Miao Fu

Hi, thanks for stopping by! 👋

I’m a Data Scientist and Machine Learning Engineer with extensive experience in user analysis, device fingerprinting, and advertising optimization. My career has focused on leveraging advanced data techniques to solve complex problems and drive business results.

In my recent roles, I’ve led impactful projects including:

Intelligent Advertising Decision System: Developed a high-efficiency performance calculation process for over 3,000 advertising channels, achieving 93% prediction accuracy and securing two patents for innovative computation methods.
Device Fingerprinting: Engineered a system handling 10-20 million data entries daily with 96.8% accuracy, saving the company 400,000 RMB annually.
User Segmentation and Analysis for Shengbei App: Enhanced customer acquisition and retention by comprehensively analyzing user data.
Intermediate Revenue Analysis: Improved operational efficiency with automated data reporting, optimized user segmentation, boosting third-party platform registrations by 8% monthly.

I thrive on bridging the gap between theoretical concepts and practical applications, combining my background in theoretical math and statistics with my technical expertise. My goal is to build scalable, efficient models that deliver precise, actionable insights and drive meaningful outcomes.

I hold a Bachelor’s degree in Information and Computing Science from Hunan University of Technology and Business (HUTB). My academic journey included a summer program at Brown University and research at the Hunan Province Mobile Business Intelligence Lab (under the guidance of Prof. Liang), where I focused on statistical models, recommendation algorithms, and classification.

Feel free to connect with me at miaofu1997@gmail.com!

news

Jun 23, 2019	Graduate from college!
Nov 07, 2017	Won the National First Prize at CUMCM 2017
Aug 26, 2017	Finished 2 summer-session classes at Brown University

selected publications

IEEE

Social Recommendation With Large-Scale Group Decision-Making for Cyber-Enabled Online Service

Xiaokang Zhou, Wei Liang, Suzhen Huang, and Miao Fu

IEEE Transactions on Computational Social Systems, Oct 2019

Abs PDF

Along with the development of several emerging computing paradigms and information communication technologies, it is said that cyber computing technology is playing an increasingly important role across cyber-related systems and applications. In this article, we focus on cyber-social computing and propose a computational model that integrates large-scale group decision-making (LSGDM) into social recommendations for cyber-enabled online services. As a concrete application example, a graph model is built to describe the LSGDM problem among researchers in scholarly big data environments. Following the basic profiling to describe decision-makers within scholarly networks, measures are defined to evaluate one researcher’s academic performance and research outcome and further quantify correlations between them based on their collaboration relationships in a constructed network model. A two-stage large-scale decision-making solution is then proposed for social recommendations: A network partition algorithm is developed based on the identification of experts along with their influence extending to a group of researchers, and a random walk with the restart-based algorithm is improved to calculate the weighted decisions for group decision aggregation and alternative ranking. Experiments using the real-world data demonstrate the usefulness and effectiveness of our proposed model and method, which can provide the target researcher with more reliable recommendations.