Field Theories", Kyle Cranmer, PREFIT20: PRecision Effective FIeld Theory School
- 27 Feb 2020 - "Allen: a GPU trigger for LHCb", Daniel Craik, IRIS-HEP Poster Session
- 27 Feb 2020 - "GNN Tracking and FPGA Acceleration (poster)", Markus Atkinson, IRIS-HEP Poster Session
- 12 Feb 2020 - "mkfit and HL-LHC tracking in CMS", Mario Masciovecchio, Joint HSF event reconstruction/trigger working group and IRIS-HEP Topical Meeting
- 11 Feb 2020 - "Accelerated Full Tracking at (CMS) HLT", Mario Masciovecchio, CMS scouting kick-off workshop
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- 29 Jan 2020 - "LHCb HLT performance- and regression-tests A hybrid deep learning approach to vertexing", Marian Stahl, IRIS-HEP Topical Meeting
- 27 Jan 2020 - "The Allen Project", Daniel Craik, IRIS-HEP Topical Meeting
- 17 Jan 2020 - "ROB: Reproducible Open Benchmarks for Data Analysis Platform", Heiko Mueller, ML4Jets 2020
- 17 Jan 2020 - "Looking into Jets with Machine Learning", Sebastian Macaluso, ML4Jets 2020
- 17 Jan 2020 - "A Worldwide Software Collaboration?", David Lange, Hong Kong IAS Mini-workshop: Experiment / Detector - Software and Physics Requirements for e+e- Colliders
- 16 Dec 2019 - "Update on mkFit developments", Slava Krutelyov, CMS Tracking POG Meeting
- 9 Dec 2019 - "The Trigger and Real-time Reconstruction at LHCb", Daniel Craik, CPAD Instrumentation Frontier Workshop 2019
- 22 Nov 2019 - "Reconstruction R&D for HEP in the next decade", Slava Krutelyov, Latin American Workshop on Software and Computing challenges in High-Energy Particle Physics (LAWSCHEP 2019)
- 20 Nov 2019 - "High-Performance Python: CPUs", Henry Schreiner, Princeton Research Computing Fall Mini-courses and Workshops
- 19 Nov 2019 - "Looking into Jets with Machine Learning", Sebastian Macaluso, Particle Theory Seminar, Harvard University
- 19 Nov 2019 - "Flows three ways", Kyle Cranmer, Deep Learning for Physics Seminar Series at Princeton Center for Theoretical Physics
- 4 Nov 2019 - "Looking into Jets with Machine Learning", Sebastian Macaluso, Data Science Tea UMass
- 1 Nov 2019 - "Looking into Jets with Machine Learning", Sebastian Macaluso, NYU Physics x ML
- 19 Oct 2019 - "FPGA ML inference as a service on AWS", Markus Atkinson, FastML Co-processors Meeting
- 17 Oct 2019 - "Simulation-based inference, interpretability, and experimental design", Kyle Cranmer, Workshop on Interpretable Learning in Physical Sciences Part of the Long Program Machine Learning for Physics and the Physics of Learning
- 16 Oct 2019 - "LHCb Status and Outlook", Daniel Craik, LHC Users Association Annual Meeting 2019
- 16 Oct 2019 - "Semantic Segmentation for CMS Pixel Clustering", Savannah Thais, US LHC Users' Association Meeting
- 5 Oct 2019 - "Particle Physics in the context of Data Science", Kyle Cranmer, The 6th IEEE International Conference on Data Science and Advanced Analytics
- 30 Sep 2019 - "What does the Revolution in Artificial Intelligence Mean for Physics?", Kyle Cranmer, Joint PITT-CMU Physics Department Colloquium
- 27 Sep 2019 - "Simulation-based inference, causality, and active learning", Kyle Cranmer, AI and the Scientific Method, ETH, Zurich
- 25 Sep 2019 - "Benchmarks for ML4Jets research", Heiko Mueller, IRIS-HEP Topical Meeting
- 25 Sep 2019 - "Benchmarks for ML4Jets research", Sebastian Macaluso, IRIS-HEP Topical Meeting
- 24 Sep 2019 - "Looking into Jets with Machine Learning", Sebastian Macaluso, Princeton Pheno & Vino Seminar
- 13 Sep 2019 - "Histogramming and more", Henry Schreiner, 2019 IRIS-HEP Institute Retreat
- 12 Sep 2019 - "Introduction and Plans", Savannah Thais, IRIS-HEP Institute Retreat
- 12 Sep 2019 - "Introduction and Plans", Markus Atkinson, IRIS-HEP Institute Retreat
