Accepted Application Spotlights
Below you will find the list of the accepted Spotlights. If you have any questions about any of the spotlight sessions, please contact the organizers listed below directly.
- In Situ Inference: Advanced Data Science for Space Weather Modeling
- Bridging Visualization with Radiation Oncology
- Challenges for Visualization in Immersive Planetarium Domes
- Uncertainty-aware visual analytics in applications - Towards a systematic approach
- Visualization Challenges in Deep Uncertainty
When: Wednesday (October 27th, 2021) 12:00-1:30 pm
Ayan Biswas, Los Alamos National Laboratory, United States Divya Banesh, Los Alamos National Laboratory, United States Natalie Klein, Los Alamos National Laboratory, United States Steven Morley, Los Alamos National Laboratory, United States Subhashis Hazarika, Los Alamos National Laboratory, United States Earl Lawrence, Los Alamos National Laboratory, United States
Solar activity may impact electric power grids, satellite communications, navigation systems, and space-borne projects. Space weather simulations model the interplanetary medium between the Sun and the Earth in efforts to predict solar activity and prevent potentially catastrophic damage. In this application spotlight session, we focus specifically on work from Los Alamos National Laboratory (LANL) to identify and characterize small-scale fast flow structures, or bursty bulk flows (BBFs), in space weather simulations. Earthward BBFs are of interest because they may penetrate spacecraft orbits or the Earth’s magnetosphere and damage critical infrastructure. However, their small spatial extent and irregular spatiotemporal frequencies makes it difficult to identify, track, visualize, and quantify their behavior. In this session, we will discuss multiple techniques to facilitate in-depth analyses of BBFs in simulation data. One such effort performs “in situ” unsupervised clustering of high-resolution simulation runs using Variational Gaussian Mixture Models. An interactive visualization framework allows scientists to analyze clustering results in a post-hoc stage to better understand solar events, including BBFs. A second technique explores the use of scalar field topology (specifically, contour tree segmentation) to detect and track BBFs. Statistical tools combined with the results of these techniques form a more comprehensive picture of BBF activity and their potential causes.
Ayan Biswas - Los Alamos National Laboratory: Ayan Biswas is a scientist in the Data Science at Scale team (CCS-3) at Los Alamos National Laboratory. His research interests include exascale data analysis and reduction, in situ workflows, uncertainty quantification, statistical analysis and high-dimensional data visualization. He also has vast experience in working with vector fields and information theory applications for visualization and analysis. He received his PhD in Data Visualization from The Ohio State University in 2016. (Contact him at firstname.lastname@example.org)
Steve Morley - Los Alamos National Laboratory: Steve Morley is a scientist in the Space Science and Applications group (ISR-1) at Los Alamos National Laboratory (LANL), where they have worked for over 10 years. Prior to joining LANL they worked at the University of Newcastle, Rutherford Appleton Laboratory, and British Antarctic Survey. They received their Ph.D. in Physics (2004) from the University of Southampton. Their work is primarily focused on physics of the coupled solar wind-magnetosphere-ionosphere system, and its application in the space weather domain. Their research interests include electron radiation belt dynamics, solar energetic particle events, magnetospheric substorms, and numerical space weather prediction.
Subhashis Hazarika – Palo Alto Research Center: Subhashis Hazarika is currently a Research Scientist at the Intelligent Systems Lab, Palo Alto Research Center. Prior to this, he was a Postdoctoral Researcher at the Data Science at Scale Team, Los Alamos National Laboratory. He received his Masters and Ph.D. in Computer Science and Engineering from The Ohio State University, with specialization in Scientific Visualization and Data Analysis. His primary research interests are large-scale data analysis and visualization, statistical data modeling, and machine learning.
Divya Banesh - Los Alamos National Laboratory: Divya Banesh received her Ph.D. in Computer Science from the University of California, Davis. She is currently a Postdoctoral Researcher in the Data Science Team at Los Alamos National Laboratory. Her core interests are feature identification and analysis in computer vision, image processing, topology and machine learning.
Natalie Klein - Los Alamos National Laboratory: Natalie Klein is a scientist in the Statistical Sciences group at Los Alamos National Laboratory. Natalie’s research centers on the development and application of statistical and machine learning approaches in a variety of application areas, including hyperspectral imaging, laser-induced breakdown spectroscopy, and high-dimensional physics simulations. Klein holds a joint Ph.D. in Statistics and Machine Learning from Carnegie Mellon University.
