جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Călin-Adrian POPA is a Full Professor at the Department of Computer and Software Engineering, Politehnica University of Timişoara. He holds a Ph.D. in Computer Science and Information Technology (2015) from Politehnica University Timişoara, and additional degrees in Mathematics from West University of Timişoara (B.Sc. 2013, M.Sc. 2015). His research focuses on artificial intelligence, machine learning, and neural networks, with specializations in complex-valued systems (octonion, quaternion, Clifford algebra), stability analysis, and synchronization of neural networks. He has been awarded the 'Profesor Bologna' Distinction (2017). Research interests include: Advanced neural network architectures Deep learning for computer vision Fractional-order systems Nonlinear dynamics and control Applications in robotics and astrophysics His publications span 2014–2025, emphasizing theoretical contributions to neural networks and practical applications in computer vision and robotics. He supervises a large cohort of PhD students in AI-related fields.

Bradley Denbyمشاهده پروفایل

استادیار

• Cyber-physical systems
• Computational nanosatellite constellations
• Machine learning for autonomy
• +۳ مورد دیگر

Bradley Denby is an Assistant Professor in the Department of Aerospace & Ocean Engineering at Virginia Tech, holding the Marty and Anna Irvine AOE Faculty Fellow position. He specializes in cyber-physical systems, computational nanosatellite constellations, and machine learning for autonomy, with a focus on orbital edge computing. Denby leads the Starbelt Lab, focusing on advanced air mobility and satellite systems research. Education: PhD in Electrical and Computer Engineering, Carnegie Mellon University (20XX) MS in Computer Engineering, Air Force Institute of Technology (AFIT) BS in Physics, Southern Illinois University Research Interests: Denby’s work bridges computer science and aerospace engineering, addressing challenges in computational satellite systems, edge computing in space, and autonomous decision-making. His projects include batteryless satellites (Tartan Artibeus) and high-coverage nanosatellite constellations (EagleEye). He explores orbital edge computing frameworks to enable real-time data processing directly on satellites. Recent Research Trends: His publications emphasize scalable nanosatellite constellations, overcoming computational bottlenecks in space, and integrating machine learning for on-orbit inference. Themes include energy-efficient systems, inter-satellite communication, and mission-critical autonomy. Awards: Marty and Anna Irvine AOE Faculty Fellow (Virginia Tech) Teaching & Labs: Instructs courses on space engineering, satellite design, and advanced air mobility. Runs the Starbelt Lab (website/GitHub linked).

• Biomedical Engineering
• 3D Printing in Healthcare
• Pediatric Medical Devices
• +۳ مورد دیگر

Dr. Tegan Cheng is an NHMRC Emerging Leadership Fellow and Adjunct Lecturer in the School of Biomedical Engineering at the University of Sydney. She leads the EPIC Lab at Kids Research, Children's Hospital at Westmead, focusing on pediatric medical devices and 3D printing applications. Her work bridges engineering and clinical needs to address musculoskeletal challenges in children. She holds a BE(Mechanical/Biomedical) and BMedSc from the University of Sydney (2011) and a PhD (2016). Research Interests: Developing implantable/wearable medical devices for children, 3D printing in healthcare, pediatric orthotic design, and commercialization pathways. Her Printhotics initiative aims to create equitable access to personalized ankle-foot orthoses (AFOs) using 3D scanning and AI. Key collaborations include clinicians, engineers, and designers to advance pediatric device solutions. Grants and Awards: 2021 Young Tall Poppy Science Award, NHMRC Investigator Grant for Printhotics (2020), and grants from the American Orthotic & Prosthetic Association and University of Sydney's Educational Innovation Fund. Active projects include smart orthotics with embedded sensors and biodegradable bone implants. Labs/Teams: Group Leader of the EPIC Lab, collaborating with Kids Research, University of Sydney, and industry partners. Focus areas include translational research, medical device innovation, and interdisciplinary education using 3D-printed anatomical models.

