Kun Liu's Email Addresses & Phone Numbers

📖 Summary

Intern @ Microsoft Research Asia From May 2005 to August 2005 (4 months) Staff Software Engineer and Applied Researcher @ LinkedIn Worked on various exciting problems such as: user intent and interest modeling for personalization; recommendation models/systems that match job candidates to job postings; and subscription churn prediction. From June 2012 to March 2014 (1 year 10 months) Senior Engineering Manager @ Amazon Leading a team of over 50 engineers/scientists/product managers/technical program managers focusing on building machine learning driven optimization for Amazon’s ads product. San Francisco Bay AreaScientist, Yahoo! Labs @ Yahoo [** Engineering Aspect **]: built state-of-the-art ad-targeting system that brings the highest quality traffic to advertisers and optimizes revenue for publishers by serving the most relevant ads to consumers. - As a Science Lead, designed and implemented large-scale audience-targeting pipelines that create user segments based on various targeting options: declared interests, inferred interests, geographic/demographic information, online purchase history, etc. The pipeline was processing 500+ million event records from 100+ million users on a daily basis. - As a Science Lead, designed and developed a terabyte-scale system for evaluating behavioral-targeting models in online bucket tests (A/B tests). It was one of the largest reporting pipelines running on the production Grid on a daily[** Research Aspect**]: performed large-scale data-mining research with a specific focus on behavioral ad targeting and social-network analysis. - Conducted large-scale research (e.g., tens of terabytes of data and hundreds of billions events) to quantify the value of social-network data for behavioral ad targeting across 60 consumer domains on a large online network of over 180 million users. It was the most comprehensive social-network data analysis in the context of behavioral ads targeting. From June 2009 to June 2012 (3 years 1 month) Postdoctoral Researcher @ IBM Almaden Research Center ** Conducted data mining research with a specific focus on privacy issues in social networks (privacy-preserving social-network analysis). - Invented a mathematical model to measure the privacy risks of online social-networking users, enabling individual users to actively manage and safeguard personal information. - Developed algorithms to scramble/encrypt large networks in order to protect the identity of registered users, allowing enterprises to outsource their network data for research and commercial purpose without compromising users' privacy. From January 2007 to June 2009 (2 years 6 months) Senior Engineering Manager @ LinkedIn Leading a stellar team of research engineers launching LinkedIn's brand new initiative - Pay for Performance Jobs & Jobs Marketplace. By leveraging all the great stuffs we've build for our ads relevance, we are developing machine-learning and optimization models/infrastructure to optimize job poster's ROI (cost per applicant/hire), boost LinkedIn's jobs revenue, and deliver compelling experience to job seekers. We are working on a wide array of challenging problems: multi-objective optimization-based ranking/auction for jobs, jobs applicant quality models, automatic bidding optimization, supply forecasting, pricing, budget pacing, auction mechanism (GSP / VCG), etc. Also led a rock star team of engineers and applied researchers working on the full spectrum of computational advertising problems: ads CTR and conversion prediction, bidding optimization, lookalike modeling, data-driven reserve price, bid landscaping, campaign insights and optimization recommendation. My team has been generating double-digit lift of ads revenue year over year. From October 2015 to September 2017 (2 years) Engineering Manager, Applied Researcher @ LinkedIn To certain extent I am both an Engineering Manager and a Product Manager leading an amazing team of engineers and applied researchers to develop innovative machine learning and optimization algorithms, products and infrastructure to optimize LinkedIn's ads revenue, improve advertiser ROI, and deliver compelling experiences to our members. My team are working on the full spectrum of computational advertising problems: ads CTR and conversion prediction, bidding optimization, lookalike modeling, data-driven reserve price, bid landscaping, campaign insights and optimization recommendation. My team has been generating double-digit lift of ads revenue year over year. From April 2014 to September 2015 (1 year 6 months)