جستجوی استاد راهنمای دانشگاهی

Shahrokh Valaeeمشاهده پروفایل

استاد

• Wireless Networks
• Signal Processing
• Vehicular Networks
• +۴ مورد دیگر

Shahrokh Valaee is a Professor and Associate Chair for Undergraduate Studies in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, part of the Faculty of Applied Science and Engineering. He founded and directs the Wireless and Internet Research Laboratory (WIRLab). Education: BSc and MSc in Electrical Engineering from University of Tehran PhD in Electrical Engineering from McGill University Research Interests: Focuses on wireless networks (vehicular/sensor networks, B5G/6G), signal processing (indoor localization, machine learning for medical imaging), and integrated sensing/communication. His work spans: Localization in GPS-denied environments Machine learning for healthcare with limited/imbalanced data Reconfigurable Intelligent Surfaces (RIS) and drone networks Publications: Recent articles (2014-2016) show strong focus on indoor localization techniques, vehicular network protocols, and network coding, with emerging trends in machine learning applications for wireless systems and healthcare. Awards: Connaught Award (2012, 2013) NSERC Discovery Accelerator Award (2010) MaRS Innovations cPOP Award (2012) IEEE Fellow (FIEEE) Engineering Institute of Canada Fellow (FEIC) Leadership: Advises graduate students at WIRLab, where research combines theory with practical implementation (GPU-based ML, Android localization). Manages projects in integrated sensing/communication, ML for health, and B5G networks. Labs/Teams: Directs WIRLab with focus on wireless signal processing, networking, and ML implementations. Current team includes postdocs and PhD students working on localization, B5G networks, and medical ML applications.

Antoine Doucetمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Text Mining
• +۷ مورد دیگر

Antoine Doucet is a Full Professor at the University of La Rochelle, where he teaches in the Computer Science department of the University Institute of Technology (IUT). He conducts his research at the Computer Science, Image and Interaction Laboratory (L3i) within the 'Images and Content' team, which he has led since 2015. He is also a member of the Franco-Vietnamese laboratory ICTLab and serves as Director of the ICT Department at the University of Science and Technology of Hanoi since 2016. His research focuses on information retrieval, natural language processing, text mining, and artificial intelligence, with emphasis on automatic analysis of text in all forms across languages. His work prioritizes generic methods that work across languages without relying on language-specific linguistic resources. This approach is particularly valuable for under-resourced languages and noisy texts from sources like social media or OCR output. As coordinator of the Horizon 2020 NewsEye project, he led efforts to improve access to European historical newspapers through semantic enrichment and advanced search capabilities. His research has practical applications in epidemic surveillance, document fraud detection, and historical content analysis. The NewsEye project involved 11 teams across Europe, including 3 national libraries and multiple research groups. Best paper award from IMIA Yearbook 2016 (among 1,272 candidates) Best paper award at HCI International with Ilona Nawrot Press coverage for ACL 2013 paper in major publications Recipient of French scientific excellence award (Prime d'Excellence Scientifique) Doucet actively supervises PhD and Master's students, with recent advisees including Chloé Artaud (Document fraud detection), Paul Martin (Photograph Time-Stamping), Ilona Nawrot (Temporal and Multilingual Text Analysis), and Gaël Lejeune (Multilingual Epidemic Surveillance). His research has been funded through multiple projects including ANR Digistory, AmeliOCR, PHC Nusantara, and USTH SWARMS. He has also coordinated significant European projects like NewsEye and Embeddia. At L3i, he leads a research group of approximately 40 persons focused on Images and Digital Content. His work bridges theoretical advances in multilingual text processing with practical applications in historical document analysis, epidemic surveillance, and document security.

Maxim Romanovمشاهده پروفایل

پژوهشگر ارشد

• Social history of premodern Islamic world
• Arabic written tradition
• Computational methods
• +۳ مورد دیگر

Maxim Romanov heads 'The Evolution of Islamic Societies' project at University of Hamburg's Asia-Africa-Institut, funded by DFG's Emmy Noether Program. Former positions include senior research fellow at KITAB Project and University of Vienna. Research reconstructs social history of Islamic world (c.600-1600 CE) through computational analysis of Arabic chronicles and biographical collections. Research Focus: Digital humanities approaches to premodern Islamic history including OCR development for Arabic manuscripts, corpus linguistics, and geospatial modeling of historical data. Technical Contributions: Developed OpenITI corpus infrastructure, al-Ṯurayyā gazetteer system, and computational methods for large-scale historical text analysis. Recent work enhances NLP for classical Arabic with OCR accuracy exceeding 90%.

