جست‌وجوی استاد و پژوهشگر

Frauke Kreuterمشاهده پروفایل

استاد

• Statistics
• Data Science
• Survey Methodology
• +۶ مورد دیگر

Professor Frauke Kreuter holds the Chair of Statistics and Data Science at Ludwig-Maximilians-Universität (LMU) Munich, Germany, and is a faculty member at the Joint Program in Survey Methodology (JPSM) at the University of Maryland, USA. She is Co-Director of the Social Data Science Center (SoDa), and plays a leading role in advancing data science for social research. Her research focuses on the intersection of statistics, survey methodology, and data science. Key areas include: Development and application of modern survey methods Integration of big data and traditional survey data Machine learning in social science research Secure and ethical access to sensitive data Training and capacity building in data science for researchers and policymakers Professor Kreuter has been recognized with several honors, including: Elected Fellow of the American Statistical Association Warren Mitofsky Innovators Award (2020) from the American Association for Public Opinion Research She is the Founder of the International Program for Survey and Data Science (IPSDS), Co-Founder of the Coleridge Initiative—which enables secure access to government data for research—and Co-Founder of the German-language podcast Dig Deep , promoting public understanding of data science. Her work involves extensive collaboration across academia, government, and policy sectors, and she leads initiatives that bridge methodological innovation with real-world impact.

Masoud Daneshtalabمشاهده پروفایل

استاد

• Deep Learning Acceleration
• Hardware/Software Co-Design
• Interconnection Networks
• +۷ مورد دیگر

Masoud Daneshtalab is a Professor at Mälardalen University, leading the Heterogeneous System research group (HERO). He previously held roles as a European Marie Curie Fellow at KTH Royal Institute of Technology (2014) and as a university lecturer and group leader at the University of Turku, Finland (2012-2014). His research focuses on interconnection networks, hardware/software co-design, deep learning acceleration, and evolutionary optimization. He specializes in fault-tolerant DNN accelerators, time-sensitive networking (TSN), and embedded systems. His work bridges theoretical advancements with practical implementations, emphasizing reliability and efficiency in edge computing and AI applications. Research interests include: Network-on-Chip (NoC) architectures and congestion prediction Fault resilience in deep neural networks (DNNs) Optimization of federated learning and homomorphic encryption for edge AI Integration of TSN with 5G and automotive systems Hardware acceleration techniques for computational efficiency Recent publications emphasize advancements in robust AI architectures, fault tolerance mechanisms, and TSN-based communication protocols. His work often addresses practical challenges in deploying machine learning models on resource-constrained devices. He actively contributes to interdisciplinary projects in autonomous systems, healthcare monitoring via FMCW radar, and neural architecture search for embedded applications. Labs/Teams: Leads the HERO group at Mälardalen University, focusing on heterogeneous computing systems and real-time embedded systems.

• Semantic Web
• Linked Data
• Blockchain
• +۵ مورد دیگر

John Domingue is a prominent researcher at The Open University, UK , specializing in Semantic Web technologies, Blockchain applications, and Educational Technology. He has co-authored over 211 publications since 1987, with recent work focusing on decentralization, data privacy, and AI integration. Current research themes include: Decentralized learning analytics Blockchain-based micro-accreditation Semantic knowledge graphs AI for education Trusted decentralized systems His recent publications (2023-2025) demonstrate ongoing leadership in Semantic Web and Blockchain research applied to education and data governance. Key collaborations include Alexander Mikroyannidis, Aisling Third, and Carlos Pedrinaci. Notable projects: FORGE Toolkit for remote experimentation ScienceMiles digital currency for researchers ADA system for automated learning analytics Decentralized trust frameworks Semantic PETs (Privacy Enhancing Technologies) While formal awards aren't listed, his extensive publication record in top venues (WWW, ISWC, ESWC) and editorial contributions establish him as a key figure in Semantic Web research. His work bridges technical innovation with practical educational applications, particularly in decentralized architectures.

