Computer Vision
NLP
Deep Learning
Data Science/Analytics
AutoML
Machine Learning
| 8:00am | Check In | |||
| 9:00am Keynote | AI Infrastructure for Autonomous Vehicles - challenges and learnings Nicolas Koumchatzky, Director of AI, Nvidida | |||
| 9:50am Keynote | Responsible AI Development in Practice Sarah Bird, Microsoft | |||
| 10:40am | Coffee break and networking | |||
| 11:00am Keynote | AI to empower AI (Agent Intelligence) Joseph Sirosh, CTO, Compass | |||
| 11:50am | Lunch break and networking | |||
1:00pm - 1:50pm | ||||
| Track 1 | Introduction to TensorFlow 2.0 Brad Miro, Google | |||
| Track 2 | A Bayesian Approach to Model Overlapping Objects Sandhya Prabhakaran, Memorial Sloan Kettering Cancer Centre | |||
| Track 3 | Simulation-Based Inference Nelson Ray, OpenDoor | |||
2:00pm - 2:50pm | ||||
| Track 1 | ML Workflows at Twitter: Lessons Learned Jonathan Jin, Twitter | |||
| Track 2 | How embeddings power Machine Learning (move to 7/24) Douwe Osinga, Sidewalk Labs | |||
| Track 3 | Using a Bayesian Neural Network in the Detection of Exoplanets Esperanza Aguilera, Kx | |||
2:50pm -3:10pm: Coffee break and networking | ||||
3:10pm - 4:00pm | ||||
| Track 1 | BERT, Natural Language Representations and Challenges Emily Pitler, Google AI | |||
| Track 2 | Machine Learning for Artist, GAN Gene Kogan | |||
| Track 3 | Elastic Distributed Deep Learning Training at large scale Yonggang Hu, IBM Watson | |||
4:10pm - 5:00pm | ||||
| Track 1 | Real World Reinforcement Learning John Langford, Microsoft | |||
| Track 2 | Deep MCMC: Training deep neural networks with Markov Chain Monte Carlo Marcelo Labre, Advanced Institute for Artificial Intelligence (AI2) | |||
| Track 3 | Easy Machine Learning with AutoML Dale Markowitz, Matt Ritter, Google | |||
5:30pm - 8:00pm: Evening Session (conference plus, workshop/training tickets holders only) | ||||
| Dinner Reception with Speakers, Invited Guests. | ||||
| 8:30am | Check In | |||
| 9:30am | The Ethical Algorithm Aaron Roth, University of Pennsylvania | |||
| 10:30am | Coffee break and networking | |||
| 10:50am | Women in AI (Special Session) Sarah Bird(Microsoft), Emily Pitler(Google), Esperanza Aguilera (Kx), Reshama Shaikh(WiMLDS) | |||
| 11:50am | Lunch break and networking | |||
1:00pm - 1:50pm | ||||
| Track 1 | Time Prediction for Uber Eats Marketplace Lei Kang, Katherine Chen, Uber | |||
| Track 2 | Choosing a Deep Learning Library: There are a lot of them Jesse Brizzi, Curalate | |||
| Track 3 | Realtime Recommendation at Massive Scale Kexin Xie, Yuxi Zhang, Salesforce | |||
2:00pm - 2:50pm | ||||
| Track 1 | Real-Time Object Detection with Core ML Nicholas Bourdakos, IBM Watson | |||
| Track 2 | How embeddings power Machine Learning Douwe Osinga, Sidewalk Labs | |||
| Track 3 | Deep Learning for Image Acquisition and Image Interpretation in Healthcare Michal Sofka, Hyperfine | |||
3:00pm - 3:50pm | ||||
| Track 1 | Step up your machine learning process with Azure Machine Learning service Heather Spetalnick, Microsoft | |||
| Track 3 | Faster Time to Insights using Automated Data Visualization and Machine Learning Ram Seshadri, Google | |||
4:00pm - 4:50pm | ||||
| Track 1 | Hybrid Methods for the Integration of Heterogeneous Multimodal Biomedical Data Ina Fiterau, UMass Amherst | |||
| Track 3 | Many Ways to Lasso Jared Lander, Lander Analytics | |||
9am - 12pm | |
| Track 1 | Reinforcement Learning in Action by John Langford , Rafah Hosn, Microsoft *hands-on workshop, with tech talks, demo, and code labs. |
| 12pm-1:30pm | lunch break and networking |
1:30pm - 4:30pm | |
| Track 1 | Scale Machine Learning and NLP on Social Media Data by Brad Miro, Google *hands-on workshop, with tech talks, demo, and code labs. |
9am - 12pm | |
| Track 1 | Automated Data Visualization and Machine Learning by Ram Seshadri, Google *hands-on workshop, with tech talks, demo, and code labs. |
| 12pm-1:30pm | lunch break and networking |
1:30pm - 4:30pm | |
| Track 1 | The Future of Machine Learning in R by Jared Lander, Lander Analytics *hands-on workshop, with tech talks, demo, and code labs. |
40+ tech lead speakers from Engineering Teams at Microsoft, Google, Amazon, Facebook, Uber, Linkedin, Pinterest, Nvidia, Twitter, and more.
50+ deep dive tech topics and practicial experiences in machine learning, deep learning, computer vision, speech reconginition, NLP, data science and analytics. specially geared to tech engineers who want to grasp AI tech applied to their daily project.
Connect with 500+ tech engineers, developers, data scientists; learn from peers, small-group discussions, office-hour, and lunch with speakers, happy hours.
Continue to learn and practice AI post conference, join our free online AI learning group with 400+ tech speakers, 85,000+ tech engineers. Learn more.
The speakers and sponsors teams are hiring tech engineers, developers, data scientitst, machine learning engineers and algorithm engineers. Come to talk and connect to the hiring manager and tech lead of the teams.
