Vahid Behzadan is an Assistant Professor in Data Science and Computer Science at the University of New Haven's Tagliatela College of Engineering. He leads the SAIL Lab, focusing on AI safety and security, particularly in autonomous systems like driverless cars and smart cities. His work addresses adversarial attacks on machine learning and reinforcement learning, with applications in cybersecurity and healthcare. Behzadan has held prior positions at Kansas State University, University of Nevada Reno, and University of Birmingham, UK. He holds a Ph.D. in Computer Science and an M.S. from the University of Nevada Reno, and a B.Eng. from the University of Birmingham. His research spans AI ethics, cybersecurity, and complex systems. Behzadan advises the UNH hacking team and actively participates in policy initiatives, including Connecticut's AI Working Group and the Connecticut AI Alliance. He has contributed to over 30 peer-reviewed articles and frequently engages in media discussions on topics like AI safety, facial recognition, and cybersecurity threats. Key research areas include adversarial machine learning, AI forensics, and ethical AI design. His work bridges theoretical advancements with real-world applications in transportation, healthcare, and national security. Behzadan collaborates with organizations such as the Transportation Research Laboratory (TRL) and Open Web Application Security Project (OWASP).
Alexandra Kirsch is an Assistant Professor in the Media Informatics Department at the University of Tübingen's Faculty of Informatics. She held the Carl von Linde Junior Fellowship at the Technical University of Munich (TUM) Institute for Advanced Study (TUM-IAS) from 2010. Previously, she was a senior research scientist at TUM's Intelligent Autonomous Systems Group and led the independent Junior Research Group “Planning for Adaptive Robot Assistance” within the Excellence Cluster CoTeSys (Cognition for Technical Systems). Education: Diploma in Computer Science from TUM, 2003 Doctoral degree from TUM, completed between 2003-2007 Research Interests: Kirsch focuses on developing control mechanisms for autonomous robots using artificial intelligence, aiming to create systems that collaborate closely and transparently with humans. Her work emphasizes models of world dynamics, robot action effects, and human behavior. She created the Robot Learning Language (RoLL) to automate model acquisition and update processes during robot operations. Collaborations with psychologists and neuroscientists explore joint human-robot planning tasks and model development for seamless interaction. Scientific Awards: Member of the Bayerische Akademie der Wissenschaften Förderkolleg (2012) Award by Comet Computer GmbH for excellent graduation results (2003) Advising & Grants: Managed interdisciplinary research projects during her junior fellowship at TUM. Previously worked as a management consultant at Booz & Co., 2007-2008. Her grants include the Carl von Linde Fellowship and support for the Junior Research Group. Labs/Teams: Active in the Planning for Adaptive Robot Assistance group (CoTeSys) and collaborates with the Cognitive Technology focus group at TUM-IAS. Engages in cross-disciplinary teams involving neuroscience and psychology for human-robot interaction studies.
Talal Shaikh is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences in Dubai. He serves as Director of Undergraduate Studies and Programme Director for BSc Computer Science, BSc CS (AI), and MSc Software Engineering. With a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. Research Interests: Pervasive Computing, IoT/M2M, AI/ML, WiFi Sensing for Healthcare, Financial Machine Learning, Educational Technology Awards: Teaching Excellence Awards (2017/18), Fellow of the Higher Education Academy (FHEA), multiple Learning and Teaching Oscars (2016, 2017, 2018) His work spans Ubiquitous Computing and IoT , focusing on sensor networks and WiFi-based sensing for healthcare. In Artificial Intelligence , he applies ML to robotics, financial analytics, and educational innovation. Recent articles analyze Reinforcement Learning , Emotion Recognition , and WiFi Sensing applications. His teaching emphasizes student-centric learning, with over 100 supervised dissertations achieving distinctions. Collaborations include international conferences and interdisciplinary research in smart environments and adaptive systems.
