جست‌وجوی استاد و پژوهشگر

• Auditory Perception
• Cognitive Neuroscience
• Music Psychology
• +۷ مورد دیگر

Joel S. Snyder, Ph.D., is a Professor in the Department of Psychology at the University of Nevada, Las Vegas (UNLV), where he also directs the Auditory Cognitive Neuroscience Laboratory (ACNL). His research integrates cognitive psychology and neuroscience to explore how humans perceive and remember complex auditory environments, with a special focus on musical rhythm, groove, and emotional responses to sound. Education: While specific degrees are not listed in the provided text, Dr. Snyder holds a Ph.D. and is a tenured Professor at UNLV, indicating extensive academic training in psychology and neuroscience. Research Interests: Auditory Scene Perception: How listeners parse and remember real-world auditory environments. Musical Rhythm and Groove: Neural and cognitive mechanisms underlying beat perception, rhythm production, and emotional responses like groove and chills. Memory for Natural Sounds: Long-term memory for auditory and visual stimuli in naturalistic contexts. Misophonia and Musicality: The relationship between sound sensitivity disorders (e.g., misophonia), musical training, and brain function. Cognitive Neuroscience Methods: Use of EEG, fMRI, and brain stimulation to study auditory cognition. Publication Themes: Dr. Snyder’s recent work spans consciousness theory critique, replication studies in EEG and rhythm perception, auditory scene analysis in natural environments, and the psychological impact of sound in both neurotypical and clinical populations (e.g., autism, misophonia). His collaborative output includes theoretical reviews and empirical studies in high-impact journals like Nature Neuroscience , Nature Reviews Psychology , and Philosophical Transactions B . Scientific Contributions & Recognition: Dr. Snyder has co-authored open letters critiquing prominent theories of consciousness, participated in large-scale replication efforts (e.g., #EEGManyLabs), and contributed to public science communication through interviews with The New York Times and podcasts. His lab’s work has been featured in international conferences (e.g., Neurosciences and Music, ICMPC, Timing Research Forum). Teaching & Mentorship: He teaches undergraduate and graduate courses in Perception and Cognitive Neuroscience. His lab has mentored students including Maggie McMullin, Solena Mednicoff, Dan Berkowitz, and Karli Nave, some of whom have gone on to medical school or presented at international venues. Labs & Collaborations: The Auditory Cognitive Neuroscience Laboratory (ACNL), founded in 2007, collaborates with scholars worldwide on projects exploring auditory cognition, rhythm, and consciousness. The lab uses state-of-the-art EEG, behavioral, and neuroimaging techniques to study auditory perception in both natural and controlled settings.

• Machine Learning
• Constrained Clustering
• Knowledge Representation
• +۳ مورد دیگر

Christel VRAIN is a full-time University Professor affiliated with the University of Orleans, specializing in Machine Learning and Constraint Programming. Her research focuses on constrained clustering, knowledge integration, and hybrid AI systems, with applications in image classification, time series analysis, and geospatial data. She collaborates extensively with researchers like Thi-Bich-Hanh DIEP-DAO and Samir LOUDNI. University Professor at University of Orleans Affiliated with Laboratoire d'Informatique Fondamentale d'Orléans (LIFO) Her work bridges declarative programming with machine learning, emphasizing explainability and optimization. Recent publications explore continual learning, graph models, and constraint-based clustering frameworks. She contributes to interdisciplinary research through the Kay R. Amel group, investigating synergies between reasoning, knowledge representation, and data mining. Her methodological innovations include memory-efficient algorithms for large-scale datasets and shapelet transforms for time series.

