جست‌وجوی استاد و پژوهشگر

• Environmental History
• Aquatic Ecology
• River Management
• +۴ مورد دیگر

Gertrud Haidvogl serves as Associate Professor at the University of Natural Resources and Life Sciences, Vienna (BOKU), affiliated with the Institute of Hydrobiology and Water Management (IHYAM) and Institute of Social Ecology (iISEC). Her research bridges environmental history and aquatic ecology to examine long-term socio-ecological transformations in river systems, with special focus on the Danube River Basin. She holds teaching authorization in Historical Aquatic Ecology and Environmental History and maintains active scholarly output through 2025. Her research interests encompass historical ecology of rivers, socio-ecological river dynamics, and human-river coevolution. She investigates how industrialization, urbanization, and infrastructure projects have altered river connectivity, fish migration, and floodplain ecosystems over centuries. Her interdisciplinary methodology integrates archival research, archaeological data, ecological modeling, and policy analysis to reconstruct historical baselines and inform contemporary river restoration. Key themes include longitudinal connectivity disruption, biodiversity loss in freshwater systems, and the evolution of flood control paradigms. Analysis of her 2022-2025 publications reveals increasing emphasis on policy-relevant research, particularly regarding EU Nature Restoration Law implementation and transboundary river management. Her work consistently applies historical perspectives to establish ecological reference conditions while addressing contemporary challenges like climate change adaptation and free-flowing river restoration. The interdisciplinary nature of her scholarship is evident in collaborations spanning environmental history, ecology, hydrology, and social sciences. Dr. Haidvogl actively participates in the FWF Doctoral Program "Industrialized River Landscapes" under the HR21 Doctoral School, indicating her role in supervising doctoral candidates and securing competitive research funding. Her collaborations with the International Association for Danube Research (IAD) demonstrate engagement in transnational research networks addressing basin-scale challenges. While specific grant details aren't provided, her leadership in major interdisciplinary programs reflects substantial research funding. Embedded within BOKU's Hydrobiology and Water Management group, she contributes to interdisciplinary teams developing tools like the River Network Toolkit (RivTool) and iPODfish framework for historical fish occurrence reconstruction. Her work frequently involves partnerships with historians, ecologists, and policymakers to address complex river management challenges in urbanizing landscapes.

• Information Management
• Data Security
• Scientific Databases
• +۵ مورد دیگر

Marianne Winslett is a Professor at the University of Illinois' Siebel School of Computing and Data Science, affiliated with the Department of Computer Science since 1987. Her research focuses on data security, information management, and privacy in cyber-physical systems. She co-led the TrustBuilder project, advancing access control and authentication in open computing environments, and directed the Advanced Digital Sciences Center (ADSC) in Singapore from 2009–2013, addressing challenges in data analytics and smart grids. Her work includes pioneering methods to ensure privacy in biomedical data analysis. Education: Earned her doctorate in Computer Science from Stanford University and worked at Bell Labs before joining Illinois. Awards: ACM Fellow (2006), NSF Presidential Young Investigator (1989), University Scholar, and Stanley H. Pierce Award for advising. She has supervised 24 PhD theses and mentored numerous graduate students, particularly supporting female scholars. Research Interests Secure data management in distributed systems Privacy-preserving techniques for biomedical data Adversarial attack detection in cyber-physical systems like smart grids Elastic resource scheduling in cloud environments Query optimization under differential privacy constraints Key Contributions Developed frameworks for self-supervised learning in smart grid cybersecurity Pioneered causal mechanism transfer networks for mechanical system domain adaptation Advanced auto-scaling strategies for real-time stream processing (DRS/Elasticutor systems) Labs & Teams Former Director of the Advanced Digital Sciences Center (ADSC), a University of Illinois research outpost in Singapore focusing on data analytics and IoT applications.

