Plenary Talks

  • Prof. Hojjat Adeli, Ohio State University
    https://ceg.osu.edu/people/adeli.1
    https://scholar.google.com/citations?user=TQMpoh8AAAAJ

    Machine Learning and Classification Algorithms

    Abstract:

    Some of the recent advances in machine learning and classification algorithms are reviewed with a focus on new classification algorithms developed by the author and his associates including the Enhanced Probabilistic Neural Networks (EPNN) of Ahmadlou and Adeli and the Neural Dynamic Classification (NDC) algorithm devel-oped recently by Rafiei and Adeli (2017) based on the robust patented neural dynamics optimization model of Adeli and Park. Recent applications of the Deep Boltzmann Machine (DBM), EPNN, and NDC are presented from both engineering and medical fields such as computer-aided diagnosis of Parkinson’s disease, earthquake early warn-ing systems, and damage detection in highrise building structures.

    Bio:

    Hojjat Adeli received his Ph.D. from Stanford University in 1976 at the age of 26. He has authored over 590 research and scientific publications including 16 books in vari-ous fields of computer science, engineering, applied mathematics, and medicine. In 1998 he received the Distinguished Scholar Award, from The Ohio State University (OSU) “in recognition of extraordinary accomplishment in research and scholarship”. He is the recipient of numerous other awards and honors such as the OSU College of Engineering Lumley Outstanding Research Award (quadruple winner); Peter L. and Clara M. Scott Award for Excellence in Engineering Education, and Charles E. MacQuigg Outstanding Teaching Award, a Special Medal from The Polish Neural Network Society in Recognition of Outstanding Contribution to the Development of Computational Intelligence, Eduardo Renato Caianiello Award for Excellence in Scien-tific Research from the Italian Society of Neural Networks and an Honorary Doctorate from Vilnius Gediminas Technical University, Lithuania. He is the Founder and Edi-tor-in-Chief of Computer-Aided Civil and Infrastructure Engineering, now in 32nd year of publication and Integrated Computer-Aided Engineering, now in 25th year of publi-cation. He is also the Editor-in-Chief of International Journal of Neural Systems. He is a Distinguished Member of ASCE, and a Fellow of AAAS, IEEE, AIMBE, and Ameri-can Neurological Association.

  • Prof. Xiaoyang (Sean) Wang, Fudan University & University of Vermont
    http://www.cs.fudan.edu.cn/en/?page_id=2380
    https://scholar.google.com/citations?user=AHOG_MgAAAAJ

    Supporting Smart Exploratory Data Analysis

    Abstract:

    To achieve desired analysis results, data analysis traditionally repeats the following process: Data analytic professionals work together domain experts to carefully select the data and the analysis models, and then apply analysis tools. With the rapid growth of data volume, and the emergence of new data, applications increasingly need to use "dark data” (or unfamiliar data) in addition to familiar, domain-specific data. At the same time, the demand for data analysis in various areas is increasing rapidly, creating a severe shortage of professional data analysts. As a result, data analysis is experiencing two changes: (1) The data used in analysis is changing from mostly domain-specific data to data from multiple, often unfamiliar, sources; (2) Data analysis practitioners are changing from only computer scientists or statisticians and other technical experts to experts in the application domains. Therefore, how to provide tools that will help user in their analysis tasks has become an important research subject. This talk will discuss the possibility in providing such tools that are collectively called "intelligent data analysis system”, and introduce several preliminary attempts.

    Bio:

    Xiaoyang Sean Wang is Professor at the School of Compute Science of Fudan University. He received his PhD degree in Computer Science from the University of Southern California in 1992. Before joining Fudan University in 2011, he was the Dorothean Chair Professor in Computer Science at the University of Vermont between 2003-2011 and Assistant/Associate Professor in the Department of Information and Software Engineering at George Mason University during 1992-2003, and during 2009-2011, he served as a Program Director at the National Science Foundation in the Division of Information and Intelligent Systems. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. `

  • Prof. Xizhao Wang, Shenzhen University
    Big Data Institute
    College of Computer Science and Software Engineering
    http://wang.hebmlc.org/en/
    http://scholar.google.com/citations?user=NyLTM0wAAAAJ&hl=en

    Big Data Learning with Uncertainty  

    Abstract:

    Big data refers to the datasets that are so large that conventional database management and data analysis tools are insufficient to work with them. Big data has become a bigger-than-ever problem with the quick developments of data collection and storage technologies. Model simplification is one of the most popular approaches to big data processing. After a brief tutorial of the existing techniques of processing big data, this talk will present some key issues of learning from big data with uncertainty, focusing on the impact of handling uncertainty and the challenges uncertainty brings to big data learning. It shows that the representation, measure, and handling of the uncertainty have a significant influence on the performance of learning from big data. Some new advances in our Big Data Institute regarding the research on big data analysis and its applications to different domains are briefly introduced.

    Bio:

    Prof. Wang’s major research interests include uncertainty modeling and machine learning for big data. Prof. Wang has edited 10+ special issues and published 3 monographs, 2 textbooks, and 200+ peer-reviewed research papers. By the Google scholar, the total number of citations is over 5000 and the maximum number of citation for a single paper is over 200. Prof. Wang is on the list of Elsevier 2014/15/16 most cited Chinese authors. As a Principle Investigator (PI) or co-PI, Prof. Wang's has completed 30+ research projects. Prof. Wang is an IEEE Fellow, the previous BoG member of IEEE SMC society, the chair of IEEE SMC Technical Committee on Computational Intelligence, and the Chief Editor of Machine Learning and Cybernetics Journal.