ALEX ZHANG
STEM & VEX
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
First, Second, and Third Prize, Formative Assessment
Second Prize, Knowledge in Practice
Outstanding Advisor’s Assistant Award (2 years)
School Excellence Award (Morality, Academics & Sports)
BNDS Talent Scholarship (Top 1% of 5000), Gold & Silver Award
Outstanding Student (Moral, Academic, and Physical Excellence), Dongcheng District
Outstanding Student (Moral, Academic, and Physical Excellence), Beijing Municipality
VEX Robotics Competition
As Team Captain and Lead Engineer, I competed in the Wuxi Invitational, National Championship, and the VEX World Championship, earning the Asia Regional Championship and First Prize. Among 2,000+ teams from 50+ countries, only 80 teams qualified for the World Championship, placing our team in the top 4% globally.
I committed 6+ hours per week to lab work and led the team through 20+ competitions, coordinating training schedules, mentoring 20+ team members, and overseeing both mechanical and software development. I engineered a custom catapult mechanism, programmed autonomous routines, and guided iterative prototyping through testing and failure analysis.
Beyond competition performance, I built introductory curricula for new members, developed match strategies, and conducted detailed post-match technical analysis at major events—treating robotics not just as a contest, but as a system for engineering rigor, teamwork, and applied problem-solving.
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.
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.
Sustainable Energy Scheduling — IEEE Conference Paper
@EEICAI 2026: IEEE Intl Conference on Elec Engineering, Intelligent Control & AI
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.
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.