جست‌وجوی استاد و پژوهشگر

Hua Dongمشاهده پروفایل

استاد

• Inclusive Design
• User-Centered Design
• Human-Computer Interaction
• +۶ مورد دیگر

Professor Hua Dong is a leading academic in inclusive design at Brunel University London , serving as Full Professor in Design, Director of Brunel Design Research Centre, and Inaugural Dean of Brunel Design School (2020-2024). She was elected Vice-Chair of the Design Research Society (DRS) in 2024, is a Fellow of DRS, and will chair QAA's Art and Design Subject Benchmarking Statement in 2025-26. Research Expertise: Dong pioneers inclusive design theory and application across industrial, engineering, architectural, and service design domains, with a focus on AI-powered products, mHealth for aging populations, and multisensory design. Her work bridges academia and industry through projects like the Design Bugs Out Challenge and collaborations with Ant Financial, Shanghai M&G Stationery, and UK-China initiatives. Scientific Contributions: She has secured major grants including an EPSRC grant and Newton Funds . Her publications span 2007-2024, covering AI-enhanced design ideation, service ontology for aging populations, and sustainable inclusion frameworks. She co-founded the Inclusive Design Research Centre in China and curated a special issue on inclusive design for Design magazine. Scientific Awards: Fellow of Design Research Society (2019) AHRC SEED Fellowship Member of Peer Review College of AHRC Teaching & Leadership: Dong holds a PGCert in Academic Practice and has shaped design education through co-design methodologies. She has supervised 14 PhD and 1 EngD student completions, organized the Cambridge Workshop for Universal Access since 2014, and served as External Examiner at Imperial College. Global Impact: As International Judge for SDGs Design Awards and advisor to UK charities (AgeUK), governments (Nanjing), and corporations (Reckitt Benkiser), she translates design research into tangible societal benefits. Her work has been exhibited at international venues including the Milan Triennale and Zurich Design Museum .

Maitreyee Deyمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Artificial Intelligence
• Low Carbon Technologies
• +۹ مورد دیگر

Dr. Maitreyee Dey serves as a Visiting Fellow in Mechanical Engineering and Design and works as a Post-doctoral research fellow at London South Bank University's Department of Electrical and Electronics Engineering. She concurrently holds the position of Data Mining Lead at Neuville Grid Data Management Limited, where she contributes to significant energy research projects. Her research expertise spans machine learning/artificial intelligence and low carbon technologies, with additional interests in pattern recognition, big data analysis, deep learning, image processing, energy optimization in smart buildings, renewable energy technology, and Computer-aided Diagnosis for smart health applications. Her work directly contributes to UN Sustainable Development Goals related to clean energy and sustainable communities. Analysis of her recent publications reveals a strong research trajectory focusing on two primary domains: power grid analysis using machine learning techniques and microwave-based medical imaging for breast cancer detection. Her work demonstrates sophisticated applications of neural networks and data mining methods to solve complex problems in both energy systems and medical diagnostics. Best Paper Award at the CUE conference, Elsevier (2020) Endorsement by the Royal Academy of Engineering Dr. Dey serves as an Editorial board member for MDPI journals and as an Adhoc reviewer for high-impact publications including IEEE transactions and Nature Scientific Data. She is actively involved in the Access to Innovation (A2i) and Low Carbon London (LCLDN) projects funded by the EU commission through its regional development Fund, working in collaboration between London South Bank University and Neuville Grid Data Management Limited. Her professional affiliations include active membership in IEEE, CIGRE NGN, and the Energy Institute, demonstrating her engagement with key professional organizations in her fields of expertise.

• Clinical Prediction Modeling
• Health Informatics
• Big Data Analysis
• +۴ مورد دیگر

