Prof. Franz Rottensteiner is an Adjunct Professor and Deputy Director at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover. His work focuses on remote sensing, photogrammetry, and AI applications in geospatial data analysis. He leads projects like Gauss Centre for temporal geospatial data analysis and EU ChemiNova for cultural heritage conservation. Key research areas include CNN-based 3D reconstruction, satellite image time series classification, and cooperative vehicle positioning using UAV imagery. He collaborates on initiatives such as OER4Ukraine, providing educational resources in image analysis for Ukrainian academia. His recent projects emphasize interdisciplinary AI applications, including the Leibniz AI Academy for trans-curricular learning. Notable contributions include deep learning frameworks for land cover classification and vehicle pose estimation, with publications spanning 2020–2025. Current research integrates transformer models for temporal data analysis, semantic segmentation, and domain adaptation techniques. His work bridges theoretical advancements with practical applications in autonomous systems, environmental monitoring, and heritage preservation.
Jenny Lin is an Assistant Professor at the University of Utah's Kahlert School of Computing, specializing in computational fabrication and textile manufacturing. Her research bridges computer science with mathematics, focusing on algorithmic design for textile fabrication processes. She holds a PhD from Carnegie Mellon University (CMU) and a B.E. in Computer Science and Molecular Biology from MIT. Before joining Utah, she was a postdoctoral scholar under Cem Yuksel at Utah, advised by James McCann during her PhD. Research interests include formalizing knit object equivalence via knot theory, machine knitting compilers, and robotic fabrication optimization. She actively seeks students interested in graphics, textiles, and mathematical modeling. Teaching experience includes serving as a TA for CMU's Computational Photography course and guest lecturing on algorithmic textiles at CMU and the University of Washington. PhD: Carnegie Mellon University (Computer Science) B.E.: Massachusetts Institute of Technology (Computer Science & Molecular Biology) Her work has been recognized with a Best Conference Paper nomination at ICRA 2021. She co-runs the University of Utah's Graphics Seminar and maintains the Textiles Lab's Knitout Office Hours program at CMU, offering machine knitting instruction.
Jenny Han Lin is an Assistant Professor at the University of Utah's Kahlert School of Computing. Her research focuses on computational fabrication, particularly the intersection of computer science, textiles, and mathematics. She holds a PhD from Carnegie Mellon University (2024) and a B.E. from MIT, dual-degree in Computer Science and Molecular Biology. Prior to her current role, she was a postdoctoral scholar under Cem Yuksel at the University of Utah, and advised by James McCann during her PhD. Her research interests include algorithmic textiles, robotic fabrication, and mathematical modeling of knitting/crochet processes. Notable work includes applying knot theory to formalize knit object equivalence and developing machine knitting compilers. She actively mentors students interested in graphics, textiles, and mathematical applications in fabrication. Recent publications emphasize textile automation and fabrication planning, with applications in both robotics and computer graphics. Her work on Artin Braid Group representation for knitting machines received recognition as a finalist for the Best Conference Paper at ICRA '21. Jenny has contributed to academic service through roles like organizing SIGBOVIK (a satirical conference fostering early academic writing) and running TechNights STEM outreach. She has co-chaired the SIGBOVIK organizing committee (2019-2022) and developed educational materials for algorithmic textiles through guest lectures at CMU and UW.
Cem Yuksel is an Associate Professor at the Kahlert School of Computing, University of Utah, and founder of Cyber Radiance LLC. His research focuses on computer graphics, including real-time rendering, physically-based simulations, global illumination, and hair/cloth modeling. He holds a PhD from Texas A&M University and has been a postdoc at Cornell University. Education: PhD in Computer Science (2010, Texas A&M), MS in Computer Engineering (2003, Bogazici University), BS in Physics (2000, Bogazici University). Research interests include Hair Meshes for real-time hair rendering, high-performance polynomial solvers, mesh color textures, and wearable 3D knitting. He has authored over 100 peer-reviewed papers and received awards such as the Wolfgang Straßer Best Paper Award (2022) and multiple Dean’s commendations for teaching. Courses taught include Introduction to Computer Graphics and Rendering with Ray Tracing. Key projects: Hair Meshes (SIGGRAPH 2024), Stochastic Lightcuts (HPG 2019), Mesh Color Textures (2020), and high-performance polynomial solvers (SIGGRAPH 2022). His work has been implemented in commercial software like Hair Farm for 3ds Max. Grants and service roles include steering committee member for High-Performance Graphics (HPG) and organizing SIGGRAPH conferences. He runs a graphics research lab at the University of Utah.