Yang Wangمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Domain Adaptation
• +۳ مورد دیگر

Dr. Yang Wang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on computer vision and machine learning, with a particular emphasis on domain adaptation, meta-learning, and privacy-preserving techniques. He actively advises prospective graduate students through a dedicated webpage outlining application procedures. Research interests include few-shot learning, test-time adaptation, and cross-modal applications such as handwritten text recognition and gaze estimation. His work explores how models can adapt dynamically to new domains using limited labeled data, with applications in crowd counting, medical data analysis, and cybersecurity. He also investigates privacy-preserving methods for deep learning models to protect user attributes and sensitive information. Recent publications highlight advancements in meta-auxiliary learning frameworks and efficient user adaptation techniques. His contributions span journals and conferences, showcasing innovations in both foundational machine learning methodologies and real-world applications.

Arnab Nandiمشاهده پروفایل

استاد

• Database Systems
• Human-Computer Interaction
• Human-in-the-Loop Data Analytics
• +۵ مورد دیگر

Arnab Nandi is a Professor of Computer Science and Engineering at The Ohio State University, with a courtesy appointment in Biomedical Informatics. He holds leadership roles including Steering Committee Member for the Human-in-the-Loop Data Analytics (HILDA) Workshop and has served as Workshops co-chair for SIGMOD 2025-26 and Demonstrations co-chair for SIGMOD 2024. His research focuses on bridging human interaction and data infrastructure, spanning database systems, human-in-the-loop data analytics, and next-generation query interfaces. Nandi's work emphasizes interactive data exploration through projects like DICE (Distributed Interactive Cube Exploration), GestureDB (Querying Beyond Keyboards), and Omni (Multimodal Data Exploration). His recent research explores integrating LLMs into database education, augmented reality interfaces for data analytics, and multimodal approaches to video querying. Nandi has received numerous honors including the NSF CAREER Award, Google Faculty Research Award, IEEE TCDE Early Career Award, and the University's Alumni Award for Distinguished Teaching. He was also named to Columbus Business First's '40 under 40' and became an ACM Distinguished Member in 2024. As an educator, he teaches courses including CSE 3241 (Introduction to Database Systems), CSE 5242 (Advanced Database Systems), and CSE 5251 (Introduction to Software Startups). His educational innovations include DBTutor, which integrates LLMs into database systems education. At Ohio State, Nandi co-founded the OHI/O Program, which fosters tech culture through hackathons, and The STEAM Factory, an interdisciplinary research collaboration network. Prior to academia, he was founder and CEO of Mobikit, a connected vehicles data analytics startup acquired by Azuga Inc. (a Bridgestone company). His research has been supported by the NSF and industry partnerships, with applications spanning precision agriculture (CropFusion), clinical data pipelines (ICARUS), and interactive visualization systems (Perceptvis).

Gonzalo Nápolesمشاهده پروفایل

استادیار

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

Gonzalo Nápoles is an Assistant Professor at Tilburg University's School of Humanities and Digital Sciences, Department of Cognitive Science and AI. He holds a Doctoral Degree in Computer Science from Rough Cognitive Networks (2014–2017). His research focuses on AI applications in cognitive modeling, pattern classification, and neural networks with interdisciplinary applications in healthcare, finance, and social sciences. Key research areas include Fuzzy Cognitive Maps, data augmentation techniques for neuroimaging, and interpretable machine learning systems. He actively contributes to UN Sustainable Development Goals related to education and innovation. Recent work explores AI ethics, financial risk assessment using dynamic networks, and sensory processing disorder analysis through neural networks. Education: Doctoral Degree in Computer Science, 2017 (Thesis: Rough Cognitive Networks) Prize-winning research includes Best Paper Awards at CIARP 2021 and IWAIPR 2023. He collaborates internationally, hosting academic visitors and serving on multiple PhD committees. Current projects involve stock prediction using graph neural networks and fMRI data augmentation methodologies. Awards: Best Paper Award - CIARP 2021 Best Paper Award - IWAIPR 2023 Nápoles advises on PhD theses in cognitive science and AI applications. His work bridges theoretical advancements with practical implementations in healthcare, finance, and urban systems.

