Speakers

Title of the talk: Three Major Approaches to Tackling Many Objectives

Abstract: Many optimisation problems in the real world need to consider multiple conflicting objectives simultaneously. Evolutionary algorithms are excellent  candidates for finding a good approximation to the Pareto optimal front in a single run. However, many multi-objective optimisation algorithms are effective for two or three objective only. It is an on-going challenge to deal with a larger number of objectives. In this talk, I will explain several methods for dealing with many objectives. First, we will describe a method for reducing a large number of objectives to a smaller one, especially when there is redundancy among different objectives. Second, alternative dominance relationship, other than the Pareto dominance, will be introduced into to make previously non-comparable solutions comparable. Lastly, new algorithms will be introduced to cope with many objectives through the use of two separate archives, for convergence and diversity, respectively. Our studies show that these methods are very effective and outperform other popular methods in the
literature.

Bio-sketch of the speaker:

Xin Yao is a Chair Professor of Computer Science at the Southern University of
Science and Technology, Shenzhen, China, and a part-time Chair Professor of Computer Science at the University of Birmingham, UK. He is an IEEE Fellow, a former President (2014-15) of IEEE Computational Intelligence Society, and a former Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation. His major research interests include evolutionary computation, ensemble learning and search-based software engineering. 
His work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010, 2016 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards. He received the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013.

Internet of Things Revolution:  Can Predictive Analytics Create Sustainable and Fair Smart Cities
Abstract: Currently there are about 30 billion IoT devices connected to the Internet. By 2020, an estimated 75 billion devices will be connected. Already, 55% of the 7.4 billion population have internet connection. By 2050, 70% of the world's population and over 6 billion people are expected to live in cities and surrounding regions. Increasing population density in urban centres demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. Managing city, people and resources (water, electricity, air, land, transport, public health) are set to become challenging. The utilization of information and communications technologies to achieve these objectives present an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. Smart city is the one that uses information and communications technologies to make the city services more interactive, efficient and citizen centric. Cities need to be smart and sustainable to survive while allocating resources and developing platforms that enable economic, social and environmental wellbeing. This talk presents how IoT can seamlessly integrate physical infrastructure and digital information across diverse platforms and applications to develop a common operating picture (COP) of the city. 

Bio-sketch of the speaker:

Marimuthu Palaniswami is a Fellow of IEEE and a distinguished lecturer of the IEEE Computational Intelligence Society. He received his Ph.D. from the University of Newcastle, Australia before joining the University of Melbourne, where he is a Professor of Electrical Engineering and Director/Convener of a large ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) with about 100 researchers on various interdisciplinary projects. Previously, he was a Co-Director of Centre of Expertise on Networked Decision & Sensor Systems. He served in various international boards and advisory committees including a panel member for National Science Foundation (NSF). He has published more than 480 refereed journal and conference papers, including 3 books, 10 edited volumes.
He was given a Foreign Specialist Award by the Ministry of Education, Japan in recognition of his contributions to the field of Machine Learning and communications. He received University of Melbourne Knowledge Transfer Excellence Award and Commendation Awards. He served as associate editor for Journals/transactions including IEEE Transactions on Neural Networks, Computational Intelligence for Finance. He is editor of Journal of Medical, Biological Engineering and Computing and the Subject Editor for International Journal on Distributed Sensor Networks. Through his research, he supported various start-ups, local and international companies.
As an international investigator, he is involved in FP6, FP7 and H2020 initiatives in the areas of smart city and Internet of Things (IoT). To enhance outreach research capacity, he founded the IEEE international conference series on sensors, sensor networks and information processing and served as General Chair for over 15 IEEE and IEEE sponsored Conferences. He has given several keynote/plenary talks in the areas of sensor networks, IoT and machine learning. His research interests include Smart Sensors and Sensor Networks, Machine Learning, IoT and Biomedical Engineering and Control.

Title: Networking and Big Data: Challenges and Opportunities

Abstract: Big Data is one of the hottest topics in data science community. However, we found that the fancy learning and mining applications would be impossible without the support from the underneath layers, which is generally named as  Networking for Big Data or Big Data Networking. It is an emerging and attractive topic in networking and communication communities. In this talk, we will firstly overview the current landscape of this energetic area, and then present the unprecedented challenges in this new domain, and finally discuss the current research directions in the field. We humbly hope this talk will shed light for forthcoming researchers to further explore the uncharted part of this promising land.

