Abstract:
In the field of medicine, there has been a tremendous growth in the amount of medical imaging data that is acquired, such as computed tomography (CT) or magnetic resonance imaging (MRI) scans. In medical visualization, computer-based interactive visual representations of such data are made, often aiming at improved diagnosis, treatment planning, treatment guidance, and/or doctor-patient communication. At times, there are structures that are not visible in the original medical imaging scans. When these invisible structures are nerves, and they are at risk for damage during surgery, visualizing these invisible structures can be crucial to avoid such damage. In this talk, I will present an overview of my research in this area, aiming at enhancing medical imaging data by integrating information from various sources into a combined interactive visual representation for educational, clinical, and research purposes.

About the Speaker:
Noeska Smit is an Associate Professor in Medical Visualization in the Visualization Group at the Department of Informatics, University of Bergen, Norway. She is also affiliated to the Mohn Medical Imaging and Visualization Centre at the Department of Radiology, Haukeland University Hospital, where she holds a position as a Senior Researcher. Her research interests include model-based visualization for surgical planning and education, as well as interactive visualization of multi-modal medical imaging data. She received the Dirk Bartz Prize for Visual Computing in Medicine (Eurographics Medical Prize) in 2019. Dr. Smit received her PhD from Delft University of Technology in the Netherlands.

Abstract:
Projection mapping dynamically augments the appearance of a real surface by digital image projection. It has been applied in many application fields such as medicine, entertainment, and product design. However, the projected results are always suffered from the reflectance properties of the surface such as spatially varying textures, subsurface scattering, and inter-reflection, and also from projector's technical limitations such as low dynamic range, narrow depth-of-field, and latency. To resolve these issues and enhance the image quality of projection mapping, several technologies based on computational display approaches have been developed. Computational display is the joint design of hardware with computational algorithms. A unique and interesting property of computational display for projection mapping is that the hardware we try to optimize includes not only optics but also surfaces, while normally only optics has been considered in computational display researches for other displays. This talk introduces a series of computational projection mapping researches, and also discuss its new application field---human augmentation.

About the Speaker:
Daisuke Iwai received his B.S., M.S., and Ph.D. degrees from Osaka University, Japan, in 2003, 2005, and 2007, respectively. He was a visiting scientist at Bauhaus-University Weimar, Germany, from 2007 to 2008, and a visiting Associate Professor at ETH, Switzerland, in 2011. He is currently an Associate Professor at the Graduate School of Engineering Science, Osaka University. His research interests include spatial augmented reality (a.k.a. projection mapping) and projector-camera systems. He published 100+ research papers in the related areas and received several academic awards including Best Paper Award in IEEE VR 2015, Best Paper Award in IEEE 3DUI 2015, Best Paper Runner-up Award in IEEE ISMAR 2016, and Best Research Demonstration Runner-up Award in IEEE VR 2019. He is a member of the IEEE.

Abstract:
Visual object tracking is a fundamental and important task in computer vision. Tracking is an essential prerequisite of motion analysis which is important to many problems such as motion capturing, object recognition and scene understanding. Tracking research started in the early 1950s with Claude Shannon’s exceptional work on information theory and Philip Woodward’s contributions to radar research. Since then, tracking has become a research field in various scientific disciplines, considering beside visual data, state estimation in dynamical systems or the analysis of time series. Despite the efforts, visual tracking is an open problem, lacking in a sufficient theoretical understanding and in practical algorithms for a large number of applications. This lecture will give an introduction to tracking and motion analysis, its challenges and applications supported by practical examples. We will learn the theoretical views on the problem, which are currently prevalent in literature, especially by focusing on tracking single objects. Finally, the talk will present a rather popular view on tracking by seeing the problem from a machine learning perspective. A compact overview of different learning situations such as adaptation, semi-supervised learning, unsupervised deep learning with different representational models under different algorithmic design concepts is given. The talk concludes with a summary of the current state-of-the-art concerning performance, open problems and potential future work.

