Activities and Research Products

Software products   Projects   Partner Projects   DIANA Topical Meetings   Presentations by the DIANA team   Papers / Documents

Software products

• DIANA/HEP organization on GitHub

• Carl: a toolbox for likelihood-free inference - docs, GitHub

• Histogrammar: a multilingual specification for histogram aggregation with implementations in Python and Scala - docs, GitHub

• Histbook: a versatile, high-performance histogram toolkit for Numpy - docs, GitHub

• uproot: pure Python implementation of ROOT I/O - GitHub

• OAMap: tools for handling hierarchically nested data as arrays with an Object Array Map - GitHub

• yadage: a language and engine for flexible, distributed workflows - docs GitHub

• TreeNiN: QCD-aware recursive neural networks - GitHub

• pyhf: a pure-python implementation of the widely-used HistFactory p.d.f. template - GitHub

• MadMiner: likelihood-free inference for particle physics using machine learning - GitHub

Projects

• PyHEP: creation of a "Python in HEP" community. Several activities / actions:
• PyHEP 2018 Workshop
• GitHub repository for "Python in HEP" resources

• Gitter channel for informal discussions of Python in HEP

• Scikit-HEP: a community-driven Python project for HEP core and common tools, containing at present about 12 packages (web page, GitHub)

• Participation in the GSoC 2017 program:

• Project Efficient Python routines for analysis on massively multi-threaded platforms - Python bindings for the Hydra C++ library - Eduardo Rodrigues, mentor for this CERN-HSF organisation project.

• Participation in the GSoC 2018 program:

• Project ROOT Package Manager - Oksana Shadura, Brian Bockelman - mentor in CERN-HSF organisation project.

Partner Projects

• Scikit-Optimize: a library for sequential model-based optimization (documentation, code)

• REANA: a system that permits to instantiate research data analyses on the cloud. website docs code

• RooFit / RooStats: workbook

DIANA Topical Meetings

The DIANA team organizes periodic meetings on various topics of interest, with both invited external speakers and DIANA team contributions. These and other DIANA meetings can be found in the DIANA-HEP Organization in the INDICO agenda system. Videos of these meetings are published in YouTube.

Papers / Documents

• Inferring the quantum density matrix with machine learning - Kyle Cranmer, Siavash Golkar, Duccio Pappadopulo arXiv:1904.05903

• Machine learning and the physical sciences - Guiseppe Carleo, Ignacio Cirac, Kyle Cranmer, et. al. submitted to Reviews of Modern Physics

• The Machine Learning Landscape of Top Taggers - G. Kasieczka, T. Plehn, Kyle Cranmer, Sebastian Macaluso, et. al. submitted to SciPost

• Optimizing Frameworks Performance Using C++ Modules Aware ROOT - Yuka Takahashi, Vassil Vassilev, Oksana Shadura, Raphael Isemann. Proceedings of the 23nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) (Sofia, Bulgaria) arXiv:1812.03992

• Continuous Performance Benchmarking Framework for ROOT - Oksana Shadura, Vassil Vassilev, Brian Paul Bockelman. Proceedings of the 23nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) (Sofia, Bulgaria) arXiv:1812.03149

• Extending ROOT through Modules - Oksana Shadura, Brian Paul Bockelman, Vassil Vassilev. Proceedings of the 23nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) (Sofia, Bulgaria) arXiv:1812.03145

• HEP Community White Paper on Machine Learning - Kyle Cranmer, Jim Pivarski, Eduardo Rodrigues, Michael D Sokoloff in K. Albertsson et al. arXiv:1807.02876

• Open is not enough Xiaoli Chen, Sünje Dallmeier-Tiessen, Kyle Cranmer, Lukas Heinrich, et. al. Nature Phys. 15 (2019) no.2, 113-119 (2018-11-15) DOI: 10.1038/s41567-018-0342-2

• Likelihood-free inference with an improved cross-entropy estimator. -- Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer. arXiv:1808.00973

• Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. - Atilim Gunes Baydin et al. arXiv:1807.07706

• Deep Learning and its Application to LHC Physics. - Dan Guest, Kyle Cranmer, Daniel Whiteson. 10.1146/annurev-nucl-101917-021019. Ann.Rev.Nucl.Part.Sci. 68 (2018) 161-181

• Backdrop: Stochastic Backpropagation - Siavash Golkar, Kyle Cranmer. arXiv:1806.01337

• Mining gold from implicit models to improve likelihood-free inference - Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer. arXiv:1805.12244 [stat.ML].

