جست‌وجوی استاد و پژوهشگر

• Pharmacokinetics
• Pharmacodynamics
• Inflammation
• +۵ مورد دیگر

Letao Li is a Researcher in the Pharmacy department at Erasmus MC, specializing in clinical pharmacology with focus on pharmacokinetics and pharmacodynamics in vulnerable patient populations. Affiliated with Rotterdam-based research teams, Li's work bridges pharmaceutical sciences and critical care medicine. Research interests center on drug behavior in inflammatory conditions, particularly examining how biomarkers like C-reactive protein alter pharmacokinetics in sepsis, pre-eclampsia, and pediatric oncology. Key themes include protein binding dynamics, glucocorticoid therapy optimization, and precision dosing for antibiotics in critically ill patients. Recent publications reveal strong emphasis on translating pharmacokinetic findings into clinical practice, with trends showing increasing focus on model-informed precision dosing (MIPD) frameworks. The work consistently addresses therapeutic challenges in high-risk groups including neonates, pregnant women, and sepsis patients. Key research areas: Pharmacokinetic alterations during inflammation Dexamethasone/betamethasone optimization in special populations Temocillin protein binding in critical illness Biomarker-guided dosing strategies Li's collaborative network spans multiple European institutions with significant work in pediatric oncology pharmacology and critical care pharmacotherapy. Current research appears focused on implementing precision dosing algorithms for vulnerable populations through model-informed approaches.

Charles Yangمشاهده پروفایل

استاد

• Linguistics
• Cognitive Science
• Computational Linguistics
• +۵ مورد دیگر

Charles Yang is a Professor at the University of Pennsylvania's Department of Linguistics, with joint appointments in Computer and Information Science and Psychology. As Chair of Linguistics and Director of the Program in Cognitive Science since 2011, he co-directs the Integrated Language Science and Technology group with John Trueswell. PhD in Computer Science, MIT AI Lab Faculty at Yale (2000-2008) Current roles: Chair of Linguistics, Director of Cognitive Science Program His research bridges linguistics, cognitive science, and computational modeling, focusing on human and machine language acquisition, phonological and morphological learning, and computational modeling of historical language change. He develops the Tolerance Principle for quantifying language acquisition thresholds and investigates neural language models as cognitive theories. Recent publications analyze language acquisition through computational models (2021-2024), with the 2025 work evaluating LLMs for low-resource languages. His 2016 book The Price of Linguistic Productivity won the Leonard Bloomfield Award and explores productivity in language systems. Guggenheim Fellowship (2018) Bloomfield Book Award (2018) NSF GRFP Fellowship Leonard Bloomfield Book Award (2016) Yang advises students in language acquisition, computational linguistics, and cognitive modeling. His lab collaborates with researchers like Bob Berwick, Johan Bolhuis, and Noam Chomsky on integrated approaches to language science.

