User: Lil112

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Lil112
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Li Liping at TCL 2015 Product Release Conference
Name
Li Liping
LanguagesChinese, English
Education and employment
EmployerTCL Corporation
EducationMS in Information Science(in progress) University of Pittsburgh
MS in Applied Psychology (2012) Sun Yat-sen University
Contact info
Facebookhttps://www.facebook.com/liping.li.37017
LinkedInhttps://www.linkedin.com/in/liping-li-5b465991/

My name is Li Liping and I am a graduate student at University of Pittsburgh School of Computing and information. I was born in Yongxing County, Chenzhou City, Hunan Province, China. I earned a BS degree from Sun Yat-sen University Department of Psychology in 2010 followed by a MS degree in 2012. I entered Pitt's MSIS program in 2016, studying data analysis skills and technology understanding.

I have over three years of user experience research and design professional experience at Smart TV manufacturer TCL corporate. I'm interested in data-driven experience-centered product/service design, striving to improve product design based on user feedback data.

I have a solid background on user research area. As the experience quality leader of TCL Iqiyi TV, I organized a 10-day national home test participated by 300+ users as well as comprehensive usability tests and researches on TV sets, TV remotes, content service and in-store experience. This product was honored with 2013 Innovative Award of Asian Electronic Forum.

In my previous product manager job, I worked for “TCL Wei Dian Shi”. This service involves VOD control on TV by Phone, serving 1 million Smart TV users. It is based on WeChat platform, the hottest social App in China (600m users). It builds an instant control on a TCL TV by using WeChat to scan the QR code on a TV. This service was recognized as WeChat IOT INNO Case Top 5 in 2014.

Recently, I worked on colorsenserecommendation service.(github repository for detailed introduction) Inspired by the way that professional designers study palettes, I want to present people a service recommending them Kuler highly-rated palette based on their pictures. Colorsense can extract colors of user-upload pictures, compare them with palette dataset, rate them with similarity of palettes, and show similar palettes recommendation.