جست‌وجوی استاد و پژوهشگر

Arnab Bhattacharjeeمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Energy Systems
• Power Engineering
• +۷ مورد دیگر

Dr. Arnab Bhattacharjee is a Postdoctoral Research Fellow at the University of Queensland's School of Electrical Engineering and Computer Science, specializing in AI-driven solutions for energy systems, power engineering, and cybersecurity. He holds a PhD (with distinction) in Electrical Engineering and Computer Science from the University of Queensland and the Indian Institute of Technology Delhi Academy of Research, alongside a B.Tech (Honors) in Electrical & Electronics Engineering from NIT Trichy, where he graduated as valedictorian. His research focuses on optimizing energy systems through machine learning and deep learning, with emphasis on battery degradation modeling, cyber-physical security for smart grids, and AI applications in healthcare (e.g., xViTCOS for COVID-19 screening). Key collaborations include TATA Power, Alt Mobility, and government agencies to advance energy forecasting and EV infrastructure planning. Dr. Bhattacharjee has contributed to leading institutions such as IIT Delhi, the Max Planck Institute for Intelligent Systems, and the University of Queensland. His work spans adversarial attacks on power systems, explainable AI, and stochastic optimization algorithms inspired by biological processes.

• Machine Learning
• Medical Image Analysis
• Computer Vision
• +۵ مورد دیگر

Junxi Chen is an academic affiliated with Dalian University of Technology's School of Software. His research focuses on advanced machine learning techniques, medical image analysis, and computer vision applications in healthcare. He has contributed significantly to adversarial machine learning, robust deep learning models, and medical image segmentation. His work bridges theoretical advancements with practical applications in medical diagnostics and data security. Key research areas include adversarial attacks/defense mechanisms, medical imaging segmentation (e.g., skin lesions, tumors), and innovative neural network architectures like UNet variants. He also explores secure data-hiding techniques in encrypted images and hybrid memory systems optimization. Publications span top venues such as CVPR, IEEE Transactions, and Medical Image Analysis journals. Collaborations with institutions like Dalian University of Technology and international co-authors highlight his global research impact.

Syeda Fizzah Jilaniمشاهده پروفایل

مدرس

• Electromagnetics
• Radio Wave Propagation
• RF Spectrum Monitoring
• +۷ مورد دیگر

Dr. Syeda Fizzah Jilani is a Lecturer in the Department of Physics at Aberystwyth University, UK, and a course coordinator for the MSc Radio Spectrum Engineering program. She holds a PhD in Antennas and Electromagnetics from Queen Mary University of London (2018) and previously worked on US DOE-funded research at the University of Maine. Her research focuses on advanced antenna systems, 5G/6G wireless technologies, millimeter-wave applications, and AI-driven environmental and medical imaging solutions. She has authored/co-authored over 50 papers, a book titled Antennas and Propagation for 5G and Beyond , and secured grants from L3Harris and QinetiQ. Key achievements include the 2024 Aberystwyth University Visibility Award and inclusion in the global '100 Brilliant and Inspiring Women in 6G' list. She leads projects on spectrum monitoring, reconfigurable antennas, and smart city applications, while actively supervising PhD students and contributing to IEEE and IET committees. Education: PhD in Electronic Engineering, Queen Mary University of London (2015–2018) Research Interests: Electromagnetics and Antenna Design Millimeter-Wave and Terahertz Systems Flexible Wearable Antennas Deep Learning in Remote Sensing and Medical Imaging 6G/5G Wireless Communication Networks Sustainable Transportation Solutions Recent Research Trends: Her work spans cutting-edge antenna technologies for 6G, AI-driven environmental monitoring (e.g., landslide detection, air quality analysis), and medical imaging advancements (e.g., breast lesion classification). Recent publications highlight innovations in reconfigurable phased arrays, liquid metal phase shifters, and explainable neural networks for healthcare. Awards and Grants: Principal Investigator: 3-year Serapis Project with QinetiQ/DSTL (£X) PI: L3Harris Technologies Grant (£Y) Featured in '100 Brilliant and Inspiring Women in 6G' (2024) Aberystwyth University Visibility Award (2024) Advising & Grants: Supervises PhD scholars and leads industry-academia collaborations. Projects include spectrum monitoring systems with the UK Spectrum Centre and smart city MIMO antenna arrays. Active in professional service as an IEEE AP-S Young Professional Ambassador and IET Antennas Technical Committee member. Labs/Teams: Works within the Department of Physics' Antennas and Propagation group, focusing on 6G infrastructure and wearable electronics research.