Title of the talk: Teamwork in Multi-Robot Systems

Abstract: The increasing number of robots around us will soon create a demand for connecting these robots in order to achieve goal-driven teamwork in heterogeneous multi-robot systems. In this presentation we focus on the engineering viewpoint of robot teamwork. While the conceptual modelling of multi-agent teamwork has been studied extensively during the last two decades, related engineering concerns have not received the same degree of attention. Now is the time to change this because real robots are available and increasingly used in real applications. Our presentation has two parts: The analysis part discusses general design challenges that apply to robot teamwork in dynamic application domains. The constructive part presents existing engineering approaches in response to these challenges. Thus, we aim at creating awareness for the manifold challenges and dimensions of the design space, and we highlight characteristics of viable technical solutions. Finally, we present some open research questions that need to be tackled in future work. 

Bio-sketch of the speaker:

Kurt Geihs is a full professor in the EECS Department at the University of Kassel (Germany) and founding director of the Interdisciplinary Research Center for Information System Design (ITeG). His research and teaching interests include distributed systems, multi-robot systems, and software technology. Current research projects focus on self-adaptive context-aware systems, collaborative autonomous mobile robots, and socio-technical development methods. He has published more than 200 refereed articles and is author / co-author / editor of several books. Before joining the University of Kassel he was professor at TU Berlin and University of Frankfurt, and researcher at the IBM European Networking Center in Heidelberg. He received a David Lorge Parnas Fellowship from Lero – the Irish Software Research Centre in 2016, and an Alexander von Humboldt South African Research Award in 2004. From 2007-2013 he was a member of the Computer Science panel of the European Research Council. He was a visiting professor and guest scientist at IMT (Lucca/Italy), LERO (Limerick/Ireland), FBK (Trento/Italy), Sintef and NTNU (Trondheim/Norway), University of Pretoria (Pretoria/South Africa), Microsoft Research (Cambridge/UK) and IBM Research (Hawthorne/USA). He holds a PhD from RWTH Aachen (Germany), a M.Sc. from UC Los Angeles (USA), and a Diplom Degree from TU Darmstadt (Germany), all in Computer Science.

Title of the talk: Massive fungal biodiversity data re-annotation and visualization with multi-level clustering
Abstract: With the availability of newer and cheaper sequencing technologies, genomic data are being generated at an increasingly fast pace. In spite of the high degree of complexity of currently available search routines, the massive number of sequences available virtually prohibits quick and correct identification of large groups of sequences sharing common traits. Hence, there is a need for clustering tools for automatic knowledge extraction to enable the curation of large-scale databases. Current sophisticated approaches on sequence clustering are based on pairwise similarity matrices. This is impractical for databases of hundreds of thousands of sequences since such a similarity matrix alone would exceed the available computer memory. 
In this talk, I will present a new approach called MultiLevel Clustering (MLC) to avoid a majority of sequence comparisons, and therefore, the total runtime for clustering is significantly reduced. An implementation of the algorithm allowed clustering of all 344,239 ITS (Internal Transcribed Spacer) fungal sequences from GenBank utilizing only a normal desktop computer within 22 CPU-hours whereas the greedy clustering method took up to 242 CPU-hours. MLC has been applied to predict optimal thresholds to identify fungal species and higher taxa using the DNA barcode datasets generated at the Westerdijk Institute, and to reveal the most frequently sampled environmental sequence types that have been difficult to be assigned to meaningful taxonomic levels.  

Bio-sketch of the speaker:

Duong Vu obtained a PhD degree in computer science from the University of Amsterdam, The Netherlands in 2002. After two years working as a postdoc at the same university, she moved to the Westerdijk Fungal Biodiversity Institute, Utrecht, to work as a scientific researcher. She has been developing information systems, algorithms and software tools to manage, analyze and extract knowledge from large amounts of data generated at the Westerdijk Institute. Her current research interest is to advance computer algorithms for big data analytics and efficient computing.