People / Students / Class of 2026
Rachel Gong graduated from the University of California, Berkeley in 2025 with Bachelor’s degrees in Data Science and Computer Science. Through advanced coursework in machine learning, deep learning, data engineering, and databases, she built a solid technical foundation and applied it to real-world projects. Notably, she investigated the impact of PM2.5 on asthma mortality using causal inference and hypothesis testing, uncovering region-specific disparities that informed tailored asthma policy recommendations. In addition to technical training, Rachel developed strong communication skills by mentoring peers as an academic intern for Berkeley’s Data 8 course.
Beyond the classroom, Rachel explored her interests in tech, healthcare, and finance through a series of internships. At Shenwan Hongyuan, she retrieved large-scale recruitment data through SQL and built quantitative stock selection models to identify high-performing investments. At MicroPort, a medical device company, she assessed market performance across 13 subsidiaries. She developed a Python-based web scraping pipeline to collect hospital data across East China and constructed key metrics to evaluate product penetration, presenting insights through Tableau dashboards for non-technical stakeholders.
Her time at MicroPort made her aware of the lack of standardized, accessible platforms in healthcare, motivating her to build structured databases that support professionals in the field. Therefore, she later served as a student assistant at the California Institute for Quantitative Biosciences (QB3) at Berkeley, where she led the migration of a researcher database and web app across institutional platforms, adding new functionalities during the process. This experience strengthened her engineering, collaboration, and project planning skills.
During the summer before joining the program, Rachel is exploring the intersection of biomedical data and AI as a Data Scientist Intern at Elimu Informatics. She is building a hybrid pipeline that integrates deterministic logic with LLMs, leveraging APIs and evidence scoring to predict the oncogenicity of gene variants and support clinical decision-making. As a new member of Northwestern’s MLDS program, she hopes to grow as a data scientist through practicum projects and contribute to data-driven solutions in healthcare, technology, and beyond.
