جست‌وجوی استاد و پژوهشگر

Margrit Betkeمشاهده پروفایل

استاد

• Computer Vision
• Medical Image Analysis
• Human-Computer Interaction
• +۴ مورد دیگر

Margrit Betke is a Professor of Computer Science at Boston University, serving as Director of the Master's Admissions and Director of the Master's in AI Program. She co-leads the Artificial Intelligence Research Initiative and the Image and Video Computing Research Group. Her research focuses on computer vision, medical image analysis, human-computer interfaces, and assistive technologies, with notable contributions to the Camera Mouse and lung tumor tracking systems. She holds a PhD from MIT and has published over 200 papers. Her honors include the NSF Early Career Award and being named a Top 10 Woman to Watch in New England. Education: PhD in Computer Science and Electrical Engineering from MIT (1995). Research spans medical imaging, AI for disabilities, and computer vision applications. Her work includes developing patented algorithms for pulmonary nodule detection and the Camera Mouse, a free assistive technology. Awards include the National Science Foundation Early Career Award (2001), Top 10 Women to Watch (2005), and Senior Member statuses in ACM and IEEE (2012). She has advised numerous students, including fourteen PhD graduates and current researchers in AI and assistive tech. Grants include NSF BIGDATA (2018) for public communication analysis and ONR MURI (2018) for neuroscience-inspired robotics. Her labs and teams focus on AI ethics, medical imaging, and multimodal data analysis.

• Distributed Computing
• Internet Computing
• Cloud Computing
• +۹ مورد دیگر

George Pallis is a Professor in the Department of Computer Science at the University of Cyprus, where he also serves as Associate Director of the Laboratory of Internet Computing. He is a programme director for the Master in Data Science and leads major international research initiatives funded by the European Commission, national agencies, and industry partners such as Google. His academic foundation includes a BSc and PhD in Informatics from Aristotle University of Thessaloniki, Greece. PhD, Department of Informatics, Aristotle University of Thessaloniki, Greece, 2006 BSc, Department of Informatics, Aristotle University of Thessaloniki, Greece, 2001 Dr. Pallis’s research focuses on Distributed and Internet Computing , with specialized interests in Big Data Analytics, Cloud/Edge/Fog Computing, Content Delivery Networks, and Online Social Networks . His work bridges theoretical innovation and practical deployment, particularly in scalable and energy-efficient computing infrastructures. He has contributed to international standards through the German Institute for Standardization (DIN) and has developed frameworks for cloud elasticity, fog emulation, and misinformation detection. His 15 most recent publications reflect a strong trend in edge and fog computing, AI-driven analytics, privacy-preserving data processing, and misinformation detection . These works span high-impact venues such as IEEE IC2E, IEEE/ACM SEC, IEEE CloudCom, and IEEE BigData, with increasing integration of machine learning and human-in-the-loop systems. Dr. Pallis has received multiple scientific awards, including: Best Paper Award, IEEE CloudCom 2024 Best Paper Award (2nd place), IEEE/ACM UCC 2023 Best Paper Award, IEEE IoTDi 2022 Best Student Paper, IEEE ISCC 2022 Best Paper, IEEE BigData 2016 Best Paper, ICSOC 2014 Best Demo Award, ACM/IEEE SEC 2020 World’s Top 2% Scientists (Stanford) Golden Core Member, IEEE Computer Society He has supervised several PhD students in areas such as fog computing emulation, big data in entrepreneurship, and polarization detection. His research is supported by over 5.5 million euros in grants from the European Commission (e.g., RAINBOW, UNICORN, ICARUS), the Research Promotion Foundation in Cyprus, and industry. He has served as General Chair of IEEE/ACM SEC 2024 and IEEE IC2E 2024, and as Editor-in-Chief of IEEE Internet Computing (2019–2023), now holding the Emeritus title. He is currently Associate Editor for the Computing Journal (Springer). Dr. Pallis leads the Laboratory of Internet Computing, a hub for research in cloud, edge, and social computing. The lab develops tools like Fogify for emulation, RAINBOW analytics, and Check-It for fake news detection, fostering collaboration across academia and industry.

