جست‌وجوی استاد و پژوهشگر

Rong Chenمشاهده پروفایل

دانشیار

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

Rong Chen is an Associate Professor in the Department of Diagnostic Radiology and Nuclear Medicine at the University of Maryland School of Medicine. He serves as Associate Vice Chair of AI and leads the Biomedical Data Mining Laboratory, focusing on integrating machine learning, computational neuroscience, and neuroimaging to decode brain-behavior relationships. His work spans clinical and translational research for disorders like Alzheimer’s, Parkinson’s, autism, and HIV, and he develops open-source software (GAMMA suite, Advanced Connectivity Analysis) for neuroimaging data analysis. Education: BS in Biomedical Engineering, Southeast University, China (1996) MS in Electrical Engineering, The Graduate School of Chinese Academy of Sciences (1999) PhD in Electrical and Computer Engineering, Washington State University (2003) Postdoctoral Researcher in Radiology, University of Pennsylvania (2005) MTR in Translational Research, University of Pennsylvania (2012) Research Interests: Computational modeling of neural activity and behavior Development of machine learning frameworks for neuroimaging Brain-inspired AI and therapeutic concepts Longitudinal analysis of brain disorders Distributed data mining for heterogeneous databases Software tools for biomarker detection and functional connectivity Scientific Contributions: 20+ years of advanced modeling and algorithm development Two open-source neuroimaging software packages (GAMMA suite, ACA) NIH and BRAIN initiative-funded research Editorial roles in journals like Frontiers in Computational Neuroscience Honors: Senior Member of IEEE Labs & Collaborations: Dr. Chen collaborates with institutions like NIH and Oracle, and his lab has developed tools used in studies on sickle cell disease, autism, and traumatic brain injury.

İLKAY SİBEL KERVANCIمشاهده پروفایل

استادیار

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

İLKAY SİBEL KERVANCI serves as an Assistant Professor in the Department of Computer Engineering at Gaziantep University's Faculty of Engineering. Her academic career spans teaching and research in artificial intelligence, machine learning, and data mining with practical applications across finance and bioinformatics sectors. PhD in Computer Engineering, Çukurova University (2023) MSc in Informatics, Kahramanmaraş Sütçü İmam University (2017) BSc in Computer Engineering, Kocaeli University (2004) Her research centers on machine learning applications for cryptocurrency price forecasting using LSTM and GRU networks, neutrosophic logic implementations in regression problems, and drug-target interaction prediction. Recent work demonstrates expertise in hyperparameter optimization, time series analysis, and handling imbalanced datasets through hybrid feature reduction techniques. She actively contributes to advancing neural network architectures for financial and biomedical challenges. Publication trends reveal consistent focus on Bitcoin price prediction (6 publications since 2017), expanding into drug-target interaction modeling and neutrosophic applications. Her work bridges theoretical machine learning with industrial applications in cement manufacturing, stock markets, and pharmaceutical research through recurrent neural networks and optimization frameworks. Dr. Kervanci teaches graduate courses including Introduction to Artificial Intelligence Methods and Introduction to Data Mining Methods, alongside undergraduate courses such as Artificial Intelligence in Engineering and Discrete Mathematics, demonstrating commitment to computational education across academic levels.

