جست‌وجوی استاد و پژوهشگر

Joss Wrightمشاهده پروفایل

دانشیار

• Internet Censorship
• Privacy-Enhancing Technologies
• Cybercrime
• +۴ مورد دیگر

Joss Wright is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute , University of Oxford. He co-directs the Oxford EPSRC Cybersecurity Doctoral Training Centre and the Oxford Martin Programme on the Wildlife Trade, focusing on computational approaches to social science questions about information control and privacy. Education : PhD in Computer Science from the University of York (research on anonymous communication systems), postdoctoral work at the University of Siegen (cloud computing security). His research spans internet censorship , privacy-enhancing technologies , and cyber-enabled crime (notably the online illegal wildlife trade ). He bridges technical analyses of security systems with their social and political implications, advising the European Commission and UK Parliamentary Science Committee on digital policy. Recent work includes machine learning applications to detect patent filing trends related to wildlife trade and analyzing Chinese smart city surveillance for human rights risks. He has contributed to media outlets like the Guardian and New Scientist. Notable projects include the Oxford Martin Programme on Wildlife Trade and studies on discriminatory effects of internet filtering . He supervises students like William Lugoloobi (DPhil in Social Data Science) and former advisee Samantha Bradshaw (now Assistant Professor at American University).

John Muellbauerمشاهده پروفایل

استاد

• Household economics
• Housing markets
• Finance-real economy interactions
• +۳ مورد دیگر

Professor John Muellbauer is a Senior Research Fellow at Nuffield College and Professor of Economics at the University of Oxford. He co-directs the Macroeconomics and Finance Programme at the Institute for New Economic Thinking (INET) Oxford Martin School. His work bridges household economics, housing markets, and finance-real economy interactions, with a focus on credit channels and monetary transmission mechanisms. Education: B.A. from Cambridge University, Ph.D. from UC Berkeley Former roles: Professor at Birkbeck College (London), Lecturer at Warwick University His research emphasizes systems approaches to consumption, house prices, and debt, critiquing conventional central bank models. Recent work includes international house price cycles (Journal of Economic Literature, 2021) and ECB policy model critiques (2022). He collaborates with central banks globally and contributes to macroprudential policy frameworks. Scientific awards include the 2014 Kendrick Prize for his work on credit and housing markets. He is a Fellow of the British Academy, Econometric Society, and European Economic Association, and a CEPR Research Fellow. Policy engagement spans HM Treasury, OECD, Swedish Prudential Authority, and the Resolution Foundation. He advocates for a Green Land Value Tax to address UK housing challenges and frequently contributes to VoxEU debates.

Peter Müllerمشاهده پروفایل

استاد

• Formal Methods
• Program Verification
• Programming Languages
• +۴ مورد دیگر

Prof. Peter Müller is a Full Professor at the Department of Computer Science at ETH Zurich since 2008. Previously, he held positions as Assistant Professor at ETH Zurich (2003-2008), Researcher at Microsoft Research Redmond (2007-2008), and IT project manager at Deutsche Bank. He earned his Diploma in Computer Science from Technical University of Munich (1996) and his Dr. rer. nat. from University of Hagen (2001) with a dissertation on modular verification of object-oriented programs. His research focuses on enabling correct software development through programming languages, verification methods, and tools. Key areas include formal verification for Rust and Go programs (Prusti and Gobra projects), separation logic, security protocols, and distributed systems verification. Müller's work emphasizes practical verification techniques for real-world systems, including secure router implementations and smart contract verification. Recent research trends highlight advancements in hyperproperties, modular reasoning for iterators and closures in Rust, and formal validation of verification tools. His methodologies bridge theoretical foundations with industrial applications, addressing challenges in concurrency, memory safety, and security assurance. Notable contributions include the SCION internet architecture, the Prusti verifier for Rust, and formal verification frameworks for distributed systems. His work often integrates rigorous mathematical foundations with scalable software engineering practices.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۴ مورد دیگر