Simon Hentschelمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Climate Modeling
• +۴ مورد دیگر

Simon Hentschel, M.Sc., is a researcher at the Chair of Data Science (Informatik X) within the Faculty of Mathematics and Computer Science at the University of Würzburg. He joined the institution in November 2024 after completing his Master's degree in Computer Science. His work focuses on interdisciplinary applications of machine learning, particularly bridging climate modeling with computer vision techniques. His research spans topics including: Climate modeling using deep learning architectures Computer vision for environmental data interpretation Transformer-based models for vegetation activity analysis Image aesthetic assessment using CLIP and vision-language frameworks Transfer learning from weather prediction models to ecological monitoring Recent work demonstrates his expertise in adapting foundational AI models like FourCastNet and CLIP to novel ecological and visual domains. His publications reveal a pattern of innovation in leveraging pre-trained architectures for specialized tasks while optimizing computational efficiency. Beyond technical contributions, he maintains active involvement in: Collaborative research projects like BigData@Geo 2.0 Laboratory infrastructure at CAIDAS (Center for Artificial Intelligence and Data Science) Development of open-source models and datasets Contact: Room 50.03.009, Emil-Fischer-Straße 50, 97074 Würzburg, Germany

Véronique Benzakenمشاهده پروفایل

استاد

• Data-Centric Programming Languages
• Formal Verification of Data Management Systems
• XML/JSON Data Processing
• +۳ مورد دیگر

Véronique Benzaken is a Full Professor at Université Paris-Saclay, affiliated with the Laboratoire de Méthodes Formelles (LRI) and INRIA Saclay's Toccata group. Her research focuses on Data-Centric Programming Languages , formal verification of data management systems, and type-safe XML/JSON processing. Key projects: ℂDuce XML-centric functional language, Datacert data certification initiative Collaboration with Oracle Labs on multi-lingual query interfaces using QIR Active in Coq formalization of SQL, Datalog, and data provenance Research interests include: Formal foundations for NoSQL/BigData languages Coq-based verification of database systems Type systems for data models Cloud computing data abstraction Her work bridges theory and practical implementation through collaborations with both academic and industrial partners.

Mohammad Abuosbaمشاهده پروفایل

استاد

• Engineering Informatics
• IT Systems in Engineering
• Database Systems
• +۷ مورد دیگر

Prof. Dr.-Ing. Mohammad Abuosba is a faculty member at the University of Applied Sciences Berlin, affiliated with the School of Engineering—Technology and Life. His teaching and research focus on engineering informatics, database systems, product data management, quality assurance, and sustainable development in technical contexts. Research clusters: Sustainable Smart Cities, Software systems for sustainable development Specialties: BigData modeling, service process optimization in mechanical/plant engineering

• Deep Learning for Medical Imaging
• BigData for Medical Image Analysis
• Quantitative MR Analysis
• +۲ مورد دیگر

Pierrick Coupé is a CNRS Research Director affiliated with the Laboratoire Bordelais de Recherche en Informatique (LaBRI) at the University of Bordeaux. His career spans academic research in medical image processing, with leadership roles including co-leading the Digital Health Axis and directing the in2brain research group since 2023. PhD in Image Processing (2008) - University of Rennes I Postdoctoral Researcher (2008-2011) - Montreal Neurological Institute Research Leadership (2013-2023) - PICTURA research group Dr. Coupé's research focuses on medical image processing innovations: Deep Learning for medical imaging (denoising, registration, segmentation) BigData analysis for lifespan brain studies Quantitative MR techniques for neuroanatomical validation Computer-Aided Diagnosis (SNIPE scoring method for Alzheimer's) Image enhancement via nonlocal means filters His recent publications highlight: Cloud-based medical image analysis (volBrain platform processing 250,000 MRIs) Whole-brain segmentation frameworks Longitudinal Alzheimer's disease modeling Lifespan brain maturation studies Scientific recognition includes: IEEE Innovation Prize for best thesis CECR Fellowship during postdoctoral training Dr. Coupé leads significant research initiatives and has developed widely used medical image analysis tools, contributing extensively to NeuroImage, Human Brain Mapping, and IEEE Transactions.

