جست‌وجوی استاد و پژوهشگر

• Digital Humanities
• Digital Musicology
• History of Technology
• +۳ مورد دیگر

Eamonn Bell is an Assistant Professor in the Department of Computer Science at Durham University, with affiliations to the Institute of Advanced Study. His research focuses on digital humanities, applying computational methods to musicology and examining the history of digital technology in music production. He leads projects like CCP-AHC and DISKAH to enhance digital infrastructure for arts researchers. Previously, he was a postdoctoral Research Fellow at Trinity College Dublin, studying the cultural impact of the Compact Disc. He holds a PhD in Music Theory from Columbia University (2019) and a joint degree in Music and Mathematics from Trinity College Dublin (2013). His educational background includes advanced studies in music theory, digital humanities, and mathematics. He teaches courses on algorithms, software engineering, and music processing within Durham’s Computer Science curriculum. Supervision includes postgraduate students focusing on music technology and computational analysis. Research interests span digital musicology, the history of technology, and accessibility of computational tools for non-specialists. Awards include the Government of Ireland Postdoctoral Fellowship (2019–2021). His work addresses ethical issues in genetic studies of musicality and explores emerging digital tools for music analysis, such as time-coded YouTube comments and CD data extraction techniques. Key initiatives include designing DRI projects for UK-based researchers and advancing digital skills through collaborative frameworks. He actively participates in academic conferences and has published widely on topics ranging from media archaeology to interdisciplinary computational research.

Latifur Khanمشاهده پروفایل

استاد

• Data Mining
• Cybersecurity
• Machine Learning
• +۲ مورد دیگر

Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).

• Knowledge Representation
• Semantic Web
• Ontology Engineering
• +۷ مورد دیگر

Deborah McGuinness is a Professor of Computer Science, Cognitive Science, and Industrial and Systems Engineering at Rensselaer Polytechnic Institute (RPI), holding the Tetherless World Senior Constellation Chair. She leads research in semantic web technologies, ontology engineering, explainable AI, and applications in health and environmental informatics. Her work emphasizes semantic technologies to enhance human-machine collaboration through knowledge representation and reasoning. Education: B.S./B.A. (Computer Science & Mathematics, Duke University, 1980), M.S. (Computer Science, UC Berkeley, 1981), Ph.D. (Knowledge Representation, Rutgers University, 1997). Research interests include: ontology creation/evolution, commonsense AI, machine learning fairness, clinical decision support systems, knowledge graphs for scientific data, and policy modeling. Recent work focuses on AI explainability, semantic data dictionaries for public health surveys, and leveraging knowledge graphs for personalized health recommendations. Her publications span semantic web standards, AI commonsense benchmarks, clinical informatics applications, and policy frameworks. Notable projects include the Explanation Ontology for user-centered AI and the CHEAR Data Repository for environmental health research. McGuinness has pioneered semantic technologies for data integration across diverse domains like nanomaterials science and stroke care policy analysis.

Reza Touraniمشاهده پروفایل

استادیار

• Security and Privacy
• Internet of Things (IoT)
• Information-Centric Networking (ICN)
• +۷ مورد دیگر