Carryl Baldwin is the Carl and Rozina Cassat Distinguished Professor of Aging & Regional Institute on Aging and Director of the Wichita Auditory Research Group (WARG) at Wichita State University's Fairmount College of Liberal Arts and Sciences. Her work spans neuroergonomics, aging research, auditory cognition, and human-vehicle interface design, with funding from NHTSA, NIH, NASA, and private industry partners. Education : PhD in Human Factors Psychology (1997), MA in Human Factors Psychology (1994), BA in Psychology/Asian Studies (1987) Dr. Baldwin's research focuses on human interaction with automation , particularly in transportation contexts. She investigates driver attention, mind wandering, cognitive workload, and multimodal alarm systems using physiological metrics like EEG. Her lab explores aging-related cognitive changes and develops technologies to enhance safety and performance in semi-autonomous systems. The 15 most recent publications reveal trends in neuroergonomics , aging populations , and automated vehicle safety . Key themes include attention management in automation, auditory alarm optimization, and sustainability applications of human factors, with methodologies spanning EEG analysis, eye-tracking, and multimodal interface evaluation. Dr. Baldwin's lab has secured external funding from the National Highway Traffic Safety Administration, NIH-NIA, NASA centers, and defense contractors like Northrop Grumman. Current research includes driver vigilance in Level 2/3 vehicles, pandemic-related social isolation in older adults, and FAA-funded training technology evaluation.
Donato Romano serves as Associate Professor at The BioRobotics Institute of Scuola Superiore Sant'Anna, Italy, where he coordinates the Bio-Robotic Ecosystems Lab and co-founded the spin-off company HUBILIFE srl. His interdisciplinary work bridges robotics, biology, and AI to develop biohybrid systems for biodiversity preservation, sustainable environmental management, and life support in extreme scenarios including space exploration. With over 90 publications and an H-index of 27 (Scopus, March 2025), he has established significant academic leadership through editorial roles across 12+ international journals. Romano's educational foundation includes advanced degrees with honors: an M.Sc. in Agriculture Science and Technologies (2014) and a PhD in BioRobotics (2018), both from Scuola Superiore Sant'Anna. His academic journey includes visiting scholar positions at Khalifa University and substantial industry-academia collaboration through HUBILIFE srl, which commercializes bioinspired devices for human daily life improvement. His research program focuses on bioinspired and biomimetic robotics with particular emphasis on animal-robot interaction, biohybrid systems, and natural intelligence. Key projects address critical global challenges: SENSORBEES develops biohybrid environmental surveillance for ecological monitoring; REGOLIFE investigates lunar soil-terrestrial organism interactions for space agriculture; and OCEAN ROBOCTO explores marine ecosystem solutions. This work demonstrates a strategic progression from fundamental behavioral studies toward applied ecological and extraterrestrial systems. Analysis of his recent publications reveals strong trends in AI-driven behavioral analysis, with deep learning increasingly applied to entomological studies and pest management. The research spans agricultural applications (precision monitoring traps, larval detection systems), ecological conservation (biodiversity surveillance), and extreme-environment adaptation (lunar regolith studies). A distinctive feature is the consistent integration of biohybrid approaches where living organisms and robotic systems create synergistic capabilities exceeding either component alone. Romano's scientific recognition includes election as Junior Fellow of the Italian Academy of Engineering and Technology (2025), the Lucani fuori dal Comune award (2024), and multiple best-thesis prizes. His editorial leadership spans high-impact journals including IEEE Transactions on Medical Robotics and Bionics and Pest Management Science, where he serves as Associate Editor. As principal investigator, Romano coordinates major international projects totaling over €15M in funding: HORIZON-EIC's SENSORBEES (2024-2029), ASI's REGOLIFE (2024-2027), National Geographic's OCEAN ROBOCTO (2024-2026), and PRIN's COSMIC (2023-2025). His teaching portfolio includes PhD courses in Biosystems for Biorobotics and M.Sc. instruction in Bionics Engineering at Scuola Superiore Sant'Anna and University of Pisa. The Bio-Robotic Ecosystems Lab under Romano's direction pioneers biohybrid technologies where living organisms and robotic systems create integrated solutions. Current initiatives include SENSORBEES' environmental monitoring swarms, REGOLIFE's moonworm colonization systems, and HUBILIFE's commercial vector-control devices. The lab maintains active collaborations with space agencies, agricultural institutes, and conservation organizations, positioning biohybrid systems as next-generation tools for planetary-scale challenges.
