جست‌وجوی استاد و پژوهشگر

Shakes Chandraمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Deep Learning
• Biomedical Engineering
• +۴ مورد دیگر

Dr. Shakes Chandra is a Senior Lecturer at the University of Queensland, leading a research team focused on medical image analysis and deep learning. He holds a Ph.D. in Theoretical Physics from Monash University and has over a decade of experience applying machine learning to medical imaging. His work integrates mathematical concepts like fractals and group theory into imaging algorithms, with applications in MRI reconstruction, musculoskeletal analysis, and disease prognosis. Affiliations: School of Electrical Engineering and Computer Science, University of Queensland Co-leads the Chaotic Sensing research initiative Collaborator with Siemens Medical Solutions Research Interests: Medical imaging (MRI/CT segmentation, artifact suppression) Deep learning applications in healthcare Computational imaging and signal processing Mathematical foundations of imaging systems Key Projects: Developed Chaotic Sensing framework for faster MRI using fractal sampling patterns Automated tools for knee/hip joint segmentation deployed in clinical platforms Leader of a 20+ researcher team in AI-driven medical imaging Grants & Collaborations: Principal investigator on multi-center TBI prognosis studies (PREDICT-TBI trial) Industry partnerships with Siemens Healthcare Active in translational research bridging physics/mathematics with clinical applications Teaching: Course leader for Theory of Computation and Pattern Recognition & Analysis Advances rapid application development frameworks for biomedical imaging

• Computer Vision
• Image Processing
• Machine Learning
• +۴ مورد دیگر

Professor Lilian Tang is a faculty member in the School of Computer Science and Electronic Engineering at the University of Surrey, holding a position in the Computer Science Research Centre. She holds degrees of BEng, MEng, and PhD (Cantab) from the University of Cambridge. Her research focuses on applying machine learning to image and natural language understanding, with emphasis on medical domains such as surgical instrument tracking, retinal image analysis, bacterial cell imaging, and collaborations with NHS hospitals globally. She also explores nature-related projects with institutions like the Royal Botanic Gardens, Kew. Education: BEng, MEng, PhD (Cantab) from University of Cambridge. Research Interests: Computer vision, image processing, machine learning, natural language processing, medical imaging analysis, and nature image data analysis. Collaborations: Moorfields Eye Hospital, Royal Surrey County Hospital, UCL NHS hospitals, Royal Botanic Gardens, Kew, and institutions in China, Australia, and Italy. Teaching: Courses include COM2028 Introduction to AI and COM3025 Deep Learning and Advanced AI. Research Highlights: Her work spans automated surgical instrument tracking, retinal image analysis for systemic conditions, bacterial cell image analysis, and heritage plant studies. Projects emphasize clinical applications and cross-disciplinary collaboration.

Johan Perssonمشاهده پروفایل

دانشیار

• Computer Aided Product Development
• Surrogate Models
• Finite Element Analysis (FEM)
• +۴ مورد دیگر

Johan Persson is an Associate Professor and Head of Unit at Linköping University's Department of Management and Engineering (IEI). He leads the Product Realisation (PROD) division, focusing on advancing Computer Aided Product Development (CAD) and optimization techniques. His research emphasizes computationally efficient surrogate models to enhance FEM/CFD analyses and streamline product development processes. Key areas of interest include design automation frameworks, additive manufacturing integration, and multidisciplinary optimization strategies. He teaches courses such as Machine Elements (TMKT39), Design Optimization (TMKT48), and Collaborative Multidisciplinary Optimization (TMKT79). His work bridges academic research and industry applications, with notable contributions to frameworks supporting Additive Manufacturing (AM) and OpenMDAO-based optimization. Research trends in his publications highlight advancements in AM applications, design automation tools, and collaborative optimization methods. Notable projects include a framework for AM design integration and studies on hydraulic pump design using additive techniques. Johan supervises PhD students including Erik Gustafsson and Javier Villena Toro. His research is supported by collaborations with colleagues like Anton Wiberg and Johan Ölvander. The Automation Lab, a key platform for his work, pioneers innovations in CAD automation and digital design processes.