• Neuroscience
• Cognitive Psychology
• Visual Perception
• +۴ مورد دیگر

Assoc. Prof. Ivan Ivanov, PhD, serves as an Associate Professor in the Department of Telecommunications at New Bulgarian University (NBU), teaching core courses including SECB810 (Information Management Assurance) and TCMB862 (Internet-Based Networks and Protocols) within the Bachelor's Program in Telecommunications and Computer Technology. His academic role integrates engineering education with cutting-edge cognitive neuroscience research. Ivanov's research spans visual neuroscience and neurolinguistics, with dual emphases on face/body perception mechanisms and bilingual language processing. His methodological expertise encompasses electrophysiology (ERP/EEG) and functional neuroimaging (fMRI), applied to investigate neural coding in primate and human models. This interdisciplinary approach bridges telecommunications signal processing with cognitive science, particularly in analyzing neural responses to visual and linguistic stimuli. Analysis of his 15 most recent publications (2015-2025) reveals dominant trends in mesoscale neural organization for face/body processing and bilingual cognitive control. His work frequently employs primate neurophysiology to map body-selective cortical patches while simultaneously exploring second-language activation dynamics in humans. Key methodological innovations include fixation-related potentials for bilingual studies and fMRI adaptations for trauma evaluation in refugee populations. Scientific Awards: No awards were documented in the provided materials. Advising and Grants: Available documentation indicates no formal student advisees or research grants. Ivanov's academic profile emphasizes independent research and course instruction, with publications primarily reflecting departmental collaborations rather than externally funded projects. Laboratory and Team Affiliations: While specific labs are unmentioned, his fMRI/ERP work implies access to neuroimaging facilities, likely through cross-departmental partnerships between Telecommunications and Psychology/Neuroscience units at NBU. His trauma-stabilization studies suggest clinical collaborations with refugee support organizations.

Recep Firat Cekinelمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Multimodal Fact-Checking
• Explainable AI
• +۷ مورد دیگر

Recep Firat Cekinel is a Turkish NLP researcher who recently obtained his Ph.D. in Computer Engineering from Middle East Technical University (METU). He spent 13 months as a visiting predoctoral researcher at the University of Tübingen and is currently a researcher on the EU-funded EXA4MIND project, where he develops NLP pipelines that convert natural language into database queries using large language models. His research focuses on responsible, scalable AI systems and bridges foundational NLP work with real-world applications. Education: Ph.D. in Computer Engineering, Middle East Technical University (METU), Türkiye Visiting Predoctoral Researcher, University of Tübingen, Germany (13 months) Research Interests: Dr. Cekinel’s work spans natural language processing , multimodal fact-checking , explainable AI , and large language models . He is particularly interested in building responsible and scalable AI systems that integrate foundational research with practical deployments, such as natural-language interfaces for high-performance computing environments. Recent Publication Trends: His 2025 publications reveal a concentrated effort on multilingual and multimodal fact-checking , satire-style debiasing , and NL-to-database-query generation . Earlier work explores graph-based event extraction , Turkish irony detection , and cultural-heritage text mining , demonstrating a trajectory from low-resource Turkish NLP toward globally applicable, responsible-AI systems. Contact & Code: Email: rfcekinel@ceng.metu.edu.tr Office: METU Computer Eng. Dept. A-206, 06800 Ankara, Turkey Phone: +90-(312)-210-5593 GitHub: firatcekinel Google Scholar: profile available

Binghao Liمشاهده پروفایل

دانشیار

• Indoor Positioning
• Pedestrian Navigation
• Satellite Positioning
• +۳ مورد دیگر

Associate Professor Binghao Li leads the MIoT & IPIN Lab at the School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney. He holds a PhD in Spatial Information Systems from UNSW and advanced degrees in Civil and Electrical/Mechanical Engineering from Tsinghua University and Beijing Jiaotong University. Expertise in indoor/outdoor positioning systems Pioneering mine IoT applications Leader in pedestrian navigation research His research spans indoor positioning technologies, satellite navigation, and mining IoT solutions. Key grant projects include: 2021 CRC-P grant ($2m) for underground mine LoRa networks 2020 ARC Research Hub ($5m) for connected sensors 2018 Digital Grid Seed Funding for indoor navigation 2015-2019 ARC Linkage grants for positioning systems Award highlights: 2019 Best Paper & Presentation Awards 2010 VC's Post-Doctoral Fellowship 2004-2005 student research awards He supervises research in indoor positioning and mine IoT, and teaches courses including ENGG1000 Engineering Design and MINE8710 Mine Slope Stability.

