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Catherine BurnsView profile

Professor

• Human factors engineering
• Systems engineering
• Safety
• +7 more

Catherine Burns is a Professor in the Department of Systems Design Engineering at the University of Waterloo, Canada, and holds the Tier 1 Canada Research Chair in Human Factors in Healthcare Systems. She also serves as the Associate Vice President, Health Initiatives, overseeing research in health and health technology alignment with the university's strategic goals. Her roles include leadership in institutional initiatives such as the Health Initiatives Task Force and the Centre for Bioengineering and Biotechnology. Education: She earned a Bachelor's in Systems Design Engineering (1992, University of Waterloo), a Master's in Industrial Engineering (1994, University of Toronto), and a Doctorate in Mechanical and Industrial Engineering (1998, University of Toronto). Research: Her work focuses on human factors engineering, systems engineering, safety, ergonomics, and cognitive engineering, with applications in healthcare systems and technology. Key interests include interface design, usability testing, and decision-making processes in complex environments. Awards: Notable recognitions include multiple Outstanding Performance Awards from the University of Waterloo, the Research Excellence Award (2009), and Fellow status in the Human Factors and Ergonomics Society (2015). Advising & Grants: As the Principal Investigator of an NSERC CREATE Training program, she has trained over 40 graduate students. Her grants include the NSERC Discovery Accelerator Supplement (2008). Labs & Teams: Founded and led the Centre for Bioengineering and Biotechnology (2012–2019), advancing interdisciplinary research in biomedical and health technologies.

Tania MorimotoView profile

Associate Professor

• Robotics
• Medical Robotics
• Soft Robotics
• +3 more

Tania Morimoto is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego. Her research focuses on the design and control of flexible and soft robots for medical and exploration applications, including personalized surgical robots and intuitive human-in-the-loop interfaces. She leads efforts in haptic device development for education, notably creating the Hapkit and H3Kit systems used in STEM labs globally. She holds a B.S. in Mechanical Engineering from MIT, and M.S. and Ph.D. from Stanford University's CHARM Lab. Her work bridges robotics, haptics, and medical engineering, with key innovations in continuum robot design, wireless sensing, and surgical teleoperation systems. Key research areas include soft robotics for minimally invasive surgery, wearable haptic devices, and scalable pneumatic control systems. Her team has developed novel methods for patient-specific robot design using preoperative medical imaging, along with virtual reality interfaces for surgical planning. Notable achievements include the NSF CAREER Award (2022) for work on handheld continuum robots, and contributions to wireless force sensing technologies. Her educational initiatives have expanded access to hands-on robotics learning through low-cost hardware kits adapted for remote instruction.

Sarah HarrisView profile

Professor

• Computer Architecture
• FPGA Design
• RISC-V Systems
• +3 more

Sarah Harris is Professor and Undergraduate Coordinator in the Department of Electrical and Computer Engineering at University of Nevada, Las Vegas. Her research focuses on computer architecture education, FPGA-based systems, and hardware implementations of neural networks. Research areas include: RISC-V architecture education tools FPGA-based hardware design Embedded AI systems Computer engineering pedagogy Her publication portfolio shows strong focus on educational innovation with 7 publications (2021-2024) on RISC-V teaching tools and MOOCs, alongside hardware implementations of neural networks and contributions to bioinformatics tools. Earlier work includes digital design fundamentals and memory systems.

Gian Luca MarcialisView profile

Associate Professor

• Biometrics
• Presentation Attack Detection
• Facial Recognition
• +4 more

Gian Luca Marcialis is an Associate Professor at the University of Cagliari, leading the Biometric Unit of the Pattern Recognition and Applications Laboratory (PRA Lab) within the Department of Electrical and Electronic Engineering. He holds an M.S. and Ph.D. in Electronic Engineering and Computer Science from the University of Cagliari (2000 and 2004). His research focuses on biometric security, including fingerprint presentation attack detection, facial deepfake detection, and crowd anomaly analysis. Key areas include multimodal biometric systems, liveness detection in authentication, and the ethical implications of AI in forensics. Marzialis has contributed to initiatives like the LivDet competitions and BullyBuster project against cyberbullying. His work integrates machine learning with real-world applications in surveillance, healthcare, and public safety, emphasizing both technical innovation and societal impact.