• A Guide to Constraining Effective Field Theories with Machine Learning - Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez. arXiv:1805.00020 [hep-ph].

• Constraining Effective Field Theories with Machine Learning - Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez. arXiv:1805.00013 [hep-ph].

• HEP Community White Paper on Data analysis and interpretation - Brian Bockelman, Kyle Cranmer, Peter Elmer, David Lange, Jim Pivarski, Eduardo Rodrigues, Michael D Sokoloff in Lothar Bauerdick et al. arXiv:1804.03983

• HEP Community White Paper on Software trigger and event reconstruction: Executive Summary - David Lange, Eduardo Rodrigues, Michael D Sokoloff in Johannes Albrecht et al. arXiv:1802.08640

• HEP Community White Paper on Software trigger and event reconstruction - David Lange, Eduardo Rodrigues, Michael D Sokoloff in Johannes Albrecht et al. arXiv:1802.08638

• A Roadmap for HEP Software and Computing R&D for the 2020s - Brian Bockelman, Kyle Cranmer, Peter Elmer, Lukas Heinrich, David Lange, Eduardo Rodrigues, Michael D Sokoloff in Antonio Augusto Alves Jr et al. arXiv:1712.06982

• Increasing Parallelism in the ROOT I/O subsystem - Guilherme Amadio, Brian Bockelman, Philippe Canal, Danilo Piparo, Enric Tejedor, Zhe Zhang, submitted to Proceedings of 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) (Seattle) arXiv:1804.03326

• Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators - By Mario Lezcano Casado et al. arXiv:1712.07901 [cs.AI].

• Optimizing ROOT IO For Analysis - Brian Bockelman, Zhe Zhang, Jim Pivarski, submitted to Proceedings of 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) (Seattle) arXiv:1711.02659

• Toward real-time data query systems in HEP - Jim Pivarski, David Lange, Thanat Jatuphattharachat, submitted to Proceedings of 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) (Seattle) arXiv:1711.01229

• CMS Analysis and Data Reduction with Apache Spark - Oliver Gutsche, Luca Canali, Illia Cremer, Matteo Cremonesi, Peter Elmer, Ian Fisk, Maria Girone, Bo Jayatilaka, Jim Kowalkowski, Viktor Khristenko, Evangelos Motesnitsalis, Jim Pivarski, Saba Sehrish, Kacper Surdy, Alexey Svyatkovskiy, submitted to Proceedings of 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) (Seattle) arXiv:1708.08319

• Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes -Meghan Frate, Kyle Cranmer, Saarik Kalia, Alexander Vandenberg-Rodes, Daniel Whiteson. arXiv:1709.05681 [physics.data-an].

• Fast Access to Columnar, Hierarchical Data via Code Transformation - Jim Pivarski, Peter Elmer, Brian Bockelman, and Zhe Zhang. Submitted to IEEE Big Data 2017 (Boston) arXiv:1708.08319

• Adversarial Variational Optimization of Non-Differentiable Simulators - Gilles Louppe and Kyle Cranmer arXiv:1703.07113 | source and code

• Yadage and Packtivity - analysis preservation using parametrized workflows - Kyle Cranmer, Lukas Heinrich. Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco) arXiv:1706.01878 [physics.data-an]. DOI: 10.1088/1742-6596/898/10/102019. J.Phys.Conf.Ser. 898 (2017) no.10, 102019.

