Find Academic Supervisors

• Digital Humanities
• Media Studies
• History of the Book
• +2 more

J. Elyse Graham is a Professor in the Department of English at Stony Brook University , specializing in Digital Humanities , Media Studies , and the History of the English Language . Her research bridges traditional literary analysis with digital innovation, focusing on textual culture across print and digital media. Ph.D. in English, Yale University (2015) She has authored books for prestigious publishers like Oxford University Press , Stanford University Press , and McGill-Queen's University Press , with a current book project underway for HarperCollins . Her teaching includes courses such as Introduction to Poetry , Science, Technology and Humanities , and graduate seminars on Storytelling With Data and Studies in Language and Linguistics . Graham's research explores the evolution of media, from the Printing Press as Metaphor to contemporary Digital Art and Annotation Practices . Her work on Database Thinking and Deep Description highlights her commitment to developing digital tools for humanities scholarship. She has contributed to debates on Boundary Maintenance and the Origins of Trolling , examining how digital communities shape discourse. Recent publications include Digital Art in Ireland (2023) and Joyce and the Graveyard of Digital Empires (2019).

Robert Godwin-JonesView profile

Professor

• Computer-Assisted Language Learning
• Second Language Acquisition
• Artificial Intelligence in Language Education
• +3 more

Robert Godwin-Jones is a Professor in the Department of Foreign Language at Virginia Commonwealth University's College of Humanities and Sciences, where he has served since 1979. He holds the position of Foreign Language Liaison and maintains active scholarly contributions through his "German Stories" digital repository and "Communicating Across Cultures" blog, focusing on technology-enhanced language pedagogy and intercultural communication. His educational background includes: B.A. in French and German from Franklin and Marshall College (1971) M.A. in Comparative Literature (German, French, and English) (1973) Ph.D. in Comparative Literature from the University of Illinois Urbana-Champaign (1977) Dr. Godwin-Jones specializes in Computer-Assisted Language Learning (CALL), with research spanning artificial intelligence applications, virtual reality environments, and digital literacies for language learners. His work examines how emerging technologies transform second language acquisition through ecological semiotics, distributed agency models, and multimodal communication frameworks. He investigates pedagogical translanguaging strategies for less commonly taught languages and analyzes authenticity in AI-mediated language interactions, particularly in pragmatics and writing development. Analysis of his 2021-2025 publications reveals three dominant trajectories: (1) generative AI integration in language instruction with emphasis on ethical implementation and threat mitigation; (2) extended reality applications creating immersive language spaces in the metaverse; and (3) ecological approaches connecting smart devices, ambient intelligence, and situated learning contexts. His scholarship consistently bridges theoretical frameworks with practical classroom applications across 19th-century German literature studies and contemporary language education. He maintains active professional engagement through memberships in: Delta Phi Alpha German Honorary Society Phi Kappa Phi Honorary Society Modern Language Association American Association of Teachers of German American Association of Teachers of French Foreign Language Association of Virginia As director of the "German Stories" project since 1994, he has curated verified editions of 19th-century German narratives with English translations under Creative Commons licensing, creating what has become a foundational resource for literary study and language acquisition. His blog extends this work through contemporary analyses of intercultural communication challenges, while his scholarly leadership in organizations like CALICO shapes national discourse on technology in language education. His laboratory functions as the "German Stories" digital ecosystem, which has evolved into a comprehensive platform hosting annotated texts, pedagogical resources, and research archives. This environment supports both formal classroom instruction and informal learning communities, demonstrating his commitment to porous classroom models that integrate physical and digital learning spaces.

Emmanuel MorinView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Bilingual Terminology Extraction
• +5 more

Emmanuel Morin is a Full Professor in Computer Science at the University of Nantes, France. He is affiliated with the Computer Science Department of Nantes Institute of Technology (IUT) and leads the Natural Language Processing (TALN) team at the Digital Sciences Laboratory of Nantes (LS2N UMR CNRS 6004). His research focuses on computational linguistics with particular emphasis on multilingualism and multimodality. His academic leadership includes serving as Head of the NLP team (2017-present), Co-responsible for the ATAL (Machine Learning and Natural Language Processing) option of the Computer Science Master (2017-present), and Director of the Nantes computer science training department (2014-present). He also co-edits the Traitement Automatique des Langues (TAL) journal and serves on the steering committee of ATALA since 2004. Current projects: Atlantic 2020, ALALA Project (2019-present), ANR ADDICTE (2017-present), and Atlanstic 2020 RAPACE Project (2016-present) Past projects: Labex CominLabs LIMAH (2014-2019), ANR CRISTAL (2012-2016), and European FP7-ICT TTC project (2010-2012) Professor Morin's research spans natural language processing with a focus on bilingual terminology extraction and comparable corpora analysis. His recent work (2020-2022) shows strategic expansion into medical informatics applications like FrenchMedMCQA, while maintaining his foundational work on domain adaptation of language models using graph-based networks. His publications demonstrate a consistent trajectory from early terminology extraction methods to contemporary applications in specialized language processing. He has supervised numerous PhD students, with current advisees including Kévin Espasa, Martin Laville, Merieme Bouhandi, and Antoine Caubrière. His past students have made significant contributions to the field of natural language processing, continuing his research legacy in academia and industry.

