People / Students / Class of 2027
Chongyao (Charlie) Ma graduated Summa Cum Laude and Phi Beta Kappa from Colby College in 2024 with a double major in Mathematics and Economics. During an econometric forecasting seminar, Charlie first discovered his passion for data analytics, realizing the power of transforming raw datasets into predictive insights through the development of time-series models to forecast macroeconomic trends. This interest in real-world data challenges led him to UCLA’s Computational and Applied Mathematics REU program, where he leveraged natural language processing and dynamic knowledge graphs to analyze online discourse clusters—a project he co-authored and presented at the 2023 IEEE International Conference on Big Data.
Prior to joining the MLDS program, Charlie worked full-time for two years as a Data Analyst at FoW Partners, a private equity firm based in Portland, Maine. During his tenure, he integrated machine learning models with demographic algorithms to develop a market prioritization framework that informed a multi-million-dollar capital deployment into healthcare clinics. Pivoting toward AI development and business transformation, Charlie designed and implemented an end-to-end agentic LLM framework using LangChain and LangGraph to automate complex operational analyses. By deconstructing businesses into highly granular activities, the tool evaluated technology transformation potential and aggregated individual insights into concrete, underwritable, and executable AI initiatives. The initiative attracted interest from leading private equity firms, whose portfolio management and deal teams utilized the tool for both target evaluation during acquisitions and the identification of optimization opportunities within portfolio companies. Applied across more than 20 enterprise businesses with over $10 billion in combined revenue, the system identified an average of more than 15 revenue-focused opportunities per company while improving operational efficiency by an average of 60%.
At Northwestern, Charlie approaches data science with an AI-native mindset, viewing artificial intelligence not merely as a standalone tool but as a foundational catalyst for the future of the discipline. He aims to leverage the MLDS program’s rigorous theoretical and quantitative curriculum to build a career as a data scientist who bridges traditional analytical depth with advanced AI capabilities. Keen to explore new frontiers in the rapidly evolving intersection of AI and data science, he is particularly interested in applying generative frameworks to unlock predictive value from unique alternative datasets and develop models that capture dynamic market signals. Outside of coding and refining algorithmic frameworks, Charlie enjoys exploring his interests in art history, esports, and figure skating.
