Applied Data Science Invited Speakers

The Applied Data Science Invited Talks will provide a venue for leading experts in the world of applied data mining and knowledge discovery. These invited talks will feature highly influential speakers who have directly contributed to successful data mining applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or government applications of data mining in areas such as finance, health-care, bio-informatics, public policy, infrastructure, telecommunications, social media and computational advertising.

Keynote: Joseph Bradley

Joseph Bradley

Databricks, Inc.

Addressing Challenges in Data Science: Scale, Skill Sets and Complexity

Data science in modern applications is pushing the limits of tools and organizations. The scale of data, the breadth of required skill sets, and the complexity of workflows all cause organizations to stumble when developing data-powered applications and moving them to production. This talk will discuss these challenges and Databricks’ efforts to overcome them within open source software projects like Apache Spark and MLflow.

Apache Spark has simplified large-scale ETL and analytics, and its Project Hydrogen helps to bridge the gap between Spark and ML tools such as TensorFlow and Horovod. MLflow, an open source platform for managing ML lifecycles, facilitates experimentation, reproducibility and deployment. We will present insights from our collaborations on these projects, as well as our perspective at Databricks in facilitating data science for a wide variety of organizations and applications.

Joseph Bradley is an Apache Spark PMC member and a Machine Learning Software Engineer at Databricks. At the intersection of Big Data and Machine Learning, his team works on scaling ML, integrating Big Data and ML tools, and simplifying the management of environments and workflows for Data Science. Major projects include Apache Spark MLlib and GraphFrames. Previously, Joseph was a postdoc at UC Berkeley after receiving his Ph.D. in Machine Learning from Carnegie Mellon. His research interests included optimization techniques for sparse regression, systems for peer grading and rating, and probabilistic graphical models.

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