• System Identification
• Automatic Control
• Decision Systems
• +۵ مورد دیگر

Bo Wahlberg is a Professor of Chair in Automatic Control at KTH Royal Institute of Technology since 1991. He leads the Division of Decision and Control Systems within the School of Electrical Engineering and Computer Science. As a co-founder of the Centre of Autonomous Systems (CAS) and the Linnaeus Center ACCESS on networked systems at KTH, he has been instrumental in establishing key research initiatives. Currently, he serves as the KTH director and member of the program management for the Wallenberg AI, Autonomous Systems and Software Program (WASP). Professor Wahlberg's research spans system identification, automatic control, and signal processing with applications across multiple domains. His expertise in orthonormal basis functions for system modeling has been foundational in the field. Recent work focuses on model predictive control for autonomous vehicles, inverse filtering problems, distributed control strategies, and applications in transportation systems. His research demonstrates consistent evolution from theoretical foundations to practical implementations in autonomous systems and robotics. His publication record shows a clear progression from fundamental system identification techniques to cutting-edge applications in autonomous systems. The recent publications (2020-2024) increasingly focus on multi-agent systems, autonomous vehicle control, inverse problems in estimation, and security applications of control theory. This reflects the broader trend in control systems research toward more complex, networked, and safety-critical applications. IEEE Fellow (2007) for contributions to system identification using orthonormal basis functions Fellow of IFAC (2019) for contributions to system identification and development of orthonormal basis function models Fellow of the Royal Swedish Academy of Engineering Sciences (IVA) (2023) IEEE Transactions on Automation Science and Engineering Best New Application Paper Award (2016) Professor Wahlberg has supervised more than 130 master's students and 25 PhD students throughout his career. His research has been supported by major initiatives including the Wallenberg AI, Autonomous Systems and Software Program (WASP). He has contributed to establishing research arenas like WARA-PS for public safety demonstrations and autonomous rescue robotics experimentation. His leadership extends to program management and directing significant research centers at KTH. Professor Wahlberg leads the Division of Decision and Control Systems at KTH, which is part of the broader ecosystem including the Centre of Autonomous Systems (CAS) and the Linnaeus Center ACCESS. His work connects with multiple research groups focusing on networked systems, autonomous vehicles, and robotics applications. The Wallenberg AI, Autonomous Systems and Software Program (WASP) represents a major collaborative effort he directs, bringing together academic and industry partners for large-scale research in AI and autonomous systems.

Nasreen Anjumمشاهده پروفایل

مدرس

• Cybersecurity
• Cyber-Biosecurity
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Nasreen Anjum is a Lecturer in Computer Science at the University of Portsmouth's Faculty of Technology, with a dual appointment at the Institute of Life Sciences and Healthcare. Her work bridges cybersecurity, artificial intelligence, and biomedical data protection, focusing on securing emerging technologies like IoT, digital twins, and next-generation sequencing systems. PhD in Computer Science, King's College London (2014-2019) Specialist in cyber-biosecurity and AI-driven network security Principal Investigator for UK-Saudi Challenge Fund (2024-2025) Her research spans four key areas: IoT Security : Lightweight cryptography, edge computing integration, and AI-based anomaly detection Cyber-Biosecurity : Genomic data protection, AI-bio convergence security, and NGS threat mitigation Digital Twins : Resilient architectures for healthcare and smart manufacturing applications Edge/Cloud Security : Adaptive access control and hybrid ecosystem protection Selected publications demonstrate interdisciplinary breadth: 2025: Foundational work on cyber-biosecurity in genomic sequencing 2024: Malware awareness education through gamification 2023: Cross-national cybercrime deterrence strategies 2022: IoT applications in pandemic monitoring Scientific recognition includes: Schlumberger Faculty for the Future Fellowship Best Paper Award nomination at IEEE WINCOM Fellow of the Higher Education Academy Actively supervising postgraduate research, Dr. Anjum welcomes PhD candidates in cybersecurity, AI, and technology ethics domains.

• Learning Analytics
• Computational Psychometrics
• Adaptive Learning Systems
• +۷ مورد دیگر

Dr. Matthieu Brinkhuis is an Associate Professor at the Faculty of Science , Utrecht University , with a focus on Software Technology for Learning and Teaching . He serves as Director of Education for the Department of Information and Computing Sciences and is a Senior Fellow at the Center for Academic Teaching and Learning. His work bridges Learning Analytics , Computational Psychometrics , and Human-Centered AI . Research Themes : AI Labs, Governing the Digital Society, Human-Centered Artificial Intelligence Technical Expertise : Data Science, Learning Analytics, Adaptive Learning Systems, Process Mining His scholarly output includes pioneering methods like the Urning Algorithm for dynamic ability tracking and Federated Learning Analytics balancing privacy and performance. He leads the Utrecht Platform for Applied Data Science and contributes to Higher Education Research through large-scale online learning systems. Notable collaborations include projects on Privacy in SMEs , E-Assessment Validation , and Cognitive Diagnostic Models for actionable educational feedback. His work has been published in venues such as the British Journal of Mathematical and Statistical Psychology and Frontiers in Education .

