People / Students / Class of 2026
Zikun Zheng earned his Bachelor of Science degree magna cum laude from the University of Washington in Computational Finance & Risk Management (Data Science minor), graduating with a 3.95 GPA and consecutive quarterly and yearly Dean’s List honors. While completing his coursework, he secured a year-long research appointment in an interdisciplinary neuroscience lab at Seattle Children’s Research Division—an initiative that investigates how lived experience shapes brain function and mental health. There, Zikun spearheaded the PsycTest project, acquiring and processing metadata and PDFs for 70 000+ psychological surveys (the largest collection of its kind on the web). He built end-to-end Python pipelines that parsed XML, applied UMAP, TF-IDF, clustering, and rich visual analytics to create a “survey genome,” laying the groundwork for fine-tuning language models in psychiatry and sparking multiple downstream studies. His meticulous documentation, initiative, and resilience in refining complex analyses earned high praise from his mentor. Alongside research, Zikun has thrived as an educator. For one year and a half at University of Washington’s CLUE Center he led evening tutorials in multivariable calculus and linear algebra, turning abstract proofs into intuitive, real-world insights and consistently boosting student exam performance—a role that honed his ability to convey complex ideas with clarity. He also translated theory into practice during his stint as a Quantitative Research Intern at CCB Principal Asset Management, where he combined logistic regression and XGBoost to predict “Special Treatment” risk in Chinese equities and integrated index-futures hedges that raised a 150-stock portfolio’s Sharpe ratio by 0.5. These experiences cultivated a rare blend of research rigor, teaching acumen, and industry impact, underpinned by a toolbox that spans Python (Pandas, Scikit-learn, PyTorch), SQL, Git, and modern data-pipeline orchestration.
Now pursuing an M.S. in Machine Learning and Data Science at Northwestern, Zikun is charting a path toward a tech-industry role as a Data Scientist or Machine Learning Engineer. He commands a production-ready stack—Python (Pandas, NumPy, scikit-learn, PyTorch, etc), SQL (PostgreSQL, MySQL, BigQuery), Spark for distributed processing, and AWS or Azure for scalable compute and model serving. His portfolio spans NLP transformer fine-tuning, tabular prediction, time-series forecasting, and causal-inference pipelines, each delivered with rigorous experiment tracking, performance monitoring, and A/B testing. Bolstered by product intuition from finance and healthcare projects, Zikun is determined to dive deeper into representation learning, responsible AI, and real-time inference, and is eager to join an innovative tech team where he can turn complex data into high-impact solutions while mentoring the next generation of data talent.
