ECE SEMINAR Speaker: Dhruv Rohatgi, Massachusetts Institute of Technology Title: Designing Principled ML Algorithms via Modularity Day: Wednesday, March 18, 2026 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Pramod Viswanath Abstract: Designing the right algorithm for a problem requires modelling the problem well. But modern machine learning increasingly tackles problems that appear far too complex to mathematically model: empirical successes are driven by elusive properties of natural data, as well as significant engineering expertise. Can we still develop theory to help illuminate the space of possible algorithms for these problems? In this talk, I will argue the benefits of a modular approach to theory-building, where instead of defining "natural data" explicitly, we define what machine learning can already do with it. More precisely, we take existing building blocks of machine learning -- classifiers, regressors, even generative models -- as given, and we ask: how should we use these building blocks to provably solve more complex problems? I will apply this lens at several stages of the language model pipeline -- fine-tuning, reinforcement learning, and generation -- to prototype principled new algorithms and illuminate fundamental limits. Throughout, a key theme will be understanding when and how the "curse of horizon" -- a folklore principle throughout machine learning, that long decision-making horizons tend to amplify learning errors -- can be algorithmically mitigated. Bio: Dhruv Rohatgi is a final-year PhD student at the Massachusetts Institute of Technology, advised by Ankur Moitra. His research focuses on understanding how fundamental building blocks of machine learning can be fit together to solve more complex problems, particularly those that involve reliably making sequences of decisions. His PhD has been supported by an Akamai Presidential Fellowship and an NDSEG Fellowship.