Yang Zhangمشاهده پروفایل

عضو هیئت علمی

• Human-centered Artificial Intelligence
• Crowd Intelligence
• AI Fairness
• +۱۳ مورد دیگر

Yang Zhang is a Teaching Assistant Professor at the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign (UIUC). He holds a Ph.D. in Computer Science and Engineering from the University of Notre Dame and is affiliated with the Social Sensing & Intelligence Lab at UIUC as a Senior Researcher. His research focuses on Human-Centered Artificial Intelligence , emphasizing collaboration between humans and AI to enhance model performance, fairness, and accountability. Education: Ph.D. in Computer Science and Engineering, University of Notre Dame; First Class Scholarship, Wuhan University Yang’s work integrates Crowd Intelligence to optimize AI models for applications in disaster response, smart urban sensing, and social impact-aware systems. His recent publications explore hybrid learning frameworks, neural architecture search, and hyperparameter optimization. Yang has received multiple accolades, including a Best Paper Award at ACM/IEEE ASONAM 2022 and a Data Science Scholar Fellowship from Indiana University. Scientific Awards: Best Paper Award (ASONAM 2022), Best Paper Honorable Mention (SMARTCOMP 2022), Outstanding Graduate Research Assistant (Notre Dame), Video Presentation Award (IWQoS 2020), IEEE Student Travel Awards (BigData 2018, 2019), Data Science Scholar Fellowship (Indiana University), First Class Scholarship (Wuhan University) Yang is actively involved in graduate education as a Graduate Faculty member at UIUC. He has cohosted international scholars through the Bolashak International Scholarship Program and been recognized for excellence in teaching. His collaborations span institutions like Argonne National Laboratory, where he served as a W. J. Cody Research Associate.

• Data Management
• Big Data
• Databases
• +۶ مورد دیگر

Panos K. Chrysanthis is a Professor of Computer Science at the University of Pittsburgh , where he directs the Advanced Data Management Technologies Lab (established in 1996). He holds adjunct professorships at Carnegie-Mellon University and University of Cyprus , and has served as Honorary Visiting Professor at the University of Diaspora of the University of Cyprus since 2021. With a PhD from the University of Massachusetts Amherst (1991), he has over 30 years of academic experience at Pitt and 25 years at CMU/Cyprus. Research Focus: Data management (Big Data, Databases, Data Streams), Distributed & Mobile Computing, Workflow Management, Real-Time Systems Leadership: Program Co-Chair of IEEE ICDE 2018, General Co-Chair of BIRTE 2018, and editorial roles at VLDB Journal , IEEE TKDE , and DAPD Awards: 7 teaching awards, NSF CAREER Award (1995), Provost Award for Mentoring (2015), ACM Distinguished Scientist , IEEE Senior Member Scientific Contributions span over 300 publications, including keynote speeches at SEKE 2017 and invited talks at UMass (2017), University of Michigan (2016), and Missouri S&T (2017). His work includes the CAPRIO path recommendation system, SlickDeque stream processing framework, and CausalMGM tool for causal discovery in biomedical data. Current research integrates AI with visual analytics, IoT data systems, and mobility science. Grants include a $1.8M NIH U01 grant (2017) for causal discovery in healthcare data and multiple NSF awards, including a 2016 Big Data grant. He advises PhD students in data systems and mentors award-winning lab teams, with presentations at VLDB 2017 and IEEE BigData 2016.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Md Azher Uddin is an Assistant Professor at Heriot-Watt University's School of Mathematical & Computer Sciences. His email is M.Uddin@hw.ac.uk . He holds a Gold Medal Award for academic excellence from his undergraduate studies (2012) and has received scholarships from Kyung Hee University (2015) and the Woojung Education and Culture Foundation (2016). His research interests span Artificial Intelligence, Computer Vision, and Deep Learning, with applications in agriculture, healthcare, and human-computer interaction. Notable contributions include paddy disease recognition, automated depression severity estimation, and sign language recognition systems. Key awards include the Best Paper Award (Korea Computer Congress, 2019) and Excellent Paper Award (Korea Computer Congress, 2020). His work aligns with UN Sustainable Development Goals, particularly in innovation and infrastructure. He has been actively involved in peer-review activities for journals like Signal, Image and Video Processing , and conferences such as IEEE BigData. He delivered an invited talk at the 2022 International Conference on Data Science in Dubai, UAE.

