Marc Pollefeys is a Full Professor of Computer Science at ETH Zurich and Director of the Microsoft Mixed Reality and AI Zurich Lab. He has held roles such as Visiting Professor at Stanford University (2007) and Assistant/Associate Professor at UNC-Chapel Hill (2002–2009). His research focuses on 3D computer vision, robotics, machine learning, and augmented reality. Education: PhD in Computer Science from KU Leuven (1999), followed by postdoctoral research there until 2002. He transitioned to academic roles at UNC-Chapel Hill before joining ETH Zurich in 2007. Research interests include 3D reconstruction, visual localization, SLAM, and applications in archaeology, urban modeling, and robotics. Notable projects include real-time 3D scanning, city-scale reconstruction, and autonomous vision-based drones. Key awards include ACM Fellow (2022), IEEE Fellow (2012), and ERC Starting Grant (2008). He advises numerous PhD students and collaborates with institutions like Google and Microsoft. Labs and teams: Leads the Computer Vision and Geometry (CVG) lab at ETH Zurich and directs the Microsoft Mixed Reality and AI Lab. His work bridges academia and industry, focusing on perception for mixed reality and autonomous systems.
Roman Kuc is a Professor of Electrical Engineering at Yale University, affiliated with the School of Engineering & Applied Science. He directs the Intelligent Sensors Laboratory, focusing on biomimetic sensors for robotics and bioengineering. His research explores brain-based devices (BBDs), sonar sensing, and neuromorphic processing inspired by biological systems. He holds a BSEE from Illinois Institute of Technology and a PhD from Columbia University. Dr. Kuc’s work bridges signal processing, robotics, and bioengineering, with applications in autonomous systems and clinical diagnostics. He has published over 200 papers and authored textbooks like Electrical Engineering in Context and The Digital Information Age . Notable honors include an honorary doctorate from the Glushkov Institute of Cybernetics and the Yale Sheffield Distinguished Teaching Award. His research themes include cognitive mapping via sonar echoes, neural network-based classification of environmental features, and biomimetic approaches to echolocation. Recent work emphasizes sensorimotor integration and robust performance in uncertain environments. Scientific awards highlight his contributions to robotics, signal processing, and education. His lab develops systems that emulate biological sensory mechanisms, aiming to advance robotics, medical applications, and assistive technologies.
Prof. Dr. Enkelejda Kasneci is a Distinguished Professor at the Technical University of Munich (TUM), leading the Chair of Human-Centered Technologies for Learning. She holds dual affiliations within TUM School of Social Sciences and Technology and TUM School of Computation, Information and Technology. Her research integrates AI, eye-tracking, and immersive technologies to advance educational paradigms. She directs the TUM Center for Educational Technologies and chairs the MSc program 'AI in Society.' Education: PhD in Computer Science from University of Tübingen (2013), M.Sc. from University of Stuttgart (2007). Earlier roles include Assistant Professor and Dean of Studies at University of Tübingen. Research Focus: Human-centered AI applications in education, multimodal interaction design, and privacy-preserving eye-tracking. Her work bridges technology and pedagogy through projects like AI tutor PEER, VR Classroom, and Privacy-Preserving Eye-tracking. Key Projects: Leads EU-funded projects VIVA (€1.125M), DigiProMIN (€163K), and SARA Kids (€244.8K). Active in policy initiatives like Europe’s AI Imperative. Awards: TUM Heinz Maier-Leibnitz Medal (2024), Liesel Beckmann Distinguished Professorship (2022), and Südwestmetall Research Prize (2014). Grants & Advising: Over €5M in secured funding across 12+ projects. Supervises 14+ PhD researchers and mentors postdocs in AI education and HCI. Labs & Teams: IT-Stiftung EdTech Lab houses advanced VR/eye-tracking setups. Research group includes 20+ members spanning AI, HCI, and educational technology.