- 9 Sep 2019 - "The interplay between physically motivated simulations and machine learning", Kyle Cranmer, Machine Learning for Physics and the Physics of Learning Long Program at IPAM
- 8 Aug 2019 - "ACTS Status", Xiaocong Ai, USATLAS Summer Workshop
- 31 Jul 2019 - "ACTS: a common track reconstruction software", Xiaocong Ai, DPF 2019
- 31 Jul 2019 - "Ambiguity Resolution: Using Machine Learning", Nicholas Cinko, ACTS Developers Meetings
- 31 Jul 2019 - "Overview and Future directions for ML in particle and astro physics", Kyle Cranmer, Hammers & Nails 2019
- 23 Jul 2019 - "The Machine Learning Landscape of Top Taggers", Sebastian Macaluso, 11th International Workshop on Boosted Object Phenomenology, Reconstruction and Searches in HEP (BOOST 2019)
- 24 Jun 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, ATLAS Software and Computing Week
- 19 Jun 2019 - "SCAILFIN: Reproducible Open Benchmarks", Heiko Mueller, Analysis Systems Topical Meeting
- 19 Jun 2019 - "SCAILFIN: Reproducible Open Benchmarks", Sebastian Macaluso, Analysis Systems Topical Meeting
- 14 Jun 2019 - "Advances in Deep Learning motivated by Physics Problems", Kyle Cranmer, Theoretical Physics for Deep Learning
- 4 Jun 2019 - "Deep Learning for Higgs Boson Identification and Searches for New Physics at the Large Hadron Collider", Mark Neubauer, Blue Waters Symposium for Petascale Science and Beyond
- 29 May 2019 - "The Primacy of Experiment", Kyle Cranmer, The Universe Speaks in Numbers
- 29 May 2019 - "Machine learning in high-energy particle physics experiments, from simulation, through reconstruction to physics analysis", Heather Gray, Physics in Machine Learning Workshop
- 1 May 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, Gotham City Physics X ML
- 17 Apr 2019 - "A hybrid deep learning approach to vertexing", Henry Schreiner, 3rd IML Machine Learning Workshop
- 15 Apr 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, 3rd IML Machine Learning Workshop
- 5 Apr 2019 - "Scalable Cyberinfrastructure for Artificial Intelligence and Likelihood-Free Inference", Mark Neubauer, NSF Large Facilities Workshop
- 3 Apr 2019 - "A hybrid deep learning approach to vertexing", Henry Schreiner, Connecting The Dots and Workshop on Intelligent Trackers 2019
- 20 Mar 2019 - "Machine Learning for the Primary Vertex reconstruction", Henry Schreiner, 2019 Joint HSF/OSG/WLCG Workshop
- 11 Mar 2019 - "A hybrid deep learning approach to vertexing", Henry Schreiner, 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research
- 17 Feb 2019 - "Selected recent ideas in computing and their impact on high-energy physics", Heather Gray, 25th ICEPP Symposium
- 13 Feb 2019 - "Using ML on FPGAs to enhance reconstruction output", Dylan Rankin, IRIS-HEP Topical Meeting
- 6 Feb 2019 - "IRIS-HEP Innovative Algorithms", David Lange, IRIS-HEP Steering Board Meeting
- 6 Feb 2019 - "IRIS-HEP Innovative Algorithms", Heather Gray, IRIS-HEP Steering Board Meeting
- 13 Jan 2019 - "Ambiguity Resolution Studies with ACTS", Nicholas Cinko, Berkeley Tracking Workshop
- 13 Jan 2019 - "Workshop introduction", Heather Gray, Berkeley Tracking Workshop
- 13 Jan 2019 - "Ambiguity Resolution Studies with ACTS", Heather Gray, Berkeley Tracking Workshop
- 15 Nov 2018 - "Pulling Out All the Tops with Computer Vision and Deep Learning", Sebastian Macaluso, Machine Learning for Jet Physics
- 19 Oct 2018 - "Design Roadmap for Future Collaborations", Mark Neubauer, Deep Learning for Multimessenger Astrophysics Real-time Discovery at Scale
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IA Publications
- Flows for simultaneous manifold learning and density estimation, J. Brehmer and K. Cranmer, arXiv 2003.13913 (30 Mar 2020) [1 citation].