Jaafar Alghazoمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۷ مورد دیگر

Dr. Jaafar Alghazo is an Associate Professor in the Math, Science and Technology Department at the University of Minnesota. He currently teaches courses in Information Technology Management and Software Engineering, including ITM 3900-E90: Internship, SE 2200-001: Intro to Software Engineering, and SE 4500-001: Senior Project I. Research Focus: Machine Learning, Deep Learning, Medical Image Analysis, Cybersecurity, IoT, UAV Communications Key Publication Trends: 15 most recent works span 2025–2023, emphasizing neural network optimization, Arabic sign language recognition, flood detection, and secure medical imaging techniques

Thanassis Tiropanisمشاهده پروفایل

استاد

• Decentralised information systems
• Decentralised information retrieval
• Data observatories
• +۷ مورد دیگر

Thanassis Tiropanis is a Professor at the University of Southampton's School of Electronics and Computer Science (ECS), specifically within the Web and Internet Science Group. He serves as enterprise champion for ECS and impact champion for computer science. His academic affiliations extend to the Institute for Life Sciences and the Southampton Marine and Maritime Institute, reflecting his interdisciplinary research approach. Professor Tiropanis specializes in decentralised information systems and infrastructures, with research interests spanning decentralised information retrieval, data and web observatories, bias in datasets, inaccessible algorithms, and IoT analytics. His work focuses on the technical and socio-technical aspects of the Web and Internet as evolving socio-technical artifacts, particularly examining how individuals can maintain control over their personal data through decentralised architectures. His recent publications reveal a strong research trajectory in decentralised search technologies, particularly through the ESPRESSO framework for personal online datastores. The publications demonstrate expertise in federated learning, privacy-preserving techniques, and bias detection in machine learning systems. His work bridges theoretical computer science with practical applications in health data, cybersecurity, and social linked data environments. Fellow of BCS Chartered IT professional with BCS Senior member of IEEE Fellow of the Higher Education academy in the UK Member of ACM Member of the Technical Chamber of Greece Professor Tiropanis currently supervises PhD students including Engin Kocak and Wenxuan Huang, while leading active research projects such as ESPRESSO (EPSRC-funded) focused on efficient search over personal repositories. His completed projects include significant EU-funded initiatives like NoBIAS (Artificial Intelligence without Bias) and IoT Lab. He is affiliated with multiple research centers including the Centre for Internet of Things and Pervasive Systems, Centre for Machine Intelligence, and Centre for Digital Finance.

Jan Pennekampمشاهده پروفایل

پژوهشگر

• Industrial Internet of Things (IIoT)
• Security and Privacy
• Privacy Enhancing Technologies (PETs)
• +۸ مورد دیگر

Jan Pennekamp is a postdoctoral researcher at the Chair of Communication and Distributed Systems (COMSYS) within the Department of Computer Science at RWTH Aachen University. He is a member of the Security and Privacy research group and is actively involved in the Cluster of Excellence 'Internet of Production,' where he serves as deputy workstream coordinator. His academic journey includes a B.Sc. and M.Sc. in Computer Science from RWTH Aachen, with exchange studies at Aalto University and an internship at the University of Luxembourg. His research interests center on security and privacy in the Industrial Internet of Things (IIoT), privacy-enhancing technologies (PETs), secure computation, and interdisciplinary applications in healthcare, particularly synthetic data and single-cell genomics. He has led and contributed to numerous research projects, including CALCIPROTECT, RFC, RUST, SUSTAINET-guardian, and SYNCLIVER. His methodological focus includes both technical innovation and rigorous evaluation in real-world scenarios. His recent publications demonstrate a strong trend toward privacy-preserving data sharing in industrial and healthcare contexts, leveraging techniques such as confidential computing, blockchain, and machine learning. His work bridges computer science with industrial engineering and clinical research, emphasizing secure, interoperable, and accountable data ecosystems. Scientific Awards: Klaus Tschira Boost Fund Fellow 2025 Attendee of the 12th Heidelberg Laureate Forum 2025 Young Researcher Award 2022, Cluster of Excellence Internet of Production ICT Young Researcher Award 2021 TDWI Award 2018 (best master thesis) Google Scholarship 2018 Finalist, Artifacts Competition and Impact Award at ACSAC 2022 Outstanding Reviewer Award, TheWebConf/WWW 2024 Jan has advised numerous B.Sc. and M.Sc. theses on topics ranging from privacy-preserving benchmarking to intrusion detection and blockchain-based accountability. He has received research stipends and travel grants from RWC and ACSAC and has held leadership roles in academic service, including organizing conferences and serving on program committees for top venues like IEEE S&P, CCS, and EuroS&P. He is also a Certified ScrumMaster and RWTH Research Manager, reflecting his strong project and team leadership skills. He is actively engaged in interdisciplinary research labs and teams, particularly within the 'Internet of Production' initiative, collaborating with industrial partners and medical researchers to develop secure and privacy-preserving information systems for real-world applications.