Ambrose Agweyuمشاهده پروفایل

استاد

• Infectious Disease Epidemiology
• Global Health
• Pediatric Health
• +۳ مورد دیگر

Professor Ambrose Agweyu is a faculty member at the London School of Hygiene & Tropical Medicine (LSHTM), specifically within the Department of Infectious Disease Epidemiology and International Health under the Faculty of Epidemiology and Population Health. He specializes in pediatric and infectious disease epidemiology, with a focus on global health challenges in Africa. His research addresses gaps in understanding pneumonia mortality disparities, health policy adaptation, and vaccine efficacy through large-scale studies like the SEARCH trial and the Kenya Multi-Site Serosurveillance (KEMIS) collaboration. Professor Agweyu also explores the intersection of dietary aflatoxin exposure and severe childhood pneumonia, contributing to evidence-based health interventions. His work emphasizes integrating routine health services with research and surveillance, particularly in low-resource settings. Notable projects include adapting WHO guidelines for pneumonia risk stratification in Kenya and evaluating HIV prevention knowledge in postconflict regions of South Sudan. Agweyu collaborates closely with institutions such as the Kenya Medical Research Institute (KEMRI) and has contributed to high-impact publications in BMJ , The Lancet , and BMC Infectious Diseases . His research aims to strengthen health systems through improved data utilization, advocacy, and policy implementation, addressing both immediate pandemic responses and long-term childhood health challenges. While no formal academic awards are explicitly listed, his leadership in pivotal studies and his inaugural lecture at LSHTM underscore his contributions to advancing global health equity. His advisory and grant-related activities remain unspecified in the provided texts, though his collaborations and trials imply significant institutional support. Agweyu’s work is deeply embedded in African health systems, with a particular focus on Kenya, aiming to reduce preventable mortality and improve healthcare outcomes through innovative methodologies and policy-oriented research.

Lydia Bouzar-Benlabiodمشاهده پروفایل

استادیار

• Hardware-based Machine Learning
• Privacy-Preserving Machine Learning
• Explainable AI
• +۳ مورد دیگر

Dr. Lydia Bouzar-Benlabiod is an Assistant Professor at the Jodrey School of Computer Science, Acadia University, Nova Scotia, Canada. Her research focuses on hardware-based machine learning, privacy-preserving techniques, and explainable AI. She holds a Ph.D. in Computer Science from Université d’Artois, France, and an M.Eng from Ecole nationale Supérieure d’Information (ESI), Algiers, Algeria. Her work bridges theoretical advancements with practical applications in cybersecurity, medical imaging, and intelligent systems. Research interests include adversarial machine learning, neural architecture search, and AI-driven healthcare solutions. Her lab explores cost-effective hardware implementations for medical diagnostics and anomaly detection systems. Key projects involve NeuroMem® chip integration for breast cancer detection and RNN-VED models to reduce false positives in cybersecurity. Dr. Bouzar-Benlabiod’s publications span 2013–2024, emphasizing data-driven modeling, case-based reasoning, and AI ethics. She contributes to special issues on heuristic data science and reuse in intelligent systems. Her work is accessible via CILS Lab and personal page .

Omar Alamمشاهده پروفایل

دانشیار

• Software Engineering
• Model-Driven Software Engineering
• Aspect-Oriented Modelling
• +۳ مورد دیگر

Professor Omar Alam is an Associate Professor in the Department of Computer Science at Trent University. His research focuses on software engineering, particularly Model-Driven Software Engineering, Aspect-Oriented Modelling, and Mining Software Repositories. Prior to Trent, he earned his PhD at McGill University, specializing in Model-Driven Engineering. He has received notable awards such as the ACM SIGSOFT Distinguished Paper Award (2019) and the Merit Award for Excellence in Research (2022). His teaching includes courses like Software Specification (COIS 3030), Software Design and Modeling (COIS 2240), and Software Architecture (COIS 3040). Key grants include the NSERC Discovery Grant (2017-2024) and past fellowships from NSERC and FRQNT. His work spans collaborative modeling, automated grading systems, and empirical studies on developer practices. Research often intersects software development methodologies, tool integration, and socio-technical aspects of computing. His recent publications explore topics like developer workplace communication, transit schedule deviations using real-time data, and three-way domain-specific model differencing. He actively contributes to journals like SoSyM and conferences such as SLE and ICSM. His teaching excellence is recognized through student awards and nominations, reflecting his commitment to pedagogy in computer science education.

