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LLM Applied Scientist @ Microsoft Office AI
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I'm a Data and Applied Scientist focused on transforming complex business challenges into actionable insights using cutting-edge AI technology. I have a strong track record of architecting and deploying scalable AI solutions, using Large Language Models, Machine Learning, and Deep Learning to solve real-world problems in Natural Language Processing, Computer Vision, Recommendation, Searching, and Ranking.
University of Maryland Baltimore County
Ph.D., Computer Science
January 1, 2009 – January 1, 2016
Xi'an Jiaotong University
M.E., Software Engineering
January 1, 2005 – January 1, 2008
Xi'an Jiaotong University
B.E., Information Engineering
January 1, 2001 – January 1, 2005
Microsoft
Senior Applied Scientist
October 1, 2025 – Present
Redmond, Washington, United States · Hybrid
Career Break
Full-time parenting
May 1, 2024 – October 1, 2025
Microsoft
Senior Data & Applied Scientist
February 1, 2021 – May 1, 2024
Microsoft
Senior Data & Applied Scientist
October 1, 2019 – January 1, 2021
The Climate Corporation
Sr. Data Scientist - Machine Learning
July 1, 2018 – October 1, 2019
The Climate Corporation
Data Scientist - Machine Learning
September 1, 2017 – June 1, 2018
The Climate Corporation
Machine Learning Engineer
January 1, 2017 – August 1, 2017
NoizIvy.org
Content Developer
September 1, 2016 – December 1, 2016
Lake Anna, VA
Univerisity of Maryland, Baltimore County
Graduate Research Assistant
January 1, 2011 – May 1, 2016
Washington DC-Baltimore Area
Univerisity of Maryland, Baltimore County
Graduate Teaching Assistant
September 1, 2010 – December 1, 2014
Washington DC-Baltimore Area
Xi'an Jiaotong University
Graduate Research Assistant
January 1, 2007 – May 1, 2008
Xi'an, Shaanxi, China
Xi'an Jiaotong University
Graduate Research Assistant
January 1, 2006 – December 1, 2006
Xi'an, Shaanxi, China
China Mobile
Intern
June 1, 2004 – August 1, 2004
Xi'an, Shaanxi, China
Recurrent Deep Learning Machines
January 1, 2011 – May 1, 2016
Supported by NSF • Contributed to the development of convexification and deconvexification method to handle the high-dimensional non-convex optimization in training recurrent deep learning machines • Conducted theoretical and experimental research of the normalized risk-averting error criterion to overcome the local minimum problem in training neural networks • Designed and implemented deep learning frameworks using C++ and Theano with GPU acceleration, supporting deep multilayer perceptrons and convolutional neural networks • Implemented neural network training algorithms based on the normalized risk-averting error criterion to achieve superior performances on both optimization and generalization compared with the mean squared error criterion
Classification of fMRI data
January 1, 2011 – May 1, 2011
"Introduction to Machine Learning" Course Project • Reduced the dimension of fMRI data to reasonable ranges for discriminating healthy controls and schizophrenia patients with one-sample t-test, two-sample t-test, and kernel principle component analysis using Matlab • Selected significant features of fMRI data and analyzed the importance of feature selection for classification • Evaluated classification performances with/without the aid of significant features using correlation classifier, K-nearest neighbors algorithm, and support vector machine in Weka
Factoid Question Answering
September 1, 2010 – December 1, 2010
"Introduction to Nature Language Processing" Course Project - Conducted knowledge learning on factoid question answering and its system structure - Analyzed evaluation criteria based on different question answering goals
Name Entity Disambiguation
January 1, 2010 – December 1, 2010
Ph.D. Pre-candidacy Research, Advised by Prof. Tim Finin • Acquired the entity similarity by implementing exact match, regular expression, edit distance, Jaccard distance, dice score, and longest common sequences algorithms using Python for feature engineering • Enhanced the similarity-based algorithm by adding logic rules using Markov logic network to interpret ambiguities and distinguish similar abbreviations of different author names on citations • Evaluated performance of the name entity disambiguation algorithm on the Cora dataset using SVMLight
Finding Synonyms Using Combined PMI from Two Windows
September 1, 2009 – December 1, 2009
"Foundations of Data Mining" Course Project - Contributed to the development of an similarity calculation using two Pointwise Mutual Information (PMI) values acquired from a window of ordinary size and a very small window to find synonyms - Applied the TOEFL test to evaluate the developed approach, which outperforms the single window based PMI approach and is close to what the best latent semantic analysis approaches can do
Chinese Automatic Abstract System
January 1, 2007 – May 1, 2008
Supported by the National Key Laboratory in Institute of Automation, Chinese Academy of Science • Contributed to the design and implementation of an extraction-based Chinese summarization system, which has been applied to generate the abstract of internet news for real time analysis • Developed the Chinese segmentation module and the semantic similarity calculation method based on the HowNet words concept to distinguish Chinese characters and calculate their similarities using Java
Azure Machine Learning Development: 1 Basic Concepts
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Deep Learning Specialization - Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Deep Learning Specialization - Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Python for Data Visualization
June 24, 2026 – Present
Azure Machine Learning Development: 3 Deploying and Managing Models
June 24, 2026 – Present
Management Excellence at Microsoft: Model, Coach, Care
June 24, 2026 – Present
Data Visualization: Storytelling
June 24, 2026 – Present
Statistics Foundations 1: The Basics
June 24, 2026 – Present
Get Started with Python
June 24, 2026 – Present
Deep Learning Specialization - Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Deep Learning Specialization - Sequence Models
Coursera
June 24, 2026 – Present
Machine Learning with TensorFlow on Google Cloud Platform Specialization - Intro to TensorFlow
Coursera
June 24, 2026 – Present
Regression Analysis: Simplify Complex Data Relationships
June 24, 2026 – Present
Go Beyond the Numbers: Translate Data into Insights
June 24, 2026 – Present
The Nuts and Bolts of Machine Learning
June 24, 2026 – Present
Google Advanced Data Analytics
June 24, 2026 – Present
Taming Big Data with Apache Spark and Python
Udemy
June 24, 2026 – Present
AI For Everyone
Coursera
June 24, 2026 – Present
The Power of Statistics
June 24, 2026 – Present
Google Advanced Data Analytics Certificate
Coursera
June 24, 2026 – Present
Deep Learning Specialization - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Coursera
June 24, 2026 – Present
Azure Machine Learning Development: 2 Learning ML Studio
June 24, 2026 – Present
Foundations of Data Science
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project experience, ranging from academic research to industry applications at Microsoft and The Climate Corporation, demonstrates adaptability and a broad interest in various ML domains. The involvement in non-profit education and mentoring indicates a collaborative and knowledge-sharing mindset. The career progression from ML Engineer to Senior Data & Applied Scientist roles, coupled with a Ph.D., suggests a strong drive for continuous learning and professional growth. The projects and roles align well with a culture that values innovation, problem-solving, and practical application of advanced ML techniques.
Soft Skills & Operational Fit
The candidate's resume highlights collaboration with business stakeholders, program managers, software engineers, and data scientists, indicating strong teamwork and communication skills. Experience mentoring a junior data analyst suggests leadership potential. The career break for full-time parenting shows a commitment to personal responsibilities, which can be a positive indicator of work-life balance awareness. The role as a content developer for a non-profit education advocator also suggests a willingness to contribute to the community and share knowledge.