Christopher BidinostiView profile

Professor

• Nuclear Magnetic Resonance
• Medical Imaging
• Machine Learning
• +3 more

Dr. Christopher Bidinosti is a Professor in the Department of Physics at the University of Winnipeg. He holds a Ph.D. from the University of British Columbia (2000) and specializes in nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), and machine learning applications in agriculture. His research spans low-field NMR/MRI, hyperpolarized nuclei, and collaborations like the TerraByte project, aimed at advancing agricultural machine learning through open-access plant image datasets. His research interests include TRASE MRI, neutron electric dipole moment (EDM) searches, GPU computing, and magnetics (coil design, shielding). He teaches courses such as Electricity & Magnetism and Optics and Waves. His work bridges physics and applied technology, with a focus on interdisciplinary innovation. Bidinosti collaborates with institutions like UWinnipeg’s Computer Science department and has developed hardware solutions for NMR/MRI systems. His contributions span experimental physics, computational methods, and agricultural technology, leveraging both theoretical and applied approaches.

• Artificial Intelligence
• Machine Learning
• Algorithm Selection
• +4 more

Lars Kotthoff is the Templeton Associate Professor and Derecho Professor at the University of Wyoming's School of Computing, Department of Electrical Engineering and Computer Science. He leads the MALLET lab, focusing on meta-algorithmics, learning, and large-scale empirical testing. His research integrates AI and machine learning to develop robust systems, particularly in algorithm selection, configuration, and automated machine learning (AutoML). He has held sabbaticals and collaborations globally, including at the University of Warsaw and NASA Ames. Awards include the Templeton Endowed Chair and Open Source Machine Learning Award. His work bridges computational mechanics, materials science, and optimization heuristics. Research interests include Bayesian optimization for materials science, automated parameter tuning, and interpretable machine learning models. Key projects include optimizing laser-induced graphene production and developing the mlr3 framework. Grants include NASA EPSCoR funding for in-space manufacturing and a Microsoft grant for biomedical imaging. He advises graduate and undergraduate students, mentors Google Summer of Code projects, and organizes workshops/conferences like COSEAL and Dagstuhl seminars. Publications span automated algorithm design, performance benchmarking, and interdisciplinary applications. His work emphasizes making machine learning accessible to non-experts through tools like Auto-WEKA and the mlr3 ecosystem. Current projects include NASA-funded advanced electronics manufacturing and collaborations with institutions worldwide.

Jacob K. WhiteView profile

Professor

• Computer-aided design
• CAD
• Computational prototyping
• +11 more

Jacob K. White is the Cecil H. Green Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). He is a principal investigator at the Research Laboratory of Electronics (RLE) and served as its Associate Director since 2001. His academic journey includes positions as an assistant professor (1987), associate professor (1991), and full professor (1996) at MIT. Education: Bachelor's degree in Electrical Engineering and Computer Science from MIT (1980) Master's degree from the University of California, Berkeley (1983) Doctorate from UC Berkeley (1985) Research Interests: Computer-aided design and computational prototyping Numerical algorithms and simulation techniques Optimization of integrated circuits and MEMS devices BioMEMS and biomolecule design Systems biology and fluidic analysis of sensors/actuators Scientific Awards: 2008 IEEE Fellow Major Award at the 50th Design Automation Conference (DAC) Advising and Grants: No student advisees listed. Details on grants are not provided in the text. Labs/Teams: Principal Investigator at the Research Laboratory of Electronics (RLE) and leads the Computational Prototyping Group.

