ALEX ZHANG
STEM
My academic work in STEM focuses on using data, mathematical modeling, and computational methods to analyze complex systems—from public health and energy infrastructure to sports policy and algorithmic fairness. Across coursework, competitions, and peer-reviewed research, I approach STEM as both a technical discipline and a tool for evidence-based decision-making.
Honors & Recognition
BNDS Outstanding Student Award (2 years)
BNDS Excellence in Specialized Areas Award (Ice Hockey)
Thinking Scholarship for Academic Excellence
Second & Third Prize, Formative Assessment
Second Prize, Knowledge in Practice
Outstanding Advisor’s Assistant Award (2 years)
School Excellence Award (Morality, Academics & Sports)
University-Level Coursework
STAT10118 — UChicago Summer Session
I completed STAT10118: Data Science during the UChicago Summer Session, exploring the role of data science in business, government, and research. I gained hands-on experience with Python for data collection, visualization, regression, and classification.
As team lead, I directed a final project using XGBoost to identify drivers of life expectancy, compare continental trends, and explain U.S. outliers. My role included data cleaning, feature engineering, model tuning, and result interpretation. We co-authored a 23-page analytical report, translating statistical findings into policy-relevant insights.
Machine Learning & Global Competition
Kaggle
Silver Medal — Top 5%, Image Matching Challenge 2025
Competing against 943 teams of high school students and professionals worldwide, I earned Silver (Top 5%) in Kaggle’s Image Matching Challenge.
To achieve this result, I authored a 15-page technical paper and developed machine-learning-based image grouping algorithms for large-scale Structure from Motion (SfM). My models separated visually similar but unrelated images, removed outliers, and clustered valid views—improving 3D reconstruction accuracy in unstructured photo datasets with applications in AR, robotics, and scientific modeling.
Sustainable Energy Scheduling — IEEE Conference Paper
Math Modeling Paper @ IEEE Conference on Knowledge Graphs
As Lead Mathematical Modeler and Programmer, I led a four-member team and co-authored a 28-page paper on optimizing green energy scheduling for data centers. We developed a nonlinear multi-objective optimization modelincorporating price volatility, carbon cost, workload priority, and supply uncertainty.
Using Genetic and Greedy Algorithms, our approach reduced operational cost by 20%, increased renewable utilization, and demonstrated robustness under uncertainty. The paper underwent rigorous peer review and was recognized through international academic channels.
Mathematical Modeling & Systems Optimization
Olympic Sport Optimization — International Math Modeling
As Program Lead, I co-authored a 26-page paper addressing the lack of objective criteria for adding or removing Olympic sports. Using AHP, EWM, and TOPSIS, I built a multi-criteria ranking system across seven dimensions, then applied grey forecasting and logistic regression to predict candidates for the 2032 and 2036 Olympics, including Radio Direction Finding, Parkour, and Frisbee.
Applied & Computational Engineering (CPCI)
My 2,000-word paper on algorithmic bias and structural inequity in AI was accepted to CONF-CDS 2025 after double-blind peer review and published in Applied & Computational Engineering, with submission for indexing in CPCI, Crossref, and related databases.
Drawing on 26 academic sources, the paper examined the limits of ethics-only AI frameworks and proposed adapting the FDA’s Total Product Life Cycle (TPLC) model to strengthen transparency, accountability, and equity—supported by real-world case studies.
Physics & Peer Learning
Advanced Physics Scholar & Peer Tutor, BNDS Physics Club
Selected through a competitive exam admitting fewer than 20 students from 60+ applicants, I joined the advanced physics track focusing on mechanics, electromagnetism, and calculus-based modeling. Alongside weekly problem sets and labs, I provided 10+ hours of one-on-one tutoring, helping eight peers improve physics scores by an average of 10% through targeted concept review and applied practice.