Corey Clarkمشاهده پروفایل

استادیار

• Artificial Intelligence
• Game Development
• Human Computation
• +۴ مورد دیگر

Corey Clark, Ph.D., serves as Deputy Director of Research and Assistant Professor in the Department of Computer Science and Engineering at Southern Methodist University's Lyle School of Engineering. He leads the Human and Machine Intelligence (HuMIn) Game Lab, pioneering research at the intersection of gaming, artificial intelligence, and human computation to solve large-scale problems in healthcare, education, and national security. Dr. Clark holds a Ph.D., M.S., and B.S. in Electrical Engineering from The University of Texas at Arlington, where he graduated Magna Cum Laude for his undergraduate degree. His doctoral research focused on nanoscale modeling and simulation techniques for Molecular Beam Epitaxy and Chemical Vapor Deposition of exotic materials. His research spans Artificial Intelligence, Game Development, Human Computation, Distributed Computing, Machine Learning, Computational Biology, and Educational Technology. Clark is renowned for transforming commercial video games into distributed computing platforms through human computation, enabling breakthroughs in medical diagnostics and educational technology. His technical innovations in HTML5/JavaScript multithreading have been featured at major international game conferences. Analysis of Clark's 15 most recent publications (2022-2024) reveals a dominant focus on generative AI applications integrated with gaming mechanics, particularly for knowledge graph enhancement, medical diagnostics, and computational thinking education. Key trends include explainable AI systems, human-AI collaboration through gameplay, blockchain applications, and privacy-preserving machine learning, demonstrating consistent innovation in applying game-based approaches to real-world problems. Dr. Clark has secured over $1.7 million in competitive research funding, including: Human Computation for Ocular Tomography Analysis ($62,924) with Retina Foundation Cryptocurrency Transactional Analysis via Gaming ($60,000) with Raytheon Game-Based Adult Literacy (XPrize) ($480,000) Flexible Electronics for Airborne Laser ($820,000) with Missile Defense Agency Networked C4ISR Chip ($850,000) with US Army As CTO for Dallas-based game technology companies, Clark has helped raise over $12 million in startup funding. His HuMIn Game Lab currently develops immersive gameplay systems for cancer treatment research using crowdsourced computation and machine learning, with recent projects including therapeutic discovery platforms and blockchain-based task completion systems.

• Cybersecurity
• Social Media Analytics
• Software Quality
• +۳ مورد دیگر

Dr. Manish Agrawal is a Professor in the School of Information Systems and Management at the University of South Florida (USF) , and currently serves as the Academic Director for Education at Cyber Florida . He has previously held the role of Chair of the Department of Information Systems and Decision Sciences (ISDS) at USF. His expertise lies in cybersecurity, social media analytics, software quality, offshoring, and extreme event response . Education: PhD in Information Systems – SUNY Buffalo Undergraduate studies – Indian Institute of Technology, Kanpur Research Interests: Dr. Agrawal’s research is deeply interdisciplinary. He investigates how cybersecurity systems can be improved through social media analytics , particularly in the context of crisis response and extreme events . His work also explores software quality assurance in outsourced and offshored IT projects, and how decision fusion can be used to improve information retrieval and knowledge discovery in high-stress environments. His recent publications reflect a consistent focus on information systems in crisis , social media behavior during disasters , and outsourcing economics . These works are published in top-tier journals such as MIS Quarterly , Management Science , and IEEE Transactions on Software Engineering . Grants and Funding: NSF Grant #IIS1651475 (2016–2018): Co-PI, $199,999 – “EAGER: An Investigation of the Propagation of Error-Resistant and Error-prone Messages over Large-Scale Information Networks” NSF Grant #1043919 (2014–2018): Co-PI, $299,468 – “Developing Open Authentic Case Studies for an MS in Cybersecurity Capstone Course” Service and Leadership: Department Chair, ISDS, USF (2016–Present) Conference Co-Chair, IFIP Dewald Roode Workshop (2017) Member, USF General Education Council (2011–2013) USF Faculty Senate Representative (2009–2012) Faculty Advisory Committee, Center for India Studies, USF (2010–Present) Teaching: Dr. Agrawal teaches courses in information security , business data communications , and web application development at both undergraduate and graduate levels. His teaching philosophy emphasizes real-world application and case-based learning .

