BDVA’15 will host several internationally renowned keynote speakers. The following speaker(s) have already confirmed their attendance.

We are proud that BDVA'15 will host several internationally renowned keynote speakers.

"Is what you see really there?", Dianne Cook, Monash University


Plots of data often provoke the response "is what we see really there". In this talk we will discuss ways to give visual methods an inferential framework.  Statistical significance of "graphical discoveries" is measured by having the human viewer compare the plot of the real dataset with collections of plots of null datasets: plots take on the role of test statistics, and human cognition the role of statistical tests, in a process modeled after the "lineup", popular from criminal legal procedures.  This is a simple but rigorous protocol that provides valid inference, yielding p-values, estimates of the test power, for discoveries made from graphics. And it provides a framework for determining which plot design is more effective than another. Amazon's Mechanical Turk is used to implement the lineup protocol and crowd-source the inference.

Prof. Cook is a Fellow of the American Statistical Association. Her research is in data visualization, exploratory data analysis, multivariate methods, data mining and statistical computing. She has experimented with visualizing data in virtual environments, participated in producing software including xgobi, ggobi, cranvas and several R packages. Methods development include tours, projection pursuit, manual controls for tours, pipelines for interactive graphics, a grammar of graphics for biological data, and visualizing boundaries in high-d classifiers.  Her current work is focusing on bridging the gap between statistical inference and exploratory graphics. She is doing experiments using Amazon's Mechanical Turk, and eye-tracking equipment. Some of the applications that she has worked on include backhoes, drug studies, mud crab growth, climate change, gene expression analysis, butterfly populations in Yellowstone, stimulus funds spending, NRC rankings of graduate programs, technology boom and bust, election polls, soybean breeding, common crop population structures, insect gall to plant host interactions, soccer and tennis statistics.

"Visual Analytics of Big Complex Networks", Seok-Hee Hong, University of Sydney

Recent technological advances have led to big complex network models in many domains, including social networks and biological networks. Good visualisation can reveal the hidden structure of the networks and amplifies human understanding, thus leading to new insights and findings. However, visualisation of massive complex networks is challenging due to scalability and complexity. This talk will address the challenging issues for visual analytics of big complex networks, and review latest methods for visual analytics of such networks.
Prof. Hong is a Professor and a Future Fellow at the School of IT, University of Sydney. She was a Humboldt Fellow, and a project leader of VALACON (Visualisation and Analysis of Large and Complex Networks) project at NICTA (National ICT Australia) in 2004-2007. Her research interests include Graph Drawing, Algorithms, Information Visualisation and Visual Analytics. In 2006, she won the CORE (Computing Research and Education Association of Australasia) Chris Wallace Award for Outstanding Research Contribution in the field of Computer Science, for her research "Theory and Practice of Graph Drawing". The award was given for notable breakthroughs and a contribution of particular significance.  Prof. Hong has held research funding of 5 Million, from her Fellowships, ARC Discovery Projects and ARC Linkage Projects including her latest project on "Algorithmics for Visual Analytics of Massive Complex Networks". She has more than 140 publications, and she has given 10 invited talks at international conferences as well as 50 invited seminars worldwide. In particular, she has developed an open source visual analytic software GEOMI with her research team members. Prof. Hong serves as a Steering Committee member of GD (International Symposium on Graph Drawing), IEEE PacificVis (International Symposium on Pacific Visualization) and ISAAC (International Symposium on Algorithms and Computations) and an editor of JGAA (Journal of Graph Algorithms and Applications). She has served as a Program Committee Chair of AWOCA, APVIS, GD, ISAAC and IEEE PacificVis, and a Program Committee Member of 60 international conferences. In particular, she has formed the Information Visualisation research community in the Asia-Pacific Region, by founding IEEE PacificVis Symposium.

"Answering questions we did not know how to ask", Alfred Inselberg, Tel Aviv University



I want to be stunned by a visualization discovery ... a WOW moment! And I do not mean that some variable values turned out to be much different than expected or the location of an event was different than expected etc. But rather that something far-reaching we had no idea existed was found, something like ... penicillin! This should be the measure of visualization's success. And just how do we do that? For one thing luck helps but, as I tell my students, "when you work harder your luck ... improves"!

For a dataset with M items there are 2M possible subsets anyone of which may turn out to be the one satisfying our objectives. With our fantastic pattern-recognition ability we can cut great swaths through this combinatorial explosion discovering patterns corresponding to relational information from a good data display. This is something that simply can NOT be automated ... thank goodness!

Patterns are geometrical creatures and so we need to learn GEOMETRY. Actually from our point-of-view we are not interested in rigid patterns but malleable ones e.g. "gaps" which can be different in shape but are gaps non-the-less. That is we are really in the TOPOLOGY of the patterns. It has been shown that multidimensional patterns cannot be discovered directly from their points. Rather they can be synthesized from lower dimensional information. Even in 3-D we learn to look at planes not by their points but by their planar surface/shape consisting of their lines and ditto for surfaces. We need to discuss and adopt a rigorous syllabus for the discipline of Visualization involving geometry, topology and cognition among others. This is our best investment for the future.

Research on the geometry and topology induced by ||-coords has made great strides. Many patterns corresponding to multivariate relations have been discovered. We have embarked on a project to transform these results into powerful tools for our exploration and data mining arsenal. They revolutionize the power of modern ||-coords.