Riccardo Rosatiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Biomedical Engineering
• +۵ مورد دیگر

Riccardo Rosati (born 1992) is a Research Fellow and Ph.D. student at the Polytechnic University of Marche, where his work focuses on Machine Learning and Deep Learning applications in biomedical engineering and Industry 4.0. He holds a Master of Science in Biomedical Engineering (cum laude, 2018) from the same institution and completed an internship at the Image Processing Division of Leiden University Medical Center (2018-2019). His current Ph.D. scholarship, funded by the Marche Region, supports research in "Embedded Automation and Edge Computing for Industry 4.0". Research Areas: Deep learning for biomedical imaging (e.g., facial liveness detection, lung ultrasound analysis) AI-driven predictive maintenance in industrial systems Augmented reality frameworks for industrial design Steganalysis and biometric-based security systems Human-centered AI applications Key Publications Trends: His recent work spans facial liveness detection, wastewater sensor forecasting, excavation monitoring, and predictive maintenance of ATMs. The research combines convolutional neural networks, LSTM architectures, and transformer models with applications in healthcare, industrial automation, and cybersecurity. Contact: Email r.rosati@univpm.it , Office: +39 071 2204465, Room: Q165_121.

David Doermannمشاهده پروفایل

استاد

• Document Image Understanding
• Video Analysis
• Pattern Recognition
• +۹ مورد دیگر

David Doermann is a Professor and Department Chair of the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on document image understanding, video analysis, pattern recognition, computer vision, media forensics, and artificial intelligence. Education: PhD in Computer Science, University of Maryland, College Park (1993) MS in Computer Science, University of Maryland, College Park (1989) BS in Computer Science/Mathematics, Bloomsburg University of PA (1986) Research Interests: Dr. Doermann's work spans computer vision, document analysis, and AI, with recent emphasis on histopathology image synthesis, motion prediction, and personalized vision-language models. His publications highlight advancements in neural architecture search, federated learning, and binary neural networks. Scientific Awards: Award for Excellence from the Under Secretary of Defense (2017) DARPA Results Matter Award (2016) IEEE Fellow (2014) IAPR Fellow (2014) Royal Academy of Engineering Fellowship (2011-2013) Honorary Doctorate from University of Oulu (2002) Future Work: He is exploring the integration of generative AI in software development and advancing techniques for human-centric video-depth generation.

• Biomedical Ontologies
• Medical Informatics
• Semantic Networks
• +۳ مورد دیگر

Yehoshua Perl is a Professor of Computer Science and Director of the Structural Analysis of Biomedical Ontologies Center at New Jersey Institute of Technology (NJIT). His research focuses on biomedical ontologies, semantic networks, and quality assurance methodologies for medical terminologies. He leads projects funded by the National Institutes of Health and U.S. National Library of Medicine, including frameworks for auditing biomedical ontologies and developing interface terminologies to improve EHR usability. His work integrates computational methods like large language models and convolutional neural networks with ontology engineering to address challenges in medical informatics. Key research interests include ontology evolution visualization, SNOMED CT refinement, and the application of abstraction networks for knowledge summarization. He has pioneered methodologies for auditing complex concepts in ontologies such as ChEBI, NCIt, and Gene Ontology. Recent projects emphasize improving patient comprehension of EHRs through AI-driven simplification and clinical entity recognition systems. Federal Grants: Family-Based Framework Of Quality Assurance For Biomedical Ontologies (NIH, 2015–2019) Taxonomies Supporting Orientation, Navigation and Auditing of Terminologies (NLM, 2007–2011) His publications span ontology design patterns, semantic type assignments, and visualization tools like BLUSNO for SNOMED CT. He collaborates internationally on standards for ontology reuse and has contributed to the CIDO coronavirus ontology. Current work includes leveraging generative AI for hierarchical relationship prediction and EHR annotation frameworks tailored to cardiology and other specialties.