Introduction - Ayan Biswas (10 mins) Application Domain Background - Steve Morley (10 mins) In situ Clustering and Visualization - Subhashis Hazarika (15 mins) Topological Data Analysis - Divya Banesh (15 mins) Statistical Analysis - Natalie Klein (10 min) Conclusion - Ayan Biswas (10 mins) Q & A (20 mins)
When: Thursday (October 28th, 2021) 8:00-9:30 am
Renata G. Raidou, TU Wien, Austria Katarína Furmanová, Masaryk University, Czech Republic Ludvig P. Muren, Aarhus University Hospital, Denmark Wouter van Elmpt, Maastricht University Medical Centre, the Netherlands
Radiation oncology (RO) is the multidisciplinary medical specialty that addresses cancer treatment. One of its fundamental enablers—from diagnosis to treatment delivery—is visual computing . RO was also one of the first medical visualization applications and is still an active field of vis research. It is a challenging application area that requires the development of approaches spanning the entire basic vis research portfolio. It revolves around multimodal, multiparametric, time-varying, heterogeneous, and uncertain data, involves several users (specialists and patients), and includes a variety of visual computing sub-applications and challenges. Yet, few vis approaches have made it into daily clinical practice and care. This may indicate that current approaches are still complex, not mature, or costly. Closer collaborations between clinic, academia, and industry are necessary to bridge the gap between development of new techniques and their clinical adoption. This is especially crucial, given the future challenges of personalization and predictiveness for more effective treatment, patient participation for more informed treatment, selection of adequate (and explainable) AI methods to deal with the available data volume, and follow-up or inter-patient analysis.
The structure of our spotlight will include talks from vis and RO specialists, and a structured discussion with the audience to identify problems, challenges, and future directions. We hope to provide a better understanding of how vis can support RO, and act as a starting point for discussing trends and opportunities for joint research.
 M. Schlachter, R. G. Raidou, L. P. Muren, B. Preim, P. M. Putora, and K. Bühler. State-of-the-art report: Visual computing in radiation therapy planning. In Computer Graphics Forum, vol. 38, pp. 753–779. Wiley Online Library, 2019. doi: 10.1111/cgf.13726
Talk 1: Challenges and opportunities in fusing AI and visual computing for radiotherapy planning
Katja Bühler (VRVis Research Center, Austria): Katja Bühler is Scientific Director of VRVis GmbH Vienna, an applied research center for Visual Computing founded in 2000 in Austria with the mission to bridge the gap between science and industry. She completed her studies in Mathematics at KIT, Karlsruhe, Germany, and holds a PhD in Computer Science from TU Wien. In 2003 she became head of the Biomedical Image Informatics Group at VRVis. She is member of the management board of Austrian Bioimaging and associate editor of Computers and Graphics, the Visual Computer and Frontiers in Bioinformatics. Katja’s scientific roots are in reliable computing, numerics and geometry processing. Today’s focus of her research is on the development of highly efficient methods to provide access to information encoded in biomedical images with the aim to accelerate image-based decision making. For this purpose, she is fusing expertise in image processing, machine and deep learning, high performance computing, data mining, visualization and human computer interaction to novel visual computing solutions for medicine and life science. The software emerged from the groups research is helping radiologists, radiotherapists, cancer biologists and surgeons to efficiently cope with multi-modal data and to accelerate diagnostic tasks in their daily clinical routine. The e-science platform Brain* addresses the urgent need to manage and exploit image intense data collections and accelerates multi-omics and image-based research in neuroscience.
Talk 2: From medical visualization research to clinical practice: obstacles and opportunities
Noeska Smit (University of Bergen & Haukeland University Hospital, Norway): Noeska Smit is an Associate Professor (tenure track) in the visualization research group at the University of Bergen, Norway, since 2017, where she leads a team researching multimodal medical visualization. She is also affiliated with the Mohn Medical Imaging and Visualization (MMIV) center as a senior researcher at the Haukeland University Hospital. After working as a radiographer for three years, she completed her studies in Computer Science at the Delft University of Technology, the Netherlands, specializing in Computer Graphics and Visualization in 2012. In 2016, she obtained her PhD in medical visualization at the same institute in collaboration with the Anatomy and Embryology department at the LUMC in Leiden. Currently, she is researching novel interactive visualization approaches for multimodal medical imaging data. Her current focus in this context is on multi-parametric MR acquisitions.