Michał Chromiakمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Computer Vision
• +۴ مورد دیگر

Dr. Michał Chromiak is an Assistant Professor at the Department of Cybersecurity and Computational Linguistics , part of the School of Mathematics, Physics and Computer Science at Maria Curie-Skłodowska University . With a PhD in Computer Science from the Polish Academy of Sciences and dual MSc degrees in Mathematics and Computer Science, he focuses on understanding the chaos in data through research in Machine Learning, Deep Learning, and Software Engineering. PhD in Computer Science (Polish Academy of Sciences) MSc in Mathematics and Computer Science His scientific activity spans cross-domain feature transfer in computer vision (e.g., DINOv2, MAE), reinforcement learning (e.g., Decision Transformer), and neural architecture analysis (MLP-Mixer, Transformers). He actively shares insights through his blog, covering topics like Attention Mechanisms , Self-Supervised Learning , and Sequence Modeling . Dr. Chromiak serves on the Program Committee for ECAI 2024 and maintains a strong online presence via ORCID , GitHub , and LinkedIn . Contact for teaching matters: chromiakm@office.umcs.pl . Consultations available via MS Teams on Thursdays 15-17:15 and Fridays after 15:00.

Alexander Eckerمشاهده پروفایل

استاد

• Machine Learning
• Computational Neuroscience
• Visual Perception
• +۳ مورد دیگر

Prof. Dr. Alexander Ecker is Professor of Data Science at the Institute of Computer Science, University of Göttingen, and concurrently holds the prestigious Max Planck Fellow position at the Max Planck Institute for Dynamics and Self-Organization. Since 2020 he also serves on the Executive Board of the Campus Institute Data Science in Göttingen. He leads the Neural Data Science research group, comprising 14 PhD students and 2 postdoctoral researchers, focusing on the interface of machine learning and computational neuroscience. His educational background includes a Dr. rer. nat. in Neuroscience (2014) from the Graduate School of Neural and Behavioral Sciences/IMPRS, University of Tübingen, followed by post-doctoral and group-leader positions at the University of Tübingen and the Max Planck Institute for Biological Cybernetics. Research Interests Machine Learning & Deep Learning: developing novel algorithms for representation learning and generative modeling. Computational Neuroscience: large-scale data-driven modeling of visual cortical circuits. Visual Perception: bridging biological vision and computer vision via biologically inspired architectures. His work has produced a steady stream of influential publications (2019-2025) in leading journals such as Nature Communications , Nature , Nature Methods , PLOS Computational Biology , ICLR , NeurIPS , and CVPR . The publications trend toward integrating high-resolution neural recordings with state-of-the-art machine-learning models to uncover principles of sensory processing, neuron-type classification, and behavior. Scientific Awards & Honors Max Planck Fellow, Max Planck Institute for Dynamics and Self-Organization (ongoing) Executive Board Member, Campus Institute Data Science, Göttingen (since 2020) Teaching, Advising & Grants Regularly teaches advanced courses: “Deep Learning for Image Synthesis”, “Current Topics in Deep Learning”, and “Graph Machine Learning”. Supervises 14 current PhD students and 2 postdocs within the Neural Data Science Group. Offers numerous Bachelor’s and Master’s thesis projects, with topics ranging from neuronal morphology clustering to primate vocalization analysis. Leads or co-leads large collaborative consortia with labs in Göttingen, Tübingen, Baylor College of Medicine, and other institutions across the US and Germany. Labs & Teams The Neural Data Science Group operates at the Institute of Computer Science, University of Göttingen, and is tightly integrated with the Max Planck Institute for Dynamics and Self-Organization. The group maintains active collaborations with over a dozen partner laboratories, including groups led by Fabian Sinz, Andreas Tolias, Thomas Euler, Tim Gollisch, and Viola Priesemann, fostering an interdisciplinary environment that spans computer science, physics, biology, and psychology.

