Lecture 1. Ning Zhong “Research Issues and Challenges on Brain Informatics”
Ning Zhong, firstname.lastname@example.org
The International WIC Institute/Beijing University of Technology, China, and Maebashi Institute of Technology, Japan
Brain Informatics (BI) is a new interdisciplinary and multidisciplinary field that focuses on studying the mechanisms underlying the human information processing system. It brings together researchers and practitioners from diverse fields to explore the main research problems that lie in the interplay between the studies of human brain and the research of informatics, by using powerful equipment, including functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), positron emission tomography (PET), and eye-tracking as well as various wearable, ubiquitous, active, micro and nano devices. The systematic BI methodology has resulted in the brain big data, including various raw brain data, data-related information, extracted data features, found domain knowledge related to human intelligence, and so forth. In this talk, I demonstrate a systematic approach to an integrated understanding of macroscopic and microscopic level working principles of the brain by means of experimental, computational, and cognitive neuroscience studies, as well as utilizing advanced Web intelligence centric information technologies. I discuss research issues and challenges from three aspects of Brain Informatics studies that deserve closer attention: systematic investigations for complex brain science problems, new information technologies for supporting systematic brain science studies, and Brain Informatics studies based on Web intelligence research needs. These three aspects offer different ways to study traditional cognitive science, neuroscience, mental health and artificial intelligence.
Lecture 2. Boris Mirkin “Data Clustering: Some Topics of Current Interest”
Professor of Department of Data Analysis and Artificial Intelligence of HSE, Leading Research Fellow of International Laboratory of Decision Choice and Analysis http://www.hse.ru/en/org/persons/3954058
Areas of expertise: mathematical methods of cluster analysis and decision making, based on experimental and textual information and applied to sociology, bioinformatics, organizational systems, development of interpreting systems.
Lecture 3. Luiz F. Autran M. Gomes “Robustness in Discrete Multi-Criteria Decision Analysis”
Luiz F. Autran M. Gomes, email@example.com
Professor of Management in Ibmec University, Rio de Janeiro, Brazil; Researcher of the National Research Council (CNPq) of Brazil; Member of the National Academy of Engineering (Brazil), http://br.viadeo.com/en/profile/luiz-f-autran-m.gomes
Areas of expertise: Operations Research, Decision Making, Decision Aids, Organizational Decision Making, Graduate and undergraduate teaching of Multicriteria Decision Aiding.
Multi-Criteria Decision Analysis (MCDA) is the area of the Decision Sciences that focus on structuring, analyzing, modeling, solving, and recommending a solution to decision problems in the presence of multiple criteria. One important issue in MCDA has to do with Robustness Analysis in discrete MCDA. Although there is not a single definition accepted by the scientific community, we can conveniently refer to robustness as the ability of a solution to cope with uncertainties. Different sources of uncertainty interact in a decision problem, some reflecting more or less arbitrary choices of the decision analyst and others concerning external uncertainties. In this sense, it has been proposed that the robustness concern needs to be explicit in a problem such that the robustness analysis is driven by a specific aim. A new robustness analysis framework is proposed where robustness of a solution in a decision aiding process is measured by the distance from that solution to an expected outcome, chosen by the decision-aiding analyst. Therefore, the robustness concern concentrates on changes in criteria weights as well as in trade-off rates, as they are defined in a given MCDA method. Two main contributions are introduced: a local robustness measure, defined in terms of a distance among rankings; and a global robustness measure, as an adaptation of the minimax-regret rule to select a global robust solution.