Jenny Han Lin is an Assistant Professor at the University of Utah's Kahlert School of Computing. Her research focuses on computational fabrication, particularly leveraging knot theory and low-level computer representations to advance textile manufacturing and real-world phenomenon modeling. She completed her PhD in Computer Science at Carnegie Mellon University under James McCann, preceded by a B.E. in Computer Science and Molecular Biology from MIT. Her work bridges computer graphics, robotics, and mathematics, with notable contributions to machine knitting compilers, braid group theory applications, and crochet representation. She has been a postdoctoral scholar at the University of Utah and taught courses at CMU and the University of Washington. Lin actively promotes textile fabrication through initiatives like Knitout Office Hours and served as SIGBOVIK organizing committee chair (2019-2022), creating an award-winning conference website. Lin's research has been recognized with a finalist award at ICRA 2021. She collaborates widely, mentoring students in interdisciplinary projects at the intersection of computing and physical fabrication.
Roles & Affiliations: Professor of Computer Graphics at the Department of Computer Science, University of Hong Kong (since 2020). Previously at University of Edinburgh (2006-2020), City University of Hong Kong (2002-2020), and RIKEN (2000-2002). Holds a BSc, MSc, and DSc in Information Science from the University of Tokyo. Research Interests: Focuses on physically-based animation, character animation, 3D modeling, cloth animation, and robotics. Recent work emphasizes machine learning integration into animation synthesis. Notable projects include MotionNet (3D motion reconstruction), neural state machines for character interactions, and fracture simulation with material points. Publications: Over 100 peer-reviewed papers spanning SIGGRAPH, Eurographics, and IEEE journals. Recent work emphasizes deep learning applications in animation, medical imaging analysis, and physics-based simulations. Key trends include neural networks for motion synthesis, transformer-based models for 3D gestures, and topology-aware shape reconstruction. Awards: Royal Society Industry Fellowship (2014), Google AR/VR Research Award (2017). Recognized for contributions to physically-based animation and medical imaging AI. Labs & Collaborations: Leads research groups in computer graphics and AI at HKU. Collaborates with institutions on robotics, VR/AR, and medical imaging projects. Active in organizing SIGGRAPH and Eurographics conferences.
Dr. Karel Matous is a Professor of Aerospace and Mechanical Engineering at the University of Notre Dame and Director of the Center for Shock-Wave Processing of Advanced Reactive Materials (C-SWARM). He holds a Ph.D. in Theoretical and Applied Mechanics from the Czech Technical University in Prague. His research focuses on predictive computational science and engineering, multiscale modeling, and high-performance computing, with applications in materials science and energetic systems. Education: Ph.D. in Theoretical and Applied Mechanics (Czech Technical University, 2000), M.S. in Theoretical and Applied Mechanics (Czech Technical University, 1998). His work bridges applied mathematics, computational science, and materials science, emphasizing data-driven modeling and experimental-computational synergy. Key contributions include multiscale models for heterogeneous materials, adaptive wavelet algorithms, and computational homogenization at extreme scales. His research has been funded by agencies like the DOE, NSF, and industry partners, with over $12M in grants for the C-SWARM center. Publications span topics such as solid propellant modeling, particle debonding in elastomers, and microstructure-property relations. Awards include ASME Fellow, Melosh Medal (student award), and Rector's Award. He collaborates with institutions globally and teaches advanced mechanics courses at Notre Dame. Labs/Teams: Directs C-SWARM, leading teams in multiscale simulations and shock-wave processing of materials. Active in computational physics and materials research groups.
Dr. Hannes Raddatz is a researcher at the University of Rostock, focusing on embedded systems and industrial communication protocols. He works within the Department of Embedded Systems, contributing to projects involving OPC UA, MQTT-SN, and Time-Sensitive Networking (TSN) for Smart Manufacturing and IIoT applications. His expertise spans protocol evaluation, secure communication frameworks, and virtual prototyping of distributed systems. PhD in Embedded Systems Research Interests: Industrial IoT, Smart Factories, Cybersecurity His recent work includes protocol integration for sensor networks and optimizing real-time communication in industrial environments. He collaborates with institutions like the University of Rostock and researchers such as Prof. Christian Haubelt and Dr. Frank Golatowski. Key publications address OPC UA server evaluation, MQTT-SN binding for IIoT, and secure frameworks for Smart Home systems. He has presented at conferences like INDIN, ETFA, and WF-IoT. Scientific Contributions: Best Paper Award Nominee at PATMOS 2019 Key roles in projects: GenerIoT, SUSTAIN Dr. Raddatz contributes to advancing secure and efficient communication protocols in industrial and embedded contexts, with applications in logistics, crane systems, and smart manufacturing environments.
