جست‌وجوی استاد و پژوهشگر

Christof Wolfمشاهده پروفایل

استاد

• Social Inequality
• Social Change
• Health
• +۴ مورد دیگر

Prof. Dr. Christof Wolf holds the Chair of Social Structural Analysis at the University of Mannheim's Faculty of Social Sciences. His research focuses on social inequality, health, social networks, and religion, approached through a structural lens. He collaborates with the Mannheim Centre for European Social Research (MZES) and GESIS – Leibniz Institute for the Social Sciences. His work emphasizes methodological advancements in data collection and analysis, particularly in open science frameworks. Research interests span interdisciplinary topics including the interplay between social structures and individual well-being, quantitative methodologies, and the application of computational tools to sociological inquiry. His team explores innovative approaches to social science research, leveraging advanced data analysis techniques. Publications highlight expertise in multimedia systems, adaptive streaming, and serious games for educational and therapeutic purposes. Collaborations with technical institutions reflect a commitment to bridging social science and computational research. He actively contributes to teaching, developing interactive learning scenarios and leveraging mobile technologies for classroom engagement.

• Clinical Informatics
• Natural Language Processing
• Medical Data Mining
• +۳ مورد دیگر

Pradeep Mutalik, MD, is an Associate Research Scientist in Psychiatry at Yale School of Medicine with over 35 years of experience. His work spans neurophysiology, clinical informatics, radiology, and medical data mining. Current research focuses on NLP for electronic medical record extraction at the VA Clinical Epidemiological Research Center. Research areas include clinical NLP, virtual trial emulation, medical informatics, and VA health data systems. He developed methods for negation detection in EMRs and validation techniques for clinical data extraction. Publications demonstrate a progression from early neuroscience and pain research (1980s) to radiology informatics (1990s) and contemporary clinical NLP applications for COVID-19 and diabetes research.

• Computational Imaging
• Compressed Sensing
• Signal Processing
• +۵ مورد دیگر

Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.

Luo Jieboمشاهده پروفایل

استاد

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Jiebo Luo is a Professor of Computer Science at the University of Rochester, where he has held this position since 2014. He earned his BS and MS in Electrical Engineering from the University of Science and Technology of China (1989 and 1992) and a PhD in Electrical Engineering from the University of Rochester (1995). Prior to academia, he spent 15+ years at Kodak Research Laboratories as a Senior Principal Scientist. His research focuses on computational social science, natural language processing, digital health, computer vision, data mining, and multimedia. Dr. Luo’s work has been recognized through numerous awards, including the ACM SIGMM Technical Achievement Award (2021), Fellowships from ACM, AAAI, IEEE, IAPR, and SPIE. He has authored over 500 peer-reviewed papers, holds 90+ patents, and serves as Editor-in-Chief of the IEEE Transactions on Multimedia. He actively contributes to conference organization (e.g., ACM Multimedia, CVPR) and editorial roles for top journals. Key contributions include pioneering work in social media analytics, sentiment analysis, and digital health, as well as foundational research in multi-label classification and action recognition datasets like UCF 101. His labs and collaborations span the Goergen Institute for Data Science and the Greater Rochester Data Science Industry Consortium.

Kun Luمشاهده پروفایل

دانشیار

• Natural Language Processing
• Information Retrieval
• Text Mining
• +۳ مورد دیگر

Kun Lu is an Associate Professor in the School of Library and Information Studies (an iSchool) at The University of Oklahoma, serving as Co-Director of the Data Scholarship Program. His research focuses on applied natural language processing (NLP), information retrieval, text mining, and science informatics. He develops NLP methods for domains like oncology clinical trials, materials science, and Native American literature analysis. His work emphasizes interdisciplinary applications, including improving patient-clinical trial matching and accelerating materials discovery through machine learning. Dr. Lu teaches courses such as Information Retrieval & Text Mining and Database Design, and has secured grants totaling over $750,000 from NIH and institutional seed funds. Key projects include NIH-funded work on clinical trial optimization ($717,666), seed-funded studies on LLM applications in materials design, and HathiTrust-based Native American literature analysis. His award-winning research includes a 2011 Highly Commended Award for health information retrieval interface evaluation. Publications span 35+ peer-reviewed articles in top journals like Scientometrics , Journal of the Association for Information Science and Technology , and PLOS ONE , with recent focus on generative AI, domain-specific NLP, and scientific literature analysis. Affiliations: School of Library and Information Studies, Data Scholarship Program, Dodge Family College of Arts and Sciences Grants: NIH grant (2024-2026), OU seed funds for materials science and clinical trials projects Research Themes: NLP applications, scientometrics, health informatics, interdisciplinary data science

