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.
Dr Luke Hespanhol is a Senior Lecturer in Design and Computation at the University of Sydney's School of Architecture, Design and Planning. He is affiliated with the Sydney Southeast Asia Centre and Sydney Nano Institute, and previously served as a guest researcher at Aarhus University's Department of Aesthetics and Communication. His work focuses on interactive media installations that foster reflection on human-environment relationships, blending urban design, technology, and social engagement. He holds a PhD in interactive media architecture from the University of Sydney's Design Lab, with research exploring user-centred hybrid urban environments. His doctoral work laid the foundation for his current interests in digital placemaking, urban informatics, and smart cities. Research Interests: - Designing technology-infused public spaces - Media architecture as a tool for inclusive urban development - Ethical implications of AI and blockchain in cultural contexts - Temporal and spatial dimensions of urban interaction design - HCI methodologies for multiperspectival cultural heritage interpretation - Antifragile city frameworks integrating citizen participation His recent publications highlight trends in reimagining urban futures through speculative design, ethical AI systems, and autonomous vehicle interaction paradigms. While no specific scientific awards are listed, his work often addresses critical societal issues like decolonization in technology and climate-resilient infrastructure. Grants and Collaborations: - 2025: 'Waste to Resilience: Sanitation Against Stunting and Climate Vulnerability in Informal Coastal Areas' (Sydney Southeast Asia Centre) - 2022: 'The Metaverse as Vehicle for Aboriginal Digital Inclusion, Wellness and Self-Determination' (Facebook Research) Dr Hespanhol is actively involved in the Media Architecture Institute and contributes to cross-disciplinary initiatives uniting technology, art, and urban planning. His research emphasizes co-creation processes with communities to ensure equitable technological integration in urban systems.
Florian Shkurti is an Assistant Professor in the Department of Computer Science at the University of Toronto Mississauga (UTM), affiliated with the UofT Robotics Institute, Vector Institute, and Acceleration Consortium. His research focuses on robotics, machine learning, and computer vision, emphasizing safe and effective autonomous systems in dynamic environments. He directs the Robot Vision and Learning (RVL) lab, exploring areas like environmental monitoring, autonomous navigation, and mobile manipulation. Research Interests: His work spans robotics, machine learning, and computer vision. Key areas include robot perception, planning under uncertainty, safe exploration, imitation learning, and applications in field robotics, autonomous vehicles, and chemistry lab automation. He develops methods enabling robots to perceive, reason, and act safely in collaboration with humans. Publications: Recent work includes advancements in safe multitask learning, interactive crowd navigation, and diffusion models for trajectory planning. His research bridges theoretical foundations with real-world applications in environmental science and autonomous systems. Affiliations: Faculty Member, UofT Robotics Institute; Faculty Affiliate, Vector Institute; Faculty Member, Acceleration Consortium. He also holds positions at UTM's Mathematical & Computational Sciences department. Teaching: Courses include Imitation Learning for Robotics, Neural Networks, and Mobile Robotics. He emphasizes hands-on experience with autonomous systems through projects involving RC cars and simulation tools. Labs & Teams: Leads the RVL lab, collaborating on projects like RoboCulture (automated biological experimentation) and SICNav (safe crowd navigation systems). The lab focuses on cross-disciplinary robotics solutions for real-world challenges.