About the Speaker:
Roman Pflugfelder is Scientist at the AIT Austrian Institute of Technology and lecturer at TU Wien. He received in 2002 a MSc degree in informatics at TU Wien and in 2008 a PhD in telematics at the TU Graz, Austria. In 2001, he was academic visitor at the Queensland University of Technology, Australia. His research focuses on visual motion analysis, tracking and recognition applied to automated video surveillance. He aims to combine sciences and theories in novel ways to gain theoretical insights into learning and inference in complex dynamical systems and to develop practical algorithms and computing systems. Roman contributed with more than 55 papers and patents to research fields such as camera calibration, object detection, object tracking, event recognition where he received awareness of media as well as several awards and grants for his scientific achievements. Roman is senior project leader at AIT where he has been managing cooperations among universities, companies and governmental institutions. Roman co-organised the Visual Object Tracking Challenges VOT'13-14 and VOT'16-18 and was program co-chair of AVSS'15. Currently he is steering committee member of AVSS. He is regular reviewer for major computer vision conferences and journals.

Abstract:
Given the widespread adoption of electronic health systems, clinical institutions are amassing ever-larger collections of patient-centered data. These repositories hold detailed longitudinal records capturing a vast variety of patient histories and outcomes.  Visual analysis techniques--designed to mine, analyze, and visualize data-driven insights--are enabling new opportunities to leverage these vast longitudinal resources for population health applications.  This talk will provide an overview of my research in this area, with a focus on visual analytics methods for large-scale temporal event data such as longitudinal electronic health records.  It will include demonstrations of new methods being developed to provide practitioners with exploratory cohort selection and analysis tools that are faster, more intuitive, and more reliable.

About the Speaker:
David Gotz is an Associate Professor of Information Science at the University of North Carolina at Chapel Hill (UNC), where he leads the Visual Analysis and Communications Lab (VACLab).  He is also Assistant Director for the Carolina Health Informatics Program (CHIP) and Associate Director for UNC's NIH-funded Big Data to Knowledge Pre-Doctoral Training Program.  Prior to joining UNC in 2014, Dr. Gotz was a Research Scientist at the IBM T.J. Watson Research Center in New York where he served as technical lead for visual analytics and data analysis systems in the Department of Health Informatics Research, and member of the Intelligent Information Interaction research group.  Dr. Gotz received his Ph.D. in Computer Science from UNC in 2005.

Abstract:
Designing effective visualizations requires a careful consideration of factors beyond aesthetics and functionality — it requires deeply understanding the needs, intuitions, and goals of target users. Visualization design studies are a methodical approach for acquiring this understanding. In this talk I’ll discuss the way we conduct design studies in my group, and how we use what we learn to contribute new visualization knowledge. I’ll also layout some open challenges in design study research along with a several proposed solutions that we’ve developed.

About the Speaker:
Miriah is an associate professor in the School of Computing at the University of Utah and a faculty member in the Scientific Computing and Imaging Institute. She co-directs the Visualization Design Lab, which focuses on the design of visualization systems for helping analysts make sense of complex data, as well on the development of design methods for helping visualization designers make sense of real-world problems. She obtained her bachelors degree in astronomy and astrophysics at Penn State University, and earned a PhD in computer science from the University of Utah. Prior to joining the faculty at Utah Miriah was a postdoctoral research fellow at Harvard University and a visiting scientist at the Broad Institute of MIT and Harvard.

Miriah is the recipient of a NSF CAREER grant, a Microsoft Research Faculty Fellowship, and a NSF/CRA Computing Innovation Fellow award. She was named a University of Utah Distinguished Alumni, both a TED Fellow and a PopTech Science Fellow, and included on MIT Technology Review's TR35 list of the top young innovators. She was also awarded an AAAS Mass Media Fellowship that landed her a stint as a science writer for the Chicago Tribune.