Amos Folarinمشاهده پروفایل

پژوهشگر

• Mobile Health (mHealth)
• Clinical Informatics
• Bioinformatics
• +۷ مورد دیگر

Amos Folarin is a Senior Software Development Group Leader and Statistician at King’s College London's NIHR Maudsley Biomedical Research Centre (BRC), based in the Department of Biostatistics & Health Informatics within the Institute of Psychiatry, Psychology & Neuroscience (IoPPN). His work focuses on developing embedded software teams and digital health technologies for clinical research, particularly in mental health and chronic disease monitoring. Education Background: Trained in biochemistry/molecular biology, he transitioned to computational biology and has over 16 years of experience in data science and software development across institutions like Birkbeck, UCL, and King’s College London. Research Interests: Mobile Health (mHealth), Clinical Informatics, Bioinformatics, Genetics, and wearable device applications for remote monitoring of conditions like ADHD, depression, and chronic illnesses. Key projects include the RADAR-base platform for wearable data collection and the ART program for ADHD research. Grants & Funding: Received £2.5M MRC funding for the ART-transition study on ADHD in young adults. Involved in large-scale projects like CONVALESCENCE (long-term effects of COVID-19) and EDIFY (eating disorders in youth). Teams & Collaborations: Leads the Maudsley BRC Bioinformatics group and collaborates with global consortia (e.g., RADAR-CNS, BigData@Heart) on digital phenotyping and wearable technologies.

Paul L Bendichمشاهده پروفایل

استاد مدعو

• Topological Data Analysis
• Machine Learning
• Computational Topology
• +۴ مورد دیگر

Paul L Bendich is an Adjunct Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from Duke University (2008). His research focuses on adapting topological and geometric methods for data analysis, particularly in topological data analysis (TDA). He has pioneered TDA methodologies for applications in machine learning, sensor fusion, and environmental modeling. Current appointments include leading research initiatives in multi-modal data analysis and reinforcement learning optimization. Key areas of expertise include computational topology, persistent homology, and topological signal processing. He teaches courses on topological data analysis (COMPSCI 434, MATH 412) and has developed educational programs like Data+ at Duke. Grants include NSF-funded projects (BIGDATA: F: DKA: CSD) and Air Force Office of Scientific Research initiatives. Recent work emphasizes topological methods in AI safety (topological parallax), reinforcement learning efficiency, and geophysical feature tracking. Professional activities include conference presentations on TDA applications and editorial work for journals. His research bridges theoretical mathematics with practical data-driven challenges in science and engineering.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Paulo Miguel Torres Duarte Quaresma is a Full Professor at the Department of Informatics, Universidade de Évora, Portugal, and Senior Researcher at Centro ALGORITMI. His career spans roles as Vice-Rector for Research and Innovation (2022–2025), Director of the School of Science and Technology (2009–2013), and Member of FCT’s Board of Directors (2021). Holding a PhD in Informatics (specialized in AI/NLP) from Universidade Nova de Lisboa and a Habilitation in Informatics from Universidade de Évora, he leads the AI & BigData Lab (equipped with 10 petaflop NVIDIA DGX-A100 supercomputers) and co-coordinates PORTULAN CLARIN, a €2M FCT-funded language technology infrastructure. Current Affiliation: Universidade de Évora (since 1997) Research Labs: AI & BigData Lab, PORTULAN CLARIN, VISTA Lab, NOVA LINCS (2019–2021) His research focuses on Artificial Intelligence and Natural Language Processing , with applications in legal reasoning , medical informatics , geospatial accident analysis , and semantic web technologies . Recent work includes creating European Portuguese BERT models, analyzing 18th-century health texts, and developing AI solutions for clinical triage. Advising highlights include supervising 7 PhD and 26 MSc theses. He chairs international conferences like PROPOR (2020) and IDEAL (2023), and participates in projects integrating AI with public administration and regional development through Alentejo2020 funding.

