About me

I am an engineering leader with over a decade of industry experience in Growth and Machine Learning, and have a proven track record in driving growth in social media platform and e-commerce. I led business-critical products such as friending search and recommendation systems, push notifications, search engine marketing (SEM), and risk management, and supported multiple teams of client, backend, infrastructure, and machine learning engineers across geolocations and time zones.

Currently, I lead the Friending product team, the Growth Machine Learning team, the Growth Foundational team, and the Graph Understanding Platform at Snap. We develop features to enhance the Friending experience, deliver ML solutions and infrastructure to power various growth products (e.g., friend recommendations, notification optimization, search, invite, and off-platform sharing), and build a new Graph Understanding Platform to mine signals from complex entity relationships on Snapchat, unlocking new business opportunities. Before Snap, I worked as a senior applied scientist and tech lead at Amazon, where I led a team of scientists and engineers to forecast sales velocity for various promotions and rank recommendations on the Seller Central homepage for selling partners. Prior to Amazon, I was a research tech lead at eBay, where our team developed machine learning solutions for search engine marketing on Google and Facebook to maximize return on investment and risk management for sellers and buyers.

I am also passionate about teaching and helping more people enter the field of machine learning and data science. From 2018 to 2023, I taught several Machine Learning courses at the Foster School of Business at the University of Washington, including Advanced ML, Deep Learning and Big Data, and Natural Language Processing.

I received my Ph.D. in Computer Science from Oregon State University, working with Dr. Weng-Keen Wong. During my Ph.D. studies, my research focused on using probabilistic graphical models and crowdsourcing to predict species distribution using citizen science data and quantify the skill level of citizen scientists in the eBird project. Please refer to my Publication Page for details.