Benjamin BachView profile

Lecturer

• Network Visualization
• Spatio-temporal Data Visualization
• Data-driven Storytelling
• +5 more

Benjamin Bach is a Lecturer (Assistant Professor) in Design Informatics and Visualization at the University of Edinburgh , affiliated with the School of Informatics and the Centre for Design Informatics . Education : PhD in Computer Science from Université Paris Sud (2014), MSc in Computer Science (Diplom Medieninformatik) from University of Technology Dresden (2010). Research Interests focus on designing interactive visualization interfaces to explore, communicate, and understand complex data. Key areas include: Network Visualization Immersive Analytics (Augmented/Virtual Reality) Data-driven Storytelling Collaborative and Non-digital Visualization Visualization of Spatio-temporal Data Graph Databases and Dynamic Networks His work integrates these themes into tools like networkcube and Vistorian , emphasizing interdisciplinary applications in biology, neuroscience, and history. Current projects explore annotation systems, dynamic network analysis, and geographic network visualization. Scientific Awards : Honorable mention for Best PhD Thesis by IEEE Visualization Committee (2014). Supervision : Mentored 14 graduate students (MSc, MA, B.Hons) and actively seeks PhD/MSc candidates in network visualization, data storytelling, and immersive analytics. Collaboration : Works within the interdisciplinary Centre for Design Informatics , which bridges data science, design, and digital humanities.

Rrezarta KrasniqiView profile

Assistant Professor

• Software Engineering
• Requirements Engineering
• Software Quality
• +3 more

Rrezarta Krasniqi is an Assistant Professor in the Department of Software and Information Systems at the University of North Carolina at Charlotte. She holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2024) and has previously taught at multiple institutions while working as a senior Java developer in industry. Research Focus: Her work centers on improving software quality through automated detection of quality-related bugs, using AI-driven approaches and empirical methodologies to address challenges in code scattering, requirement vagueness, and system-wide reliability issues. She specializes in semantic analysis, classifier development, and 3D visualization tools for codebase monitoring. Key Contributions: Developed RetroRank for bug-fixing comment recommendation (2021-2023) Pioneered SoftQualDetector for semantic quality concern mapping (2023-2024) Co-authored surveys on quality concern management in open-source communities Publications: Her work spans leading venues like EMSE'23, ICSME'23, SANER'23, and SQJ'23, with earlier contributions at ICSE'2017 and FSE'2018 through the TraceLab reproducibility framework.

Vitaveska LanfranchiView profile

Research Fellow

• Human-Computer Interaction
• Social Media
• Visual Analytics
• +3 more

Vitaveska Lanfranchi is a Senior Research Fellow at the University of Sheffield , School of Computer Science, focusing on Human-Computer Interaction , Social Media , and Visual Analytics . Her work addresses real-time data analysis for emergency response and knowledge management, with projects funded by EPSRC, UK/SBRI, and EU Framework 6. Research Interests : Human-Computer Interaction Social Media for Emergency Response Visual Analytics Knowledge Management Mobile Interaction Recent publications (2008–2012) explore semantic user networks, knowledge dashboards, hybrid search techniques, and AI usability, particularly in aerospace engineering and emergency response. Projects like Randms and WeKnowIt emphasize collective intelligence and web-scale data analysis. Contact : v.lanfranchi@dcs.shef.ac.uk