Dr. Muhammad Faisal is an Associate Professor at the Centre for Digital Innovations in Health & Social Care, University of Bradford. He holds a PhD in Biostatistics from the University of Vienna and has over 15 years of applied health research experience. His work focuses on developing equitable clinical prediction tools and leveraging big health data for improved healthcare outcomes. Key contributions include leading the CARSS (Computer Aided Risk Scoring System) project, highlighted in the Goldacre review, and co-founding the NHS-R community to advance R-based analytics in healthcare. Education: MSc in Statistics (Bahauddin Zakaryia University, 2006), PhD in Biostatistics (University of Vienna, 2012). Professional Affiliations: Royal Statistical Society (RSS), International Society for Clinical Biostatistics (ISCB). Awards: Wolfson Centre for Applied Health Research Fellow, Fellow of Advance HE. Research Interests: Clinical prediction modeling, machine learning applications in healthcare, big data analytics, and translational research for population health. He leads the £5.8M Yorkshire & Humber Patient Safety Research Collaboration and evaluates interventions for cancer screening in South Asian Muslim women (funded £440,699). Teaching: Courses include Health Data Science, Health Informatics, and Clinical Prediction Modelling. Grants: Reviewed NIHR and UKRI grants, served on UKRI Peer Review College and NIHR HS&DR committees. Notable Publications: Over 100 peer-reviewed articles, with one achieving top 5% Altmetric attention (17 news stories). Labs/Teams: CARSS Research Group, NHS-R community. Future Work: Expanding equitable prediction tools and digital health innovation.

Yuan Liمشاهده پروفایل

دانشیار

• Online Information Security
• Privacy Management
• Computer-Aided Decision Making
• +۳ مورد دیگر

Yuan Li is an Associate Professor and Gibbs Faculty Fellow at the University of Tennessee, Knoxville's Haslam College of Business, affiliated with the Accounting & Information Management department. His expertise spans online information security, privacy management, computer-aided decision-making, and business analytics. Education: Ph.D. in Management Information Systems, University of South Carolina (2006) M.S. in Business Administration, Northeastern University, China (2000) B.S. in Industrial Engineering and Management, Northeastern University, China (1996) Research focuses on cybersecurity policy compliance, digital identity management, and decision support systems. His work explores psychological factors in phishing detection, cloud computing adoption barriers, and victim responses to identity theft. Recent studies investigate the interplay between stress and security policy adherence in organizational settings. Notable awards include Best Paper recognitions at the Midwest Association for Information Systems (2017) and Southeast Decision Science Institute (2012/2011). His early career was marked by an Excellent Master Thesis award (2000). Research interests bridge technical systems and human behavior, with emphasis on: Cybersecurity workforce training Consumer privacy decision-making Technology adoption in auditing Knowledge management frameworks

Tzu-Chieh Kurt Hongمشاهده پروفایل

استادیار

• Shape Grammars
• Visual Computing
• Generative Modeling
• +۴ مورد دیگر

Dr. Tzu-Chieh Kurt Hong is an Assistant Professor at the Department of Architecture within the School of Architecture and Design at the University of Kansas. He teaches design studios, algorithmic modeling, parametric design, and computer programming in design studies. He founded K9K Research & Design, focusing on innovative computational design methodologies. His research emphasizes shape grammars, visual computing, and generative modeling, particularly through the development of the Shape Machine—a computational tool integrating shape recognition and rewriting into CAD systems. Education: Ph.D. in Architecture (Design Computation), Georgia Institute of Technology (2021) M.S. in Architecture and Design, University of Michigan (2016) Master of Architecture, National Chiao Tung University (2008) M.S. and B.S. in Electronics Engineering, National Chiao Tung University (Taiwan) Research Interests: Shape embedding and rewriting in CAD systems Algorithmic design and generative systems Integration of traditional methods (e.g., Euclidean geometry) with computational tools Applications in architectural education and practice Awards: 2022 ARCC Dissertation Award for contributions to CAD systems through shape recognition research Professional Experience: Research Scientist at Georgia Tech's Shape Computation Lab (2016–2022) Contributions include the Shape Machine framework and the Smart 3D Atlanta visualization platform Labs/Teams: Leads the K9K Research & Design initiative, collaborating with academic and industry partners on computational design tools and generative workflows.

John Rasmussenمشاهده پروفایل

استاد

• Biomechanics
• Rehabilitation Robotics
• Musculoskeletal Simulation
• +۲ مورد دیگر

John Rasmussen is a Professor and Vice Head of the Department of Materials and Production at Aalborg University's Faculty of Engineering and Science. He founded the AnyBody Research Group and served as CEO of AnyBody Technology A/S. His research focuses on biomechanics, rehabilitation robotics, and musculoskeletal simulation, with applications in exoskeleton design and orthopedic interventions. Education: Ph.D. in Computer-Aided Design (Aalborg University, 1989) M.Sc. in Mechanical Engineering (Aalborg University, 1986) Research Interests: Biomechanics, CAD optimization, finite element analysis, and biomedical engineering. His work bridges computational modeling with clinical solutions for mobility disorders. Article Trends: Recent publications emphasize exoskeleton design, spinal biomechanics, and osteoarthritis interventions, utilizing experimental and simulation approaches across robotics and orthopedics. Awards: Wearable Robotics Innovation Prize (2018) Grand Challenge Competition Winner (2014) Steno Prisen (2012) Teacher of the Year (2011) Leadership: Manages the Center for Rehabilitation Robotics and oversees 37 research projects. Advises industry collaborations in assistive technology.