S. Mohadeseh Taheri-Mousavi is an Assistant Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. She joined CMU in September 2022 after postdoctoral appointments at MIT and Brown University. Her research is supported by major grants from NASA STRI, DARPA, the Army Research Laboratory, and the Naval Nuclear Laboratory, and she is affiliated with the NextManufacturing Center and the Wilton E. Scott Institute for Energy Innovation. Her educational background includes a Ph.D. from EPFL, Switzerland, and M.Sc. and B.Sc. degrees from Sharif University of Technology, Iran. She was awarded both early and advanced Swiss National Science Foundation fellowships during her postdoctoral studies. Taheri-Mousavi’s research focuses on the intersection of materials science, mechanical engineering, and computer science. She develops multi-scale computational models and AI-driven frameworks—such as AlloyGPT and generative AI agents—to design next-generation structural alloys, particularly for additive manufacturing and extreme environments. Her work emphasizes materials sustainability, industrial decarbonization, and uncertainty quantification in alloy design. The integration of machine learning with Integrated Computational Materials Engineering (ICME) and CALPHAD methods enables rapid exploration of high-dimensional composition and processing spaces. Her recent publications (2023–2025) show a strong trend toward AI/ML applications in alloy discovery, hydrogen embrittlement modeling, and high-temperature aluminum and tungsten alloys. These works reflect a deep commitment to accelerating materials innovation through human-AI collaboration and smart experimental validation. Her scientific honors include prestigious Swiss National Science Foundation fellowships. She has also received seed funding from the Scott Institute for Energy Innovation to study hydrogen embrittlement. She advises a dynamic team of doctoral students and a postdoctoral researcher, working on topics including hydrogen embrittlement, generative AI for welding, and gradient alloys. Her research is funded by high-impact grants from NASA, DARPA, the Army, and the Naval Nuclear Laboratory, supporting transformative projects in structural alloy design. She leads the Taheri-Mousavi Group, which operates within CMU’s Materials Characterization Facility and the NextManufacturing Center. The group focuses on developing novel AI-integrated computational frameworks to guide efficient and intelligent experimentation in alloy development.
Fu-Kuo Chang is a Professor in the Department of Aeronautics and Astronautics at Stanford University, with a secondary affiliation in the Bio-X program. His research focuses on multifunctional materials, intelligent structures, and structural health monitoring (SHM), emphasizing applications in aerospace, robotics, and medical devices. He has pioneered work on embedded sensors, self-diagnostic systems, and energy storage composites. Academic Appointments: Professor (Stanford), Editor-in-Chief of International Journal of Structural Health Monitoring (since 2012), and Chair of the International Workshop on Structural Health Monitoring (since 1997). Honors: Multiple lifetime achievement awards in SHM, AIAA and ASME Fellowships, and the NSF Presidential Young Investigator Award (1988). Research interests include bio-inspired sensory materials, autonomous systems (e.g., 'fly-by-feel' vehicles), and multidisciplinary integration of structural mechanics, electrical engineering, and materials science. His recent work addresses challenges in smart skins for robotics, thermoplastic composites, and predictive modeling of material degradation. Publications span structural health monitoring, advanced composites, and robotics, reflecting expertise in both theoretical and applied domains. His lab, the Structures and Composites (SACL) laboratory, drives innovation in smart materials and system integration. Advising: Supervises doctoral and master’s students in aeronautics and materials science. Grants/Contributions: Active in industry and government collaborations, including roles on the US Army Research Laboratories Advisory Board and leadership in SHM industry initiatives.
Karthik Dantu is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, within the School of Engineering and Applied Sciences. His research focuses on mobile sensor networks, robot networks, networked embedded systems, mobile computing, wireless networks, and embedded operating systems. He leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab and has received significant funding including an NSF CAREER Award. Dr. Dantu's educational background includes: PhD in Computer Science from University of Southern California (2009) BE in Computer Science from Sri Jayachamarajendra College of Engineering (1999) His research interests center on algorithmic and systems challenges in Edge Computing Systems, with particular focus on enabling seamless vision sensing in cloud-edge environments. Dantu's work bridges mobile systems and robotics, developing novel approaches for UAV software, visual SLAM, and distributed sensing. His research addresses critical challenges in resource-constrained environments, security, and real-time performance for mobile and robotic systems, with emphasis on practical implementations that solve real-world problems in autonomous systems. Dr. Dantu's publication record shows a strong trajectory in mobile systems and robotics research, with increasing focus on edge computing applications for visual sensing. His recent work demonstrates expertise in adapting visual SLAM to edge environments, securing mobile systems through technologies like Rushmore, and developing novel approaches for UAV software reliability and depth sensing. The research spans theoretical algorithms and practical system implementations, with particular strength in bringing academic research to practical applications in robotics and mobile computing. Dr. Dantu has received several scientific honors: NSF CAREER Award on Enabling Seamless Vision Sensing in Cloud-Edge Systems Outstanding service award from the Office of International Services NSF Travel Grant for SenSys 2005 Conference Travel Grant for SIGCOMM 2002 As an advisor, Dr. Dantu has mentored numerous PhD students to completion, with graduates now working at companies like Samsung Research and Zoox Inc., or continuing academic careers as Assistant Professors. His research is supported by substantial grants including a DARPA OFFSET Sprint 4 award ($470k), an NSF CAREER award ($550k), and multiple NSF collaborative grants totaling over $1.5 million. He serves on numerous conference committees including Mobicom, MobiSys, and ICRA, demonstrating leadership in the mobile systems and robotics research communities. Dr. Dantu leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab at UB, which focuses on developing algorithms and systems for mobile sensor networks, robot networks, and embedded sensing applications. The lab's work spans theoretical foundations to practical implementations, with particular expertise in UAV systems, visual SLAM, and edge computing for robotics, maintaining strong collaborations with industry partners and other academic institutions to advance the state of the art in mobile and robotic systems.
