The Machine Learning Hub


NeurIPS'19 Meetup

December 10-12 2019, Auditorium between Bldg. 2 and 3

This year Neural Information Processing Systems (NeurIPS), a major international machine learning conference, has introduced NeurIPS Meetups as part of an initiative intended to build a remote presence for NeurIPS.

A NeurIPS Meetup is a local event hosted during the NeurIPS conference, leveraging conference videos, and live local content, with a duration ranging from a few hours to a full week, and bringing together either participants from a single institution (a company or a university) or the public.

The Machine Learning Hub is excited to be hosting a NeurIPS Meetup at KAUST this year!

This meetup will live stream three NeurIPS keynotes being delivered at the conference in Vancouver. It will also include a tutorial (Introduction to Deep Learning with PyTorch), a student workshop and a distributed deep learning clinic. See the schedule details below.

Call for Student Presentations

As part of the upcoming NeurIPS Meetup at KAUST, we are seeking submissions for student spotlight presentations of their recent AI/ML related research work. We would like to invite and encourage all students on campus, particularly students interested in getting more engaged with machine learning, to participate in the student workshop. Students will give short presentations about their recent and exciting work related to AI/ML ranging from theory to practice. Do not miss this exciting opportunity to engage and interact with other machine learning researchers, enthusiast and practitioners on campus!

Submit your talk proposal here.

Application Deadline: 02-Dec-2019
Accepted Submissions Notification: 05-Dec-2019

For any questions regarding the workshop, please contact the workshop chairs (contact information below).


Registration is free but required (both for planning and because adherence to the NeurIPS Code of Conduct is required). Complete this form to register.


Day 1 - December 10 2019
 Distributed deep learning clinic
 Student workshop
  • Filip Hanzely — Better optimization for deep learning
  • Uchenna Akujuobi — Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
  • Abdullah Hamdi — SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications
  • Khalid Alhazmi — Continuous Control of Complex Reaction Network with Reinforcement Learning
  • Nursulu Kuzhagaliyeva — Using deep neural networks to diagnose engine pre-ignition
 Pizza and Live Stream of Bin Yu (UC Berkeley) keynote entitled Veridical Data Science

Day 2 - December 11 2019
 Distributed deep learning clinic
 Student workshop
  • Basmah Altaf — Dataset Recommendation via Variational Graph Auto-Encoders
  • Kiran Yalamanchi — Machine Learning to Predict Standard Enthalpy of Formation of Hydrocarbons
  • Yu Li — RNA Secondary Structure Prediction by Learning Unrolled Algorithms
  • Guohao Li — DeepGCNs for Representation Learning on Graphs
  • Samuel Horv├íth — Natural Compression for Distributed Deep Learning
 Pizza and Live Stream of Blaise Aguera y Arcas (Google) keynote entitled Social Intelligence

Day 3 - December 12 2019
 Hands-on tutorial by David R. Pugh (KVL) on Introduction to Deep Learning with PyTorch
 Pizza and Live Stream of Kafui Dzirasa (Duke University) keynote entitled Mapping Emotions: Discovering Structure in Mesoscale Electrical Brain Recordings


  • Marco Canini (CEMSE)
  • David R. Pugh (KVL)
Student Workshop Co-Chairs
  • Aritra Dutta
  • Ahmed Sayed
  • Adel Bibi
  • Samuel Horvath

For any questions regarding the event in general, please contact Prof. Marco Canini. For any questions related to the student workshop, please contact the workshop chairs (email: name.surname @ kaust).