Arjen Jansenمشاهده پروفایل

مدرس

• Sports Engineering
• Drug Delivery Systems
• Sustainable Design
• +۳ مورد دیگر

Dr. Arjen Jansen is a Lecturer in Industrial Design Engineering at Delft University of Technology, affiliated with the Emerging Materials and Sustainable Design Engineering programs. His research spans sports engineering, biomedical applications (e.g., 3D drug printing), and human-powered energy systems. Notable projects include developing vibro-tactile feedback systems for skating and protective wetsuits for Olympic sailors. He teaches courses like the Design Engineering Contest and leads the Erasmus+ project A4SEE to enhance sports engineering education across Europe. Jansen's work bridges innovation theory with practical applications, emphasizing sustainability and human-centric design. Education: Doctor of Industrial Design Engineering (Dr.ir.) Research Interests: Sports technology, biomedical engineering, sustainable materials, and educational methodologies in design. His work often integrates ergonomics, environmental science, and digital tools to create impactful solutions. Grants & Collaborations: Led international projects like A4SEE (2022) and contributed to EU-funded initiatives. His research has been published in journals like Journal of Pharmaceutical Innovation and presented at conferences such as the Engineering of Sport series. Labs/Teams: Emerging Materials Program, Sports Engineering Lab.

Fei Zouمشاهده پروفایل

استاد

• Biostatistics
• Genetic Epidemiology
• Statistical Genetics
• +۴ مورد دیگر

Dr. Fei Zou is a Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill, affiliated with the Gillings School of Global Public Health. She leads research in statistical methodologies for genetic data analysis, specializing in quantitative genetics and genomic data interpretation. Her work focuses on advancing machine learning applications for big EHR data and high-dimensional genetic datasets. Education: PhD in Statistics from the University of Wisconsin-Madison (2001), MS in Mathematical Statistics from Wuhan University (1993), and BS in Mathematics from Wuhan University (1990). Research interests include genetic mapping (linkage/association studies), copy number variation analysis, and next-generation sequencing methodologies. She directs the Biostatistics/Bioinformatics Core of the UNC Neuroscience Center and is a core member of the Carolina Center for Genome Sciences. Her contributions to statistical genetics include developing algorithms for collaborative cross mouse models and genomic data analysis tools like ShrunkenPCA. Awards include the David Byar Young Investigator Award (2001) and the Gordon Research Fellowship (2001). Active in academic service, she serves on NIH review panels and curriculum committees. Teaching focuses on statistical genetics and bioinformatics courses such as BIOS 783 and BCB723. Software contributions include HapSample (SNP genotype simulation) and Semiparametric QTL mapping tools. Her research bridges statistical theory with practical applications in translational and clinical research through collaborations in the UNC Center for AIDS Research and NC TraCS Institute.

Arumugam Nallanathanمشاهده پروفایل

استاد

• Wireless Communications
• Networking
• 6G Technology
• +۶ مورد دیگر

Arumugam Nallanathan is a Professor of Wireless Communications at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. He leads the Communication Systems Research (CSR) Group and is a core member of the Centre for Networks, Communications and Systems. His research focuses on 6G technologies, AI-driven wireless systems, reconfigurable intelligent surfaces (RIS), UAV-enabled communications, ultra-reliable low-latency communications (URLLC), and IoT applications. He teaches the advanced 5G Mobile and Beyond module, integrating theoretical frameworks with practical implementations. Nallanathan's work bridges cutting-edge research with real-world challenges, emphasizing secure and energy-efficient communication systems. His recent publications explore topics such as IRS-based DOA estimation, covert communication techniques, and federated learning in edge networks. He actively contributes to the design of integrated sensing and communication systems, with applications in smart cities and beyond. His research group collaborates on grants addressing future network architectures and AI-enabled solutions. Research interests span: 6G systems, AI for communications, RIS optimization, UAV networks, URLLC protocols, and IoT security. His teaching emphasizes 5G fundamentals and emerging technologies. Notable contributions include advancements in STAR-RIS aided communication, multi-modal learning for satellite-ground networks, and secure resource allocation in NOMA systems. He leads projects on edge intelligence, digital twins for URLLC, and generative AI for channel estimation. The CSR Group actively engages in industry partnerships to translate research into practical solutions.