William Turkettمشاهده پروفایل

دانشیار

• Computational Biosciences
• Bioinformatics
• Network Security
• +۵ مورد دیگر

Dr. William Turkett serves as Associate Professor and Department Chair of the Department of Computer Science at Wake Forest University, a position he has held since 2020. He concurrently acts as Graduate Program Director and serves on Wake Forest's Information Technology Advisory Board, with prior experience as an ABET/CSAB Peer Evaluator. His educational background includes: Ph.D. in Computer Science and Engineering from University of South Carolina (2004), specializing in probabilistic reasoning B.S. in Computer Science from College of Charleston (1998) Dr. Turkett's research program bridges computational biosciences and cybersecurity, with core expertise in DNA/amino acid sequence analysis for prediction/classification tasks. His work consistently addresses systems-scale sequential and temporal datasets, while maintaining active interests in computer science education methodologies and bioinformaticians' software development practices. Recent investigations include evolutionary cyber defense strategies and community-focused mobile application development. Publication analysis reveals three dominant research thrusts: bioinformatics (sequence-based protein classification, gene expression networks), cybersecurity (resilient systems, moving target defense), and education (non-traditional student engagement, STEM incubators). His interdisciplinary approach connects biological systems with computational modeling while addressing practical security challenges. Dr. Turkett oversees graduate education as Program Director and contributes to curriculum development through courses like CSC 201 (Data Structures) and CSC 231 (Programming Languages). His ABET accreditation experience supports program quality assessment, while community outreach initiatives demonstrate commitment to expanding computer science participation. Collaborative research efforts span computational biology labs and cybersecurity teams, though specific laboratory affiliations aren't documented in available sources. Current work continues to evolve at the intersection of biological sequence analysis and adaptive security frameworks.

• Genetical Genomics
• Systems Genetics
• Bioinformatics
• +۶ مورد دیگر

Dr. L.B. Snoek serves as an Assistant Professor in the Bioinformatics group within the Faculty of Science at Utrecht University. His research bridges computational biology with experimental approaches to understand genetic and phenotypic variation across different organisms. He leads the systems genetics and network biology lab, focusing on high-throughput techniques like RNA-sequencing to analyze complex biological interactions. Dr. Snoek obtained his Bachelor's degree in Biochemistry/Biotechnology from Enschede University College in 2000, followed by a Master's in Plant Biotechnology/Genetics from Wageningen University in 2002. He completed his PhD at Utrecht University in 2009, with subsequent research positions at Wageningen University, NIOO, and his current role at Utrecht University where he became Assistant Professor in 2020. His research interests center on genetical genomics and systems genetics, particularly studying natural variation in transcript abundance in organisms like Caenorhabditis elegans and Arabidopsis . He investigates how gene regulatory networks can be constructed using Recombinant Inbred Lines and Introgression Lines, with applications to agriculturally important traits and human disease models. His work on network biology focuses on integrating multi-level biological measurements to understand complex interactions from gene to ecosystem levels. Analysis of his recent publications reveals a strong focus on quantitative trait loci mapping, expression QTL analysis, and the impact of environmental factors on gene expression. His work spans plant biology (particularly lettuce, rice, and tomato), nematode genetics, and microbiome interactions, demonstrating an interdisciplinary approach that combines computational methods with experimental validation. Recent projects show increasing integration of machine learning and big data approaches to tackle complex biological questions. Dr. Snoek is actively involved in teaching, contributing to programs including Bioinformatics and Biocomplexity, Molecular and Cellular Life Sciences, and teaching courses on Python and R for life sciences. He currently serves as Principal Investigator for the LettuceKnow project, a TTW Perspective Grant focused on establishing lettuce as a model crop through quantitative genetics approaches. His lab maintains collaborations across multiple institutions, including ongoing work with NIOO-KNAW on ecosystem studies and Wageningen University on nematode research.