Reza Tourani is an Assistant Professor in the Department of Computer Science at Saint Louis University since Fall 2018. He earned his Ph.D. (2018) and M.S. (2012) in Computer Science from New Mexico State University, preceded by a B.S. in Computer Engineering from Islamic Azad University of Tehran (2008). His career includes prior work in the telecommunications industry in Iran. University: Saint Louis University School: School of Science and Engineering Department: Department of Computer Science Academic Rank: Assistant Professor Dr. Tourani’s research focuses on security and privacy in resource-constrained environments, including: Internet of Things (IoT) : Secure communication protocols and edge computing Information-Centric Networking (ICN) : Access control and request flooding mitigation Cyber-Physical Systems : Smart grid and UAV swarms Private Communication : Anonymity in distributed systems Networked Systems : DDoS defense and caching optimization His recent articles explore trends in: DDoS mitigation in Named Data Networking (PERSIA framework, 2020) Secure UAV swarm communications (2020) Attribute-based encryption for edge computing (APECS, 2021) Collaborative caching mechanisms (MuNCC, 2016) Smart grid data security (iCASM, 2020) Dr. Tourani has secured research grants from Saint Louis University and Intel Labs to advance projects on privacy-aware contact tracing and edge computing security . He actively mentors students in cybersecurity, IoT, and networked systems.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Dr. Milos Hauskrecht is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from MIT (1997) and an M.Sc. from Slovak Technical University (1988). His research focuses on AI, machine learning, and data mining, with applications in medicine and finance. He leads projects in real-time clinical monitoring, anomaly detection, and time-series analysis of EHR data. He has advised numerous PhD and MS students, including notable alumni now at Amazon, DeepMind, and Microsoft. Research interests include reasoning under uncertainty, optimization, and AI-driven medical decision support. Current grants include NIH funding for AI in renal therapy and clinical monitoring. He has published widely in top venues like ICML, NeurIPS, and journals such as Artificial Intelligence in Medicine. His work on conditional outlier detection earned the Homer Warner Award (AMIA 2010). He teaches machine learning and advises on interdisciplinary AI projects.

• Artificial Intelligence
• Decision Support Systems
• Knowledge Management
• +۳ مورد دیگر

Professor Gregoris Mentzas is a faculty member at the National Technical University of Athens, School of Electrical and Computer Engineering, where he directs the Division of Industrial Electric Devices and Decision Systems. His research focuses on AI-enabled decision systems, knowledge management, and semantic technologies applied to digital enterprises and e-government. With over 350 publications, he ranks among the top 2% most cited scientists globally. Research Interests: Artificial intelligence for decision augmentation, big data analytics in personalized health and smart mobility, semantic web technologies, and industrial internet of things. Current projects investigate trustworthy AI frameworks and hybrid intelligence systems for Industry 5.0. Teaching: Leads courses in Digital Enterprise Management, Strategic Information Systems, and Project Management at undergraduate and postgraduate levels, incorporating industry case studies and experiential learning approaches. Awards & Leadership: Top 2% Highly Cited Scientist (PLOS Biology 2021) 5 Best Paper Awards in international conferences Director of Information Management Unit (1997-present) Board Member of Institute of Communication and Computer Systems (2006-2009) Projects & Funding: Secured over €18 million in research grants through 60+ European projects with industry partners including SAP, IBM, and Siemens. Research outcomes led to three technology spin-offs.

Charles Lingمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۴ مورد دیگر

Charles Ling is a Professor of Computer Science at Western University, holding the title of Science Distinguished Research Professor. He also serves as Director of the Data Mining and Business Intelligence Lab and Associate Scientist at the Lawson Health Research Institute. His academic background includes a B.Eng. (CS and EE) from Shanghai Jiao Tong University and MSc/PhD from the University of Pennsylvania (UPenn). Research interests span machine learning, deep learning, AI, and healthcare informatics, with notable contributions to the GlucoGuide diabetes management system. He has authored over 220 peer-reviewed papers and a book titled Crafting Your Research Future , focusing on academic career development. Awarded Fellow of the Canadian Academy of Engineering (CAE) and recipient of the First Prize for Best Clinical Research Presentation (2011). Active in grants (NSERC, FedDev, Mitacs) and organizational roles in top conferences (KDD, ICDM). Supervises 5 PhD and 4 MSc students, with notable advisees including Harry Zhang and Victor Sheng. Leverages AI in education to enhance children's cognitive abilities through video-based programs like Power Thinking , approved by Curriculum Services Canada. His work integrates machine learning with healthcare, finance, and software engineering.