• HEPData: a repository for high energy physics data - Eamonn Maguire, Lukas Heinrich, Graeme Watt. Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco) arXiv:1704.05473

• QCD-Aware Recursive Neural Networks for Jet Physics - G.Louppe, K.Cho, C.Becot, K.Cranmer arXiv:1702.00748

• Exploring compression techniques for ROOT IO - Z.Zhang, B.Bockelman; Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco) arXiv:1704.06976

• Big Data in HEP: A comprehensive use case study - O.Gutsche, M.Cremonesi, P.Elmer, B.Jayatilaka, J.Kowalkowski, J.Pivarski, S.Sehrish, C.Mantilla Surez, A.Svyatkovskiy, N.Tran; Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco) arXiv:1703.04171

• Analysis Preservation and Reinterpretation, Lukas Heinrich Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco)

• Better Higgs boson measurements through information geometry - Johann Brehmer, Kyle Cranmer, Felix Kling, Tilman Plehn. arXiv:1612.05261 [hep-ph]. DOI: 10.1103/PhysRevD.95.073002. Phys.Rev. D95 (2017) no.7, 073002.

• Learning to Pivot with Adversarial Networks - G.Louppe, M.Kagan, K.Cranmer arXiv:1611.01046

• Carl in the Journal of Open Source Software - G.Louppe, K.Cranmer, J.Pavez;

• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers - K.Cranmer, J.Pavez, G.Louppe arXiv:1506.02169

• Visualization of Publication Impact - E.Maguire, J.Montull, G.Louppe; Proceedings of Eurographics Conference on Visualization (EuroVis) 2016 (Groningen) arXiv:1605.06242

• NSF Proposal - Data-Intensive Analysis for High Energy Physics (DIANA/HEP)

Presentations by the DIANA team

• 13 Mar 2019 - "Deep Learning in Science", Kyle Cranmer, National Academy of Science, Sackler Colloquia

• 18 Mar 2019 - "Overview of Likelihood-Free Inference for Physics", Kyle Cranmer, Likelihood-Free Inference Workshop

• 15 Apr 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, 3rd IML Machine Learning Workshop

• 18 Mar 2019 - "'Mining gold' from simulators to improve likelihood-free inference", Johann Brehmer, Likelihood-free inference workshop

• 14 Mar 2019 - "Keynote: Constraining effective field theories with machine learning", Johann Brehmer, International Workshop on Advanced Computing and Analysis Techniques in Physics Research

• 13 Mar 2019 - Migrating large codebases to C++ Modules, Yuka Takahashi, Oksana Shadura, Vassil Vassilev, ACAT 2019

• 10 Mar 2019 - ROOT I/O compression algorithms and their performance impact within Run 3, Oksana Shadura, Brian Paul Bockelman, ACAT 2019

• 10 Mar 2019 - Speeding HEP Analysis with ROOT Bulk I/O, Brian Paul Bockelman, Zhe Zhang, Oksana Shadura, ACAT 2019

• 10 Mar 2019 - Evolution of ROOT package management, Oksana Shadura, Brian Paul Bockelman, Vassil Vassilev, ACAT 2019

• 7 Mar 2019 - "What does the Revolution in Artificial Intelligence Mean for Physics?", Kyle Cranmer, UC Riverside Physics Colloquium

• 28 Feb 2019 - "Bringing together simulations, physics insight, and machine learning to constrain new physics", Johann Brehmer, Dark universe seminar

• 12 Feb 2019 - "Panel Discussion on Machine Learning and Physics", Kyle Cranmer, At the Crossroad of Physics and Machine Learning

• 25 Jan 2019 - "Inverting simulation: experiences with inverse problems in particle physics", Kyle Cranmer, MATH + X Symposium on Inverse Problems and Deep Learning in Space Exploration

• 15 Jan 2019 - Computing & software work - recognition & citations in publications, Eduardo Rodrigues, LHCb Weekly Meeting, CERN