Qingjin Pengمشاهده پروفایل

استاد

• Product Design and Manufacturing
• Digital Manufacturing
• Design Methodology
• +۵ مورد دیگر

Qingjin Peng is a Professor in the Department of Mechanical Engineering at the University of Manitoba’s Price Faculty of Engineering. His work focuses on Product Design and Manufacturing, with expertise in Digital Manufacturing, Design Methodology, and Virtual Reality applications. He holds a PhD from the University of Birmingham (1998) and master’s and bachelor’s degrees from Xi’an Jiaotong University (1988, 1982). Education: PhD in Mechanical and Manufacturing Engineering, University of Birmingham (1998) M.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1988) B.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1982) Research Interests: His research spans Digital Manufacturing, Product Assembly/Disassembly Planning, CAD/CAM systems, and System Modeling. He pioneered methods integrating AI (e.g., Reinforcement Learning, Machine Learning) into product design and manufacturing processes. Notable contributions include optimizing Additive Manufacturing parameters using Taguchi methods and developing VR-based training systems for Coordinate Measuring Machines. Publications: Recent work emphasizes AI-driven innovation in manufacturing, with focus areas including sustainability in Additive Manufacturing, fault diagnosis of rotating machinery, and multi-agent systems for concept evaluation. His 2024 studies highlight advancements in product design through radical problem-solving frameworks, causal chain analysis, and genetic algorithm optimization. Advising & Grants: Currently no graduate student opportunities are listed, but his past collaborations include projects funded by national grants. Research teams often involve interdisciplinary partnerships, leveraging AI and data-driven approaches. Labs/Teams: His lab focuses on Digital Manufacturing and AI applications, though specific team names are not explicitly mentioned in the provided text.

Yingliang Maمشاهده پروفایل

دانشیار

• Computer Vision
• Medical Image Analysis
• Image-Guided Surgery
• +۳ مورد دیگر

Dr. Yingliang Ma is an Associate Professor in Computing Sciences at the University of East Anglia, leading the Vision Computing Research Group. He holds a PhD in Computer Science from the University of Manchester and serves as President of the Cardiac-SIG in MICCAI. His research focuses on computer vision, medical image analysis, and procedure risk assessment, with grants from EPSRC, NIHR, and MRC. He is also a Visiting Academic at King's College London (2022–2026) and former Honorary Lecturer (2016–2018). Education: PhD in Computer Science, University of Manchester Research Interests: Dr. Ma develops novel algorithms in computer vision and computational geometry for medical applications such as image-guided surgery, cardiac interventions, and procedure risk assessment. His work emphasizes real-time systems and AI integration in healthcare. Grants & Projects: MRC Impact Fund (2024–2025): £44,669 for a 3D guidance system for cardiac procedures. EPSRC Grant (2023–2025): £279,504 for hybrid 3D cardiac intervention guidance. NIHR Grant (2015–2018): £400,719 for MRI-augmented pediatric cardiovascular interventions. EU ERASMUS+ (2020–2022): €237,940 for Virtual Reality in education. Labs & Teams: Head of Vision and Graphics Research Group. Active in collaborations across cardiology, computer science, and AI.

