Recently, the MSBA students have switched to their “mid-semester” courses and are beginning to dive head first into data analysis. This transition serves as the perfect opportunity to highlight our wonderful faculty that inspire and equip our students with all they need for a successful future in analytics!
Teaching a course on prescriptive modeling is Yu Ma, who we are excited to shine the spotlight on as our first faculty feature of the 2025-26 school year!
Research Rooted in Real-World Impact
Yu began her journey to WSB earning a bachelor’s degree in applied mathematics from UC Berkeley and her PhD from MIT in operations research. While establishing these impressive credentials, Yu also engaged in research focused primarily in the healthcare setting, examining both operational efficiency and clinical decision-making. Through understanding fundamental methodology and working directly with physicians, Yu and her team investigated whether machine learning or artificial intelligence could be built from scratch to tackle a specific clinical question physicians have. Yu explored various areas of focus, including computer science and astrophysics, before settling on healthcare. Her passion for the field stems from the impact her work leaves. “It’s very easy to feel like everything you work on, even if it makes a small incremental difference, affects a lot of people,” shares Yu.
Yu’s research contributes to the solid foundation of expertise she is able to bring to the classroom. Her course on prescriptive modeling can be broken down into two main parts; modeling and optimization. The modeling aspect of the course, which requires understanding what the input is and how it drives the output, is greatly supplemented by her experience identifying common pain points of healthcare providers. Yu helps students understand “what are the typical problems in a healthcare setting that need optimization” and “how you usually solve them using the techniques that we learn in class.” This perspective helps students visualize with a concrete, real-world anecdote as they learn a general framework that applies more broadly to machine learning model creation.
Fostering an Inclusive and Adaptive Learning Environment
Beyond the technical side of her work, Yu’s passion for teaching shines through in how she approaches the classroom environment. “I really care a lot about making students feel as included as possible,” she shares. “I think everything should be set up so that no matter what type of strengths you have initially, you can learn something from the class.” Curating this experience for students means encouraging them to ask questions and work directly with her to ensure they are able to apply the frameworks to their specific interests. Recognizing that each student’s objectives and future plans are unique, Yu strives for an adaptable approach “to help them grow the way that they want to grow.”
Empowering the Next Generation of Problem Solvers
Beyond her professional pursuits, Yu enjoys snowboarding, a reflection of the agility and persistence she brings to her teaching. This academic year, her primary goal is to provide students with a high-level structure for approaching analytical problems and a roadmap for applying those methods to new contexts. “Let me not talk at you,” she emphasizes, “rather, I will lecture, but then let’s discuss a specific problem like how do you actually go from A to B to C.” Through this interactive approach, Yu empowers students to think critically and independently, preparing them to navigate the complex, evolving challenges of the analytics field.
Our students are so lucky to learn from you, Yu, and we look forward to seeing how you continue to inspire!
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