- Equivariant flow-based sampling for lattice gauge theory, G. Kanwar, M. Albergo, D. Boyda, K. Cranmer, D. Hackett et. al., arXiv 2003.06413 (13 Mar 2020) [2 citations].
- Compact Representation of Uncertainty in Hierarchical Clustering, C. Greenberg, S. Macaluso, N. Monath, J. Lee, P. Flaherty et. al., arXiv 2002.11661 (26 Feb 2020).
- Mining gold from implicit models to improve likelihood-free inference, Proceedings of the National Academy of Sciences; DOI:10.1073/pnas.1915980117 (20 Feb 2020) [23 citations].
- Set2Graph: Learning Graphs From Sets, H. Serviansky, N. Segol, J. Shlomi, K. Cranmer, E. Gross et. al., arXiv 2002.08772 (20 Feb 2020) [1 citation].
- Normalizing Flows on Tori and Spheres, D. Rezende, G. Papamakarios, S. Racanière, M. Albergo, G. Kanwar et. al., arXiv 2002.02428 (06 Feb 2020) [2 citations].
- Allen: A high level trigger on GPUs for LHCb, R. Aaij, J. Albrecht, P. Billoir, T. Boettcher, A. Brea Rodriguez et. al., Comput.Softw.Big Sci. 4 7 (2020) (19 Dec 2019) [1 citation].
- Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning, The Astrophysical Journal, Volume 886, Number 1; DOI:10.3847/1538-4357/ab4c41 (19 Nov 2019) [9 citations].
- The frontier of simulation-based inference, K. Cranmer, J. Brehmer and G. Louppe, arXiv 1911.01429 (Submitted to National Academy of Sciences) (04 Nov 2019) [2 citations].
- Hamiltonian Graph Networks with ODE Integrators, A. Sanchez-Gonzalez, V. Bapst, K. Cranmer and P. Battaglia, arXiv 1909.12790 (27 Sep 2019) [1 citation].
- Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale, A. Baydin, L. Shao, W. Bhimji, L. Heinrich, L. Meadows et. al., arXiv 1907.03382 (07 Jul 2019) [2 citations].
- A hybrid deep learning approach to vertexing, R. Fang, H. Schreiner, M. Sokoloff, C. Weisser and M. Williams, arXiv 1906.08306 (Submitted to ACAT 2019) (19 Jun 2019).
- Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector, G. Cerati, P. Elmer, B. Gravelle, M. Kortelainen, V. Krutelyov et. al., arXiv 1906.02253 (05 Jun 2019) [2 citations].
- FPGA-accelerated machine learning inference as a service for particle physics computing, J. Duarte, P. Harris, S. Hauck, B. Holzman, S. Hsu et. al., Comput.Softw.Big Sci. 3 13 (2019) (18 Apr 2019) [1 citation].
- Machine learning and the physical sciences, G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld et. al., Rev.Mod.Phys. 91 045002 (2019) (25 Mar 2019) [37 citations].
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- The Machine Learning Landscape of Top Taggers, G. Kasieczka, T. Plehn, A. Butter, K. Cranmer, D. Debnath et. al., SciPost Phys. 7 014 (2019) (26 Feb 2019) [35 citations].
- Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, A. Baydin, L. Heinrich, W. Bhimji, L. Shao, S. Naderiparizi et. al., arXiv 1807.07706 (20 Jul 2018) [3 citations].
- Machine Learning in High Energy Physics Community White Paper, K. Albertsson, P. Altoe, D. Anderson, J. Anderson, M. Andrews et. al., J.Phys.Conf.Ser. 1085 022008 (2018) (08 Jul 2018) [47 citations].
- Adversarial Variational Optimization of Non-Differentiable Simulators, G. Louppe, J. Hermans and K. Cranmer, arXiv 1707.07113 (22 Jul 2017) [10 citations].
- QCD-Aware Recursive Neural Networks for Jet Physics, G. Louppe, K. Cho, C. Becot and K. Cranmer, JHEP 01 057 (2019) (02 Feb 2017) [93 citations].
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