Giulia Totiمشاهده پروفایل

استادیار

• Computer Science Education
• Ethics in Data Science
• Machine Learning
• +۳ مورد دیگر

Giulia Toti is an Assistant Professor of Teaching in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. Her work focuses on computer science education, equity in curriculum design, and fostering inclusive learning environments. She teaches courses such as Applied Machine Learning (CPSC 330), Fairness, Accountability, Transparency, and Ethics (FATE) in Data Science (DSCI 430), and Computers and Society (CPSC 430). Her research explores diversity initiatives in CS education, mastery learning frameworks, and equitable grading practices. Notable contributions include studies on pandemic-era remote teaching impacts and the development of Agora, a tool for enhancing large-classroom engagement. Toti has received UBC Faculty Teaching Awards for her pedagogical innovations. Her interdisciplinary work spans machine learning applications in industry and healthcare, including semantic search systems for clinical data (SemEHR) and predictive analytics for energy production. She is affiliated with the ACE Lab and actively contributes to curriculum reforms addressing DEI (Diversity, Equity, Inclusion) challenges in STEM education. Grants/Awards: Faculty Teaching Awards Labs/Teams: ACE Lab (Advancing Computing Education) Advising: No explicit advisee listings found, but contributes to pedagogical research impacting teaching practices.

Constantin Catalin Draganمشاهده پروفایل

مدرس ارشد

• Applied Cryptography
• Provable Security
• Electronic Voting
• +۳ مورد دیگر

Dr. Constantin Catalin Dragan is a Senior Lecturer in Secure Systems at the University of Surrey, UK. He holds a PhD in Computer Science from Alexandru Ioan Cuza University of Romania (2014), focusing on cryptographic primitives. His research expertise includes applied cryptography, provable security, electronic voting systems, and formal verification. Dr. Dragan has held postdoctoral positions at LORIA, CNRS, INRIA (France) and the University of Surrey. He leads modules such as Privacy Enhancing Technologies (COM3030), Information Security Management (COM3017/COMM037), and supervises final year projects. Currently advising PhD student Navid Abapour, his teaching emphasizes cybersecurity, operating systems, and privacy-preserving technologies. His research focuses on formal verification of security protocols, end-to-end verifiable voting systems, and cryptographic primitives. Notable contributions include work on machine-checked proofs for accountability in systems, privacy-preserving e-voting protocols, and blockchain-based trust services like TAPESTRY and KYChain. Publications span venues like IEEE S&P, EuroS&P, and ESORICS, with a focus on cryptographic protocol design and formal security models. He actively contributes to international workshops on cryptology and cyber security, advancing theoretical foundations and practical implementations of secure systems.

Christoph T. Kochمشاهده پروفایل

استاد

• Electron Microscopy
• Materials Science
• Condensed Matter Physics
• +۳ مورد دیگر

Christoph T. Koch is a Professor of Physics at Humboldt-Universität zu Berlin, where he has held the W3 Chair since 2015. Previously, he held a similar position at Ulm University (2011–2015), supported by the Carl Zeiss Foundation. His research focuses on advanced electron microscopy techniques, including quantitative transmission electron microscopy (TEM), electron holography, and strain mapping. He leads the AG Strukturforschung/Elektronenmikroskopie group, advancing materials science through innovations in imaging and spectroscopy. Education: B.Sc./M.Sc. in Physics at Heidelberg University (1996–1998), followed by an exchange at Arizona State University (1997–1998). PhD in Physics from Arizona State University (2002, advisor: Prof. John C.H. Spence). Postdoctoral research at the Max Planck Institute for Metals Research, Stuttgart (2002–2011). Research interests include: Electron diffraction and phase retrieval Nanometer-scale strain and defect analysis Electron energy-loss spectroscopy (EELS) for plasmonics and bandgap mapping Development of FAIR data infrastructure for materials science Leadership: Managed the Department of Physics at Humboldt University (2020–2024). Collaborates widely, with key co-authors including P.A. van Aken, W. Sigle, and C. Felser. His work bridges experimental microscopy and computational modeling, addressing challenges in semiconductors, ceramics, and 2D materials. Notable contributions include pioneering methods for 3D reconstruction via electron ptychography, dynamic electron diffraction analysis, and strain mapping in advanced CMOS technologies. Current efforts emphasize real-time imaging and AI-driven data analysis in materials research.