• 14 Jan 2018 - "Meticulous measurements with matrix elements and machine learning", Johann Brehmer, ITS/CHEP joint seminar

• 11 Jan 2019 - "Improving inference with matrix elements and machine learning", Johann Brehmer, HK IAS Program on High Energy Physics

• 19 Nov 2018 - Updates on ROOT C++ modules, Yuka Takahashi, DIANA meeting

• 19 Nov 2018 - Updates on ROOT Package Manager, Oksana Shadura, DIANA meeting

• 15 Nov 2018 - Python/Numpy for High-Performance Numerical Processing, Jim Pivarski, Princeton Research Computing Mini-Course, Princeton

• 12 Nov 2018 - ROOT package management: lazy install approach, Oksana Shadura, ROOT Team meeting

• 23 Oct 2018 - HEP Software Foundation (R&D) activities, Eduardo Rodrigues, CMS ECOM2X WG7 Meeting, CERN

• 17 Oct 2018 - Automatic Differentiation in C/C++ Using Clang Plugin Infrastructure, Vassil Vassilev, Aleksandr Efremov, LLVM developers' meeting 2018

• 15 Oct 2018 - I/O for uproot, Pratyush Das (DIANA Fellow), DIANA meeting

• 15 Oct 2018 - Updates about ROOT I/O compression algorithms, Oksana Shadura, DIANA meeting

• 15 Oct 2018 - Updates about ROOT I/O, Brian Bockelman, DIANA meeting

• 1 Oct 2018 - Histogram interoperability, Jim Pivarski, DIANA meeting

• 20 Sep 2018 - "Learning to constrain new physics", Johann Brehmer, IPPP seminar

• 18 Sep 2018 - Python in HEP - first workshop, community activities, Eduardo Rodrigues, LHCb Weekly Meeting, CERN

• 13 Sep 2018 - "Learning to constrain new physics", Johann Brehmer, Pheno & Vino Seminar

• 12 Sep 2018 - ROOT prompt usability features and the strict option, Yuka Takahashi, ROOT 2018 Users Workshop

• 12 Sep 2018 - Future of ROOT runtime C++ modules, Yuka Takahashi, ROOT 2018 Users Workshop

• 12 Sep 2018 - ROOT Package Manager, Oksana Shadura, Brian Bockleman, Vassil Vassilev, ROOT 2018 Users Workshop

• 11 Sep 2018 - Interactive documentation for advanced statistical modelling with RooFit and RooStats, Vince Croft, ROOT 2018 Users Workshop

• 10 Sep 2018 - Writing ROOT files with uproot, Pratyush Das, Jim Pivarski, ROOT 2018 Users Workshop

• 10 Sep 2018 - ROOT I/O compression algorithms, Oksana Shadura, Brian Bockelman, ROOT 2018 Users Workshop

• 10 Sep 2018 - Vectorized processing of nested data, Jim Pivarski, Jaydeep Nandi, David Lange, ROOT 2018 Users Workshop

• 7 Sep 2018 - "What does the Revolution in Artificial Intelligence Mean for Physics?", Kyle Cranmer, Harvard Physics Colloquium

• 19 Sep 2018 - "What does the Revolution in Artificial Intelligence Mean for Physics?", Kyle Cranmer, Jefferson Lab Colloquium

• 27 Aug 2018 - "Learning to constrain new physics", Johann Brehmer, Elementary particle seminar

• 23 Jul 2018 - "Machine Learning to Probe a BSM Higgs Sector", Johann Brehmer, Higgs Hunting

• 10 July 2018 - Continuous Performance Benchmarking Framework for ROOT, Oksana Shadura, Brian Paul Bockelman, Vassil Vassilev, CHEP 2018 Conference

• 9 July 2018 - Extending ROOT through Modules, Shadura Oksana, Brian Paul Bockelman, Vassil Vassilev CHEP 2018 Conference