• Computer Vision
• Image Processing
• Photometric Stereo
• +۱۲ مورد دیگر

Vasileios Argyriou is an academic researcher affiliated with Kingston University, with extensive contributions in computer vision, image processing, and AI applications in IoT, healthcare, and smart agriculture. His work spans photometric stereo, motion estimation, federated learning, and UAV-based remote sensing. Research Interests: His research focuses on developing advanced computer vision techniques including photometric stereo for 3D reconstruction, deep learning for anomaly detection, federated learning for privacy-preserving AI, and AI-driven solutions for smart farming and healthcare. He explores motion estimation, facial recognition, and synthetic data generation for training robust models. The recent publications (2024–2025) highlight a strong trend toward applying machine learning in real-world systems—particularly federated learning for intrusion detection, UAV-based agricultural monitoring, 5G security, and medical imaging. His work integrates computer vision with IoT, wireless communications, and edge computing, demonstrating interdisciplinary innovation. Scientific Contributions: Author of a book on image and video registration with quality metrics. Extensive publication record in top journals including IEEE Transactions, Sensors, and Pattern Recognition. Pioneering work in photometric stereo, motion estimation using phase correlation, and GAN-based face reenactment. He has advised or collaborated with numerous researchers across fields, though specific student names are not listed. His work often involves large-scale data analysis, simulation frameworks, and real-time systems. He is involved in projects related to smart cities, precision agriculture, and autonomous systems. Laboratories and Teams: His collaborations suggest involvement with research groups focused on intelligent systems, computer vision, and IoT at Kingston University and beyond, particularly with Panagiotis Sarigiannidis, Thomas Lagkas, and others in networking and AI.

Johan Alibasaمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Software Engineering
• Affective Computing
• +۷ مورد دیگر

Johan Alibasa is an Assistant Professor in the Master of Cybersecurity program at Monash University Indonesia. He holds a PhD in Computer Science from the University of Sydney and has previously served as a lecturer at Telkom University and Sampoerna University in Indonesia, where he contributed to curriculum development in software engineering and information systems. Education: PhD in Computer Science, University of Sydney M.Sc. in Computer Engineering (Electrical Engineering), Institut Teknologi Bandung B.Sc. in Computer Engineering (Electrical Engineering), Institut Teknologi Bandung His research focuses on the integration of human factors with cybersecurity and software engineering. Key areas include human-computer interaction, affective computing, and the application of artificial intelligence to improve secure software development and user authentication. He investigates how cognitive load affects user interaction with security systems and designs usability-balanced, human-centered authentication mechanisms. The two most recent publications highlight a strong interdisciplinary trend, merging affective computing with cybersecurity (2022) and designing secure architectures for mobile health applications (2017). These works reflect a consistent focus on securing digital systems while maintaining user experience and cognitive efficiency, particularly in health and mobile domains. Scientific Awards: School of Computer Science Research Students Excellence Prize, University of Sydney Johan is actively involved in PhD supervision and contributes to the academic community through service on conference committees and peer review. His work aligns with the UN Sustainable Development Goals, particularly in advancing quality education and leveraging technology for societal benefit. He has contributed to curriculum design and fosters research that bridges theory and real-world applications in cybersecurity and AI. Johan is a member of international research networks and collaborates across countries, with recent work involving researchers from Australia and Indonesia. His lab-level activities focus on secure AI systems, affective user modeling, and privacy-preserving federated learning, though no formal lab name is mentioned.

• Wireless Systems and Networks
• Internet of Things (IoT)
• Digital Twin
• +۹ مورد دیگر