Abu HaddudView profile

Associate Professor

• Supply Chain Management
• Operations Management
• RFID Technology
• +6 more

Dr. Abu Haddud is an Associate Professor at the University of Southern Mississippi (USM), located at the Gulf Park campus. His research focuses on supply chain sustainability, RFID technology, ERP systems, autonomous vehicles, and digital disruption. He holds a PhD from Eastern Michigan University (2011). His work explores the integration of emerging technologies such as AI, RFID, and autonomous vehicles into supply chains. Recent studies address challenges in ERP implementation across sectors, waste reduction in lean manufacturing, and the strategic role of 3D printing in global operations. His publications emphasize practical applications of technology to enhance operational efficiency and competitiveness. Dr. Haddud’s research also extends to education innovation, analyzing value co-creation in online business programs. He actively investigates digital transformation trends, including electronic logging devices and chatbot technologies like ChatGPT in logistics processes.

• Robotics
• Sensors Technology
• Biomedical Engineering
• +5 more

Oliver Ozioko is a Lecturer in Electrical and Electronic Engineering at the College of Science and Engineering. His research focuses on advanced sensor technologies, wearable systems, and robotic applications. He specializes in developing soft, flexible sensors for robotics, prosthetics, and biomedical devices. Key areas of interest include tactile sensing, electronic skin (e-skin), additive manufacturing for sensors, and assistive technologies for the deafblind community. His work integrates interdisciplinary approaches combining material science, nanotechnology, and robotics. Notable contributions include capacitive sensors using recycled carbon fibers, 3D-printed robotic end-effectors, and multifunctional e-skin systems with temperature and pressure sensing capabilities. He has also pioneered tactile communication interfaces and haptic feedback devices for assistive applications. Publications span journals like IEEE Sensors Letters and Sensors, with conference contributions at IEEE International Conferences on Flexible Sensors and Systems. His research emphasizes sustainability (e.g., recycled materials) and real-world applications in healthcare and industrial automation. Awards and grants information is not explicitly detailed in the provided texts, though his prolific publication record indicates sustained research activity. He collaborates extensively with industry partners and academic institutions globally, though specific lab affiliations are not mentioned.

DAVID LOPEZ VILARIÑOView profile

Professor

• Computer Architecture
• Computer Vision
• FPGA Acceleration
• +3 more

David Lopez Vilariño is a **Professor** at the **University of Santiago de Compostela**, affiliated with the **Department of Electronics and Computing** within the **Faculty of Physics**. He earned his PhD in 2001 with a thesis titled *"Active contours at the pixel level: design and implementation on cellular network architectures,"* advised by Dr. Diego Cabello Ferrer. His research focuses on **Computer Architecture**, **FPGA Acceleration**, **LiDAR Data Analysis**, and **Embedded Systems**, with notable contributions to LiDAR-based applications in urban planning, infrastructure monitoring, and medical imaging. He is part of the **ARQCOMP (Computer Architecture)** and **Artificial Vision** research groups. His work spans topics such as high-performance computing, parallel processing, and hardware optimization for vision-capable systems. Key projects include developing FPGA-based solutions for real-time video surveillance, retinal vessel analysis, and autonomous navigation systems. Publications emphasize **LiDAR data processing**, including algorithms for road detection, power line characterization, and 3D point cloud analysis. He also pioneered tools like the *Open Lidar Visualizer and Analyser* for 3D stereoscopic visualization. His expertise bridges hardware design and software development, particularly in leveraging FPGAs for embedded vision systems. No scientific awards or grants are explicitly listed, but his prolific publication record highlights sustained innovation in computer vision and geospatial technologies. His research team collaborates on projects involving manycore systems, GPU acceleration, and reconfigurable computing architectures.

