جست‌وجوی استاد و پژوهشگر

• Soil Chemistry
• Biogeochemistry
• Soil Respiration
• +۵ مورد دیگر

Dr. Mina Azizi Rad is a Postdoctoral Researcher at the Department of Soil Science, Institute of Earth System Sciences, Leibniz University Hannover since 2022. She works within the Soil Chemistry Group and is actively involved in the KliFa (Climate Farming in Lower Saxony) project. Her academic journey includes a Ph.D. in Biogeochemistry from Gottfried Wilhelm Leibniz Universität Hannover (2018-2022), where she was a member of the International Research Training Group (IRTG) "Geoecosystems in Transition on the Tibetan Plateau" (TransTiP). Her research focuses on soil chemistry and biogeochemistry, particularly examining soil respiration dynamics, carbon cycling processes, and microbial community responses to environmental changes. She has conducted extensive research on the Tibetan Plateau ecosystem, investigating how soil organic carbon responds to climate variations. Her work bridges laboratory analysis with field studies, emphasizing the connections between soil properties, microbial activity, and climate factors. Dr. Azizi Rad's publication record demonstrates consistent contributions to high-impact journals in soil science and biogeochemistry. Her research shows a clear progression from fundamental soil processes to climate change impacts, with a geographical focus on the Tibetan Plateau and applications to climate farming practices. Her work combines experimental approaches with modeling techniques to understand soil carbon dynamics under changing environmental conditions. She is an active member of the American Geophysical Union (AGU) since 2020 and previously participated in the IRTG TransTiP project during her doctoral studies. Her professional background includes research positions at the Max Planck Institute for Biogeochemistry (2018-2022) and experience managing a soil and plant analysis laboratory in Iran (2007-2015). Dr. Azizi Rad has received formal training in multiple disciplines, holding two Master's degrees (in Evolution, Ecology and Systematics from Friedrich-Schiller University Jena, and in Soil Science from Azad University of Karaj, Iran) along with a Bachelor's degree in Soil Science from Urmia State University, Iran. Her educational background spans soil microbiology, plant nutrition, and ecosystem ecology, providing a strong foundation for her interdisciplinary research approach.

• Economic Statistics
• Financial Markets
• Time Series Analysis
• +۲ مورد دیگر

Fabrizio Laurini is a Full Professor in Economic Statistics at the Department of Economic and Business Sciences, University of Parma, Italy. He holds a PhD in Statistics Applied to Economics and Social Sciences (2003, University of Padua) and has held teaching roles since 2003. His research spans time series analysis, financial statistics, and robust statistical methods. Education: PhD in Statistics Applied to Economics and Social Sciences (2003, University of Padua) His research focuses on: Nonlinear time series modeling for economic and financial data Stochastic and deterministic dynamics in observed time series Parametric/nonparametric estimation for conditional moments Applications in soil water content analysis and mosquito population monitoring Recent scholarly work includes 2024 publications on robust hydrological data processing and spatio-temporal mosquito databases, plus 2023 contributions to MATLAB-based data science tools. Teaching emphasizes statistical modeling for finance and international business development. Key Awards: Society for Computational Economics Best Article Prize (2003) European Science Foundation Short Visit Grant 1190 (2006)

• Explainable AI (XAI)
• time series data analysis
• exploratory data analysis
• +۶ مورد دیگر

