OpenTech AI Summit Switzerland

OpenTech AI Summit Switzerland

Organized by

  • Susan Malaika - STSM: Open Tech for Data & AI - NoSQL, Hadoop, ODPi, Blockchain

  • Romeo Kienzler - human being

Keynote: Abdel Labbi - IBM Distinguished Engineer, A.I. Systems, IBM Research Zurich

Please find more information including the latest agenda on the link below:

Tentative Flow Monday May 28th: 8:30-11:00 Registration

9:00-12:00 Tutorials

9:00-12:00 PowerAI and OpenPOWER Bootcamp – Ganesan Narayanasamy 9:00-10:15 Node-Red & Watson APIs 101 – Yamini Rao 10:45-noon Accelerate Decisions in a Data-First World – Margriet Groenendijk 10:00-noon Visual Recognition on the edge with iOS Core ML –Yacine Rezgui & Sam Couch 10:00-noon Visually modelling neural networks for greater portability – Sean Tracey & Arlemy Turpault

noon-13:00 Lunch

13:00-13:45 Welcome by Christian Keller, CEO IBM Switzerland; Keynote by Distinguished Engineer Abdel Labbi;

13:45-14:30 Panel 14:30-15:15 Lightning Talks

  • Accelerate machine learning workloads with high-speed training of popular machine learning models on modern CPU/GPU, for large data sets and/or real-time or close-to-real-time applications, Dr. Robert Haas, Department Head, Cloud and Computing Infrastructure, IBM Research - Zurich

  • Crail: Accelerate data processing workloads with high-performance distributed ephemeral store (open-source Apache incubator project), Dr. Robert Haas, Department Head, Cloud and Computing Infrastructure, IBM Research - Zurich

  • Deep Learning for Computer Graphics, Muzahid Hussain, University of Stuttgart

  • Robust 3D Pose Estimation of Humans and Objects for Augmented Reality, Bugra Tekin, EPFL

  • From neurons to roads - machine learning for detection of curvilinear structures, Agata Mosinska, EPFL

  • Automatised Prediction of Convolutional Neural Networks Performances without training across multiple different datasets, Roxana Istrate, IBM Research

  • Learning to Find Good Correspondences, Eduard Trulls, EPFL

  • Geodesic Convolutional Shape Optimization, We train Geodesic Convolutional Neural Networks to emulate a fluidynamics simulator. This lets us introduce a new type of surrogate models to optimize aerodynamic shapes, which is more flexible, more powerful and more scalable than previous approaches, Pierre Baqué, EPFL

  • privacy-preserving ML ("Privacy-Preserving Classification with Secret Vector Machines"), Robert West, EPFL

  • Revisiting Arithmetic for AI, Babak Falsafi, ecocloud, EPFL

15:15-15:45 Break

15:45-16:30 Panel

16:30-17:00 Final Keynote & Next Steps – Romeo Kienzler

17:00-17:30 Refreshments and Networking

17:30-20:00 Meetup

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