Kay Giesecke is an Associate Professor of Management Science & Engineering at Stanford University and the Paul Pigott Faculty Scholar in the School of Engineering. He is the Director of Stanford's Center for Financial and Risk Analytics, the Director of Stanford's Quantitative Finance Certificate Program in Hong Kong, and the Co-Director of Stanford's Mathematical and Computational Finance Program. He is also the Vice-Chair of the SIAM Activity Group on Financial Mathematics and Engineering.
Kay's research addresses the quantification and management of financial risks. He is particularly interested in the stochastic modeling, valuation and hedging of financial risks; the development of statistical tools to estimate and predict these risks; and the methods for solving the significant computational problems that arise in this contex
Gary Kazantsev, Bloomberg LP
Justin Palmer, LendingHome
Michael Manapat, Stripe
Michael Manapat leads the Machine Learning team at Stripe. His team is responsible for both the data science and the production infrastructure backing machine learning products across the company. He was previously a Software Engineer at Google, a Postdoctoral Fellow at Harvard, and a graduate student at MIT.
Arshak Navruzyan, Startup.ML
Soups Ranjan, Coinbase
Soups was previously a Data Mining Engineer at Yelp where he used Machine Learning to make local ads more relevant. He is passionate about all things data and leads Yelp's university outreach efforts by organizing the Yelp Dataset Challenge, where he provides a dataset containing 2M+ reviews about local businesses in 10 cities world-wide for use in academia.
Yelp has a unique local ads product where businesses buy impression- or performance-based advertising to drive clicks. HE will draw the curtains open on how Yelp does local ads with a brief overview of the components of the ads system including: 2nd price auction based ad-exchange and auto bidding to set bid prices. We will also talk about problems such as Click-Through-Rate Prediction, Inventory Forecasting, and more.
Joel Dietz, Swarm
Scott Clark, SigOpt
Scott is currently running SigOpt, an optimization as a service startup that leverages techniques from optimal learning to automatically tune A/B tests, machine learning models, and complicated systems. Before that he worked on the Ad Targeting team at Yelp Inc leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University.