Mohamed Elawadyمشاهده پروفایل

استاد آموزشی

• Computer Vision
• Image Analysis
• Robotics Vision
• +۴ مورد دیگر

Dr. Mohamed Elawady is a Teaching Professor in the Department of Computer and Information Sciences at the University of Strathclyde, part of the Faculty of Science. He holds a PhD in Computer Vision from Lyon University (2019) and a joint MSc in Computer Vision and Robotics from Heriot-Watt University, Burgundy University, and Girona University under the Erasmus Mundus program. Previously, he served as a Lecturer in Data Science at the University of Stirling and worked as a Lead Computer Vision Researcher in a Parisian startup acquired by a unicorn company. His research focuses on image analysis, medical imaging, robotics vision, and deep learning applications. He has authored highly cited papers in top conferences like ICCV and VCIP, particularly in symmetry detection and medical image segmentation. He is a Co-Investigator on a UK-funded food waste management project and a peer reviewer for computer vision journals. He actively supervises PhD and MSc students, offering projects in computer vision and AI. Education: PhD in Computer Vision, Université de Lyon (2019) MSc in Computer Vision and Robotics, Erasmus Mundus Program (Heriot-Watt, Burgundy, Girona Universities) BSc in Computer Science, Suez Canal University (Egypt) Research Interests: Elawady’s work spans image analysis (symmetry detection, medical imaging), robotics vision, and deep learning applications. He has pioneered methods using wavelet-based feature extraction and CNNs for tasks like breast ultrasound segmentation and retinal lesion classification. His research aligns with UN SDGs related to health and innovation. Publications Trends: His articles emphasize symmetry detection innovations, medical imaging advancements, and computational methods for real-world challenges like obstacle detection in UAVs and food waste reduction through computer vision. Awards: Winner of ICCV’17 worldwide competition in symmetry detection Advising & Grants: Current Co-I on a UK Industry-funded project (Falcon Foodservice Equipment) for food waste management. He supervises students across PhD, MSc, and internship programs. His lab collaborations include projects on Human Digital Twins for occupational safety. Labs/Teams: Member of the Human Centric AI Research Group and Computer Science Education Research team at Strathclyde, focusing on AI-driven solutions for societal challenges.

Cosmina Croitoruمشاهده پروفایل

پژوهشگر

• Argumentation Frameworks
• Artificial Intelligence
• Algorithmic Graph Theory
• +۲ مورد دیگر

Cosmina Croitoru is a researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany, specializing in Algorithms and Complexity. She works within Department 1 of the institute, focusing on theoretical aspects of computer science with applications in artificial intelligence and argumentation frameworks. Her research has significant implications for knowledge representation, multi-agent systems, and computational social choice. PhD in Computer Science from Universität des Saarlandes (2017) Master's in Computer Science from Universität des Saarlandes (2012) Bachelor's in Computer Science from University AL. I. Cuza Iasi, Romania (2010) Cosmina's research interests span multiple domains within theoretical computer science and artificial intelligence. Her primary focus is on argumentation frameworks, where she has made significant contributions to the theoretical foundations. She also works extensively on algorithmic graph theory, applying these techniques to problems in artificial intelligence and data science. Her work on planning algorithms demonstrates her ability to bridge theoretical computer science with practical applications. The interdisciplinary nature of her research connects computer science with social choice theory and rational decision making. Analysis of Cosmina's publication record reveals a strong trajectory in theoretical computer science with a focus on argumentation frameworks. Starting with foundational work on abstract argumentation systems, her research evolved to address more complex problems involving combinatorial analysis, polyhedral geometry, and social interaction models. Her publications span prestigious venues in artificial intelligence, theoretical computer science, and computational logic. The consistent theme across her work is the application of rigorous mathematical methods to problems in knowledge representation and reasoning. Cosmina has been actively involved in academic service through conference organization and editorial work: Co-Organizer of ADFOCS 2014-2021 Editorial Assistant of CGTA (Computational Geometry Journal) 2014-2018 Student Organizer of ADFOCS 2012, Algo 2011, CICLing 2010 Organizer of Eurolan Junior 2009 As an educator, Cosmina has taught numerous courses including Artificial Intelligence, Data Science, Optimization, and various specialized topics in computer science. Her teaching spans multiple institutions including MPII, HTW, and Universität des Saarlandes. She has advised students on topics related to argumentation frameworks and graph algorithms, though specific student names aren't documented in the available information. Her primary research affiliation is with the Algorithms and Complexity department at the Max Planck Institute for Informatics, where she collaborates with leading researchers in theoretical computer science. Her work contributes to the institute's broader mission of advancing fundamental research in computer science.