Raquel Urtasun is a Full Professor in the Department of Computer Science at the University of Toronto and a co-founder of the Vector Institute for AI. She is also the Founder and CEO of Waabi, an autonomous vehicle company. Previously, she was Chief Scientist and Head of R&D at Uber ATG (2017–2021) and held faculty positions at the Toyota Technological Institute at Chicago (TTIC) and as a visiting professor at ETH Zurich. Her research spans machine learning, computer vision, robotics, and AI with a strong focus on autonomous driving and 3D perception. Education: Bachelor's degree, Universidad Pública de Navarra, 2000 Ph.D., Computer Science, École Polytechnique Fédérale de Lausanne (EPFL), 2006 Postdoctoral studies, MIT and UC Berkeley Raquel Urtasun's research focuses on developing AI systems for self-driving cars, emphasizing efficient perception using minimal sensors. Her work includes 3D scene understanding, stereo vision, optical flow, semantic segmentation, and object detection. She has developed the KITTI benchmark suite, widely used in autonomous driving research. Her lab is an NVIDIA NVAIL lab, reflecting its leadership in AI innovation. Her recent publications show a consistent trend in deep learning for visual perception, particularly in stereo matching, optical flow, 3D object detection, and semantic segmentation. These works integrate deep neural networks with structured models like CRFs and MRFs, pushing the boundaries of accuracy and efficiency in scene understanding for autonomous systems. Scientific Awards: NSERC E.W.R. Steacie Fellowship NVIDIA Pioneers of AI Award Google Faculty Research Awards (multiple) Amazon Faculty Research Award Connaught New Researcher Award Fallona Family Research Award Best Paper Runner Up at CVPR 2013 and 2017 UPNA Alumni Award Chatelaine 2018 Woman of the Year Adweek 2018 Toronto's Top Influencers Urtasun has advised numerous PhD and Master’s students, many of whom now hold faculty or research scientist positions at institutions like UIUC, NYU, UBC, MIT, and companies including Google, Amazon, NVIDIA, and Apple. She has secured significant research grants from NSERC, Google, Amazon, and NVIDIA. Her leadership extends to organizing workshops and serving as Area Chair and Program Chair at top conferences like CVPR, ICML, and NeurIPS. Labs and Teams: She leads a research group at the University of Toronto focused on AI for autonomous systems. Her team has been recognized as an NVIDIA NVAIL lab, and she continues to mentor students and postdocs working on cutting-edge problems in robotics and machine learning, both at UofT and through her company Waabi.
Ding Zhao is an Associate Professor in Mechanical Engineering at Carnegie Mellon University (CMU), with cross-appointments in Computer Science, Robotics Institute, CyLab Security & Privacy Institute, and Scott Institute for Energy Innovation. He directs the CMU Safe AI Laboratory, pioneering research in trustworthy AI for autonomous vehicles, robotics, and healthcare. His work emphasizes robustness, safety, and ethical deployment of AI systems. Education: • Ph.D. in Mechanical Engineering, University of Michigan (2016) • B.S. in Automotive Engineering, Jilin University (2010) Research Interests: Zhao's lab bridges machine learning theory and engineering to develop AI for high-stakes applications. Key areas include: trustworthy AI generalization, safety-critical decision-making, generative AI for digital twins, and physical AI in mobility/healthcare. His long-term mission is to create "trustworthy AI generalists" deployable in real-world critical systems. Awards & Honors: NSF CAREER Award MIT Technology Review 35 under 35 China IEEE George N. Saridis Best Paper Award Ford/Carnegie-Bosch/Toyota Industrial Fellowships Qualcomm Innovation Award Advising & Grants: He mentors 13+ PhD and 30+ Master’s students, with alumni at NVIDIA, Meta, Stanford, and Tsinghua University. His lab collaborates with Google, Amazon, Ford, Mayo Clinic, and received grants from NSF, DOT, Rolls-Royce, and Bosch. Courses taught include Trustworthy AI and Modern Control for Robotics . Lab & Projects: The Safe AI Lab develops: Safe Robotic Foundation Models (LocoMan), generative AI for autonomous driving (SafeBench), cardiac diagnostics (Heart-2), multi-agent safety systems, and robotic tool innovation (RoboTool). Projects target landslide monitoring, AV safety with Pittsburgh, and energy grid resilience.
