Name: DS@HEP 2017
Start: 2017-05-08T08:00:00-05:00
End: 2017-05-12T18:00:00-05:00
Location: Fermi National Accelerator Laboratory

DS@HEP 2017

from Monday, 8 May 2017 (08:00) to Friday, 12 May 2017 (18:00)

Monday, 8 May 2017
09:00
09:00 - 09:30
Contributions

09:00 Welcome to Fermilab - Joseph Lykken (Fermilab)

09:15 Introduction to the workshop - Maria Spiropulu (Caltech)
09:30 Computer vision I
Computer vision I
09:30 - 10:15
Contributions

09:30 Review talk on Computer vision - Pietro Perona (California Institute of Technology)
10:15 Coffee Break
Coffee Break
10:15 - 10:35
10:35 Computer vision II
Computer vision II
10:35 - 11:50
Contributions

10:35 Jet Image - Ben Nachman Benjamin Nachman (LBNL)

11:00 jet b-tagging with ConvNN - Markus Stoye

11:25 Convolutional NNs for neutrino experiments - Alexander Radovic (College of William and Mary)
11:50 Necessary Changes in the Philosophy and Practice of Probability & Statistics - William Briggs (Cornell University) William Briggs
Necessary Changes in the Philosophy and Practice of Probability & Statistics
- William Briggs (Cornell University)
- William Briggs
11:50 - 12:35
12:35 Lunch Break
Lunch Break
12:35 - 13:30
14:00 Computer Vision III
Computer Vision III
14:00 - 14:45
Contributions

14:00 Convolutional NNs for tracking - Farrell Steve (LBNL)

14:22 Image Calorimetry: status and challenges - Josh Bendavid (MIT)
14:45 IBM Minsky - Ken Halbrecht (IBM reseller)
IBM Minsky
- Ken Halbrecht (IBM reseller)
14:45 - 15:05
15:05
15:05 - 16:10
Contributions

15:05 Imaging detector datasets - Amir Farbin (University of Texas at Arlington)

15:30 Jet dataset - Javier Duarte (Fermilab)

15:50 Tracking dataset - Jean-Roch Vlimant Jean-Roch vlimant (California Institute of Technology)
16:10 Coffee Break
Coffee Break
16:10 - 16:45

16:45

16:45 - 18:15

16:45 - 18:15

16:45 - 18:15

18:30 Reception
Reception
18:30 - 20:30
Room: WH Atrium
Tuesday, 9 May 2017
09:00 Sequential/Recursive Learning
Sequential/Recursive Learning
09:00 - 10:35
Contributions

09:00 Review talk on Sequential/Recursive Learning - Kyunghyun Cho (New York University)

09:45 Jet b-tagging with RNNs - Dan Guest

10:10 Topology classifier with LSTM - Jean-Roch Vlimant (Caltech) Jean-Roch vlimant (California Institute of Technology)
10:35 Coffe Break
Coffe Break
10:35 - 10:55
10:55 Use of advanced ML techniques in operations and data analysis at LHCb - Michael Sokoloff (University of Cincinnati)
Use of advanced ML techniques in operations and data analysis at LHCb
- Michael Sokoloff (University of Cincinnati)
10:55 - 11:25
11:25 Uncertainty quantification for machine learning - Stefan Wager (Columbia University)
Uncertainty quantification for machine learning
- Stefan Wager (Columbia University)
11:25 - 12:00
12:00 The blackness of the ML box - Victoria Stodden Matthew Krafczyk
The blackness of the ML box
- Victoria Stodden
- Matthew Krafczyk
12:00 - 12:35
12:35 Lunch Break
Lunch Break
12:35 - 13:30
13:30 Anomaly/Outlier Detection
Anomaly/Outlier Detection
13:30 - 15:40
Contributions

13:30 Review on Anomaly/Outlier detection - Cecile Germain (Université Paris-Sud)

14:05 Monitoring the LHC magnets - Maciej Wielgosz (AGH University of Science and Technology, Kraków)

14:28 Good Data Certification at the LHC - Fedor Ratnikov

14:51 Machine learning to control an X-ray laser - Daniel Ratner (SLAC)

15:14 Online DQM with Machine Learning - Pol Adrian (Université Paris-Sud and CERN)
15:40 Transfer to Wilson Hall
Transfer to Wilson Hall
15:40 - 16:00
16:00 FNAL Colloquium - Zadeh Reza (Stanford & Matroid)
FNAL Colloquium
- Zadeh Reza (Stanford & Matroid)
16:00 - 17:00
17:00 Roundtable on uncertainty in machine learning
Roundtable on uncertainty in machine learning
17:00 - 18:00
Room: Directors’ Conference Room on 2E
Wednesday, 10 May 2017
09:00 Adversarial Networks I
Adversarial Networks I
09:00 - 10:35
Contributions