Umar Razaمشاهده پروفایل

مدرس ارشد

• Internet of Things
• Machine Learning
• Wireless Sensor Networks
• +۹ مورد دیگر

Dr. Umar Raza serves as a Senior Lecturer in Networking, IoT and Smart Systems at Manchester Metropolitan University's Department of Engineering, where he concurrently manages the Cisco Network Academy. His academic career includes prior lecturing roles in Robotics and Computing at Staffordshire University, with research spanning industrial applications of emerging technologies. His educational credentials include a PhD in Wireless Sensor Networks from the University of Bradford (2014), Postgraduate Certificates in Research Methods and Professional Higher Education from Staffordshire University (2005, 2007), an MSc in Electronics and Computer Systems from Huddersfield University (1995), and a BSc in Engineering Electronics from DeMontfort University Leicester. Dr. Raza's research centers on Internet of Things applications integrated with Machine Learning across industrial, agricultural, and healthcare domains. Current projects address IoT/ML solutions for domicile care monitoring, cognitive impairment assistance, natural disaster early warning systems, and smart farming in Pakistan, with emphasis on practical implementations in real-world environments. His recent publications demonstrate a pronounced interdisciplinary trajectory, particularly at the IoT-Machine Learning nexus for healthcare innovation (cardiac diagnostics, autism support wearables) and agricultural technology. Significant contributions also appear in blockchain scalability, vehicular network security, and 5G propagation modeling, reflecting both theoretical rigor and industry-relevant problem solving. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) Dr. Raza actively supervises two MSc and two PhD candidates while leading multiple funded projects: Principal Investigator for KTP projects with Kindus Solutions (completed) and Trumeter Ltd Project Advisor for KTP with RAIT Ltd Lead researcher on NATO Science for Peace Security grant for natural disaster warning systems Principal Investigator for Ignite National Technology Fund smart farming initiative in Pakistan As Cisco Network Academy Manager, he drives industry-academia collaboration in networking education, developing curricula that integrate cutting-edge IoT and cybersecurity concepts while maintaining strong professional engagement through IEEE, IET, and BCS memberships.

Kaushallya Adhikariمشاهده پروفایل

دانشیار

• Array Signal Processing
• Statistical Signal Processing
• Estimation and Detection Theory
• +۵ مورد دیگر

Kaushallya Adhikari serves as an Associate Professor in the Department of Electrical, Computer and Biomedical Engineering at the University of Rhode Island's College of Engineering. Her research bridges theoretical signal processing with practical applications in sonar, radar, and biomedical systems through innovative array design and algorithm development. Her academic credentials include: Ph.D. in Electrical Engineering, University of Massachusetts Dartmouth (2016) M.S. in Electrical Engineering, University of Massachusetts Dartmouth (2012) B.E. in Electronics and Communication Engineering, Tribhuwan University, Nepal (2008) Dr. Adhikari's research centers on array signal processing with emphases on sparse array configurations, direction of arrival estimation, and information-theoretic approaches to detection. She leads the ASPIRE lab (Array Signal Processing for Information REtrieval), which explores statistical signal processing, machine learning integration, and sensor array optimization. Her work demonstrates consistent innovation in transforming circular arrays to linear configurations, developing subspace estimation techniques, and applying neural networks to traditional signal processing challenges. Analysis of her 15 most recent publications reveals a decisive shift toward deep learning integration with classical array processing methods, particularly for direction of arrival estimation in sonar systems. While maintaining strong foundations in estimation theory and information metrics, her recent work increasingly focuses on convolutional neural networks for signal classification, sparse sampling optimization, and biomedical applications like tEEG-based cortical mapping. This evolution reflects her strategic expansion from theoretical frameworks to real-world implementation challenges. Her scientific recognition includes: Office of Naval Research Young Investigator Program (YIP) Award (2020) Dr. Adhikari has secured competitive funding including multiple Office of Naval Research grants for sonar array optimization (2020, 2023) and a NE Big Data Innovation Hub grant for neural network-based cortical mapping (2021). She actively mentors graduate researchers in the ASPIRE lab, where current projects span sonar signal processing, EEG/tEEG analysis, and renewable energy system modeling through autoregressive techniques. The ASPIRE lab maintains a cohesive research program focused on sensor array design, statistical signal processing, and machine learning applications across defense, biomedical, and energy domains, with particular expertise in coprime/nested array configurations and direction of arrival estimation algorithms.