Shuang Liuمشاهده پروفایل

پژوهشگر

• Embedded Systems
• Computer Architecture
• Networks-on-Chip (NoC)
• +۳ مورد دیگر

Shuang Liu is a Researcher at the Institute of Computer Architecture and Computer Engineering, University of Stuttgart. Their work focuses on embedded systems, networks-on-chip (NoC), and chip design methodologies. They specialize in deadlock-free routing, fault-tolerant computing, and application-specific system synthesis using advanced techniques like integer linear programming. Research interests include optimizing NoC architectures for chiplet-based systems, co-design of floorplanning and routing topologies, and energy-efficient signal processing in medical electronics. Their recent work emphasizes formal verification methods and performance optimization in hardware-software co-design. Publications span topics such as deadlock prevention in NoC, ILP-based routing synthesis, and trade-offs in hearing aid ASIP design. Office hours are Monday 2–3 p.m. in Room 1.001, Pfaffenwaldring 5b, Stuttgart.

• Auditory Neuroscience
• Hearing Science
• Speech Perception in Noise
• +۲ مورد دیگر

Skyler G. Jennings, Au.D., Ph.D., serves as an Adjunct Professor in the Department of Surgery at the University of Utah. His academic work focuses on auditory neuroscience and audiology, specifically investigating speech perception mechanisms in noisy environments and the role of the medial olivocochlear reflex in auditory adaptation. Dr. Jennings completed his undergraduate education at the University of Utah and Utah State University, followed by graduate studies at Purdue University where he earned his master's degree, clinical doctorate in Audiology (Au.D.), and Ph.D. His research program centers on understanding how the auditory system calibrates to changing acoustic soundscapes, with particular emphasis on why individuals with cochlear hearing impairment struggle to adapt due to compromised outer hair cell function affecting efferent feedback mechanisms. Analysis of his 14 publications (2009-2019) reveals consistent investigation into auditory processing in noise, cochlear mechanics, and hearing loss effects. Key research themes include amplitude modulation detection, overshoot phenomena, auditory filter tuning, and computational modeling of individual differences. His work demonstrates how notched-noise precursors improve low-frequency modulation detection and reveals sharper cochlear tuning in musicians, with significant implications for hearing assistive technologies. Dr. Jennings has contributed to methodological advancements in auditory research through the development of the PsyAcoustX MATLAB toolbox for psychoacoustic experimentation. His collaborative research with investigators including Bidelman, Strickland, and Dubno spans auditory neuroscience, computational modeling, and clinical audiology applications.

Chadi Assiمشاهده پروفایل

استاد

• Wireless Networks
• Information Security
• Cybersecurity
• +۶ مورد دیگر

Chadi Assi is a Professor and Tier II Concordia Research Chair at the Concordia Institute for Information Systems Engineering, Concordia University. His research focuses on wireless networks, information security, and smart grid systems, with particular emphasis on reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), cybersecurity for electric vehicles (EVs), and machine learning-driven network optimization. He has pioneered work on mitigating cyber-physical attacks in power grids and IoT ecosystems, while advancing cooperative communication protocols like RSMA and NOMA. His technical contributions span theoretical frameworks for energy efficiency maximization in hybrid SDMA/NOMA schemes, low-complexity RIS element selection algorithms, and adversarial PINN models for grid dynamics. He also investigates vulnerabilities in EV charging infrastructure and O-RAN synchronization protocols, proposing robust detection mechanisms like PEACE and Grid Mirror. His interdisciplinary work bridges communications, power systems, and AI, addressing challenges in 5G/6G security and resilient IoT provisioning. Key Research Areas: RIS-enabled ISAC networks, EV cybersecurity, meta-learning in communications, IoT malware analysis Current Projects: Grid resilience against load-altering attacks, Movable antenna optimization, federated learning for AGC systems Recent publications (2024-2025) emphasize deep reinforcement learning frameworks for RIS-aided networks, cooperative RSMA performance enhancement, and defense mechanisms against dynamic trigger-based attacks. He has also developed novel datasets for advanced persistent threats and frameworks like ChargePrint for EV charging security analysis. His work is published in top venues including IEEE Transactions on Smart Grid, IEEE JSAC, and IEEE ICC, reflecting contributions to both theoretical advancements and practical system implementations.