Indika Kahandaمشاهده پروفایل

استادیار

• Bioinformatics
• Biomedical Informatics
• Machine Learning
• +۳ مورد دیگر

Dr. Indika Kahanda is an Assistant Professor in the School of Computing at the University of North Florida , where he leads the BioMedInfo Lab focusing on bioinformatics and biomedical informatics. Previously, he held the same position at the Gianforte School of Computing (Montana State University) from 2018 to 2023. Education: PhD in Computer Science (2016), Colorado State University MS in Electrical and Computer Engineering (2010), Purdue University BS in Computer Engineering (2007), University of Peradeniya, Sri Lanka His research applies machine learning and natural language processing to large-scale biomedical data, with key focuses on computational methods for functional genomics and automated text mining tools for biomedical literature. Current projects include Pangenomics , student misconception detection , and inconsistency detection in medical literature . Recent publications highlight his work in LLM evaluation for healthcare applications, protein function prediction , and medical transcription error analysis . He contributes to biomedical datasets like miRNAFinder and MLHCBugs, with technical expertise in deep learning, ensemble methods, and metamorphic testing.

Michael Ryooمشاهده پروفایل

دانشیار

• Deep Learning
• Computer Vision
• Robotics
• +۳ مورد دیگر

Michael Ryoo serves as a SUNY Empire Innovation Associate Professor in the Department of Computer Science at Stony Brook University while concurrently working as a Research Scientist with Google Brain's "Robotics at Google" team. Previously, he held positions as an Assistant Professor at Indiana University Bloomington and a Staff Researcher at NASA's Jet Propulsion Laboratory. His academic background includes a Ph.D. from the University of Texas at Austin (2008) and a B.S. from Korea Advanced Institute of Science and Technology (KAIST) in 2004. Ryoo's research centers on deep learning and computer vision with specific focus on convolutional neural network (CNN) models for video semantic understanding. His work bridges visual perception and robotic action through applications in robot perception, robot learning, and human-robot interaction. Key innovations involve developing efficient architectures for processing multimodal data and translating visual understanding into robotic control systems. Analysis of his 15 most recent publications reveals strong emphasis on multimodal AI integration, particularly vision-language models applied to robotics. His 2025 work shows significant advancement in token-efficient video representation, motion-controllable diffusion models, and zero-shot learning frameworks specifically designed for robotic control systems. The research trajectory demonstrates consistent focus on making video understanding more accessible and applicable to real-world robotic scenarios. Scientific awards: No awards were mentioned in the provided source material. Regarding academic advising, the source text does not list any students or mentoring activities. Similarly, no grant funding information is provided, though his dual academic-industry role suggests substantial research support. His Google Brain affiliation likely involves industry-sponsored research initiatives. Ryoo maintains active laboratory affiliations through Stony Brook's AI Innovation Institute and Google's Robotics team. His prior work at NASA JPL indicates experience with space robotics systems, while his current Google role focuses on large-scale robot learning infrastructure. The LAM SIMULATOR project represents his current focus on advancing data generation techniques for training large action models.