Matthew Dunbabin is a Professor at Queensland University of Technology (QUT) and Chief Investigator at the Australian Centre for Robotic Vision (ACRV). His expertise spans environmental robotics, with a focus on vision-based autonomous systems for marine conservation, water quality monitoring, and greenhouse gas management. He holds a PhD from QUT and a BEng (Aerospace) from RMIT. Dunbabin has led projects at CSIRO and QUT, developing robots like COTSBot and RangerBot to combat marine pests and promote reef restoration. His work has earned national and international awards, including the 2019 Australian Water Association Award and 2016 Google Impact Challenge. Education: PhD in Engineering, Queensland University of Technology (Queensland, Australia) BEng (Hons) in Aerospace Engineering, Royal Melbourne Institute of Technology (Melbourne, Australia) Research Interests: Environmental robotics and autonomous systems Vision-based perception and classification Marine habitat restoration and pest control Greenhouse gas monitoring via autonomous vehicles Cooperative robotics and sensor networks Awards & Recognition: 2019 Australian Water Association Award (SAMMI Project) 2019 Good Design Award - Sustainability (RangerBot) 2016 Google Impact Challenge People’s Choice Award (RangerBot AUV) 2010 Australian ICT Industry Association National iAward (iSnet) 2006 Queensland Engineering Excellence Innovation Award (Starbug Project) Grants & Projects: ARC Centre of Excellence for Robotic Vision (ACRV): Leading robotic vision research (2014–present) Revolutionising Protection Against Air Pollution: Air quality monitoring networks (2015–present) Establishing Advanced Networks for Air Quality Sensing: Sensor development for urban environments (2017–present) Labs & Collaborations: ACRV at QUT Institute for Future Environments (QUT) CSIRO Autonomous Systems Laboratory (2001–2013)
Bilal Farooq is an Associate Professor and Program Director for the Master of Engineering in Interdisciplinary Engineering (MEIE) at Toronto Metropolitan University, holding the Canada Research Chair in Disruptive Transportation Technologies and Services within the Department of Civil Engineering. His educational background includes a PhD from the University of Toronto (2011), MASc from Lahore University of Management Sciences (2004), and BSc from the University of Engineering and Technology (2001). Dr. Farooq's research pioneers disruptive transportation solutions through cyber-physical systems, AI/machine learning applications, behavioral modeling, and optimization techniques. His work specifically targets on-demand multimodal systems, sustainable urban transportation, urban air mobility, automated vehicles, and extended reality applications, addressing critical urban mobility challenges with human-centered approaches. Analysis of his recent publications reveals a strong trend toward quantum-enhanced computational methods, privacy-preserving federated learning frameworks, and sustainability-focused decarbonization strategies across transportation domains, with increasing emphasis on human factors and real-world implementation. Notable scientific awards include: Ontario Early Researcher Award (2018) Canada Research Chair (2017) MassMotion Academic Pedestrian Modelling Project of the Year (2016) Québec Early Researcher Award (2014) Dr. Farooq actively supervises graduate students and secures significant research funding through his Canada Research Chair position and Early Researcher Awards. He directs the Laboratory of Innovations in Transportation (LiTrans), which develops interdisciplinary solutions integrating mathematics, engineering, computer science, and economics to address emerging transportation challenges. LiTrans focuses on disruptive transportation technologies, complete streets design, cyber-physical systems, pedestrian dynamics, resilience, and climate change impacts, collaborating with industry and government partners to translate research into practical urban mobility innovations for smart cities worldwide.