Luciano Bononi is a Full Professor and Deputy Head of the Department of Computer Science and Engineering at the University of Bologna. He holds a PhD in Computer Science (2002) and has been active in academia since 2001, progressing through roles like Researcher, Senior Researcher, Associate Professor (2011), and Full Professor (2020). His research focuses on Wireless Systems, IoT, Digital Twins, Smart Cities, and Mobile Applications , with over 160 peer-reviewed publications and major contributions to EU projects like Arrowhead and IoE. Education: MS (Laurea) in Computer Science, University of Bologna (1997, Summa cum Laude) PhD in Computer Science, University of Bologna (2002) Research Interests: Bononi's work spans Wireless Protocols, IoT Platforms, Digital Twin Integration with AI, Smart Mobility/ Energy Systems, and Edge Computing . He leads the WiLMA Lab and the ALMA-AI CoInnovation Lab , focusing on real-world IoT deployments and smart city solutions. Key Contributions: Coordinated EU projects like Arrowhead (2013-2017) and national initiatives like PNRR-Mobility-Spoke 7 Recipient of 4 Best Paper Awards and top rankings in global scientist databases Editorial roles at 7 journals including Wiley's Wireless Communications and Elsevier's Ad Hoc Networks Organized over 15 international conferences as Chair and participated in 200+ TPC roles Labs & Teams: Directs the WiLMA Lab (Wireless Systems and Mobile Apps) and the ALMA-AI CoInnovation Lab , emphasizing interdisciplinary collaboration between academia and industry.

Ashutosh Dhar Dwivediمشاهده پروفایل

استادیار

• Cybersecurity
• Post-Quantum Cryptography
• Blockchain Security
• +۴ مورد دیگر

Ashutosh Dhar Dwivedi is an Assistant Professor in the Cybersecurity Group at Aalborg University, Copenhagen, Denmark. He specializes in blockchain security, applied cryptography, post-quantum cryptography, and advanced cybersecurity. His interdisciplinary research spans cryptography, IoT security, and AI-driven security analytics. Education: PhD in Cryptography, with postdoctoral research at institutions including the University of Waterloo, Technical University of Denmark, and the Polish Academy of Sciences. His pedagogical focus includes professional upskilling in cyber defense and post-quantum resilience. Research interests include post-quantum cryptographic protocols, privacy-preserving blockchain systems, and machine learning for security. His work has yielded over 50 peer-reviewed papers, including contributions to high-impact journals and conferences. Notable achievements: 2023 and 2024 Stanford University Top 2% Scientist ranking. Contributions: Editorial roles in international journals, program committees for premier conferences, and leadership in academic-industry collaborations like the Quantum Communication Infrastructure (QCI) consortium. Active in developing quantum-secure systems for national and industrial infrastructure.

• Digital and precision agriculture
• AI systems for the Internet of Things (IoT)
• Programming systems
• +۳ مورد دیگر

Chandra Krintz is a Professor and Vice Chair in the Department of Computer Science at the University of California, Santa Barbara (UCSB). She co-directs the RACELab (Lab for Research on Adaptive Computing Environments) and leads research initiatives in IoT/AI systems, cloud-edge computing, and precision agriculture. Her work focuses on hybrid edge-cloud systems, energy-efficient computing, and agricultural sustainability through projects like SmartFarm and Where's the Bear (WTB). Affiliations: UCSB Computer Science, RACELab Roles: Academic Leadership (Vice Chair), Research Director, NSF-funded projects Research interests span IoT systems, distributed computing, and AI-driven agriculture. Notable projects include SmartFarm for sustainable farming and WTB for wildlife monitoring using edge-cloud integration. She has authored over 100 publications and secured grants from NSF, DOE, and industry partners. Awards: 2022 Best Paper Award (MSDBench) NSF and DOE grants for research in distributed systems and agriculture Teaching: Courses on runtime systems (CS263), cloud-edge IoT (CS190B), and software project management (CS148). Supervised numerous graduate and undergraduate students in research and capstone projects. Labs/Teams: RACELab collaborates with academia and industry on IoT, cloud computing, and AI applications. Key projects include xGFabric, SmartFarm, and WTB, with deployments in environmental monitoring and agricultural analytics.

• Mechanical Engineering
• Manufacturing Engineering
• Finite Element Analysis
• +۵ مورد دیگر

Ambarish Kulkarni is an Associate Professor at Swinburne University of Technology's School of Engineering, specializing in mechanical design, virtual/augmented reality (VR/AR), and electric vehicle (EV) technologies. His research focuses on product design, EV drivetrains, battery packaging, medical devices, and bushfire shelter design. He holds a PhD from Swinburne, a Master's from Queensland University of Technology, and a Bachelor's from the Indian Institute of Industrial Engineering. Research interests span finite element analysis, bio-mechanical modeling, and Industry 4.0 applications. Notable projects include Bombardier Tram development, therapeutic sleep systems, and smart manufacturing frameworks. He has supervised over 20 HDR students on topics ranging from predictive maintenance to extended reality in education. Awards include the Teaching Excellence Award (2016) and Premier's Design Awards for medical and safety innovations. He is a Fellow of the Institution of Engineers (Australia) and actively involved in professional bodies like SAE International. Grants funded projects on hydrogen generators, smart manufacturing systems, and graphene supercapacitors. His work bridges academia and industry through collaborations with firms like Bambach Wires and iMOVE Australia.