Andrej Pustogowمشاهده پروفایل

دانشیار

• Solid State Physics
• Condensed Matter Physics
• Electronic Correlations
• +۴ مورد دیگر

Andrej Pustogow is an Associate Professor at the Vienna University of Technology (TU Vienna), affiliated with the Faculty of Physics and the Institute of Solid State Physics . He leads the research group "Tuning Charge and Spin of Correlated Electrons" within the Functional and Magnetic Materials research area (E138-03). His academic journey includes a Bachelor's degree in Physics from Ludwig Maximilian University of Munich, a Master's in Physics of New Materials from ETH Zurich, and a PhD in Experimental Solid State Physics from the University of Stuttgart (2017). After postdoctoral work at the University of Stuttgart and UCLA, he joined TU Vienna in 2020 on a tenure track, transitioning to Associate Professor in 2025. Education Bachelor's: Ludwig Maximilian University of Munich Master's: ETH Zurich (Physics of New Materials) Doctorate: University of Stuttgart (2017) Pustogow's research focuses on experimental exploration of correlated electron systems, particularly layered molecular conductors. His work investigates the interplay of charge, spin, and orbital degrees of freedom in quantum materials, with applications to functional and magnetic materials. His publications are listed in Scopus and TU Vienna's ReposiTUm database. Scientific Awards Alexander von Humboldt Foundation Research Fellowship At TU Vienna, he contributes to the Functional and Magnetic Materials research area (E138-03) and oversees the research group dedicated to tuning charge and spin dynamics in correlated electron systems.

Michael Rohlfingمشاهده پروفایل

استاد

• Condensed Matter Physics
• Electronic Structure Theory
• Many-Body Perturbation Theory
• +۷ مورد دیگر

Prof. Dr. Michael Rohlfing is a theoretical physicist at the Institute of Solid State Theory , University of Münster. His research focuses on electronic structure , optical excitations , and many-body perturbation theory in 2D materials and hybrid heterostructures . He leads the AG Rohlfing group, which develops and applies ab initio methods to study surfaces , interfaces , and nanoscale systems . Current projects include optical excitations in TMDC heterostructures under pressure (DFG since 2020) and electronic interface states in weakly bound systems (SFB 103, A13). His group investigates phenomena such as excitonic effects , electron-phonon interactions , and spin-orbit-driven surface states , with applications in nanotechnology and optoelectronics . Recent work explores trions , image potential effects , and valley-selective interlayer coupling in materials like CrSBr and MoS2 . Key publications highlight exciton dynamics in 2D systems, strain-tunable optical properties , and first-principles simulations of scanning tunneling microscopy images. His research team is involved in the Collaborative Research Center 1083 (Structure and Dynamics of Internal Interfaces) and the Computational 2D Materials Database (C2DB) . Scientific awards include the Heisenberg Fellowship (DFG, 2001-2004) and recognition for STM simulations and electron correlation studies . Prof. Rohlfing supervises numerous Bachelor’s , Master’s , and PhD students , including recipients of the Infineon PhD Award (Matthias Drüppel, 2017). His group regularly contributes to public workshops and collaborative experimental-theoretical studies .

• Temporal Databases
• Time Series Analysis
• Database Systems
• +۴ مورد دیگر

Anton Dignös is a professor at the Free University of Bozen-Bolzano , specializing in temporal databases , time series analysis , and database systems . His research focuses on efficient query processing for interval data, temporal joins, and schema design, with significant contributions to in-memory and time series databases. Key research areas include: Temporal Data Management : Advanced techniques for interval and duration queries. Time Series Analytics : Machine learning integration and pattern detection. Schema Optimization : Automated design and tuning of database schemas. Visual Analytics : Tools for period data comparison and correlation analysis. His work spans collaborations with researchers like Johann Gamper and Michael H. Böhlen , addressing challenges in healthcare systems, industrial applications, and financial data analytics. Notable contributions include algorithms for temporal anti-joins , range-duration queries , and machine learning-based anomaly detection .

Gregory Pawloskiمشاهده پروفایل

دانشیار

• Neutrino Physics
• Experimental High Energy Physics
• Particle Physics
• +۳ مورد دیگر

Gregory Pawloski serves as an Associate Professor at the School of Physics and Astronomy, University of Minnesota, with his office located in the Physics And Nanotechnology Building at 115 Union St. SE, Minneapolis. His research centers on experimental high energy physics with emphasis on neutrino sector investigations. Key research questions include neutrino mass ordering, CP-violation in the lepton sector, leptogenesis mechanisms, and potential discoveries beyond the Standard Model. His work directly addresses fundamental questions about the physical universe through precision neutrino measurements. Dr. Pawloski actively leads critical components of major international collaborations: NOvA: Current leader of Near Detector Physics Working group; previously led muon neutrino disappearance and cross-section tuning working groups DUNE: Leads hardware database effort for detector construction; develops machine learning techniques for atmospheric neutrino analysis MINOS: Past involvement in Main Injector Neutrino Oscillation Search His experimental approach leverages Fermilab's neutrino beamlines with distantly separated detectors to measure neutrino flavor composition changes, driving advancements in fundamental particle physics understanding.