• Artificial Intelligence
• Machine Learning
• Spatial Reasoning
• +۳ مورد دیگر

Dr. Paulo Santos is a Senior Lecturer at Flinders University's College of Science and Engineering, specializing in Artificial Intelligence with a focus on explainable AI systems. He holds a PhD from Imperial College London (2003) and has over 20 years of research experience in spatial reasoning, machine learning, and robotics. His work bridges knowledge representation with deep learning to enhance transparency in AI decision-making. Dr. Santos has led research groups in Brazil, collaborated internationally, and secured funding from organizations like the British Council and EU. His expertise spans robotics, computer vision, and cognitive science. Notable achievements include the British Computer Science Machine Intelligence Prize (2004) and the Santander Prize for Science and Innovation (2006). Research interests include reinforcement learning for autonomous underwater vehicles (AUVs), scene graph generation in computer vision, and spatial reasoning for multi-robot systems. Recent work emphasizes sim-to-real transfer learning and fault recovery in underwater robotics.

• Pervasive Sensing
• Tiny Machine Learning (TinyML)
• Edge AI
• +۷ مورد دیگر

Prof. Eiman Kanjo is a Professor of Pervasive Sensing & TinyML and Head of the Smart Sensing Lab at Nottingham Trent University. She leads the Smart Nottingham and Smart Campus initiatives, focusing on impactful applications of mobile sensing, wearable tech, and edge AI. Her work bridges academia, industry, and community, emphasizing privacy-preserving AI for mental health, environmental monitoring, and social prescribing. Education: PhD in Computer Science (2005), University of Abertay Dundee. Formerly a Research Associate at the University of Cambridge and researcher at the University of Nottingham’s Mixed Reality Lab. Collaborates with organizations like the tinyML Foundation, Health Data Research UK (HDRUK), and the Alan Turing Institute. Research Interests : Pervasive computing, TinyML, Edge AI, wearable devices, environmental sensing, mental health technologies, and community-driven solutions for urban wellbeing. She pioneered early mobile sensing systems like NoiseSPY (2010) and MobSens (2009). Key Projects : 5G Connected Forest (£10m DCMS grant) : Smart environmental monitoring in Sherwood Forest. TagWithMe : AI-driven proximity-based games for community wellbeing. EPSRC Green+ Network : Tech for healthier environments. Awards & Recognition : Top 50 Women in Engineering (2023). Outstanding Educator of the Year Award (EDGE AI Foundation, 2023). 2021 Vice-Chancellor’s Outstanding Research Team Award (Smart Sensing Lab). Grants & Collaborations : Over £10m in funding from EPSRC, InnovateUK, MOD, and DEFRA. Partners include Nottingham City Council, tinyML Foundation, Health Data Research UK, and industry stakeholders. Labs & Initiatives : Leads the Smart Sensing Lab , developing technologies for Green Social Prescribing at Rufford Country Park and Highbury Mental Health Hospital.

• Deep Learning
• Artificial Intelligence
• Graph Neural Networks
• +۳ مورد دیگر

Dr. Hua Mao is an Assistant Professor in Computer and Information Sciences at Northumbria University, holding a PhD in Computing Science from Aalborg University. Previously served as Associate Professor at Sichuan University, China. Research Focus: Specializes in deep learning and artificial intelligence with applications spanning graph representation, multiview clustering, and explainable AI systems. Recent work advances techniques for processing incomplete data and invariant representation learning. Publication Trends: Her 45+ research outputs demonstrate consistent innovation in machine learning fundamentals, particularly graph-based algorithms and interpretable models. Recent articles focus on explainability frameworks and adaptive learning techniques for complex data structures including IP analytics and flood mapping applications.