• 9 July 2018 - The Scikit-HEP project, Eduardo Rodrigues, CHEP 2018 Conference

• 8 July 2018 - PyHEP Workshop Summary and Outcomes, Graeme Stewart, Eduardo Rodrigues, PyHEP 2018 Workshop

• 7 July 2018 - Welcome and workshop overview, Eduardo Rodrigues, PyHEP 2018 Workshop

• 27 Jun 2018 - "Constraining EEffective Theories with Machine Learning", Johann Brehmer, Theory seminar

• 4 June 2018 - RNNs and Beyond, Kyle Cranmer IML Working Group

• 29 Mar 2018 - Summary of Programming for Concurrency and Co-Processors, Jim Pivarski (with Vincenzo Innocente), Joint WLCG/HSF Workshop 2018

• 29 Mar 2018 - Summary of Analysis Facilities and Use Cases, Eduardo Rodrigues (with Oliver Gutsche), Joint WLCG/HSF Workshop 2018

• 28 Mar 2018 - Overview of Serialization Technologies, Jim Pivarski, Joint WLCG/HSF Workshop 2018

• 28 Mar 2018 - Becoming Mainstream, Eduardo Rodrigues, Joint WLCG/HSF Workshop 2018

• 27 Mar 2018 - Spark-like and query-like analysis systems and tools, Jim Pivarski, Joint WLCG/HSF Workshop 2018

• 26 Mar 2018 - CWP Roadmap, Eduardo Rodrigues, Joint WLCG/HSF Workshop 2018

• 28 Feb 2018 - Uproot: rapidly moving data from ROOT to Numpy and Pandas, Jim Pivarski, IML Meeting

• 26 Feb 2018 - APPAVO - Augmented tools for Particle Physics Analysis, Visualisation and Outreach, Eduardo Rodrigues, CERN EP Department Software R&D Second Lightning Talks Session

• 21 Feb 2018 - Enhance TTreeCache defaults, Brian Bockelman, ROOT I/O Workshop, Early Spring 2018

• 21 Feb 2018 - LZ4, ZStd updates, Oksana Shadura, ROOT I/O Workshop, Early Spring 2018

• 21 Feb 2018 - Parquet Data Format Performance, Jim Pivarski, ROOT I/O Workshop, Early Spring 2018

• 12 Feb 2018 - GooFit Python bindings, Himadri Pandey, DIANA Meeting

• 5 Feb 2018 - Activities in Data Analysis, Jim Pivarski, CMS Offline/Computing R&D

• 26 Jan 2018 - ZSTD and CloudFlare/ZLIB update, Oksana Shadura, ROOT I/O Meeting

• 11 Dec 2017 - Updates on ROOT Modularization Project, Oksana Shadura, DIANA Meeting

• 16 Nov 2017 - Data Management with Columnwise Stores, Jim Pivarski, Workshop on Data Organisation, Management and Access (DOMA) in Astronomy, Genomics and High Energy Physics

• 13 Nov 2017 - Unfolding in python, Vincent Croft, DIANA Meeting

• 25 Oct 2017 - Bridging the Particle Physics and Big Data Worlds [ talk abstract ], Jim Pivarski, APS Division of Nuclear Physics 2017

• 11 Oct 2017 - In-memory file merging, Vassil Vassilev, Oksana Shadura ROOT I/O Workshop

• 11 Oct 2017 - LZ4, BulkIO, and offset removal performance, Jim Pivarski, ROOT I/O Workshop

• 11 Oct 2017 - DIANA I/O Contributions Update, Brian Bockelman, ROOT I/O Workshop

• 11 Oct 2017 - How to make a petabyte ROOT file: proposal for managing data with columnar granularity, Jim Pivarski, ROOT I/O Workshop

• 30 Sep 2017 - Particle Physics, 10000 Times Faster, [ talk abstract, talk video ], Jim Pivarski, Strange Loop Conference

