Biography: Oge Marques (http://faculty.eng.fau.edu/omarques/) is Professor of Computer and Electrical Engineering and Computer Science at Florida Atlantic University (FAU) (Boca Raton, Florida). He received his Ph.D. in Computer Engineering from Florida Atlantic University in 2001, his Masters in Electronics Engineering from Philips International Institute (Eindhoven, NL) in 1989 and his Bachelor’s Degree in Electrical Engineering from Universidade Tecnologica Federal do Parana (UTFPR) (Curitiba, Brazil), where he also taught for more than 10 years before moving to the USA.
He has more than 25 years of teaching and research experience in the fields of image processing and computer vision, in different countries (USA, Austria, Brazil, Netherlands, Spain, France, and India), languages (English, Portuguese, Spanish), and capacities. Recent teaching awards include the FAU Excellence and Innovation in Undergraduate Teaching Award (received both in 2004 and 2011) and the Outstanding Mid-Career Teaching Award, presented by the American Society for Engineering Education - Southeastern Section (ASEE-SE) (2011).
His research interests are in the area of intelligent processing of visual information, which combines the fields of image processing, computer vision, image retrieval, machine learning, serious games, and human visual perception. He is particularly interested in the combination of human computation and machine learning techniques to solve computer vision problems. He is the (co-) author of two patents, more than 50 refereed journal and conference papers, and several books in these topics, including the textbook Practical Image and Video Processing Using MATLAB (Wiley-IEEE Press, 2011) (http://www.ogemarques.com/), which has been adopted by more than 20 universities in more than a dozen different countries and has also been translated to Chinese.
He is Editor-in-Chief (with Borko Furht) of the 3rd edition of the Encyclopedia of Multimedia (http://encyclopediaofmultimedia.com). He serves as Editorial Board member for the journal Multimedia Tools and Applications and as a reviewer for several leading journals in computer science and engineering, including: IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, IEEE Transactions on Systems, Man and Cybernetics, and the EURASIP Journal on Image and Video Processing. He has also served as reviewer for several science and technology funding agencies, including: the Hong Kong’s Innovation and Technology Commission, the Netherlands Organisation for Scientific Research (NWO), and the Austrian Science Fund (FWF).
He has numerous international collaborations and appointments, including: Visiting Professor at Alpen-Adria Universitat Klagenfurt, Austria (since 2008), Visiting Professor at ENSEEIHT Engineering School, University of Toulouse, France (since 2010), Visiting Professor at UTFPR, Curitiba-PR, Brazil (since 2003), and collaborative projects and publications with Universitat Politecnica de Catalunya (UPC), Barcelona, Spain (since 2012) and Universidade Federal de Goias (UFG), Goiania, Brazil (since 2010).
He was one of the co-chairs for the Doctoral Symposium of the ACM International Conference on Multimedia 2014 (Orlando, FL, November 2014), the organizer and co-chair of a special track on Image and Video Databases for the ACM SAC 2003 (Melbourne, FL, March 2003), and has served as reviewer and Program Committee member for several ACM conferences and workshops. He is a senior member of both the ACM and IEEE. He is also a member of the American Society for Engineering Education (ASEE), and the honor societies of Tau Beta Pi, Sigma Xi, Phi Kappa Phi, and Upsilon Pi Epsilon.
Prof. Hideyuki TAKAGI, Kyushu University, Japan
First, we introduce two approaches for accelerating evolutionary computation (EC). The first approach is to use frequency information of a fitness landscape. Assume a fitness landscape as sound or image signal. Then, you may think whether its frequency information can be used for EC search. This first acceleration approach is a method to approximate a fitness landscape using a sine curve obtained from its frequency domain and estimate rough area of the global optimum. The second acceleration approach is to estimate the global optima using the moving trajectories of individuals analytically. EC is a method to approach to the global optimum iteratively. This proposed method estimates the convergence point of individuals mathematically. These two acceleration approaches are still in the early research stage and must face several difficulties until they become completed methods. However, their viewpoints are unique and informative for future research.
