Luca Spalazzi is an Associate Professor at the Department of Information Engineering , Università Politecnica delle Marche , Italy. His research spans multiple domains including cybersecurity , blockchain technology , machine learning , and telerehabilitation systems for Parkinson's disease. He applies formal methods to software verification and security analysis, with a focus on real-time systems and distributed architectures . Key research areas: Cybersecurity, Blockchain, Machine Learning, IoT, Formal Verification Recent work: Blockchain-based sustainable supply chains, Zero-Knowledge Proofs, Smartphone health monitoring His publications (2013-2025) demonstrate expertise in malware detection , smart contract verification , and AI-driven health solutions . Articles include BRAIN 2024 workshop organization and RAPIDO system for Parkinson's telerehabilitation.
Sinan Haliyo is a Professor at the Institute of Intelligent Systems and Robotics (ISIR), Sorbonne University, Paris, where he leads the 'Multiscale Interactions' research team. His academic career spans over two decades, with significant contributions to robotics, particularly in micromanipulation, haptics, and human-computer interaction. Dr. Haliyo earned his Doctorate from Pierre and Marie Curie University in 2002 with a thesis on "Adhesion forces and dynamic effects for micromanipulation," supervised by Prof. Jean-Claude Guinot. In 2019, he obtained his Habilitation to Direct Research (HDR) from Sorbonne University with the thesis "Multi-Scale Interactions," demonstrating his capacity to lead independent research and supervise doctoral candidates. His research broadly focuses on robotics and interactivity—designing teleoperated robots for specific applications and developing systems that allow operators to perceive what they are controlling through haptic and multimodal interfaces. Starting with microrobotics in the late 1990s, his work has expanded to encompass human-robot interaction, haptics, virtual reality, and human-machine interfaces. His multidisciplinary approach involves collaboration with roboticists, psychologists, neuroscientists, and biologists. Analysis of his recent publications (2023-2025) reveals a strong focus on optical microrobotics, biomedical applications, and haptic interfaces. His team is advancing techniques for precise micro-manipulation using optical tweezers combined with deep learning, developing medical microrobots for applications like biliary procedures and in vitro fertilization, and creating innovative haptic interfaces for surgical training and virtual reality. The research spans multiple disciplines including robotics, biomedical engineering, computer vision, and neuroscience. IROS'09 Best application Mechatronics Journal 2010 Triennal Best paper MARSS 2019 Best Paper World Haptics 2021 Best ToH Short Paper Hon. Ment. ISOT 2021 Best Student Paper INTERACT 2021 IFIP TC13 Pioneers' Award for Best Doctoral Student Paper Dr. Haliyo has supervised 14 theses to completion with 6 currently in progress, representing a total supervision rate of 390% (approximately 60% per thesis on average throughout his career). He has coordinated multiple significant research projects including ANR IOTA (Interactive Optical Tweezers), ANR OptoBots, and 3BIOT (Robotic Optical Tweezers for Biology). His research is funded by Sorbonne University, the Ile-de-France Region, ANR grants, and private companies including Percipio Robotics, Robeauté, and Segula. As leader of the Multiscale Interactions team at ISIR (since January 2019), Dr. Haliyo oversees a research group comprising 4 faculty members, 4 researchers, and 15 PhD students. Previously, he animated the Microrobotics group within the ISIR interaction team (2016-2019) and has been responsible for the ISIR Assisted Micromanipulation Platform since 2015, which serves as a node of the CNRS National Micro-Nanorobotics Platform and recipient of the Labex Robotex.
Alexandra Golby, MD is a Professor of Neurosurgery and Radiology at Harvard Medical School, and Haley Distinguished Chair in the Neurosciences at Brigham and Women's Hospital. She directs the Golby Lab, a surgical brain mapping laboratory focused on advanced imaging technologies for neurosurgical applications. Her clinical expertise centers on brain tumor and epilepsy surgery, with a focus on lesions near critical brain structures. Dr. Golby holds multiple leadership roles including Director of Image-guided Neurosurgery and Co-Director of AMIGO at Brigham and Women's Hospital. Her research integrates disciplines such as computer science, applied mathematics, and biomedical engineering to improve surgical planning and intraoperative decision-making. Notable innovations include technologies for real-time tumor resection monitoring and low-cost neuronavigation systems (e.g., NousNav) for low-resource settings. Dr. Golby completed her BA at Yale University and MD at Stanford University School of Medicine, followed by neurosurgery residency at Brigham and Women's Hospital. Dr. Golby's translational work emphasizes global health equity, including Fulbright-supported initiatives to develop locally adapted medical technologies in Rwanda and Morocco. Her research spans image-guided neurosurgery, brain-computer interface applications, and neuro-oncology advancements.
