Topic Archives: GGAM
Feb 17, 2017, 4:47 PM
The GGAM Mini-Conference 2017 took place on February 16. Three speakers, Professors Miles Lopes, Xiaodong Li, and Patrice Koehl, presented their research. Two poster sessions were held, in which graduate students presented their applied mathematics research. The poster session and the following reception were also an opportunity for visiting prospective graduate students to learn about the research activities in GGAM.
Dec 2, 2016, 5:39 PM
Dr. Rademacher works in theoretical computer science and some related areas. He is primarily interested in the foundations of data science and artificial intelligence and this has lead to a focus on problems in convex geometry, machine learning, matrix computations and optimization. Dr. Rademacher received his Ph.D. in mathematics from the Massachusetts Institute of Technology. He has received an NSF CAREER award.
Oct 10, 2016, 3:45 PM
Topics: GGAM member Prof. Kevin Luli receives NSF CAREER award; 2009 Applied Math Ph.D. Deanna Needell receives 2016 IMA Prize, joins UCLA faculty; new faculty and researchers; research highlight by Naoki Saito: "Analyzing Data on Graphs and Networks"; emeritus focus by Roger Wets: "A Treasure Trove of Fundamental Questions"; department awards.
Aug 19, 2016, 12:29 PM
Dr. Lopes' main research areas are in high-dimensional statistics and machine learning, with a particular focus on resampling and bootstrap methods in high dimensions.
Apr 28, 2016, 3:52 PM
Dr. Hsieh's research is in machine learning and optimization.
GGAM Colloquium, Friday, April 8, 2016, 5:10pm – Prof. John Hunter will speak about Nonlinear Surface Waves
Mar 2, 2016, 1:12 PM
GGAM member Prof. John Hunter (Mathematics) will give the GGAM Spring Colloquium.
Mar 2, 2016, 11:15 AM
Feb 20, 2016, 10:00 PM
The GGAM Mini-Conference, our annual day-long event, took place on Saturday, February 20.
Feb 19, 2016, 6:00 PM
Dr. Sharpnack works at the intersection of machine learning and statistics, and has studied signal processing under heterogeneity assumptions such as graph structure and heteroscedasticity.