جست‌وجوی استاد و پژوهشگر

Peyman Najafiradمشاهده پروفایل

دانشیار

• Artificial Intelligence
• AI Security
• Machine Learning
• +۳ مورد دیگر

Peyman Najafirad is an Associate Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), part of the College of Sciences. His academic journey includes a Ph.D. and M.S. in Electrical and Computer Engineering and Computer Science from UTSA, alongside an M.S. in Computer Engineering and Artificial Intelligence from Iran's Polytechnic, and a B.S. in Computer Science from Sharif University of Technology. His research focuses on AI security, knowledge representation, probabilistic decision-making, and reinforcement learning. Notable contributions include work on adversarial machine learning, LLM-driven cybersecurity solutions, and multimodal AI systems for remote sensing and medical imaging. Najafirad's publications address challenges in secure code generation, AI fairness, and real-time decision-making frameworks for IoT systems. His work bridges theoretical advancements with practical applications, such as vulnerability analysis in software systems, automated defense mechanisms, and explainable AI for healthcare and disaster response. Current research trends highlight innovations in AI safety, ethical algorithm design, and cross-disciplinary solutions for complex societal challenges. While no awards or grants are explicitly listed, his prolific publication record reflects sustained engagement with cutting-edge topics in computer science. His advising role and academic leadership contribute to fostering the next generation of researchers in interdisciplinary AI applications.

• Artificial Intelligence
• Remote Sensing
• Climate Science
• +۳ مورد دیگر

Jordi is an Associate Professor at the University of Valencia, specializing in interdisciplinary research at the intersection of artificial intelligence, environmental science, and education technology. His work focuses on applying machine learning to climate forecasting, vegetation mapping, and improving educational tools through AI-driven methodologies. Research interests include AI applications in hurricane and medicane prediction, causal inference for environmental displacement, satellite data analysis for crop yield monitoring, and leveraging social media for climate studies. He also explores technology adoption in higher education, particularly using short videos and learning management systems. His recent publications highlight advancements in GPT-4 for educational assessment tools, Gaussian processes for vegetation trait mapping, and physics-aware ML models for carbon flux analysis. Jordi has contributed to initiatives like enhancing Sentinel-2 data processing and developing hybrid predictive tools for extreme weather events. His work emphasizes interdisciplinary collaboration, blending signal processing, remote sensing, and statistical learning to address global challenges in agriculture, climate resilience, and sustainable development.

Márton Karsaiمشاهده پروفایل

دانشیار

• Network Science
• Computational Social Science
• Data Science
• +۵ مورد دیگر

Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at Central European University (CEU). He also serves as a Research Professor at the Rényi Institute of Mathematics (Hungary) and Editor-in-Chief of Advances in Complex Systems . His work focuses on computational social science, human dynamics, and data-driven modeling of socioeconomic systems. Karsai holds advanced degrees including a DSc from the Hungarian Academy of Sciences and an HDR (Habilitation) in Computer Science from École Normale Supérieure de Lyon. His research integrates temporal networks, human mobility, and social contagion phenomena, often using large-scale datasets from digital platforms and wearable sensors. Notable projects include studies on evacuation behavior during disasters, vaccination hesitancy, and urban socioeconomic stratification. Karsai leads interdisciplinary initiatives like the DyLNet project, which examines social interactions and language development in preschool environments through sensor technology. Recent publications highlight innovations in network clustering algorithms (PASCO), epidemic modeling with generalized contact matrices, and the application of machine learning to infer socioeconomic status from satellite imagery. His work bridges computational methods with real-world challenges in public health, urban planning, and humanitarian development.