09:00 Review of Adversarial Networks - Prasanna Balaprakash

09:45 GANs for Jets - Michela Paganini (Yale) Michela Paganini (Yale University)

10:10 GANs for simulation - Sofia Vallecorsa (Gangneung-Wonju National University, Korea & CERN)
10:35 Coffee Break
Coffee Break
10:35 - 11:00
11:00 Adversarial Networks II
Adversarial Networks II
11:00 - 11:50
Contributions

11:00 Systematics mitigation - Gilles Louppe (New York University)

11:25 Training neural networks on data - Francesco Rubbo (SLAC National Accelerator Laboratory) Francesco Rubbo (Università degli Studi di Torino)
11:50 Recursive jets - Kyle Cranmer (NYU)
Recursive jets
- Kyle Cranmer (NYU)
11:50 - 12:20
12:20 Lunch Break
Lunch Break
12:20 - 13:25
13:25 The power of DRL and Latest Advancements in Learning Systems - Rocky Duan
The power of DRL and Latest Advancements in Learning Systems
- Rocky Duan
13:25 - 14:05
14:05 Programming Neuromorphic Computing Systems - Catherine Schuman (Oak Ridge National Laboratory)
Programming Neuromorphic Computing Systems
- Catherine Schuman (Oak Ridge National Laboratory)
14:05 - 14:45
14:45

14:45 - 16:00

14:45 - 16:00

14:45 - 16:00

16:00 Coffee Break
Coffee Break
16:00 - 16:30

16:30

16:30 - 18:00

16:30 - 18:00

16:30 - 18:00
Thursday, 11 May 2017
09:00 Computing Infrastructure I
Computing Infrastructure I
09:00 - 10:25
Contributions

09:00 Infrastructure for Large Scale HEP data analysis - Matteo Cremonesi (FNAL)

09:25 A path toward HEP data analysis using high performance computing - Saba Sehrish (Fermilab)

09:50 Large-scale network training with MPI - Dustin Anderson (California Institute of Technology)
10:25 Coffee Break
Coffee Break
10:25 - 10:55
10:55 Computing Infrastructure II
Computing Infrastructure II
10:55 - 11:50
Contributions

10:55 Data plumbing: moving data across frameworks - Jim Pivarski (Fermilab)

11:20 CERN OpenLab: Machine Learning, HEP, and Industry - Maria Girone (CERN)
11:50 The Berkeley Institute for Data Science - Peter Nugent
The Berkeley Institute for Data Science
- Peter Nugent
11:50 - 12:35
12:35 Lunch Break
Lunch Break
12:35 - 13:30
13:30 Large scale Bayesian computations - Michael Betancourt (Columbia University)
Large scale Bayesian computations
- Michael Betancourt (Columbia University)
13:30 - 14:05
14:05 AI/Deep Learning applications in Industry - Kathy Copic
AI/Deep Learning applications in Industry
- Kathy Copic
14:05 - 14:40
14:40

14:40 - 16:00

14:40 - 16:00

14:40 - 16:00

16:00 Coffee Break
Coffee Break
16:00 - 16:30

16:30

16:30 - 18:00

16:30 - 18:00

16:30 - 18:00
Friday, 12 May 2017
09:00 Summary of Hands-on Session II: Jets - Alexx Perloff (Texas A&M University)
Summary of Hands-on Session II: Jets
- Alexx Perloff (Texas A&M University)
09:00 - 09:30
09:30 Summary of Hands-on Session III: Tracking with Machine Learning - Jamie Antonelli
Summary of Hands-on Session III: Tracking with Machine Learning
- Jamie Antonelli
09:30 - 10:00
10:00 Summary of Hands-on Session I: Imaging Detector datasets - Gabriel Perdue (Fermilab)
Summary of Hands-on Session I: Imaging Detector datasets
- Gabriel Perdue (Fermilab)
10:00 - 10:30
10:30 Coffee Break
Coffee Break
10:30 - 11:00
11:00 Community Efforts in Machine Learning - Fernanda Psihas (Indiana University)
Community Efforts in Machine Learning
- Fernanda Psihas (Indiana University)
11:00 - 11:25
11:25 Introduction to CWP discussion - Sergei Gleyzer (University of Florida)
Introduction to CWP discussion
- Sergei Gleyzer (University of Florida)
11:25 - 11:30
11:30 Summary & outlook: planning work in view of DS@HEP2018 - Amir Farbin (University of Texas at Arlington)
Summary & outlook: planning work in view of DS@HEP2018
- Amir Farbin (University of Texas at Arlington)
11:30 - 12:30
13:30
13:30 - 16:00
Room: Big Room in Building 327
Contributions

13:30 Introduction to CWP-ML - Sergei Gleyzer (University of Florida)

13:45 Group breakout editing session I

15:05 Group breakout editing session II