Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.
Wendy Ju is an Associate Professor of Information Science at Cornell Tech, with appointments in the Cornell Ann S. Bowers College of Computing and Information Science, the Jacobs Technion-Cornell Institute, and the Technion-Israel Institute of Technology. Previously, she served as executive director of interaction design research at Stanford University's Center for Design Research and as an associate professor of interaction design at the California College of the Arts. Her work bridges human-computer interaction, design, and robotics with a focus on how interactive devices can communicate with people without interrupting them. PhD in Mechanical Engineering from Stanford University Master's degree in Media Arts and Sciences from MIT Professor Ju's research centers on implicit interactions, human-robot collaboration, and automotive interfaces. She investigates how people interact with automated systems in natural contexts, develops methods for early-stage prototyping of autonomous technologies, and examines the social implications of robotics in urban environments. Her work spans from theoretical frameworks to practical applications, with particular emphasis on designing systems that integrate seamlessly into human activities without demanding constant attention. Her recent publications reveal a strong trajectory toward understanding human-robot interaction in public urban spaces, with increasing focus on robot navigation in city streets, the social implications of autonomous vehicles, and the integration of generative AI in design processes. Her work consistently bridges theoretical HCI frameworks with practical applications in transportation, urban design, and everyday robotics. Inducted into the ACM SIGCHI Academy (2025) Multiple Honorable Mention Awards at ACM CHI and DIS conferences Best Paper Award at Multimodal Technologies and Interaction (2023) Best Student Paper Award at IEEE Intelligent Vehicles Symposium (2017) Best Demonstration Award at HRI (2017) Professor Ju actively mentors numerous PhD and master's students, many of whom have become leading researchers in HCI and robotics. Her research has been supported by significant grants from NSF and industry partners, enabling extensive field studies of human-robot interaction in real-world settings. She has pioneered methodologies for studying autonomous vehicle interactions through both simulated and naturalistic driving environments. Her work with the Jacobs Technion-Cornell Institute supports interdisciplinary research at the intersection of computing, design, and urban technology. She has established research partnerships with transportation authorities, automotive companies, and urban planning organizations to study how emerging technologies can enhance urban mobility while respecting human needs and social contexts.
Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.
Prof. Alois Christian Knoll is a full professor at the Technical University of Munich (TUM) in the School of Computation, Information and Technology. His academic career includes roles at Bielefeld University and leadership in major EU initiatives like the Human Brain Project and ECHORD++. He specializes in robotics, AI, and autonomous systems, with a focus on medical robotics, sensor-based systems, and neuromorphic engineering. Knoll has supervised over 100 doctoral theses and authored/co-authored over 1,000 publications. Education: Diploma in Electrical Engineering (University of Stuttgart, 1985); PhD in Computer Science (Technical University of Berlin, 1988); Habilitation (TU Berlin, 1993). He has been at TUM since 2001, leading the Robotics, AI, and Real-Time Systems department. Research interests span autonomous systems, neuro-IT integration, and traffic simulation. Key projects include fortiss (Bavarian State Institute for Computer Science) and TUM-CREATE (Singapore collaboration). Awards include IEEE Fellow, University of Tokyo Fellow, and the Carl-Ramsauer-Prize (1990). Current roles include editorships in robotics journals, leadership in EU flagship projects, and teaching across multiple programs. His work bridges computer science, neuroscience, and engineering, with applications in healthcare, automotive systems, and urban mobility.
Cyrill Stachniss is a Full Professor at the University of Bonn , where he heads the Lab for Photogrammetry and Robotics and is affiliated with the Lamarr Institute for Machine Learning and Artificial Intelligence . He was previously a Visiting Professor in Engineering at the University of Oxford until 2025. His academic journey includes positions at the University of Freiburg, University of Zaragoza, and the Swiss Federal Institute of Technology. University: University of Bonn School: Faculty of Engineering Department: Department of Photogrammetry Academic Rank: Professor His research spans robotics, photogrammetry, SLAM, autonomous navigation, perception systems, agricultural robotics, and unmanned aerial vehicles . He emphasizes probabilistic techniques for mobile robots and has made significant contributions to visual and LiDAR-based localization, scene understanding, and 3D reconstruction. The recent publications highlight a strong trend toward neural implicit representations, agricultural phenotyping, radar-based perception, and active learning . His team develops robust systems for real-world deployment in dynamic and unstructured environments, particularly in precision farming and autonomous vehicles. Scientific Awards: IEEE RAS Early Career Award (2013) Microsoft Research Faculty Fellow (2010) 7th EURON Georges Giralt Award (2008) Multiple Best Paper Awards at ICRA, IROS, RSS, and RAL Faculty Teaching Award, University of Freiburg He has advised numerous students and leads the DFG Cluster of Excellence PhenoRob and Research Unit FOR 1505 Mapping on Demand . His lab has co-founded three startups, reflecting strong industry and societal impact. He also runs the educational video series 5 Minutes with Cyrill , explaining key robotics concepts.