• Smart and Sustainable Grids
• Intelligent Systems
• Bioengineering
• +۴ مورد دیگر

Dr. Gregorio López López is an Associate Professor at the School of Engineering (ICAI) of Universidad Pontificia Comillas and a member of the Institute for Research in Technology (IIT). His research focuses on Smart and Sustainable Grids, Intelligent Systems, and Bioengineering. He holds a PhD in Telecommunications Engineering from Universidad Carlos III de Madrid (2014), recognized with the Extraordinary PhD Dissertation Award and CISCO-COIT Prize. Education: PhD in Telecommunications Engineering (UC3M, 2014), MSc and BSc in similar fields. His research interests include M2M communications optimization, IoT cybersecurity, and smart grid technologies. He has led over 17 national/EU projects including eFORT and InteGrid. His work spans 64 conference papers and 31 journal publications. Key awards: CISCO-COIT Prize (2015), Extraordinary PhD Award (2015), Deloitte Prize (2021), Lyntia Prize (2022). He advises 4 PhD students, teaches telecommunications subjects, and contributes to cybersecurity initiatives like the RAYUELA project. His lab focuses on integrating cybersecurity with smart grid systems and youth tech safety.

• High-performance Computing
• Energy-efficient Computing
• Machine Learning
• +۳ مورد دیگر

Dr. Ngoc Nha Vi Tran is an Associate Professor of Computer Science at UiT The Arctic University of Norway. She holds a PhD from UiT and was a visiting scholar at Rutgers University, USA. Her research focuses on high-performance and energy-efficient computing, machine learning, and bioinformatics. She is a member of the NORA.startup Steering Group and leads the Arctic Green Computing Group. Education: PhD in Computer Science (UiT), M.Sc. in Software Engineering via Erasmus Mundus (Blekinge Institute of Technology, Sweden & Technical University of Kaiserslautern, Germany). Research interests include energy-efficient algorithms, bioinformatics tools (e.g., vCOMBAT), and applications of machine learning in healthcare and robotics. She teaches courses such as INF-2200 Computer Architecture, INF-2900 Software Engineering, and INF-2202 Concurrent Programming. Her work spans computational models for antibiotic target-binding, runtime energy optimization (REOH framework), and power models for embedded systems (RTHpower/ICE). She contributed to the EXCESS project on energy-efficient computing systems. Labs/Teams: Arctic Green Computing Group, EXCESS consortium.

Regan Zaneمشاهده پروفایل

استاد

• Power Electronics
• Transportation Electrification
• Battery Management Systems
• +۳ مورد دیگر

Regan Zane is the David G. and Diann L. Sant Endowed Professor in the Department of Electrical and Computer Engineering at Utah State University. His research focuses on high-efficiency power converters for transportation electrification, battery systems, and microgrid applications. PhD, MS, BS in Electrical Engineering, University of Colorado Boulder IEEE Fellow and recipient of 15+ scientific awards including NSF CAREER and IEEE Bass Award Recent work emphasizes dynamic wireless charging for electric vehicles, second-life battery reconditioning , and high-frequency converter design . Publications span topics in ZVS analysis, resonant topologies, and grid-integrated systems. Key awards include: 2020 Utah Clean Cities Sustainability Partner of the Year 2019 Utah Innovation Award in Clean Technology 2008 IEEE Richard M. Bass Outstanding Young Engineer Mentored 20+ graduate students in power electronics and electrified transportation projects. Led development of USU's EVR Research Facility and contributed to DARPA/MIT collaborations on solid-state hydraulic systems.

Michael Reichhardt Hansenمشاهده پروفایل

دانشیار

• Formal Methods
• Embedded Systems
• Real-Time Systems
• +۳ مورد دیگر

Michael Reichhardt Hansen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark. He holds a MSc in Engineering (1982) and a PhD in Computer Science (1987) from DTU. He has held postdoctoral and visiting roles, including a fellowship at IBM Research Center (1984–1985) and a guest professorship at Oldenburg University (1992–1993). His research focuses on formal methods, embedded systems, and real-time systems, emphasizing mathematically grounded software development. He has authored/co-authored three books and over 70 scientific articles. Research interests include model-based program construction, temporal logic, functional programming, and ontology alignment. He has led projects on ontology matching frameworks (e.g., MDMapper) and formal methods for real-time systems. Hansen has supervised PhD students in areas like data integration and genetic circuit synthesis. He has contributed to international conferences and serves as a reviewer/examiner for academic programs. His work bridges theoretical foundations with industrial applications, particularly in embedded systems and data management. He has collaborated internationally, with projects addressing ESG reporting standards and regulatory compliance through automated data traceability.