Kevin A. Shinpaugh is Collegiate Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech’s College of Engineering. Since 2019 he has led instruction and research in spacecraft design and propulsion, leveraging decades of experience in high-performance computing and space-systems engineering. Education Ph.D., Aerospace Engineering, Virginia Tech (1994) M.S., Aerospace Engineering, Virginia Tech (1989) B.S., Aerospace Engineering, Virginia Tech (1986) Research Focus Dr. Shinpaugh’s scholarship centers on the intersection of high-performance computing (HPC) and space systems engineering . He develops and applies advanced computational techniques to spacecraft design, propulsion analysis, and mission planning. His work spans numerical simulation of complex aerospace systems, optimization of propulsion architectures, and creation of scalable HPC frameworks that enable rapid design iteration for spacecraft and launch vehicles. Publication Trends Across more than thirty refereed papers and design-competition reports, a clear trajectory emerges: early contributions in experimental fluid-mechanics instrumentation (laser-Doppler velocimetry, fiber-optic sensors) evolved into large-scale computational studies of space systems, and most recently into student-led mission-concept designs for CubeSats, lunar exploration, and interplanetary missions. Keywords consistently include spacecraft design, propulsion, deployable structures, and mission architecture. Service & Committees Chair, Virginia Tech HPC User Committee (2004–2011) Member, VT HPC Advisory Board (2007–present) NSF TeraGrid/XSEDE Campus Champion for Virginia Tech (2006–2013) IBM HPC/AI Customer Advisory Council DC (2019–present) Member, VT AOE Seminar Committee (2019–present) Laboratory & Computing Resources Dr. Shinpaugh has long stewarded Virginia Tech’s high-performance computing ecosystem. He directs students and collaborators in leveraging the university’s Advanced Research Computing (ARC) clusters, as well as national facilities through XSEDE and DoD HPCMP, to execute spacecraft-design simulations and propulsion analyses at scale.
Michael Mühlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, leading the independent Learning and Dynamical Systems group. His academic journey began at ETH Zurich where he earned his B.Sc. (2010) and M.Sc. (2013) in mechanical engineering, specializing in robotics, systems, and control. He completed his Ph.D. at ETH Zurich in 2018 under Prof. R. D'Andrea, followed by postdoctoral research at UC Berkeley with Prof. Michael I. Jordan. Dr. Mühlebach's research spans machine learning, dynamical systems, control theory, and optimization . His work bridges theoretical foundations with practical applications in robotics, developing methods that incorporate physical constraints and system dynamics into learning frameworks. His group focuses on online learning, physics-informed machine learning, and large-scale optimization for cyber-physical systems, with applications in electromagnetic navigation, robotic table tennis, and energy-efficient flight systems like the shape-changing robot Floaty . His publication record shows a strong focus on constrained optimization, with recent work exploring decision-dependent stochastic optimization, nonlinear feedback, and the theoretical foundations of reinforcement learning. His research integrates perspectives from control theory, dynamical systems, and optimization to develop algorithms with strong theoretical guarantees and practical performance. Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellow (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) Dr. Mühlebach actively mentors doctoral researchers and is seeking talented students for PhD and Master's projects. His research group has received funding from multiple prestigious fellowships and maintains collaborations across institutions including ETH Zurich, UC Berkeley, and various Max Planck research units. The group's work spans theoretical developments to practical implementations on robotic systems, demonstrating strong connections between mathematical theory and physical realization.
Mike Grimble is a Research Professor in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. His work is centered on advanced control systems with applications across automotive, aerospace, marine, and industrial domains. He is actively involved in theoretical and applied research, particularly in nonlinear and robust control methodologies. Research Interests: Theory and application of nonlinear and robust control for multivariable systems Adaptive control and estimation methods Benchmarking and performance assessment of control systems Condition monitoring and industrial applications Real-time control and embedded systems His recent publications highlight a strong trend in applying predictive and adaptive control techniques to electric vehicles, battery systems, underwater robotics, and industrial machinery. These works emphasize real-time implementation, energy optimization, and robustness—critical for modern sustainable and autonomous systems. Scientific Recognition and Activities: Invited speaker on the benefits and challenges of advanced control in industrial applications (2013) Contributor to UN Sustainable Development Goals, particularly in sustainable industry and innovation Active research output with over 158 publications, including journals, conferences, and book chapters Research Leadership and Funding: Principal Investigator on multiple EPSRC and RSE-funded projects Co-investigator in interdisciplinary initiatives such as the Medical Devices Doctoral Training Centre Organizer of international workshops on hybrid and predictive control Labs and Research Teams: He is associated with the Industrial Control Centre at the University of Strathclyde, a leading hub for control engineering research. His collaborations span departments and institutions, involving real-time LabVIEW implementations, hardware demonstrations, and partnerships with industry players like National Instruments and Quanser Inc.