Andrew W. Ellisمشاهده پروفایل

استاد

• Climate Science
• Meteorology
• Hydroclimatic Variability
• +۴ مورد دیگر

Andrew W. Ellis is a Professor and Director of Graduate Studies in the Department of Geography at Virginia Tech’s College of Natural Resources and Environment. He holds a Ph.D. in Climatology from the University of Delaware (1997), following earlier degrees from the same institution. Prior to joining Virginia Tech in 2011, he was on the faculty at Arizona State University. Education: Ph.D. Climatology, University of Delaware, 1997 M.S. Geography, University of Delaware, 1994 B.A. Geography, University of Delaware, 1991 Research Interests: Ellis specializes in hydroclimatic variability and change, focusing on drought dynamics, water resource sustainability in arid regions, and synoptic climatology. His work bridges atmospheric science and hydrology, addressing topics like snow-cover teleconnections, urban heat islands, and climate impacts on water systems. Recent projects include drought monitoring frameworks, climate change adaptation strategies for water management, and analysis of precipitation extremes. Grants & Funding: NOAA COMET Program: Mixed-precipitation forecasting for the Mid-Atlantic ($19,325, 2019) NSF Decision Center for a Desert City II ($7.5M, 2010) Bureau of Reclamation: Drought forecasting in the Colorado River Basin ($65,079, 2008) Labs/Teams: Leads Virginia Tech’s climatology research group and collaborates with the Center for Geospatial Information Technology. Active in interdisciplinary projects with NOAA, Salt River Project, and the U.S. Geological Survey.

Shizhe Chenمشاهده پروفایل

استادیار

• Statistics
• High-Dimensional Data Analysis
• Point Processes
• +۳ مورد دیگر

Shizhe Chen is an Assistant Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional data, point processes, network reconstruction, and machine learning applications in neuroscience and biostatistics. He holds a Ph.D. and teaches advanced statistical courses such as STA 290. His work emphasizes flexible modeling approaches for complex systems and nonparametric methods for independence testing. Key research interests include: High-dimensional statistical inference and graphical models Point process modeling with applications to neuroscience and social networks Development of assumption-light screening techniques for Hawkes processes Network reconstruction using ordinary differential equations Recent work explores temporal dynamics in hippocampal sequences and distribution-free statistical tests for modern data structures. His methods bridge theoretical advancements with practical applications in computational biology and econometrics.

Ganesh Krishnasamyمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Pattern Recognition
• +۶ مورد دیگر

Ganesh Krishnasamy is a Lecturer at the School of Information Technology, Monash University Malaysia. He holds a PhD in Electrical Engineering from the University of Malaya, with earlier degrees from Universiti Kebangsaan Malaysia (B.Eng and M.Eng in Electrical and Electronic Engineering). His research focuses on computer vision, pattern recognition, manifold learning, and semi-supervised learning, with applications in multimedia processing and understanding. He has led three research projects including 'AEinstein: Adversarial AI amongst Materials Discovery Domains' and pioneered semi-supervised action recognition in videos. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure. Research Interests: Computer Vision Pattern Recognition Manifold Learning Semi-Supervised Learning Optimization Algorithms HDR Image Reconstruction Recent Projects: AEinstein Project (2024-2026): Focuses on adversarial AI in materials discovery. Research Management System (2023-2024): Aims to optimize research workflows. Semi-Supervised Image-to-Video Adaptation (2020-2023): Developed ActNetFormer and other hybrid models. Publications: Over 16 outputs since 2014, with recent emphasis on HDR imaging, sports video analysis (badminton rally detection), and optimization techniques. His work bridges theoretical machine learning with commercial applications.

Luca Magriمشاهده پروفایل

استاد

• Scientific Machine Learning
• Data Assimilation
• Fluid Mechanics
• +۸ مورد دیگر

