COCOON 2018, July 2-4, 2018, Qingdao, China
The 24th International Computing and Combinatorics Conference

Keynotes


Michael Segal:

Communication Systems Engineering Department,
Ben-Gurion University of the Negev,
Beer-Sheva 84105, POB 653, Israel

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Michael Segal is a Professor of Communication Systems Engineering at Ben-Gurion University of the Negev, known for his work in ad-hoc and sensor networks. After completing his undergraduate studies at Ben-Gurion University in 1994, Segal received a Ph.D. in Mathematics and Computer Science from Ben-Gurion University in 2000 under the supervision of Klara Kedem. The topic of his PhD Dissertation was: Covering point sets and accompanying problems. After continuing his studies with David G. Kirkpatrick at University of British Columbia, and Pacific Institute for the Mathematical Studies he joined the faculty at Ben-Gurion University in 2000, where he also served as the head of the Communication Systems Engineering department between 2005-2010. He is known (equally with his coauthors) for being first to analyze the analytical performance of the well-known Least Cluster Change (LCC) algorithm that is widely used in ad hoc networks for re-clustering in order to reduce the number of modifications. He also was one of the first to introduce and analyze the construction of multi-criteria spanners for ad hoc networks.
Segal has published over 140 scientific papers and was a recipient of the Toronto Prize for Research in 2010. He is serving as the Editor-in-Chief for the Journal of Computer and System Sciences. Along with his Ben-Gurion University professorship, he also is visiting professor at Cambridge University.

Abstract: To Come Soon


Ming Li :

Department of Computer Science,
University of Waterloo,
Waterloo, Ontario, Canada

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Ming Li is a CRC Chair Professor in Bioinformatics, of Computer Science at the University of Waterloo. Together with Paul Vitanyi he pioneered applications for Kolmogorov complexity. He is also a co-founder of RSVP Technologies Inc., an artificial intelligence startup company in Waterloo.
His research interests are bioinformatics tools (protein structures, genome mapping, conducting homology searches); protein structure prediction, and automated NMR protein structure determination; stem cell image recognition; deep learning, natural language processing and automated conversation, AI.
Ming Li has published over 250 scientific papers and was a recipient of the Outstanding Contribution Award in 2010. He has published 4 books and he also is the Canada Research Chair from 2002 to present.



Abstract: To Come Soon


Russell Schwartz :

Department of Biological Sciences and Computational Biology Department,
Carnegie Mellon University,
4400 Fifth AvenuePittsburgh, PA 15213, USA

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One major interest of his group is the analysis of genetic variations, with specific application to inference of population subgroups and phylogenetics. They have focused for a number of years on the analysis of single nucleotide polymorphism (SNP) data and how they can help them understand the formation of human population subgroups and our history as a species as well as assist them in identifying correlations between genotype and phenotype. Their work includes basic theory on models and algorithms for these inference problems as well as application to studies of large-scale variation patterns in the human genome. They have recently extended this work into examination of the phylogenetics of tumor development.
Their other major direction is modeling and simulation of biological systems, particularly self-assembly systems. Self-assemblies systems are ubiquitous in biology and essential to nearly every biological function, yet they are difficult to analyze either experimentally or theoretically. They seek to address these problems by developing and applying stochastic simulation methods for complex self-assemblies. Their lab works in part on theoretical issues in the development of accurate and efficient simulation of self-assemblies and in part on the application of these methods to specific systems of interest, most prominently virus capsid assembly. A recent focus of their works has been better understanding how models must be adapted to realistically model assembly in living cells versus the test tube environment in which most available data is gathered.

Abstract: To Come Soon



Past Editions

  • COCOON 2017
  • COCOON 2016