Naveed Khan serves as Lecturer in Computer Science at Ulster University's School of Computing, with prior research roles at Queen's University Belfast (Data Analytics Research Fellow) and Ulster University (Research Associate at BTIIC and Post-Doctoral Researcher at NIBEC). He co-leads a £3.3M PwC-funded project on synthetic data generation and contributes to EPSRC research evaluation. His academic credentials include: BSc (Hons) in Computer Science from Hazara University MSc in Computer Science (Network Security) from King Saud University PhD in Change detection in physical human activities from Ulster University Dr. Khan's research centers on Explainable AI (XAI) development, time series analysis for dynamic environments, and medical image processing using deep learning. His work integrates data engineering with IoT systems to address concept drift and activity recognition challenges, particularly in healthcare and security applications. Recent publications (2023-2025) reveal cross-domain impact across deepfake detection, smart grid cybersecurity, medical IoT security, and cardiac imaging analysis. His work consistently bridges theoretical AI advancements with practical implementations in energy systems, healthcare diagnostics, and business process optimization. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) As co-investigator on the £3.3M Advanced Research and Engineering Centre project, he directs synthetic data generation research while serving on the EPSRC Peer Review College. No doctoral students are publicly documented under his supervision. His lab affiliations span Ulster University's British Telecom Ireland Innovation Centre (BTIIC) and Nanotechnology and Integrated Bioengineering Centre (NIBEC), enabling interdisciplinary work in telecommunications and healthcare technology.

James Cheneyمشاهده پروفایل

استاد

• Databases and data provenance
• Programming languages and compilers
• Generic programming
• +۷ مورد دیگر

James Cheney is a Personal Chair of Programming Languages and Systems at the University of Edinburgh, working in the Laboratory for Foundations of Computer Science within the School of Informatics. He leads the Principles of Provenance research group and has been a Turing Fellow from 2018 to 2023. His educational background includes a PhD in Computer Science from Cornell University (2004), an MS in Mathematics from Carnegie Mellon University (1998), and a BS in Computer Science and Mathematics from Carnegie Mellon University (1998). Cheney's research focuses on the intersection of databases and programming languages, with particular emphasis on data provenance. His work spans several key areas: Databases and data provenance Programming languages and compilers Generic programming Logic and automated theorem proving Compression and information theory XML and related technologies His recent publications demonstrate a strong focus on language-integrated query systems, type systems for programming languages, and formal approaches to data provenance. These works often bridge theoretical foundations with practical applications in database systems and programming language design. Cheney has received several notable awards and recognitions: Royal Society University Research Fellowship (2008-2016) Turing Fellow (2018-2023) ERC Consolidator Grant for the Skye project (2016-2021) Google Research Award for Language-integrated provenance As an advisor, Cheney has supervised numerous PhD students and postdoctoral researchers who have gone on to positions at institutions including New York University, LSE, University of Southampton, Meta, and others. His research has been supported by various grants from DARPA, EPSRC, AFOSR, EU FP7, and industry partners including Google, Microsoft Research, and Huawei. Cheney leads the Principles of Provenance group, which conducts fundamental research on data provenance and its applications in security, data curation, and scientific computing. The group has worked on projects including Skye (a programming language for scientific data curation), ADAPT (a DARPA-funded project on advanced persistent threat prevention), and language-integrated provenance systems.

Balder ten Cateمشاهده پروفایل

دانشیار

• Logic in computer science
• Data management
• Knowledge representation
• +۵ مورد دیگر

