Learning and privacy with incomplete data and weak supervision

Montreal, Canada on December 12th @ NIPS'15

Speakers: Wendy Cho, Kamalika Chaudhuri, Nando de Freitas, Max Ott
- with a special issue on the Journal of Privacy and Confidentiality -


Wendy K. Tam Cho is a Professor in the Departments of Political Science and Statistics, Senior Research Scientist at the National Center for Supercomputing Applications, a Guggenheim Fellow (2015-2016), Faculty in the Illinois Informatics Institute, and Affiliate of the Cline Center for Democracy, the CyberGIS Center for Advanced Digital and Spatial Studies, and the Computational Science and Engineering Program at the University of Illinois at Urbana-Champaign.

Kamalika Chaudhuri is Assistant Professor at the University of California, San Diego. Her research is on machine learning. Much of her work is on privacy-preserving machine learning and unsupervised learning, but she is broadly interested in a number of topics in learning theory, such as confidence-rated prediction, online learning, and active learning.

Nando de Freitas is a Computer Science Professor at Oxford University and a senior staff research scientist at Google DeepMind. He is also a fellow of the Canadian Institute For Advanced Research (CIFAR) in the Neural Computation and Adaptive Perception Program. He received his PhD from Trinity College, Cambridge University in 2000 on Bayesian methods for neural networks. From 1999 to 2001, He was an artificial intelligence postdoctoral fellow at UC Berkeley.

Max Ott has been working his entire career on turning bleeding edge research ideas into useful and commercially viable products and services. He recently returned to NICTA/Data61 from a company he founded previously which is using embedded ML to dramatically improve user experience around mobile video. He is now focusing on productizing confidential computing solutions to bring a healthy balance between companies’ need to better understand their customers and an individual right for privacy.