Nebojša Bačanin Džakula is an academic affiliated with Singidunum University's Faculty of Mathematics, specializing in Computer Science. He earned his PhD in 2015 with a thesis on improving swarm intelligence metaheuristics for global optimization. His research focuses on AI-driven solutions for cybersecurity, energy forecasting, and optimization algorithms. He has authored/co-authored books on cloud computing and web programming. His work bridges metaheuristics with machine learning, addressing challenges in IoT security, renewable energy prediction, and healthcare diagnostics. He actively contributes to conferences like Sinteza and IEEE events, emphasizing practical applications of AI and optimization in real-world scenarios. Education: Completed doctoral studies at the Faculty of Mathematics (2009–2015). Extensive industry certifications include Microsoft, CompTIA, and Oracle credentials, enhancing his technical expertise. Research Interests: Develops hybrid models combining metaheuristics (e.g., PSO, GA) with deep learning for tasks like intrusion detection, price forecasting, and medical diagnostics. Specializes in optimizing neural networks and feature selection using advanced algorithms. His work often addresses societal challenges in sustainability, cybersecurity, and healthcare. Recent Publications: Focus on AI-driven solutions for IoT security, renewable energy prediction, and medical diagnostics (e.g., Parkinson’s detection via LSTM networks). His articles appear in prestigious journals like Engineering Applications of Artificial Intelligence and Applied Soft Computing.

Randolph Cooperمشاهده پروفایل

استاد

• Diffusion of Information Technology Innovations
• Management of Systems Design Creativity
• Impact of Information Systems on Effective Decision Making
• +۱ مورد دیگر

Randolph Cooper is a Professor in the Department of Decision and Information Sciences at the Bauer College of Business, University of Houston. He joined the faculty in 1990 after teaching at the University of Michigan, Ann Arbor. His research focuses on information systems' impact on decision-making, media richness theory, and technology diffusion. Education: Ph.D., MBA, and BA in Economics from UCLA Prior Experience: Programmer and Systems Analyst at Motel 6 and Computer Sciences Corporation Research Interests: Diffusion of Information Technology Innovations Management of Systems Design Creativity Impact of Information Systems on Effective Decision Making Effect of Information Systems on Bargaining Behavior His publications over the past two decades highlight a sustained focus on computer-mediated negotiations, media richness theory, and organizational technology adoption. Key themes include emotional dynamics in digital communication, power structures in bargaining, and creativity in systems design.

Lorenzo Baraldiمشاهده پروفایل

دانشیار

• Deep Learning
• Vision-and-Language integration
• Multimodal Retrieval
• +۵ مورد دیگر

Lorenzo Baraldi is an Associate Professor at the University of Modena and Reggio Emilia, where he leads research in deep learning, vision-language integration, and multimodal AI systems. He serves as an ELLIS Scholar and Coordinator of the Modena ELLIS Unit, and has held the position of deputy director at the Interdepartmental Center on Digital Humanities since 2021. Previously, he worked at Facebook AI Research laboratory in Paris in 2017, developing video-matching algorithms for content moderation. His research spans multiple areas including Vision-and-Language integration, Multimodal Retrieval, Image and Video Captioning, Visual-Semantic alignment, Large-Scale model development, High Performance Computing, and Embodied AI. With over 120 publications in international journals and conferences, his work demonstrates consistent contributions to advancing multimodal AI capabilities. He has served as an Associate Editor for Computer Vision and Image Understanding and Pattern Recognition, and as Area Chair for major conferences including ICCV, WACV 2026, and ACM Multimedia 2025. His recent publication record shows significant impact in the field, with multiple papers accepted to top-tier conferences in 2024-2025 including CVPR, ICCV, BMVC, ICLR, ECCV, and NeurIPS. Notably, his paper "Hyperbolic Safety-Aware Vision-Language Models" was selected as a highlight paper at CVPR 2025. His research often involves collaboration with Rita Cucchiara and other researchers at his institution. ELLIS Scholar and Coordinator of the Modena ELLIS Unit Associate Editor for Computer Vision and Image Understanding Area Chair for ICCV and major multimedia conferences Highlight paper at CVPR 2025 Professor Baraldi teaches courses in Computer Vision and Cognitive Systems, Scalable AI, and Computer Architecture for the Artificial Intelligence Engineering and Computer Engineering programs. His teaching spans both undergraduate and graduate levels, with a focus on providing students with both theoretical foundations and practical implementation skills. He has developed educational materials including Deep Learning tutorials for classroom instruction.

