I’m so proud of Dr. Alex Ritchie for his excellent PhD defense! His dissertation is titled “Covariate Structure as Weak Supervision in Estimation Problems,” and includes interesting theoretical and empirical results for Supervised PCA as well as learning nonparametric mixtures. We look forward to all the good Dr. Ritchie will certainly do in the world!
August 18, 2026 By Laura Balzano
Congratulations Dr. Can Yaras for successfully defending his PhD in July. His dissertation was titled “Low-dimensional Structures of Learning and Computation in Deep Learning,” including work that is already making an impact in the crowded field of deep learning efficiency and algorithm understanding. He will be moving on to a postdoc at Princeton Language and Intelligence.
July 10, 2026 By Laura Balzano
I’m so proud of Dr. Alex Ritchie for his excellent PhD defense! His dissertation is titled “Covariate Structure as Weak Supervision in Estimation Problems,” and includes interesting theoretical and empirical results for Supervised PCA as well as learning nonparametric mixtures. We look forward to all the good Dr. Ritchie will certainly do in the world!
July 1, 2026 By Laura Balzano
Congratulations Dr. Soo Min Kwon for successfully defending his PhD in May and for winning the AIStats CauScale Workshop best paper award!
Soo Min’s dissertation was titled “Deep Learning through Low-Dimensional Representations: Theory and Algorithms” and included a range of exciting results on theory of low-dimensional structure in deep learning, as well as a novel algorithm for learning Grassmannian geodesic structure from compressive measurements.
Soo Min was co-advised by my colleague Qing Qu. Another of Qing’s students, Alec Xu, was lead co-author on the paper that won a workshop best paper award. That paper studies a mathematical model for out-of-distribution generalization we sometimes see empirically with in-context learning. You can read about the best paper award here.
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