Alfred received a Ph.D. in Mathematics and Physics from the University of Illinois (Champaign-Urbana) and continued as a Research Professor. Subsequently, he held research positions at IBM, where he developed a Mathematical Model of Ear (TIME Nov. 74), concurrently having joint appointments at UCLA, USC and later Technion and Ben Gurion University. Since 1995 he is professor at the School of Mathematical Sciences at Tel Aviv University. He was elected Senior Fellow at the San Diego Supercomputing Center in 1996, was Distinguished Visiting Professor at Korea University in 2008 and National University of Singapore in 2011. Alfred invented and developed the multidimensional system of Parallel Coordinates for which he received numerous awards and patents. His textbook "Parallel Coordinates: VISUAL Multidimensional Geometry and Its Applications" published by Springer was praised by Stephen Hawking among others.

"The Power of Visual Analytics: Unlocking the Value of Big Data", Daniel Keim, University of Konstanz

Never before in history data is generated and collected at such high volumes as it is today. For the analysis of large data sets to be effective, it is important to include the human in the data exploration process and combine the flexibility, creativity, and general knowledge of the human with the enormous storage capacity and the computational power of today's computers. Visual Analytics helps to deal with the flood of information by integrating the human in the data analysis process, applying its perceptual abilities to the large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses for better problem-solving and gaining deeper domain knowledge. Visual analytics techniques have proven to be of high value in exploratory data analysis. They are especially powerful for the first steps of the data exploration process, namely understanding the data and generating hypotheses about the data, but they also significantly contribute to the actual knowledge discovery by guiding the analysis using visual feedback.

In putting visual analysis to work on big data, it is not obvious what can be done by automated analysis and what should be done by interactive visual methods. In dealing with massive data, the use of automated methods is mandatory - and for some problems it may be sufficient to only use fully automated analysis methods - but there is also a wide range of problems where the use of interactive visual methods is necessary. The presentation discusses when it is useful to combine visualization and analytics techniques, and it will provide examples from a wide range of application areas to illustrate the power of visual analytics.

Daniel Keim is a full professor and the head of the Information Visualization and Data Analysis Research Group in the University of Konstanz's Computer Science Department. Keim received a habilitation in computer science from the University of Munich. He has been program cochair of the IEEE Information Visualization Conference, the IEEE Conference on Visual Analytics Science and Technology (VAST), and the ACM SIGKDD Conference on Knowledge Discovery and Data Mining. He's on the steering committees of IEEE VAST and the Eurographics Conference on Visualization

"Visual Analytics of Biological Data", Sean O'Donoghue, CSIRO and the Garvan Institute of Medical Research

The rapidly increasing volume and complexity of biological and medical data requires new, combined computational approaches in the methods and tools used to derive knowledge from these data. Visual analytics plays a critical and integrating role, and is often the key step in obtaining insight into underlying biological processes. I will briefly discuss the impact and perspectives on efforts to raise the global standard of visual analytics in bioinformatics (, with specific examples drawn from my own lab (

Sean O'Donoghue has been actively engaged in bioinformatics research since 1988. He is currently an Office of the Chief Executive Science Leader in Australia's Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney. He is also Group Leader and Senior Faculty Member at the Garvan Institute of Medical Research in Sydney. He holds a B.Sc. (Hons) and PhD in biophysics from the University of Sydney. Much of his career was spent in Heidelberg, Germany, where he worked in the Structural and Computational Biology programme at the European Molecular Biology Laboratory (EMBL) and at Lion Bioscience AG, where he was Director of Scientific Visualization. He has been awarded a C. J. Martin Fellowship from the National Health & Medical Research Council of Australia and an Achievement Award from Lion Bioscience. He was recently elected a Fellow of the Royal Society of Chemistry, and was a finalist in the 2015 NSW Emerging Creative Talent Award. He is also CSIRO's representative on the EMBL-Australia council, and chairs the EMBL-Australia Bioinformatics Advisory Committee.

"Egocentric Design in Geospatial Mapping", Shigeo Takahashi, University of Aizu

Due to the contemporary advancement of measurement and sensor technologies, the amount of data associated with geospatial information has been rapidly increasing. Thus effectively understanding of such geotagged data requires us to significantly enhance the readability of the associated map representations. This talk presents techniques for producing egocentric views in the map composition in order to fully facilitate our understanding of important geospatial features. The techniques include layout design, label placement, and cartographic generalization. Our approach here is to formulate layout design rules as constraints and solve the associated mapping problems using optimization techniques. Examples are provided to demonstrate how the present approaches can improve the readability of map itself and can further create plausible metaphors in the context of data visualization.

Shigeo Takahashi is currently a professor in the School of Computer Science and Engineering at the University of Aizu, Japan. He received his B.S., M.S., and Ph.D. in computer science from the University of Tokyo in 1992, 1994, and 1997, respectively. His research interests include scientific and information visualization, visual perception modeling, and geometric modeling. He is currently serving as an associate editor of IEEE Transactions on Visualization and Computer Graphics (2011-2014), served and is serving as a program co-chair of IEEE Pacific Visualization Symposium (2014, 2015) and International Symposium on Visual Information Communication and Interaction (2015). He also served as a program committee member for more than 50 computer visualization and graphics conferences. He received the Most Cited Paper Award for Graphical Models (2004-2006) from Elsevier.