Simon Clematideمشاهده پروفایل

مدرس

• Natural Language Processing
• Computational Linguistics
• Text Mining
• +۷ مورد دیگر

Simon Clematide is an Academic Associate at the Department of Computational Linguistics within the Faculty of Arts and Social Sciences at the University of Zurich, where he has been actively engaged in research and teaching since the early 2000s. His work spans computational linguistics, natural language processing, and text mining with a particular focus on historical document processing, multilingual applications, and practical implementations of machine learning techniques. Dr. Clematide's research interests encompass Natural Language Processing , Computational Linguistics , Text Mining , Machine Learning , Sentiment Analysis , Named Entity Recognition , and Historical Document Processing . His interdisciplinary approach bridges computer science with humanities applications, particularly in analyzing historical newspapers and multilingual corpora. His work demonstrates strong expertise in developing practical NLP systems that address real-world challenges in document analysis and language processing. Over the past five years, his publication record reveals a consistent focus on historical text processing, multilingual NLP applications, and shared task competitions. His research shows particular strength in named entity recognition for historical documents (CLEF HIPE shared tasks), grapheme-to-phoneme conversion (SIGMORPHON shared tasks), and OCR post-processing for historical newspapers. The interdisciplinary nature of his work is evident in collaborations spanning computer science, linguistics, history, and geography. Dr. Clematide has been instrumental in organizing and participating in numerous shared tasks including CLEF-HIPE (2020-2022), SIGMORPHON (2017-2021), and CoNLL-SIGMORPHON (2017-2020), where his team achieved multiple first and second places. His teaching portfolio includes courses on Text Mining, Machine Learning for NLP, Deep Learning in Language Technology, and Sentiment Analysis, demonstrating his commitment to educating the next generation of computational linguists. He has led or participated in diverse research projects including NFP 77, impresso, Citizen Linguistics Projects (tonaccent.ch, dindialaekt.ch), SPARCLING, KTI project with Eurospider, and biomedical text mining initiatives (MANTRA, SASEBio). His interdisciplinary work extends to collaborations with material scientists and social scientists on concept extraction and text zoning applications. Dr. Clematide has contributed to the development of several computational tools and resources, including finite-state morphology systems for Rumansh Grishun, Standard German, and Swiss German, as well as the CLab web-based virtual laboratory for computational linguistics. His work on crowdsourcing OCR ground truth for heritage corpora demonstrates practical solutions to real-world digitization challenges.

Ka Ho Chowمشاهده پروفایل

استادیار

• Machine Learning
• Cybersecurity
• Artificial Intelligence
• +۶ مورد دیگر

Ka Ho Chow is an Assistant Professor in the Department of Computer Science at the University of Hong Kong, part of the School of Computing and Data Science. He holds a PhD from Georgia Institute of Technology and was previously a research scientist at IBM Research. His research focuses on the intersection of machine learning, cybersecurity, and scalable systems, emphasizing trustworthy AI and defense against security/privacy threats in federated learning, large language models, and visual recognition systems. Key achievements include IBM PhD Fellowship (2022) and Croucher Scholarship (2021). Education: PhD in Computer Science from Georgia Tech (2020), advised by Prof. Ling Liu. His work spans algorithmic optimization, infrastructure resilience, and adversarial machine learning. Current research explores attack-resilient solutions for centralized/federated learning and AI system vulnerabilities. Recent articles highlight innovations in federated learning security, gradient inversion attacks, backdoor detection, and privacy-preserving techniques. He has openings for PhD students interested in AI security and trustworthy systems. His lab collaborates on projects involving blockchain fraud detection (ZipZap), facial recognition privacy (Personalized Masks), and graph neural network robustness. Awards: IBM PhD Fellowship (2022), Croucher Scholarship (2021). Active in guiding PhD candidates and advising on microservices cloud migration (Atlas/SCAD systems). Research outputs include over 30 peer-reviewed papers spanning cybersecurity, AI ethics, and distributed learning frameworks.

Philipp Koehnمشاهده پروفایل

استاد

• Machine Translation
• Natural Language Processing
• Artificial Intelligence
• +۲ مورد دیگر

Philipp Koehn is a Professor in the Department of Computer Science at Johns Hopkins University, with additional affiliation at the University of Edinburgh. His primary research focuses on statistical and neural machine translation, specifically developing methods to leverage large-scale digital information for cross-lingual communication. He leads the Machine Translation Research Group and maintains key resources like the Moses toolkit and Europarl corpus. His research interests span: Core machine translation techniques (statistical/neural approaches) Low-resource and unsupervised translation methods Cross-lingual representation learning Speech-to-speech translation systems Large-scale parallel data mining and alignment Evaluation methodologies for generated text Koehn's recent publications demonstrate strong focus on improving translation efficiency (dynamic compression, streaming models), robustness (noise handling, error correction), and accessibility (low-resource languages, radio speech processing). Key trends include multilingual generalization, document-level coherence, and human-centered evaluation. Significant scientific recognition includes: ACL Fellow (2024) IAMT Award of Honor (2015) European Inventor Award Finalist (2013) He currently advises PhD students Rachel Wicks, Elina Baral, Bismarck Odoom, and Weiting Tan. His Machine Translation Group develops widely-used open-source tools and organizes major conferences including WMT and MT Marathon.