Luca Magri is a Professor of Scientific Machine Learning at Imperial College London's Department of Aeronautics (Faculty of Engineering), leading the MagriLab . He holds dual roles as Director of Research in Aeronautics and Director of the Research Centre in Data-Driven Engineering. He is also a Professor in Fluid Mechanics at Politecnico di Torino under the PNRR project on Twin Real-time digital twins. His research bridges physics-aware machine learning, data assimilation, and fluid mechanics, with applications in quantum computing, turbulence modeling, and thermoacoustics. Magri completed his PhD in Engineering at the University of Cambridge (2012-2015) and held postdoctoral roles at Stanford University (2015-2016). He has been a Royal Academy of Engineering Research Fellow (2016-2021), Simons Fellow (2022-2023), and Hans Fischer Fellow at TUM (2018-2021). He leads the Alan Turing Institute's Scientific Machine Learning group and collaborates with institutions like Pembroke College and the Isaac Newton Institute. His research interests span quantum reservoir computing, data-driven stability analysis, and real-time digital twins for combustion systems. Key projects include optimizing wind farm layouts and suppressing extreme events in chaotic flows. He has pioneered methods like Proper Latent Decomposition (PLD) and physics-constrained neural networks for turbulence reconstruction. Magri has secured funding from EPSRC, Royal Academy of Engineering, and EU initiatives. His group includes engineers, physicists, and computer scientists addressing net-zero challenges in aerospace propulsion and energy systems. Recent work focuses on quantum computing for nonlinear PDEs and AI-driven process design in manufacturing.

Laura Swiftمشاهده پروفایل

پژوهشگر

• Early Modern Literature
• Discourse Analysis
• Abjection Studies
• +۳ مورد دیگر

Laura Swift is a researcher at the University of Manchester , specializing in early modern literature and urban studies. She holds a PhD in The Body and the City from the University of Manchester (2019), an MA in Early Modern English Literature from King's College London (2012), and a BA in English Literature from the University of Manchester (2011). Her work intersects with the UN Sustainable Development Goals related to education and urban aging. Doctor of Philosophy (The Body and the City: Feeling Paranoid in Ben Jonson's London), 1 Jun 2019 Master of Arts (Early Modern English Literature: Text and Transmission), 1 Dec 2012 Bachelor of Arts (English Literature), 1 Jun 2011 Her research focuses on abjection , queer temporalities , and the interplay between urban spaces and bodily experiences in early modern drama. She has contributed to debates around Deleuze and Guattari’s philosophy and its application to literary analysis. Laura’s publications analyze themes such as paranoia in Bartholomew Fair , gendered discourse in Epicene , and the socio-political anxieties in Jonson’s plays like The Devil is an Ass . Her work often bridges literary theory with urban studies, emphasizing temporal and spatial dynamics. She has earned recognition as an Associate Fellow of the Higher Education Academy (AFHEA) and actively participates in research groups like the Manchester Urban Ageing Research Group and the Data Skills and Training Research Group . Laura has organized academic events, including the Cities of Strangeness, 1350-1700 conference (2018), and contributed to public discourse through media reviews of performances at HOME, Manchester, covering works like Macbeth (2016) and The Encounter (2016).

• 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.

Habtamu Bizuayehuمشاهده پروفایل

عضو هیئت علمی

• Cancer Research
• Indigenous Health
• Health Equity and Disparities
• +۳ مورد دیگر

Dr. Habtamu Bizuayehu is a Postdoctoral Research Fellow at the First Nations Cancer & Wellbeing Research Program, School of Public Health, University of Queensland. His primary focus is cancer research among Indigenous peoples, including cancer outcomes, treatment disparities, and global screening initiatives. Previously, he worked at Cancer Council Queensland and as a researcher/lecturer at Debre Markos University. He holds a PhD in Public Health from the University of Newcastle, Australia, and expertise in epidemiological methodologies such as longitudinal analysis and causal modeling. Education: Doctor of Philosophy in Public Health and Behavioral Sciences, University of Newcastle, Australia Master of Public Health in Epidemiology, Addis Ababa University, Ethiopia Bachelor of Science in Public Health, Hawassa University, Ethiopia Research Interests: Cancer disparities, Indigenous health, health equity, maternal and child health, infectious disease epidemiology, and diabetes. He has authored/co-authored 28 peer-reviewed articles cited over 5000 times and presented at 11 conferences. His work spans high-income (Australia) and low-income (Ethiopia) contexts. Advising & Grants: Supervised seven master's students and mentored junior staff. Specializes in task prioritization, student engagement, and stakeholder collaboration. His research has addressed neonatal mortality, maternal near-miss events, and HIV/AIDS knowledge barriers in Ethiopia. Labs/Teams: Active in the First Nations Cancer & Wellbeing Research Program, focusing on Indigenous cancer outcomes and global health equity initiatives.