Talk 3: Modeling dose-response data in radiation therapy
Vitali Moiseenko (University of California San Diego, USA): Professor at UCSD where he leads research on biological effects of ionizing radiation with primary focus on analyzing outcomes data in cancer patients receiving radiation therapy. Having completed his post-doctoral fellowship at the National Radiological Protection Board, UK, Vitali obtained his board certification from the Canadian College of Physicists in Medicine in 2004. He has extensively published on the topic of radiation-induced toxicity in cancer patients with emphasis on adverse effects seen in patients treated for genitourinary, gastrointestinal, and head&neck cancers. At the time of preparing this biosketch Vitali has 147 peer-reviewed papers, 9 book chapters and 2 Task Group reports published. Vitali has served on the American Association of Physicists in Medicine/American Society for Radiation Oncology (AAPM/ASTRO) Working Groups, QUANTEC (Quantitative Analysis of Normal Tissue Effects in the Clinic), and HyTEC (High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic), which were set to summarize and produce consensus guidelines for normal tissue tolerance following conventionally fractionated and hypofractionated radiation therapy. His current interest is finding ways to make radiation therapy personalized by accounting for patient-specific features, from biomarkers to anatomic signatures. In particular, visual analytics may provide tools to predict how patients respond to radiotherapy when commonly used metrics omit features impacting response.
Talk 4: Multi-parametric imaging for tumor characterization and target delineation
Uulke van der Heide (Netherlands Cancer Institute, the Netherlands): Uulke van der Heide received his training as medical physicist at the department of radiotherapy of the University Medical Center in Utrecht, the Netherlands and worked there as a medical physicist until 2011. He now works as a medical physicist and group leader at the Netherlands Cancer Institute in Amsterdam, the Netherlands. He holds a chair as professor of imaging in radiotherapy at the Leiden University Medical Center. He participates as teacher and course director in the ESTRO school. His research group works on the improvement of target definition in radiotherapy by application of MRI and the development and validation of quantitative imaging methods for tumor characterization for radiotherapy dose painting. He further leads the MR-guided radiotherapy program at the Netherlands Cancer Institute.
Opening: By the organizers Four short talks:
- Challenges and opportunities in fusing AI and visual computing for radiotherapy planning by Katja Buehler (VRVis, Austria)
- From medical visualization research to clinical practice: obstacles and opportunities by Noeska Smit (University of Bergen, Norway)
- Modeling dose-response data in radiation therapy by Vitali Moiseenko (University of California San Diego, USA)
- Multi-parametric imaging for tumor characterization and target delineation by Uulke van der Heide (Netherlands Cancer Institute, the Netherlands) Guided panel discussion: same as the talks above, moderated by the organizers Audience discussion: open session with the four speakers above and the audience Final closing remarks: By the organizers (1-2 minutes)
When: Thursday (October 28th, 2021) 10:00-11:30 am
Anders Ynnerman, Linköping University, Norrköping, Sweden Mark SubbaRao, Scientific Visualization Studio, NASA Goddard Space Flight Center, United States Alexander Bock, Linköping University, Norrköping, Sweden
Planetariums have been at the forefront of science education since their inception 100 years ago. While initially focussed on accurately depicting the stars in the night sky to teach astronomy to planetarium visitors, the recent two decades have seen a rapid transition of this immersive, but relatively static display environment. The planetarium now encompasses the dissemination of non-astronomical topics, and employs sophisticated interactive immersive software suites. The current focus of these environments is on public visitors while the usage of the almost 3000 planetariums worldwide for scientific purposes remains largely untapped. While visualization techniques have evolved rapidly in the space of flat monitor and virtual reality environments, planetariums with their large-scale non-reciprocal display surfaces pose some unique rendering and interaction challenges. This application spotlight will feature speakers with experience from the visualization domain and the planetarium world that introduce some of the terminology of the field, highlight the ongoing endeavors in this rapidly evolving field, as well as discuss some of the open challenges with a call-to-action to the community to utilize more of these underappreciated display environments. A portion of the application spotlight will be dedicated to an interactive discussion of the speakers with the audiences.
