جست‌وجوی استاد و پژوهشگر

Cain Evansمشاهده پروفایل

استاد آموزشی

• Software Engineering
• Cyber Security
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Cain Evans is a Teaching Professor at Aston University, serving as co-Programme Director for the MSc DTSS Degree Apprenticeship within the College of Engineering and Physical Sciences. He holds a prominent role in leading degree apprenticeship programs and has over 24 years of teaching experience across the UK and Far East, specializing in Computer Science, Software Engineering, and pedagogical research. His research focuses on intelligent systems, context-aware technologies, digital health (smart-care spaces), and AI-driven applications. He has served as an external examiner at multiple UK universities and has held senior academic roles at institutions like Birmingham City University and Northumbria University. His professional affiliations include Fellow of the Higher Education Academy (FHEA), STEM Ambassador, and memberships in BCS and IEEE. Research Interests: Dr. Evans' work spans interdisciplinary areas including software engineering, cyber security, AI (data science/mining), and pedagogy. Recent trends in his publications highlight advancements in smart care systems (e.g., pervasive sensing for at-home care), algorithmic trading models using neural networks, and mobile advertising frameworks like iMAS. His contributions also address challenges in e-education and future iCampus developments. Awards : Birmingham and Solihull STEM Ambassadors Award (2010) 15th Anniversary Outstanding Reviewer Awards (2016) Nominated for Engaging & Inspiring Teacher of the Year (2017) Advising & Grants : Supervisor for MSc Data Science and DTSS Professional projects Past roles include Programme Director for BSc DTS Professional and external examiner at Bradford, Brighton, and University of Wales Labs/Teams : Active in interdisciplinary collaborations, particularly in smart-care and AI applications, though no specific lab names are mentioned.

James Lawمشاهده پروفایل

پژوهشگر

• Human-Robot Collaboration
• Trustworthy Autonomous Systems
• Responsible Innovation
• +۳ مورد دیگر

Dr. James Law serves as a Senior Experimental Officer at Sheffield Robotics and leads the Collaborative Robotics Group within the School of Computer Science at the University of Sheffield. He holds a joint appointment between the Department of Computer Science and the Advanced Manufacturing Research Centre, and serves as Director of Innovation and Knowledge Exchange at Sheffield Robotics. His work integrates academic research with industrial applications in advanced manufacturing. His research focuses on human-robot collaboration, trustworthy autonomous systems, and responsible innovation in manufacturing robotics. Law develops safety assurance frameworks and digital twinning solutions for collaborative workspaces, with particular emphasis on trust engineering, user authentication, and graphical communication systems that reduce human anxiety in robot co-working environments. His interdisciplinary approach bridges computer science, engineering psychology, and industrial implementation requirements. Law's publication portfolio demonstrates consistent advancement in collaborative robotics safety, with recent work (2022-2024) focusing on verified safety controllers, digital twin frameworks, and trust modeling in human-robot triads. His research shows progression from foundational developmental robotics (2010-2015) toward applied industrial solutions, with increasing emphasis on security, safety assurance, and human factors in manufacturing contexts. As an active science communicator, Law serves on the UK Robotics and Autonomous Systems network (UK-RAS) management board and the EPSRC Early Career Forum in Manufacturing Research. EPSRC STAMAN: Robotic skill transfer for manufacturing (Co-PI, £1M+) Horizon Europe OpenSwarm: Energy-aware swarm orchestration (Co-PI, £463k) EPSRC Security of Digital Twins in Manufacturing (Co-PI, £775k) EPSRC UKRI Trustworthy Autonomous Systems Node (Co-PI, £3M) Law leads the Collaborative Robotics Group and contributes to the Complex Systems Modelling research group, focusing on translating theoretical safety frameworks into practical industrial implementations through close collaboration with manufacturing partners and policy stakeholders.