• Robotics
• Biomedical Engineering
• Control Systems
• +۳ مورد دیگر

Jason Gu is a Professor in the Department of Electrical and Computer Engineering at Dalhousie University, cross-appointed to the School of Biomedical Engineering. His research integrates robotics, control systems, and biomedical engineering to develop innovative solutions for mobile robotics, surgical systems, and rehabilitation technologies. His primary research domains include: Robotics : Mobile robotics, surgical robots, rehabilitation assistive devices, wireless control systems, and multi-sensor data fusion. Biomedical Engineering : Artificial eye implant control, medical robotic devices, and rehabilitation technology design. Control Systems : Real-time intelligent control, nonlinear systems theory, and embedded control applications. Alternative Energy : Development of novel energy technologies and systems. Analysis of his recent publications (2023-2025) reveals a strong convergence of AI with robotics, particularly in vision-language models for human-robot interaction, semantic SLAM for dynamic environments, and medical image processing. His work shows increasing emphasis on lightweight algorithms for UAVs, neural interfaces, and energy-efficient control systems for aerospace applications. His distinguished honors include: IEEE Canada President (2020-2021) and President-elect (2018-2019) Fellow of the Engineering Institute of Canada (FEIC) Fellow of the Canadian Academy of Engineering (FCAE) Professional Engineer (PEng) designation Professor Gu leads a dynamic research laboratory developing advanced robotic platforms including the PA10 Portable General-Purpose Intelligent Arm and B21r Mobile Robotic System. His team actively pursues real-world applications in surgical robotics, terrain perception for legged robots, and alternative energy systems through industry-academic partnerships and competitive research grants.

• Medical Image Analysis
• Machine Learning for Personalised Medicine
• Biomedical Engineering
• +۳ مورد دیگر

Gemma Piella Fenoy is a Full Professor at the Universitat Pompeu Fabra (UPF), Department of Engineering. She holds a Telecommunication Engineering degree from the Universitat Politècnica de Catalunya (UPC) and a PhD in Applied Sciences from the Universiteit van Amsterdam. Her career includes postdoctoral research at Télécom Paris (Marie Curie Fellowship) and a Ramón y Cajal Researcher position. She co-founded the SIMBIOsys research group and the Barcelona Center of New Medical Technologies (BCN-MedTech), leading its research in Medical Image Analysis and Machine Learning for Personalized Medicine. Her work bridges theoretical foundations with clinical applications, recognized by awards like the ICREA Academia (2021) and DonaTIC (2022). She is a member of ELLIS and Sigma Xi, and served as co-chair of the Avicenna Alliance's AI Task Force (2023-2024). Research focuses on medical imaging, machine learning in healthcare, fetal MRI analysis, and AI-driven diagnostics. Notable projects include the LiverColor platform for liver graft assessment and BabyFM for 3D fetal facial modeling. Her work emphasizes interdisciplinary collaboration between engineering and clinical medicine, addressing challenges in prenatal care, neurological disorders, and personalized medicine. She actively contributes to global health initiatives and AI ethics in clinical applications. Education PhD in Applied Sciences, Universiteit van Amsterdam (The Netherlands) Telecommunication Engineering Degree, Universitat Politècnica de Catalunya (Spain) Her scientific contributions span over 150 peer-reviewed articles in top journals and conferences, with a focus on medical image analysis, AI in healthcare, and clinical decision support systems. She leads BCN-MedTech, a consolidated research group recognized by the Catalan government, and collaborates with international institutions on AI-driven medical solutions.

Thomas Åstebroمشاهده پروفایل

استاد

• Entrepreneurship
• Innovation Economics
• Behavioral Economics
• +۱۲ مورد دیگر

Thomas Åstebro is a Professor in the Department of Economics and Decision Sciences at HEC Paris, where he serves as Executive Director of the ION Management Science Lab and Scientific Director and Founder of the Creative Destruction Lab (CDL) in Paris. His work focuses on AI-augmented talent selection, training, and venture development, with large-scale randomized trials conducted in collaboration with public institutions such as France Travail and in Ecuador. His education includes a Ph.D. from Carnegie Mellon University, a Master of Engineering, and an MBA from Chalmers University of Technology in Sweden. He has held academic positions at the University of Waterloo, University of Toronto, and KU Leuven. Åstebro’s research spans entrepreneurship, innovation economics, behavioral decision-making, and AI in management. He investigates market entry behavior, academic entrepreneurship, venture financing, and the role of overconfidence and ambiguity in entrepreneurial decisions. His recent work emphasizes scalable interventions using AI tools in education and employment training. His 15 most recent publications reveal a strong focus on behavioral and empirical entrepreneurship, with increasing integration of AI and data science in private equity, crowdfunding, and talent development. Key themes include investor herding, science-based startups, diversity in venture capital, and AI use in decision processes. Best Empirical Paper, AoM Entrepreneurship Division (2016) Chair of Excellence (2016) Management Science Distinguished Service Award (2014) Finalist, Prix Academique de la Recherche en Management (2015) Listed in Marquis Who's Who in the World Top 2% most downloaded author on SSRN Åstebro has advised on innovation policy and led major research initiatives including CDL Paris and the ION Lab. He has organized numerous academic workshops on entrepreneurship and served in editorial roles for journals such as Management Science and Small Business Economics . He has secured funding for large-scale RCTs and training programs aimed at social entrepreneurship and unemployment reduction. He has also been involved in advising and mentoring startups through HEC Incubateur. He leads the ION Lab, a research group dedicated to improving human decision-making through AI augmentation, and co-leads CDL Paris, a venture accelerator focused on high-growth science-based startups. These labs integrate academic research with real-world entrepreneurial training and mentorship.