• Artificial Intelligence
• Collaborative Robotics
• Human-Machine Interaction
• +3 more

Madeleine EL ZAHER is a Researcher-Lecturer at CESI, affiliated with the Engineering and Numerical Tools research team. Her work focuses on Artificial Intelligence, Collaborative Robotics, Human-Machine Interaction, and Multi-Agent Systems. She holds a PhD in Computer Sciences from the University of Technology of Belfort-Montbéliard (2013) and a Master’s degree in Computer Sciences and Telecommunications from Paul Sabatier University (2010). Teaching responsibilities include Computer Sciences and Electronics at the Engineering program level, emphasizing project-based learning and training through research. She co-supervises PhD students in industrial robotics and cyber-physical systems, including Abdessalem ACHOUR (defending in 2024) and Badra Souhila GUENDOUZI (defending in 2025). Her research spans semantic mapping in mobile robotics, federated learning for industrial systems, and platooning algorithms for autonomous vehicles. Notable publications include work on semantic mapping with 3D models (2024), federated learning frameworks using genetic algorithms (2023), and verification of platooning systems (2012–2015). No scientific awards are explicitly listed, but her contributions reflect impactful work in autonomous systems and robotics. Grants and lab affiliations are not detailed in the provided text, though her team’s research aligns with CESI’s focus on engineering and numerical tools.

Richard PaigeView profile

Professor

• Software Engineering
• Model-Driven Engineering
• Safety-Critical Systems
• +10 more

Richard Paige is a Joseph Ip Distinguished Engineering Professor at McMaster University's Department of Computing and Software, Faculty of Engineering. He also holds a part-time position as Professor of Enterprise Systems at the University of York, UK. His primary research focuses on software engineering, particularly model-driven engineering, safety-critical systems, and low-code development. He leads the McMaster Centre for Software Certification (McSCert) and has extensive industry collaborations, including with Rolls-Royce, Leonardo, and NASA. Paige holds a PhD in Computer Science from the University of Toronto (1997), an MSc from the same institution (1994), and a BSc from McMaster University (1992). He has supervised over 22 PhD and 72 Master’s students, many of whom pursue careers in academia and industry. His funding exceeds $24M CAD from NSERC, Ontario Research Fund, and industry grants. His research emphasizes model management, assurance cases, and safety-critical systems. Key contributions include the Epsilon framework and advancements in model-driven tools. Paige chairs the STAF conferences and serves on editorial boards for journals like Software and Systems Modeling . He actively promotes open-source tooling and has won multiple best paper awards, including ACM Distinguished Paper at MoDELS (2010, 2017) and the Ten-Year Most Influential Paper Award (2016). Paige’s teaching focuses on problem-based learning and flipped classrooms, with awards for pedagogical innovation. He also leads interdisciplinary projects, such as SECT-AIR (reducing aerospace software costs) and CROSSMINER (developer-centric knowledge mining). His work bridges academia and industry, addressing challenges in automotive, healthcare, and aerospace sectors. Current projects include safety assurance for autonomous vehicles and model-driven sustainability evaluation.

Mark LawfordView profile

Professor

• Software engineering
• Safety critical systems
• Digital & Smart Systems
• +3 more

Mark Lawford is a Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. His expertise spans software engineering, safety critical systems, and model-based development, with a focus on digital & smart systems and transportation research clusters. Contact: Email: lawford@mcmaster.ca Website: www.cas.mcmaster.ca/~lawford His research explores automotive systems , formal verification , and model-driven engineering , particularly addressing challenges in automotive electrification , centralized E/E architectures , and safety assurance cases . Recent publications emphasize test case generation , change impact analysis , and model transformation tools for Simulink, SysML, and AUTOSAR. Key trends in his work include autonomous vehicle safety , real-time control systems , and integration of formal methods with industrial practices across automotive, biomedical, and nuclear domains.