Sang-Hoon Kimمشاهده پروفایل

دانشیار

• Operating Systems
• Memory Systems
• Storage Systems
• +۴ مورد دیگر

Sang-Hoon Kim is an Associate Professor in the Department of Software and Computer Engineering and Department of Artificial Intelligence at Ajou University, South Korea. He leads the Systems Software Lab (Paldal Hall 1004-2) and maintains active collaborations with Virginia Tech as a Visiting Scholar since August 2024. His academic journey includes a Ph.D. in Computer Science from KAIST (2016) under advisors Seungryoul Maeng and Jin-Soo Kim, and a B.S. in Computer Science from KAIST (2002). His research spans operating systems, memory management, and storage systems with focus on mobile platforms, heterogeneous architectures, and SSD technologies. Key interests include memory fragmentation control , distributed thread execution , key-value storage optimization , and resource disaggregation . His work bridges theoretical innovation with practical system implementations, particularly for mobile and datacenter environments. Kim's publication portfolio shows consistent output in top-tier venues including USENIX FAST, VLDB, ICDCS, and ASPLOS. His research demonstrates evolution from mobile memory management (2015-2017) toward distributed systems and hardware-aware software (2019-present), with recent emphasis on resource-disaggregated environments and heterogeneous-ISA computing. The 2024 Best Paper Award at USENIX FAST highlights his impact in storage systems research. Best Paper Award at USENIX FAST'24 Multiple patents including US-9588912B2 for memory control He directs significant research projects funded by ETRI, NRF, and US ONR, including current work on memory-centric computing systems (2020-2023) and disaggregated non-volatile memory systems using RDMA (2018-2020). His Systems Software Lab maintains strong industry partnerships with Samsung Electronics and NHN, with prior projects improving Android memory management and developing SSD-based storage systems for large-scale internet services.

Ding Liمشاهده پروفایل

استادیار

• Program Analysis
• Energy Optimization
• Mobile Applications
• +۲ مورد دیگر

Ding Li is an Assistant Professor in the School of Computer Science at Peking University. He holds a Ph.D. in Computer Science from the University of Southern California (USC) and a B.S. from Peking University. His research focuses on program analysis, energy optimization for mobile applications, and security, with publications in top conferences including ICSE, FSE, and ASE. His research interests span: Program Analysis : Techniques to optimize mobile application energy consumption. System Security : Identifying vulnerabilities in Android apps and WebAssembly binaries. Cloud/Edge Computing : Enhancing serverless computing efficiency and federated learning security. Dr. Li's recent work explores the integration of large language models into pointer analysis and automated optimization of resource inefficiencies. His publications demonstrate a consistent focus on practical system optimizations and security enhancements across mobile, cloud, and machine learning domains. Awards: Viterbi Undergraduate Research Mentoring Award (2014)

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.

• Machine Learning
• Deep Learning
• Remote Sensing
• +۵ مورد دیگر

Fabian Cristian Gieseke serves as an Associate Professor in the Department of Computer Science at the University of Copenhagen's Faculty of Science. His research focuses on applying machine learning techniques to environmental monitoring and remote sensing applications. He maintains an active research profile with significant contributions to the field of computational environmental science. Dr. Gieseke's research interests center on Machine Learning, Deep Learning, and their applications in Remote Sensing and Environmental Monitoring. His work particularly emphasizes forest resource monitoring, tree detection, and biomass estimation using advanced AI techniques. He has developed innovative approaches for analyzing satellite and aerial imagery to address critical environmental challenges related to forest management and climate change. His recent publications demonstrate a strong trend toward applying deep learning models to environmental monitoring problems, particularly in forest ecosystems. His research spans from national-scale tree counting and crown segmentation to above-ground biomass estimation using LiDAR data. His work shows increasing interdisciplinary collaboration between computer science and environmental science communities, with publications appearing in high-impact journals across both domains. Dr. Gieseke maintains an active research profile with 76 total research outputs as documented, including 41 article in proceedings, 24 journal articles, and various other publication types. His work has received significant attention with mentions across multiple social media platforms, news outlets, and policy documents, indicating real-world impact of his research. Based at the Department of Computer Science (DIKU) at the University of Copenhagen, Dr. Gieseke's research appears to be part of broader environmental informatics initiatives that bridge computer science with ecological monitoring and climate science applications.