Jimmy McGibneyمشاهده پروفایل

مدرس

• Network Security
• Artificial Intelligence
• Cybersecurity
• +۵ مورد دیگر

Jimmy McGibney is a Lecturer in the Department of Computing and Mathematics at Waterford Institute of Technology (WIT), now part of South Eastern Technological University (SETU). He holds a Master of Engineering from Dublin City University (1995) and a Bachelor of Engineering (Electronic) from University College Dublin (1992). His research focuses on network security, AI-driven cybersecurity solutions, trustworthiness in service compositions, and resource-constrained environments. External roles include serving as a Researcher at the Telecommunications Software and Systems Group (2000), a Research Assistant at Dublin City University (1996–1997), and a Systems Engineer at Aldiscon (1994–1996). His work emphasizes applied research in intrusion detection systems, network forensics, and trust management frameworks. Key research interests include AI applications in cybersecurity, trust metrics for service compositions, and securing edge computing environments. He has organized workshops on digital forensics and incident response, and his recent work explores AI methodologies for resource-limited systems. McGibney’s publications span over 38 works, including peer-reviewed chapters and conference contributions. Notable areas include network forensic readiness frameworks, deep learning-based intrusion detection, and trust overlays for spam protection. He has contributed to projects funded by industry and academic collaborations, focusing on practical cybersecurity solutions.

Darshika G. Pereraمشاهده پروفایل

استادیار

• Computer Engineering
• FPGA-Based Hardware Acceleration
• Neuromorphic Computing
• +۵ مورد دیگر

Darshika G. Perera is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Colorado Colorado Springs (UCCS). Her research focuses on FPGA-based hardware acceleration, neuromorphic computing, and embedded systems optimization with applications in machine learning, cryptography, and battery management systems. She holds a PhD and has extensive experience in designing reconfigurable architectures for compute-intensive tasks. Her work bridges theoretical algorithms with practical hardware implementations, emphasizing efficiency and real-time performance. Key research areas include FPGA design methodologies, neuromorphic hardware for AI applications, and embedded systems security. She has published widely on topics such as hardware-software co-design for edge computing, optimization algorithms for genomics, and blockchain applications in healthcare IoT. Her contributions also span data mining hardware accelerators and dynamic reconfiguration techniques for cryptographic systems. Dr. Perera’s work is characterized by interdisciplinary collaborations, combining principles from electrical engineering, computer science, and applied mathematics. She is committed to advancing next-generation edge-computing platforms through innovative architectures and methodologies.

Nikhil Gargمشاهده پروفایل

استادیار

• Algorithms
• Data Science
• Mechanism Design
• +۶ مورد دیگر

Nikhil Garg is an Assistant Professor of Operations Research and Information Engineering at Cornell Tech, Cornell University , since July 2021. He holds a Ph.D. in Electrical Engineering from Stanford University (2020) and dual B.S./B.A. degrees in Electrical and Computer Engineering from the University of Texas at Austin (2015). His research bridges computer science, economics, and operations research, focusing on algorithms, data science, and mechanism design for societal systems like democracy, markets, and public policy. Key research interests include surge pricing mechanisms, rating system design, voting systems, and addressing biases in AI (e.g., stereotypes in word embeddings). He has held roles at Uber, NASA, Microsoft, and the Texas Senate, and previously served as Principal Data Scientist at PredictWise. He is also completing a postdoctoral fellowship at UC Berkeley’s Department of Electrical Engineering and Computer Science. His work has received recognition such as the NSF CAREER Award (2024), INFORMS George Dantzig Dissertation Award (2020), and M&SOM Student Paper Award (2020). Research outputs span topics like fair recommendation systems, equitable congestion pricing, and analyzing polarization in social media. Recent projects emphasize improving public decision-making through statistical methods that address information gaps and heterogeneous behavior. His NSF-funded work aims to develop transparent, efficient systems for public interest applications.