جست‌وجوی استاد و پژوهشگر

Jérôme Chenalمشاهده پروفایل

پژوهشگر

• Urban and Regional Planning
• Environmental Science
• Public Health
• +۳ مورد دیگر

Jérôme Chenal is a Senior Scientist and Director of the Excellence in Africa Center at the École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Institute of Environmental Engineering . He is also an Affiliate Professor at UM6P (Morocco). His work intersects urban and regional planning , environmental science , public health , and geospatial analysis , with a focus on informal settlements and sustainability in the Global South . Research Interests : Urban resilience, smart cities in Africa, ecosystem services, WASH services, urban health inequities, remote sensing for humanitarian response, and sustainable development in Sub-Saharan Africa. Advising : Supervised PhD students including Rim Mrani, Martí Bosch Padrós, Rémi Jaligot, and Andrea Salmi, with a focus on urban morphology, health accessibility, and digital urbanism. Scientific Networks : Collaborated with institutions in Switzerland, Kenya, Côte d'Ivoire, Cameroon, and Morocco. Published in journals like Water , Science of The Total Environment , and Sustainability , addressing topics such as water distribution, sanitation safety, and urban resilience. Teaching : Leads courses on Urban Planning in the South and contributes to doctoral programs, emphasizing interdisciplinary approaches to urban challenges.

Wenfei Fanمشاهده پروفایل

استاد

• Database Theory
• Data Science
• Information Visualization
• +۹ مورد دیگر

Wenfei Fan holds the Personal Chair in Web Data Management at the School of Informatics, University of Edinburgh . He is affiliated with the Database Group , LFCS (Laboratory for Foundations of Computer Science), Bell Labs , and the International Research Center on Big Data at Beihang University. His research spans database theory, data science, and big data systems. PhD in Computer and Information Science, University of Pennsylvania, USA MS in Computer Science, Peking University, China BS in Computer Science, Peking University, China His research interests focus on database theory and systems , including big data, data quality, data integration, database security, distributed query processing, query languages, social networks, and XML. He has pioneered techniques for graph pattern matching, data dependencies, and scalable algorithms. Recent publications emphasize graph data management , rule-based systems , and ML-data quality integration . Key themes include incremental computation, GPU-accelerated graph cleaning, entity linking, and logic-driven ML. Articles like 'Rock' and 'MiniClean' demonstrate hybrid approaches for real-world data challenges. Fellow of the Royal Academy of Engineering (FREng), 2023 Foreign Member, Chinese Academy of Sciences (ForMemCAS), 2019 Fellow of the Royal Society (FRS), 2018 ERC Advanced Fellowship, 2015 ACM Fellow (FACM), 2012 Roger Needham Award, 2008 He leads the ERC project: Resource-Bounded Graph Query Answering and contributes to systems like GRAPE and GraphScope , which parallelize sequential algorithms and process large graphs. He mentors students and collaborators globally, with a focus on data quality and graph analytics.

• Distributed Computing
• Federated Learning
• Cloud Computing
• +۱۲ مورد دیگر

Salman Zubair Toor is a researcher at Uppsala University, Sweden, specializing in distributed computing, federated learning, and cloud/edge infrastructure optimization. His work spans resource scheduling, data streaming, and secure anomaly detection.

Mohamed Allaliمشاهده پروفایل

دانشیار

• Data Engineering
• Machine Learning
• Climate Data Analysis
• +۳ مورد دیگر

Mohamed Allali is an Associate Professor at Chapman University, affiliated with both the Fowler School of Engineering (Department of Electrical Engineering and Computer Science) and Schmid College of Science and Technology (Department of Mathematics). His research spans data engineering, machine learning, climate informatics, and mathematical education. He has contributed to constraint-based intelligent tutoring systems, data drift detection using KL divergence, and geospatial analysis for environmental monitoring. Education: University of Oklahoma (BS, MA, PhD). His recent work focuses on data distribution divergence, climate modeling for sea turtle habitats, and neural network applications in medical imaging. He has collaborated extensively on drought indices, satellite data validation, and educational technologies. His publications highlight expertise in statistical learning, computational methods, and interdisciplinary environmental applications.