• Biostatistics
• Epidemiology
• Medical Statistics
• +۷ مورد دیگر

Matteo Bottai is Professor of Biostatistics and Head of the Department of Biostatistics at Karolinska Institutet, where he also serves as Scientific Director of the Biostatistics Core Facility. He holds additional academic appointments as Visiting Professor in the Department of Mathematical Statistics at Stockholm University and Adjunct Professor in the Department of Biostatistics at the University of South Carolina. Education: Doctor of Science, Harvard University, USA His research focuses on advanced biostatistical methods, particularly in quantile regression, modeling of bounded and censored outcomes, longitudinal data analysis, and methods for handling missing data in distributed networks. His work bridges theoretical statistics and practical applications in epidemiology, environmental medicine, and public health. He has contributed to methodological developments in Laplace regression, parametric quantile modeling, and optimal probability weighting. His recent publications span a wide range of topics, including statistical methodology, cancer outcomes, respiratory health, cardiovascular disease, and mental health interventions. The articles reflect a strong emphasis on population-based cohort studies, clinical trials, and the use of complex statistical models to derive meaningful clinical and public health insights. Diagnostic guidelines for familial hemophagocytic lymphohistiocytosis Effect of internet-delivered cognitive behavioral therapy in pediatric OCD and Tourette syndrome Risk of thromboembolic events after cancer surgery Impact of air pollution on infant lung function Statistical methods for missing data and quantile regression Matteo Bottai actively collaborates across disciplines, working with researchers in oncology, neurology, cardiology, psychiatry, and environmental health. His role as Scientific Director of the Biostatistics Core Facility underscores his leadership in supporting high-quality statistical analysis in biomedical research. He has been involved in numerous clinical trials and large-scale cohort studies, contributing statistical expertise to study design, analysis, and interpretation. He leads the Biostatistics Unit at the Institute of Environmental Medicine, Karolinska Institutet, which provides statistical support and fosters methodological research in environmental and occupational health.

• Differential Privacy
• Machine Learning
• Stochastic Optimization
• +۳ مورد دیگر

Abhradeep Thakurta is a researcher at Pennsylvania State University (College of Engineering, Computer Science and Engineering Department) and Microsoft Research Silicon Valley, focusing on Differential Privacy and Machine Learning . His work explores privacy-preserving techniques in optimization, model training, and data analysis. Key affiliations: Pennsylvania State University (College of Engineering), Microsoft Research Silicon Valley Academic rank: Researcher His research spans Differential Privacy in Stochastic Optimization , Convex Optimization , and Deep Learning . He investigates methods to enhance privacy guarantees while maintaining model accuracy and efficiency, particularly through matrix factorization, adaptive clipping, and noise correlation. Recent publications (2023–2025) highlight advancements in privacy amplification , checkpoint reuse , and secure model training . Collaborations include top researchers from institutions like Google, Stanford, and MIT.

Frank Pallasمشاهده پروفایل

استاد

• Privacy Engineering
• Policy-Aligned Systems
• Techno-Legal Implications
• +۷ مورد دیگر

Frank Pallas is a Professor at the Paris Lodron University of Salzburg, affiliated with the Faculty of Digital and Analytical Sciences. His research focuses on Privacy Engineering and Policy-Aligned Systems (PEPSys), integrating technical and legal aspects of emerging technologies. Education: Dipl.-Inform. and Dr.-Ing. in Computer Science from TU Berlin His work emphasizes techno-legal privacy engineering , policy-alignment mechanisms , and experimental assessment of privacy techniques , addressing anonymity, utility metrics, and societal impacts. Recent publications explore data anonymization, GDPR compliance, and privacy in distributed systems. Key projects include co-leading Salzburg's EXDIGIT initiative for digital sciences. Activities span organizing international workshops on privacy engineering, media contributions, and third-mission engagements like public lectures on digital privacy.