Slides of the following seminar talks are available online.

MADDD Seminar in Winter 2020:

01/28: Gal Mishne (UCSD) Multiway Tensor Analysis with Neuroscience Applications

The third and last roundtable discussion of this quarter was held before Dr. Ken Clarkson (IBM Research)’s statistics seminar talk: Dimensionality Reduction for Tukey Regression. His talk slides can be found here.

From: Henry Kautz, Division Director IIS, National Science Foundation

The White House Office of Science & Technology Policy (OSTP) has released the report American AI Initiative: Year One Annual Report to mark critical milestones made since February 2019:

https://www.whitehouse.gov/wp-content/uploads/2020/02/American-AI-Initiative-One-Year-Annual-Report.pdf

The second roundtable discussion of this quarter was held before Prof. Bin YU (UC Berkeley, Stat)'s Joint Math/Stat Colloquium talk: Veridical Data Science. Her talk slides can be found here.

The roundtable discussion kicked off by the host asking the guest speaker about the status of data science activities at the Berkeley campus.

The format of our roundtable discussions for Winter 2020 has been changed from that of Fall 2019: we have decided to have a roundtable discussion once a month instead of every week, and schedule it before a seminar talk with refreshments instead of after a seminar talk.

Happy New Year to you all!

I just recently found out two potentially quite useful websites for people who are interested in data science and machine learning.

(1) Nextjournal https://nextjournal.com/ : allows you to develop you notebooks for reproducible research and publish them or share them with your collaborators.

The tenth and final roundtable of the quarter was held after Krishna Balasubramanian’s Statistics seminar talk, “Normal Approximations for Stochastic Iterative Estimators (and Martingales)”. His talk slides can be found here.

The first part of the roundtable followed a question and answer format, so we recreate that here.

I found the following article on Ars Technica: https://arstechnica.com/science/2019/12/how-neural-networks-work-and-why-theyve-become-a-big-business/ quite interesting and well written although the author does not trace some important history of the development of neural networks such as K.

Our ninth roundtable discussion took place after Javd Lavaei’s MADDD seminar talk “Computational Techniques for Nonlinear Optimization and Learning Problems”. His talk slides can be found here.

Our eighth roundtable took place after Grace Yi’s statistics seminar talk “Making Sense of Noisy Data: Why and How?” Her talk slides can be found here.