Mia Silfverمشاهده پروفایل

عضو هیئت علمی

• Social Psychology
• Moral Emotions
• Environmental Sustainability
• +۴ مورد دیگر

Mia Silfver is a University Lecturer in Social Psychology at the University of Helsinki, affiliated with the Faculty of Social Sciences. She holds a visiting scholar position in the Department of Economics and Management. Her research focuses on moral emotions, environmental sustainability, cross-cultural values, and the intersection of work-life balance with caregiving. Silfver's work bridges social psychology with policy analysis, exploring topics like pro-environmental behavior, populist discourse, and moral vitalism across cultures. She has contributed to projects such as 'Food Futures' and 'Embedding Environmental Sustainability Norms', addressing food system transformations and societal norms. Silfver has collaborated internationally, presenting at conferences like the European Conference on Psychological Assessment and engaging in media discussions on topics ranging from moral emotions to historical pedagogy. Her research employs mixed methods, including psychophysiological measurements and large-scale survey analysis. Key research areas include analyzing moral foundations theory in political contexts, examining emotional responses in narcissistic individuals through facial electromyography, and investigating how social networks influence dietary choices. Silfver's work on workplace practices emphasizes supportive policies for employees balancing caregiving and employment. She has published over 35 peer-reviewed articles, with recent contributions focusing on Finland's societal challenges related to climate policy and food systems. Her projects often involve interdisciplinary collaboration with economists, policymakers, and historians. Professional activities include peer reviewing for journals, organizing conferences, and delivering invited talks on history education and moral consciousness integration. Silfver's research has informed policy recommendations on sustainable food systems and has been featured in media discussions on topics like shame and body image in societal contexts.

Or Brookمشاهده پروفایل

دانشیار

• Competition Law and Policy
• Antitrust
• Regulation
• +۳ مورد دیگر

Dr. Or Brook is an Associate Professor of Competition Law and Policy at the School of Law, University of Leeds, and Deputy Director of the Centre for Business Law and Practice. They hold interdisciplinary qualifications, including a PhD from the Amsterdam Centre for European Law and Governance, an LLM in European Competition Law from the University of Amsterdam, and degrees in Law and Economics from the Hebrew University of Jerusalem. Research focuses on international EU competition law, antitrust, and empirical legal methods. Notable projects include the Priority Setting Project (funded by ESRC) and a comprehensive study of judicial review in EU competition law. They co-edit the Research Handbook on Abuse of Dominance and lead the UK branch of ASCOLA, contributing to policy discussions through international collaborations. Education: PhD in Law, University of Amsterdam LLM in European Competition Law, University of Amsterdam LLB and BSc in Economics, Hebrew University of Jerusalem Research interests emphasize empirical analyses of legal texts, institutional enforcement practices, and competition law’s intersections with other regulatory frameworks. Recent monograph Non-Competition Interests in EU Antitrust Law (Cambridge UP, 2022) presents novel empirical data on public policy influences in EU competition enforcement. Key awards include the Michael Beverley Innovation Fellowship (associated member), supporting entrepreneurial academic initiatives. Their work bridges law and economics through interdisciplinary methods, advocating for empirical rigor in European legal scholarship.

Tania Morimotoمشاهده پروفایل

دانشیار

• Robotics
• Medical Robotics
• Soft Robotics
• +۳ مورد دیگر

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.

Christopher Bidinostiمشاهده پروفایل

استاد

• Nuclear Magnetic Resonance
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

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.