Jun Luo is an Associate Professor in the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He earned his PhD in Computer Science from EPFL under the supervision of Prof. Jean-Pierre Hubaux and completed postdoctoral research at the University of Waterloo. He joined NTU in 2008 as an Assistant Professor and was promoted to Associate Professor in 2014. He served as Deputy Director of the Centre for Multimedia and Network Technology from 2010 to 2013. Education: PhD in Computer Science, Swiss Federal Institute of Technology in Lausanne (EPFL), 2006 MS in Electrical Engineering, Tsinghua University, 2000 BS in Electrical Engineering, Tsinghua University, 1997 Research Interests: Jun Luo's research focuses on mobile and pervasive computing, wireless networking, machine learning, and applied operations research. His primary research thrusts include: Contact-free Sensing Driven by Deep Learning : Leveraging RF, acoustic, and visible light signals for human activity recognition, respiration monitoring, and localization without wearable devices. Visible Light Communication and Sensing : Exploring LED-camera systems for data transmission, occupancy inference, and indoor broadcasting. Indoor and Outdoor Localization and Tracking : Developing systems using WiFi, geomagnetism, and crowdsourced data for precise positioning. Machine Learning for Mobile Networking : Applying deep learning and optimization to improve wireless network performance, mobile crowdsensing, and resource allocation. Publication Trends: His recent publications (2021–2023) demonstrate a strong focus on deep learning-enhanced sensing using RF and acoustic signals, particularly for health monitoring (e.g., respiration, heartbeat), multi-person tracking, and privacy-preserving techniques. He frequently collaborates with researchers in signal processing, computer vision, and networking, publishing in top venues like IEEE Transactions on Mobile Computing, MobiCom, and INFOCOM. His work emphasizes practical deployment on commodity devices and integration of sensing with communication systems. Scientific Recognition: IEEE Fellow Advising and Grants: Dr. Luo has advised numerous PhD and Master's students, as evidenced by the extensive list of student co-authors across his publications. He has led significant research projects in wireless sensor networks, mobile computing, and IoT systems, likely supported by competitive grants from Singaporean and international funding agencies. His role as Deputy Director of a research center indicates leadership in managing research teams and collaborative efforts. Labs and Teams: He leads a research group focused on mobile and distributed computing, deep learning, and computer vision. His team actively publishes in top-tier conferences and journals, working on projects involving RF sensing, acoustic platforms, visible light communication, and privacy-aware systems. The group collaborates with researchers both within NTU and internationally, particularly in Canada and China.
Benoit Guillard is a researcher specializing in 3D surface reconstruction and neural network applications. He earned his PhD in 2023 from EPFL’s Computer Vision Lab (CVLAB), focusing on representation learning for 3D geometry. His work bridges computer vision, machine learning, and computer graphics through innovative solutions for garment modeling, LiDAR simulation, and implicit surface processing. Notable contributions include: MeshUDF (ECCV 2022): Differentiable meshing for unsigned distance fields DrapeNet (CVPR 2023): Self-supervised garment draping technique UCLID-Net (NeurIPS 2020): Single-view 3D reconstruction with hybrid architectures His research has garnered recognition through CVPR 2022 Best Reviewer and NeurIPS 2022 Top Reviewer awards. He has served as a reviewer for top-tier conferences including CVPR, ICCV, and SIGGRAPH.
Damien Rohmer is a Professor of Computer Science at École polytechnique , Institut Polytechnique de Paris. He leads the VISTA research team at LIX (CNRS UMR 7161) and serves as Deputy Director of LIX since 2024. Current PhD supervisions with ANR/CIFRE funding Open-source tool development for computer graphics education Editorial roles in SCA, MIG, SIGGRAPH conferences His research spans 3D modeling , real-time animation , and user-interactive simulation , with applications in entertainment, medical sciences, and design. Key sub-areas include: Character motion retargeting Multi-scale natural phenomenon simulation Field-based implicit modeling Wearable technology visualization Gesture-controlled animation systems Recent publications in SIGGRAPH , Eurographics , and SCA earned multiple Best Paper/Poster awards in 2024-2025. He co-develops the CGP library for educational 3D programming and maintains community resources for French computer graphics institutions.