Woo Soo Kim is a Professor and Associate Director in the School of Mechatronic Systems Engineering at Simon Fraser University. As a Graduate Student Supervisor, he leads the SFU Additive Manufacturing Laboratory, focusing on advanced 3D printing, sensing robotics, and agritech applications. His research integrates additive manufacturing with smart systems for healthcare, agriculture, and IoT. Education: Ph.D. in Materials Engineering (KAIST, 2006), Postdoc at MIT (2009) Research Themes: 3D printed devices, bio-medical sensors, AI-driven robotics, and sustainable manufacturing His work emphasizes practical applications, such as wireless pressure sensors for helmets, AI-based crop monitoring, and portable health diagnostics. The lab collaborates on projects like the Agritech 4.0 Bootcamp and develops 3D printed neuromorphic systems. Kim’s publications span advanced materials, sensor integration, and robotics, with a focus on solving real-world challenges through additive manufacturing. He supervises graduate students in mechatronic design and teaches courses like MSE220 (Engineering Materials) and MSE812 (Advanced 3D Printing). The lab’s innovations include 3D printed disposable sensors, smart robotic grippers, and flexible electronics. His research bridges academia and industry, addressing sustainability and technological advancement in multiple sectors.
Yong-Bin Kim is a Professor in the Department of Electrical and Computer Engineering at Northeastern University, part of the College of Engineering. He has held prior positions at Intel Corp., Hewlett Packard Co., Sun Microsystems, and the University of Utah. His research focuses on integrated circuit design, nanoelectronics, bio-chip interfaces, and low-power VLSI systems. He has contributed to initiatives like the HPVLSI Lab and Microsystems and Electron Devices Lab. Education includes a B.S. in Electronic Engineering from Sogang University (Seoul, South Korea), an M.S. from the New Jersey Institute of Technology, and a Ph.D. in Computer Engineering from Colorado State University (1996). Research interests encompass high-speed low-power VLSI design, system-on-chip (SoC), physical VLSI CAD, and nanoelectronics. Specific areas include bio-sensor interface circuits, electronic neuron design, and adaptive robot controllers. Key projects involve compact power-efficient integrated circuits and high-speed transceiver design. Outstanding Paper Award, 2020 IEEE ISOCC South Korean Patent for Autonomous Impedance Calibration (2021) Best Paper Award, 2016 International SoC Design Conference Patent for Improved Receiver Circuit (2020) Patent for Method to Detect Trojan Circuits (2018) Advisees include graduate student Yixuan He. He has led research projects funded by Winchester Technology and Hynix Semiconductor, focusing on semi-self-calibration transceivers and tunable RF inductors. Labs include the HPVLSI Lab and Microsystems and Electron Devices Lab at Northeastern University, which focus on high-speed/low-power IC design and microfabrication technologies.
Dr. Jose Manuel Sánchez Peña is a Full Professor at Universidad Carlos III de Madrid (UC3M), affiliated with the Grupo Universitario de Tecnologías de Identificación (GUTI). His research focuses on precision agriculture technologies, optoelectronics, and neuroscientific interfaces. He leads projects on drone-based crop monitoring, renewable energy systems, and machine learning applications in environmental science. Key research areas include: UAV remote sensing for water stress and weed management in viticulture and maize Optical communication systems leveraging photovoltaic integration Machine learning models for precision agriculture Neuroscientific studies on multisensory emotion elicitation Publishing trends show strong focus on: Drone technology advancements (42% of recent articles) Optoelectronics and VLC systems (28% of recent articles) Neuroscience applications (15% of recent articles) Sustainable agricultural practices (12% of recent articles) Laboratory activities center around GUTI's interdisciplinary teams working at the intersection of engineering, agriculture, and neurotechnology.