Secondly, we introduce a new research direction of interactive EC (IEC). IEC is a method optimize a target system based on human evaluations. Then, we may analyze the human evaluation characteristics indirectly by analyzing the target system optimized by the human. It is somehow similar to reverse engineering. We introduce some such challengeable approaches including analyzing human mental scale, finding unknown facts, and modeling human awareness mechanism.
Biography: Zoran S. Bojkovic received his B.Sc, M.Sc and Ph.D degrees all in electrical engineering from the University of Belgrade, Faculty of Electrical Engineering. He is full professor from the University of Belgrade and periodically the Visiting Professor of the University of Texas at Arlington, UTA, TX, USA, EE Department, Multimedia System Lab. In 1986, he was a visiting researcher at Stanford University, Stanford, USA, Information System Lab, hosted by Prof. Robert M. Gray. From 1993 up to now, he has been working on monographies/books projects together with Prof. K.R.Rao from the UTA, Arlington, Texas. He was a visiting professor at more than 20 universities worldwide and taught a number of courses in the field of digital signal processing, communication and computer networks, multimedia communications. Prof.Bojkovic is the co-author of 7 international books published by Prentice-Hall, Wiley, CRC Press Taylor& Francis Group, WSEAS Press,etc. Also, he is the co-author in 20 Chapters of the books published by Springer, Elsevier, WSES Press,etc. As co-editor, he participated in 76 International Books and Conference Proceedings. He has published more than 460 papers in peer reviewed journals, conference proceedings and publications. He served and is yet serving as Editor-in-Chief, Associate Editor and Guest Editor in 8 International Journals. Prof.Bojkovic has conducted many Keynote/Plenary Lectures, Workshops/Tutorials, Seminars and participated in many scientific and industrial projects. He is a member of review board of more than 10 high level journals and conference proceedings. Prof.Zoran Bojkovic is a Senior Member of IEEE, member of EURASIP, IASTED Canada, SERC Korea, expert in IAMSET, full member of Engineering Academy of Serbia and a member of Serbian Scientific Society. More information at: http://www.zoranbojkovic.com
Prof. Gregorio Martinez Perez, University of Murcia (UMU), Spain
Sri Krishnan, PhD, PEng, FCAE
Professor and Canada Research Chair in Biomedical Signal Analysis, Dept. Electrical and Computer Engineering, Ryerson University, Toronto, Canada
Biography: Sridhar (Sri) Krishnan received the B.E. degree in Electronics and Communication Engineering from Anna University, Madras, India, in 1993, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Calgary, Calgary, Alberta, Canada, in 1996 and 1999 respectively. He joined Ryerson University, Toronto, Canada in 1999 and since October 2007 he has been appointed as a Canada Research Chair in Biomedical Signal Analysis. Sri Krishnan has published 260 papers in refereed journals and conferences, and five of his papers have won best paper awards. He is a Fellow of the Canadian Academy of Engineering, Senior Member of IEEE and a member of the Professional Engineers of Ontario. Sri Krishnan is a recipient of many national and provincial awards including the 2013 Achievement in Innovation Award from Innovate Calgary, 2011 Sarwan Sahota Distinguished Scholar Award, 208 Biodiscovery Award, 2007 Engineer Achievement Award from Engineers Canada; 2006 South Asian Community Achiever Award; 2006 New Pioneers Award in Science and Technology; 2006 Best IEEE Chapter Chair Award (Toronto Section); and 2005 Research Excellence Award from the Faculty of Engineering, Ryerson University. More information at: www.ee.ryerson.ca/~krishnan.
Talk description: Most of the real world signals possess non-stationary and non-linear characteristics. Information processing and feature extraction from these signals is a challenging task. This talk will focus on four generations of signal processing algorithms developed for analysis and interpretation of signals. Recent advances in using sparse signal representation and compressive sensing of long-term signals will also be covered. The application of the extraction and classification of features from audio and speech signals, and biomedical signals (cardiac electrograms, pathological speech signals and gait rhythms) will be discussed in detail.