Dimitrije D. Čvokićمشاهده پروفایل

استادیار

• Bioinformatics
• Software Development
• Operations Research
• +۵ مورد دیگر

Dimitrije D. Čvokić serves as Assistant Professor and Head of the Department of Computer and Information Sciences within the Faculty of Science and Mathematics at the University of Banja Luka. His academic leadership spans curriculum development across computer science and mathematics disciplines, with teaching responsibilities including Data Structures, Algorithms, Software Engineering, and Mobile Application Development. His research focuses on competitive hub location problems, bioinformatics tool development, and blockchain applications. Čvokić pioneered the integration of Linux-based bioinformatics tools like BioLinux and UGene into biology curricula, while developing novel optimization algorithms for Stackelberg games in logistics networks. His recent work explores large language model terminology standardization and blockchain-based property investment platforms. Analysis of his publication trends reveals a strong trajectory from theoretical optimization (2015-2020) toward applied computational solutions (2021-2024), with increasing interdisciplinary collaboration spanning public health, real estate, and linguistic computing. His work demonstrates consistent methodological rigor in operations research while adapting to emerging technological domains. As an academic advisor, Čvokić has mentored multiple thesis students including Aleksandar Vrhovac (Interval Arithmetic, 2023), Danijela Kovačević (Automatic Control Systems, 2023), and Nemanja Marjanović (CAPTCHA systems, 2022). His teaching innovations include introducing Python programming, RDKit cheminformatics tools, and BASH scripting into diverse science curricula across chemistry, biology, and geodesy programs. Čvokić established computational laboratories featuring specialized software environments including Chemistry Add-in for Word, ChemSketch, UGene, and NET Bio Extension for Excel. His cross-departmental collaborations have modernized computer science instruction across the university's technical and natural science programs through systematic integration of open-source tools and modern development frameworks.

Jan Wilkensمشاهده پروفایل

استاد

• Medical Radiation Physics
• Radiation Therapy with Photons and Particles
• Laser-driven Ion Beams
• +۳ مورد دیگر

Jan Wilkens is a Professor for Advanced Technologies in Radiation Therapy at the Department of Radiation Oncology and Radiotherapy , Technical University of Munich (TUM), since 2008. As head of the Medical Physics Unit since 2012, he focuses on innovations in Medical Radiation Physics Radiation Therapy with Photons and Particles Laser-driven Ion Beams Treatment Planning and Dose Optimization Medical Imaging . His research emphasizes advanced radiation therapy technologies, including microbeam irradiation, X-ray dark-field imaging, and spectral CT for electron density measurement. Recent publications highlight applications in Radiation-induced lung injury detection Synctrotron-based imaging Proton and ion beam therapy Dose redistribution strategies . Key trends in his work include radiation biology studies, medical imaging innovations for early damage detection, and machine learning integration (implied through departmental collaborations). His experimental models span murine lung injury and synchrotron radiation applications in clinical contexts.

