Data Council Austin
March 28-30, 2023

Jennifer Prendki is the founder and CEO of Alectio. The company is the direct product of her beliefs that good models can only be built with good data, and that the brute force approach that consists in blindly using ever larger training sets is the reason why the barrier to entry into AI is so high. Prior to starting Alectio, Jennifer was the VP of Machine Learning at Figure Eight, the company that pioneered data labeling, Chief Data Scientist at Atlassian and Senior Manager of Data Science in the Search team at Walmart Labs. She has spent most of her career creating a data-driven culture wherever she went, succeeding in sometimes highly skeptical environments.

She is particularly skilled at building and scaling high-performance machine learning teams and is known for enjoying a good challenge. Trained as a particle physicist (she holds a PhD in particle physics from Sorbonne University), she likes to use her analytical mind not only when building complex models but also as part of her leadership philosophy. She is pragmatic yet detail oriented. Jennifer also takes great pleasure in addressing both technical and nontechnical audiences alike at conferences and seminars and is passionate about attracting more women to careers in STEM.

Andreas Mueller is a lecturer at the Data Science Institute at Columbia University and author of the O'Reilly book "Introduction to machine learning with Python", describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon.

Spoke at NYC '17, NYC '18

Wes McKinney is an open source software developer focusing on data processing tools. He created the Python pandas project and has been a major contributor to many other OSS projects. He is a Member of the Apache Software Foundation and a project PMC member for Apache Arrow and Apache Parquet. He is the director of Ursa Labs, an innovation lab for open source data science tools powered by Apache Arrow.

Spoke at BCN '18, NYC '16

Joseph Gonzalez is a 6th year PhD student in Dr. John Gilbert's CSC lab here at UCSB. His areas of interest include graph theory, scientific computing, and high performance computing.

Matei is an Assistant Professor of Computer Science at Stanford and Chief Technologist at Databricks. During his PhD, he started the Apache Spark computing engine and developed other widely used open source software including Apache Mesos, Alluxio and job schedulers for Apache Hadoop. Today, he also works on MLflow (https://mlflow.org), an open source machine learning platform from Databricks.

Spoke at SF '17

Maxime Beauchemin recently joined Airbnb as a data engineer developing tools to help streamline and automate data-engineering processes. He mastered his data-warehousing fundamentals at Ubisoft and was an early adopter of Hadoop/Pig while at Yahoo in 2007. More recently, at Facebook, he developed analytics-as-a-service frameworks around engagement and growth-metrics computation, anomaly detection, and cohort analysis. He’s a father of three, and in his free time, he’s a digital artist.

Spoke at SF '17, SF '18

Andy is Assistant Professor of Databaseology in the Computer Science Department at Carnegie Mellon University. His research interest is in database management systems, specifically main memory systems, self-driving / autonomous architectures, transaction processing systems, and large-scale data analytics. At CMU, he is a member of the Database Group and the Parallel Data Laboratory.

Spoke at NYC '16

Catherine Tarsney is the Analytics Director at the Democratic National Committee. In this role, she leads a team of analysts and data scientists who maintain the core data of the Democratic party and provide analytics resources to help Democrats around the country leverage data-driven decision making to win elections from school board to the presidency.

Prior to the DNC, she worked at the consulting firm 270 Strategies, working at the intersection of analytics and political organizing to help campaigns, non-profits, and companies develop data-driven strategies to identify, engage, and empower their supporters. During the 2016 presidential primaries, Catherine worked with the Hillary Clinton campaign to support strategic planning, training program development, voter targeting, and program measurement in Iowa and other early primary states. She then served as the Data Director for the Kansas caucus before joining the campaign's Voter Targeting & Analytics Team for the general election. In that role, Catherine managed voter targeting for the national organizing program – determining the campaign's prioritization of potential volunteers and voters for phone calls, canvassing, and text messages – and helped coordinate the campaign's approach to data-driven targeting across volunteer and paid voter outreach programs.

Catherine's political campaign experience also includes work for Wendy Davis’ 2014 gubernatorial campaign in Texas and President Obama's 2012 reelection. Catherine graduated with honors in public policy from the University of Chicago, where she studied the role of education in promoting civic engagement.