Håkan Grahnمشاهده پروفایل

استاد

• Computer Architecture
• Multicore Systems
• GPU Computing
• +۷ مورد دیگر

Håkan Grahn is a Professor of Computer Engineering at the Department of Computer Science, School of Computing, Blekinge Institute of Technology (BTH) in Sweden. He has been a faculty member since 1996, becoming a full professor in 2007. His academic leadership includes serving as Head of Department (1999-2002) and Dean of Research (2011-2013) at BTH. He leads multiple significant research projects including GPAI (General Purpose AI Computing) and Green Clouds, with funding from ELLIIT, the Knowledge Foundation, and Vinnova. His educational background includes: M.Sc. in Computer Science and Engineering (1990) from Lund University Ph.D. in Computer Engineering (1995) from Lund University Håkan's research spans several interconnected domains in computer science and engineering, with a strong emphasis on practical applications. His work in computer architecture focuses on optimizing system performance through innovative cache coherence protocols and memory management techniques. In the realm of parallel computing , he investigates multicore systems, GPU computing, and thread-level speculation to enhance computational efficiency. His research in AI and machine learning addresses energy efficiency, data stream mining, and practical applications in areas like district heating systems and airborne networks. The integration of image processing with machine learning forms another significant strand of his work, particularly in historical document analysis and medical imaging applications. These research areas converge in his leadership of major initiatives like BigData@BTH and GPAI, where he bridges theoretical advances with real-world implementation challenges. Analysis of Håkan's recent publications reveals a clear trajectory toward increasingly applied research with strong industry connections. While maintaining foundational work in computer architecture, his output increasingly focuses on practical AI applications, energy efficiency in computing, and domain-specific implementations in sectors like telecommunications, energy systems, and defense. The interdisciplinary nature of his work is evident in collaborations spanning computer science, engineering, and domain-specific applications, with a growing emphasis on sustainability and resource optimization in computing systems. Håkan has successfully supervised numerous doctoral students, with ten graduates and six current Ph.D. candidates. His research has been supported by substantial funding from: The Knowledge Foundation (BigData@BTH, HINTS, Green Clouds) ELLIIT (GPAI project) Vinnova (FANET-MCA, Directed COM & EW) Industry partners including Ericsson, Saab, Telenor, and Fortnox He is actively involved in multiple research groups including DISL (Distributed and Intelligent Systems Lab), CCS-Lab (Communication and Computer Systems Research Lab), and previously PAARTS (Parallel Architectures and Applications for Real-Time Systems). His leadership extends to organizing academic events like the Nordic workshop on Multi-Core Computing and the Swedish Artificial Intelligence Society workshop.

• Mobility Data Analytics
• Big Data
• Machine Learning
• +۸ مورد دیگر

Dr. Omid Isfahani Alamdari serves as Assistant Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, bringing expertise in mobility data analytics and big data systems developed through international research experience. Education PhD in Computer Science, University of Pisa, Italy MSc in Computer Engineering - Software (Distributed Systems), Iran University of Science and Technology BSc in Computer Engineering - Software, Urmia University, Iran Research Focus His research centers on developing efficient trajectory analysis methods and advanced indexing techniques for massive mobility datasets. Key applications include sustainable transportation solutions (electric vehicle adoption, carpooling optimization) and explainable event prediction systems combining historical patterns with real-time data streams. He actively explores generative AI applications for mobility challenges and time series analysis. Publication Trends Publications from 2018-2023 reveal consistent contributions to transportation analytics, with dominant themes in trajectory processing (40%), sustainable mobility (30%), and prediction systems (30%). His work appears in top transportation venues (IEEE Transactions on ITS) and data science conferences (IEEE BigData, SIGSPATIAL), featuring strong international collaboration patterns. Academic Activities Teaching: Agile Software Development, Python for Data Analytics, SQL Databases, and Data Analytics Case Studies Research: Currently leads EU-inspired projects on sustainable mobility and event prediction Specialization: Trajectory analysis, spatio-temporal indexing, EV simulation, graph embedding