Sai Manoj Pudukotai DinakarraoView profile

Assistant Professor

• Hardware security
• Adversarial machine learning
• Internet of Things networks
• +9 more

Sai Manoj Pudukotai Dinakarrao is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University's College of Engineering and Computing. He leads the HArt (Hardware and AI Research) Group, focusing on cutting-edge research at the intersection of hardware security and artificial intelligence. His educational journey includes a BTech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University (2010), an MTech in Information Technology from International Institute of Information Technology Bangalore (2012), and a PhD in Electrical Engineering from Nanyang Technological University, Singapore (2015). Following his doctoral studies, he completed post-doctoral research at TU Wien, Vienna (2015-2017) and George Mason University (2017-2018). Dr. Dinakarrao's research spans hardware security, adversarial machine learning, IoT networks, and deep learning in resource-constrained environments. His work integrates hardware design with AI techniques to address security challenges in computing systems, with particular focus on side-channel attack detection, malware detection in IoT networks, on-chip security, and hardware accelerator design for machine learning applications. His research has resulted in numerous publications in top-tier conferences and journals including IEEE Transactions, ACM conferences, and Design Automation Conference. Analysis of his recent publications reveals a strong trend toward hardware security solutions using machine learning techniques. His work increasingly focuses on Processing-in-Memory architectures, energy-efficient security solutions for IoT devices, and innovative approaches to hardware Trojan detection. Many publications demonstrate interdisciplinary collaboration across electrical engineering, computer science, and cybersecurity domains. Young Research Fellow Award at Design Automation Conference (DAC) 2013 Best paper award at International Conference on Data Mining (ICDM) 2019 Best paper award at International Conference on Consumer Electronics (ICCE) 2020 Best paper nomination at International Conference on Computer-Aided Design (ICCAD) 2019 Best paper nomination at Design Automation and Test in Europe (DATE) 2018 Dr. Dinakarrao has successfully mentored numerous PhD and MS students, with alumni securing positions at AMD-Xilinx, US Government agencies, and academic institutions. His research has been supported by significant grants from NSF, DARPA, and Virginia Commonwealth Cyber Initiative. Current projects include securing supply chains with UVA, developing novel architectures for machine learning acceleration, and creating energy-preserving cryptography protocols. The HArt Group maintains active collaborations with industry partners including AMD-Xilinx and government agencies. The lab focuses on practical implementations of theoretical security concepts, with particular emphasis on creating deployable security solutions for real-world hardware systems. Current research directions include intermittent computing with energy harvesting, hardware fuzzing techniques, and robust machine learning models resistant to adversarial attacks.

Tamilselvan GajapriyaView profile

Assistant Professor

• Aviation Safety
• Pilots' Selection and Training
• Human Factors in Aviation
• +7 more

Tamilselvan Gajapriya is an Assistant Professor in the Department of Aviation Science at Saint Louis University, part of the School of Science and Engineering. He holds a Ph.D. in Aviation Science from Florida Institute of Technology, and master’s and bachelor’s degrees in Avionics and Electronics Engineering from Anna University, India. His research focuses on aviation safety, pilot selection/training, human factors, and physiological monitoring in flight environments. He directs the Aviation Safety, Aviation Physiology, and Ergonomics (ASAPE) Lab, which uses advanced equipment like EEG sensors and eye-tracking devices to study pilot performance under stress. Key research areas include cognitive workload assessment during emergencies, UAV privacy concerns, and consumer attitudes toward autonomous aircraft. He has published extensively in journals like Safety Science and Journal of Unmanned Vehicle Systems , with peer-reviewed work on topics ranging from pilot retention strategies to electronic chart usability. Professional affiliations include the Human Factors & Ergonomics Society and Women in Aviation International. His lab investigates physiological markers (e.g., galvanic skin response, EEG) to evaluate pilot decision-making and stress levels during simulated emergencies. Current projects explore factors influencing pilot career retention in commercial aviation and public acceptance of unmanned systems. Dr. Gajapriya collaborates with industry partners to translate research into practical aviation safety solutions.