• Wireless Communications
• Polarization Techniques
• Knowledge Graph Completion
• +۷ مورد دیگر

Fangfang Liu is an active researcher with an extensive publication record spanning from 2005 to 2025, demonstrating significant contributions across multiple domains in computer science and engineering. Their work shows consistent collaboration with researchers including Weimin Li, Caili Guo, Zhimin Zeng, and Chunyan Feng, suggesting strong institutional ties within their research community. Fangfang Liu's research spans several key areas including wireless communications, knowledge graph completion, semantic communications, and fake news detection. Their work demonstrates expertise in applying machine learning techniques to solve complex problems in network security, IoT systems, and multimedia analysis. The research portfolio shows a progression from foundational work in wireless communications and polarization techniques toward more recent applications in AI-driven security and knowledge representation. The publication trends reveal a strategic expansion from traditional communications engineering into cutting-edge AI applications. Early work focused on polarization techniques and wireless channel modeling, while recent publications emphasize knowledge graphs, multimodal fake news detection, and semantic communications. This evolution demonstrates adaptability to emerging research frontiers while maintaining technical depth in signal processing and network analysis. Fangfang Liu has made substantial contributions through numerous publications in prestigious venues including IEEE Transactions, Expert Systems with Applications, and Knowledge-Based Systems. Their collaborative approach is evident through extensive co-author networks across academia and research institutions. While specific advising information isn't detailed in the publication record, Fangfang Liu appears to lead research groups working on precision assembly, knowledge graph applications, and IoT security. The research program demonstrates strong connections between theoretical foundations and practical implementations in communication systems and AI applications.

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Prof. Allan Hanbury is a full professor at TU Wien's Faculty of Informatics, leading the E-Commerce Research Area. His work focuses on domain-specific systems for information extraction and retrieval, with expertise in machine learning, natural language processing, and medical informatics. Key projects include developing tools like CRUISE-Screening for systematic literature reviews and VoMBaT for high-recall search evaluation. He coordinates initiatives in transparent automated content moderation and image mining for patents. His research bridges theoretical advancements with practical applications in healthcare, legal domains, and digital humanities. Publications span over 240 works, emphasizing reproducibility, explainability in AI, and cross-lingual systems. Active in organizing conferences like PatentSemTech and contributing to CLEF challenges. Advises on thesis topics involving hate speech detection, legal text analysis, and graph-based methods. Collaborates internationally on projects like CSMeD (clinical trial matching) and BRISE (legal corpus development). His work integrates NLP, IR, and data mining to address real-world challenges in information access and AI transparency.

• Digital Media Studies
• Human-Computer Interaction (HCI)
• Social Media Analysis
• +۶ مورد دیگر

Lecturer Sarvas Risto is affiliated with Aalto University's Department of Computer Science within the School of Science. His research focuses on digital media studies, human-computer interaction (HCI), and the societal implications of technology. Key areas include social media analysis, digital saturation, metadata creation, and citizenship in the digital age. Risto has contributed to over 20 peer-reviewed publications since 2001, with notable work exploring photography's technological evolution, mobile social interaction platforms, and user-centric design principles. His research activities include organizing academic events like the 2021 Final Seminar of the 'Everyday Media Imagined by Teenagers' project and presenting at international conferences on topics such as digital natives' connectivity experiences and citizenship skills. While primarily focused on academic contributions, his work bridges technical innovation with sociocultural analysis, addressing both practical and ethical dimensions of emerging technologies. Risto has collaborated on projects like the RISE initiative, which explored semantically supported media services, and the DIMAS system for distributing multimedia content on peer-to-peer networks. His ongoing interests span future print media design, metadata systems for digital photography, and the legal aspects of user-generated content.