Martin v. Mohrenschildtمشاهده پروفایل

دانشیار

• Control Systems
• Signal Processing
• Simulation
• +۵ مورد دیگر

Dr. Martin v. Mohrenschildt is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. He holds academic positions as Undergraduate Advisor for Mechatronics and Mechatronics Coordinator. His expertise spans control systems, signal processing, hybrid systems, and immersive simulation. He earned a Dr. sc. math. ETH and Dipl. math. ETH (Swiss Federal Institute of Technology), and is a Professional Engineer (P.Eng.). Research focuses include Model Predictive Control, vibration analysis for industrial machinery, and multisensory integration in motion simulation. His Motion Simulator Laboratory features a 6-DOF simulator with advanced visualization and physiological measurement capabilities. The Embedded Systems Laboratory explores mechatronics and robotics, including alternative fuel injection systems. Teaching includes courses like CAS-748 (Time Series Analysis) and SFWR ENG 2SO3. Recent work emphasizes sensor fusion (LiDAR/camera/thermal), biofeedback systems for muscle interventions, and cognitive effects of motion cues in virtual environments. Collaborations with Dr. Shedden investigate perceptual neuroscience in immersive simulators. His lab facilities support interdisciplinary projects combining engineering, computer science, and cognitive science. Publications span 30+ years, covering hybrid systems theory, control algorithms, and applied sensor technologies. Current research directions include autonomous vehicle perception systems and aging-related changes in multisensory integration.

• Process Systems Engineering
• Sustainable Chemical Processes
• Environmental Engineering
• +۴ مورد دیگر

Professor Benoit Chachuat is a Professor of Process Systems Engineering in the Department of Chemical Engineering at Imperial College London, UK. He holds affiliations with the Centre for Process Systems Engineering, Industrial Biotechnology Hub, Institute for Molecular Science and Engineering, and Robotics Forum. His research focuses on developing computational methods for sustainable chemical and biological processes, integrating rigorous modeling, optimization, and data analysis to improve industrial processes. Key areas include process optimization, enviro-economic assessments, and advanced control strategies for bioreactors and energy systems. Prof. Chachuat's career includes roles as Senior Lecturer (2010–2015), Reader (2015–2020), and current Professorship (since 2020) at Imperial College. Prior to this, he held positions at McMaster University (2008–2010), EPFL (2005–2008), MIT (2003–2005), and INRIA (2002–2003). His educational background includes a Research and Teaching Assistantship at Lorraine Institute of Technology (1998–2002). His research interests span computational methods for process systems engineering, with emphasis on global optimization, robust control, and Bayesian estimation. Notable projects include enviro-economic assessments of bioenergy, waste-to-X technologies, and sustainable aviation fuels. His work bridges theoretical advancements with practical industrial applications, supported by software tools for method dissemination. Publications highlight contributions to modeling microalgae cultures, membrane contactors in natural gas processing, and vaccination campaign optimization. His enviro-economic analyses address carbon footprints and externalities in chemical processes, aiming for net-zero solutions. Current projects explore machine learning integration in industrial processes and resilience in biopharmaceutical supply chains. Prof. Chachuat collaborates with experimentalists and industry partners, addressing challenges in energy sustainability, circular economy, and pandemic response. His methodologies prioritize safety, economic viability, and environmental stewardship across chemical and biological systems.

