Data Science & Machine Learning

Europe/Zurich
Room: New York (T-Systems)

Room: New York

T-Systems

T-Systems Kloten (Balsberg), Balz-Zimmermann-Strasse 7, CH-8302 Kloten
Bolliger Christian (ETH Zurich) , Valerio Zanetti-Überwasser (T-Systems Schweiz AG)
Description
Introduction

Data Science and Machine Learning have become relevant in many research areas and industries. The amount of collected data repeatedly breaks before known speed and volume barriers, which creates the need for automated data processing. Before automated data processing can take place, a machine or algorithm has to be trained for the intended task, which can be information search/retrieval, gaining insights or taking actions. The training phase might last long and occupy a large part of the available infrastructure. Especially if it has to be repeated on new incoming data. To minimize infrastructure costs, machine learning workloads tend to be offloaded to specialized hardware.

Machines are trained to take semantic action in specific domains. To act successfully in such a context, machine learning can't solely rely on data and general purpose algorithms, domain models play an important role in generating accurate results. Data Science - which can be seen as a combination of mathematics, heuristics and domain knowledge - helps discovering patterns and regularities in data, which ideally give birth to new models that help understanding the digitized ocean.

Key questions
  • What algorithms and computational models are best fit for machine learning at scale?
  • How scalable are the current implementations of support vector machines and deep neuronal networks?
  • Which type of hardware is a good match for offloading machine learning workloads: DSPs, ASICs, GPUs?
  • What deployment models and data flows are most supportive for machine learning applications?
  • How does machine learning impact resource usage in a HPC cluster?
  • What can Data Science adopt from HPC and vice versa?
Participants
  • Alexander Kashev
  • André Kunz
  • Christian Bolliger
  • Christian Gerster
  • Christian Iseli
  • Danuta Paraficz
  • Derek Feichtinger
  • Diana Coman Schmid
  • Douglas Potter
  • Gianfranco Sciacca
  • Hamid Hussain-Khan
  • Hardik Kothari
  • Hayk Sargsyan
  • Ingo Elsen
  • Ivan Usov
  • Jens Zudrop
  • Jiří Kunčar
  • Jonathan Blazek
  • Manuel Kohler
  • MARC GENTILE
  • Marcel Schoengens
  • Mario Jurcevic
  • Martin Jacquot
  • Mattia Belluco
  • Michael Rolli
  • Michele De Lorenzi
  • Nicholas Cardo
  • Nico Färber
  • Norman Del Puppo
  • Olivier Byrde
  • Patrik Burkhalter
  • Ralph Larisch
  • Raluca Hodoroaba
  • Ricardo Silva
  • Riccardo Murri
  • Richard Braendli
  • Roberto Fabbretti
  • Rok Roskar
  • Samuel Fux
  • Sebastian Schubert
  • Sigve Haug
  • Silvan Hostettler
  • Sona Hunanyan
  • Stadler Hans-Christian
  • Steffen Oettich
  • Teodoro Brasacchio
  • Thomas Arter
  • Thomas Wuest
  • Timothée Delubac
  • Tyanko Aleksiev
  • Urban Borštnik
  • Valerio Zanetti-Ueberwasser