Prashant Mehta is a Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Laboratory . His research focuses on controlled interacting particle systems and machine learning applications , particularly in human activity recognition using motion sensors. Education: Ph.D. in Mathematics, Cornell University (2004) M.S. in Electrical & Computer Engineering, University of Massachusetts Amherst (1996) B.E. in Electrical & Electronics Engineering, Birla Institute of Technology & Sciences (1993) Mehta's work has pioneered the feedback particle filter (FPF) algorithm for nonlinear estimation, applied in robotic systems and gesture recognition. His research spans control of combustion instabilities in jet engines, mean-field games , and dynamical systems in aerospace engineering. His publications emphasize nonlinear control theory and stochastic filtering , with recent trends in sensor data pattern recognition and cyber-physical systems . He has received multiple scientific awards , including the MURI award for the Cyberoctopus project and Excellence in Undergraduate Advising Awards . Scientific Honors: MURI Award (2019) for Cyberoctopus Excellence in Undergraduate Advising (2010, 2008) Outstanding Teaching Assistant Award (1994) Senior Member, IEEE Control Systems Society Member, ASME Energy Systems Subcommittee Member, SIAM Dynamical Systems Group Mehta has supervised students like Jin Kim (IEEE CDC Best Student Paper, 2019) and co-founded the startup Rithmio , acquired by Bosch Sensortec . His laboratory develops gesture-detection filters for applications in soft robotics and human-machine interfaces .
Dr. Michael Kleeberger is a Researcher at the Chair of Materials Handling, Material Flow, Logistics (FML) at the Technical University of Munich, based at Boltzmannstr. 15 in Garching. He collaborates closely with Prof. Johannes Fottner and maintains an active research profile in crane dynamics and mechanical systems simulation. His research specializes in Materials Handling and Logistics with emphasis on Crane Dynamics, Flexible Multibody Systems, and Control Systems. He develops advanced models for hydraulic actuated cranes, focusing on dynamic behavior during hoisting, slewing, and trajectory operations using port-Hamiltonian formulations and geometrically exact beam theory. His work bridges theoretical mechanics with industrial applications in heavy machinery. Analysis of his 15 most recent publications reveals consistent focus on numerical methods for flexible crane structures, with growing emphasis on optimal control strategies (2020-2025). Key trends include port-Hamiltonian system applications, lunar crane feasibility studies, and vibration mitigation techniques for lattice boom and knuckle boom configurations across diverse operational scenarios. As part of FML, Dr. Kleeberger contributes to TUM's leadership in logistics engineering through industry-collaborative projects and fundamental research in material flow systems, maintaining the chair's reputation for excellence in mechanical dynamics and practical engineering solutions.
Jan Skaloud serves as an Adjunct Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC). He holds positions across multiple departments including SSIE (Institute of Earth Surface Dynamics), EDCE (Doctoral Program in Environmental Sciences and Engineering), and leads the Earth Sensing and Observation (ESO) Lab. His office is located at GC C2 397 in the EPFL campus in Lausanne, Switzerland. Dr. Skaloud's research expertise spans satellite positioning, inertial and integrated navigation systems, sensor orientation and calibration, attitude determination, mobile mapping, airborne laser scanning, and Kalman filtering techniques. His work bridges theoretical development with practical applications in UAV navigation, photogrammetry, and remote sensing. He teaches across three EPFL sections and two faculties, demonstrating his interdisciplinary approach to education. His publication record shows consistent contributions to the field, with recent work (2023-2025) focusing on vehicle dynamic model-based navigation for various UAV platforms, including delta-wing and fixed-wing drones. His research demonstrates a clear trajectory toward increasingly sophisticated navigation systems that integrate aerodynamic modeling with traditional sensor fusion approaches. This trend reflects the growing importance of model-based navigation in achieving higher precision and autonomy in UAV operations. 2021: Samuel Gamble Award for career contribution in photogrammetry & sensing (ISPRS) 2020: U.V. Helava Award for best paper in ISPRS Journal (2016-2019) 2017: Best Demo Award at IEEE International Workshop on Metrology & Aerospace 2014: Hansa Luftbild Award for best paper in PFG journal 2012: Karl Kraus Medal for best textbook in Photogrammetry 2009: GNSS Leader to Watch Innovation Award (GPS World) Dr. Skaloud has supervised numerous PhD students whose work focuses on advanced navigation systems, sensor calibration, and UAV applications. His research has received funding for projects involving direct georeferencing, mobile mapping systems, and UAV-based search and rescue operations. The ESO lab he directs serves as a hub for cutting-edge research in Earth observation technologies. The Earth Sensing and Observation Lab under Dr. Skaloud's direction brings together researchers working on navigation systems, sensor integration, and data processing techniques for geospatial applications. The lab maintains strong connections with industry partners and international research organizations, facilitating technology transfer and collaborative research projects.