• Artificial Intelligence
• Health Informatics
• Education Analytics
• +۴ مورد دیگر

Professor Prabal Barua is an Adjunct Professor at the University of Southern Queensland (UniSQ) and holds academic positions at multiple institutions, including Taylor's University Malaysia and SRM University of Science and Technology. His expertise spans Artificial Intelligence (AI), health informatics, education analytics, and ICT for business transformation. He leads strategic roles such as Academic Dean at the Australian International Institute of Higher Education and participates in governance committees at UniSQ. With over 25 years of global leadership in education and IT, Barua has secured $3M+ in research grants, focusing on AI applications in healthcare and education. He has supervised over 15 doctoral and master's students, overseeing projects like AI-driven assistive tools in education and healthcare diagnostics. His research outputs include over 100 top-tier publications and reviews on AI in biomedical signals, energy prediction, and neurological disorders. Barua is affiliated with the IEEE Computer Society, Australian Computer Society, and other professional bodies. His educational background includes a PhD in Information Systems from USQ and advanced degrees in Mass Communication and Data Analytics. Research interests emphasize leveraging AI for healthcare (e.g., disease detection via EEG, MRI, and sound signals) and education (e.g., student performance prediction, assistive technologies). His recent work addresses challenges in Alzheimer’s detection, fibromyalgia diagnosis, and automated tools for early education intervention. He collaborates widely, contributing to interdisciplinary projects at the intersection of AI, medicine, and education. Barua’s articles span AI-driven health diagnostics, energy forecasting, and educational analytics, reflecting his focus on practical, impactful applications. His grants and industry partnerships underscore his role in bridging academia and industry, particularly in AI and healthtech. He is actively involved in student supervision and academic leadership, shaping policies and strategic frameworks in higher education.

Alexandre Mendesمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Optimization
• Deep Learning
• +۶ مورد دیگر

Dr. Alexandre Mendes is a Senior Lecturer at the University of Newcastle, affiliated with the School of Information and Physical Sciences and the Department of Computing and Information Technology. His research focuses on machine learning, optimization, and deep learning, with applications in robotics, energy systems, and transport logistics. Education: PhD in Electrical Engineering, Universidade Estadual de Campinas, Brazil MSc in Electrical Engineering, Universidade Estadual de Campinas, Brazil Bachelor in Applied Mathematics, Universidade Estadual de Campinas, Brazil Graduate Certificate in the Practice of Tertiary Teaching, University of Newcastle Research Interests: Mendes specializes in applying machine learning to real-world problems such as optimizing railway logistics, predicting solar energy output, and improving robotic systems. His work bridges theory and practice, collaborating with industries like CSIRO and Hunter Valley Coal Chain. Grants and Funding: He has secured over AU$1.5M in grants from ARC, CSIRO, and industry partners. Key projects include coal train scheduling optimization and solar energy prediction using deep learning. Awards: Work Integrated Learning Staff Member of the Year (2020) Best Student Paper Award (2012) Pro Vice-Chancellor's Research Excellence Award (2011) Labs and Teams: He leads the NUbots robotics team, focusing on humanoid robots and autonomous systems. His research group collaborates on projects like the Sydney Metro and Hunter Region rail network optimization.

Minjeong Kimمشاهده پروفایل

دانشیار

• Biomedical Image Analysis
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Minjeong Kim is an Associate Professor and Interim Department Head in the Department of Computer Science at The University of North Carolina at Greensboro (UNCG), serving as a CAS Dean's Fellow. She holds a Ph.D. from Ewha Womans University, Korea, with postdoctoral research at the Biomedical Research Imaging Center (BRIC) at UNC Chapel Hill and the University of Pennsylvania. Her research focuses on biomedical image analysis, deep learning applications in neurodegenerative diseases, and computational neuroscience. Education includes a Ph.D., M.S., and B.S. in Computer Science and Engineering from Ewha Womans University. Professional experience includes roles as a Research Fellow at Ewha Womans University and visiting researcher at the University of Pennsylvania's Department of Radiology. Research interests emphasize graph neural networks for brain connectivity analysis, Alzheimer’s disease diagnosis via machine learning, and multimodal medical imaging techniques. Her work bridges computational methods with clinical applications, particularly in uncovering disease mechanisms through advanced imaging analysis. Her recent articles highlight innovations in graph representation learning, tau protein propagation modeling, and functional MRI analysis. She maintains a lab at Moore Building 301A and teaches graduate courses in software engineering and computer vision.