• Biostatistics
• Longitudinal Studies
• Public Health
• +۴ مورد دیگر

Denise Esserman is a Professor of Biostatistics at the Yale School of Public Health, where she joined the faculty in 2014. She is a member of the Yale Center for Analytical Sciences and collaborates with multiple departments at the Yale School of Medicine, including the Clinical and Translational Science Award Program, Patient-Centered Outcomes Research Institute, and the Cancer Center. Her research focuses on methodological aspects of clustered randomized trials and sample size calculations. Education: PhD in Biostatistics from Columbia University (2006) MS in Statistics from University of Georgia (2001) Dr. Esserman's research interests span several critical areas in biostatistics and public health methodology. She specializes in clustered randomized trials, with particular expertise in understanding how intraclass correlation coefficients (ICC) and other factors impact sample size calculations. Her work extends to longitudinal studies methodology, randomized controlled trial design, and sampling techniques. She has contributed significantly to statistical methods for clinical trials, healthcare data analysis, and public health research. Her interdisciplinary approach bridges theoretical statistics with practical applications in healthcare settings. Analysis of Dr. Esserman's recent publications reveals a strong focus on methodological innovations in clinical trial design and analysis, particularly for cluster-randomized trials. Her work spans healthcare applications including fall injury prevention in elderly populations, opioid use disorder treatment in international settings, pain management for hemodialysis patients, and validation of medical coding algorithms. She frequently employs advanced statistical techniques including Bayesian methods, mediation analysis, and methods for handling clustered data. Her research demonstrates a consistent commitment to improving the rigor and applicability of statistical methods in public health and clinical research. Dr. Esserman serves as a reviewer for several prestigious journals including the American Journal of Epidemiology, Arteriosclerosis, Thrombosis and Vascular Biology; Statistics in Biopharmaceutical Research; Clinical Trials; and Obesity. As a member of the Yale Center for Analytical Sciences, Dr. Esserman collaborates with numerous researchers across Yale University. Her current projects include the EQuIP trial (HIC ID 2000033355), where she serves as Sub Investigator with primary completion date of 08/31/2027, focusing on mental health and behavioral research for sexual minority women.

• Cybersecurity
• Privacy-enhancing technologies
• Payment system security
• +۵ مورد دیگر

Professor Steven J. Murdoch is a leading figure in cybersecurity and privacy-enhancing technologies at the Department of Computer Science , University College London . His work bridges computer science and law, focusing on payment system security, electronic evidence reliability, and dispute resolution mechanisms. Head of Information Security Research Group at UCL (2022–present) Director at Open Rights Group (2022–present) Research spans payment security , privacy protocols , and legal implications of computer evidence . Key contributions include analyzing EMV protocol flaws, designing secure authentication mechanisms, and improving Tor's anonymity. Publications cover privacy-preserving federated learning , time-lock puzzles , and malware ecosystem analysis Awards include Fellowships from the Institution of Engineering & Technology and British Computer Society , and a Royal Society University Research Fellowship . Teaches COMP0055 Computer Security II and COMP0061 Privacy Enhancing Technologies at UCL

• Hydrogen fuel
• Combustion engineering
• Alternative energy systems
• +۷ مورد دیگر

Dr Stathis Tingas is a Lecturer at Edinburgh Napier University's School of Computing Engineering and the Built Environment. His research focuses on hydrogen fuel systems, combustion engineering, and sustainable transportation technologies. With numerous publications in high-impact journals and conference proceedings, Dr Tingas has established himself as a significant contributor to the field of alternative energy systems. Dr Tingas' research interests center on hydrogen and ammonia as alternative fuels for transportation, with particular emphasis on combustion characteristics, engine performance, and emissions control. His work spans theoretical modeling, computational analysis, and practical applications for decarbonizing various transportation sectors including aviation, heavy-duty vehicles, and maritime transport. Recent publications demonstrate his focus on hybrid propulsion systems combining fuel cells with traditional engine technologies. Dr Tingas' publication record shows consistent productivity with research outputs spanning from fundamental combustion science to applied engineering solutions. His work often employs computational singular perturbation techniques for analyzing complex combustion phenomena, with recent focus shifting toward practical applications of hydrogen and ammonia fuels in real-world engine systems. The trend in his publications indicates growing emphasis on zero-emission transportation solutions aligned with net-zero targets. Dr Tingas serves as a second supervisor for PhD students, including Richard Wallace who is working on subsurface hydrogen storage simulation. He has successfully secured multiple research grants from UK government bodies including the Department for Science, Innovation & Technology, Scottish Government, and the Royal Society of Edinburgh, with projects totaling over £500,000 in funding. His current research portfolio includes projects focused on accelerating clean energy technology development, creating sustainable cities, advancing electromobility, and developing zero-carbon hydrogen engines for heavy transport applications. These projects demonstrate his commitment to addressing practical challenges in the transition to sustainable energy systems.

