This month, we are still testing out a pizza and talk format. RSVP is required, but we can take payment on site by either check or cash. We only can accept credit cards in advance, unfortunately. Please note our new location at Brandman University in Irvine! We are now offering this event free to people who are unemployed or students, as well as to people who are interested in only showing up to the presentation. If you are interested in only showing up for the presentation, please show up at 6:45 PM.

Abstract

The current estimated worldwide data volume is 2 zettabytes (1 zettabyte is 1 billion terabytes) and doubling every 1.5 years. Google alone stores 120 billion images in their databases (That is 38 images per second for 100 years). McKinsey's 2011 report calls Big Data "The next frontier for innovation, competition, and productivity". Google's search engine and IBM's Watson show that we are witnessing the birth of a new kind of artificial intelligence that is quite different from the human inspired artificial intelligence that we anticipated 20 years ago.

In my talk I will discuss topics from the field of machine learning and how it has contributed to the "Intelligence from Big Data" revolution. We will see how through crowd-sourced market places, humans are helping to build more intelligence machines, and how machines can multiply their intelligence by pooling their wisdom. I will describe some of the most common and useful machine learning algorithms at a high level and end with an outlook on how the combined power of computing, big data and learning algorithms will change our world in the near future.   

Biography

Dr. Max Welling is a Professor of Computer Science at UC Irvine with a joint appointment in the statistics department. Before joining UCI he held postdoctoral positions at Caltech (1998-2000), University College London (2000-2001) and the University of Toronto (2001-2003). He received his PhD in 1998 in Theoretical Physics.

He is associate editor in chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and the associate director of the Center for Machine Learning and Intelligent Systems at UCI. He also serves on the editorial boards of JMLR and JML and was an associate editor for Neurocomputing, Journal of Computational and Graphical Statistics and TPAMI. In 2009 he was the conference chair for AISTATS.

He received multiple grants from NSF, NIH and ONR-MURI among which was an NSF career grant in 2005. He was recipient of the Dean's midcareer award for research in 2008 and the ECCV Koenderink Prize in 2010.

His research focuses on large-scale statistical learning. He has made contributions in distributed Bayesian learning, variational Bayesian learning, learning of Markov random Field models, approximate inference in graphical models, hierarchical models of complex cells and products of expert models, algorithms for learning image taxonomies, visual object recognition, information retrieval, image de-noising, and statistical shape analysis. He has over 100 academic publications.

Schedule

6:00PM - Social

6:30PM - Dinner

7:00PM - Presentation