Sep 23–26, 2019

Jennifer Kloke
VP of Product Innovation, Ayasdi

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Dr. Jennifer Kloke is the VP of Product Innovation at Ayasdi. For the last three years, she has been responsible for the automation and algorithm development for the entire Ayasdi codebase and cutting edge analysis techniques utilizing TDA and AI. During that time, she was the principal investigator for a Phase 2 DARPA SBIR developing automation and data fusion capabilities. These have led to breakthroughs in the field and several patents. Jennifer also served five years as a Senior Data Scientist analyzing a wide variety of data including point cloud, text, and networks from diverse industries including large military contractors, finance, bio-tech, and electronics manufacturing. Her work includes developing prediction algorithms for reducing the number of false alarms for a large military jet manufacturer as well as developing and deploying a predictive program management application at a large government contractor. Jennifer and her team’s efforts landed Ayasdi spots on Fast Company’s Most Innovative Companies in Big Data, AIconics, and consecutively on Forbes Fintech50.

Jennifer received her Ph.D. in Mathematics from Stanford University with an emphasis on topological data analysis. She has collaborated with chemists at Lawrence Berkeley National Laboratory and UC Berkeley to develop topological methods to mine large databases of chemical compounds to identify energy efficient compounds for carbon capture. She also developed a de-noising algorithm to efficiently process high dimensional data and has published in the Journal of Differential Geometry.

Sessions

10:00am10:30am Tuesday, September 24, 2019
Location: 1A 08
Jennifer Kloke (Ayasdi)
Jennifer Kloke will discuss how banks and financial enterprises can adopt and integrate actual risk models with existing systems to enhance the performance and operational efficiency of the financial crimes organization. In doing so, she will explain how actual risk models can reduce segmentation noise, utilize unlabeled transactional data, and spot unusual behavior more effectively. Read more.

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