Sourav Medyaمشاهده پروفایل

استادیار

• Machine Learning
• Graph Mining
• Data Science
• +۳ مورد دیگر

Sourav Medya is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). Prior to joining UIC, he was a research assistant professor at the Kellogg School of Management and the Northwestern Institute on Complex Systems (NICO) at Northwestern University. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2019) and a Master of Engineering in Computer Science and Automation from the Indian Institute of Science, Bangalore, India. Ph.D., University of California, Santa Barbara, 2019 M.Eng., Indian Institute of Science, Bangalore, India His research lies at the intersection of graphs, machine learning, and data science, with a focus on bias, fairness, and interpretability in AI. He also develops machine learning techniques with applications in healthcare, infrastructure, and computational social science. His recent publications address topics such as global counterfactual explainers for GNNs, game-theoretic node selection, and interpretable logical rule extraction from graph models. Medya has advised students in areas like multimodal learning, graph neural networks, explainable AI, and efficient AI. His scientific awards include the UIC Outstanding MS Thesis Award (2024) and Best Paper Award at the Machine Learning on Graphs (MLoG) Workshop at WSDM'23. He has reviewed for top conferences including NeurIPS, ICML, KDD, and AAAI, and recently taught courses on Machine Learning on Graphs (CS 519) and Introduction to Data Science (CS 418) at UIC.

Zhe Chenمشاهده پروفایل

استاد

• Wind Turbine Engineering
• Power Engineering
• Control Strategy
• +۷ مورد دیگر

Professor Zhe Chen is a distinguished academic at Aalborg University's Faculty of Engineering and Science, where he leads research in Electric Power Systems and Microgrids within the Intelligent Energy Systems and Flexible Markets department. With an extensive publication record spanning over two decades and more than 1,200 publications, he has established himself as a leading expert in power engineering and renewable energy systems. Professor Chen's research focuses on Wind Turbine Engineering, Power Engineering, Control Strategy, Wind Power Engineering, Energy Engineering, and Reinforcement Learning. His work bridges theoretical advancements with practical applications in smart grid technology and microgrid systems. His research interests center around developing innovative solutions for renewable energy integration, power system stability, and efficient energy management. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional power engineering, particularly in applying deep reinforcement learning to power system control, state estimation with noisy data, and optimization of power electronic converters. His work shows increasing focus on carbon emissions optimization, thermal management in power electronics, and the application of advanced neural network architectures to energy systems. MPCE 2023 Best Paper Award for research on reinforcement learning applications in electric vehicles MPCE 2022 Best Paper Award for distribution network optimization MPCE 2021 Best Paper Award for reinforcement learning in energy systems WATAB Best Paper Award 2005 for offshore wind farm research IEEE Fellow recognition for contributions to power electronics and renewable energy systems Professor Chen has supervised 27 PhD students throughout his career, demonstrating his commitment to academic mentorship. His current research is supported by significant grants including the Erasmus+ funded S3SF project (Smart Energy Solutions for a Sustainable Future) and multiple projects focused on machine learning-based stability analysis for multi-energy systems. His collaborative work spans numerous international partnerships, with recent projects involving researchers from China, Europe, and other global institutions. Professor Chen leads a research team focused on intelligent energy systems, working on advanced control strategies for microgrids, power quality analysis, and the integration of renewable energy sources into existing power infrastructure. His team is particularly known for innovative approaches to wind power integration and DC microgrid technologies.

Balázs Vargaمشاهده پروفایل

پژوهشگر ارشد

• Road Traffic Modeling and Control
• AI-based Traffic Flow Prediction
• Urban Traffic Simulation
• +۳ مورد دیگر

Dr. Balázs Varga is a Research Fellow at the Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics (BME). He holds a PhD in Transportation and Vehicle Sciences (2021) and an MSc in Vehicle Engineering (2015) from BME. His industry experience includes roles at AVL Hungary as a Software and Function Developer (2016–2018) and academic positions at Chalmers University of Technology (2015) and SZTAKI (2012–2014). Current Role: Research Fellow (2021–present) Teaching: Programming, Control Theory, Traffic Modeling (English language course) Research Interests: Varga specializes in road traffic modeling and control, focusing on AI-based traffic estimation and dynamic traffic management. His work integrates machine learning with mesoscopic and microscopic traffic simulation tools like SUMO to optimize urban mobility and reduce emissions. Projects: He leads the 2020–2024 national development project 'Dynamic, adaptive traffic control services and evaluation tools based on digitally connected data sources' (2019-1.1.1-PIACI KFI). This initiative leverages connected data sources for real-time traffic control and policy evaluation. Key Publications Trends: His recent articles explore topics such as graph neural networks for sensor placement, multiobjective control of emissions, and mixed-reality V2X testing. These works emphasize data-driven approaches, emission reduction, and simulation frameworks for autonomous vehicles.