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۷ مورد دیگر

Tarek Gaber is a prominent academic researcher affiliated with the Faculty of Computers and Informatics at Suez Canal University and leads the Scientific Research Group in Egypt (SRGE). He maintains strong collaborative ties with international institutions including the University of Salford, Frankfurt University of Applied Sciences, and Ain Shams University, reflecting a globally engaged research profile. His research spans artificial intelligence, cybersecurity, blockchain, and smart systems with applications in agriculture, healthcare, aerospace, and industrial automation. Key areas include biometric authentication, satellite communication security, plant identification, and IoT-based monitoring systems. The body of his recent work demonstrates a trend toward secure, intelligent, and decentralized systems, particularly in using blockchain for space communications and federated learning for medical data. His publications appear in high-impact journals such as Computers and Industrial Engineering , Signal, Image and Video Processing , and Aerospace , with a strong citation record and an H-index categorized as 'Very High'. National Research Foundation National Institutes of Health Consejo Nacional de Ciencia y Tecnología Princess Nourah Bint Abdulrahman University International Atomic Energy Agency Deanship of Scientific Research, King Faisal University Wellcome Trust Nelson Mandela Metropolitan University VLIRUOS Petroleum Technology Development Fund He has supervised numerous research projects and likely mentors students through the Scientific Research Group in Egypt. While specific advisees are not listed, his leadership role implies active academic guidance. Dr. Gaber is associated with advanced research facilities and has contributed to over 100 publications. His work is supported by diverse international sponsors, highlighting the global relevance and interdisciplinary nature of his research.

Xin Xingمشاهده پروفایل

استادیار

• Medical Imaging
• Machine Learning
• Neuroscience
• +۳ مورد دیگر

Xin Xing is an Assistant Professor at the University of Nebraska at Omaha, College of Information Science & Technology, Department of Computer Science. Their research bridges machine learning, neuroscience, and biomedical applications. Education: Ph.D. in Computer Science (2023), University of Kentucky M.S. in Information Technology (2016), University of Stuttgart B.S. in Communications Engineering (2011), Shandong University Research Interests: Medical Imaging AI: Developing models for Alzheimer's disease diagnosis using 3D PET/MRI and transformers (e.g., ADViT, CAT-XPLAIN) Gut-Brain Axis: Investigating microbiome impacts on neurodegeneration, particularly in APOE4 carriers Computer Vision: Innovating diffusion models, self-supervised learning, and attention mechanisms for biomedical and geospatial applications Publication Trends: Recent work focuses on neuroimaging biomarkers for Alzheimer's, microbiome interventions, and scalable vision-language architectures. They integrate cutting-edge ML techniques with clinical data analysis. Collaborations: Engaged in interdisciplinary projects spanning genetics, nutrition, and traffic safety. Their work appears in journals like Communications Biology and explores real-world applications such as roadway hazard detection via satellite imagery.

Pari Delir Haghighiمشاهده پروفایل

مدرس ارشد

• Ubiquitous Computing
• Context-aware Computing
• Mobile Health
• +۴ مورد دیگر

Pari Delir Haghighi is a Senior Lecturer in the Department of Human Centred Computing at Monash University's Faculty of Information Technology. She holds a PhD in Computing (2010) and a Bachelor of Computer Science (Honours) from Monash University (2004). Her research focuses on ubiquitous computing, context-aware systems, mobile health, and AI-driven decision support. She has pioneered solutions for chronic disease management, emergency response systems, and healthcare data integration. Notable projects include developing the DO4MG ontology for emergency management, AI-powered construction safety systems, and mobile interventions for eating disorders. She has led 13 research projects, including collaborations on digital health, safety engineering, and body image interventions. Her work aligns with UN Sustainable Development Goals related to health and education. Teaching contributions include awards for Honours supervision (2014) and student learning excellence (2020). She teaches courses such as Mobile and Distributed Computing Systems and Systems Analysis and Design. Pari actively organizes conferences like the International Conference on Advances in Mobile Computing and Multimedia. Her lab, the Data Visualisation and Immersive Analytics Research Lab, explores innovative visualisation techniques. Current research emphasizes culturally inclusive digital health interventions, LLM auditing for harmful content detection, and IoT middleware benchmarking. She advises on interdisciplinary projects, fostering collaboration between IT and healthcare sectors.