Dr. Tilo Arens is a Lecturer at the Institute for Applied and Numerical Mathematics , part of the Karlsruhe Institute of Technology (KIT) . His research focuses on numerical methods for scattering problems , particularly involving electromagnetic waves , chiral scatterers , and inverse scattering problems . He actively contributes to the Working Group 4: Inverse Problems and collaborates on projects within the CRC 1173 Wave Phenomena . His teaching portfolio includes courses such as Higher Mathematics II for engineering disciplines, Optimization Theory , and Scattering Theory . He also supervises bachelor’s and master’s theses on topics like Approximation of rotation-minimizing frames using B-spline curves and Numerical methods for integral equations . Dr. Arens has authored numerous publications on inverse scattering, integral equations, and computational methods. Notable works include studies on monotonicity-based shape reconstruction , high-order numerical methods for bi-periodic structures , and electromagnetic chirality measurement . His research intersects with applied mathematics, physics, and computational science. He previously collaborated on a student seminar with Heisenberg-Gymnasium Karlsruhe to bridge university and secondary education in mathematics. His habilitation (2010) and PhD (2000) are in electromagnetic and elastic wave scattering, respectively.
Emre Akbas is an Associate Professor at the Department of Computer Engineering, Middle East Technical University (METU). He previously worked as a researcher at the Vision and Image Understanding Lab at University of California Santa Barbara (UCSB) and earned his PhD from University of Illinois at Urbana-Champaign (UIUC) under Prof. Narendra Ahuja. His academic journey includes MS and BS degrees from METU, where he ranked 1st in his undergraduate class. Education : PhD (UIUC), MS (METU), BS (METU) Honors : Young Scientist Award (2022), METU Thesis Awards (2023, 2022, 2020), Beckman Institute Cognitive Science/AI Award (2010) His research focuses on visual detection and machine learning, particularly addressing imbalance problems in object detection. He co-developed innovative methods like HoughNet, a generalized Hough transform for object detection, and formulated the Localization Recall Precision (LRP) metric for visual detection evaluation. His work spans deep learning, domain adaptation, and generative adversarial networks (GANs). Recent publications highlight his contributions to object detection metrics, ranking-based loss functions, and domain adaptation techniques. His research group has produced significant works at venues like CVPR, ECCV, and TPAMI, including the textbook Signals and Systems: Theory and Practical Explorations with Python (Wiley, 2024). Awards : Young Scientist Award (Science Academy, Turkey), METU Thesis Awards Grants : ERC Starting Grant (NONWESTLIT), TUBITAK Career Development Grant, AWS Cloud Credits As an advisor, he has guided multiple thesis awardees. His community service includes organizing ECCV and CVPR workshops and serving as a reviewer for top-tier conferences and journals.
Prof. Dr. Konrad Polthier is a full professor of mathematics at Freie Universität Berlin since 2005, leading the Mathematical Geometry Processing Group. He holds roles as chair of the Berlin Mathematical Society and co-chair of the Berlin Mathematical School. His research focuses on discrete differential geometry, applied geometry, and mathematical visualization, with contributions to geometry processing, computer graphics, and educational media. He has authored books like A Mathematical Picture Book and produced award-winning films such as MESH – A Journey through Discrete Geometry . His work bridges academic research and public engagement, including the MathFilm Festival and the platform math.berlin . Polthier's scientific accolades include international film awards for his videos and funding for projects like QuadCover. He has served on editorial boards for journals and conferences, including co-editing Computer Aided Geometric Design .
Dr. Jin Yang is an Assistant Professor in the Department of Aerospace Engineering & Engineering Mechanics at the University of Texas at Austin. He joined the faculty in Fall 2022 after completing a postdoctoral position at the University of Wisconsin-Madison and a PhD from the California Institute of Technology (Caltech). His research focuses on developing experimental techniques and analytical tools to study viscoelastic materials under extreme loading conditions, particularly leveraging machine learning and advanced imaging methods like digital image correlation (DIC) and laser-induced cavitation. Key areas include material characterization of hydrogels, biological tissues, and foams at ultra-high strain rates (10 -4 to 10 7 s -1 ). Education: B.En. in Engineering Mechanics, Tsinghua University (2013) Ph.D. in Engineering Mechanics, California Institute of Technology (2019) Research Interests: Dynamic instability and failure mechanisms in viscoelastic materials Machine learning-driven material characterization Laser-induced microcavitation for rheometry Development of high-speed full-field deformation measurement techniques (DIC, DVC) Notable Awards: National Science Foundation CAREER Award (2025) Haythornthwaite Initiation Grant (ASME, 2022) ICTAM Fellowship (International Congress of Theoretical and Applied Mechanics) Grants and Initiatives: Academic Development Funds for enhancing aerospace materials labs (2024-25) Funding for high-speed imaging equipment (Shimadzu HPV-X2 camera) Labs & Teams: Active in the Yang Research Group at UT-Austin, focusing on cutting-edge mechanics of solids and biomaterials.