ZHANG Jiahengمشاهده پروفایل

استادیار

• Cryptography
• Security
• Artificial Intelligence
• +۷ مورد دیگر

ZHANG Jiaheng is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His work bridges cryptography, artificial intelligence, and system security, with a focus on scalable and privacy-preserving technologies. He teaches CS3235 – Computer Security and leads research in zero-knowledge proofs, LLM safety, and trustworthy AI. Research Interests: His research spans Cryptography , Security , Machine Learning & AI , Privacy , and Algorithms & Theory . He specializes in making zero-knowledge proofs practical at scale and securing large language models against jailbreaking, backdoors, and privacy leaks. His recent projects include zkGPT, BatchZK, and Guardreasoner, highlighting his dual focus on theoretical foundations and real-world applications. The recent publications show a strong trend toward scalable zero-knowledge systems and AI security , particularly in verifying and protecting LLMs. These works integrate cryptographic rigor with modern AI challenges, reflecting a cohesive research vision at the frontier of trustworthy computing. Scientific Contributions: Developed scalable collaborative zk-SNARKs for efficient proof generation. Pioneered techniques for secure LLM inference and jailbreak detection. Advanced GPU-accelerated and distributed zero-knowledge proof systems. Advising & Grants: While specific students and grants are not listed, his active publication record in top-tier venues suggests ongoing research supervision and external funding in cybersecurity and AI. He is likely involved in advising PhD and Master’s students in cryptography and AI security. Labs & Teams: He is part of the NUS School of Computing research ecosystem, potentially affiliated with cybersecurity or AI labs, contributing to Singapore’s leadership in privacy-preserving technologies.

Javed I. Khanمشاهده پروفایل

استاد

• Computer Networking
• Cyber Infrastructure
• Active and Programmable Networking
• +۱۴ مورد دیگر

Javed I. Khan is a Professor and Chair of the Department of Computer Science at Kent State University . He leads cutting-edge research in networking, cyber infrastructure, and perceptual systems, with funding from DARPA, NSF, NASA, AFRL, and the World Bank. He is the director of the Media Communications and Networking Research Laboratory (MEDIANET) and actively contributes to international education networks. Ph.D., University of Hawaii at Manoa B.Sc., Bangladesh University of Engineering and Technology (BUET) His research spans active and programmable networking , peer-to-peer systems , semantic design , multimedia communication , and medical imaging . He has pioneered work in holographic associative memory and complex system visualization . His interdisciplinary work integrates computer science with human-centered design and global development. The recent publications reflect a strong trend in decentralized systems , next-generation architectures , and intelligent networking . Themes include P2P computing, dynamic routing, content-based search, and perceptual engineering, indicating a forward-looking research vision in network evolution and human-machine symbiosis. Fulbright Senior Specialist on High Performance Education Networking Open Grants Fellow, East West Center, Hawaii Dr. Khan has advised national and international bodies including the World Bank on research and education networks (RENs) for sustainable development. His lab has secured grants from DARPA , NSF , NASA , and AFRL , supporting innovative projects in space communication, multisensory fusion, and digital library systems. He leads the MEDIANET Lab , which houses the Internet Teaching Lab (ITL) and Student Research Portal . The lab fosters collaboration through the MEDIANET Collaboration Server and supports projects in active networking, P2P computing, and perceptual media.

Junya Shiraishiمشاهده پروفایل

پژوهشگر ارشد

• Wireless Sensor Networks
• Internet of Things
• Energy Efficiency
• +۴ مورد دیگر

Junya Shiraishi is a Postdoctoral Researcher (Marie Curie Fellow) at the Department of Electronic Systems, Technical Faculty of IT and Design, Aalborg University, Denmark. His research focuses on energy-efficient communication systems for the Internet of Things (IoT) and wireless sensor networks. Dr. Shiraishi's research spans wireless communications and IoT, emphasizing energy efficiency, content-based wake-up systems, and pull/push communication coexistence. His work addresses critical challenges in reducing sensor node energy consumption while ensuring timely data retrieval in IoT applications through innovative networking protocols. His recent publications (2023-2025) demonstrate a cohesive research trajectory centered on energy-efficient IoT protocols, featuring top-k query processing, multi-hop wake-up control, and AI integration for 6G systems. These works bridge theoretical networking concepts with practical sensor network deployments, showing increasing focus on AI-driven solutions for next-generation wireless systems. Scientific Awards: Marie Curie Postdoctoral Fellowship Supported by the Marie Curie Fellowship, Dr. Shiraishi conducts cutting-edge research in EU-funded projects. No student advisement information is available in current records. Dr. Shiraishi collaborates internationally with researchers across Europe and Japan within the Connectivity section at Aalborg University, contributing to global initiatives in 6G wireless systems and IoT standardization.