Yu Yinمشاهده پروفایل

استادیار

• Computer Vision
• Generative Models
• 3D Modeling and Animation
• +۷ مورد دیگر

Yu Yin is an Assistant Professor in the Department of Computer and Data Sciences at Case Western Reserve University (CWRU), joined in Fall 2023. Previously, they earned a Ph.D. (2023) and M.S. (2018) in Computer Engineering and Electrical & Computer Engineering from Northeastern University, and a B.E. (2016) in Electrical and Information Engineering from Wuhan University of Technology. Their research focuses on visual synthesis, 3D modeling/animation, and applications like face restoration, deepfake detection, and generative models. They teach courses including Deep Generative Models (CSDS 570) and Computer Vision (CSDS 465). Key achievements include a 2024 Teaching Award, NeurIPS 2024 Oral paper, and organizing workshops like ICCV-AMFG 2023. Active in program committees for CVPR, NeurIPS, and ICLR. Over 20+ publications in top venues (CVPR, AAAI, ACM MM) and 2 patents. Open to advising motivated students in machine learning and computer vision.

Mustapha S. Fofanaمشاهده پروفایل

دانشیار

• Nonlinear Machine-Tool Chatter
• Manufacturing Systems
• Time Delay Dynamics and Control
• +۳ مورد دیگر

Mustapha S. Fofana is an Associate Professor in the Department of Mechanical & Materials Engineering at Worcester Polytechnic Institute (WPI). He holds a BS and MS from the Technical University of Budapest (1986), followed by an MS (1989) and PhD (1993) from the University of Waterloo. His research focuses on nonlinear dynamics in manufacturing processes, including machine-tool chatter, time delay systems, and stochastic modeling. He is affiliated with WPI’s Higgins Labs and can be reached at msfofana@wpi.edu. Education: BS, Technical University of Budapest (1986) MS, Technical University of Budapest (1986) MS, University of Waterloo (1989) PhD, University of Waterloo (1993) Research Interests: Prof. Fofana explores advanced topics in manufacturing dynamics, including nonlinear machine-tool chatter mechanisms, time-delay control strategies for manufacturing systems, and stochastic analysis of machining processes. His work integrates delay differential equations and degenerate Hopf bifurcation theories to improve manufacturing health monitoring and process stability. Publications: His notable work includes analyzing nonlinear effects in orthogonal turning operations (2002) and ongoing research on deterministically and stochastically perturbed systems under review in engineering journals. He also investigates parametric software integration into computer-aided manufacturing (CAM) environments. Awards & Professional Highlights: No specific awards or professional highlights are listed in the provided text. Labs & Teams: Affiliated with Higgins Labs at WPI, though specific lab teams or collaborative projects are not detailed here.

• Computer-Integrated Manufacturing
• Computer-Aided Design
• Computer-Aided Manufacturing
• +۹ مورد دیگر

Olusanjo Fadiya is a Lecturer in Mechanical Engineering at the Faculty of Computing, Engineering and Science, University of South Wales. He is a member of the Engineering Research and Innovation Group and his work contributes to the UN Sustainable Development Goals. His educational background includes: PhD in Industrial Engineering (Materials Logistics Systems) from the University of Wolverhampton (2008-2012) MSc in Mechanical Engineering from University College London (2004-2005) BEng in Mechanical Engineering (Manufacturing option) from the University of Ilorin, Nigeria (1997-2002) Dr. Fadiya has developed expertise in computer-integrated manufacturing (CIM) with specialization in computer-aided design (CAD), computer-aided manufacturing (CAM), and computer-aided process planning (CAPP). His research spans design, modeling, analysis, CNC programming, additive manufacturing, and process planning. His scholarly fingerprint reveals strong connections across manufacturing (100%), engineering (96%), mechanical engineering (81%), logistics (80%), construction industry (72%), and planning processes (64%). His publication record shows a progression from construction logistics research (2010-2015) examining plant theft, construction waste, and ICT-based logistics frameworks, to his most recent 2023 work focusing on student-centered educational methodology using appreciative inquiry. This represents a notable expansion of his research into engineering education while maintaining connections to quality management principles. Professional contributions include serving as an External Examiner at the University of Southern Queensland (April-June 2019).