Dr. Brett J. Borghetti is a Professor of Computer Science in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), Graduate School of Engineering and Management, Wright-Patterson AFB, OH. He was promoted to Professor in July 2022, following prior appointments as Associate Professor (2017) and Assistant Professor (2008/2013). His expertise lies in artificial intelligence, machine learning, deep learning, cybersecurity, and human-machine teaming. Education: Ph.D. in Computer Science, University of Minnesota, Twin Cities (2008) M.S. in Computer Systems, Air Force Institute of Technology (1996) B.S. in Electrical Engineering, Worcester Polytechnic Institute (1992) Dr. Borghetti's research focuses on applying machine learning to physical science sensors (hyperspectral, seismic, RF), cybersecurity, and enhancing human-machine team performance. He teaches graduate courses in machine learning, AI, data security, and algorithm design, and advises numerous MS and PhD students in areas such as sensor exploitation, cognitive workload, and cyber situational awareness. His recent publications demonstrate strong trends in deep learning for multimodal sensor fusion, nuclear security, and neuroergonomics. Scientific Awards: AETC Educator of the Year (2021, Civilian) AFIT Ezra Kotcher Teaching Award (2021) AFIT Teaching Excellence Award (2019) AF STEM Outstanding Science and Educator Award (2015) Multiple Eta Kappa Nu Outstanding Instructor Awards Air Force Meritorious Service Medal and other military honors Dr. Borghetti has advised numerous graduate students and led research projects with significant funding and applications in defense and national security. He has directed research in AI-driven sensor analysis, cyber defense systems, and adaptive automation. His work often involves collaboration with national labs and DoD agencies. He has contributed to major research initiatives in human factors, cyber intruder detection, and machine learning for operational environments. Labs and Research Teams: His work is associated with AFIT's research in cyber security, sensor exploitation, and human-machine systems. He collaborates with teams working on the Cyber Intruder Alert Testbed (CIAT), neuroergonomic modeling, and machine learning for defense applications.
Jonas Schorlemer is a Researcher at the Department of High Frequency Systems within the Faculty of Electrical Engineering and Information Technology at Ruhr University Bochum. His work focuses on radar systems, AI integration in sensor technologies, and applications in humanitarian demining. He collaborates with Prof. Dr.-Ing. Ilona Rolfes and contributes to projects like KI-ROJAL and Terahertz-NRW. His research emphasizes radar echo simulation, GPR-based localization, and SAR algorithm development. Research interests include radar-based particle tracking, sensor fusion in indoor environments, and electromagnetic localization in granular materials. Recent work highlights AI-driven approaches for improving training data generation and scenario augmentation in demining applications. Publications span high-impact journals like Sensors and conferences such as IEEE MTT-S and ICEAA. He actively participates in academic events including the Faculty Colloquium and international workshops. He maintains a lab website at www.etit.ruhr-uni-bochum.de/hfs/ and holds an ORCID ID for scholarly tracking.