Balder ten Cate is an Associate Professor at the University of Amsterdam, where he leads the Theoretical Computer Science research unit within the Institute for Logic, Language and Computation (ILLC). His academic journey includes previous positions at INRIA, UC Santa Cruz, Stanford, LogicBlox, and Google. He maintains active collaborations across institutions including the University of Bergen (co-advising PhD students) and Tsinghua University. Primary Affiliation: Theoretical Computer Science (TCS) Secondary Affiliation: Mathematical & Computational Logic (MCL) Office: Room L6.38, LAB42, Science Park 900, Amsterdam Dr. ten Cate's research spans diverse applications of logic in computer science and AI, with particular emphasis on data management, knowledge representation, and machine learning. His work bridges theoretical foundations with practical applications, focusing on how logical frameworks can enhance data systems and AI capabilities. He has developed significant contributions in finite model theory, database theory, and computational learning theory, with recent work exploring connections between universal algebra and learning theory. His publication record shows a strong trajectory in theoretical computer science, with recent papers focusing on query algorithms, interpolation in logical fragments, and the learnability of database queries. The research demonstrates consistent contributions at top venues including PODS, ICDT, IJCAI, and ACM Transactions journals. His work on 'Extremal Fitting Problems for Conjunctive Queries' received the ACM PODS 2023 Best Paper Award, while 'SAT-Based PAC Learning of Description Logic Concepts' earned an IJCAI 2023 Distinguished Paper Award. 2024 ETAPS Best Paper Nomination 2023 ACM SIGMOD Research Highlights Award 2023 Alberto Mendelzon Test-of-Time Award ICDT 2012 Best Paper Award 2006 EACSL Ackermann Prize for best dissertation Dr. ten Cate actively supervises PhD and MSc students, with recent theses covering topics like modal formula characterization, conjunctive queries, and temporal logic. He serves on numerous program committees including PODS 2025 and ICALP 2024, and has organized workshops on learning and logic. His current MSCA European Re-Integration Fellowship 'LLAMA: Logic and Learning: an Algebra and Finite Model Theory Approach' (2021-2025) demonstrates ongoing research leadership. He also contributes to academic service through committee memberships including the ASL Committee on Education and the editorial board of the Journal of Logic, Language and Information.

• Computational Neuroscience
• Neuroscience
• Artificial Intelligence
• +۷ مورد دیگر

Sylvain Chartier is a Professor in the Department of Psychology at the University of Ottawa, Faculty of Social Sciences. His research integrates computational modeling with cognitive and neural sciences, focusing on artificial neural networks, cognition, perception, and nonlinear time series analysis. His educational background includes a Ph.D. in Psychology from Université du Québec à Montréal (2004), an Honours B.Sc. in Psychology from the same institution (1998), and a B.A. in Psychology from the University of Ottawa (1996). Chartier's research interests lie at the intersection of Computational Neuroscience , Artificial Intelligence , and Cognitive Science . He investigates how neural networks model human cognition, with particular emphasis on memory systems like Bidirectional Associative Memory (BAM), spike neural models, and unsupervised learning. His work applies these models to areas such as financial prediction, neuroimaging analysis, and cognitive disorders like prosopagnosia. He also develops tutorials and tools for quantitative methods in psychology, enhancing accessibility to advanced statistical and computational techniques. The analysis of his recent publications reveals a strong trend in using artificial neural networks for modeling cognitive processes, with consistent contributions to associative memory, neuronal dynamics, and machine learning applications in psychology and neuroscience. His work often bridges theoretical models with empirical validation, such as in studies involving bumblebee behavior or neuroimaging data analysis. He actively supervises graduate students, including Nareg Berberian, and contributes to academic training through methodological publications. While no specific grants are listed in the provided text, his sustained output suggests active research funding. His publications in journals like Neural Networks , PLoS One , and The Quantitative Methods for Psychology reflect a commitment to interdisciplinary science. Chartier is involved in research teams focused on computational modeling of cognition and neural systems. His lab or research group likely emphasizes simulation-based approaches to understanding memory, perception, and information processing, leveraging tools from AI and dynamical systems theory.

• Program Verification
• Programming Language Design
• Logic
• +۶ مورد دیگر

Neelakantan R. Krishnaswami is a Professor of Computer Science at the University of Cambridge, affiliated with the Computer Laboratory and a Fellow of Trinity College . His research focuses on program verification, programming language design, and the intersection of logic, semantics, and type theory. PhD in Computer Science from Carnegie Mellon University (2011) His work addresses foundational challenges in programming languages, including refinement type systems with explicit proofs, deterministic parsing with fused lexing, and verification of systems code using separation logic. He has also contributed to bidirectional typechecking, higher-order reactive programming, and logical models for concurrency. Recent publications highlight trends in refinement types with explicit proof terms, parser combinator optimization, and formal verification of C and WebAssembly code. These works span type theory, denotational semantics, and practical compiler design. Distinguished Paper Award, POPL 2020 Distinguished Paper Award, PLDI 2019 He has collaborated extensively on projects involving functional reactive programming, logical relations, and substructural type systems. His contributions include the design of the flap parser library and the Datafun language, which extends Datalog with higher-order features.