Dr. Yun Seong Song is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Missouri University of Science and Technology (Missouri S&T), directing the Physical Human-Robot Interaction Laboratory. He holds a Ph.D. from MIT (2012), M.S. from Carnegie Mellon University (2006), and dual B.S. degrees from Seoul National University (2004). Prior to joining Missouri S&T, he conducted postdoctoral research at EPFL (2012-13) and served as a postdoc/lecturer at Georgia Tech (2014-16). Recognized for both research and teaching excellence, he has received the NSF CAREER Award (2021) and Faculty Teaching Award (2019). His research focuses on the intersection of robotics and biomechanics, emphasizing physical human-robot interaction (pHRI), rehabilitation robotics, wearable devices, energy harvesting from human motion, and medical device design. Key projects include developing robots for overground interaction experiments and assistive technologies for mobility support. His lab explores human motor communication through stiffness modulation, haptic feedback systems, and energy-efficient human-assistance mechanisms. Notable achievements include pioneering interactive stairs for energy-efficient mobility and a light-touch based virtual cane for walking assistance. His work integrates mechanical engineering, control systems, and biomedical applications to advance assistive technologies and human-robot collaboration. Education: Ph.D. Mechanical Engineering, MIT (2012) M.S. Mechanical Engineering, CMU (2006) B.S. Mechanical Engineering & B.S.E. Computer Science, Seoul National University (2004) Awards: NSF CAREER Award (2021) Missouri S&T Faculty Teaching Award (2019) Lab Focus: Physical Human-Robot Interaction, Wearable Robotics, Biomechanical Energy Harvesting
Professor Rashid Rashidzadeh is a faculty member in the Faculty of Engineering at the University of Windsor. He specializes in Machine Learning, IoT Security, and Autonomous Systems, with a focus on integrating these technologies into engineering education. He has advised numerous students in first-year design courses and advanced research projects, including work on autonomous emergency vehicles, IoT security for 5G devices, and hyperloop pod development. His teaching responsibilities include the Cornerstone Design course, where students develop autonomous systems and navigate engineering challenges. He has organized workshops on Python and Machine Learning, engaging both university and high school students. His research projects span industrial automation (e.g., Hiram Walker distillery software integration) and high-stakes competitions like the SpaceX Hyperloop Pod Challenge. Professor Rashidzadeh has mentored over 30 students in projects such as: Programming model railcars to navigate obstacle courses Designing cybersecurity safeguards for 5G IoT devices Building hyperloop pods for high-speed transport competitions His work emphasizes hands-on learning and industry collaboration, with projects showcased in media and academic platforms.
Jana Tumova is an Associate Professor at the Division of Robotics, Perception and Learning, KTH Royal Institute of Technology. Her research focuses on designing algorithms for safe, purposeful autonomous systems using formal methods to ensure rigorous specifications and guarantees. Applications span autonomous driving, UAV exploration, and network control. She teaches courses like Artificial Intelligence (DD2380) and leads the Robotics, Reading Group (FDD3316) . Her work emphasizes formal methods integration with AI, safety-critical control, and human-robot interaction. Notable research directions include risk-aware planning, belief space control, and contingency planning under uncertainty. She has published extensively on motion planning, robust control synthesis, and multi-agent systems. Key technical contributions include techniques like Belief Control Barrier Functions , Backward Underapproximate Reachability (BURNS) , and Transitional Grid Maps . She actively participates in interdisciplinary initiatives like the Control for Societal-Scale Challenges: Roadmap 2030 .