Associate Professor Vic Ciesielski is affiliated with RMIT University's School of Computing Technologies. His research focuses on Artificial Intelligence, Evolutionary Computing, Computer Vision, and Genetic Programming, with applications in areas like robot soccer and aesthetic analysis of images. He has supervised projects including efficient neural architecture search and off-line handwritten text recognition. His work bridges computational techniques with creative fields such as art history and digital media. Key research interests include machine learning, data management, and graphics/augmented reality. He actively contributes to conferences like GECCO and IJCNN, publishing on topics ranging from neural architecture optimization to sensor-based activity recognition. His research often integrates evolutionary algorithms with deep learning methodologies. He can be contacted via vic.ciesielski@rmit.edu.au and has an ORCID identifier: 0000-0001-7273-9566 .
Gianluca Setti is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, where he has been serving since 2017. He previously held positions at the University of Ferrara from 1997 to 2017. His institutional roles include being the Contact Person for the Research Quality Evaluation process, Member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and Member of the University Quality Assurance Committee. He serves as Editor-in-Chief of the Proceedings of the IEEE, the first non-US editor to hold this position. Dr. Setti's research spans multiple interdisciplinary fields including machine learning, artificial intelligence, big data analytics, Internet of Things, biomedical signal processing, power electronics, and electromagnetic compatibility. His work bridges theoretical foundations with practical applications, particularly focusing on compressed sensing, neural networks, and circuit design for specialized applications. His research has significant implications for healthcare, sustainable infrastructure, and next-generation electronics. His publication record reveals a consistent trajectory from foundational work in chaotic systems and neural networks to contemporary applications in AI, IoT, and edge computing. The most recent publications demonstrate his focus on anomaly detection at the edge, neural oracles for biosignal processing, and power electronics innovations. His work shows strong integration between theoretical signal processing and practical circuit implementation. 1998 Caianiello prize (best Italian Ph.D. thesis on Neural Networks) IEEE Fellow (2006) IEEE Circuits and Systems Society Distinguished Lecturer (2004, 2015) 2004 IEEE CAS Society Darlington Award 2013 IEEE CAS Society Meritorious Service Award 2013 IEEE CAS Society Guillemin-Cauer Award 2019 IEEE Transactions on Biomedical Circuits and Systems best paper award Multiple best paper awards at major conferences including ECCTD2005, EMCZurich2005, ISCAS2011, PRIME2019, and EMCCOMPO2019 Dr. Setti has supervised numerous PhD students across various research domains including electromagnetic compatibility, signal and power integrity, communication networks, mechatronics and robotics. His research is supported by significant funding including national PRIN projects, EU-funded JTI-ECSEL initiatives, and commercial contracts. He leads the VLSILAB Group at DET, focusing on circuit architectures, embedded systems, and AI applications. His current projects include DECORI (anomaly detection), StorAIge (embedded storage for AI), PROGRESSUS (energy infrastructure), CONNECT (smart grid), and CONVERGENCE (wearable healthcare applications).
Florina Almenares Mendoza is an Associate Professor at the Telematics Engineering Department of Carlos III University of Madrid , where she also serves as the Director of the University Master's Degree in Cybersecurity. Her research focuses on addressing security challenges in emerging technologies such as IoT, post-quantum cryptography, and privacy-preserving systems. Email: florina.almenares@uc3m.es Contact: 916246234 Location: 4.0.F06 - Quevedo Towers (Leganés) Research Interests Florina's work spans cybersecurity , Internet of Things (IoT) , and post-quantum cryptography , with a focus on scalable authentication, quantum-resistant protocols, and privacy. She explores machine learning applications for security, federated identity management , and smart grid security frameworks. Recent Publications Her recent research includes papers on DNSSEC soft delegation, hybrid quantum security for TLS/IPsec, PUF-based authentication in IoT, and blockchain-enabled auditability. These studies emphasize IoT security , quantum-resistant algorithms , and privacy-enhancing technologies .
Ernest Davis is a Professor at the Department of Computer Science , Courant Institute of Mathematical Sciences , New York University . His research focuses on representing commonsense knowledge in AI systems , with an emphasis on spatial and physical reasoning , and he collaborates with Gary Marcus on integrating AI and psychological models. He has authored over 50 scientific papers and three books, including Linear Algebra and Probability for Computer Science Applications (2012). His teaching includes courses on Artificial Intelligence and Fundamental Algorithms. Research Trends: His recent work examines benchmarks for commonsense reasoning , limitations of large language models (e.g., GPT-4, DALL-E 2), mathematical reasoning in AI, and the Winograd Schema Challenge . Professional Activities: He has served as an ACM reviewer, program committee member for 50+ conferences, and area editor for ACM Transactions on Computational Logic . He contributes book reviews to Computing Reviews , SIAM News , Artificial Intelligence journal, and others. Non-Technical Writing: Davis writes for general audiences on topics spanning computer science, mathematics, cognitive psychology, and literary themes, published in outlets like The New Yorker , Wired , and The Times Literary Supplement .