• 25 Sep 2017 - DIANA Big Data projects, Jim Pivarski, DIANA Meeting

• 11 Sep 2017 - Scikit-HEP Project Status, Eduardo Rodrigues, DIANA Meeting

• 24 Aug 2017 - Optimizing ROOT I/O for Analysis (poster), Brian Bockelman, Zhe Zhang, Jim Pivarski, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017), Seattle

• 22 Aug 2017 - Toward real-time data query systems in HEP, Jim Pivarski, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017), Seattle

• 4 Aug 2017 and 23 June 2017 - BulkIO to Numpy in ROOT (with sample notebooks), Jim Pivarski

• 28 Jul 2017 - RECAST and Data Reinterpretation, Lukas Heinrich at Open Session of US ATLAS Workshop, ANL

• 19-27 Jul 2017 - Broad HEP/ML Framing and Future Directions, Kyle Cranmer at Hammers and Nails Workshop, Weizmann Institute

• 20 Jul 2017 - Perspective of ML person working with physicists and Adversarial variational optimization of non-differentiable simulators, Gilles Louppe at Hammers and Nails Workshop, Weizmann Institute

• 13 Jul 2017 - Language Interoperability, Jim Pivarski, CoDaS-HEP school (CoDaS-HEP 2017 program), Princeton [Training]

• 10 Jul 2017 - Functional Programming for Data Analysis, Jim Pivarski, CoDaS-HEP school (CoDaS-HEP 2017 program), Princeton [Training]

• 17 Jul 2017 - IPCC ROOT Code Modernization Project, Vassil Vassilev, DIANA Meeting

• 19 Jun 2017 - Watch this space: emerging trends and techniques that could transform physics and astronomy, Kyle Cranmer at Big data tools for physics and astronomy at GRAPPA Institute, Amsterdam, Netherlands

• 12 Jun 2017 - DIANA/HEP: TTree Bulk I/O and More, Brian Bockelman, ROOT I/O Workshop, CERN

• 12 Jun 2017 - Compression using on-chip hardware, David Abdurachmanov, ROOT I/O Workshop, CERN

• 12 Jun 2017 - Parallel Unzipping, Zhe Zhang, ROOT I/O Workshop, CERN

• 24 May 2017 - QCD-Aware Neural Networks for Jet Physics, Kyle Cranmer, CERN Theory Colloquium

• 23 May 2017 - Femtocode, Jim Pivarski, HSF Analysis Ecosystem Workshop, Amsterdam

• 23 May 2017 - Survey of data formats and conversion tools, Jim Pivarski, HSF Analysis Ecosystem Workshop, Amsterdam

• 22 May 2017 - The Analysis Ecosystem - My Remarks, Eduardo Rodrigues, HSF Analysis Ecosystem Workshop, Amsterdam

• 22 May 2017 - Data Analysis in LHCb, Eduardo Rodrigues, HSF Analysis Ecosystem Workshop, Amsterdam

• 11 May 2017 - Data plumbing: moving data across frameworks, Jim Pivarski, DS@HEP 2017, FNAL

• 10 May 2017 - QCD-Aware Recursive Neural Networks, Kyle Cranmer, DS@HEP 2017, FNAL

• 10 May 2017 - Learning to Pivot with Adversarial Networks, Gilles Louppe, DS@HEP 2017, FNAL

• 9 May 2017 - Use of advanced ML techniques in operations and data analysis at LHCb, Mike Sokoloff, DS@HEP 2017, FNAL

• 3 May 2017 - Cyberinfrastructure for high-level science goals, Kyle Cranmer, Future Trends in Nuclear Physics Computing Workshop, Jefferson Lab

• 3 May 2017 - Lowering Boundaries between Data Analysis Ecosystems, Jim Pivarski, Future Trends in Nuclear Physics Computing Workshop, Jefferson Lab

• 2 May 2017 - Emerging trends in software for statistics and machine learning, Kyle Cranmer, 2nd S2I2 HEP/CS Workshop, Princeton University

