معرفی
Madeleine Udell is an Assistant Professor of Management Science and Engineering at Stanford University, with affiliations at the Institute for Computational and Mathematical Engineering (ICME) and a courtesy appointment in Electrical Engineering. She holds a PhD from Stanford University in Computational and Mathematical Engineering and completed a postdoc at Caltech. Her research focuses on accelerating optimization and machine learning, with applications in healthcare, finance, and engineering. Udell's work bridges algorithmic efficiency, interpretability, and practical deployment in data-driven systems.
Education:
- BS in Mathematics and Physics, Yale University (2009)
- PhD in Computational and Mathematical Engineering, Stanford University (2015)
Research Interests: Her research emphasizes optimization algorithms, scalable machine learning techniques, and their applications in real-world domains. Key areas include low-rank matrix methods, automated machine learning (AutoML), and interpretable models for healthcare. She develops tools like OptiMUS for natural language-based optimization modeling and explores second-order methods for improving deep learning efficiency.
Awards:
- Kavli Fellowship (2023)
- Alfred P. Sloan Research Fellowship (2021)
- NSF CAREER Award (2020)
- ONR Young Investigator Award (2020)
Grants & Advising: Udell leads projects funded by ONR and NSF, focusing on randomized numerical linear algebra and automated machine learning. She advises PhD students in optimization modeling, deep learning, and causal inference. Her group collaborates on tools like PINNs for scientific computing and OpenReviewer for bias-aware academic reviews.
Labs & Teams: She directs the Udell Research Group at Stanford, emphasizing interdisciplinary work in optimization, machine learning, and data science. Collaborations span academia and industry, including work with Aurora Solar on photovoltaic array design and Google Brain on scalable AutoML.
