Abstract:
In the last fifteen years, we have witnessed a dramatic trans formation of our society through the miniaturization of computing technologies. This introduced a new age in which people can access the power of computing devices on-demand, just by reaching into their pockets. While such electronics can be helpful in numerous situations, these technologies alone have limited reach and impact. With “Imperceptible Textile Interfaces”, our vision is to embed intelligence into the ubiquitous medium of textiles, thereby ushering in a new era in which everyday objects and even the very clothes we wear can play an unobtrusive yet always-accessible and powerful role in enhancing our experience of all aspects of our lives ranging from transportation and communication to work and play. In this talk, we will present a novel sensing approach enabling a new kind of yarn-based, resistive pressure sensing.

About the Speaker:
Michael Haller is a professor at the department of Interactive Media of the University of Applied Sciences Upper Austria (Hagenberg, Austria), where he is founder and director of the Media Interaction Lab (www.mi-lab.org), responsible for computer graphics & human-computer interaction. His core areas of expertise are smart graphics and the development of next-generation user interfaces. He received Dipl.-Ing. (1997), Dr. techn. (2001), and Habilitation (2007) degrees from Johannes Kepler University of Linz, Austria. His current focus is on innovative interaction techniques and smart interfaces for next generation working environments. Currently, he leads a team of over 10 researchers and students. He has been awarded the Erwin Schrödinger Fellowship Award, Google Research Award, Europrix Top Talent Award, Best ACM SIGGRAPH Emerging Technologies Award, and Microsoft Imagine Cup. Six of his papers were awarded best paper or honorable mention at top HCI venues including ACM CHI and ACM UIST.

Abstract:
With the increasing adoption of machine learning models across domains, we have to think about the human role when interacting with these models. In the last years, my collaborators and I have created a series of tools that utilize visualization and visual user interaction to help investigate behavior of machine learning models (for NLP and CV). I will present a selection of these scientific tools that makes humans play and then understand.

About the Speaker:
Hendrik Strobelt is the Explainability Lead at the MIT-IBM Watson AI Lab and Research Scientist at IBM Research. His recent research is on visualization for and human collaboration with AI models to foster explainability and intuition. The majority of his work is for NLP models and generative models while he is advocating to utilize a mix of data modalities to solve real-world problems. His work is applied to tasks in machine learning, in NLP, in the biomedical domain, and chemistry. Hendrik joined IBM in 2017 after postdoctoral positions at Harvard SEAS and NYU Tandon. He received a Ph.D. (Dr. rer. nat.) from the University of Konstanz in computer science (Visualization) and holds an MSc (Diplom) in computer science from TU Dresden. He received multiple best paper/honorable mention awards at EuroVis, BioVis, VAST, and ACL Demo. He received the Lohrmann medal from TU Dresden as the highest student honor. Hendrik has served in program committees and organization committees for IEEE VIS, BioVis, EuroVis. He was the virtual chair for ICLR 2020, AIstats 2020, and Online Experience Chair for NeurIPS 2020. Hendrik is affiliated as visiting researcher with MIT CSAIL.

Abstract:
This talk will give an overview of our group's recent progress in water wave animation. Existing methods for solving wave equations (like finite elements, finite differences, and spectral/Fourier methods) tend to fall short of the level of detail required for visual effects, or they fail to convincingly animate interactions with boundaries (like a wave reflecting off a rock).

Over the past few years, our group has experimented with new ways to animate water waves, including boundary integrals, wave packets and wavelets, and analytic solutions of the Navier-Stokes equations, and even simple geometric heuristics. During this talk, I will explain the problem and give an overview of our various methods and results.

The talk describes research collaborations with the following co-authors:
Stefan Jeschke, Camille Schreck, Christian Hafner, Tomas Skrivan, Ken Museth, Andreas Soderstrom, Christoph Sprenger, John Johansson, Matthias Mueller-Fischer, Nuttapong Chentanez, Miles Macklin

References:

• "Water Wave Packets," Stefan Jeschke and Chris Wojtan. ACM Transactions on Graphics (SIGGRAPH 2017).
• "Water Surface Wavelets," Stefan Jeschke, Tomas Skrivan, Matthias Muller-Fischer, Nuttapong Chentanez, Miles Macklin, and Chris Wojtan. ACM Transactions on Graphics (SIGGRAPH 2018).
• "Fundamental Solutions for Water Wave Animation," Camille Schreck, Christian Hafner, Chris Wojtan. ACM Transactions on Graphics (SIGGRAPH 2019).
• "Wave Curves: Simulating Lagrangian water waves on dynamically deforming surfaces", Tomas Skrivan, Andreas Soderstrom, John Johansson, Christoph Sprenger, Ken Museth, Chris Wojtan, ACM Transactions on Graphics (SIGGRAPH 2020).
• "Making Procedural Water Waves Boundary-Aware", Stefan Jeschke, Christian Hafner, Nuttapong Chentanez, Miles Macklin, Matthias Muller-Fischer, Chris Wojtan
  ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2020 (SCA 2020).

About the Speaker:
Chris Wojtan is a Professor at the Institute of Science and Technology Austria (IST Austria). He received his Ph.D. in Computer Graphics from the Georgia Institute of Technology in 2010. He has written over 40 research papers on physics simulation and geometry processing. His research aims to find efficient numerical solutions for physics problems that couple complicated geometry and topology, with particular emphasis on generating liquid motion and fracture patterns. His work has been awarded an ERC Starting Grant, the Eurographics Young Researcher Award, and the ACM SIGGRAPH Significant New Researcher Award.

Abstract:
Drones are small scale flying robots and it is predicted that the drone market as well as the general robotics market will see a major growth in the near future. AI and computer vision will play a major role in shaping the future autonomous drones. In my talk I will give an overview of the use of AI and computer vision techniques in robotics and for drones in particular. I will then follow up by presenting developments in this area done in my lab, which will include the topics of 3D mapping, localization, path planning and environment interpretation.

About the Speaker:
Friedrich Fraundorfer is Assistant Professor at Graz University of Technology, Austria since October 2014. He received the Ph.D. degree in computer science from TU Graz, Austria in 2006 working at the Institute of Computer Graphics and Vision headed by Franz Leberl and Horst Bischof. He had post-doc stays at the University of Kentucky (US), at the University of North Carolina at Chapel Hill (US) and at ETH Zürich (Switzerland). From 2012 to 2014 he acted as Deputy Director of the Chair of Remote Sensing Technology at the Faculty of Civil, Geo and Environmental Engineering at the Technische Universität München.
Friedrich Fraundorfer has been involved in multiple international and multinational research projects as project leader, investigator and collaborator, the EU project SFly (http://www.sfly.org/, opens an external URL in a new window), a 4-year SNFproject about autonomous micro UAV’s, the EU project VCHARGE (http://www.v-charge.eu/, opens an external URL in a new window). Currently he acts as PI for the H2020 project SLIM (http://www.slim-project.eu/, opens an external URL in a new window) and RESIST and as project leader for various industry collaborations. His main research areas are 3D Computer Vision and its use for robotics using modern machine learning techniques. He is the author of a well perceived two-part tutorial about visual odometry in the IEEE Robotics and Automation Magazine. https://www.tugraz.at/institute/icg/research/team-fraundorfer/, opens an external URL in a new window