Shadow (Hongying) is currently a staff data scientist at LinkedIn. Her main focuses are in the go-to-market problem space, including optimizations of marketing targeting, sales planning, and sales outreach productivity. She has been leading a series of customer journey related initiatives that maximizes the LinkedIn business efficiency and profitability via machine learning, causal inference and experimentation. 

Zhenyu is the Head of Data Science at Tencent Global Games. His team provides data science solutions across users’ lifecyle, from user acquisition to in-game optimization. He has experience in experimentation, causal inference, modeling, product analytics, and data platform solutions. He co-founded CausalML open-source project, which is focused on providing complementary and practical solutions for causal learning, such as uplift modeling and observational causal inference.

Mert is the Head of Business Data Science at Noom solving data science challenges on growth marketing, strategy and finance related to increasing customer value, revenue, optimizing cost-of-acquisition by building data solutions for financial planning and forecasting, measurement and optimization across investment levers and audiences for paid & owned acquisition, re-engagement, retention and win-back, pricing using analytics, machine learning, predictive modeling, mathematical optimization and causal inference methods.

Anbang Xu is a Senior Manager at NVIDIA. He leads a Machine Learning team to develop enterprise AI solutions. His research is a mix of Applied Machine Learning and HCI. He is the Associate Editor of ACM Transactions on Interactive Intelligent Systems. He has published 50+ research articles and 25+ patents and received 2,700+ citations. He received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign.

Neha Gupta is Head of Data Science| Machine Learning at Common Room. Before joining Common Room, Neha spent her time at companies such as Linkedin, Uber and Adobe working on challenging problems ranging from personalization, recommendations, forecasting and go-to-market ML.  Neha graduated with her Ph.D degree in Computer Science from University of Maryland, College Park. She has several publications in top tier conferences and has chaired two prior workshops on Offline and Online evaluation of ML Systems  in KDD (2019) and WWW (2015).