Qi Alfred Chen is an Assistant Professor in the Department of Computer Science at the University of California, Irvine (UCI), within the Donald Bren School of Information and Computer Sciences. He also holds affiliations with the Department of Electrical Engineering and Computer Science (EECS), the Institute of Transportation Studies at UC Irvine (ITS-Irvine), the Center for Embedded and Cyber-physical Systems (CECS), the Institute for Software Research (ISR), and the UC Irvine Cybersecurity Policy & Research Institute (CPRI). His research focuses on network and systems security, with particular emphasis on autonomous vehicle and IoT security. Dr. Chen received his Ph.D. from the University of Michigan in 2018. His educational background has prepared him for his current research in security of critical computer systems. His work bridges theoretical security principles with practical implementations in real-world systems. Chen's research interests center on network and systems security , with a major focus on smart systems and IoT security , particularly in transportation and autonomous vehicle systems . His work addresses security challenges through systematic problem analysis and mitigation, discovering and mitigating security problems in next-generation transportation systems, smartphone OSes, network protocols, DNS, GUI systems, and access control systems. His research has high impact in both academic and industry contexts, with over 10 top-tier conference papers, a DHS US-CERT alert, multiple CVEs, and coverage in major news media like Fortune and BBC News. His publication record shows a clear evolution from foundational work on network protocols and smartphone security toward increasingly sophisticated security analyses of autonomous vehicle systems and AI-powered transportation technologies. The research trajectory demonstrates growing technical sophistication and real-world impact, with recent work focusing on physical-world adversarial attacks against autonomous driving perception systems, LiDAR spoofing, and security of multi-sensor fusion in autonomous vehicles. NSF CAREER Award (2022) on securing the AI stack in emerging autonomous and connected CPSs Chancellor's Award for Excellence in Undergraduate Research Mentorship, UC Irvine (2021) 5th place nation-wide at National CCDC competition (2021, as faculty advisor) 1st place (Gold Medal) at CCDC Western Regional competition (2021) ProQuest Distinguished Dissertation Award, University of Michigan (2019) Dr. Chen has mentored numerous successful students, including PhD candidates and undergraduates who have gone on to positions at major tech companies like Meta, Uber, Amazon, and Intel. His research group has secured significant funding, including an NSF CAREER award, and has made substantial contributions to the field through high-impact publications and vulnerability disclosures. He is also the co-founder of the ISOC VehicleSec Symposium and has organized the AutoDriving CTF contest at DEF CON. Chen leads the AV & IoAT Security Research Group at UCI, focusing on security challenges in autonomous vehicles and the broader Internet of Autonomous Things. His team has developed numerous attack demonstrations and security analyses that have received significant media attention and influenced industry practices. The group maintains an active YouTube channel showcasing their security research.
Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
Elahe Soltanaghai is an Assistant Professor in the Department of Computer Science and a Faculty Affiliate in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. She is also a 2022 NCSA Fellow and received her PhD in Computer Science from the University of Virginia (2019), MS in Computer Engineering from Sharif University of Technology (2014), and dual BS degrees in Computer and Information Technology Engineering from Amirkabir University of Technology (2011, 2013). PhD: University of Virginia, Computer Science, 2019 MS: Sharif University of Technology, Computer Engineering, 2014 BS (Computer Engineering): Amirkabir University of Technology, 2011 BS (Information Technology Engineering): Amirkabir University of Technology, 2013 Her research spans wireless sensing and communication, focusing on Millimeter-wave Radar Sensing (for automotive, mixed reality, structural monitoring), Machine Learning for Wireless Systems (adaptive sensing/communication), Forest IoT (through-canopy biomass and soil sensing), Metaverse Technologies (gaze-based VR/AR), and Low-Power Backscatter Communication (WiFi/power-line tags). She directs the Wireless, Sensing & Embedded Networked Systems (iSENS) Lab and co-directs the Illinois Center for IoT. Her work bridges wireless networking with cyber-physical sensing , emphasizing environmental monitoring (e.g., wildfire fuel detection via radar tags) and human-computer interaction (e.g., gaze-tracking in VR). Recent articles include innovations in passive radar profiling , through-canopy biomass characterization , and integrated communication-sensing protocols . Scientific Awards: Google Research Scholar Award (2022) N2Women Rising Star (2021) ACM SIGMOBILE Dissertation Award (2020) EECS Rising Stars (2019) NCSA Faculty Fellowship (2023) Best Demo Runner-up, IPSN (2023) Teaching Excellence Award (2023) Grants: NASA FireTech Program Grant (2025) NSF Grant for Radar-based Perception (2024) Insper-Illinois Grant for VR Research (2024) Keysight Research Gifts (2022, 2023) T-Mobile Research Gift (2022)
Anna Choromanska is an Associate Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering, with affiliations to NYU Center for Data Science (CDS), NYU Center for Urban Science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and the C2SMART Center. Her research focuses on deep learning optimization, generalization, and applications in autonomous driving and large-scale data analysis. She holds an Alfred P. Sloan Fellowship and NSF CAREER Award, and her work impacts industries like NVIDIA and Facebook. She directs the Learning Systems Laboratory (LSL), emphasizing interdisciplinary experimental/theoretical work. Research Interests: Machine Learning fundamentals, DL optimization, continual learning, autonomous vehicle systems, large data analysis. Her lab explores DNN learning dynamics, training architecture design, and scalable algorithms. Professional Impact: Over 50 invited talks, workshop organization for top ML conferences, and contributions to open-source projects like Vowpal Wabbit. Her algorithms are deployed in production systems at Facebook and Baidu. Awards: NSF CAREER Award Alfred P. Sloan Fellowship IBM Global University Program Academic Award (2x) Columbia University Presidential Fellowship Advising & Labs: Leads LSL, supervising interdisciplinary projects in optimization and autonomy. Actively involved in NYU's Modern AI seminar series and industry partnerships through CATT. Personal Interests: Accomplished pianist, salsa dancer, and fashion design enthusiast with notable performances and certifications in dance and music.
Raj Rajkumar is the George Westinghouse Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He holds leadership roles as Director of the USDOT Mobility21 and Safety21 National University Transportation Centers, and Director of the Metro21: Smart Cities Institute. His work focuses on cyber-physical systems, autonomous driving, and vehicular networking. Education: Ph.D. in Electrical and Computer Engineering from CMU (1989). Research interests include connected/autonomous vehicles, cyber-physical systems, embedded systems, and vehicular networking. Key projects include the General Motors-CMU Collaborative Research Lab and the development of autonomous vehicle protocols like Ballroom Intersection Protocol. Notable awards include the 2016 National Academy of Inventors Fellowship and the 2017 Pittsburgh Business Times Innovation Award. His publications span real-time systems, sensor networks, and automotive cyber-physical frameworks. Rajkumar advises on autonomous vehicle safety, energy efficiency, and societal impacts, with frequent media engagements on self-driving technologies.
Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools
Dr. phil. Stefan Ladwig is a researcher at the Institute for Automotive Engineering (Institut für Kraftfahrzeuge) at RWTH Aachen University, specializing in traffic psychology, user acceptance, and human-machine interaction for automated driving systems. His work addresses psychological and ergonomic aspects of emerging mobility technologies, including seating ergonomics, trust-building mechanisms, and communication strategies for autonomous delivery robots. Research Focus: Automated driving, interior design psychology, driver/passenger behavior, and safety requirements. Projects: Lead roles in UrbANT (autonomous delivery robot) and SteeringBow (dynamic driving experience). Collaborations: Affiliated with Aldenhoven Testing Center, Future Mobility Partnership e.V., and innocam.NRW. Ladwig’s recent publications examine rotated seating positions, visual dominance in speed adaptation, and trust-relevant driving scenarios, reflecting his interdisciplinary approach to optimizing human-vehicle interactions. His work spans ergonomics, energy management, and behavioral interventions to enhance safety and usability in next-generation mobility solutions.