Dr. Jie Gu is an Associate Professor in the Department of Electrical and Computer Engineering at Northwestern University’s McCormick School of Engineering. His research focuses on energy-efficient computing architectures, machine learning accelerators, and AI-driven biomedical devices. Key areas include neuromorphic computing, edge computing systems, and hardware-software co-design for real-time applications. Education : Ph.D. Electrical and Computer Engineering (University of Minnesota), M.S. (Texas A&M University), B.S. (Tsinghua University) Labs : VLSI Research Lab His work emphasizes mixed-signal computing, with innovations in neural interface systems and physics-informed AI accelerators. Recent projects include headset-integrated brain-computer interfaces and scalable robotic control systems.
Dr. Oiwi Parker Jones is a Hugh Price Fellow in Computer Science at Jesus College, University of Oxford, and Principal Investigator leading the Parker Jones Neural Processing Lab (PNPL) at the Oxford Robotics Institute. Their research focuses on neural speech prosthetics, combining machine learning with neuroscience to develop technologies for speech restoration. They hold an honorary fellowship in the Nuffield Department of Clinical Neurosciences and teach in Engineering Science, Computer Science, and Neuroscience, including roles as a stipendiary lecturer in neuroscience and medical teaching at St Peter’s and Oriel Colleges. Jones’ work spans robotics, neural decoding, and endangered language preservation, with a particular emphasis on Hawaiian linguistics. Their lab develops large-scale machine learning methods for neural data analysis and collaborates on interdisciplinary projects involving clinical neuroimaging and AI ethics. Jones is also engaged in cultural preservation efforts through computational linguistics and has published widely on language contact, phonology, and indigenous protocols in technology. Education: Doctoral research in NLP and machine learning at Oxford Neuroscience training at UCL and Oxford Research Interests: Neural prosthetics, speech decoding, small-data machine learning, robotics, and endangered language preservation. Current projects include non-invasive brain-to-text systems, clinical fMRI applications, and computational tools for Hawaiian linguistic analysis. Teaching Contributions: Undergraduate lectures on generative deep learning and robotics, postgraduate supervision in DPhil projects across Engineering, Computer Science, and Neuroscience. Known for interdisciplinary teaching methods integrating neuroscience and AI. Awards & Recognition: No explicit awards listed, but recognized for innovative interdisciplinary research and leadership in neural engineering. Labs & Teams: Leads PNPL, collaborates with Oxford Robotics Institute and Applied Artificial Intelligence Lab. Active in global networks for clinical neuroimaging and indigenous AI ethics.
Sanjay Sarma is the Fred Fort Flowers (1941) and Daniel Fort Flowers (1941) Professor of Mechanical Engineering at MIT, currently on leave. He previously served as President, CEO and Dean of the Asia School of Business and as VP for Open Learning at MIT. Sarma co-founded the Auto-ID Center at MIT and developed key technologies behind the EPC suite of RFID standards used worldwide. He was also founder and CTO of OATSystems, acquired by Checkpoint Systems in 2008. Bachelor's Degree, Indian Institute of Technology (1989) Master of Engineering, Carnegie Mellon University (1992) Ph.D., University of California at Berkeley (1995) Professor Sarma's research spans multiple interdisciplinary fields with a focus on RFID, sensors, and Internet of Things technologies. His work in automotive and autonomous systems explores innovative applications of sensing technology. In augmented reality and brain-computer interfaces, he investigates novel human-machine interaction paradigms. His research in digital learning examines how technology can transform educational experiences at scale, with particular interest in university design and operations. Analysis of Professor Sarma's recent publications reveals a strong focus on integrating physical and digital systems. His work demonstrates increasing convergence between RFID technology, energy harvesting, and machine learning applications. Key themes include self-powered sensor networks, augmented reality interfaces for IoT devices, and security frameworks for connected systems. The research shows progression from foundational RFID work toward more complex integrated systems that combine sensing, computation, and communication. Scientific Awards NSF Career Initiation Grant (1997) Cecil and Ida Green Career Development Chair (1999) Den Hartog Teaching Excellence Award (2001) Joseph H. Keenan Award for Innovation in Undergraduate Education (2002) MacVicar Fellowship (2008) Industry Recognition Information Week's Innovators and Influencers (2003) Business Week's e.biz 25 Innovators (2003) New England Business and Technology Award (2005) MIT Global Indus Award (2005) Fast Company Magazine's "Fast 50" (2005) Boston Magazine's 40 under 40 (2006) RFID Journal Special Achievement Award (2010) Professor Sarma has advised numerous doctoral and master's students, though specific names are not listed in the available information. His grant portfolio includes significant funding from the National Science Foundation and industry partnerships. He serves on the boards of GS1US and Hochschild Mining, and advises several startup companies including Top Flight Technologies. His research has been supported by both government agencies and industry collaborators interested in RFID, IoT, and digital learning applications. Sarma leads research in the Auto-ID Labs, which he co-founded, focusing on RFID and sensor technologies. He has also been involved with the Office of Digital Learning at MIT and edX. His work extends to developing world applications through projects focused on low-cost sensing solutions. The research environment he has cultivated brings together electrical engineers, computer scientists, and mechanical engineers to tackle interdisciplinary challenges in sensing and connectivity.
