جست‌وجوی استاد و پژوهشگر

Saeed Shiry Ghidaryمشاهده پروفایل

مدرس

• Artificial Intelligence
• Robotics
• Machine Learning
• +۳ مورد دیگر

Dr. Saeed Shiry Ghidary is a Lecturer at Staffordshire University’s Digital, Tech, Innovation & Business department. He holds a Ph.D. in Robotics and Intelligent Systems from Kobe University (Japan), alongside M.Sc. and B.Sc. degrees in Computer Architecture and Electronics from Amirkabir University of Technology (Iran). His research focuses on interdisciplinary areas including Machine Learning, Robotics, AI, Computer Vision, and Cognitive Science. He has led international research teams, including chairing the Amirkabir Robotic Center, and contributed to over 20 peer-reviewed publications. **Education:** Ph.D., Robotics and Intelligent Systems, Kobe University, Japan (2002) M.Sc., Computer Architecture, Amirkabir University, Iran (1994) B.Sc., Electronics Engineering, Amirkabir University, Iran (1990) **Research & Teaching:** Dr. Ghidary specializes in AI and robotics education, teaching graduate and undergraduate courses. His research emphasizes applications like BCI systems, EEG signal processing, and kernel methods for dimensionality reduction. He has collaborated with institutions in Japan, France, Australia, and the U.S., and serves as an HEA Fellow. **Awards & Recognition:** HEA Fellowship **Consulting & Industry:** He advises industries on AI, robotics, and automation for sectors like banking, insurance, and oil production, focusing on efficiency and profitability through technological integration.

David Schneiderمشاهده پروفایل

استادیار

• Surgical Decision-Making
• Biomedical Informatics
• Machine Learning in Healthcare
• +۳ مورد دیگر

Dr. David Schneider is an Assistant Professor in the Department of Surgery (Endocrine Division) at the University of Wisconsin School of Medicine and Public Health. His research focuses on improving clinical decision-making through informatics tools, including machine learning and natural language processing applied to surgical outcomes and endocrine care. He leads the Dr. Schneider’s Lab , which collaborates with the Wisconsin Surgical Outcomes Program to optimize surgical care for endocrine conditions like hyperparathyroidism and thyroid cancer. Education: MD, MS (degree details not explicitly stated in provided texts, but inferred from lab staff listing). His work emphasizes data-driven approaches to address challenges such as delayed diagnosis of hyperparathyroidism, post-thyroidectomy hormone management, and disparities in healthcare access. Research interests include: Machine learning for predictive analytics in surgery NLP applications for clinical data extraction Quality improvement in endocrine surgery Healthcare equity analysis Grants and Awards: AAES Paul LoGerfo Award (2018–2019) ICTR Novel Methods Pilot Award (2019–2021) Labs/Teams: Principal Investigator of the Schneider Lab, which develops tools like the Thyroid Hormone Dosing App to aid clinical decisions. Collaborates with multidisciplinary teams on surgical quality improvement initiatives.

• Bioinformatics
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Dr. Matloob Khushi is an academic staff member at the University of Sydney's School of Computer Science, where he has held roles since joining in 2017. He earned his Ph.D. from the University of Sydney and has published over 50 research papers. His research focuses on big data analysis, machine learning applications in health, finance, and genomics, with notable contributions to adverse drug event prediction, bioinformatics tools, and predictive modeling in healthcare. Dr. Khushi's work has been recognized in media for innovations like the MatCol tool aiding cancer research. Education: Ph.D. in Computer Science, University of Sydney (2008). Research Interests: Machine learning for big data, biomedical informatics, financial analytics, and health informatics. Recent articles highlight advancements in graph neural networks, predictive models for chronic diseases, and tools for genomic analysis. He advises two Ph.D. students and collaborates on interdisciplinary projects.