• Computer and Network Security
• Computer Networking
• Network Modeling and Performance Evaluation
• +۴ مورد دیگر

Cliff C. Zou is a Professor at the Department of Computer Science, University of Central Florida (UCF), and Program Coordinator for Master's degrees in Digital Forensics and Cyber Security and Privacy. He serves as a UCF Cybersecurity and Privacy Cluster member, Point-of-Contact for UCF CAE-CD/CAE-R designations, and leads the Data Systems Group. With a Ph.D. from University of Massachusetts, Amherst (2005), and B.S./M.S. from USTC, he has received numerous awards including the UCF-TIP Award (2013, 2024), Best Paper Awards, and Fellowships. Ph.D., Electrical & Computer Engineering, University of Massachusetts, Amherst (2005) M.S., Automation, University of Science & Technology of China (USTC) (1999) B.S., Automation, USTC (1996) His research spans Computer and Network Security , IoT Security , Blockchain Systems , Wireless Network Security , and Malware Analysis . Recent work focuses on adversarial machine learning, side-channel attacks, encrypted traffic classification, and secure IoT architectures. His publications (2025-2020) highlight trends in deep learning-based defense , WiFi fingerprinting , TrustZone security , and malware propagation . Scientific Awards: UCF Teaching Incentive Program (TIP) Award (2024, 2013) Best Paper Award (2023) Best Student Paper Award (ACSAC 2007) Guo Moruo Scholarship (USTC, 1996) Graduate Fellowships (University of Massachusetts, 1999-2001) Prof. Zou has advised 14 PhD graduates and numerous Master's/BS students, many now holding academic/industry roles. His funded projects include NSF grants (DGE-2325452, DGE-2042996, DGE-1723587, DGE-1915780), US Army PEO STRI collaborations, and Florida Center for Cybersecurity seed grants. He leads the cyberforensic.net educational platform and has contributed to free IoT security labs for professionals.

DAVID LOPEZ VILARIÑOمشاهده پروفایل

استاد

• Computer Architecture
• Computer Vision
• FPGA Acceleration
• +۳ مورد دیگر

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.

Jon R. Marstranderمشاهده پروفایل

استادیار

• Embedded Systems
• Digital Signal Processing
• Neurological Image Analysis
• +۷ مورد دیگر

Jon R. Marstrander is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Alabama at Birmingham (UAB) , where he joined the faculty in 2005. His expertise bridges Embedded Systems , Digital Signal/Image Processing , and Neurological Signal Analysis , supported by a Professional Engineering License since 1992. Education : B.S. in Electrical Engineering, UAB M.S. in Electrical Engineering, UAB Ph.D. in Computer Engineering, UAB His research focuses on collaborative neuroscience projects with UAB clinicians, combining industry-grade hardware/software design (from 16+ years of aerospace/medical engineering) with medical image analysis . Recent work includes high-performance computing workflows for neuroimaging and pathology modeling in Parkinson's/Schizophrenia. Key publication themes: Neurological Imaging (2015–2020), Exercise Neurology , Antipsychotic Treatment Analysis , and Optical Navigation Systems .

Ryan T. Whiteمشاهده پروفایل

دانشیار

• Deep Learning
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.

Sheng Liمشاهده پروفایل

استادیار مدعو

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۴ مورد دیگر

Sheng Li is an Adjunct Assistant Professor in the School of Computing at the University of Georgia (UGA). He holds roles such as Graduate Program Faculty and Courtesy Faculty in the Institute of Bioinformatics. Previously, he served as an Assistant Professor at UGA from August 2018 to July 2022. His research focuses on Machine Learning, Computer Vision, Data Mining, Natural Language Processing, Causal Inference, and User Modeling. Li earned his Ph.D. in Computer Engineering from Northeastern University in 2017, following degrees from Nanjing Institute of Post and Telecommunications in China. His work bridges theoretical advancements and practical applications, including AI solutions for healthcare (e.g., medication adherence monitoring), computer vision for wildlife tracking (fish re-identification), and causal inference methodologies. He has received notable awards like the Fred C. Davison Early Career Scholar Award (2022) and the Aharon Katzir Young Investigator Award (2020). His research is funded by grants from agencies such as the US Department of Defense and NIH, supporting projects like Knowledge-Guided Scene Graph Generation and Reasoning for Visual Understanding. Li’s publications span top venues in AI and computer science, addressing challenges in domain adaptation, trustworthy AI, and multimodal learning. He has collaborated on interdisciplinary projects, including bioinformatics and agricultural NLP. His contributions emphasize both technical innovation and societal impact.