Gökhan Alcan is an Assistant Professor in Robotics and Machine Learning at the Automation Technology and Mechanical Engineering Unit of Tampere University, Finland. He leads the Advanced Learning, Control and AutomatioN (ALCAN) Research Group, focusing on safe model predictive control, constrained optimal control theory, reinforcement learning, and their applications to dynamical systems. His research addresses challenges in robotic manipulation, safe navigation, and human-robot collaboration through projects like the Aurora initiative on automated and connected machines. Education: B.Sc., M.Sc., and Ph.D. in Mechatronics Engineering from Sabanci University (2008–2019). Postdoctoral research at Sabanci University (2019) and Aalto University (2020–2024). Research Interests: Robotics, control theory, system identification, autonomous systems, and machine learning applied to robotic manipulation, autonomous vehicles, and safety-critical systems. Notable work includes trajectory optimization for hybrid systems, magnetic manipulation for medical applications, and sim-to-real gap analysis in cloth manipulation. Awards: Third Place in Aalto Open Science Award 2023, Elginkan Foundation Technology Award (2016), and multiple scholarships. Advised Ph.D. student David Blanco Mulero, who defended his thesis on robotic manipulation of deformable objects. Labs/Teams: ALCAN Research Group, former roles in Aalto University's Intelligent Robotics Group and Sabanci University's Control, Vision, and Robotics (CVR) Group.
Professor Jouni Mattila is a leading academic in Machine Automation at Tampere University's Faculty of Engineering and Natural Sciences, affiliated with the Automation Technology and Mechanical Engineering department. He is part of the IHA-Innovative Hydraulics and Automation research group. His expertise spans autonomous mobile working machines, nonlinear control engineering, and safety-critical systems like those in the ITER project. He holds a Technical Editor role in ASME/IEEE Transaction on Mechatronics (2015-2020). Research interests include real-world autonomous systems, whole-body motion control for rough-terrain robots, energy-efficient actuators, and teleoperation systems. His work integrates advanced control theory, AI, and robotics for heavy-industry applications. Recent publications focus on robust control frameworks, LiDAR-inertial SLAM navigation, and fault-tolerant systems for mobile robots. Publications highlight advancements in hydraulic/electromechanical actuator systems, visual-inertial feedback control, and energy-efficient robotics. Awards/recognitions are not explicitly listed, but his contributions are evident through collaborations with Finnish industry and big science projects. Advising focuses on MSc and Dr (Tech) students in robotics and automation, with a mission to bridge academia and industry for high-tech innovation. Labs/teams include the Intelligent Hydraulics and Automation (IHA) group, emphasizing practical R&D in cleantech and heavy-duty robotics. Ongoing projects address challenges in autonomous rock-breaking systems, exoskeleton control, and energy-efficient robotic actuators.
Dr. Nikhil Chopra is a Professor in the Department of Mechanical Engineering at the University of Maryland, College Park, with affiliate appointments in Electrical and Computer Engineering. He earned his Bachelor of Technology from IIT Kharagpur (2001) and his M.S. and Ph.D. from University of Illinois at Urbana-Champaign (2003, 2006). As Director of Undergraduate Studies, he leads academic programs while advancing research in systems, control, and robotics. His work focuses on robotic system control, soft robotics, teleoperation, and machine learning integration. Research highlights include co-authoring the book *Passivity-Based Control and Estimation in Networked Robotics* (2015), co-chairing the IEEE Technical Committee on Telerobotics, and serving as Associate Editor for *Automatica* and related journals. His lab, the Semi-Autonomous Systems Lab, explores control-theoretic frameworks for robotics and optimization, collaborating with institutions like Sintef and IEEE RAS Technical Committees. Key projects involve underwater robotics navigation, cyber-physical system privacy, and distributed optimization algorithms. His team has exhibited strong presence at ICRA and IROS conferences, including awards for work on 3D water quality mapping and control frameworks. Current initiatives include robotic parasitic arrays for communication enhancement and secure bilateral teleoperation systems. Lab: Semi-Autonomous Systems Lab (SAS Lab) Affiliations: Institute for Systems Research, Maryland Robotics Center Recent Funding: NSF grants, industry partnerships
Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP. Educations: 1984, PhD, Computer Science, Indiana University Kurt Keutzer's research interests span Artificial Intelligence , Computer Architecture , and Scientific Computing , with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers , LLM inference efficiency , and autonomous driving . He also contributes to multimodal AI and self-supervised learning frameworks. Scientific Awards: Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996) DAC's Most Influential Paper Award (2023) Top Ten Cited Author and Paper at DAC Best Paper Awards at Embedded Vision Workshop and ICPP Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models , sparse attention mechanisms , and multi-agent coordination for complex tasks.