Biography: Sameep Mehta is a Senior Researcher and Manager at IBM Research - India. He received his Ph.D. in Data Mining and Visualization from The Ohio State University. His current research interests are Data Mining, Text Mining, Machine Learning, Big Data Technologies, Social Data Analytics and Knowledge Graph. He has published extensively in top conferences in Data Mining, Services and Visualization. He is a regular speaker at conferences and served PC chair for Big Data Analytics Conference 2014. He also serves as Adjunct Faculty at IIIT-Delhi in the area of Data Analytics and regularly mentors Ph.D. and MS students at IIIT, IITs and University of Delhi. He can be reached at email@example.com .
Dr. Alex Aravind
Professor, Department of Computer Science at the University of Northern British Columbia (UNBC), Canada
Biography: Alex Aravind is a Professor in the Department of Computer Science at the University of Northern British Columbia (UNBC), Canada. Alex received his Ph.D in Computer Science from Indian Institute of Science (IISc), Bangalore, and M.Tech. in Computer Science from Indian Institute of Technology (IIT), Kharagpur, India. After a brief stint at the Supercomputer Education and Research Center (SERC), IISc, he was a Post-doctoral fellow at the Memorial University of Newfoundland, St. John's, Canada. Alex is an expert in his field and is well known for his contributions to the distributed and concurrent programming field. He has co-authored a book on Operating Systems, published by Pearson Education, and has published more than 50 research articles in leading journals and conferences. Recently, a research contribution of Alex was recognised as one of the major research results in concurrent programming in the last 30 years. He won two UNBC University Achievement Awards, one for Excellence in Teaching (in 2012) and another for Excellence in Research (in 2013). More information at: http://web.unbc.ca/~csalex/
Talk description: Computer architecture is undergoing a revolutionary change, and in the future nearly all computers are expected to be “multicore”. As the multicore processors are becoming the norm, concurrent programming is expected to emerge as the mainstream software development approach. This new trend poses several challenges including performance, power management, system utilization, and fair and predictable response. Such a demand is hard to meet without the cooperation from hardware, compilers, operating system, and applications. Particularly, an efficient scheduling of shared resources to the application threads is fundamentally important in assuring the above mentioned system performance. Among the problems, scheduling of cores to threads (scheduling) and coordinating threads in accessing shared resources (synchronization) are most important. In this talk, after briefly reviewing the state of the art, I will discuss some of our work relevant to multicore scheduling and synchronization.
Title of Talk: Signal Processing of Electrical Consumption - Providing Deep Insights into Our Lives
Biography: Dr. Amarjeet Singh received his Ph.D. in Electrical Engineering at University of California, Los Angeles (UCLA) in 2009. As part of his Ph.D. thesis work, he developed mobile sensing systems for environmental monitoring. He was part of multiple real world deployments in collaboration with application scientists such as Biologists and Ecologists for critical applications such as monitoring pollution content in rivers and lakes. Amarjeet joined as Asst. Professor at IIIT Delhi in 2009. At IIIT Delhi, he has led multiple efforts in both energy and healthcare that involve data collection through sensors and other information systems along with developing suitable analytics that helped improve the overall operations. He has recently co-founded a startup called Zenatix (www.zenatix.com) that helps large consumers of energy identify inefficient operations and work towards reducing the energy bill significantly. Notable customers of Zenatix include office spaces for Google, NIIT and Tata Docomo. He has led several project grants worth crores and actively engages with the industry through multiple research and consulting projects. He did his B.Tech. from IIT Delhi in 2002 and MS from UCLA in 2007. He was awarded Outstanding MS student in UCLA School of Engineering in 2007 and Chorafas Foundation Award for research with long term impact.
Talk description: Electrical consumption, for both residential and industrial consumers, is currently provided through monthly bills that give little insights into our consumption patterns. This data if collected at a higher resolution - say every minute, can provide deep insights into our personal lives such as what time we wake up in the morning, what time we go to bed and how long do we watch television every day, among others. In addition, big data generated from such high resolution consumption pattern can be used to automatically dis-aggregate home level energy consumption into appliance level energy consumption without monitoring these appliances individually. In this talk, I will use real datasets collected by our group (and other publicly available datasets) to discuss deep learning opportunities possible with high resolution electrical consumption data. I will further talk about recent work in advanced analytics possible by combining this electrical consumption data with data from mobile phone sensors to provide personalized energy consumption feedback.