William M. Jones Jr.مشاهده پروفایل

استاد

• High Performance Computing (HPC)
• Fault Tolerance
• Resilience Engineering
• +۵ مورد دیگر

Dr. William M. Jones Jr. is a Professor in the Department of Computing Sciences at Coastal Carolina University (CCU) since 2017. He concurrently serves as a Guest Scientist at Los Alamos National Laboratory (LANL) and is the founder of the LANL-CCU Collaboration initiative, established in 2007. His roles include HPC Visiting Scholar/Sabbatical at LANL (2025–present) and Guest Scientist at LANL (2023–present). Former positions include Department Chair of Computing Sciences at CCU (2012–2018) and Assistant Professor at the U.S. Naval Academy (2006–2008). Education: Ph.D. & M.S. in Computer Engineering (2005, 2000, Clemson University); B.S. in Computer Engineering (1999, Clemson University); B.A. in Spanish (2017, Coastal Carolina University). Research focuses on resilience and fault tolerance in HPC systems, including soft error mitigation, parallel computing optimization, and applied machine learning. He leads collaborative projects with LANL, emphasizing high-performance computing and student research engagement. Key contributions include the LANL-CCU partnership, yielding numerous publications and external funding since 2007. Articles highlight advancements in HPC resilience, machine learning applications, and fault tolerance, with recent trends emphasizing spatio-temporal modeling and statistical frameworks for system reliability. His work also extends to language pedagogy, with contributions to Spanish as a foreign language teaching methodologies. Advising and grants: Directs student researchers through the LANL-CCU initiative and has secured external funding for collaborative projects. Grants and partnerships focus on advancing HPC resilience and educational outreach in STEM. Labs/Teams: Co-leads the LANL-CCU Collaboration, fostering interdisciplinary research between academia and national labs. Active in the Ultrascale Systems Research Center (since 2014) and previously in the National Security Education Center at LANL (2019–2023).

Matthew Kupilikمشاهده پروفایل

دانشیار

• Control Systems
• Renewable Energy
• Signal Processing
• +۳ مورد دیگر

Matthew Kupilik is an Associate Professor in the Department of Electrical Engineering at the University of Alaska Anchorage (UAA), within the College of Engineering. He holds a B.S., M.S., and Ph.D. in Electrical Engineering from the Colorado School of Mines. His research spans control systems, environmental modeling, and educational technology, with recent focus on Gaussian process regression for climate change studies and Arctic coastal erosion forecasting. He has taught multiple engineering courses at UAA including MATLAB for Electrical Engineers, Power Systems, and Control Systems. Educations: B.S. in Engineering Systems (Electrical Specialty), Colorado School of Mines, 2001 M.S. in Electrical Engineering, Colorado School of Mines, 2008 Ph.D. in Electrical Engineering, Colorado School of Mines, 2012 Professional Service: Chair of Curriculum Committee, UAA College of Engineering (2015–present) NSF GK-12 Fellow (2008–2010) Reviewer for Journal of the Acoustical Society of America, IEEE Conferences, and others His research trends show a progression from early work on fuel cell control systems to current interdisciplinary applications in environmental science and education. Collaborations include acoustic tracking of salmon migrations and development of pedagogical strategies for engineering education. Teaching and service contributions include developing energy curriculum for summer academies and leading instructional technology initiatives in eLearning at UAA.

Christian Katzenbachمشاهده پروفایل

استاد

• Platform Governance
• Artificial Intelligence
• Digital Society Discourse
• +۳ مورد دیگر

