Pablo Samuel Castro (@pcastr) — Google Research

Neil Lawrence (@lawrennd) — Cambridge University

Yale Song (@yalesong) — Microsoft Research

EXPO Chairs NeurIPS 2020

The NeurIPS EXPO will be held on Sun, Dec 6, 2020, the day before the NeurIPS 2020 main conference tutorials. This is the third year of the Expo, which aims to showcase the industrial application of the research topics discussed during the main conference.

The tenets of the EXPO are:

  • Present what is taking place in the field of AI/ML research and applications generally.
  • Help the practitioner community gain a wider perspective on the context in which algorithms are being used to solve the real-world problems.
  • Reflect and explore the practical challenges of implementing and deploying AI in the real world.
  • Present attendees technically useful ideas and actionable thought leadership from an industrial perspective.

Historically, the EXPO has been limited to sponsors of the conference, but this year we opened it up to non-profit organizations and actively encouraged their participation. Organizations who chose to apply could do so for the following:

  • Talks and Panels. A 45-minute slot with one or two speakers, followed by a Q&A.
  • Demonstrations. A 1-hour slot where one product, model, or approach is demonstrated to attendees.
  • Workshops and Active Trainings. Half-day slot where a body of content is presented around a larger theme or idea.

We received 51 applications in total (32 talks/panels, 9 demonstrations, and 10 workshops) out of which we accepted 22 talks, 9 demonstrations, and 8 workshops. Our decisions were based on whether the application has adequate technical content aligned well with the research interests of the conference, and is not a duplicate submission (e.g. the same proposal as both a talk and a workshop).

We are working on finalizing the schedule for the EXPO day, and will share the full details once it is complete. For now, here is the list of accepted presenters:

Talks and Panels

  • Zalando — Scikit-learn and Fairness, Tools and Challenges
  • CausaLens — The challenges and latest advances in the field of causal AI
  • Benevolent AI — How we leverage machine learning and AI to develop life-changing medicines — a case study with COVID-19
  • Apple — Accelerated Training with ML Compute and Apple Silicon
  • QUANTUMBLACK — Making boats fly by scaling Reinforcement Learning with Software 2.0
  • Wild Me — Automating Wildlife Conservation for Cetaceans
  • IBM — AI against COVID-19
  • Amazon Science — Privacy, explainability and fairness in healthcare: how we can address these challenges for ML systems
  • Amazon Science — Challenges in the adoption of Machine Learning in Health Care
  • IBM — AI4Code @ IBM and Red Hat
  • Hudson River Trading AI Labs — Modern ML Meets Financial Markets: Insights and Challenges
  • IBM — Human-Centered AI @ IBM Research –Automation versus Collaboration in the Age of AI
  • Microsoft — The Unpaved Path of Deploying Reliable and Human-Centered Machine Learning Systems
  • Scale — Scaling Data Labeling with Machine Learning
  • Sony — Hypotheses Generation for Applications in Biomedicine and Gastronomy
  • Scale — Visually Debugging ML Models With Scale Nucleus
  • Cruise — Driving New Frontiers of Machine Learning with Cruise
  • Google Research — Accelerating eye movement research via smartphone gaze
  • Microsoft — How to Teach Machines When Experts Disagree
  • Kuaishou — New AI applications and challenges met by short-video companies
  • Facebook — Building Neural Interfaces: When Real and Artificial Neurons Meet


  • Microsoft — Real World RL with Vowpal Wabbit: Beyond Contextual Bandits
  • Google Research — Graph-based Learning at Scale
  • IBM — Neurips 2020 EXPO Workshop Proposal -Perpsectives on Neurosymbolic Artificial Intelligence Research
  • IBM — DAQA — Domain Adaptation for Question Answering: An Industry Perspective
  • Facebook — Building AI with Security and Privacy in mind
  • Alibaba group — New Challenges in User-Generated Content
  • Ant Group / Alipay — Machine Learning for All-Inclusive Finance
  • Netflix Research — Machine Learning at Netflix


  • Alibaba Group — Whale: Accelerate EasyTransfer training workloads within one unified distributed training framework
  • Sony — Accelerating Deep Learning for Entertainment with Sony’s Neural Network Libraries and Console
  • IBM — Beyond AutoML: AI Automation & Scaling
  • Amazon Science — AWS Computer Vision Science
  • Amazon Science — Medical Transcription Analysis
  • Zalando — GAN Applications in Fashion Article Design and Outfit Rendering
  • Deep Genomics — Discovering genetic medicines using the Deep Genomics AI Drug Discovery Platform
  • Sony — The Intelligent Vision Sensor
  • Neural Magic — Using Sparse Quantization for Efficient Inference on Deep Neural Networks

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