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۱۰ مورد دیگر

Hoda Eldardiry is an Associate Professor in the Department of Computer Science at Virginia Tech, where she directs the Machine Learning Laboratory. Her research focuses on artificial intelligence and machine learning, particularly in building human-machine collaborative AI systems that can learn context-aware and explainable models from multisource and interconnected data. Prior to joining Virginia Tech, she led research at Palo Alto Research Center (Xerox PARC) in the machine learning research group. Dr. Eldardiry received her educational qualifications from: BE in Computer and Systems Engineering from Alexandria University, Egypt MS and PhD in Computer Science from Purdue University Her research interests span multiple domains of AI and machine learning. She specializes in robust machine learning for information extraction, forecasting, and control. Her work integrates graph neural networks, time-series analysis, and relation extraction to develop explainable and context-aware AI systems. She also investigates the intersection of AI with ethics, policy, and governance, exploring how to build responsible AI systems that align with human values and societal needs. Dr. Eldardiry's recent publications demonstrate a strong focus on advancing graph-based time-series modeling, zero-shot learning techniques, and optimal control systems. Her work bridges theoretical advancements with practical applications in healthcare, transportation, and e-commerce. She has made significant contributions to knowledge graph construction, explainable AI, and federated learning frameworks that operate efficiently in resource-constrained environments. Her scientific achievements have been recognized with several prestigious awards: Purdue University College of Science Early Career Scientist Award for the Department of Computer Science (2021) Honorable Mention Best Paper Award for Exploring Approaches to Artificial Intelligence Governance: From Ethics to Policy (IEEE Ethics 2023) Most Cited Paper Award for COVID-19 Pandemic Impacts on Traffic System Delay, Fuel Consumption and Emissions (2023) Purdue CS Women's History Month Celebration Recognition (2022) VT CS Women's History Month Celebration Recognition (2023) Early Career Distinguished Scientist Award from Purdue University College of Science (2021) Purdue University College of Science Distinguished Alumni (2021) Dr. Eldardiry has successfully secured substantial research funding, with total grant funding of $27,424,460 ($13,808,328 share) from diverse sources including VT, IARPA, DOE, NSF, DARPA, NIH-iTHRIV, CCI, EBAY, SIEMENS, ADOBE, P&G and XEROX. Her current projects include NSF-funded research on Advancing Health Equity using Interactive Condition Assessment and Monitoring and Exploring How AI Engineers Perceive and Develop Translational Ethical Competency, as well as industry collaborations with EBAY on Heterogeneous Hypergraph Modeling for Zero-Shot Product Aspect Identification. As director of the Machine Learning Laboratory at Virginia Tech, Dr. Eldardiry leads a research team that bridges theoretical AI advancements with real-world applications. Her lab collaborates extensively with industry partners and government agencies to develop practical AI solutions while maintaining a strong commitment to ethical considerations and societal impact.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +۲ مورد دیگر

Daisuke Kawahara is a Professor at Waseda University's Faculty of Science and Engineering and a Visiting Professor at the National Institute of Informatics. He holds a PhD in Informatics from Kyoto University (2005) and has previously served as Associate Professor at Kyoto University and Senior Researcher at NICT. His research spans natural language processing, computational linguistics, and AI infrastructure. Education: Ph.D. in Informatics, Kyoto University (2005) Graduate Studies in Intelligent Informatics, Kyoto University (1999–2002) M.Eng. in Electronic & Communication Engineering, Kyoto University (1997–1999) B.Eng. in Electrical Engineering, Kyoto University (1993–1997) Research Focus: Kawahara specializes in NLP, including syntactic parsing, semantic role labeling, language resource development (e.g., JGLUE benchmark), and multilingual corpus construction. His work integrates machine learning with linguistic theory to improve text understanding systems, error correction tools, and dialogue agents. Publication Trends: His recent articles emphasize Japanese and Chinese NLP, neural network-based parsing, and practical applications like educational tools and pandemic information systems. Common themes include benchmarking, corpus annotation, and cross-lingual adaptation. Awards: 情報処理学会 自然言語処理研究会 優秀研究賞 (2025) 言語処理学会最優秀論文賞 (2024, 2023) 科学技術分野の文部科学大臣表彰 (2017) Multiple Best Paper Awards from NLP conferences (2000–2025) Projects & Advising: He leads JSPS-funded projects like Building General Language Understanding Infrastructure (2021–2025) and Acquisition of Knowledge Frames (2018–2021). No student advisees are listed. Labs & Teams: Collaborates with RIKEN Center for Advanced Intelligence Project and maintains ties to Kyoto University's NLP lab. Focuses on large-scale language modeling and collaborative AI-human intelligence frameworks.