• Particle and high energy physics
• Astronomical sciences
• Nuclear and plasma physics
• +۳ مورد دیگر

Wayne Springer is a Professor in the Department of Physics & Astronomy at the University of Utah, with a career spanning over 25 years. He has been actively involved in experimental particle astrophysics, ultra-high-energy cosmic ray (UHECR) physics, and gamma-ray astronomy. Ph.D. in Physics from University of Maryland (1991) B.S. in Physics from University of Maryland (1985) Postdoctoral training at University of Maryland and University of Alberta His research focuses on particle astrophysics, cosmic ray detection, and gamma-ray astronomy. He has made significant contributions to the development of the HiRes and Telescope Array cosmic ray observatories, as well as the HAWC and SWGO gamma-ray observatories. His recent work includes deployment of the Trinity neutrino detector prototype and serving as SWGO project manager for Chile site infrastructure. Article trends show strong emphasis on TeV gamma-ray observations (HAWC, SWGO), cosmic ray diffusion mechanisms, dark matter searches, and high-energy astrophysical source characterization (pulsars, microquasars, supernova remnants). He has secured multiple NSF grants for particle astrophysics research and leads detector working groups in international collaborations. Professor Springer actively participates in astronomy outreach, co-developing observatories and implementing computational physics teaching tools with Gradescope auto-graders for enhanced pedagogy. His work bridges experimental high-energy physics, detector development, and multiwavelength astrophysical studies.

Farzan Banihashemiمشاهده پروفایل

پژوهشگر ارشد

• Urban Building Energy Modeling
• Building Energy Simulation
• Machine Learning and Deep Learning
• +۱ مورد دیگر

Farzan Banihashemi serves as a Research Fellow at the Chair of Energy Efficient and Sustainable Design and Building at the Technical University of Munich (TUM), maintaining this affiliation since 2019 while concurrently working as a Data Scientist at Climateflux GmbH since 2023. His work bridges sustainable building design and data science, focusing on computational approaches for urban energy systems. His academic credentials include: Master in Management from TUM School of Management (2019) Master in Energy Efficient and Sustainable Building from TUM (2017) His research centers on data-driven urban building energy modeling (UBEM) , building energy simulation , and machine learning applications for occupant behavior analysis . He develops non-intrusive sensing methodologies to model window operations and occupancy patterns using environmental data streams, with significant contributions to CO2-based occupancy detection systems and predictive modeling for office environments. His work integrates climate change considerations into early-stage building design processes. Analysis of his 2022-2024 publications reveals a concentrated research trajectory applying artificial intelligence to building energy challenges. Over 60% of his recent work addresses occupant behavior modeling—particularly window operations and space occupancy—using explainable AI techniques. His publications also demonstrate growing engagement with urban-scale applications, including urban heat island mitigation and vertical densification strategies, often incorporating life cycle assessment frameworks. No scientific awards were documented in the source materials. While specific advising activities aren't detailed, his collaborative publication pattern (average 4.3 co-authors per paper) indicates active participation in research teams. Grant involvement is implied through project affiliations though specific funding mechanisms aren't specified. He operates within TUM's Chair of Energy Efficient and Sustainable Design and Building, contributing to major initiatives including Building Climate–Municipal (BauKlima-Kommunal), CircularFTmehrRAUM, CircularGreenSimCity, and the NAWAREUM project. These efforts focus on sustainable urban development, climate adaptation strategies, and circular economy implementation in the built environment, particularly examining urban densification under climate change scenarios.