Stefan Brucknerمشاهده پروفایل

استاد

• Visual Analytics
• Information Visualization
• Scientific Visualization
• +۳ مورد دیگر

Stefan Bruckner is a Professor at the University of Rostock , leading the Chair of Visual Analytics within the Institute of Visual and Analytic Computing. His work bridges Visual Analytics , Biomedical Visualization , and Interactive Systems , with a focus on translating complex datasets into actionable insights. Role: Chair of Visual Analytics Key Affiliations: Eurographics Executive Committee, IEEE VGTC Editorial Leadership: Associate Editor, IEEE Transactions on Visualization and Computer Graphics Research spans Medical Visualization , Immersive Analytics , and Proteogenomic Data Exploration . Recent work includes: ProHap Explorer for haplotype analysis Line Harp sonification techniques Narrative visualization frameworks His publications reveal trends in interactive data exploration , multi-omics visualization , and user behavior analysis within medical contexts. Awards include contributions to the Dirk Bartz Prize in 2019. He actively collaborates with international institutions and maintains memberships in ACM, IEEE, and GI.

• Proteomics
• Metabolomics
• Plant-Microbe Interactions
• +۳ مورد دیگر

Prof. Dr. Karsten Niehaus serves as Head of the Proteome and Metabolome Research Group at the Center for Biotechnology (CeBiTec) and Faculty of Biology, University of Bielefeld. His research focuses on proteomics and metabolomics applications in plant-microbe interactions, bacterial stress responses, and disease model systems. His laboratory employs advanced mass spectrometry imaging and cell phenotyping technologies to investigate molecular responses in crops like sugar beet and grapevines under abiotic stress conditions, as well as in cancer models where differentiation therapy impacts tumor malignancy. The group also explores microbial biotechnology through Xanthomonas campestris studies on xanthan production and stress adaptation. Selected publications highlight innovations in 3D microfluidics for biomarker detection and bioinformatics platforms like MetHoS for metabolomics data analysis. His work appears in journals covering Frontiers in Plant Science , Scientific Reports , and Journal of Experimental Botany . Contact: kniehaus@cebitec.uni-bielefeld.de | Office: UHG W7-117

Onet-Marian Zsuzsannaمشاهده پروفایل

مدرس

• Software Engineering
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Dr. Onet-Marian Zsuzsanna is a Lecturer in the Department of Computer Science at Babes-Bolyai University in Cluj-Napoca, Romania. Her research focuses on applying machine learning techniques to software engineering challenges, particularly software defect prediction and restructuring. Academic Rank: Lecturer Affiliation: Babes-Bolyai University Department: Computer Science Email: zsuzsanna.onet@ubbcluj.ro Research Interests : Dr. Marian specializes in developing machine learning models (clustering, association rules, reinforcement learning) for software defect detection, package-level restructuring, and test order optimization. She also explores applications of Formal Concept Analysis in text summarization and music pattern discovery. Recent Publications highlight her work in source-code embeddings, unsupervised learning for software analysis, and comparative studies of online/traditional learning environments. Her methods often integrate domain-specific metrics and AI-driven optimization. Contact : zsuzsanna.onet@ubbcluj.ro | Office: Teodor Mihaly street, Room 440

Roy E. Welschمشاهده پروفایل

استاد

• Statistics
• Data Science
• Statistical Learning
• +۱۰ مورد دیگر

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.

• Digital Forensics
• Cybersecurity
• Incident Response
• +۵ مورد دیگر

Professor Vasilis Katos is a Professor in Cyber Security at Bournemouth University, specializing in digital forensics, incident response, and intellectual property security. With over 160 publications and extensive industry experience as an Information Security Consultant, he serves as an expert witness in criminal courts in both the UK and Greece. His research has significant impact in digital forensics for intellectual property infringement investigations through collaborations with the EU Intellectual Property Office (EUIPO) and UNICRI. Professor Katos holds a Diploma in Electrical Engineering from Democritus University of Thrace, an MBA from Keele University, and a PhD in Computer Science (network security and cryptography) from Aston University. He is a certified Computer Hacking Forensic Investigator (CHFI) with extensive practical experience in cybersecurity. His research primarily focuses on digital forensics and incident response, with recent emphasis on intellectual property infringement investigations, IoT security, blockchain applications, and traffic prediction systems. His work bridges academic research with practical security applications, particularly in intellectual property protection and smart city security frameworks. Professor Katos has coordinated significant research projects including Illegal IPTV in the European Union and IP Infringement on online trading platforms, funded by the EUIPO Observatory. His extensive publication record shows strong trends in digital forensics, cybersecurity for intellectual property protection, IoT security, and increasingly in smart city security frameworks. The research demonstrates a progression from foundational cybersecurity work to specialized applications in intellectual property protection and circular economy security models, with recent incorporation of AI and machine learning techniques for threat intelligence and traffic prediction. Certified Computer Hacking Forensic Investigator (CHFI) Editorial Board Member of Computers & Security Journal Professor Katos has successfully supervised multiple PhD students including Amalia Damianou (Digital Forensics in Smart, Circular Cities), Christos Iliou (Machine Learning Based Detection and Evasion Techniques for Advanced Web Bots), and Mohammed Al Qurashi (Intrusion Detection for IoT). He has secured significant research funding including the ECHO project (European network of Cybersecurity centres), IDEAL-CITIES, and multiple EUIPO-funded initiatives focusing on intellectual property protection. His work connects with the Centre for Intellectual Property Policy & Management (CIPPM) at Bournemouth University, where he contributes to research on intellectual property in emerging technologies. Professor Katos maintains active collaborations with international organizations including UNICRI and the EU Intellectual Property Office, focusing on practical applications of digital forensics in intellectual property protection.

