Symposium Artificial Intelligence for Science, Industry and Society

Session

Plenary

Keynote Adresses

21 Oct 2019, 09:30

Conveners

Plenary: Keynotes 1

• Steven Schramm (Universite de Geneve (CH))

Plenary: Plenaries 1

• Steven Schramm (Universite de Geneve (CH))

Plenary: Public lecture

• Guy Paic (Universidad Nacional Autonoma (MX))

Plenary: Keynotes 2

• RAFAEL MAYO (CIEMAT)

Plenary: Plenaries 2

• Alan Paic (OECD)

Plenary: Plenaries 3

• Boris Escalante-Ramírez (UNAM)

Plenary: Keynotes 3

• Gergely Gabor Barnafoldi (Wigner RCP Hungarian Academy of Sciences (HU))

Plenary: Plenaries 4

• Gergely Gabor Barnafoldi (Wigner RCP Hungarian Academy of Sciences (HU))

Plenary: Keynotes 4

• Federico Carminati (CERN)

Plenary: Plenaries 5

• Federico Carminati (CERN)

9. AI Innovation in the Pharmaceutical Sector - Accelerating Research

Dan Faggella (CEO/founder of Emerj Artificial Intelligence Research)

21/10/2019, 09:30

Oral

26. Machine Learning in High Energy Physics

Michael Aaron Kagan (SLAC National Accelerator Laboratory (US))

21/10/2019, 10:15

Oral

17. Amazon Web Services AI - Deep learning at scale

Nathalie Rauschmayr (Amazon Web Services AI)

21/10/2019, 11:30

Oral

Deep learning is driving rapid progress in fields such as computer vision, natural language processing, and speech recognition. It has become one of the most disruptive technologies and nowadays many products feature artificial intelligence. However, creating production-ready deep learning models involves many challenges: starting from generating good high-quality training datasets to...

18. Machine learning for neutrino identification

Mr Saul Alonso Monsalve (CERN)

21/10/2019, 12:00

Oral

This talk will cover the current state of machine learning (ML) in neutrino experiments. In experiments like the Deep Underground Neutrino Experiment (DUNE), NuMI Off-axis νe Appearance (NOvA), the Micro Booster Neutrino Experiment (MicroBooNE), and Argon Neutrino Teststand (ArgoNeuT), deep learning (DL) approaches based around convolutional neural networks have been developed to provide...

62. Scaling Deep Learning to Exascale – ACM Gordon Bell Prize 2018

Pedro Mario Cruz e Silva (NVidia)

21/10/2019, 12:30

Oral

In this talk I will present technical details about the ACM Gordon Bell Prize winner project at Supercomputing 2018. In this work the joint team from NERSC and NVIDIA succeeded in scaling a Deep Learning training across 27.000+ GPUs in Summit (world’s largest HPC system) and obtained a high fraction of peak performance. This research showed that realistic scientific applications could leverage...

61. AI Research at Atos

Cédric Bourrasset (Atos)

21/10/2019, 18:00

Oral

This lecture will cover the AI research challenges currently addressed by Atos research teams over different domains like Cyber Security, Predictive Maintenance of IT and Privacy by Design needs for Video Intelligence solution in the context current European regulations.

11. Trustworthy AI - The AI4EU approach

Ulises Cortes (Barcelona Supercomputing Center)

22/10/2019, 09:00

Oral

59. Regulating Emerging Technologies: Opportunities and Challenges for Latin America

Mirjana Stankovic (Vice President, Emerging Tech and Intellectual Property, Tambourine Innovation Ventures)

22/10/2019, 09:45

Oral

“Can regulators keep up with fintech?” “Your Apps Know Where You Were Last Night, and They’re Not Keeping It Secret.” “Regulators scramble to stay ahead of self-driving cars.” “Digital health dilemma: Regulators struggle to keep pace with health care technology innovation.” Headlines like these capture a central challenge to today’s regulators.

Existing regulatory structures are often slow...

20. Simulation and generative models in high energy physics

Tobias Golling (Universite de Geneve (CH))

22/10/2019, 11:00

Oral

High-energy particle physic experiments rely heavily on billions of CPU hours per year for data processing purposes. The production of synthetic data through Geant4-based Monte Carlo simulation describing particle shower developments in the calorimeter is the single most compute-intensive tasks. Fast simulation techniques are already today indispensable. With the upcoming high-luminosity...

21. Towards inverse design in chemistry: from prediction to deep generative models

Benjamin Sanchez-Lengeling (Google Brain)

22/10/2019, 11:30

Oral

Many of the challenges of the 21st century, from personalized healthcare to energy production and storage, share a common theme: materials are part of the solution. Groundbreaking advances are likely to come from unexplored regions of chemical space. A central challenge is, how do we design molecules and materials according to a desired functionality?
In this talk I showcase how we can apply...