• 2 May 2017 - Femtocode: query system for HEP (poster), Jim Pivarski, 2nd S2I2 HEP/CS Workshop, Princeton University

• 2 May 2017 - Data Intensive Analysis Tools & Visualization (Parallel Session Introduction), Jim Pivarski, 2nd S2I2 HEP/CS Workshop, Princeton University

• 17 Apr 2017 - Femtocode: querying HEP data - Status update and illustration of working examples, Jim Pivarski, DIANA Meeting, [ Video Recording ]

• 8 Apr 2017 - Bayesian Optimization with Scikit-Optimize, Gilles Louppe, PyData Amsterdam, The Netherlands

• 7 Apr 2017 - Learning to Pivot with Adversarial Networks, Gilles Louppe, Seminar at IIHE, VUB, Brussels

• 6 Apr 2017 - The Scikit-HEP Project, Eduardo Rodrigues, LHCb Analysis & Software Week, CERN

• 20-22 March 2017 - Introduction to Machine Learning, Gilles Louppe, IML Machine Learning workshop, CERN

• 27 Feb 2017 - The Scikit-HEP Project, Eduardo Rodrigues, DIANA Meeting, [ Video Recording ]

• 21-22 Feb 2017 - Data-Intensive Analysis for High Energy Physics (DIANA/HEP), poster at 2017 NSF SI2 PI Workshop

• 8 Feb 2017 - Survey of columnar file formats and the techniques they use, Jim Pivarski, ROOT I/O Workshop

• 8 Feb 2017 - Executing code on columnar data, Jim Pivarski, ROOT I/O Workshop

• 8 Feb 2017 - CMS/DIANA I/O Update, Brian Bockelman, ROOT I/O Workshop

• 26 Jan 2017 - DIANA Advisory Board Meeting: , Cincinnati, NYU, Princeton, Nebraska

• 16 Dec 2016 - Updates & planning: Nebraska, Cincinnati, NYU, Princeton

• 14 Dec 2016 - Data Plumbing (whiteboard talk), Jim Pivarski, LPC Coffee Hour

• 13 Dec 2016 - Machine Learning & Likelihood Free Inference in Particle Physics, Kyle Cranmer NIPS 2016 Keynote

• 12 Dec 2016 - Femtocode: development of a query language for HEP, Jim Pivarski, DIANA Meeting

• 5 Dec 2016 - Focus groups: physicist attitudes toward software, Jim Pivarski, DIANA Meeting

• 17 Nov 2016 - Learning to Pivot with Adversarial Networks, Gilles Louppe, ATLAS ML Forum

• 14 Nov 2016 - TTreeProcessor: A toy framework for parallel ntuple processing, Brian Bockelman, DIANA Meeting

• 7 Nov 2016 - Overview of file formats, Jim Pivarski, DIANA Meeting

• 7 Nov 2016 - Reading ROOT data in Java and Spark, Jim Pivarski, ROOT Team meeting; follow up with CERN IT

• 30 Oct 2016 - Plotting data on GPUs with Histogrammar, Jim Pivarski, Parallel Kalman Meeting

• 13 Oct 2016 - Analysis Preservation and Reinterpretation, Lukas Heinrich CHEP 2016

• 13 Oct 2016 - HEPData - a repository for high energy physics data exploration, Lukas Heinrich & Eamonn Maguire for HEPData team CHEP 2016

• 10 Oct 2016 - Exploring Compression Techniques for ROOT IO, Zhe Zhang, CHEP 2016

• 20 Oct 2016 - Histogrammar: Making Histograms Functional, Jim Pivarski, Chicago Hadoop User's Group (CHUG)

• 17-21 Oct 2016 - Lectures on Machine Learning, Gilles Louppe, ICCUB School Machine Learning and Data Science in Physics, Barcelona, Spain

• 21 Sep 2016 - Reconstructing Particle Trajectories in High Energy Physics with Xeon and Xeon Phi, David Abdurachmanov, Intel Xeon Phi User Group Meeting 2016