• Digital Humanities
• Palaeography
• Computer Science
• +۳ مورد دیگر

Peter A. Stokes is a Research Professor at the École Pratique des Hautes Études (EPHE) within Université PSL in Paris, specializing in Digital Humanities applied to historical writing. He holds a PhD from the University of Cambridge and has held roles including Leverhulme Early Career Fellowship at the University of Cambridge and Senior Lecturer at King's College London. His research focuses on computational palaeography, multigraphism, and the analysis of historical handwriting. He co-directs eScriptorium, a software project for automatic transcription of manuscripts, and leads the Biblissima+ Cluster 4 project. Education: PhD, University of Cambridge (2006), Dissertation: English Vernacular Script, ca 990 – ca 1035 BA (Hons) in Classics and English Literature & BE (Hons) in Computer Engineering, University of Melbourne (2001) Exchange Studies, Georgetown University (1998) Research Interests: Combining palaeography with digital humanities and computer science, focusing on theoretical and practical aspects of handwriting analysis, multigraphism, and transversal palaeography. Key projects include eScriptorium, DigiPal Framework, and contributions to Exon Domesday and Models of Authority projects. Grants & Awards: European Research Council (ERC) grant for DigiPal (2011–14) AHRC grants for Exon Domesday and Models of Authority (2015–17) Medieval Academy of America Prize (2016) Teaching & Supervision: Leads courses in digital humanities and palaeography at EPHE and King's College London. Supervised PhD students in medieval manuscript studies and digital humanities, including Salomé Pichon and Matilda Watson. Labs & Collaborations: Co-director of eScriptorium; member of Comité International de Paléographie Latine and Humanistica (Digital Humanities association). Active in international projects like Biblissima+ and CultureLab.

Thomas Stäckerمشاهده پروفایل

استاد

• Digital Humanities
• Library Science
• Digitization of Early Printed Books
• +۷ مورد دیگر

Prof. Dr. Thomas Stäcker is a Part-time Professor for Digital Humanities at the Department of Information Sciences, University of Applied Sciences Potsdam. He serves as Director of the University and State Library Darmstadt (since 2017) and Deputy Director of the Herzog August Library Wolfenbüttel (since 2009). His career spans roles as a librarian at Herzog August Bibliothek (since 1998) and Johannes a Lasco Library in Emden (1997-1998). Education: Master's degree in History of Philosophy and Latin (1991), doctorate (1994) from TU Braunschweig, University of Essex, and University of Osnabrück Research: Digital Humanities, digitization of early printed books, XML/TEI encoding, OCR technologies, and cultural heritage preservation Leadership: Key roles in major digitization projects (e.g., VD17, Dünnhaupt Digital) and digital library development His work bridges library science with scholarly research, emphasizing open access, digital editions, and metadata standards. He has authored/co-authored numerous monographs, including the 2015 'Grenzen und Möglichkeiten der Digital Humanities' and the 2016 Festschrift chapter on open access. Stäcker co-edited digital editions like Christoph Heidmann's Oratio de Bibliotheca Julia (2013) and maintained active engagement through blogs like 'dhd-blog.org' (2015 post on humanities research data).

• Image Processing
• Pattern Recognition
• Machine Learning
• +۶ مورد دیگر

Jean-Christophe Burie is a Professor at the University of La Rochelle, serving as Vice-President for Digital Campus and Information Systems, Deputy Director of the L3i Laboratory (Informatics, Imaging, and Interaction), and Director of the joint research laboratory SAIL (Sequential Art Image Laboratory). His interdisciplinary work bridges computer science and humanities. His research focuses on image processing, pattern recognition, and artificial intelligence , with applications in historical manuscripts, comics indexing, and digital security. Key projects include enhancing palm-leaf manuscript analysis (AMADI/STIC ASIE), developing e-BDthèque for comics content extraction, and identity verification systems (MOBIDEM/IDECYS). He collaborates internationally with institutions like Leiden University and Vietnam's ICT Lab on medical imaging and cultural heritage preservation. He advises doctoral students in areas spanning ancient text analysis, document security, and manga character recognition. His recent publications demonstrate a strong emphasis on deep learning adaptations for document analysis, biometric security, and cross-modal recognition systems.