Benjamin Brackeمشاهده پروفایل

پژوهشگر

• Machine Learning
• Medical Imaging
• Computer Vision
• +۳ مورد دیگر

Benjamin Bracke, M.Sc., is a researcher with a strong focus on interdisciplinary applications of machine learning and computer vision. His work spans medical imaging, biodiversity informatics, and educational technology. He has contributed to projects such as ImageCLEF challenges, advancing multimedia retrieval systems in healthcare and ecological contexts. Recent research includes diabetic foot ulcer diagnosis, snake species classification using multimodal data, and kidney tumor segmentation via U-Net architectures. Key research interests include machine learning pedagogy, integrating industrial projects into Master’s curricula, and developing automated solutions for medical diagnostics. His technical expertise lies in object detection, semantic segmentation, geospatial data fusion, and metadata-driven classification systems. Bracke’s publications demonstrate a decade-long commitment to medical engineering (e.g., femoral neck axis detection for hip surgeries) alongside modern AI applications. Despite no listed awards or grants, his work bridges academic research with practical clinical and ecological challenges. No lab affiliations or student advisements are explicitly mentioned in the provided texts.

Sampath Jayarathnaمشاهده پروفایل

دانشیار

• Neuro-Information Retrieval
• Data Science
• Human-Computer Interaction
• +۵ مورد دیگر

Sampath Jayarathna is an Associate Professor in the Department of Computer Science at Old Dominion University, part of the College of Science. His research focuses on Neuro-Information Retrieval, Data Science, and Human-Computer Interaction, with a particular emphasis on eye tracking, biometrics, and wearable devices. He holds a Ph.D. from Texas A&M University and has been at ODU since 2018. Jayarathna has received multiple accolades, including the NSF CAREER Award in 2021 and the ODUAA New Faculty Award. His work bridges disciplines, applying computational techniques to understand user behavior and develop adaptive interfaces for accessibility. Education: Ph.D. in Computer Science (Texas A&M University, 2016), M.S. (Texas State University, 2010), B.S. (University of Peradeniya, Sri Lanka, 2006). Prior to ODU, he was on the faculty at Cal Poly Pomona. Research Interests: Neuro-Information Retrieval (eye tracking, EEG, oculomotor systems), Data Science (multi-modal sensing, user interest modeling), Human-Computer Interaction (recommender systems, accessibility for visually impaired users). His lab develops tools like the Neuro Information Retrieval and Data Science Lab, supported by grants such as NSF CAREER and COVA-CCI. His recent work includes enhancing accessibility for blind users in e-commerce and data visualization, detecting deceptive web advertisements, and real-time cognitive load analysis via pupillary metrics. He has authored over 50 publications in top-tier venues like IEEE Transactions and ACM conferences. Awards include the 2025 COS Outreach Award, 2023 NSF S&CC-PG grant, and the 2021 Provost's Award for Mentoring. He advises numerous students, with a focus on inclusive technologies and interdisciplinary research. Labs/Teams: Leads the Neuro Information Retrieval and Data Science Lab, collaborating on projects like distributed eye-tracking systems (DisETrac) and gaze analytics dashboards. Active in grants involving disinformation detection and secure IoT applications.

• Modality
• Evidentiality
• Evaluative Language
• +۷ مورد دیگر

Marta Carretero Lapeyre is a Full Professor at the Complutense University of Madrid, specializing in the Department of English Studies, Linguistics and Literature. Her research focuses on modality, evidentiality, and evaluative language in English and contrastive studies, alongside syntactic, semantic, and pragmatic analysis of noun phrases. Supervised 3 PhD dissertations Co-supervised 2 PhD dissertations Currently supervising 1 PhD dissertation Her work spans corpus linguistics, pragmatics, and digital communication. Recent publications address topics like machine learning applications in modal analysis, cross-linguistic pragmatic markers, and epistemic stance in pandemic discourse. She welcomes proposals on pragmatics, corpus linguistics, and digital communication research. Email: mcarrete@filol.ucm.es