• 16 Sep 2016 - Expressing Complex Data Aggregations With Histogrammar, [ talk abstract, talk video ], Jim Pivarski, Strange Loop Conference

• 18 Aug 2016 - Computing for Data Analysis, Jim Pivarski, LPC Physics Forum

• 3 Aug 2016 - Learning to generate with adversarial networks, Gilles Louppe, US ATLAS Physics Support, Software and Computing meeting

• 21 Jul 2016 - Learning to generate with adversarial networks, Gilles Louppe, ATLAS ML forum

• 18 Jul 2016 - Potential Issues with Julia, Jim Pivarski, DIANA Meeting

• 7 Jul 2016 - Learning to generate with adversarial networks, Gilles Louppe, DS @ HEP, New York, USA

• 27 Jun 2016 - Learning to generate with adversarial networks, Gilles Louppe, Software Tech Forum

• 22 Jun 2016 - Approximating likelihood ratios with Calibrated Classifiers, Gilles Louppe, 2nd MLHEP summer school, Lund, Sweden

• 20 Jun 2016 - High-level analysis scripts with low-level performance, Jim Pivarski, DIANA Meeting

• 13 Jun 2016 - Python/ROOT Interoperability, David Lange, DIANA Meeting

• 25 May 2016 - DIANA IO Update, Brian Bockelman, ROOT I/O Workshop

• 23 May 2016 - Chains of functional primitives, Jim Pivarski, ROOT Team Meeting

• 19 May 2016 - The DIANA-HEP Project, Jim Pivarski, US-CMS Collaboration Meeting

• 2 May 2016 - Introduction to DIANA/HEP, Peter Elmer, HEP Software Foundation Workshop

• 25 Apr 2016 - Histogramming in map-reduce, Jim Pivarski, DIANA Meeting

• 18 Apr 2016 - Introduction to DIANA/HEP, Peter Elmer, ROOT Team Meeting

• 13 Apr 2016 - Robust and Calibrated Classifiers with Scikit-Learn, Gilles Louppe, Zurich ML meetup, Switzerland

• 13 Apr 2016 - Approximating likelihood ratios with Calibrated Classifiers, Gilles Louppe, ETH, Zurich, Switzerland

• 11 Apr 2016 - Bayesian Optimization, Gilles Louppe, DIANA Meeting

• 11 Apr 2016 - Introduction to DIANA/HEP, Peter Elmer, DIANA Meeting

• 30 Mar 2016 - Bayesian Optimization, Gilles Louppe, ATLAS ML workshop

• 29 Mar 2016 - Approximating likelihood ratios with Calibrated Classifiers, Gilles Louppe, ATLAS ML workshop

• 7 Mar 2016 - Computing practices observed in industry, Jim Pivarski, ROOT Team Meeting

• 7 Mar 2016 - ROOT-Spark integration, Jim Pivarski, ROOT Team Meeting

• 3 Mar 2016 - The DIANA/HEP Project, Eduardo Rodrigues, LHCb-wide Amplitude Analysis Meeting on an Ampl. Ana. software library

• 22 Feb 2016 - Accessing ROOT from the JVM (update), Jim Pivarski, DIANA Meeting

• 22 Feb 2016 - Notes from Heavy Flavour Data Mining Workshop, Eduardo Rodrigues, DIANA Meeting

• 22 Feb 2016 - Approximating density ratios with machine learning for likelihood-free inference, Gilles Louppe, DIANA Meeting

• 18 Feb 2016 - An introduction to machine learning with Scikit-Learn, Gilles Louppe, Heavy Flavour Data Mining workshop

• 16-17 Feb 2016 - Data-Intensive Analysis for High Energy Physics (DIANA/HEP), Peter Elmer, 2016 NSF SI2 PI Workshop

• 8 Feb 2016 - Accessing ROOT from the JVM (Java/Scala), Jim Pivarski, CMS Software and Computing R&D Working Meeting