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Data Science | Machine Learning
• Data Scientist with technical expertise in programming, data analysis/visualization, statistical modeling/machine learning • PhD in Materials Science and Engineering with 9 years of experience in applying quantitative analysis and analytical thinking to solve a broad spectrum of problems • Team player with extensive experience working in interdisciplinary, collaborative environment • Creative problem solver with excellent attention to details and demonstrated ability in multitasking • Fast learner and self-starter, quick to adapt to new areas and changing priorities
Udacity
Nanodegree, Machine Learning Engineer
January 1, 2017 – Present
University of Minnesota
Doctor of Philosophy (Ph.D.), Materials Science and Engineering
January 1, 2009 – January 1, 2016
Fudan University
Bachelor’s Degree, Materials Chemistry
January 1, 2005 – January 1, 2009
Retail Solutions Inc.
Data Scientist
November 1, 2017 – Present
Mountain View, CA
Udacity
Machine Learning Session Lead
September 1, 2017 – April 1, 2018
San Jose, CA
The Data Incubator
Data Science Fellow
January 1, 2017 – February 1, 2017
San Francisco Bay Area
Coursera Course Certificates
Data Science Specialization
April 1, 2016 – December 1, 2016
University of Minnesota, Department of Chemical Engineering and Materials Science
Research Assistant
January 1, 2010 – January 1, 2016
Minneapolis, MN
University of Minnesota, Department of Chemical Engineering and Materials Science
Graduate Teaching Assistant
January 1, 2010 – January 1, 2012
Minneapolis, MN
Advanced Coatings Research Center, Fudan University
Research Assistant
June 1, 2007 – July 1, 2009
Shanghai, China
Creating an AI Agent to solve Sudoku
July 1, 2017 – Present
• Created an AI agent to solve Diagonal Sudokus using constraint propagation and search techniques. • Taught the agent to use Naked-Twins strategy which enforces the constraint that no squares outside the two naked twins squares (two boxes belonging to the same unit, i.e., row, column, square , and diagonal) can contain the twin values. [Python, constraint propagation, search]
Machine Learning for Employee Retention
April 1, 2017 – Present
Project goal: use machine learning techniques to provide actional insights to the management and human resource team to implement an effective employee retention program • Trained a variety of supervised classification models including Logistic Regression, K-Nearest Neighbors, Decision Trees, Support Vector Machines, and ensemble models to predict employee churn • Selected performance metric for the system and compared model performance; performed model optimization • Identified the most important features for employee churn with feature importance analysis [Python, Sklearn]
Text Prediction App
September 1, 2016 – Present
• Cleaned and analyzed an English corpus consisting of news, blogs, and tweets with about 100 million words • Built a text predictive model based on N-gram frequency with back-off algorithm • Integrated the model into a web application to provide text input prediction for faster and easier typing [R, Shiny, Natural Language Processing]
Deep Learning
DeepLearning.AI
June 24, 2026 – Present
Artificial Intelligence Nanodegree and Specializations
Udacity
June 24, 2026 – Present
Machine Learning Engineer Nanodegree
Udacity
June 24, 2026 – Present
Data Science
The Data Incubator
June 24, 2026 – Present
Mining Massive Datasets
Stanford University
June 24, 2026 – Present
Statistical Learning
Stanford University
June 24, 2026 – Present
Data Science Specialization
Coursera Course Certificates
June 24, 2026 – Present
Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background shows a strong inclination towards continuous learning and skill development, evidenced by numerous certifications and nanodegrees in data science and machine learning. The diverse range of projects, from AI agents to employee retention and text prediction, indicates adaptability and a broad interest in applying data science to various domains. The academic and teaching experience suggests a structured and methodical approach, which can be beneficial in a data-driven environment. However, the target role is 'Data Analyst' while the candidate's experience leans heavily towards 'Data Scientist' and 'Machine Learning Engineer', which might indicate a potential mismatch in expectations or a need for clarification on the desired scope of work for the Data Analyst role.
Soft Skills & Operational Fit
The candidate's extensive research and teaching experience suggests strong analytical thinking, problem-solving, and communication skills. The mentoring roles indicate leadership potential and ability to collaborate. The diverse project portfolio, including personal projects, demonstrates initiative and a proactive approach to learning and applying new skills. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is limited.