Rich Westمشاهده پروفایل

استاد

• Real-time systems
• Embedded systems
• Operating systems
• +۱۲ مورد دیگر

Rich West is a Professor in the Department of Computer Science at Boston University, with a secondary affiliation in Electrical and Computer Engineering. He joined BU in 2000 after earning his PhD from Georgia Tech. His research focuses on real-time and embedded systems, operating systems, and resource management, leading the development of the Quest real-time OS and its separation kernel, Quest-V. His work emphasizes safety, predictability, and efficiency in systems like IoT devices and automotive/avionics applications. Education: PhD, Computer Science, Georgia Institute of Technology (2000) MS, Computer Science, Georgia Institute of Technology (1998) MEng, Microelectronics and Software Engineering, University of Newcastle-upon-Tyne (1991) Research Interests: Rich's work spans real-time operating systems, embedded systems design, multicore resource management, kernel architecture, hardware-software co-design, and secure separation kernels. He emphasizes practical applications, such as autonomous drones, automotive systems, and IoT devices. His lab develops systems like Quest-V for predictable execution and FlyOS for drone avionics. Awards and Recognition: Outstanding Paper Award at ECRTS 2020 Best Student Paper Award at RTAS 2017 Nominated for Best Paper Award at EMSOFT 2021 Best Paper Award at RTAS 2022 (FlyOS) Advising and Labs: He advises numerous PhD/MSc students on real-time systems, embedded computing, and safety-critical software. His lab collaborates on projects like the Quest OS, ModelMap automotive frameworks, and drone control systems (FlyOS). He also leads the BOSS, RTCC, and iBench research groups.

Cullen Blakeمشاهده پروفایل

دانشیار

• Astronomy
• Cosmology
• Exoplanet Detection
• +۶ مورد دیگر

Cullen Blake is an Associate Professor in the Department of Physics and Astronomy at the University of Pennsylvania School of Arts & Sciences. His primary research focuses on exoplanet detection around low-mass stars and the astrophysics of low-mass stars and brown dwarfs. He is deeply involved in observational astronomy, developing techniques to detect Earth-like planets and improving stellar characterization through precision radial velocity measurements and synoptic surveys. Blake plays a key role in projects like the NEID Earth Twin Survey and the Brown Dwarf Kinematics Project, leveraging instrumentation such as the NEID spectrograph and robotic telescopes. Education: Ph.D. (Astronomy) Harvard University (2009), A.M. (Astronomy) Harvard University (2006), A.B. (Astrophysics) Princeton University (2003). Research Interests: Search for Earth-like planets around low-mass stars Stellar astrophysics of low-mass stars and brown dwarfs Instrumentation development for exoplanet surveys Robotic telescopes and synoptic survey strategies Stellar activity mitigation in radial velocity data He has contributed to major collaborations including the APOGEE survey, the MINERVA telescope array, and the design of the NEID spectrometer. His work emphasizes bridging observational techniques and theoretical models to advance our understanding of planetary systems and stellar evolution. Key projects include the NEID Sun-as-a-Star program and the development of algorithms to reduce noise from telluric and stellar variability. Blake's research also extends to brown dwarf kinematics and the analysis of transient phenomena in stellar systems.

• Space Robotics
• Control Theory
• Computer Vision

Pedro Delgado Roque is a Postdoctoral Researcher at the Royal Institute of Technology (KTH) in Stockholm, affiliated with the Wallenberg AI, Autonomous Systems and Software Program (WASP). He is part of the Division of Decision and Control Systems (DCS) and leads the establishment of the Space Robotics Laboratory at KTH. His research focuses on Space Robotics, Control Theory, and Computer Vision, with contributions to projects like DISCOWER, Space Cobot, and MoonHopper. He obtained his Ph.D. in 2024 under Professors Dimos Dimarogonas, Mikael Johansson, and Jana Tumova. **Research Interests**: Space Robotics: Weightless robotics, collaborative robotics, exploration robotics. Control Theory: Model predictive control, collaborative load transportation. Computer Vision: Visual-servoing, distributed vision systems. **Awards**: ICRA 2022 Outstanding Coordination Award for work on decentralized collaborative UAV control. **Advising**: Supervised 11+ Master's and thesis students, including works on failsafe control for space systems, predictive controllers in microgravity, and multi-agent inspection systems. Collaborated with NASA Astrobee and PX4 projects. **Labs & Projects**: Co-founded the DISCOWER project with 4 PhD students, 2 master students, 6 professors, and a postdoc team. Developed the Space CoBot aerial robot for indoor microgravity testing.