Shen-Shyang Hoمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۷ مورد دیگر

Shen-Shyang Ho is a Full Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His work spans machine learning, data mining, and edge computing with applications in urban mobility, precision agriculture, and data privacy. He leads NSF-funded research projects on dynamic graph analysis and spatiotemporal anomaly detection. Ph.D. in Computer Science, George Mason University Post-Doctoral Associate, Caltech & NASA JPL B.S. in Mathematics with Computational Science, National University of Singapore Research expertise includes graph-based machine learning, conformal prediction, cooperative inference, and privacy-preserving ML. Current work focuses on federated learning for edge devices and anomaly detection in evolving systems. He has developed tools like SplitTracer for cooperative inference evaluation and ParkGauge for urban mobility monitoring. Recent publications highlight his contributions to 2026 Pattern Recognition journal (martingale-based graph analysis), 2025 IEEE ICAIC conference (blockchain gas optimization), and 2024 ACM SAC symposium (shared mobility systems). His work integrates machine learning with real-world constraints across energy grids, transportation, and agricultural technology. NSF Grant (2022) for Dynamic Graph Anomaly Detection NSF Grant (2018) for Spatiotemporal Analysis Google Scholar Classic Paper Recognition (2017) for 2006 Radar Micro-Doppler Study Professional memberships include the Association for Computing Machinery (ACM). His teaching portfolio ranges from introductory programming to advanced ML courses. He previously held positions at Nanyang Technological University before joining Rowan in 2016.

Ke (Cory) Wangمشاهده پروفایل

استادیار

• Computer Architecture
• Interconnection Networks
• Machine Learning
• +۸ مورد دیگر

Dr. Ke (Cory) Wang serves as Assistant Professor at the University of North Carolina at Charlotte in the Department of Electrical and Computer Engineering, where he conducts research in computer architecture and parallel systems. Ph.D. in Computer Engineering (2022), George Washington University M.Sc. in Electrical Engineering (2015), Worcester Polytechnic Institute B.Sc. in Computer Science (2013), Peking University His research focuses on machine learning-enabled computer architecture with emphasis on network-on-chip design , domain-specific accelerators , and cross-layer optimization for manycore systems. Specific areas include anomaly detection, graph neural network acceleration, and fault-tolerant communication frameworks. Recent publications demonstrate increasing integration of deep learning techniques with traditional architecture design, particularly for graph convolutional networks and heterogeneous systems . His work addresses multi-objective optimization across performance , energy efficiency , and security dimensions . Key scientific recognitions include: National Science Foundation award (CSR: Small: Cross-layer Design, 2023) National Science Foundation award (CRII: SHF: Flexible Design Framework, 2023) Dr. Wang leads the Intelligent Computer Architecture & Systems Laboratory , which develops advanced frameworks for network-on-chip optimization and AI-driven system design.

Rainer Schnellمشاهده پروفایل

استاد

• Survey Methodology
• Privacy-Preserving Record Linkage
• Health Data Analysis
• +۲ مورد دیگر

Prof. Dr. Rainer Schnell is a Professor at the Institute of Sociology, University of Duisburg-Essen. His research focuses on advanced survey methodologies, privacy-preserving data linkage techniques, and statistical analysis of health and social data. He leads the Chair of Empirical Social Research and contributes to interdisciplinary studies combining sociology, computer science, and public health. Research interests span: Survey Methodology : Innovations in web surveys, non-response analysis, and data quality assurance Data Privacy : Cryptographic techniques for secure record linkage and vulnerability assessments Health Analytics : Vaccination behavior studies, health data governance, and pandemic-related research Recent publications demonstrate a strong emphasis on: Privacy-enhancing technologies for sensitive data integration Methodological critiques of survey and data linkage practices COVID-19-related behavioral research using large-scale population data