Blei is recognized for significant contributions to machine learning, information retrieval, and statistics. His signature accomplishment is in the machine learning area of “topic modeling”, which he pioneered in the foundational paper “Latent Dirichlet Allocation” (LDA).
Blei is recognized for his groundbreaking work in machine learning, in particular his field-defining contributions in the areas of topic models and stochastic variational inference.
The award recognizes the excellence of faculty as teachers, scholars, and mentors within and outside the classroom, with a particular focus on teaching and mentoring undergraduate and graduate students.
Professor David Blei, with co-authors Matthew Hoffman and Francis Bach, is recognized with a Test of Time Award at NeurIPS, the world’s top machine learning conference, for scaling his topic modeling algorithm to billions of documents.
Teaching AI to distinguish between causation and correlation would be a game changer— well, the game may be about to change.
David Blei and Chong Wang were named winners of the Test of Time Award for Research at the 27th SIGKDD Conference on Knowledge Discovery and Data Mining. The duo was recognized for their 2011 paper, “Collaborative topic modeling for recommending scientific articles.”
Faculty from the department have been named recipients of the 2020 Amazon Research Awards.
Blei cited for development of novel models and methods to explore, understand, and make predictions using probabilistic machine learning
Add to GMail