Keynote 1: Tracking in Dynamic Networks

Keynote Speaker: Jian Pei, Simon Fraser University.

Abstract:

In many application scenarios ranging from social networks to IoT, we need to process and analyze a huge amount of data, connected, evolving, linkages being more interesting than entities individually.  Modeling such temporal data in nature as graphs provides a conceptually convenient way to support novel and meaningful intelligent applications.  At the same time, it also posts grant challenges in many aspects, such as algorithm design and computing system development.  In this talk, I will present some interesting and novel application scenarios where graphs play a central role, as well as the corresponding algorithms.  Moreover, I will briefly introduce our on-going effort to build a distributed cloud-based graph computing engine that can query huge graphs and networks in seconds.

Short Bio:

Jian Pei is a Canada Research Chair (Tier 1) in Big Data Science and a Professor in the School of Computing Science at Simon Fraser University. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is one of the most cited authors in data mining, database systems, and information retrieval. Since 2000, he, with H-index 73, has published one textbook, two monographs and over 200 research papers in refereed journals and conferences, which have been cited by more than 67,000 in literature. His research has generated remarkable impact substantially beyond academia. He is the recipient of several prestigious awards, such as the IEEE ICDM Research Contributions Award and the ACM SIGKDD Service Award. He is an ACM Fellow and an IEEE Fellow.

Keynote 2: Deep Learning in Bioinformatics

Keynote Speaker: Ming Li, University of Waterloo.

Abstract:

I will discuss several applications of deep learning in bioinformatics, and focus on our recent work on de novo peptide sequencing by deep learning. De novo peptide sequencing from tandem mass spectrometry data is the key technology in proteomics for the characterization of proteins, especially for antibodies. During the past 20 years, many traditional computer science algorithms were developed for this problem. These algorithms include dynamic programming, linear programming, HMM, graph algorithms, and statistical methods. Deep learning significantly improves these traditional approaches.

This is joint work with H.N. Tran, X. Zhang, L. Xin and P. Shan, to appear in PNAS.

Short Bio:

Dr. Ming Li is a Canada Research Chair in Bioinformatics and a University Professor at the University of Waterloo. He is a fellow of the Royal Society of Canada, ACM, and IEEE. He is a recipient of E.W.R. Steacie Fellowship Award in 1996, the 2001 Killam Fellowship, and the 2010 Killam Prize. Together with Paul Vitanyi they have co-authored the book "An Introduction to Kolmogorov Complexity and Its Applications".