Miles Hansardمشاهده پروفایل

مدرس ارشد

• Computer Vision
• 3D Scene Reconstruction
• Robotics
• +۶ مورد دیگر

Dr. Miles Hansard is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on 3D scene reconstruction, multi-view imaging, depth cameras, radar localization, and human vision. He specializes in geometric aspects of vision, image processing, and robotics applications. His teaching includes modules on Computer Graphics (for both postgraduate and undergraduate students) and Introduction to Multimedia Systems. These courses cover topics like 3D graphics algorithms, human perception of color, and multimedia system fundamentals. Dr. Hansard's work integrates sensor technologies such as time-of-flight cameras and millimeter-wave radar to improve 3D reconstruction and robotic systems. He contributes to projects like the GRIP software framework for robotic grasping and has published extensively in journals like IEEE Transactions on Image Processing and the International Journal of Neural Systems. He is affiliated with the Centre for Multimodal AI, where he collaborates on research involving sensor fusion, autonomous systems, and visual perception. His expertise bridges computer vision, robotics, and multimedia engineering.

Henry Leungمشاهده پروفایل

استاد

• Machine Learning
• Autonomous Systems
• Intelligent Sensing
• +۳ مورد دیگر

Henry Leung is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. His research focuses on autonomous systems, intelligent sensing, and machine learning applications in engineering. He leads the Autonomous Systems and Intelligent Sensing Laboratory. Bachelor of Math in Mathematics, University of Waterloo (1984) Master of Engineering in Engineering Science, McMaster University (1986) M.S. in Mathematics, University of Toronto (1985) Doctorate in Electrical Engineering, McMaster University (1991) His research interests include advanced control systems, robotics, and sensor networks. He has authored numerous conference proceedings in these areas. Recipient of multiple teaching awards, including the 2019 Teaching Achievement Award (Schulich School of Engineering and University of Calgary) IEEE Fellow (2015) Leung has taught courses like Machine Learning for Engineers (ENEL 525) and actively engages in research grants related to autonomous systems. He oversees the Autonomous Systems and Intelligent Sensing Laboratory, fostering interdisciplinary innovation in robotics and intelligent technologies.

• Automotive Engineering
• Railway Engineering
• Mechanical Engineering
• +۶ مورد دیگر

Vicente Díaz López is a Full Professor in the Department of Mechanical Engineering at Carlos III University of Madrid. He serves as Director of the Duque de Santomaró Institute of Motor Vehicle Safety and Director of the Master's Degree in Railway Engineering. Academic Department: Mechanical Engineering Research Group: Experimental Mechanics, Calculations and Transport Group (MECATRAN) Contact: vdiaz@uc3m.es Research Interests span automotive and railway engineering, focusing on: Intelligent tire systems for grip and friction estimation Vehicular dynamics and stability control Braking efficiency and safety analysis Structural mechanics in transport systems Sensor fusion and neural networks for vehicle parameter estimation Roadworthiness testing methodologies Recent Publications demonstrate expertise in: Brake force estimation techniques Intelligent tire development Vehicle roll stability control Structural failure analysis in transport Low-cost sensor systems for driver monitoring Projects include: Principal investigator of the fBrake software suite (2014–present) for braking efficiency analysis Multiple contracts with technical inspection agencies (ITV) and companies like Bombardier Patent development for driving style evaluation systems