Arash Mohammadiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Medical Imaging
• Cybersecurity
• +۴ مورد دیگر

Arash Mohammadi is an Associate Professor at the Concordia Institute for Information Systems Engineering (Concordia University), specializing in artificial intelligence, medical imaging, and cybersecurity. His research focuses on advancing AI-driven solutions in healthcare diagnostics, autonomous systems, federated learning, and edge computing. He supervises graduate programs in Information Systems Security, Electrical and Computer Engineering, and related disciplines. Key research interests include medical image classification using deep learning, reliability of autonomous driving systems, and secure communication in smart grids. His work integrates transformer architectures, Bayesian methods, and graph neural networks to address challenges in healthcare, robotics, and network optimization. Notable contributions include frameworks for lung nodule malignancy prediction, ECG-based emotion recognition, and secure federated learning in resource-constrained environments. Dr. Mohammadi has published extensively in top-tier venues such as IEEE conferences and journals, with a focus on practical applications of AI in healthcare and engineering systems. He actively engages in interdisciplinary projects involving biomedical signal processing, human-computer interaction, and resilience engineering for cyber-physical systems.

• Computer Vision
• Image Processing and Analysis
• Medical Imaging (Computer Aided Detection/Diagnosis)
• +۵ مورد دیگر

Prof. Paul F. Whelan is a Full Professor and holds a Personal Chair in Computer Vision at Dublin City University's School of Electronic Engineering. He joined DCU in 1990 and established the Vision Systems Laboratory (1990) and the Centre for Image Processing & Analysis (CIPA, 2006). His research focuses on image segmentation, mathematical morphology, texture analysis, and their applications in medical imaging, industrial vision, and computer-aided diagnosis. He has authored 3 books and over 190 peer-reviewed publications, filed 7 patents since 2007, and spun out Jaliko Ltd to commercialize imaging technology. Education: BEng (First Class Honours) in Electronic Engineering, DCU MEng in Electronic & Computer Engineering, University of Limerick PhD in Computing Mathematics/Computer Vision, Cardiff University Professional Roles: Director of CIPA (2006–2015) Elected Member of DCU Governing Authority (2006–2011) President of Irish Pattern Recognition and Classification Society (1998–2007) Member of IAPR Governing Board & IFCS Council Research Contributions: Developed the NeatVision/IPA Toolbox (Java/MATLAB), licensed biomedical technology, and contributed to €7M+ in competitive research funding. His work bridges translational research in medical imaging and industrial applications. Affiliations: Fellow of IET, Senior Member of IEEE, Chartered Engineer, and Royal Irish Academy nominee (2009–2013).

• Artificial Intelligence
• Structural Engineering
• Computer Vision
• +۳ مورد دیگر

Marco Plate is a Lecturer at the Department of Industrial and Information Engineering, University of Pavia. His research focuses on applying artificial intelligence and computer vision techniques to structural engineering challenges, particularly post-earthquake damage detection using semi-synthetic image generation. He has published in journals like Engineering Applications of Artificial Intelligence and Lecture Notes in Computer Science . His work emphasizes improving crack detection accuracy through advanced machine learning methods. Education & Affiliations: Department of Industrial and Information Engineering, University of Pavia Research Interests: Dr. Plate investigates the intersection of AI-driven technologies and civil engineering infrastructure resilience. His current projects include developing tunable semi-synthetic image datasets to enhance post-disaster structural analysis systems. This research bridges computer science methodologies (e.g., neural networks) with practical civil engineering applications like bridge and building damage assessment. Grants & Advising: No specific grants or advised students listed Labs/Teams: Active in the university's Smart Infrastructure Research Group, focusing on AI applications for civil infrastructure monitoring.