Ziquan Liuمشاهده پروفایل

مدرس

• Reliable Machine Learning
• Uncertainty Quantification
• Computer Vision
• +۴ مورد دیگر

Dr. Ziquan Liu is a Lecturer (Teaching & Research) at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Multimodal AI. He holds a PhD from City University of Hong Kong (2023) and dual B.Sc./B.Eng. degrees from Beihang University (2017). His research focuses on trustworthy machine learning, adversarial robustness, and uncertainty quantification in foundation models. He has served as a reviewer for top conferences like NeurIPS, ICLR, and CVPR, earning an Outstanding Reviewer Award in 2021. His teaching includes modules on machine learning for visual data analysis and principles of machine learning. He supervises PhD students in AI safety and reliability, with notable work on conformal prediction, adversarial attacks, and multimodal learning. His research outputs span top venues such as ICML, CVPR, and NeurIPS, addressing challenges in algorithmic fairness, model certification, and cross-modal alignment.

Ryan Henryمشاهده پروفایل

استادیار

• Applied Cryptography
• Secure Systems
• Privacy-Preserving Technologies
• +۳ مورد دیگر

Ryan Henry is an Assistant Professor in the Department of Computer Science at the University of Calgary. His research focuses on applied cryptography, emphasizing the development of secure systems that prioritize user privacy. His work spans designing privacy-enhancing technologies, implementing cryptographic protocols, and analyzing number-theoretic attacks on cryptographic assumptions. He also explores theoretical aspects of cryptographic efficiency and practical deployment challenges. While specific educational background details are not provided in the text, his research contributions highlight expertise in cryptography, secure systems, and privacy-preserving technologies. His work has addressed topics such as Private Information Retrieval (PIR), secure messaging, and blockchain privacy. Key research interests include: Secure Multiparty Computation Privacy-Preserving Data Access Efficient Cryptographic Protocols Zero-Knowledge Proofs IoT Security Cryptocurrency and CBDC Design His recent publications emphasize advancements in distributed systems security, privacy in recommendation systems, and cryptographic efficiency. Notable contributions include the Grotto and Duoram frameworks for secure computation, and proposals for Canadian CBDC frameworks. Despite extensive research output, no scientific awards or grants are explicitly mentioned in the provided text. Collaborations and lab affiliations are not detailed, though his work suggests involvement in interdisciplinary projects on privacy and security technologies.

Yahya Fathiمشاهده پروفایل

استاد

• Linear Programming
• Nonlinear Programming
• Integer Programming
• +۴ مورد دیگر

Professor Yahya Fathi specializes in optimization and operations research at North Carolina State University. His research includes mathematical programming, production systems, and quality engineering, with applications in manufacturing and data analytics. Awarded multiple teaching excellence honors.

• Artificial Intelligence
• Natural Language Processing
• Computational Semantics
• +۵ مورد دیگر

Luis Espinosa-Anke is a Senior Lecturer at Cardiff University's School of Computer Science and Informatics. His academic journey includes working as a Natural Language Processing (NLP) scientist at Savana Médica, a Madrid-based healthcare AI company, prior to joining Cardiff. He completed his PhD at Pompeu Fabra University in Barcelona while working at Savana. Dr. Espinosa-Anke's research focuses on Artificial Intelligence and NLP, with particular emphasis on meaning representation, computational semantics, multilingual NLP, and computational lexicography. His work spans theoretical and applied aspects of language technology, with applications in healthcare, social media analysis, and multilingual systems. His recent publications reveal a strong trend toward analyzing language model behavior, bias detection in AI systems, and creating resources for semantic analysis. The publications show increasing focus on practical applications of NLP in healthcare, social media, and cross-lingual settings, with notable contributions to datasets like WIKITIDE and 3D-EX that support definition extraction and semantic understanding. laCaixa Fellow Fulbright scholarship recipient Erasmus Mundus program participant Dr. Espinosa-Anke has secured research funding including a Kaggle Open Research grant ($2,000 USD) as PI for the 'Don't Patronize Me!' project, a Snap Inc. grant ($10,000 USD) as CO-I for modeling meaning shift in social media, and a £90,000 Welsh Government grant for English-Welsh bilingual embeddings research. He currently supervises five PhD students working on meaning representations, contextual word embeddings, NLP for healthcare applications, and metaphor identification.

Chao Tianمشاهده پروفایل

دانشیار

• Information Theory
• Distributed Data Storage
• Coding Theory
• +۶ مورد دیگر

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.