Davide Bacciuمشاهده پروفایل

استاد

• Machine Learning
• Graph Neural Networks
• Continual Learning
• +۵ مورد دیگر

Davide Bacciu is a Full Professor of Machine Learning at the University of Pisa, where he leads the Pervasive Artificial Intelligence Laboratory (PAILab) and coordinates the EIC-Pathfinder EMERGE project. He is also Director of AI Research at Aptus.AI and founder of ContinualIST and QuantaBrain startups. His academic roles include Vice President of AIxIA (Italian Association for Artificial Intelligence) and former Senior Editor of IEEE Transactions on Neural Networks and Learning Systems. He holds a PhD in Computer Science from IMT Lucca and is affiliated with the Department of Computer Science at the University of Pisa. His research focuses on machine learning fundamentals, graph neural networks, continual learning, and applications in robotics and healthcare. Key projects include the EMERGE initiative exploring collective awareness in AI systems and QuantaBrain’s diagnostic tools for autism spectrum disorders. Bacciu’s work emphasizes interdisciplinary collaboration, integrating AI with cyber-physical systems and societal impact. His contributions span foundational theory (e.g., causal abstractions, graph structure learning) and applied domains (e.g., autonomous systems, medical AI). He has organized workshops on AI for Space exploration and Geometric Deep Learning, showcasing his commitment to advancing AI frontiers. Education: PhD in Computer Science (IMT Lucca), MSc & BSc from University of Pisa. Labs/Teams: PAILab, EMERGE project, QuantaBrain startup. Grants/Projects: EIC Pathfinder EMERGE, TEACHING_H2020, AI for Space initiatives. His recent publications address challenges in dynamic graph learning, continual learning, and causal discovery. Bacciu advocates for a national AI strategy in Italy, stressing long-term vision and ethical AI governance aligned with EU regulations like the AI Act.

Javier Gonzalez-Sanchezمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Software Engineering
• Affective Computing
• +۱۱ مورد دیگر

Javier Gonzalez-Sanchez is an Assistant Professor in the Department of Computer Science and Software Engineering at the College of Engineering, California Polytechnic State University (Cal Poly). His work bridges software engineering and human-computer interaction, focusing on creating intelligent systems capable of understanding and responding to human emotions and cognitive states. His research interests lie at the intersection of affective computing, software engineering, and interactive technologies. He aims to develop adaptive systems that enhance user experiences in education, healthcare, manufacturing, and entertainment by leveraging physiological signals such as brain activity, eye movements, and biometric data. His approach involves using cutting-edge tools including brain-computer interfaces, eye-tracking devices, and virtual/augmented reality platforms. The absence of publication data prevents detailed analysis of article trends; however, his research direction suggests strong interdisciplinary engagement with psychology, education, and health sciences. He actively involves students in hands-on research, blending teaching with innovation. Javier contributes significantly to diversity, equity, and inclusion through participation in programs such as the Learning Aligned Employment Program (LEAP), Engineering Possibilities in College (EPIC), and the Summer Undergraduate Research Program (SURP). He also supports K-12 outreach in partner schools through robotics and computational thinking initiatives funded by the Office of University Diversity and Inclusion. Additionally, he mentors Hispanic undergraduate students pursuing graduate education through the Society of Hispanic Professional Engineers (SHPE). He is involved in grant-funded projects aimed at expanding educational pipelines for underrepresented communities. While specific grants are not listed, his engagement with university diversity grants and outreach programs highlights his commitment to inclusive engineering education. Javier leads or participates in research teams focused on empathetic computing and intelligent tutoring systems, though specific lab names or team structures are not mentioned in the text.

Daniel Strüberمشاهده پروفایل

دانشیار

• Model-Driven Engineering
• AI Engineering
• Software Quality Assurance
• +۷ مورد دیگر

