I have a broad range of interests, from theoretical modeling and analysis (such as mathematical modeling, optimization, statistical analysis, and algorithm design) to their applications (such as supply chain design, inventory management, demand forecasting, capacity planning, and data analysis). My current focus is to improve JD.com’s supply chain efficiency with OR techniques.
- 2016.04 – present: Applied Operations Research Scientist in the Data Science Lab at JD.com, Beijing,China.
- 2014.08 – 2016.04: Quantitative Analyst. Google, Search Infrastructure. CA, Mountain View, US.
Primary goal: monitor and improve search latency.
– Built a prediction model that predicts search latency based on several factors.
– Estimated the impact of draining datacenters on search latency, providing a guide line on drain planning.
– Built a search latency monitoring system. Enabled users to easily visualize latency overtime and quickly identify non-organic changes.
– Developed an optimization tool to determine the best locations for future datacenters in order to minimize search latency.
- 2013.02 – 2014.07: Operations Reasrch Scientist. Amazon, Inventory Planning and Control. WA, Seattle, US.
Primary goal: automate inventory planning and improve inventory models.
– Integrated order size calculation and rounding into one optimization problem resulting in more balanced inventory placement.
– Developed a hybrid algorithm to solve for optimal inventory levels with implicit profit function.
– Developed inventory models for hard-to-manage items, reducing shortage and cost.
– Partnered closely with business team and software development team to determine location-specific parameters in inventory models, fulfilling the launch plan of new buying systems ahead of schedule.
– Collaborated with other teams to further improve Amazon’s supply chain on a global level improving overall efficiency.
- 2011.05 – 2011.08: Operations Research Intern. Revenue Analytics,Inc.. Georgia, Atlanta, US.
– Actively engaged in a project for one of world’s largest hospitality company, providing 2-year forecast on daily demand of 8 segments and over 4,000 properties worldwide.
– Closely collaborated with data engineers, strategy consultants and other operations research consultants to understand clients’ need and deliver client-praising results.
– Improved the reconciliation method that reduced the forecast error by 12%.
– Investigated data integrity issues and implemented better time series models, resulting in more robust forecasts.
- 2007 – 2012: Ph.D., Industrial Engineering and Operation Research, University of California, Berkeley.
- 2007 – 2009: M.E., Industrial Engineering and Operation Research, University of California, Berkeley.
- 2006 – 2007: Industrial and Systems Engineering, Lehigh University.
- 2001 – 2005: B.E., Automation, Tsinghua University.