Chaomin Luo is an Associate Professor in the Department of Electrical & Computer Engineering at Mississippi State University (MSU), part of the Bagley College of Engineering. His research focuses on Robotics, Autonomous Systems, Computational Intelligence, Machine Learning, Control Systems, and Embedded Systems. He holds a Ph.D. from the University of Waterloo, Canada (Electrical and Computer Engineering), an M.Sc. from the University of Guelph (Engineering Systems and Computing), and a B.S. from Southeast University, China (Electrical Engineering). His work emphasizes bio-inspired algorithms for robot navigation, graph-based path planning, and human-autonomy teaming. Notable contributions include models for integrated robotic systems, underwater vehicle navigation, and safety-aware crowd-avoidance systems. He also develops pedagogical methods to enhance student learning in robotics and computer engineering topics like MIPS instruction set design and datapath optimization. Research highlights include: Autonomous systems integration with cognitive mapping and digital twin technology Multi-agent navigation algorithms using RRT*-smart and simulated annealing approaches Process monitoring via low-rank projections and fractal analysis Brain-computer interfaces for unmanned vehicle control His teaching responsibilities include courses related to embedded systems (VLSI/FPGA design) and robotics. His research has been published in over 50 peer-reviewed articles since 2018, with a focus on sustainable manufacturing systems, bio-inspired intelligence applications, and mechatronic systems optimization.
Dr. Max Pandit is a Senior Lecturer in Computer Game Design at Teesside University's Department of Computing & Games. He holds a PhD in applied artificial intelligence for biomedical signal processing from Northumbria University (2017). His expertise spans machine learning, AI in games, evolutionary optimization, and biomedical applications. He specializes in integrating Unreal Engine and visual scripting for game development. Research Interests include Machine Learning , AI-driven Game Development , Biomedical Signal Processing , and Deep Learning . His work explores applications such as brain-computer interfaces and ensemble models for medical signal analysis. Recent projects include: Visual Machine Learning Plugin for Unreal Engine 4 (2024, ongoing) Play2Secure: AI-enabled cybersecurity awareness game (2019) HackEscape : Cybersecurity awareness using serious games (2021) Collaborations focus on virtual training environments, digital twins, and procurement optimization in industry. He has led or co-led 5 funded research projects totaling over £1.5M in external funding.
Victoria Chester is Full Professor and Co-Director of the Andrew and Marjorie McCain Human Performance Laboratory at the University of New Brunswick's Faculty of Kinesiology. She holds a Ph.D. in Mechanical Engineering from UNB with specialization in biomechanics. Research programs focus on clinical biomechanics across the lifespan, particularly: Multisegment foot and upper extremity mechanics Machine learning applications for gait classification Wearable sensor development for mobility assessment Rehabilitative strategies for neuropathic conditions Diabetes-related mobility impairments Laboratory resources include 12-camera Vicon motion capture, force plates, EMG systems, and pressure mapping technology supporting gait analysis and movement studies.
George Vincent Kondraske is a Research Professor in Electrical Engineering and Bioengineering at the University of Texas at Arlington (UTA), where he has served since 1982. He is also the founding director of UTA’s Human Performance Institute and holds adjunct roles at multiple institutions. His expertise spans human performance measurement, biomedical instrumentation, and systems engineering. Education: PhD (1982), MS (1980), and BS (1978) in Biomedical/Electrical Engineering from UTA/UT Southwestern and University of Rochester. Affiliations: Texas Center for Performing Arts Medicine, Human Performance Measurement, Inc., and multiple medical centers. Research focuses on General Systems Performance Theory (GSPT), predictive analytics, and web-based tools like RC21X for cognitive/neuromotor assessment. His work is supported by NIH, NASA, and industry grants totaling over $2M. He has authored 250+ publications and holds several patents. Key contributions include the Elemental Resource Model (ERM) for human performance and Nonlinear Causal Resource Analysis (NCRA). Awards include the IEEE Early Career Award and Becton-Dickinson Career Achievement Award. Grants span robotics, laparoscopic surgery, and athlete performance. He advises on human-system integration and has pioneered wearable sensors for medical applications. Labs/teams include the Human Performance Institute and collaborations with UNT School of Music and Presbyterian Hospital labs.