- Introduction to the topic by Mark Subbarao, NASA Scientific Visualization Studio
- Lucia Marchetti, University of Cape Town: Big Data visualisation in immersive environments at the IDIA visualisation Lab and Iziko planetarium
- Tom Kwasnitschka, GEOMAR: A dome for ocean science, and the palette of tools it comes with
- KaChun Yu, Denver Museum of Nature and Science: Educational Research Using Digital Planetariums
- Panel Discussion. Moderator: Anders Ynnerman, Linköping University
- Ryan Wyatt, California Academy of Sciences
- Carter Emmart, American Museum of Natural History
- Jackie Faherty, American Museum of Natural History
- Dayna Thompson, Ball State
- Summary by Alexander Bock, Linköping University
When: Friday (October 29th, 2021) 8:00-9:30 am
Christina Gillmann, Leipzig University, Germany Petra Gospodnetic, Fraunhofer ITWM, Kaiserslautern, Germany Karsten Rink, Helmholtz-Centre for Environmental Research, Leipzig, Germany
Visual analytics has been demonstrated to be a powerful tool in a variety of applications. However, the analysis process itself is affected by a variety of uncertainties, such as uncertainty arising from data or computational models. On top of this processing uncertainty, human-computer-interaction introduces yet another type of uncertainty. Any of these uncertainties may influence the decision-making process, especially if they are not known to the user. Although this problem is quite known, there does not exist a standard workflow that assists in examining and analyzing uncertainties in visual analytics cycles. This may be due to a variety of challenges. On the one hand, visual analytics and the incorporation of uncertainties are not fully understood yet. Especially when considering choices made by humans, the resulting uncertainty is hard to quantify and determine. On the other hand, applications are manifold and each of them introduces specific requirements for the visual analytics process, thus making the standardization difficult. In this application spotlight, we aim to shed light on the uncertainty-aware visual analytics and provide three different application fields showing where uncertainties arise and how they influence the decision-making process. The spotlight is intended to identify common workflows in all shown applications and foster discussions on how to provide a unified approach to handle uncertainty in visual analytics approaches.
Introduction (given by the Organizers) (10 mins) Katja Schladitz, Fraunhofer ITWM Kaiserslautern (15 mins) Tushar Athawale, SCI Institute Utah (15 mins) Ibrahim Demir, University of Iowa (15 mins) Open Discussion (45 mins)
When: Friday (October 29th, 2021) 10:00-11:30 am
Evan Savage, National Renewable Energy Laboratory, United States
The energy sector faces many deep uncertainties about the future, including climate change, growth in energy demand and technologic capabilities, and cybersecurity. Deep uncertainty is characterized by a level of uncertainty where we do not know how to model a system or how to value the desirability of outcomes. Methodologies exist to evaluate the level of uncertainty in a problem, and to help reason on risk and decisions; however, traditional making and visualization techniques may not be applicable when facing deeply uncertain problems. In this application spotlight, we will provide an overview of deep uncertainty and the unique challenges it poses, the need for decision making under deep uncertainty (DMDU) methodologies, followed by several lightning talk topics. Our speakers will discuss the challenge of visualizing deep uncertainty, deep uncertainty in power sector resilience planning and how to visualize alternatives across a wide range of possible futures, and specific visual analytic tools relevant to deep uncertainty planning. We will finish with a half hour moderated discussion where audience participation is encouraged.
|Speakers||Topic||Length of Talk|
|Steven Popper||Overview of Deep Uncertainty||20 mins|
|Kristi Potter||Challenges to Visualization with Deep Uncertainty||15 mins|
|Nathan Lee||Power Sector Resilience: Planning for a Deeply Uncertain Future||15 mins|
|David Gold||Visual analytics as a tool for decision making under deep uncertainty: an exploration of adaptation, vulnerability and robustness||15 mins|
|Moderated by Robert Lempert||Guided discussion: planned set of questions and audience discussion||40 mins|
Steven W. Popper is a senior economist at the RAND Corporation and a professor at the Pardee RAND Graduate School. As associate director of the Science and Technology Policy Institute FFRDC (1996–2001), Popper provided analytic support to the White House Office of Science and Technology Policy and other Executive Branch agencies. His current work is on decisionmaking under deep uncertainty, science and technology policy, multi-stakeholder strategic decision processes, foresight and future studies, and security planning. He coauthored the flagship study of the RAND Pardee Center for Longer Range Global Policy and the Future Human Condition, Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis (2003), which provides a methodological framework for decisionmaking under deep uncertainty that has been applied to an expanding set of policy issues as Robust Decision Making (RDM). His research also focuses on regional economic development and international economics. He has served as a consultant to the World Bank and OECD. He is past chair of the Industrial Science and Technology section of the American Association for the Advancement of Science, and has served as an external advisor to the Ohio State Battelle Center for Science and Technology Policy, the Shoresh Institution and the Israel Innovation Policy Institute. He is the current chair for education and training of the international Society for Decision Making under Deep Uncertainty. Popper received his Ph.D. in economics from the University of California, Berkeley and a B.S summa cum laude in biochemistry from the University of Minnesota.