James Hadleyمشاهده پروفایل

دانشیار

• Translation Studies
• Digital Humanities
• Literary Translation
• +۳ مورد دیگر

James Hadley is an Associate Professor in Russian and Slavonic Studies and holds the Ussher Assistant Professorship in Literary Translation at Trinity College Dublin. He directs the MPhil in Literary Translation program and leads the Irish Research Council-funded QuantiQual Project. His research focuses on translation studies, particularly indirect translations, employing digital humanities and empirical methods to explore previously unanswerable questions. He is based at the Trinity Centre for Literary and Cultural Translation, fostering public engagement through events and initiatives. Dr. Hadley’s work bridges literary translation, technology integration, and computational analysis. He actively recruits doctoral/postdoctoral researchers interested in digital humanities applications in translation studies, especially regarding indirect translation. His research has addressed topics such as machine translation impacts, sentiment classification, and historical translation practices in Japan. He can be contacted at hadleyj@tcd.ie for collaborative opportunities. His academic contributions span theoretical frameworks (e.g., the Concatenation Effect Hypothesis) and practical applications like OCR tools for historical texts. The Trinity Centre serves as a hub for interdisciplinary translation studies, emphasizing both scholarly rigor and public accessibility.

Milena Dobrevaمشاهده پروفایل

مدرس ارشد

• Cultural Heritage Digitization
• Digital Libraries
• Media Literacy
• +۵ مورد دیگر

Dr. Milena Dobreva is a Senior Lecturer in Information Behavior at the Department of Computer and Information Sciences, University of Strathclyde, Scotland. She holds roles in the Faculty of Science and is affiliated with the Strathclyde iSchool. Her work focuses on innovation diffusion in cultural and scientific heritage sectors, media literacy, and combating misinformation. She has led projects funded by the European Commission, including the Bulgarian Romanian Observatory of Digital Media and the LEARN & EXCHANGE initiative to develop international MOOCs on digital transformation. Her research explores user needs in digital heritage, open innovation tools for cultural institutions, and digital skills training in Sub-Saharan Africa. Professional Activities Principal Investigator on projects addressing digital divide, GLAM innovation labs, and disinformation policy. Member of the Scientific Board of DARIAH and the Europeana Network Association. Peer reviewer for journals like Transformations: A DARIAH Journal and conferences such as ACM/IEEE-CS JCDL. Research Interests Innovation labs for GLAM sectors (Galleries, Libraries, Archives, Museums). Data spaces and digital transformation frameworks for cultural heritage. Information behavior in the context of disinformation and media literacy. Open science infrastructures and policy implementation. Grants & Projects Bulgarian Romanian Observatory of Digital Media (2022–2025): Combats disinformation through regional collaboration. LEARN & EXCHANGE (2022–2024): Develops multilingual MOOCs for digital transformation skills. eCHOIng (2021–2024): Uses open innovation to revitalize cultural heritage institutions. Labs & Collaborations Co-leads GLAM innovation labs research, focusing on digital divide mitigation and heritage digitization. Active in the CEDCHE (Common European Data Space for Cultural Heritage) initiative.

R. Manmathaمشاهده پروفایل

استاد مدعو

• Computer Vision
• Information Retrieval
• Image and Video Retrieval
• +۸ مورد دیگر

R. Manmatha is an Adjunct Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst and a Principal Scientist at Amazon A9 since 2013. His academic journey includes a Ph.D. in Computer Science from University of Massachusetts Amherst (1997), an M.S. in Electrical Engineering from University of Hawaii (1986), and a B.Tech in Electrical Engineering from Indian Institute of Technology Kanpur (1983). Research Interests Manmatha's work spans Computer Vision , Information Retrieval , and Document Analysis . Key projects include: Developing Vision-Language Models for GUI grounding and OCR-free document understanding Creating Word Spotting techniques for historical manuscripts like George Washington's papers Advancing Image Retrieval through statistical and relevance models Building Meta Search systems using score distribution analysis Optimizing Diffusion Transformers for text-to-image generation Scientific Contributions His research has led to numerous publications in conferences like SIGIR , CVPR , and ICDAR , focusing on: Automatic Image Annotation using cross-media relevance models Scale Space Techniques for handwritten manuscript segmentation Alignment Methods for document-groundtruth generation Indian Language Document Search via locality-sensitive hashing Transformer-based architectures for multimodal and document tasks Advising & Collaborations Manmatha has mentored students including Jiwoon Jeon , Shaolei Feng , Toni Rath , Jamie Rothfeder , and Nitin Srimal . He co-founded Snaptell (acquired by Amazon) and contributed to Amazon's mobile search technology. Labs & Teams He leads the Multi-media Indexing and Retrieval (MIR) group at the Center for Intelligent Information Retrieval (CIIR) , focusing on non-textual information indexing through ASCII conversion and direct content analysis.