Dr. Rhiannon Firth is a Lecturer in Sociology of Education in the Department of Education, Practice and Society at the Institute of Education, University College London (UCL). She also serves as the Programme Leader for the MA in Sociology of Education. Her academic career spans interdisciplinary research at the intersection of Sociology, Education, and Politics, with a focus on utopian ideals and pedagogical practices of social and ecological movements. Dr. Firth's educational background includes: First Class BA (Hons) in Combined Studies (Politics, English Literature and Economic and Social History) from the University of Leicester ESRC-funded MA in Political Science (Distinction) from the University of Nottingham PhD in Political Sociology from the University of Nottingham Her research focuses on how grassroots social movements create knowledge and mobilize social change around global "wicked" problems including inequality, climate change, technological developments, and pandemics. She has conducted funded research with self-managed sustainable communities in the UK and USA, grassroots disaster relief movements including Occupy Sandy New York and COVID-19 Mutual Aid London, and organizations using automation technology in experimental ways. Dr. Firth's recent publications reflect her ongoing exploration of utopian and dystopian themes across multiple domains. Her work spans analyses of pandemic responses, ecological breakdown, industrial technology, and community organizing. A strong thread throughout her scholarship examines how prefigurative politics and mutual aid practices offer alternatives to mainstream approaches to social organization, particularly in times of crisis. Her research increasingly focuses on the intersection of technology, labor, and utopian thinking, particularly examining how automation and cybernetics might be reimagined through radical social movements. Among her significant recognitions: Senior Fellow of the Higher Education Academy (SFHEA), earned in 2017 ESRC funding for her MA studies Dr. Firth has secured research funding from multiple sources including ESRC, ISRF, EPSRC, HEFCE, local councils, and UCL Grand Challenges. She supervises MA dissertations annually and currently mentors one PhD student. Her professional activities include media engagement (such as appearing on BBC Radio 4's The Moral Maze), conference presentations, and serving as ECR Representative for the Utopian Studies Society of Europe. She is actively involved with research groups and networks focused on utopian studies, social movements, and critical pedagogy, contributing to the vibrant intellectual community around alternative futures and radical educational practices.
Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.
John Valasek is a Professor in the Department of Aerospace Engineering at Texas A&M University, holding the Drs. L. Diane '88 and John E. Hurtado '91 Professorship. He directs the Vehicle Systems & Control Laboratory (VSCL) and serves as Site Director for the NSF Center for Autonomous Air Mobility and Sensing (CAAMS) and the FAA Center for General Aviation Research (PEGASAS). His research focuses on autonomous control systems, UAV navigation, and cybersecurity for aerospace vehicles. Valasek earned his Ph.D., M.S., and B.S. in Aerospace Engineering from the University of Kansas (1995) and California State Polytechnic University (1986). Education: Ph.D., Aerospace Engineering, University of Kansas - 1995 M.S., Aerospace Engineering, University of Kansas - 1990 B.S., Aerospace Engineering, California State Polytechnic University - 1986 Research Interests: Autonomous systems, nonlinear control, vision-based navigation, UAV control, bio-nano materials control, and aerospace systems engineering. Key Contributions: Over 100 invited lectures/seminars, leadership in NSF-funded research centers, and development of advanced control algorithms for aerospace systems. Notable publications include work on reinforcement learning for autonomous systems and real-time system identification for UAS. Awards: John Leland Atwood Award (2015) McElmurry Outstanding Teaching Award (2001, 2004, 2014) Engineering Hall of Fame inductee (2019) Advising & Grants: Advised over 60 graduate students, including recent NSF GRFP winner Evelyn Madewell. PI on multi-million-dollar grants, including the NSF CAAMS project and Air Force-funded research on autonomous systems. Labs & Teams: Directs the Vehicle Systems & Control Laboratory (VSCL), focusing on low-cost attritable aircraft technology and autonomy. Collaborates with industry partners like Stratolaunch and VectorNav through CAAMS initiatives.