Greg L. Rohrauerمشاهده پروفایل

دانشیار

• Advanced Composite Materials
• Composite Pressure Vessels
• Materials Testing
• +۴ مورد دیگر

Dr. Greg L. Rohrauer is the Department Chair and Associate Professor at the Department of Automotive and Mechatronics Engineering, Ontario Tech University. He holds a PhD in Mechanical Engineering from Concordia University (1999), alongside earlier degrees from Concordia and Dawson College. His research focuses on advanced composite materials, vehicle dynamics, and manufacturing technology, with notable contributions to composite pressure vessel design and hybrid vehicle development. Education: PhD (Mechanical Engineering, Concordia, 1999); BEng (Mechanical Engineering, Concordia, 1985); DEC (Mechanical Engineering Technology, Dawson College, 1981). Research interests include materials testing, alternate-fueled vehicles, and manufacturing applications. He has authored over a dozen peer-reviewed publications, including work on trailer dynamics control and composite elasticity solutions. Awardees of multiple scholarships and honors, including the Governor General’s medal nomination (1999), SAE Doctoral Scholarships, and academic excellence awards. He has held academic roles at Concordia University and the University of Windsor, alongside industrial experience in motorsports engineering. Teaching responsibilities include courses on automotive fundamentals, capstone design, and materials science. His professional activities include SAE faculty advising (2003–2005) and senate membership at the University of Windsor.

Ahmad Barariمشاهده پروفایل

استاد

• Advanced Manufacturing Technologies
• Digital Manufacturing
• Precision Manufacturing
• +۷ مورد دیگر

Dr. Ahmad Barari is a Professor in the Department of Mechanical and Manufacturing Engineering at the University of Ontario Institute of Technology. He holds a PhD in Mechanical Engineering from Western University (2006), MSc from Sharif University of Technology (1997), and BSc from Amirkabir University of Technology (1995). His research focuses on advanced manufacturing technologies, additive manufacturing, coordinate metrology, surface integrity, and design optimization. He has authored numerous publications and serves on multiple international conference committees. Education highlights include: PhD, Mechanical Engineering, Western University, 2006 MSc, Mechanical Engineering, Sharif University of Technology, 1997 BSc, Mechanical Engineering, Amirkabir University of Technology, 1995 Research interests span Advanced Manufacturing Technologies, Digital Manufacturing, Precision Manufacturing, and FEA-Based Design Optimization. He has contributed to innovations in additive manufacturing processes, surface quality analysis, and computational metrology. His work addresses challenges like measurement uncertainty, surface tribology, and topology optimization. Notable awards include the Best Paper Award at IMS2016 and multiple recognitions as academic advisor for student projects. He actively participates in professional activities such as organizing conference sessions and serving on review panels for NSERC, CFI, and OCE. His professional roles include organizing tracks at ASME, CAD Conferences, and IMECE events, reflecting his leadership in advancing manufacturing research. He also contributes to academic governance through roles like Design Chair for capstone projects.

ANG Shi Junمشاهده پروفایل

مدرس

• High throughput quantum chemistry
• AI in Chemistry
• Cheminformatics
• +۲ مورد دیگر

ANG Shi Jun is an Adjunct Lecturer in the Department of Chemistry at the National University of Singapore (NUS). He holds a B.Sc. (1st Class Honours) from NUS (2014), a Ph.D. from the NUS Graduate School for Integrative Sciences and Engineering (2018), and completed a postdoctoral fellowship at the Massachusetts Institute of Technology (2019–2021). His research focuses on high-throughput quantum chemistry, AI applications in chemistry, cheminformatics, and catalysis for sustainability. Key educational milestones include the Schering Plough Gold Medal (2013), A*STAR scholarships at undergraduate and postdoctoral levels, and the Chairman’s Honours List (2014). He has been recognized with the Royal Society of Chemistry Best Oral Presentation Award (2018) and secured the A*STAR Career Development Fund (2022). His research publications span topics like molecular language processing for compound databases, phase transitions in nanoparticles, and halogen bonding dynamics. His work bridges computational methods with practical applications in sustainable chemistry and materials science. ANG has received notable awards and grants, including the MIT Kaufman Teaching Certification (2020), highlighting his contributions to both research and education. While no specific lab or team is explicitly mentioned, his affiliations align with NUS’s Department of Chemistry, where he contributes to cutting-edge interdisciplinary research.