BigBrain: Mini-Symposium on Big Data for Brain Science

 BigBrain: Mini-Symposium on Big Data for Brain Science

in conjunction with

SIAM International Conference on Data Mining (SDM'15)

April 30 – May 2, 2015

Vancouver British Columbia, Canada

 

Overview

This mini-symposium will focus on exploring the forefront between data mining and brain science and inspiring fundamentally new ways of mining and knowledge discovery from a variety of brain data. The presentations and discussions will lead to novel insights and knowledge on the function and dysfunction of brain at various levels, ranging from molecular, cellular, circuitry to systems levels by mining, integrating, and interpreting large-scale, multi-modality brain data. The list of tentative topics includes:

·       Mining of in situ hybridization and microarray gene expression data

·       Mining of brain connectivity and circuitry data

·       Mining of structural and functional MRI data

·       Mining of EEG and related data

·       Mining of temporal developing brain data

·       Mining of spatial neuroimaging data

·       Integrative mining of multi-modality brain data

·       Mining of diseased brain data, such as Alzheimer's disease, Parkinson's disease, and schizophrenia

·       Segmentation and registration of neuroimaging data


This symposium is targeted to attract participants from academia, industry, government, and private institutions. The academia audience includes researchers in computational neuroscience, bioinformatics, biological and brain data mining, brain imaging, biomedical informatics, healthcare data mining, deep learning and neural networks. The industry target audience includes persons from pharmaceutical companies manufacturing drugs for curing neurological diseases. Federal government and funding agencies, including NSF, NIH and DARPA, have been actively organizing and participating symposium on brain research. The currently proposed symposium may also attract participants from these organizations. This symposium is also expected to attract people from private institutions, such as the Kavli Foundation, Allen Institute for Brain Science, Howard Hughes Medical Institute, and Salk Institute for Biological Studies.



Invited Speakers

·       Jieping Ye, University of Michigan

·       Dinggang shen, University of North Carolina at Chapel Hill

·       W. Art Chaovalitwongse, University of Washington




Organizer


Organizing Committee

  • Shuai Huang (University of Washington): shuaih@uw.edu
    • Dr. Shuai Huang is currently an Assistant Professor at the Departments of Industrial & Systems Engineering at the University of Washington, Seattle. He received his B.S. in statistics from the University of Science and Technology of China in 2007, and obtained his Ph.D. degree in Industrial Engineering from Arizona State University in 2012. Dr. Huang’s research interest is developing novel statistical and data mining models for facilitating discoveries in biomedical research and decision-making in clinical practice. With a particular focus on neuroimaging data analysis, he has been collaborating with Banner Alzheimer’s Institute, Byrd Alzheimer’s Institute, and the Integrated Brain Imaging Center at the University of Washington Medical Center. Shuai is the recipient of the best paper award of the IIE Transactions in 2014.  He is also the finalist/recipient of several best student paper competitions, such as the best student paper in Data Mining subdivision of INFORMS, the best student paper in Quality, Statistics and Reliability subdivision of INFORMS, the best dissertation poster award in ISERC. Two of his papers were also highlighted as the Feature Article in the IIE Magazine. Other awards include the Outstanding Graduate Award and the University Graduate Fellowship from the Arizona State University.
  • Shuiwang Ji (Old Dominion University)sji@cs.odu.edu
    • Dr. Shuiwang Ji is currently an Assistant Professor of the Computer Science Department at Old Dominion University. Dr. Ji received the Ph.D. degree in Computer Science from Arizona State University in 2010. His research interests include machine learning, data mining, computational biology, and computational neuroscience. Dr. Ji received the National Science Foundation CAREER Award in 2014. He has authored over 50 research articles and has coauthored a book. Currently, Dr. Ji serves as an Associate Editor for a number of journals, including BMC Bioinformatics, IEEE Transactions on Neural Networks and Learning Systems, and Neurocomputing. Dr. Ji will serve as the Publicity Chair for the 2015 International Joint Conference on Artificial Intelligence and a senior program committee member for the 2015 SIAM International Conference on Data Mining. In the past, he has served as a Program Chair of the International Workshop on Data Mining for Brain Science in conjunction with KDD'14. During the past five years, he has served as a technical program committee member of major conferences in machine learning (ICML, NIPS), data mining (KDD, SDM, ICDM), and bioinformatics and medical image computing (MICCAI and PSB).
  • W. Art Chaovalitwongse (University of Washington)artchao@uw.edu
    • Wanpracha Art Chaovalitwongse is Professor in the Departments of Industrial & Systems Engineering and Radiology (joint) at the University of Washington, Seattle. He also serves as the Associate Director of the Integrated Brain Imaging Center at the University of Washington Medical Center. Before moving to Seattle, he worked as Visiting Associate Professor in the Department of Operations Research & Financial Engineering at Princeton University in 2011. From 2005 to 2011, he was on the faculty in the Department of Industrial & Systems Engineering at Rutgers University. Before working in academia, he worked at the Corporate Strategic Research, ExxonMobil Research & Engineering, where he managed research in developing efficient mathematical models and novel statistical data analyses for upstream oil exploration and downstream business operations in multi-continent oil transportation. He received M.S. and Ph.D. degrees in Industrial & Systems Engineering from the University of Florida in 2000 and 2003. His research group conducts basic computational science, applied, and translational research at the interface of engineering, medicine, and other emerging disciplines. His work thus far has focused on (a) computational neuroscience, (b) computational biology, and (c) logistics optimization. He holds three patents of novel optimization techniques adopted in the development of seizure prediction system. His academic honors include 2003 Excellence in Research from the University of Florida, 2006 NSF CAREER Award, 2007 Notable Alumni of King Mongut’s Institute Technology at Ladkrabang, 2004 & 2008 (2-times winner) William Pierskalla Best Paper Award by the Institute for Operations Research and the Management Sciences (INFORMS), 2009 Outstanding Service Award by the Association of Thai Professionals in America and Canada, 2010 Rutgers Presidential Fellowship for Teaching Excellence, and several other best student paper awards with his PhD students. He has edited 3 books and published over 100 research articles including 70+ papers in leading journals.