• Technology Credit Scoring
• Operational Research
• Data Mining
• +۵ مورد دیگر

So Young Sohn is a distinguished Professor at Korea University's College of Business, Department of Management Engineering, with over two decades of impactful research in technology management and operations research. Her scholarly contributions have established her as a leading expert in technology credit scoring, data mining applications, and technology convergence analysis. Dr. Sohn's research interests span technology credit scoring for SMEs, operational research methodologies, data mining techniques, machine learning applications in business contexts, technology convergence patterns, patent analysis, and SME financing mechanisms. Her work bridges theoretical rigor with practical business applications, particularly focusing on Korean case studies that have broader international relevance. She has pioneered innovative approaches using knowledge graphs, multiplex networks, and deep learning techniques to solve complex business problems. Her publication portfolio reveals consistent research trends toward increasingly sophisticated analytical methods, evolving from traditional statistical models to advanced machine learning and network science approaches. Recent work demonstrates particular focus on technology convergence, digital therapeutics, and AI applications in business decision-making. The interdisciplinary nature of her research spans business analytics, engineering, healthcare, and environmental science. Dr. Sohn has received recognition through numerous high-impact publications in premier journals including Expert Systems with Applications, European Journal of Operational Research, Scientometrics, and IEEE Transactions. Her research has been consistently funded through competitive grants focusing on technology management and innovation. As an academic mentor, Dr. Sohn has advised numerous doctoral students who have gone on to productive research careers, with many continuing to collaborate with her on ongoing projects. Her research team has secured substantial funding for projects related to technology credit scoring, technology convergence analysis, and predictive analytics applications. Dr. Sohn leads a dynamic research laboratory focused on technology analytics and decision support systems, collaborating with industry partners and government agencies to translate research findings into practical business solutions. Her current work emphasizes sustainable technology development and AI-driven decision support systems for complex business environments.

• Computer Architecture
• Hardware Acceleration
• Deep Learning
• +۶ مورد دیگر

Hyuk-Jae Lee is a prominent researcher in computer architecture and hardware acceleration for deep learning systems, with an extensive publication record spanning over two decades. His work primarily focuses on hardware implementations for video processing, memory systems, and neural network acceleration. Through numerous collaborations with researchers at Korean institutions (particularly with Hyun Kim, Chae-Eun Rhee, and Xuan Truong Nguyen), Lee has established himself as a leading figure in circuit design for AI applications. Lee's research interests center around computer architecture, hardware acceleration, deep learning systems, video coding and compression, memory systems, and image processing. His work demonstrates a consistent focus on bridging the gap between theoretical algorithms and practical hardware implementations, with particular emphasis on optimizing performance and efficiency for real-world applications. His recent work shows a strong shift toward accelerating large language models and transformer-based architectures, reflecting current trends in AI hardware. Analysis of Lee's recent publications (2023-2025) reveals a clear research trajectory toward solving memory bandwidth and computational efficiency challenges in modern AI systems. His work spans the spectrum from low-level circuit design to high-level system architecture, with particular strength in memory systems optimization and hardware acceleration for neural networks. The consistent publication record in top-tier IEEE journals demonstrates sustained research productivity and impact in the field. Throughout his career, Lee has collaborated extensively with a core group of researchers, suggesting stable research teams and laboratories focused on hardware acceleration. His publications in IEEE Transactions on Circuits and Systems, IEEE Transactions on Computers, and IEEE Transactions on Video Technology indicate recognition by multiple relevant academic communities.