About

Casey is an Associate Professor of Systems Pharmacology and Translational Therapeutics in the Perelman School of Medicine at the University of Pennsylvania. His research lab develops deep learning methods that integrate distinct large-scale datasets to extract the rich and intrinsic information embedded in such integrated data. This approach reveals underlying principles of an organism’s genetics, its environment, and its response to that environment. He is also the Director of the Childhood Cancer Data Lab (CCDL), powered by Alex's Lemonade Stand Foundation. The goal of the CCDL is to empower childhood cancer researchers poised for the next big discovery with the tools, data, and knowledge to reach it.

About

I work on designing learning algorithms for agents that learn from and control their environments. I am interested in figuring out principles that help us to be more efficient at this job. 

About

Emmanuel is a Machine Learning Engineer at Stripe, and previously was the Head of AI at Insight Data Science, and worked in Data Science at Zipcar. He has years of experience going from product ideation to effective implementations. At Insight, he has led over a hundred AI projects from ideation to finished product in a variety of domains including Computer Vision, Natural Language Processing, and Speech Processing.

Leveraging his experience, Manu wrote “Building ML Powered Applications”, an O’Reilly book which teaches practical tools to build ML products for the real world. His talk will distill some of the most important of these lessons.

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Eric Tramel discusses the basic concepts underlying the federated Machine Learning approach, the advantages it brings, as well as the challenges associated with constructing federated solutions. Federated learning could be an interface between data science, machine learning, engineering, DevOps, software data, and security engineering, and bringing all this together in one spot.For machine learning test, the compute is happening where the data is, so now we have some other extra complications, we need to bring the compute to the data.If it's data that's generated in a hospital, never take it out of the hospital, leave it there.Eric is the head of Owkin’s Federated Learning Research group, where he heads a team of researchers and engineers to study and build applications of federated and privacy-aware machine learning techniques to medical data.

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François Chollet is the main creator of the Keras framework. Probably everyone already knows the Keras, but just for sure Keras is a leading deep learning framework for Python, with over 350,000 users and over 700 open-source contributors .Keras is an easy-to-use machine learning library that can be used for doing fast prototyping to building complex custom models for research and production.Keras has the goal to make deep learning accessible to everyone. Now let’s see how to integrate keras and tensorflow in a deeper level. It’s time to learn from it’s creator.François is Staff Software engineer at Google and a brilliant AI researcher whose main interest is understanding the nature of abstraction and developing algorithms capable of autonomous abstraction (i.e. general intelligence)  and democratizing the development and deployment of AI technology by making it easier to use and explaining it clearly.

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Lester Solbakken is a Principle Software Engineer at Verizon Media (previously Yahoo) on the vespa.ai platform, the open big data serving engine. His primary focus areas are machine learning engineering with emphasis on serving and search system ranking. Lester previously pursued a PhD within Artificial Intelligence and Machine Learning with neural networks, exploratory data analysis and self-organizing systems as main research topics.

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Reza Bosagh Zadeh is founder and CEO at Matroid and an Adjunct Professor at Stanford. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. He's served on the Technical Advisory Boards of Databricks, and has been working on Artificial Intelligence since 2005 when he worked in Google's AI research team. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award at Stanford.

About

I'm a machine learner, data scientist, ex-roboticist, and consultant.
I like turning crazy ideas into production systems using data science and software engineering.