Daniel Strüber is an Associate Professor in Software Engineering at Chalmers University of Technology and the University of Gothenburg, Sweden, and an affiliate of the Department of Software Science at Radboud University Nijmegen, Netherlands. His research focuses on model-driven engineering, AI engineering, and empirical software engineering, addressing challenges in software quality, variability management, and security. He holds a PhD (summa cum laude) from Philipps University Marburg and has held academic positions including Assistant Professor at Chalmers and postdoctoral roles at King's College London and Philipps University Marburg. Education: PhD in Computer Science, Philipps University Marburg (2011–2016) Diplom (M.Sc. equivalent) in Computer Science, Philipps University Marburg (2005–2011) Research Interests: Strüber's work spans model-driven engineering, AI integration in software systems, and empirical studies. Key areas include: Model-based languages and tools for software quality assurance Machine learning-enabled systems and their engineering principles Robotics software engineering and variability management Formal methods for consistency and security Search-based optimization in model-driven development Recent Contributions: Recent work includes studies on LLM-assisted reverse engineering, empirical analysis of manual abstraction in software systems, and model-driven approaches for edge deployment of ML models. His research bridges theory with practical implementations in domains like robotics and web-based systems. Awards: Best Reviewer Award (SoSyM 2025) Distinguished Reviewer Awards (GPCE 2024, SPLC 2023) Transformation Tool Contest Awards (2020, 2016) EASST Best Paper Awards (ICGT 2020, ICGT 2017) Grants & Leadership: Co-editor of a JSS special issue on Product Line Engineering (2024) and program co-chair of SPLC 2024. Active in conferences like ICSE, ICGT, and MODELS. Leads grants such as the VR SEMLA project (2022–2024) and the DFG EUphORia fellowship (2019). Teaching: Courses include 'Software Engineering for AI Systems' at Chalmers and 'Software Product Lines' at Radboud University. Involves students in real-world projects via GiPHouse, a student-run company.

Bill Basenerمشاهده پروفایل

استاد

• Data Science
• Applied Mathematics
• Machine Learning
• +۱۲ مورد دیگر

Bill Basener is a Professor at the School of Data Science at the University of Virginia, with a joint appointment in the Department of Systems and Information Engineering. His research spans applied mathematics and data science, focusing on machine learning, image processing, dynamical systems, and ecological modeling. He is widely recognized for developing foundational algorithms in hyperspectral image analysis and for the Basener-Ross model of ecological collapse. His research interests include applied topology, signal processing, evolutionary genetics, and mathematical modeling in complex systems. He authored one of the first textbooks in applied topology, covering applications in cosmology, chaos theory, protein folding, and robotics. His work in topological anomaly detection , gradient flow clustering , and hierarchical material identification has been instrumental in both defense and humanitarian applications, including global disaster relief efforts. While no specific publications are listed, his research output is centered on interdisciplinary applications of mathematical and topological methods in real-world data challenges, particularly in geospatial and ecological domains, with strong emphasis on algorithm development and software implementation. Bill Basener has made significant contributions through software and patents, and his methods have become industry standards in hyperspectral imaging. He is the founder and president of two data analytics companies: Geospatial Technology Associates and Spectral Solutions, reflecting his applied and entrepreneurial approach to data science. He earned his Ph.D. in Mathematics from Boston University and his B.S. in Mathematics from Marist College. His academic journey includes an emeritus position at the Rochester Institute of Technology, prior to joining the University of Virginia in 2014. He leads research with real-world impact, particularly in disaster response and environmental modeling, and continues to bridge theoretical mathematics with practical data science solutions.

• Distributed Systems
• IoT Protocols
• Big Data
• +۴ مورد دیگر

Dr. Sotirios Kontogiannis is a Researcher and Director of the Distributed MicroComputer Systems Laboratory (MCSL) at the Department of Mathematics, University of Ioannina. He holds a PhD in Software Engineering from Democritus University of Thrace (2012), with a focus on load balancing algorithms for distributed systems. His career spans academic and private sector roles, including software development, research in FP7 projects, and technical leadership at TEKMON Geomatics. He currently teaches courses on Database Systems, Parallel Algorithms, and Educational Technologies at the University of Ioannina. Education: 5-year Engineering Diploma (DUTH, 2003), MSc in Microelectronics and Information Systems (DUTH, 2006), PhD in Load Balancing Algorithms (DUTH, 2012). Research Focus: Distributed systems, IoT protocols, sensor networks, big data, and smart agriculture technologies (e.g., beekeeping monitoring systems, wine fermentation frameworks). His work integrates IoT, AI, and edge computing to address challenges in agriculture, accessibility, and environmental monitoring. Notable inventions include patented smart beehive systems and tactile navigation tools for visually impaired individuals.