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ACAT 2017

21โ€“25 Aug 2017
University of Washington, Seattle
US/Pacific timezone

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Session

Plenary

Plenary
21 Aug 2017, 07:45
Alder Hall (University of Washington, Seattle)

Alder Hall

University of Washington, Seattle

Conveners

Plenary: Opening

  • Gordon Watts (University of Washington (US))

Plenary: Plenary Session

  • Denis Perret-Gallix (Centre National de la Recherche Scientifique (FR))

Plenary: Plenary Session

  • Toby Burnett (University of Washington)

Plenary: Plenary Session

  • Niko Neufeld (CERN)

Plenary: Plenary Session

  • Daniel Maitre

Plenary: Plenary Session

  • Sergei Gleyzer (University of Florida (US))

Plenary: Plenary Session

  • Maria Girone (CERN)

Plenary: Plenary Session

  • Pushpalatha Bhat (Fermi National Accelerator Lab. (US))

Plenary: Plenary Session

  • David Britton (University of Glasgow (GB))

Plenary: Plenary Session

  • Maria Girone (CERN)

Plenary: Track Summaries and Conclusions

  • Federico Carminati (CERN)

Plenary: Registration

  • Gordon Watts (University of Washington (US))

Presentation materials

Day 2 Intro.pdf
Day 2 Intro.pptx
Day 3 Intro.pdf
Day 3 Intro.pptx
Day 4 Intro.pdf
Day 4 Intro.pptx
Day 5 Intro.pdf
Day 5 Intro.pptx
  1. 240. Welcome To ACAT 20171
    Gordon Watts (University of Washington (US))
    21/08/2017, 08:45
  2. 192. New developments in FORM
    Takahiro Ueda (KEK)
    21/08/2017, 09:15
    Track 3: Computations in Theoretical Physics: Techniques and Methods
    Oral

    Symbolic computation is an indispensable tool for theoretical particle
    physics, especially in the context of perturbative quantum field
    theory. In this talk, I will review FORM, one of computer algebra
    systems widely used in higher-order calculations, its design principles
    and advantages. The newly released version 4.2 will also be discussed.

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  3. 194. Deep Learning usage by large experiments
    Dr Ben Nachman (Lawrence Berkeley National Lab. (US))
    21/08/2017, 11:00
    Oral

    Modern machine learning (ML) has introduced a new and powerful toolkit to High Energy Physics. While only a small number of these techniques are currently used in practice, research and development centered around modern ML has exploded over the last year(s). I will highlight recent advances with a focus on jet physics to be concrete. Themselves defined by unsupervised learning algorithms,...

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  4. 195. ML in theoretical physics: from PDFs to MC tools
    Stefano Carrazza (CERN)
    21/08/2017, 11:30
    Oral

    We start the discussion by summarizing recent and consolidated
    applications of ML in TH-HEP. We then focus our discussion on recent studies about parton distribution functions determination and related tools based on machine learning algorithms and strategies. We conclude by showing future theoretical applications of ML to Monte Carlo codes.

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  5. 197. Meta: Toward generative C++
    Herb Sutter (Microsoft Corporation)
    21/08/2017, 12:00
    Oral

    Can we evolve the C++ language itself to make C++ programming both more powerful and simpler, and if so, how? The only way to accomplish both of those goals at the same time is by adding abstractions that let programmers directly express their intentโ€”to elevate comments and documentation to testable code, and elevate coding patterns and idioms into compiler-checkable declarations.

    This talk...

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  6. 213. Track reconstruction algorithms in high pile up environments
    Heather Gray (LBNL)
    21/08/2017, 12:30
    Oral

    The reconstruction of particle trajectories in the tracking detectors is one of the most complex parts in analysing the data at hadron colliders. Maximum luminosity is typically achieved at the cost of a large number of simultaneous proton-proton interactions between beam crossing. The large number of particles produced in such interactions introduces challenges both in terms of maintaining...

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  7. 198. New results on the g-2 calculation
    Dr Stefano Laporta (Dipartimento di Fisica, Universita di Bologna)
    22/08/2017, 09:00
    Oral
  8. 200. Generative models for fast simulation
    Sofia Vallecorsa (Gangneung-Wonju National University (KR))
    22/08/2017, 09:30
    Oral

    Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions.We will present results of several studies on the application of...

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  9. 201. Physics and Computing Challenges in Lattice QCD
    Andreas Kronfeld (Fermilab)
    22/08/2017, 10:00
    Oral

    In this talk, I will give a quick overview of physics results and computational methods in lattice QCD. Then I will outline some of the physics challenges, especially those of interest to particle physicists. Last, I will speculate on how machine-learning ideas could be applied to accelerate lattice-QCD algorithms.

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  10. 203. Development and optimization of the MCFM generator
    Walter Giele
    22/08/2017, 11:00
    Oral
  11. 207. Games and loop integrals
    Ben Ruijl (Nikhef)
    22/08/2017, 11:30
    Oral

    Project HEPGame was created to apply methods from AI that have been successful for games, such as MCTS for Go, to solve problems in High Energy Physics. In this talk I will describe how MCTS helped us simplify large expressions. Additionally, I will describe how we managed to compute four loop (and some five loop) integrals in an automated way. I close with some interesting challenges for AI...

