Dr. Priya Ponnapalli is a senior manager and principal scientist at the Amazon Machine Learning (ML) Solutions Lab, where she leads a global team of data scientists that help AWS customers accelerate their ML and cloud adoption across industries, from healthcare and finance to sports.
As leader for Amazon ML Solutions Lab’s sports business, she works with customers including Major League Baseball (MLB), National Football League (NFL), Formula 1 (F1), and sports organizations worldwide to enhance the fan experience and transform sports using ML. Viewers love seeing her team’s work integrated into game broadcasts, such as the stolen base success probability used in MLB games. Ponnapalli served on a panel of judges for the NFL 1st and Future Analytics Competition — the NFL’s annual Super Bowl competition designed to spur novel advancements in athlete safety and performance. She also served as a Design Inclusivity in AI panelist at the Grace Hopper Celebration 2019, discussing how diverse perspectives lead to better AI solutions.
Ponnapalli is also a senior research affiliate at the Genomic Signal Processing Lab at the University of Utah. She publishes in applied mathematics and data science, and gives numerous invited lectures at international institutions and conferences, such as European Organization for Nuclear Research CERN and Society for Industrial and Applied Mathematics (SIAM) conferences. As faculty at Rutgers Business School, she teaches ML to business leaders and works to inspire the next generation of leaders. Prior to joining Amazon, she co-founded Eigengene, a data-driven personalized medicine startup and has helped companies like Genentech and Roche establish and build data science teams.
Previously, Ponnapalli was the founding data science lead at JPMorgan Chase, where she created the first implicit feedback recommender system for Chase Ultimate Rewards. Before that, as a senior software engineer on the machine learning team at Bloomberg L.P., she developed and deployed the first social media analytics tool in finance, Bloomberg Social Velocity. For her Ph.D. in electrical and computer engineering at the University of Texas at Austin, Ponnapalli defined and demonstrated the higher-order generalized singular value decomposition (HO GSVD), the only framework that can create a single coherent model from multiple two-dimensional datasets by extending the GSVD from two to more than two matrices.