Cathy Wu is an Associate Professor at MIT, with affiliations in the Laboratory for Information and Decision Systems (LIDS), Department of Civil and Environmental Engineering (CEE), and Institute for Data, Systems, and Society (IDSS). Her research group focuses on integrating machine learning with model-based optimization to solve complex problems in transportation systems and cyber-physical systems. Academic Leadership: Class of 1954 Career Development Associate Professor (MIT) Research Grants: NSF CAREER Award, Amazon Robotics, Mathworks, MIT Mobility Initiative, US DOT, Microsoft Research, Cintra, Symbotic Research Interests : Wu's work bridges AI and engineering challenges in transportation. Key areas include: Hybrid ML/Model-based Optimization (large neighborhood search, branch-and-cut) Sustainable Mobility (Project Greenwave, eco-driving) Multi-Agent Coordination (warehouse automation, cooperative driving) Cyber-Professional Systems (generalization in RL, transfer learning) Recent work demonstrates significant advances in eco-driving (11-22% emissions reduction), large-scale multi-agent path finding (1000+ agents), and foundational RL methods for traffic control. Her group has produced 15+ major publications since 2015, with notable media coverage in Science, Wired, and NewScientist. Selected Scientific Awards NSF CAREER Award (2023) Ole Madsen Mentoring Award (2025) IEEE ITSS WiE/YP Fellowship (2024) Harold L. Hazen Teaching Award (2022) Her lab has advised 12+ graduate students and postdocs, including: Vindula Jayawardana (PhD '24, now at Anthropic) Sirui Li (PhD '25, now at Microsoft Research) Yining Ma (Postdoc, active researcher) Zhongxia Yan (PhD '24, now at Anthropic)
Jonathan Shihao Ji is an Associate Professor in the School of Computing at the University of Connecticut (UConn), leading the Intelligent Systems Lab. He holds a Ph.D. in Electrical and Computer Engineering from Duke University and previously served as an Associate Professor at Georgia State University and Director of the DoD Center of Excellence (CiARE). His research focuses on deep learning applications in computer vision, NLP, robotics, and high-performance computing, with over 50 publications in top venues like CVPR, NeurIPS, and IEEE journals. He has secured grants from NSF, NIH, DoD, and industry partners including VMware and Nvidia. His work emphasizes efficient algorithms for large-scale data processing, parameter-efficient model fine-tuning (e.g., VB-LoRA), and 3D perception benchmarks for UAVs (UAV3D). Notable contributions include sparse network optimization (Dep-L0), energy-based models (M-EBM), and robust defenses against adversarial attacks (Defense-VAE). He is a Senior Member of IEEE and has developed open-source tools like Parallel Word2Vec and WordRank. Recent projects include accelerating Llama2 models on FPGAs (LlamaF) and improving text-to-image synthesis via contrastive learning. His research spans theoretical advancements and practical applications, with industry collaborations in healthcare, robotics, and embedded systems.