• Analogue and Mixed Signal IC Design
• Digital System Design (FPGA)
• CMOS Lab-on-Chip Platforms
• +۴ مورد دیگر

Junming Zeng is a Researcher at the Department of Electrical and Electronic Engineering, Faculty of Engineering at Imperial College London. His work focuses on advanced CMOS technologies for biomedical applications, including lab-on-chip platforms and ion imaging systems. He holds a PhD from Imperial College London (2022), following a Master's in Analogue and Digital Integrated Circuit Design (2017) and a Bachelor's in Electronic Engineering (2016), both from UK institutions. His research interests span analogue/mixed-signal IC design, FPGA-based digital systems, and ultra-high-speed ion sensing solutions. He has pioneered CMOS lab-on-chip platforms for real-time chemical monitoring and developed compressed sensing techniques for optimizing sensor array performance. His work integrates deep learning for applications like diabetes glucose prediction and drift compensation in ISFET sensors. Zeng has received the Best Student Paper Award (1st Prize) at ISCAS 2018 and Imperial's Department PhD Scholarship. His research bridges electrical engineering and biomedical engineering, with a focus on scalable, energy-efficient systems for healthcare and diagnostics. He leads projects involving edge computing, temporal fusion transformers, and microfluidic integration, demonstrating expertise in both hardware innovation and algorithmic development. His lab develops cutting-edge systems such as 1000fps ISFET SoCs with programmable gain and high-throughput digital readout architectures. Recent work includes live demonstrations of real-time pH monitoring in 3D-printed microfluidic systems and spatio-temporal ion membrane characterization platforms.

• Machine Learning
• Image Retrieval
• Data Clustering
• +۳ مورد دیگر

Mohammed Lamine Kherfi is a researcher affiliated with Université de Ouargla, Algeria. His work focuses on machine learning, image retrieval, and data clustering with applications in computer vision and optimization. He has collaborated extensively with researchers like Oussama Aiadi, Mebarka Allaoui, and Djemel Ziou. His research bridges theoretical advancements in machine learning with practical applications in areas such as fruit classification, semantic image retrieval, and deep learning models. Key research areas include optimization algorithms (e.g., PSO integration with t-SNE), multi-view learning, and Bayesian methods for image representation. He has contributed to improving clustering techniques, feature extraction, and the development of lightweight neural network architectures. His work often emphasizes efficient and energy-aware solutions for real-world problems. Over 30 publications span prestigious venues like Expert Systems with Applications, IEEE Access, and Multimed Tools Appl. His collaborative network includes institutions in Algeria and international partners, reflecting a global impact in computational intelligence and computer vision.

• Power Amplifiers
• Signal Processing
• Radar Systems
• +۷ مورد دیگر

Souheil Ben Smida is an Associate Professor at the School of Engineering & Physical Sciences at Heriot-Watt University, affiliated with the Institute of Sensors, Signals & Systems. His research focuses on power amplifiers, signal processing, radar systems, and biomedical applications, with a particular emphasis on integrating neural networks and wireless communication technologies. His work spans areas such as nonlinear systems, 5G technology, and healthcare innovation, leveraging radar and machine learning for non-invasive monitoring. Recent contributions include dual-band radar systems for vital signs detection, low-complexity neural network-based channel estimation, and predistortion techniques for power amplifiers. Key research trends include biomedical applications using radar and neural networks, optimization of communication systems for 5G, and hardware implementations on FPGA platforms. His publications reflect a balance between theoretical advancements and practical applications in electronics, healthcare, and telecommunications. Dr. Ben Smida's expertise contributes to the UN Sustainable Development Goals, particularly in advancing health technologies and sustainable industrial innovation.