Events
An inaugural conference is planned circa May 2023! More details forthcoming.
CAMDA seminar - Fall 2022
In Fall 2022, the seminar will take place on Wednesdays from 11:30am to 12:30pm in Blocker 220.
August 31: Non-linear dictionary approximation and applications to learning algorithms, by Jonathan Siegel (Texas A&M University, Mathematics)
September 14: Approximation via neural networks, by Guergana Petrova (Texas A&M University, Mathematics)
September 21: Learning from very few samples, by P. R. Kumar (Texas A&M University, Electrical and Computer Engineering)
October 05 in Blocker 302: Control and machine learning, by Enrique Zuazua (Friedrich-Alexander-Universität Erlangen-Nürnberg, Data Science)
October 19: TBA, by Mark Iwen (Michigan State University, Mathematics)
November 09: TBA, by Ankit Patel (Baylor College of Medicine, Neuroscience, and Rice University, Electrical and Computer Engineering)
November 30: TBA, by Richard Tsai (University of Texas-Austin, Mathematics)
CAMDA seminar - Spring 2022
In Spring 2022, the seminar took place on Wednesdays from 11:30am to 12:30pm in Blocker 302.
January 26: Continuous time stochastic gradient descent and flat minimum selection by Stephan Wojtowytsch (Texas A&M University, Mathematics).
February 09: Memory-efficient projection methods for metric-constrained optimization by Nate Veldt (Texas A&M University, Computer Science and Engineering).
February 23: Optimal recovery in Hilbert spaces from exact or inaccurate data by Chunyang Liao (Texas A&M University, Mathematics).
March 23: Double-matched matrix decomposition for multi-view data by Irina Gaynanova (Texas A&M University, Statistics).
April 06: Approximation and optimization via neural networks by Josiah Park (Texas A&M University, Mathematics).
April 20: Approximation properties of Gaussian process regression by Rui Tuo (Texas A&M University, Industrial and Systems Engineering)
CAMDA seminar - Fall 2021
In Fall 2021, the seminar took place on Wednesdays from 11:30am to 12:30pm in Blocker 506A.
September 01: Thoughts on Deep Learning by Ronald DeVore (Texas A&M University, Mathematics).
September 15: Near-best adaptive piecewise-polynomial approximations by Peter Binev (University of South Carolina, Mathematics).
September 29: Self-adaptive physics-informed neural networks by Ulisses Braga-Neto (Texas A&M University, Electrical and Computer Engineering).
October 11: Mathematical methods for privacy-preserving machine learning by Thomas Strohmer (University of California-Davis, Mathematics).
October 27: Numerically-stable coded distributed computing: an overview and some recent results by Anoosheh Heidarzadeh (Texas A&M University, Electrical and Computer Engineering).
November 17: Machine learning of collective behaviors from observations by Ming Zhong (Texas A&M University, TAMIDS).
Special events
October 11th, 2021: CAMDA/TAMIDS Seminar (Virtual): Thomas Strohmer (UC Davis)
October 30th, 2020: CAMDA/TAMIDS Seminar (Virtual): Justin Romberg (Georgia Tech)