Hamish Cunninghamمشاهده پروفایل

استاد پژوهشی

• Internet of Things (IoT)
• Aquaponics
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Professor Hamish Cunningham is a Research Professor of Internet Computing at the University of Sheffield's School of Computer Science, serving as Impact Lead and a member of the Natural Language Processing (NLP) research group. He is also part of the Institute for Sustainable Food's executive committee. His career spans over 40 years in AI, NLP, and information retrieval, with notable contributions to the GATE open-source platform and IoT innovations like the unPhone and MoPi devices. He holds a PhD in Software Architecture for Language Engineering from the University of Sheffield (2000). Research interests include aquaponics for sustainable food production, IoT privacy, and open-source technologies for resilience. He teaches the IoT course (COM3505) and has supervised numerous PhD students focusing on topics like distributed cloud mechanisms, neural authorship attribution, and ontology engineering. His work integrates multidisciplinary approaches, emphasizing economic democracy for environmental sustainability. Key grants include leadership roles in EU-funded projects like SoBigData (€6.5M) and AnnoMarket (€394K), alongside contributions to aquaponics research through COST Action EU Aquaponics Hub. Over 200 peer-reviewed publications span NLP, IoT, and sustainability, with GATE's adoption by organizations like the BBC and WHO. Outreach activities include STEM ambassador roles, Raspberry Pi educational initiatives, and exhibitions like the Ideas Bazaar. His aquaponics greenhouse and open-source ethos reflect a commitment to democratizing technology for societal resilience.

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

Amal Al-Shargabi is a Senior Lecturer in Data Analytics and Artificial Intelligence at the School of Computer Science and Informatics, part of the Faculty of Computing, Engineering, and Media at De Montfort University (UK). Previously, she served as an Associate Professor at Qassim University (Saudi Arabia). She holds a PhD in Information Technology and Quantitative Sciences from Universiti Teknologi MARA (UiTM), Malaysia, along with a Master's in Computer Science (awarded excellence) and a Bachelor's in Computer Science. Education: PhD in Information Technology and Quantitative Sciences, UiTM, Malaysia Master's Degree in Computer Science, UiTM, Malaysia Bachelor Degree in Computer Science Research Interests: Machine Learning, Artificial Intelligence (including Generative AI), Deep Learning, and their applications in real-world problems such as healthcare, education, and environmental management. She also explores empirical software engineering and data analytics. Research Trends in Publications: Her work focuses on AI-driven solutions for healthcare monitoring (e.g., elderly health), energy efficiency in arid climates, GAN-based generative models, and ethical AI in academia. Recent studies also address assistive technologies like Saudi Sign Language translation and Quranic speech recognition. Awards: Invention, Innovation and Design Exhibition (IIDX’16) Award, Malaysia (2016) Three Minute Thesis (3MT’16) Award, Malaysia (2016) Student-led projects under her supervision have won awards in Saudi Arabia, including Takaful and AI Challenge prizes. Advising & Grants: Research grants include projects on health monitoring via IoT, ethical considerations of ChatGPT/BARD, and solar panel energy optimization. Internally funded projects at Qassim University addressed noise pollution, COVID-19 diagnosis via AI, and job recommendation systems. Labs/Teams: Affiliated with the Institute of Artificial Intelligence (IAI) at DMU. Collaborates with institutions like Qassim University on interdisciplinary projects.