Christopher Joseph Carmona del Jesusمشاهده پروفایل

استاد

• Data Science
• Big Data
• Subgroup Discovery
• +۳ مورد دیگر

Christopher Joseph Carmona del Jesus serves as a University Professor in the Department of Computer Science at the University of Jaén, Spain, affiliated with the Andalusian Interuniversity Institute for Data Science and Computational Intelligence and leading the Intelligent Systems and Data Mining research group. His work bridges theoretical computational intelligence with practical applications across diverse domains. His academic credentials include: PhD in Computer Science, University of Jaén (2011) Master's in Computer Science, University of Jaén (2007) Dr. Carmona specializes in Data Science, Big Data, and Subgroup Discovery using evolutionary fuzzy systems for descriptive rule extraction. His research particularly emphasizes Supervised Descriptive Rule Discovery and Emerging Patterns in dynamic data streaming environments, developing algorithms that balance interpretability with predictive power for real-world datasets. Recent work extends these methods to healthcare diagnostics and renewable energy forecasting. Analysis of his 15 most recent publications (2022-2025) reveals a strong focus on data streaming algorithms (60% of works), explainable AI systems (30%), and cross-domain applications in healthcare (20%) and cybersecurity (15%). Key technical contributions include evolutionary fuzzy frameworks for multiclustering, federated descriptive rule extraction, and spiking neural networks for energy-efficient computing. No scientific awards are documented in the provided materials. While his research leadership is evident through the Intelligent Systems and Data Mining group, specific details regarding student supervision or grant funding are not included in the available information. His methodological innovations in subgroup discovery and data streaming continue to drive practical implementations through tools like SDRDPy and the Clustering R library. He actively contributes to the Andalusian Interuniversity Institute for Data Science and Computational Intelligence, fostering collaborations that integrate computational intelligence with domain-specific challenges in energy, healthcare, and education.

Thomas Liebigمشاهده پروفایل

استادیار

• Distributed Data Mining
• Reinforcement Learning
• Multi-Agent Learning
• +۱۱ مورد دیگر

Thomas Liebig is an Assistant Professor of Smart City Science at TU Dortmund University's Artificial Intelligence Unit and a Principal Investigator at the Lamarr Institute for Machine Learning and Artificial Intelligence. He also serves as a senior AI architect supporting Materna SE in integrating artificial intelligence into the public sector and industry, and previously held an Adjunct Professor position in Data Privacy and Ethics at the University of Nicosia. His research focuses on distributed data mining, reinforcement learning, multi-agent systems, privacy-preserving learning, and spatio-temporal modeling. Liebig has made significant contributions to graph neural networks, probabilistic modeling with sum-product networks, and traffic flow prediction systems. His work bridges theoretical machine learning with practical applications in smart cities, transportation, and healthcare. Liebig's recent publications demonstrate a strong trend toward privacy-preserving machine learning techniques, particularly differential privacy applied to distributed settings. His work on sum-product networks has advanced interpretable probabilistic modeling, while his research on graph neural networks has explored novel connections with classical algorithms like PageRank. His practical applications span transportation systems, healthcare, and smart city infrastructure. Liebig has supervised numerous graduate students working on topics including blockchain-based data analysis, traffic prediction systems, and privacy-preserving machine learning. His research has been supported by projects including SFB876 (Providing Information by Resource-Constrained Data Analysis) and collaborations with industry partners. He is actively involved in the academic community, having co-organized workshops such as Mining Urban Data at ICML and Computational Transportation Sciences. His work appears in top venues including IEEE International Conference on Knowledge Graph, ECML/PKDD, and IEEE Transactions on Intelligent Transportation Systems.

• Fish Nutrition
• Fish Welfare
• Aquaculture
• +۴ مورد دیگر

Grete Kristine Følsvik Hansen Aas serves as Associate Professor in the Department of Biological Sciences at NTNU's Faculty of Natural Sciences in Ålesund, focusing on sustainable aquaculture systems and fish welfare. Her research integrates nutritional science with technological innovation to address industry challenges in salmon farming and marine resource utilization. Her core competencies span fish nutrition, welfare assessment, marine ingredient development, salmon breeding, and rest raw material valorization. Recent investigations emphasize stress biomarker validation across biological matrices, computer vision applications for growth monitoring, and advanced processing techniques for fish byproducts, demonstrating interdisciplinary approaches spanning physiology, engineering, and food science. Analysis of her 2021-2025 publications reveals dominant trends in precision aquaculture monitoring (sonar/vision systems), stress physiology biomarker validation, and circular economy applications for fish byproducts. Key disciplinary intersections include aquaculture engineering, marine biotechnology, and sustainable food systems, with methodological emphasis on analytical chemistry and computational approaches. Scientific Awards No awards documented in available sources Advising and grants remain undocumented in provided materials, though her extensive collaborative network across Norwegian research institutions suggests significant project leadership. Current work indicates active engagement with industry challenges including sea lice management, welfare assessment protocols, and sustainable valorization of processing byproducts. Laboratory capabilities appear centered on fish physiology assessment and marine bioprocessing, with recent publications indicating infrastructure for stress biomarker analysis, computer vision systems, and enzymatic hydrolysis experiments, though specific facility details are not publicly itemized.