• Computer Vision
• Image Analysis
• Ontology-based Information Modeling
• +۴ مورد دیگر

Dr. Chris Town is an Affiliated Lecturer and Research Fellow in the Department of Computer Science & Technology at the University of Cambridge. He serves as a Fellow and Director of Studies in Computer Science at Wolfson College, where he also acts as a Tutor and coordinates the postgraduate mentoring scheme. Additionally, he holds a position as Bye Fellow and Director of Studies in Computer Science at Jesus College. His academic journey includes completing a PhD at Cambridge Computer Laboratory under Professor John Daugman OBE, with prior undergraduate studies at Trinity College, Cambridge. PhD in Computer Science, University of Cambridge (Computer Laboratory) Undergraduate degree with first class honours in Computer Science from the University of Cambridge (Trinity College) Dr. Town's research spans the intersection of computer vision, information retrieval, machine learning, and language processing. He pioneered the ontology-based approach to automated visual information processing alongside Dr. David Sinclair. His work demonstrates how ontologies representing task-specific attributes can narrow the semantic gap between human and computer interpretations of visual content. His research extends to pattern matching algorithms for biological sciences, including developing tools used in ecology and zoology such as Manta Matcher and Nature Pattern Match. His work has significant applications in image retrieval, automated visual surveillance, and visually mediated human-computer interaction. His publications reveal a strong focus on applying computer vision techniques to biological pattern recognition, particularly in marine wildlife identification, egg pattern analysis, and medical imaging. The articles show consistent development of methods for species identification, pattern analysis, and image recognition across diverse domains from deep-sea corals to medical diagnostics. BCS Distinguished Dissertation Award (2005) Trinity College Rouse Ball Scholarship Industrial Fellowship from the Royal Commission for the Exhibition of 1851 JRF at Wolfson College Runner-up prize for best cognitive vision paper (2003) Best Student Paper Award at ICPRS 2022 Dr. Town has supervised over 50 final-year undergraduate, Masters, and Diploma students for their projects and dissertations, as well as co-supervising PhD students. His supervision spans diverse topics including deep learning features, shell recognition, marine wildlife identification, and fossil recognition. He has developed relationships with organizations such as Imense Ltd (where he serves as Chief Technology Officer), the Foundation for the Protection of Marine Megafauna, and ECOCEAN Whale Shark Photo-identification Library. His research has been supported by fellowships from AT&T Labs Research, the Royal Commission for the Exhibition of 1851, and various scholarships from Trinity College. Dr. Town leads the PhD Mentoring Scheme at Wolfson College and has developed tools like Manta Matcher that are used by the academic community for marine wildlife identification. His work bridges academic research with practical applications, particularly in biological pattern recognition and medical imaging.

Alex Doboliمشاهده پروفایل

استاد

• Electronic Design Automation (EDA)
• Cyber-Physical Systems
• Mixed-Domain Embedded Systems
• +۵ مورد دیگر

Alex Doboli is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University, State University of New York. He is also affiliated with SUNY Korea’s Department of ECE. His academic journey includes a Ph.D. in Computer Engineering from the University of Cincinnati (2000) and earlier degrees from Politehnica University Timisoara, Romania (B.Eng. 1990, Doctorate 1997). He served as junior faculty at Politehnica University before joining Stony Brook in 2000. His research focuses on Electronic Design Automation (EDA) , Cyber-Physical Systems , and Human-inspired Machine Learning . Key areas include analog/mixed-signal design, innovation methodologies, and data-driven design approaches. He has authored over 170 peer-reviewed publications and co-authored a textbook on mixed-signal design. His lab, the Mixed-Domain Embedded Systems Laboratory , explores team behavior modeling, IoT integration, and cognitive architectures. Dr. Doboli has advised 17 Ph.D. and 12 M.S. students. Notable awards include the IBM Partnership Award (2001) and the Traian Lalescu Award (1987). He serves as an Associate Editor for Integration, the VLSI Journal and holds leadership roles in professional organizations like the IEEE Long Island Circuits and Systems Society. His teaching spans undergraduate and graduate courses in programming, algorithms, VLSI design, and machine learning. Current courses include ESE 327: Fundamental Algorithms for Machine Learning and ESE 589: Learning Systems for Engineering Applications . Recent research highlights include automated dialog systems (diaLogic), IoT-human behavior integration frameworks, and studies on artistic understanding in neural networks. His work bridges EDA, machine learning, and embedded systems to advance design innovation and human-centric technologies.