Data Analyst, Machine Learning, Leadership, Communication

Jia Guo (Andy)

Phone: 734-***-**** Email: *******@*****.*** Ann Arbor, MI 48105 linkedin.com/in/jia-guo-4422622b5/ EDUCATION

Master of Science Apr. 2026 (Expected) University of Michigan, Ann Arbor

Major: Applied Statistics; GPA: 3.8/4.0

Related Coursework: Causal Inference, Statistical Inference, Spatial Analysis, Survival Time Analysis, Time Series Analysis Master of Science Apr. 2024 University of Michiagan, Ann Arbor

Major: Mathematics; GPA: 3.9/4.0

Related Coursework: Real Analysis, Bayesian Model, Regression Analysis, Statistical Learning, Stochastic Modeling

Honors: Outstanding Achievement in Mathematics Award (Apr. 2024) Bachelor of Science Apr. 2023 University of Michigan, Ann Arbor

Major: Honor Mathematics; GPA: 3.9/4.0

Related Coursework: Numerical Linear Algebra, Number Theory, Algebraic Combinatorics, Data Structure

Honors: Merit-based Scholarship (Jun. 2022), Outstanding Achievement in Mathematics Award (Apr. 2023) RESEARCH EXPERIENCE

Project Developer: Statistical Model for Weather Prediction University of Michigan, Ann Arbor Dec. 2024 - Jan. 2025 Supervisor: Professor Johann Gagnon-Bartsch

Developed and implemented dynamic web scraping pipelines using Python to collect real-time weather data from NOAA and weather.gov, resulting in a reliable dataset for predictive modeling

Collaborated effectively with teammates using GitHub for version-controlled updates, ensuring seamless project progress and teamwork

Designed and conducted cross-validation with grid search to optimize Gaussian process, random forest, and ridge regression models, achieving an improvement in predictive accuracy over baseline models for two-week forecasts

Maintained detailed data logs, actively contributed to workflow improvements, and participated in regular team meetings, leading to a streamlined process and robust prediction outcomes Independent Researcher: Causal Inference on Revenue Growth University of Michigan, Ann Arbor Sept. 2024 - Dec. 2024 Supervisor: Professor Micheal Elliot

Independently initiated and executed a causal inference research project, culminating in a well-received presentation and comprehensive research report on customer revenue growth strategies

Conducted exploratory data analysis using correlation heatmaps, revenue distribution visualizations, and scatterplots to validate causal assumptions and prepare for rigorous data analysis

Designed and implemented Bayesian regression models with carefully crafted priors to estimate treatment effects and explore nuanced causal pathways

Developed mediation analysis frameworks to quantify indirect effects and proportion mediated to identify actionable insights for optimizing promotional strategies

Research Team Leader: Bayesian Model for Go Predictions University of Michigan, Ann Arbor Sept. 2023 to Jan. 2024 Supervisor: Professor Long Nguyen

Spearheaded a research project to construct a Bayesian model predicting the winner of a Go game at early stage by integrating step-by-step winning rate changes and winning rate of best moves suggested by KataGo AI engine

Developed an innovative likelihood function integrating AI benchmarks and nuanced player performance dynamics, enhancing predictive accuracy and Bayesian interpretability with subtle data structure

Explored predictive possibilities by combining AI recommendations with real-time human performance, uncovering insights to optimally interpret and utilize KataGo AI data

Validated the Bayesian model using over 10,000 modern professional games, confirming high prediction accuracy of game outcomes from early-stage moves under a well-designed prior setting TUTORING EXPERIENCE

Grader for PubPol 639 University of Michigan, Ann Arbor Jan. 2025 to May. 2025 Instructor: Professor Yusuf Neggers, Professor Brian Jacob

Ensure timely and accurate grading of student assessments by following methods prescribed by the instructor and providing thorough evaluations, enhancing the reliability of course grading standards

Deliver detailed and constructive feedback to students to foster their understanding of key course concepts and support their academic growth

Collaborate with colleagues to maintain uniform grading practices using a unified rubric, contributing to a transparent and fair assessment process that improved the effectiveness of the course Course Assistant for Math 215 University of Michigan, Ann Arbor Jan. 2023 to May. 2023 Instructor: Professor Jeff Dunworth

Facilitated learning with students during class to strengthen their comprehension and academic performance

Guided students through advanced problem-solving techniques, fostering analytical skill development

Delivered clear explanations and personalized tutoring, effectively clarifying challenging mathematical concepts Administrator Online Mathematics Discussion Group Nov. 2019 to Present

Led a thriving community of 1,000 active members, fostering an engaging environment that promotes learning and collaboration

Demonstrated leadership and expertise by providing timely, insightful answers to math-related questions across diverse fields

Designed and managed monthly math challenges, inspiring participation, teamwork, and intellectual curiosity within the group

Cultivated a dynamic and inclusive platform, ensuring sustained member engagement and consistent group growth SKILLSET

Programming: Proficient in Python, R Markdown, C++, MATLAB, data analysis, computational modeling, and algorithm development

Statistical Modeling: Specialized in Markov chain Monte Carlo methods (e.g., Gibbs Sampling, Metropolis-Hastings), ANOVA, Logistic regression, and LASSO.

Machine Learning: Skilled in modern approaches such as Principal Component Analysis, Adaptive Boosting, Neural Networks, Gaussian Processes, and Random Forests

Mathematical Text: Expert in LaTeX for creating polished and professional mathematical and technical documents, with five years of consistent experience and proven ability to independently complete research reports Technical Skills: Efficient in Microsoft Suite, GitHub, Google, Zoom

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