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  12. 205. Deep Learning on the Intel Nervana platform
    Dr Ravi Panchumarthy (Intel Corporation)
    22/08/2017, 12:00
    Oral

    This presentation will share details about the Intel Nervana Deep Learning Platform and how a data scientist can use it to develop solutions for deep learning problems. The Intel Nervana DL Platform is a full-stack platform including hardware and software tools that enable data scientists to build high-accuracy deep learning solutions quickly and cost effectively than with alternative...

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  13. 239. Welcome from the Department of Physics Chair
    Prof. Blayne Heckel (University of Washington)
    23/08/2017, 08:45
  14. 196. Statistics, Data Mining, and Machine Learning in Astronomy
    Prof. Jacob Vanderplas (University of Washington eScience Institute)
    23/08/2017, 09:00
    Oral
  15. 193. Deep Learning and Particle Physics
    Kyle Stuart Cranmer (New York University (US))
    23/08/2017, 09:30
    Oral
  16. 211. Deep Learning in Particle physics
    Daniel Whiteson (University of California Irvine (US))
    23/08/2017, 10:00
  17. 206. D-wave Quantum Computing
    Dr Colin Williams (DWave Systems)
    23/08/2017, 11:00
    Oral
  18. 220. Machine Learning at NVIDIA
    Mr Tom Gibbs (NVIDIA), Dr Tom Gibbs (NVIDIA Corporation)
    23/08/2017, 11:30
    Oral
  19. 199. FPGA Datacenters -- The New Supercomputer
    Dr Andrew Putnam (Microsoft Corporation)
    23/08/2017, 12:00
    Oral

    The emergence of Cloud Computing has resulted in an explosive growth of computing power, where even moderately-sized datacenters rival the worldโ€™s most powerful supercomputers in raw compute capacity.

    Microsoftโ€™s Catapult project has augmented its datacenters with FPGAs (Field Programmable Gate Arrays), which not only expand the compute capacity and efficiency for scientific computing, but...

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  20. 209. Machine Learning in HEP
    Sergei Gleyzer (University of Florida (US))
    24/08/2017, 09:00
    Oral
  21. 210. Round Table on Machine Learning
    Pushpalatha Bhat (Fermi National Accelerator Lab. (US))
    24/08/2017, 09:30

    The round table will be animated by the following panelists

    Kyle Cranmer
    Wahid Bhijmi
    Michela Paganini
    Andrey Ustyuzhanin
    Sergei Gleyzer

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  22. 202. HEP Software Development in the next decade; the views of the HSF community
    Elizabeth Sexton-Kennedy (Fermi National Accelerator Lab. (US))
    24/08/2017, 11:00
    Oral

    Weโ€™ve known for a while now that projections of computing needs for the experiments running in 10 years from now are unaffordable. Over the past year the HSF has convened a series of workshops aiming to find consensus on the needs, and produce proposals for research and development to address this challenge. At this time many of the software related drafts are far enough along to give a...

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  23. 212. Data, Software, and Knowledge Preservation of Scientific Results
    Mike Hildreth (University of Notre Dame (US))
    24/08/2017, 11:30
    Oral

    Simply preserving the data from a scientific experiment is rarely sufficient to enable the re-use or re-analysis of the data. Instead, a more complete set of knowledge describing how the results were obtained, including analysis software and workflows, computation environments, and other documentation may be required. This talk explores the challenges in preserving the various knowledge...

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  24. 204. HPC for HEP
    Deborah BARD (LBL)
    24/08/2017, 12:00
    Oral

    High Performance Computing (HPC) has been an integral part of HEP computing for decades, but the use of supercomputers has typically been limited to running cycle-hungry simulations for theory and experiment. Todayโ€™s supercomputers offer spectacular compute power but are not always simple to use - supercomputers have a highly specialized architecture that means that code that runs well on a...

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  25. 214. Why Cultural Diversity Matters: Becoming Culturally competent
    Dr George Langford (Syracuse University)
    25/08/2017, 09:00
    Oral

    Research has shown that diversity enhances creativity. It encourages the search for novel information and perspectives leading to better decision making and problem solving, and leads to unfettered discoveries and breakthrough innovations. Even simply being exposed to diversity can change the way you think.
    Professional development opportunities are needed to train faculty and staff to improve...

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  26. 215. Round Table on Diversity
    Jerome LAURET (Brookhaven National Laboratory), Maria Girone (CERN)
    25/08/2017, 09:30
    Oral

    Our panel will cover the topics of "How to create/hire diversity into teams and the competitive advantage of diverse teams".

    We would like to collect questions you may have in advance so panelists have time to prepare comprehensive answers. We will collect them until Wednesday 23rd, noon. The form for this is at...

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  27. 238. Software Citations
    Daniel S. Katz (University of Illinois)
    25/08/2017, 11:40
  28. 218. Summary of Track 3
    Ayres Freitas (University of Zurich)
    25/08/2017, 11:55
    Oral
  29. 217. Summary of Track 2
    Sergei Gleyzer (University of Florida (US))
    25/08/2017, 12:20
  30. 216. Summary of Track 1
    Shih-Chieh Hsu (University of Washington Seattle (US))
    25/08/2017, 12:45
    Oral
  31. 219. Summary of the Conference
    Pushpalatha Bhat (Fermi National Accelerator Lab. (US))
    25/08/2017, 13:10
    Oral
  32. 236. Final remarks
    Gordon Watts (University of Washington (US))
    25/08/2017, 13:30
  33. 208. Survey of how DL is used in a large experiment
    Dr Ben Nachman (Lawrence Berkeley National Lab. (US))
    Oral
Building timetable...