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Keynote Speeches
Keynotes will be held in the room Salle des nations I & II on 2nd floor.
Bernhard Schölkopf
On causal and anticausal learning
Causal inference is an intriguing field examining causal structures by testing their statistical footprints. The talk introduces the main ideas of causal inference from the point of view of machine learning, and discusses implications of underlying causal structures for popular machine learning scenarios such as covariate shift, transfer learning and semi-supervised learning. It argues that causal knowledge may facilitate some approaches for a given problem, and rule out others.
Speaker Biography
Bernhard Schölkopf was born in Stuttgart on 20 February, 1968. He received an M.Sc. in mathematics and the Lionel Cooper Memorial Prize from the University of London in 1992, followed in 1994 by the Diplom in physics from the Eberhard-Karls-Universität, Tübingen. Three years later, he obtained a doctorate in computer science from the Technical University Berlin. His thesis on Support Vector Learning won the annual dissertation prize of the German Association for Computer Science (GI). In 1998, he won the prize for the best scientific project at the German National Research Center for Computer Science (GMD). He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). He has taught at Humboldt University, Technical University Berlin, and Eberhard-Karls-University Tübingen. In July 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in October 2002, he was appointed Honorarprofessor for Machine Learning at the Technical University Berlin. In 2006, he received the J. K. Aggarwal Prize of the International Association for Pattern Recognition, in 2011, he got the Max Planck Research Award. The ISI lists him as a highly cited researcher. He served on the editorial boards of JMLR, IEEE PAMI, and IJCV.
More information about Bernhard Schölkopf
Jure Leskovec
Mining Massive Online Networks: Challenges and Opportunities
With an increasing amount of social interaction taking place in on-line settings, we are accumulating massive amounts of data about phenomena that were once essentially invisible to us: the collective behavior and social interactions of hundreds of millions of people. Computationally analyzing this data at unprecedented levels of scale and temporal resolution offers enormous potential both to address long-standing scientific questions, and also to harness and inform the design of future social computing applications: What is the structure of online interactions? What are emerging ideas and trends? How is information being created, how it flows and mutates as it is passed from a node to node like an epidemic?
The talk discusses how computational perspective can be applied to questions involving structure of online networks and the dynamics of information flows through such networks, including analysis of massive data as well as mathematical models that seek to abstract some of the underlying phenomena.
Speaker Biography
Jure Leskovec is assistant professor of Computer Science at Stanford University where he is a member of the Info Lab and the AI Lab. His research focuses on mining large social and information networks. Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including best paper awards at KDD (2005, 2007, 2010), WSDM (2011), ICDM (2011) and ASCE J. of Water Resources Planning and Management (2009), ACM KDD dissertation award (2009), Microsoft Research Faculty Fellowship (2011), as well as Alfred P. Sloan Fellowship (2012). Jure received his bachelor's degree in computer science from University of Ljubljana, Slovenia, Ph.D. in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter @jure.
More information about Jure Leskovec
Martin Krzywinski
Needles in stacks of needles
In 2001, the first human genome sequence was published. Now, just over 10 years later, we capable of sequencing a genome in just a few days. Massive parallel sequencing projects now make it possible to study the cancers of thousands of individuals. New data mining approaches are required to robustly interrogate the data for causal relationships among the inherently noisy biology. How does one identify genetic changes that are specific and causal to a disease within the rich variation that is either natural or merely correlated? The problem is one of finding a needle in a stack of needles. I will provide a non-specialist introduction to data mining methods and challenges in genomics, with a focus on the role visualization plays in the exploration of the underlying data.
The title of the talk was drawn from the paper
Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data
Gregory M. Cooper & Jay Shendure
Nature Reviews Genetics 12, 628-640 (September 2011)
doi:10.1038/nrg3046
Speaker Biography
As a staff scientist at the Genome Sciences Center, I create visualization tools and information graphics that combine analytical clarity with an artistic dimension. I am interested in visual methods that identify and communicate core messages and patterns in large data sets.
My graduate training is in physics (University of British Columbia). In 1999 I had the opportunity to build the Genome Sciences Center computing infrastructure and this was my introduction to genomics. During that time I contributed to the field of computer security by creating the port knocking method and introduced a method of optimizing keyboard layouts, which improved the Colemak layout as well as spawn the only fashion line named after a keyboard layout (TNWMLC).
During my work on cancer genomes, I created Circos, now a community standard for displaying information in this field. More recently, I introduced hive plots, a method for rationally visualizing networks.
My information graphics have appeared in the New York Times, Wired, Conde Nast Portfolio, and on covers of books and scientific journals, like PNAS, EMBO Journal and American Scientist.
I am interested in espresso, fashion and abstract photography, and the intersection of spam and poetry. I am a former owner of the world’s most popular rat.