Theme: Machine Learning for Big Data
It is widely known that Machine Learning is a multidisciplinary field which comprises the development of algorithms that can learn from data. Machine Learning and data are inseparably united. Thus, continuous updates and improvements of Machine Learning procedures is needed in order to adapt to a relentless and massive generation of data.
In this context, it is imperative to approach the theme “Machine Learning for Big Data” where the increased processing power of ordinary computers allowed researches and professionals of different fields to combine computationally intensive methods - usually present in Machine Learning - with large amounts of data. These methodologies have been successfully implemented in leading world companies like Google, AT&T Bell Labs, Microsoft, Netflix, Facebook, Amazon, and many others.
“We are drowning in information but starved for knowledge” – John Naisbitt, Megatrends (1982)
Keynote Speakers
Three renowned researchers working in companies that are at the forefront of innovation in Machine Learning for Big Data, have honoured the Department of Statistical Sciences by accepting the invitation as keynote speakers for our Research Day:
Dr. Robert Bell (Researcher Google)
Robert Bell is scheduled to join Google on April 6, 2015. Until recently, he was a member of the Statistics Research Department at AT&T Research since 1998. He previously worked at RAND doing public policy analysis. His current research interests include machine learning methods, analysis of data from complex samples, and record linkage methods. He was a member of the team that won the Netflix Prize competition. He has served on the Fellows Committee of the American Statistical Association, the board of the National Institute of Statistical Sciences, the Committee on National Statistics, the advisory committee of the Division of Behavioral and Social Sciences and Education, and several previous National Research Council advisory committees studying statistical issues from conduct of the decennial census to airline safety.
Dr. Alekh Agarwal (Researcher Microsoft)
Dr. Alekh Agarwal is currently a researcher in the New York lab of Microsoft Research. He spent two years as a post-doctoral researcher at the same institution, where his research was primarily focused on Machine Learning, Statistics and Convex Optimization. Prior to that he obtained his Ph.D. in Computer Science from UC Berkeley, where he worked with Peter Bartlett and Martin Wainwright. He received the MSR Ph.D. Fellowship in 2009 and Google Ph.D. Fellowship in 2011.
Dr. Agarwal’s interest is in Machine Learning, Statistics and Optimization focusing on problems which arise while applying Machine Learning techniques to massive datasets. Part of his research aims to understand the tradeoffs between learning and computation, as well as designing efficient learning algorithms that can learn under a given computational budget. More recently he has been looking at approaches for learning feature representations from data in a theoretically principled and practically efficient manner.
Dr. Kevin Patrick Murphy (Researcher Google)
Dr. Kevin Murphy is a research scientist at Google in Mountain View, California where he works on AI, Machine Learning, Computer Vision and NLP. Before joining Google in 2011, he was an Associate Professor of Computer Science and Statistics at the University of British Columbia in Vancouver, Canada. Before starting at UBC in 2004, he was a post-doctoral at MIT. Kevin received his BA from University Cambridge, his MEng from University Pennsylvania, and his Ph.D. from UC Berkeley. He has published over 80 papers in refereed conferences and journals as well as an 1100-page textbook called "Machine Learning: a Probabilistic Perspective" (MIT Press, 2012), which was awarded the 2013 DeGroot Prize for best book in the field of Statistical Science. Kevin is also the (co) Editor-in-Chief of JMLR (the Journal of Machine Learning Research).
History
The Research Day was first organized in 2009 and was a very successful event. In particular, the students benefited greatly from the exchange of ideas and direct interaction with top researchers in the field. We have held the Research Day at the Fields Institute for the past few years and hope to continue to do so in the coming years.
In 2011, the theme for Research Day was Computationally Intensive Methods. This theme was selected to reflect the effect technological innovations had in the amount and data available for interpretation. The theme for 2012 was Models for Dependent Data, emphasizing how contemporary models and methods are better able to capture dependence in data. In 2013, Statistics in Networks was the topic for the Research Day
Information about the format of our last Research Day can be found here.