• Computer Graphics
• Medical Imaging
• Computer Vision
• +۴ مورد دیگر

Dr. Hammadi Nait-Charif is an Associate Professor in Animation at Bournemouth University. His research spans computer graphics, medical imaging, and neural networks, with a focus on applications in healthcare technology and interactive systems. He has contributed to advancements in motion tracking, 3D reconstruction, and assistive elderly care frameworks like ANGELAH. His work integrates interdisciplinary approaches combining computer vision, biomedical engineering, and machine learning. Research Interests : His key areas include computer animation techniques, medical image analysis (e.g., mammography and vertebra tracking), human motion capture systems, and fault-tolerant neural network architectures. Recent projects involve multi-modal feature fusion for medical report generation and holographic display technologies. Grants & Contributions : He secured a grant for developing a handheld POS system (InnovateUK, 2011). His collaborations span institutions worldwide, addressing challenges in elderly care, surgical training simulators, and spinal imaging. Labs & Teams : His work often involves interdisciplinary teams focusing on medical imaging solutions and animation systems integration.

• Machine Learning
• Neuroscience
• Biomedical Engineering
• +۳ مورد دیگر

Julien Audiffren is a Lecturer and Senior Researcher in the Department of Informatics at the University of Fribourg. His research focuses on machine learning applications in biomedical engineering, human perception, and data analysis. He holds roles in both education and research, contributing to interdisciplinary projects in informatics and health sciences. His research interests span machine learning methodologies, including multi-label classification, adaptive algorithms for perception quantification, and applications in sports science and healthcare. He has published extensively on topics such as postural control, fall prediction, and virtual reality training systems. Recent work emphasizes the development of robust adaptive procedures for threshold estimation in neuroscience experiments and the use of large language models for semi-structured data cleaning. His contributions bridge computational methods with practical applications in health monitoring and sports performance analysis. No scientific awards or grants are explicitly mentioned in the provided information. He is affiliated with the Interfaculty Informatics Department and collaborates on projects involving wearable sensors and AI-driven health solutions.

Pasi Fräntiمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Pattern Recognition
• +۷ مورد دیگر

Pasi Fränti is a Professor of Computer Science at the University of Eastern Finland (UEF) since 2000, affiliated with the School of Computing within the Faculty of Science, Forestry and Technology. He holds MSc and PhD degrees from the University of Turku (1991 and 1994). His research focuses on machine learning, data mining, pattern recognition, and location-based systems, with notable contributions to clustering algorithms, image compression, and speech technology. Fränti leads the Machine Learning research group at UEF and has supervised 25 PhD students, covering topics like clustering, image/audio compression, and location-aware systems. His work bridges theoretical advances with practical applications in healthcare optimization, mobile services, and intelligent systems. He has published extensively, with over 79 journal articles and 167 conference papers, including 14 IEEE Transactions papers. Recent research highlights include optimizing health station locations via clustering algorithms, outlier detection improvements, and gamified location-based services (e.g., Mopsi). His interdisciplinary projects address challenges in urban planning, energy systems, and biomedical signal processing. Fränti actively critiques academic publishing practices, advocating for open science and peer review reforms. Key projects include the IMPRO initiative (2018–2023) supporting health/social services reform, and contributions to journals like Applied Computing and Intelligence . His lab develops tools for real-time data clustering and spatial analysis, with applications in mobile user behavior prediction and smart city infrastructure planning.

• Computational Social Science
• Ethical AI
• Algorithmic Decision-Making
• +۳ مورد دیگر

Maria Rodriguez is a Principal Research Scientist in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. Her research explores systems of care across technology and human services, focusing on algorithmic decision-making and ethical AI. She investigates how technology impacts marginalized groups and reorients systems toward outlier cases using computational social science approaches. Education includes: PhD, Social Welfare, University of Washington (2016) MSW, Macro Social Work Practice, University of Pennsylvania (2010) BA, Clinical Counseling and Child Psychology, Alfred University (2005) Her research interests span computational social science, ethical AI, and social policy, with publications exploring digital transformation in social services, structural topic modeling, and technology's role in social justice. Recent work examines generative AI in education, neighborhood change prediction, and online discourse analysis. Awards include the Anthem Award for Human and Civil Rights (2024), EAAMO Best Reviewer (2021), and UW Presidential Dissertation Fellowship (2016).