Dr. Usman Adeel is an Associate Professor in Computer Science at Teesside University, specializing in Distributed Sensing Systems, IoT, and Smart Cities. He holds a PhD in Computing from Imperial College London (thesis: "Socio-economic aware data forwarding in mobile sensing networks and systems"). Previously, he worked as a Research Scientist at Intel Labs Europe (ICRI for Sustainable Connected Cities) and as a Research Associate at Imperial College London. His research focuses on Mobile Sensing, Low Power Networks, and Cyber-physical Systems. Recent work includes energy-efficient routing protocols for wireless sensor networks and intrusion detection systems for SDN-based VANETs. He leads the DUR007 project on Sustainable Drainage Systems innovation, emphasizing system innovation and water management. Key contributions span cybersecurity (e.g., IP camera vulnerability analysis) and biomedical applications (EEG-based brain-computer interfaces). No scientific awards are explicitly mentioned. He has supervised 2 academic works and collaborates widely in IoT, smart cities, and network security domains.
Oana Mitruț is an Associate Professor at the Polytechnic University of Bucharest, affiliated with the Department of Computer Science in the Faculty of Automatic Control and Computers. Her research focuses on virtual reality therapy, machine learning applications in healthcare, and accessibility technologies for visually impaired individuals. She has collaborated extensively on projects such as the 'Sound of Vision' initiative, exploring audio-haptic interfaces for navigation and sensory substitution. Her work integrates computational methods with clinical applications, including emotion recognition systems using biophysical signals and adaptive exposure therapy for phobias like acrophobia. She has published widely in journals like Sensors and Symmetry , as well as conferences such as EDULEARN and CHI. Her contributions address challenges in human-centered design for medical VR systems and gamification approaches to improve accessibility. Key areas of investigation include: Machine learning models for fear level detection Audio-based navigation systems for visually impaired users Therapeutic game design for mental health interventions Integration of multimodal feedback in assistive technologies Her research bridges computer science, neuroscience, and clinical practice, emphasizing real-world applications in healthcare and accessibility.
Daniel Hannon is a Professor of the Practice in the Department of Mechanical Engineering at Tufts University School of Engineering . His work bridges industry practice and academic research in human factors engineering, with a focus on transportation human factors , medical product design , educational technology , and team performance . As an educator, he specializes in teaching human factors engineering at undergraduate and graduate levels through courses like Applied Behavioral Stats Eng, Human-Machine System Design, and Special Topics in Assistive Design. Education: Ph.D. in Experimental Psychology, Brown University (1991) M.Sc. in Experimental Psychology, Brown University (1987) B.A. in Psychology, Nazareth College (1985) M.S. in Community Mental Health Counseling, Southern New Hampshire University (2008) Hannon's research interests span visual perception , cognitive systems engineering , universal design , and assessments of speech-motor coordination in autism . His recent work includes developing noninvasive biomarkers for mental state and serious games for collaborative dark network discovery . His publications highlight interdisciplinary collaborations with institutions like MIT Lincoln Laboratory , U.S. Department of Defense , and National Science Foundation . Notable grants include MIDAS (Multisensory Interactive Data Analysis System) and InterLACE (Interactive Learning and Collaborative Environment). Scientific awards include the Award of Excellence from ASME , Ergonomics in Design Best Paper Award , and Employee Excellence Award from the U.S. Department of Transportation . Hannon's professional activities include developing academic certificates (Assistive Design, Human Factors in Data Science) and coordinating the Human Factors Engineering Speaker Series at Tufts University. He serves in organizations like the Human Factors and Ergonomics Society and American Psychological Association .
Ian Howard is Associate Professor of Computational Neuroscience at University of Plymouth's School of Engineering, Computing and Mathematics. His multidisciplinary research examines brain development across motor systems, from infant speech acquisition to adult motor learning, using robotic interfaces and computational modeling. Research spans multiple periods of brain development and motor systems: infant speech acquisition, adult arm movement learning, and robotic interface design. Current projects investigate motor imagery benefits, haptic feedback in skill acquisition, and voice pathology detection using machine learning. Robotics innovations include novel manipulanda for sensorimotor research and 3D-printed robotic segments with variable-stiffness actuators. Publications demonstrate consistent focus on computational modeling of biological learning processes. Teaches machine learning, control engineering, and mobile robotics. Professional affiliations include societies for neuroscience and robotics research. Research collaborations extend internationally through robotic interface sharing.
Joseph Schmidt is an Associate Professor of Psychology at the University of Central Florida, where he directs the Attention and Memory Laboratory. His research integrates eye-tracking and EEG/ERP methodologies to study attention-memory interactions. Key focus areas include: Neural and behavioral correlates of visual working memory Attention guidance in real-world contexts Visual search errors in medical imaging Oculomotor patterns in neurological disorders His work bridges cognitive psychology and neuroscience, examining how memory representations shape attentional selection. Current projects investigate cognitive expectations in visual search, EEG classification methods, and attentional deficits in clinical populations.