• Robotics
• Artificial Intelligence
• Digital Construction Technologies
• +۹ مورد دیگر

Dr. Vijay M. Pawar is a Lecturer in Digital Construction Technology at The Bartlett School of Sustainable Construction, University College London. His work bridges Robotics, Artificial Intelligence, and Digital Construction Technologies, focusing on sustainable solutions for infrastructure and construction. Role: Lecturer in Digital Construction Technology University: University College London School: The Bartlett School of Sustainable Construction Research spans Robotics , Artificial Intelligence , and Digital Construction Technologies , with applications in sustainable infrastructure , autonomous manufacturing , and nuclear decommissioning . Articles highlight advancements in aerial additive manufacturing , VR teleoperation interfaces , and AI-driven diagnostics . Publications in high-impact journals like Nature and Science Robotics demonstrate international recognition. Teaching includes modules such as: COMP0240: Aerial Robotics: Fundamentals to Practice in Real-World Environments BCPM0022: Managing Smart Infrastructure Technologies BSSC0016: Technology 1: Data and Digital Delivery

• Mathematical Modeling
• Statistics
• Data Analysis
• +۵ مورد دیگر

Yerly Florence is an Associate Professor at the Fribourg School of Engineering and Architecture , affiliated with the ChemTech Institute . Her work bridges mathematical modeling , statistics , and data analysis across diverse domains including plant biology, systems biology, and computational methods. Education : PhD in Systems Biology (2015, University of Fribourg) Her research focuses on quantitative plant growth analysis (e.g., auxin transport modeling in Arabidopsis hypocotyls), machine learning applications (e.g., melting point prediction for ionic liquids), and interdisciplinary projects like Beyond Bézier , which reimagines digital typography through mathematical and robotic approaches. She contributes to both academic and professional publications, emphasizing computational tools and biophysical insights. The Beyond Bézier project (2023–2025), funded by the Réseau de Compétences Design et Arts Visuels , exemplifies her innovative spirit, combining mathematics, robotics, and design. Her publications span topics from gene transfer mechanisms to computational biology, reflecting her interdisciplinary expertise.

Tihomir Kostadinovمشاهده پروفایل

دانشیار

• Oceanography
• Remote Sensing
• Snow Science
• +۸ مورد دیگر

Tihomir S. Kostadinov is an Associate Professor of Geography at California State University San Marcos, affiliated with the College of Humanities, Arts, Behavioral and Social Sciences (CHABSS). His research bridges Oceanography, Remote Sensing, and Environmental Geography, with a focus on satellite algorithm development for marine ecosystem monitoring and snow cover analysis in forested regions. PhD in Marine Science from UC Santa Barbara (2009) MA in Geography from UC Santa Barbara (2005) His expertise lies in marine bio-optics and ocean color remote sensing , where he develops algorithms to retrieve particle size distributions and phytoplankton carbon biomass from satellite data. Additional work includes snow monitoring under forest canopies using lidar and scientific computing for data modeling. Recent publications highlight advancements in ocean color algorithms for the biological carbon pump and snow cover mapping . Collaborative efforts span institutions and disciplines, including Earth system models and hyperspectral sensor applications like NASA's PACE mission. Validation studies emphasize the need for global datasets to refine optical property estimates. Teaching includes foundational courses in Physical Geography, Oceanography, and Earth systems. Current research explores spectral backscattering modeling, particle composition analysis, and climate-ecosystem interactions, with applications to both marine and terrestrial environments.