Robert Lempert is a principal researcher at the RAND Corporation and director of the Frederick S. Pardee Center for Longer Range Global Policy and the Future Human Condition. His research focuses on risk management and decisionmaking under conditions of deep uncertainty. Lempert’s work aims to advance the state of art for organizations managing risk in today’s conditions of face-paced, transformative, and surprising change and help organizations adopt these approaches to help make proper stewardship of the future more commonly practiced. He is a fellow of the American Physical Society, a member of the Council on Foreign Relations, a convening lead author for Working Group II of the United Nation’s Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report, a chapter lead for the Fourth U.S. National Climate Assessment, chair of the peer review panel for California’s Fourth Climate Assessment, a member of California’s Climate-Safe Infrastructure Working Group, and has been a member of numerous study panels for the U.S. National Academies, including America’s Climate Choices and Informing Decisions in a Changing Climate. Lempert was the Inaugural EADS Distinguished Visitor in Energy and Environment at the American Academy in Berlin and the inaugural president of the Society for Decision Making Under Deep Uncertainty (http://www.deepuncertainty.org). A professor of policy analysis at the Pardee RAND Graduate School, Lempert is an author of the book Shaping the Next One Hundred Years: New Methods for Quantitative, Longer-Term Policy Analysis. He earned his Ph.D. in applied physics from Harvard University.
Kristi Potter is a Senior Scientist specializing in data visualization at the National Renewable Energy Lab (NREL). Her current research is focused on methods for improving visualization techniques by adding qualitative information regarding reliability to the data display. This work includes researching statistical measures of uncertainty, error, and confidence levels, and translating the semantic meaning of these measures into visual metaphors. She is also interested in topics related to decision making, performance visualization, method evaluation, and application specific techniques. Kristi has over 15 years of experience in visualization creation, design and deployment spanning multiple disciplines including atmospheric sciences, materials modeling, geographical mapping, and the humanities. Prior to joining NREL in 2017, she worked as a research computing consultant at the University of Oregon providing visualization services, computational training and education, and other support to researchers across campus, and as a research scientist at the University of Utah, working on projects related to the visualization of uncertainty and error in data. Her dissertation work focused on the visual representation of variability within ensemble suites of simulations covering multiple parameter settings and initial conditions. Her master’s work developed the use of sketch-based methods for conveying levels of reliability in architectural renderings. Kristi is currently working in NERL’s Insight Center on high-dimensional data visualization techniques and web-based deployment of visualization applications.
Dr. Nathan Lee is a Researcher with the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL). His research focuses on energy system planning and decision support, which includes broader power system planning as well as more focused work such as power sector resilience planning. His research focuses on decision making under deep uncertainty in energy sector planning with a focus on implementation internationally. He is currently the technical lead for the USAID-NREL Advanced Energy Partnership for Asia. His extensive international research portfolio centers on supporting energy strategy development in emerging economies. Nathan has led technical potential assessments and resilience planning activities in the Lao PDR, proactive transmission planning for renewables in the Philippines, and economic potential assessments for utility-scale wind and solar PV in Southeast Asia. Nathan’s doctoral research focused on decision support methodologies for national energy planning in emerging economies, focused on the Economic Community of West African States.
David Gold is a PhD candidate in the Reed research group at Cornell University. David’s research focuses on water supply planning under conditions of deep uncertainty, such as those caused by climate change and population growth. He is originally from Rhode Island and received a bachelor’s degree in civil and environmental engineering at Lafayette College in 2011. After graduating from Lafayette, he worked several years with the USDA Natural Resources Conservation Service, designing sustainable stormwater management plans for farms. He came to Cornell to pursue an MEng degree and stayed after graduating to study with Professor Patrick Reed.