22. Exploring Space in Cyberspace: How Computing and Big Data are Transforming Astronomy

George Djorgovski (California Institute of Technology)

22/10/2019, 12:00

Oral

69. IBM Quantum Computing

Panagiotis Barkoutsos (IBM Zurich)

22/10/2019, 12:30

Oral

23. Deep Learning for Cosmic-Ray Observatories

Jonas Glombitza (RWTH Aachen University)

22/10/2019, 14:30

Oral

Ultra-high energy cosmic rays (UHECRs) are the most energetic particles found in nature and originate from extragalactic sources. These particles induce extensive air showers when propagating within the Earth’s atmosphere. Cosmic-ray bservatories like the Pierre Auger Observatory measure such air showers using large arrays of surface-detector stations and luorescence telescopes. The...

60. HPC / AI convergence and computing architecture impacts

Cédric Bourrasset (Atos)

22/10/2019, 15:00

Oral

Science and numerical applications are evolving towards integrating Machine Learning based algorithms. This talk will review future impacts on upcoming HPC architectures.

71. Deep learning for cosmology

Celia Escamilla-Rivera (ICN-UNAM)

22/10/2019, 15:30

Oral

In this talk I will describe ongoing efforts to shed light on still-unanswered questions in fundamental physics using cosmological observations. I will explain how we can use measurements of the Supernovae data, Baryon Acoustic Oscillations, Cosmic Microwave Background and the large-scale structure of the universe to reconstruct the detailed physics of the dark universe. Also I will address...

67. The Mode of Computing

Luis Pineda (Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, UNAM)

22/10/2019, 16:00

Oral

The Turing Machine is the paradigmatic case of computing machines, but there are others, such as Artificial Neural Networks, Table Computing, Relational-Indeterminate Computing and diverse forms of analogical computing, each of which based on a particular underlying intuition of the phenomenon of computing. This variety can be captured in terms of system levels, re-interpreting and...

13. Leveraging AI Communities as Innovation Engines in Life Sciences

Pablo Meyer (IBM, Thomas J. Watson Research Center)

24/10/2019, 09:00

Oral

The generation of large-scale biomedical data is creating unprecedented opportunities for applications of AI solutions. Typically, the data producers develop initial predictions using AI, but it is very likely that the higher performing AI methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies in...

12. Upcoming Big Data and AI Challenges in Astronomy

Željko Ivezić (University of Washington)

24/10/2019, 09:45

Oral

24. Advances in classification/regression in high energy physics

Jan Kieseler (CERN)

24/10/2019, 11:00

Oral

25. HPC and AI

Isidoro Gitler (CINVESTAV)

24/10/2019, 11:30

Oral

27. Variational Autoencoders for Medical Imaging

Juan Cerrolaza (Accenture Iberia - Artificial Intelligence Group)

24/10/2019, 12:00

Oral

With the advent of deep learning-based techniques, new approaches and novel architectures are proposed every day, improving the state of the art in almost every technical discipline, including medical imaging. Autoencoders are one of the most popular unsupervised deep learning techniques. Thanks to their unsupervised nature, autoencoders, and its several variants, such as variational...

65. Huawei - Introduction to LATAM Intelligent Computing Business and CERN Case Study

Christian Eduardo Hernandez Garcia (Huawei), Su Quing (Huawei)

24/10/2019, 12:30

15. Robotics, AI, and machine vision

Jesús Savage (UNAM)

25/10/2019, 09:00

Oral

In this talk it is presented the semantic-reasoning module of VIRBOT, our proposed architecture for service robots.
We show that by combining symbolic AI with digital-signal processing techniques this module achieves competitive performance.
Our system translates a voice command into an unambiguous representation that helps an inference engine, built around an expert system, to perform...

64. Atos Quantum Computing: Future Proofing What Lies Beyond Super-Computing

Scott Hamilton (Atos)

25/10/2019, 09:45

58. Machine learning in accelerator physics: applications at the CERN Large Hadron Collider

Frederik Van Der Veken (University of Malta (MT))

25/10/2019, 11:00

Oral

With the advent of machine learning a few decades ago, Science and Engineering have had new powerful tools at their disposal. Particularly in the domain of particle physics, machine learning techniques have become an essential part in the analysis of data from particle collisions. Accelerator physics, however, only recently discovered the possibilities of using these tools to improve its...

19. Deep Learning for QCD phase transition and nuclear structure

Long-Gang Pang (CCNU & LBL)

Oral

68. IBM quantum computing

Vanessa Hernandez (IBM)

10. Machine Learning in High Energy Physics

Kyle Stuart Cranmer (New York University (US))

Oral

14. Simulating Quantum Computing System with Digital Twins for Accelerated AI

Scott Hamilton (Atos/Bull)