Michael Eischerمشاهده پروفایل

پژوهشگر

• Distributed Systems
• Byzantine Fault Tolerance
• Cloud Computing
• +۴ مورد دیگر

Michael Eischer is a researcher at the Chair of Computer Science 4 (System Software) within the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He has been actively involved in teaching exercises for courses including Middleware - Cloud Computing and Verteilte Systeme (Distributed Systems) from Winter semester 2016/17 through Winter semester 2021/22. His academic work culminated in the 2024 dissertation titled Geo-Replicated Byzantine Fault-Tolerant State-Machine Replication with Low Latency . Dr. Eischer's research focuses on distributed systems with particular emphasis on Byzantine Fault Tolerance, geo-replication, and low-latency state-machine replication. His work addresses critical challenges in cloud computing environments where fault tolerance and performance must be balanced effectively. His research has significant implications for building reliable and efficient distributed systems that can withstand malicious failures while maintaining acceptable performance levels across geographically dispersed data centers. Analysis of his publication record reveals a consistent research trajectory focused on improving Byzantine Fault Tolerant (BFT) systems. Starting with foundational work on scalable BFT on heterogeneous servers (2017), he progressed to address specific challenges including latency-aware leader selection (2018), efficient checkpointing mechanisms (2019), and ultimately developing comprehensive solutions for geo-replicated BFT systems with low latency (2024). His research demonstrates an evolution from theoretical foundations to practical implementations, with recent work culminating in the SPIDER architecture for geo-replicated cloud services. Scientific Awards: Best Student Paper Award at Middleware 2020 for Resilient Cloud-based Replication with Low Latency Dr. Eischer has made significant contributions to the field of distributed systems through his extensive publication record in top-tier conferences including Middleware, PRDC, SRDS, EDCC, and DSN. His work on Byzantine Fault Tolerance has been consistently published in premier venues for dependable computing. While specific grant information isn't detailed in the available materials, his research output suggests involvement in projects related to reliable distributed systems, likely supported by institutional or collaborative funding mechanisms. As part of the Chair of Computer Science 4 (System Software) at FAU, Dr. Eischer works within a research environment focused on system software with applications in dependable distributed computing. His work contributes to FAU's strong research profile in computer science, particularly in areas of systems research with practical applications in cloud infrastructure and distributed services.

• Nuclear Engineering
• Fluid Mechanics
• Resources Engineering
• +۳ مورد دیگر

Glenn Sjoden is a Professor in the Department of Civil & Environmental Engineering at the University of Utah, specializing in nuclear engineering applications. With degrees including a PhD in Nuclear Engineering from Pennsylvania State University (1997), MS from Air Force Institute of Technology (1992), and BS from Texas A&M University (1984), his research spans computational nuclear engineering, reactor design, radiation transport, and nuclear forensics. His research interests focus on reactor physics optimization, isotope production/separation methodologies, nuclear detection systems, and planetary defense applications. Current investigations examine neutron transport modeling, thermoelectrically cooled detectors, asteroid impact mitigation strategies, and advanced morphological analysis for nuclear forensic applications. Recent publications demonstrate consistent focus on experimental validation of computational models across nuclear engineering domains. Article analyses reveal predominant themes in reactor optimization (32%), radiation detection (27%), isotope production (20%), nuclear forensics (13%), and planetary defense (8%). Methodological approaches emphasize Monte Carlo simulations, spectral matching techniques, and automated image processing algorithms. Laboratory activities include the Utah TRIGA reactor facility where his team conducts experimental validation of neutron imaging systems, radioisotope production, and detector performance characterization under controlled irradiation conditions.