• Information Communication Systems
• Multimedia Information Processing
• Content Distribution Systems
• +۴ مورد دیگر

Wataru Kameyama is a Professor in the Department of Communications and Computer Engineering at Waseda University’s School of Fundamental Science and Engineering. Since 2014 he has held a full-time faculty position at Waseda; he previously served as Professor at GITS (2002–2014) and Associate Professor at GITI (1999–2002), both within Waseda University. He received his M.E. and Ph.D. in Electronics Engineering from Waseda University in 1987 and 1990 respectively. Education: Ph.D. in Engineering, Waseda University, 1990 M.E. in Electronics Engineering, Waseda University, 1987 B.E. in Electronics Communication Engineering, Waseda University, 1985 Research Interests: Prof. Kameyama’s research spans information communication systems, multimedia information processing, content distribution architectures, Named Data Networking, digital rights management, and high-dimensional data mining. His recent work emphasizes proactive content-caching for mobile video, producer mobility in NDN, and outlier-detection algorithms for large-scale datasets. Publications Trend: Across 85 peer-reviewed papers (h-index 9) his recent articles focus on next-generation networking (NDN, ICN), mobile multimedia delivery using transportation infrastructure, and data-mining techniques for high-dimensional outliers, often validated through large-scale field experiments. Awards & Honors: International Cooperation Award, ITU Association of Japan (2012) IEEE/IEICE Distinguished & Outstanding Contribution Awards (2009, 2007) Best Author/Best Paper Awards, Institute of Image Information and Television Engineers (2009, 2006) Professional Memberships: ACM, IEEE, Institute of Image Electronics Engineers of Japan, Information Processing Society of Japan, Institute of Electronics, Information and Communication Engineers, Institute of Image Information and Television Engineers.

• Building Information Modeling (BIM)
• Digital Twin Technology
• Road Infrastructure Management
• +۴ مورد دیگر

Ina Heise is a researcher at the Chair of Computing in Civil and Building Engineering , Technical University of Munich (TUM) . Her work focuses on leveraging Building Information Modeling (BIM) and digital twin technologies to enhance road infrastructure management through semantic modeling, knowledge graphs, and spatial data analysis. Research Interests: Her research spans BIM-based digital twins , ontology development for spatial relationships, and graph-based methods for infrastructure data. She integrates semantic modeling and knowledge graphs to enable comprehensive querying and management of road and civil structure data. Publications: Her recent publications (2023-2025) emphasize the development of digital twins for road infrastructure, focusing on spatial-temporal correlation , knowledge graph integration , and ontology-driven data representation . Supervised Theses: She has supervised several student theses, including topics like data transformation from ASB-ING to IFC4x3, graph-based analysis of BIM spatial relationships, and model-based compliance checks for bridge designs. Contact: ina.heise@tum.de | Room: 0501.03.161, Arcisstr. 21, 80333 München.

Adrien Doerigمشاهده پروفایل

استاد مهمان

• Computational Neuroscience
• Cognitive Neuroscience
• Machine Learning
• +۵ مورد دیگر

Adrien Doerig is a Visiting Professor in the Department of Education and Psychology at Freie Universität Berlin, where he leads research in the Cognitive Computational Neuroscience Lab. He is also affiliated with the Bernstein Center for Computational Neuroscience. His work bridges cognitive science, computational modeling, and neural mechanisms of perception. Doerig's research focuses on the intersection of computational neuroscience and artificial intelligence, with particular emphasis on visual perception, consciousness, and the development of biologically plausible neural network models. His work explores how the brain processes visual information, with special attention to phenomena like crowding, feature integration, and the temporal dynamics of perception. He has made significant contributions to understanding the limitations of convolutional neural networks for modeling human vision and has pioneered work on topographic neural networks that better capture cortical organization. A key trend in Doerig's recent publications is the exploration of connections between language models and visual processing in the brain. His groundbreaking 2025 Nature Machine Intelligence paper demonstrated that large language model representations align closely with visual representations in the human brain, opening new avenues for using language-based AI in modeling biological visual processing. His work consistently challenges existing paradigms while developing more biologically plausible computational frameworks. Doerig teaches a wide range of courses including Master's in Cognitive Neuroscience, Probability and statistical modeling (both theoretical and practical with focus on ANNs and neuroimaging), Introduction to programming, Applied computational cognitive neuroscience, and specialized courses on vision, language, affective & social neuroscience, and consciousness. He also supervises graduate students through the Cognitive Computational Neuroscience Lab, which includes postdocs, predocs, master's students, and interns working on cutting-edge research questions at the intersection of cognitive science and machine learning.