Jens Behley is a Lecturer (Privatdozent) and postdoctoral researcher at the Department of Photogrammetry, University of Bonn. He completed his habilitation in 2023 with a thesis on LiDAR-based spatio-temporal scene understanding for autonomous vehicles and earned his PhD in 2014 under Prof. Armin Cremers. His research focuses on LiDAR perception, agricultural robotics, and 3D scene understanding. Behley is an Associate Editor at IEEE Robotics and Automation Letters (RA-L) and has authored influential datasets like SemanticKITTI and BonnBeetClouds3D. Education: PhD in Computer Science, University of Bonn, 2014 Habilitation in Photogrammetry, University of Bonn, 2023 Research Interests: LiDAR-based perception in urban and agricultural environments, machine learning for robotics, semantic mapping, SLAM algorithms, and 3D reconstruction. His work bridges computer vision and robotics, with applications in autonomous vehicles and precision agriculture. Awards: Best Agri-Robotics Paper Award (IROS 2024) Outstanding Reviewer Awards (ECCV, CVPR, ICRA) Faculty Award for Geodesy (2021) Advisees & Grants: Behley collaborates extensively with the PRBonn lab and researchers like Cyrill Stachniss, focusing on projects funded by EU Horizon and industry partners. His team develops open-source tools for LiDAR processing (e.g., KISS-ICP, VDBFusion). Labs/Teams: Part of the Photogrammetry and Robotics Institute (IGG) at the University of Bonn, contributing to the PRBonn research group.
Prof. Dr.-Ing. Eric Sax is a Professor of Electronic Systems Engineering and Management at the Karlsruhe Institute of Technology (KIT), serving as Dean of the Department of Electrical Engineering and Information Technology (ETIT). He leads the Institut für Technik der Informationsverarbeitung (ITIV) and directs the Forschungszentrum Informatik ESS division . As Program Director of the Electronic Systems Engineering & Management (ESEM) master's program at the HECTOR School, he focuses on integrating academic and professional education. His research spans automotive systems engineering , self-learning functions , cybersecurity , and data-driven validation . Key themes include over-the-air updates, scenario-based testing, and the synergy between machine learning and automotive systems. His work addresses challenges in autonomous driving validation, software-defined mobility, and cyber-physical system security. Prof. Sax's contributions include frameworks for automotive software partitioning, cloud-enabled vehicle architectures, and methodologies for quantifying data quality impacts on perception systems. He actively collaborates with industry partners to bridge academic research with industrial application. His recent projects include OptiCAM (cloud/edge function offloading), Drive4C (autonomous driving benchmarking), and UNCOVER (data-driven security monitoring). He holds leadership roles in both KIT and the HECTOR School's technology business programs.
Marco Caccamo is a Professor at the Technical University of Munich (TUM) , holding the Chair of Cyber-Physical Systems in Production Engineering within the Faculty of Mechanical Engineering. He is also a Principal Investigator and Professor at the Department of Computer Science, with courtesy appointments in Electrical and Computer Engineering, Coordinated Science Lab (CSL), and Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC). His research spans Embedded Systems , Real-Time Systems , and Cyber-Physical Systems (CPS) , focusing on resource management, reinforcement learning architectures, and 6D pose recognition for robotics. University of Pisa (B.Sc., 1997) Scuola Superiore Sant'Anna (Ph.D., 2002) Research highlights include predictable resource management on heterogeneous platforms, security frameworks for AI-based controllers , and UAV testbed development . His work integrates deep learning and real-time constraints in industrial applications like avionics, farming, and automotive systems. His 15 most recent publications emphasize cache optimization , memory bandwidth regulation , and reinforcement learning for CPS , with a focus on multi-core processors and DNN inference . Awards include the IEEE Fellow (2018), Alexander von Humboldt Professorship (2018), and multiple Best Paper Awards at RTSS, RTNS, and RTAS. NSF CAREER Award (2003) IEEE Fellow (2018) Alexander von Humboldt Professorship (2018) Best Paper Awards (RTSS 2024, RTNS 2023, ECRTS 2019) He has advised numerous Ph.D. students and postdocs, with a track record in UAV development and industrial collaborations . His lab, the Real-Time and Embedded System Laboratory , focuses on real-time OS and predictable computing .