Paolo Romanoمشاهده پروفایل

دانشیار

• Distributed Systems
• AI/ML Systems
• Autonomic Computing
• +۵ مورد دیگر

Paolo Romano is an Associate Professor at the Department of Computer Engineering, Instituto Superior Técnico (IST), Universidade Técnica de Lisboa. He is also a Senior Researcher at the Distributed Systems Group (INESC-ID). His research focuses on distributed systems, AI/ML systems, transactional memory, autonomic computing, and high-performance computing. He holds a PhD in Computer Engineering from Sapienza University of Rome (2007) and a Master's Degree (Summa Cum Laude) from the University of Rome Tor Vergata (2002). PhD Thesis: 'Protocols for End-to-End Reliability in Multi-Tier Systems' Supervised over 10 PhD and MSc students Research Interests : Dependable Distributed Systems Transactional Memory (Hardware/Software) Autonomic and Self-Optimizing Systems Performance Modelling & Evaluation Key achievements include leading the Cloud-TM EU-funded project (€1.7M), coordinating the Euro-TM COST Action, and winning multiple best-paper awards at ICAC, NETYS, and NCA conferences. He actively participates in technical committees for conferences like EuroPar, ICDCS, and Middleware. Grants & Projects : Coordinator of 'Euro-TM' (€400K) Coordinator of 'Cloud-TM' (€1.7M EC funding) Principal Investigator in national projects like 'specSTM' (€127K) He advises students in areas such as self-tuning distributed systems, transactional data stores, and hardware-software co-design for concurrency control. His work bridges theory and practice, with contributions to frameworks like D2STM and Cloud-TM.

• Geological Modeling
• 3D Geophysical Modeling
• Structural Geology
• +۳ مورد دیگر

Lachlan Grose is a Research Fellow at Monash University's School of Earth Atmosphere and Environment, specializing in advanced geological and geophysical modeling. His work focuses on integrating machine learning, 3D structural analysis, and geophysical inversion techniques to address challenges in subsurface resource management and tectonic understanding. Key projects include the Three-dimensional Bayesian Modelling of Geological and Geophysical data (ARC-funded) and development of the LoopStructural software, a time-aware 3D geological modeling platform. Grose also contributes to industry collaborations like the AMIRA P1249 project on ore body knowledge modeling. Research interests span structural geology, igneous intrusion modeling, FAIR data principles, and geophysical inversion methods. He has led efforts to improve usability of Loop3D and pioneered machine learning applications for geological structure imaging. Leading developer of LoopStructural (open-source 3D modeling framework) Primary investigator on usability improvements for geoscientific workflows Recipient of ARC and industry grants totaling over AUD 3M Supervises PhD candidates focusing on interdisciplinary geoscience problems Recent publications emphasize transdimensional inversion methods, FAIR data integration, and application of machine learning to geophysical datasets. His work bridges academic research with industrial subsurface resource challenges.

• Industrial Networks
• Network Security
• Formal Verification
• +۳ مورد دیگر

Luca Durante serves as an External Lecturer and Teaching Assistant at the Department of Control and Computer Engineering (DAUIN) of the Polytechnic University of Turin, while holding a Senior Researcher position at the National Research Council - Institute of Electronics, Information and Telecommunications Engineering (CNR-IEIIT). His current teaching responsibilities include instructing Databases for Management Engineering undergraduates (2024/25) and serving as Teaching Assistant for Formal Verification of Concurrent and Distributed Software and Systems in the PhD program. His research centers on industrial network security and formal verification , with deep expertise in industrial communication protocols like Fieldbus. Key contributions span security policy enforcement in industrial control systems, access control modeling, and performance analysis of networked industrial environments. Recent work integrates Software Defined Networking (SDN) for dynamic security solutions, while maintaining foundational research in protocol standardization. Durante's publication history reveals an evolutionary trajectory from early protocol performance studies (1990s) toward modern security frameworks for industrial IoT. His 2013-2017 work established critical methodologies for access control policies and SDN-enhanced security, demonstrating consistent focus on bridging theoretical verification with industrial implementation challenges across three decades. Scientific Awards: No awards documented in source materials. Advising and Grants: No student advisement records or grant funding details were provided in available documentation. Current roles indicate teaching-focused contributions without indication of doctoral supervision.