Christian Katzenbach is a Professor of Media and Communication at the University of Bremen, leading the Platform Governance, Media and Technology (PGMT) Lab. He holds a doctorate from the Free University of Berlin (2016) and has been a visiting researcher at the INRS in Canada. His research focuses on platform governance, artificial intelligence, and digital society discourse, with a particular emphasis on regulatory frameworks and socio-technical systems. He has received prestigious awards for his dissertation, including the DGPuK Dissertation Prize and the Media-Culture-Communication Dissertation Prize. Key roles include heading the PGMT Lab and contributing to interdisciplinary research programs on digital society. His work explores the interplay between technology, communication, and policy, addressing topics like algorithmic governance, AI imaginaries, and copyright moderation. He has organized major events such as the 2025 AoIR Flashpoint Symposium in Bremen, highlighting his leadership in academic discourse. His research is funded by institutions like the German Research Foundation (DFG), EU Horizon 2020, and the Volkswagen Foundation. Notable publications include analyses of AI policy, sociotechnical imaginaries, and content moderation challenges under EU regulations. Katzenbach is actively involved in professional organizations, including the Association of Internet Researchers (AoIR) and the German Sociological Society (DGS). Grants and awards underscore his contributions to understanding digital governance and technology’s societal impact. His lab’s work bridges academic inquiry with practical policy recommendations, reflecting a commitment to addressing the ethical and regulatory challenges of emerging technologies.

Juan Carlos Vidal Aguiarمشاهده پروفایل

استادیار

• Knowledge Discovery
• Semantic Annotation
• Workflow Modeling
• +۷ مورد دیگر

Juan Carlos Vidal Aguiar is an Assistant Professor in the Department of Electronics and Computing at the University of Santiago de Compostela (Spain), affiliated with the Higher Technical School of Engineering and the Center for Research in Intelligent Technologies (CITIUS). His research focuses on knowledge discovery, semantic technologies, process mining, and AI-driven business intelligence. He holds a PhD from the University of Santiago de Compostela (2010) and a Bachelor's in Computer Science from the University of La Coruña (2000). His work integrates formal methods like Petri nets with machine learning and semantic web technologies, addressing challenges in workflow modeling, predictive monitoring, and healthcare analytics. Key projects include Predictive monitoring and causality for cardiac rehabilitation , Responsible AI for Process Mining 2.0 , and AQUATECHInn 4.0 (digital aquaculture training). He has led research on glucodensity analysis for diabetes monitoring and developed frameworks like OPENET for Petri net-based workflow execution. His contributions span 200+ publications in venues like IEEE Transactions, ACM T-KDD, and Advances in Data Analysis and Classification. Juan Carlos collaborates with institutions across Europe and Latin America on learning analytics, smart manufacturing, and health informatics. His lab's work emphasizes practical applications of AI in education and industry, including gamification analytics and cloud-based process monitoring tools.

Elena Di Bernardinoمشاهده پروفایل

استاد

• Spatial Statistics
• Extreme Value Theory
• Environmental Risks
• +۵ مورد دیگر

Elena Di Bernardino is a full Professor at Université Côte d'Azur, leading research in spatial statistics, extreme value theory, and environmental risk modeling. She holds the prestigious Junior membership at the Institut Universitaire de France (2025-2030) and serves as Head of the Probability & Statistics team at the J.A. Dieudonné Laboratory. Her work integrates stochastic geometry, copula theory, and machine learning to address multidisciplinary challenges such as climate risk assessment and wildfire prediction. **Education**: Dual Italian-French national, Elena earned a PhD in Applied Mathematics from Université Lyon 1 (2011) and habilitation (HDR) from Université Pierre et Marie Curie (2017). Her academic journey includes postdoctoral research in Venezuela and Canada, alongside engineering studies at the Polytechnic of Milan. **Research**: Key focus areas include spatio-temporal dependence modeling, multivariate risk measures, and geometric inference on random fields. She leads major national projects like the ANR COCHAIRS initiative under France's PEPR Risques program, and collaborates internationally via Simons-CRM and MITACS grants. **Awards**: Recognitions include the Chercheurs Simons-CRM Prize (2019), PEDR grants (2015, 2019, 2023), and continuous funding through EU and French National Research Agency (ANR) programs. **Leadership**: Currently chairs the Scientific Council of the J.A. Dieudonné Laboratory and oversees strategic initiatives at the 3IA Côte d'Azur Institute. She actively mentors 8 current PhD/postdoc researchers and has supervised over 14 completed doctoral projects. **Teaching**: Teaches advanced courses in extreme value theory, risk modeling, and environmental statistics at both undergraduate and graduate levels.