• Marketing
• Data Science
• Consumer Behavior
• +۵ مورد دیگر

Jorge Eduardo Fresneda Fernandez is an Associate Professor at the MT School of Management, New Jersey Institute of Technology (NJIT). His research focuses on marketing analytics, consumer behavior, and accessibility in digital environments. He employs machine learning techniques to analyze retail dynamics, sentiment analysis, and neuromarketing insights. Key areas include optimizing healthcare prediction models, enhancing retail website accessibility for visually impaired users, and leveraging bio-metric sensors for consumer preference analysis. His work bridges technology and human-centric approaches, addressing challenges such as service failures in retail and the role of key opinion leaders. Recent studies highlighted in media include neuromarketing techniques using EEG and skin response sensors to predict consumer emotions, and advocacy for inclusive digital experiences in retail. Collaborations include interdisciplinary projects with computer scientists, economists, and neuroscientists. Media engagements emphasize practical applications of research in technology and consumer welfare. Over 22 peer-reviewed publications since 2005 with a focus on applied data science and marketing strategy. He holds an h-index of 24 and has contributed to journals like Computer Methods and Programs in Biomedicine and Journal of Big Data . His research emphasizes real-world impact through collaborations with industry and advocacy for equitable technology design.

Serdar Bozdagمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Bioinformatics
• +۵ مورد دیگر

Serdar Bozdag is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas, with a joint appointment in Mathematics. He serves as Director of the Center for Computational Life Sciences, focusing on interdisciplinary research at the intersection of computational methods and biological systems. His research expertise spans machine learning applications in bioinformatics, multi-omics data integration, and network biology. Key areas include developing algorithms for disease biomarker discovery, integrating genomic, transcriptomic, and clinical data through graph neural networks, and creating computational tools for precision medicine. Notable contributions include frameworks like CanMod2 for co-regulatory module inference and CTDPathSim for pathway-based similarity analysis. Bozdag’s work bridges computer science and life sciences, addressing challenges in Alzheimer’s disease diagnosis, cancer systems biology, and plant-microbe interactions. He has pioneered methods for analyzing dynamic biological networks and temporal data patterns, with applications in drug response modeling and electronic health record analysis. His lab develops open-source software tools widely used in computational biology research. Current projects emphasize AI-driven approaches for multi-modal healthcare data fusion, predictive modeling of disease progression, and scalable solutions for big biological data. His research has implications for improving diagnostic accuracy, personalized treatment strategies, and understanding complex biological systems.

• Robotics
• Multi-robot Systems
• Control Systems
• +۴ مورد دیگر

Lorenzo Sabattini is an Associate Professor at the Department of Engineering Sciences and Methods (DISMI) at the University of Modena and Reggio Emilia, Italy. His research focuses on multi-robot systems, decentralized estimation/control, and human-robot interaction. He holds a Ph.D. in Control Systems and Operational Research from the University of Bologna (2012), and has been a Visiting Researcher at the University of Maryland (2010). He served as a founding co-chair of the IEEE RAS Technical Committee on Multi-Robot Systems from 2014–2021. His work has been recognized with awards such as the Fabrizio Flacco Young Author Best Paper Award (2018) and the IEEE RAS Most Active Technical Committee Award (2018). Research interests include multi-robot coordination, coverage control, human-robot collaboration, and Industry 5.0 applications. Notable projects involve digital twins for operator well-being, AGV fleet management, and socially acceptable robotics. He has organized workshops at IEEE ICRA, RSS, and ERF, and serves as an Associate Editor for International Journal of Robotics Research (2023–2024) and IEEE Robotics and Automation Letters (2015–2018). Publications span topics like connectivity maintenance in robot networks, multimodal stress assessment in HRC, and resilient multi-robot systems. He actively collaborates on industrial automation projects, such as the INCLUSIVE system for adaptive HMIs and autonomous depalletizing systems. His research bridges theory and practice, addressing challenges in logistics, manufacturing, and human-centric robotics.