François Quivigerمشاهده پروفایل

پژوهشگر ارشد

• Art History
• Renaissance Studies
• History of Cognition and Sensation
• +۶ مورد دیگر

François Quiviger is an Associate Fellow at the Warburg Institute, part of the University of London's School of Advanced Study. Born in France and educated in Canada with a B.A. and M.A. in Art History from the University of Montréal, he joined the Warburg Institute in 1983 where he completed his PhD in Combined Historical Studies. Quiviger has held multiple roles at the Institute since 1987, including librarian, webmaster, researcher, and teacher, serving as curator of digital resources from 2010 to 2016. Since October 2016, he has been an Associate Fellow while also serving as general editor of the Renaissance Lives series for Reaktion Books. His research primarily focuses on the history of cognition and sensation as it relates to the making and reception of images and the relationship between humans and nature. Quiviger has published extensively on early modern art and art theories, Renaissance academies, music, wine culture, and banqueting traditions. His notable works include The Sensory World of Italian Renaissance Art (2010) and Leonardo da Vinci: Self, Art and Nature (2019), with his upcoming biography Bernard Palissy and the Arts of the Earth scheduled for 2025. From 2002 to 2016, Quiviger developed and raised funds for the Institute's digitization program, creating a free online electronic library of out-of-print sources including early printed texts on Renaissance academies, art theory, Arabic astrology, and Giordano Bruno's works, along with 10,000 images of early illustrated books available through the Warburg Institute Iconographic Database. His recent scholarly output shows a continued focus on sensory studies, with publications examining topics such as the River Jordan in Western art, tuning figures in early modern art, and panpsychism in Bernard Palissy's work. His research demonstrates a consistent interest in how Renaissance thinkers understood the relationship between humans, nature, and artistic creation through multiple sensory channels. Quiviger has taught yearly seminars on the Survival of Classical art, Renaissance academies, art theories, early modern art, decorative arts, and material culture for the Warburg Institute's MA course, the V&A - RCA MA in the History of Design, and the UCL MA in Reception of the Classical World, sharing his expertise with multiple generations of students.

• Human-Computer Interaction
• Task Modeling
• User Interface Design
• +۳ مورد دیگر

Sybille Caffiau serves as a Lecturer in Data Engineering at ISAE-ENSMA's School of Engineering, affiliated with the LIAS laboratory across both ENSIP (Poitiers) and ISAE-ENSMA (Chasseneuil) campuses. Her research focuses on Human-Computer Interaction with specialization in task modeling and user interface validation. Her research interests span Human-Computer Interaction , Task Modeling , and User-Centered Design . She develops and evaluates tools like K-MADe for modeling user activities, with emphasis on empirical validation of task modeling approaches. Her work bridges cognitive science principles with practical software engineering applications, particularly in educational technology and interactive system design. Analysis of her publication trends reveals consistent contributions to HCI conferences and journals since 2006, with increasing focus on tool development (notably K-MADe) and empirical validation methodologies. Her recent work demonstrates strong interdisciplinary connections between cognitive modeling, software engineering, and educational technology. No scientific awards or fellowships are documented in the available materials. While her collaborative network includes prominent researchers like Laurent Guittet and Patrick Girard, specific details about student advising or research grants are not provided in the source materials. Her thesis work (2009) established foundations for model-driven interactive application design. She operates within the LIAS laboratory's Data Engineering team, contributing to research on interactive systems and task modeling frameworks. The KMADe environment represents her primary software contribution for activity description and task modeling.