'Information and Uncertainty in Data Science' Discussion Forum


April 23, 2021
4:00pm to 5:00pm
Virtual Participation

'Information and Uncertainty in Data Science' Discussion Forum
Date: Friday, April 23, 2021
Time: 4:00-5:00 PM Pacific Time
Location: Attend via Zoom

The Information and Uncertainty in Data Science Discussion Forum is a forum for open inquiry and discussion about a wide range of recurring data science fundamentals, including information, uncertainty, entropy, bits, probability, machine learning, generalization, and others. The group facilitates academic discourse on the practical use of the fundamental concepts across a wide variety of research disciplines, and strives for clarity and understanding using real-world scenarios, visual examples, cutting edge questions and unique perspectives. This group focuses on understanding and sharing concepts that are often buried in mathematical language, especially entropy, reduction of uncertainty and connections between physical systems and information systems. 

All interested members of the UC Berkeley, UCSF, LBL and LLNL communities are welcome and encouraged to attend. Please contact Gerald Friedland for more information or to be added to this Forum's mailing list. Full details about these meetings will be available at http://compdatascience.org/entropy.


Gerald Friedland

Adjunct Assistant Professor, EECS, UC Berkeley

BIDS Faculty Affiliate Gerald Friedland is an Adjunct Assistant Professor of Electrical Engineering and Computer Sciences at UC Berkeley, and the co-founder and CTO of Brainome. His work focusses on large-scale machine learning for multimedia retrieval, and he has also worked in privacy and privacy education. He has published more than 200 peer-reviewed articles in conferences, journals, and books and also co-authored the textbook Multimedia Computing. Dr. Friedland received his doctorate in computer science from Freie Universitaet Berlin, Germany.