• Distributed Systems and Algorithms
• Decentralized search algorithms
• P2P systems and applications
• +۱۴ مورد دیگر

Angelo Furno is an Associate Professor at ENTPE (National School of Public Works) within Université Gustave Eiffel in Lyon, France. He serves as a researcher at the LICIT laboratory and collaborates with INRIA AGORA and the CITI-Lab of INSA-Lyon. His academic journey began at the University of Sannio in Italy where he earned his degree in Computer Engineering in 2010, followed by a PhD in Information Engineering completed in 2014 with a thesis titled "Scalable Service Composition in Autonomic Computing." After his PhD, he conducted post-doctoral research at both the University of Sannio and INRIA in Lyon. Furno's research spans multiple domains with a strong focus on transportation systems and network analysis. His work integrates distributed systems, mobile networking, and data science to address urban mobility challenges. He specializes in analyzing multi-source traffic data to support urban planning and transportation infrastructure management. His research interests include mobility pattern recognition, travel demand estimation, road-network modeling, land use detection, and social network analysis. He has developed expertise in graph analysis, clustering algorithms, and recommendation techniques applied to transportation contexts. His technical skills encompass Python, Scala, Java, and various web technologies. His publication record demonstrates consistent contributions to transportation research, with recent work focusing on park-and-ride systems optimization, traffic demand prediction using deep learning, privacy-preserving analysis of origin-destination data, and geometric classification of urban road networks. His research often addresses practical urban challenges, including pandemic-related transportation adaptations and accessibility analysis of transit networks in cities like Lyon. Best Paper Award (2012) Furno teaches courses on Intelligent Transportation Systems, Data Science and Machine Learning, and Big Data at ENTPE. His reviewing activities span numerous prestigious journals including IEEE Transactions on Mobile Computing, Transportation Research Part C, and Sensors. His collaborative work extends to multiple institutions across Europe, with particular emphasis on French research networks.

Salam Traboulsiمشاهده پروفایل

پژوهشگر

• Smart City
• 5G Applications
• Indoor Positioning Systems
• +۲ مورد دیگر

Dr. Salam Traboulsi is a Researcher at Stuttgart University of Applied Sciences (HFT Stuttgart), affiliated with the Competence Center for Digitalization in Research, Teaching & Economics since 2019. She holds a PhD in Computer Science from the University of Toulouse, France (2008). Her work bridges technology and urban innovation, with a focus on developing scalable solutions for modern cities. Research Focus: Dr. Traboulsi specializes in: Smart City ecosystems integrating IoT and 5G Precision technologies for urban positioning and navigation Data management frameworks for large-scale sensor networks Open-source IoT platforms for building efficiency and environmental monitoring Cloud and grid computing infrastructures Key Projects: She leads/contributes to: iCity 2: UDigiT4iCity – Developing urban digital twins using IoT building data and 5G sensor networks iCity 1 – Foundational research on intelligent urban infrastructure systems Publication Trends: Her recent work (2020-2024) emphasizes 5G-enabled urban solutions, including fleet management optimization, indoor positioning systems, and IoT analytics for smart buildings. Earlier research (2005-2013) focused on distributed computing, storage virtualization, and information retrieval systems, demonstrating consistent expertise in large-scale data infrastructure. Academic Engagement: She serves as a scientific reviewer for journals and conferences and teaches in the surveying study area at HFT Stuttgart.