Zoltán Szabó: Data Science Seminar organizer (see also our departmental archive). The time information is meant in London sense. The upcoming talks are accompanied by registration links, calendar files (.ics) and Atom feed Atom feed for DSS.

2022:
Caroline Uhler.
[2pm-, Oct 17]
Florence d'Alché-Buc.
[2pm-, June 13]
Lester Mackey. Kernel Thinning and Stein Thinning.
[abstract, registration, calendar entry; 2pm-, May 30]
Rémi Flamary. Modeling Graphs with Optimal Transport.
[abstract, slides; 2pm-, May 23]
Krishnakumar Balasubramanian. Unified RKHS Methodology and Analysis for Functional Linear and Single-Index Models.
[abstract, slides; 4pm-, May 16]
Dino Sejdinovic. Recent Developments at the Interface Between Kernel Embeddings and Gaussian Processes.
[abstract, slides; 2pm-, Mar. 7]
Silvia Villa. Iterative regularization for low complexity regularizers.
[abstract, slides; 2pm-, Feb. 21]
Volkan Cevher. Optimization challenges in adversarial machine learning.
[abstract; 2pm-, Feb. 14]
Joseph Salmon. Implicit differentiation for fast hyperparameter selection in non-smooth convex learning.
[abstract, slides; 2pm-, Feb. 7]
Cynthia Rudin. Scoring Systems: At the Extreme of Interpretable Machine Learning.
[abstract, slides; 2pm-, Jan. 31]
2021:
Negar Kiyavash. Database alignment: fundamental limits and efficient algorithms.
[abstract; 1pm-, Dec. 6]
Vitaliy Kurlin. Mathematical Data Science for solid crystalline materials.
[abstract, slides; 2pm-, Nov. 22]
Po-Ling Loh. A modern take on Huber regression.
[abstract, slides; 3:30pm-, Nov. 15]
Suvrit Sra. Do we understand how to find critical points in nonsmooth optimization?
[abstract; 3pm-, Nov. 8]
Rada Mihalcea. TextRank: Bringing Order Into Texts (Revisited).
[abstract; 2pm-, Oct. 18]