• Computer Vision
• Natural Language Processing
• 3D Human Motion Synthesis
• +۳ مورد دیگر

Gül Varol is a permanent researcher at École des Ponts ParisTech's IMAGINE group, an ELLIS Scholar, and Guest Scientist at Max Planck Institute. She holds a PhD from Inria Paris/ENS with awards from ELLIS and AFRIF. Her academic service includes Program Chair at ECCV'24 and Area Chair roles at major conferences. Current affiliations: IMAGINE group (École des Ponts ParisTech), Max Planck Institute Previous roles: Postdoctoral researcher at University of Oxford Her research focuses on vision-language applications, particularly in 3D human motion synthesis, sign language technology, and audio description generation. Key techniques include text-conditioned diffusion models, temporal context modeling, and synthetic data utilization. Scientific contributions recognized through: Google Research Scholar award (2023) ELLIS PhD Award (2020) AFRIF PhD thesis award (2020) Best application paper at ACCV'20 Recent publications demonstrate expertise in: Text-driven 3D motion editing (MotionFix, 2024) Cross-dataset generalization studies (TMR++, 2024) Temporal action composition frameworks (TEACH, 2022) Sign language dense annotation methods (BOBSL, 2022) Zero-shot audio description generation (AutoAD-Zero, 2024) She actively contributes to dataset development including BOBSL (British Sign Language corpus) and SURREACT synthetic action dataset, while pioneering new evaluation metrics for audio description quality and motion retrieval benchmarks.

• Bioinformatics
• Computer Science
• Algorithms
• +۳ مورد دیگر

Dr. Juha Kärkkäinen is a University Lecturer and Principal Investigator at the Department of Computer Science, University of Helsinki, specializing in Algorithmic Bioinformatics. He supervises PhD students in the Doctoral Programme in Computer Science and actively contributes to research outputs and academic events. Institution: University of Helsinki Department: Department of Computer Science Rank: Lecturer His research focuses on string processing , data structures , and combinatorial pattern matching , with recent work on bijective Burrows-Wheeler transforms, LCP arrays, and efficient string indexing. Google Scholar lists publications spanning 2024 to 1999, highlighting his expertise in algorithm design and compression techniques . Notably, he has participated in and organized events like the International Workshop on Combinatorial Algorithms and delivered a keynote speech at the 1st Summer School on Bioinformatics Algorithms. While no explicit awards are detailed, his editorial roles in scientific collections and conferences underscore his academic influence.

Muhammad Ali Gulzarمشاهده پروفایل

استادیار

• Fault Localization
• Big Data systems
• Data Intensive scalable computing
• +۵ مورد دیگر

Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.

Franz Lennard Ricklefsمشاهده پروفایل

استاد

• Neurosurgery
• Neuro-oncology
• Molecular Neurobiology
• +۵ مورد دیگر

Prof. Dr. med. Franz Lennard Ricklefs is a Senior Physician and Head of the Working Group at the Department of Neurosurgery, University of Hamburg Faculty of Medicine. He is a Medical Specialist in Neurosurgery with cross-disciplinary expertise in neuro-oncology, molecular pathology, and extracellular vesicle research. Affiliations: University Medical Center Hamburg-Eppendorf (UKE), European Liquid Biopsy Society (ELBS), International Consortium on Meningiomas (ICOM) Research Interests: His work focuses on neurosurgical oncology, particularly glioblastoma and meningioma pathobiology. He investigates DNA methylation patterns, extracellular vesicle biomarkers, and liquid biopsy implementation in clinical neuro-oncology. Additional interests include surgical outcomes for epilepsy and aneurysm management. Article Trends: Over the last decade, Dr. Ricklefs has published extensively on: Extracellular vesicle applications as liquid biopsy markers DNA methylation subclasses for glioblastoma and meningioma Multicenter surgical outcome benchmarking Immune evasion mechanisms in neuro-oncology Technological innovations in neurosurgical visualization Molecular characterization of rare CNS tumors Professional Contributions: He co-authored the MISEV2023 guidelines for extracellular vesicle studies and participates in international consensus reviews for meningioma classification. His collaborations span institutions across Europe and North America.