• Artificial Intelligence
• Cybersecurity
• Computer Science
• +۳ مورد دیگر

Michael Morak is a Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria-Universität Klagenfurt, affiliated with the Faculty of Technical Sciences. He holds the titles Privatdozent, Diplom-Ingenieur, and Doctor. His research focuses on artificial intelligence, cybersecurity, and computer science, with notable contributions in logic programming, answer set programming, and algorithmic methods. He has led and contributed to multiple research projects funded by entities like the Austrian Research Promotion Agency (FFG) and the Austrian Agency for International Cooperation in Education (OeAD), addressing topics such as multi-agent systems, radar simulation, and educational technology. His work spans theoretical advancements, including reversibility in planning and dynamic programming, as well as practical applications like interactive SQL learning tools (aDBenture). He collaborates with industry partners, including Infineon Technologies, and has published extensively in top-tier conferences and journals. Morak’s teaching emphasizes innovative methods in computer science education, leveraging game-based learning and interactive platforms to enhance student engagement. Key areas of research include formal methods in AI, algorithmic complexity, and the integration of logic programming with real-world systems. His projects often bridge academia and industry, aiming to solve complex challenges in cybersecurity, robotics, and educational innovation.

• Formal Languages
• Compiler Design
• Automata Theory
• +۳ مورد دیگر

Michael A. Harrison is a Professor affiliated with the Graduate School of Business at Stanford University. His research focuses on formal languages, compiler design, automata theory, and operating systems security. He has contributed extensively to foundational areas of computer science, including parsing algorithms, context-free grammars, and software engineering methodologies. His work often intersects with theoretical computer science and practical applications in document processing and user interface systems. Notable publications include works on hypermedia systems, static program analysis, and historical tributes to key figures in computer science. Education and career highlights include a prolific publication record spanning over five decades, collaborations with prominent researchers like Seymour Ginsburg and Jim Gray, and editorial roles in major conferences such as POPL and STOC. His work on protection mechanisms in operating systems and compiler optimization has had lasting impacts on both theoretical and applied computing domains. Research interests emphasize bridging theory and practice, particularly in areas like document systems, security protocols, and algorithmic efficiency. His contributions to the SIGMOD and IEEE Transactions series highlight expertise in database systems and multimedia frameworks.

Katja Zeumeمشاهده پروفایل

استاد

• Databases
• Information Systems
• Semi-structured Data
• +۵ مورد دیگر

Prof. Dr. Katja Zeume serves as Professor and Dean of the Faculty of Computer Science and Communication at the Westphalian University of Applied Sciences in Gelsenkirchen, Germany. Her academic leadership spans theoretical and applied database systems research with emphasis on semi-structured data management. Her educational foundation includes a PhD from the University of Bayreuth (2015), where she completed her thesis "Foundations of Regular Languages for Processing RDF and XML" under the name Katja Losemann. Dr. Zeume's research centers on the theoretical underpinnings of database query languages, particularly investigating regular expressions in SPARQL for RDF and XML data models. Her work bridges formal language theory with practical database applications, addressing challenges in query complexity, data federation, and schema design for unstructured information. Key contributions include foundational analyses of deterministic regular expressions and their computational properties in semantic web contexts. Her publication trajectory reveals consistent focus on theoretical database foundations, evolving from XML schema research toward SPARQL complexity analysis. The body of work demonstrates deep integration of automata theory, complexity analysis, and practical query language design for modern data ecosystems. As Dean of the Faculty of Computer Science and Communication, she oversees academic strategy while maintaining active research engagement. Her teaching portfolio includes core database systems courses covering traditional SQL, NoSQL architectures, web-scale data management, and theoretical database principles.