Craig KnoblockView profile

Research Professor

• Knowledge Graphs
• Semantic Web
• Data Integration
• +7 more

Craig Knoblock serves as the Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California, Vice Dean of Engineering for the Viterbi School of Engineering, and Research Professor of both Computer Science and Spatial Sciences. He also directs USC's Data Science Program and leads the Center on Knowledge Graphs as Research Director. Dr. Knoblock's research focuses on techniques for describing, acquiring, and exploiting the semantics of data. His extensive work spans source modeling, schema and ontology alignment, entity and record linkage, data cleaning and normalization, extracting data from the Web, and building comprehensive knowledge graphs. With over 300 published works in these areas, his research has significantly advanced the field of semantic data integration. His work demonstrates strong trends toward practical applications of knowledge graphs across diverse domains including cultural heritage, geospatial information systems, human trafficking detection, and sensor networks. The evolution of his publications shows a progression from foundational ontology alignment techniques to sophisticated knowledge graph applications addressing real-world challenges. Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) Fellow of the Association of Computing Machinery (ACM) Past President and Trustee of the International Joint Conference on Artificial Intelligence (IJCAI) Recipient of the 2014 Robert S. Engelmore Award Seven best paper awards for his research contributions As Executive Director of USC's Information Sciences Institute, Dr. Knoblock leads one of the world's premier research centers in computer science and information technology. His leadership extends to directing the Center on Knowledge Graphs and serving as Associate Director of the Informatics Program at USC. His educational background includes a Bachelor of Science from Syracuse University and Master's and Ph.D. degrees in Computer Science from Carnegie Mellon University.

Robb W LindgrenView profile

Professor

• Embodied Learning
• Educational Technology
• Mixed Reality Simulations
• +5 more

Robb W Lindgren is a Professor at the University of Illinois at Urbana-Champaign with appointments in Curriculum and Instruction and Educational Psychology . He serves as Associate Dean for Research in the College of Education and holds affiliations with the National Center for Supercomputing Applications (NCSA) , Beckman Institute for Advanced Science and Technology , and Center for Social & Behavioral Science . His research focuses on Embodied learning through gesture and physical interaction Design of mixed/augmented reality educational systems Collaborative STEM education with immersive technologies Recent publications highlight his work in: Biochemistry simulations using haptic feedback (2024) VR-based spatial reasoning for astronomy education (2023) Metaverse learning environments with theory-driven design (2023) Climate change simulations with full-body tracking (2022) His research group explores how physical movement and gestural interfaces shape scientific understanding and conceptual change. Key collaborations include work with Jee Hyang Park , Jun Kang , and Thomas Kim , focusing on Gesture-mediated collaboration XR learning analytics Agency in embodied design

• Astronomy
• Astrophysics
• Machine Learning
• +5 more

Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.

Viviana CotikView profile

Associate Professor

• Artificial Intelligence
• Natural Language Processing (BioNLP)
• Information Extraction
• +5 more

Dr. Viviana Cotik is an Associate Professor at the Department of Computer Science, Faculty of Exact and Natural Sciences, University of Buenos Aires. She also serves as Professor for the Master's Degree in Data Mining and Knowledge Discovery within the same department. Additionally, she works as an Assistant Researcher at the National Council for Scientific and Technical Research (CONICET) in Argentina. Artificial Intelligence Natural Language Processing (BioNLP) Information Extraction Machine Learning Biomedical Informatics Social Applications of AI Her research focuses on applying natural language processing techniques to biomedical domains, including negation detection in clinical reports, entity recognition in radiology texts, and cross-lingual information extraction. She investigates applications of AI in social inclusion and has developed methodologies for automatic classification of medical documents through machine learning and syntactic analysis. Her work also extends to data quality assessment frameworks and historical analysis of ICT education in Argentina. Recent publications highlight her expertise in Spanish and French language processing for biomedical texts, with applications in radiology report analysis and social implications of AI. She has contributed to multiple conferences including IberLEF, RANLP, ACL BioNLP, and IJCAI workshops, while also publishing in journals like International Journal of Information Quality and Fuzzy Sets and Systems . Scientific recognition: First prize in Estudiantiles Works Contest (undergraduate category), 33rd JAIIO, 2004 She has delivered numerous guest lectures internationally, including at Stanford's Women in Data Science chapter, Universidad Nacional de La Plata, and institutions in Peru, Germany, UK, and Spain. Her outreach activities include coordinating the Computer History Museum and organizing Drunk Talks for PhD students.

• Bioinformatics
• Computational Biology
• Algorithms
• +5 more

Travis Wheeler is an Associate Professor in the Department of Pharmacy Practice & Science at the University of Arizona. His work spans bioinformatics, computational biology, and algorithm development for genomic sequence analysis. Developed tools like HMMER and Dfam Research focuses on transposable elements, sequence alignment, and epigenetics Co-author of key works with Robert Finn, Sean Eddy, and colleagues Research Interests include machine learning applications in biological sequence annotation, drug discovery, and evolutionary genomics. His work bridges computational methods with biomedical applications. Notable Contributions : Advancing profile Hidden Markov Model (HMM) methodologies Creating community resources for transposable element research Developing alignment algorithms for biological sequences Collaborations include institutions like Institute for Systems Biology, Harvard University, and Montana State University.

Joanna DolińskaView profile

Assistant Professor

• computational linguistics
• sociolinguistics
• language documentation
• +6 more

Joanna Dolińska is an Assistant Professor at the Faculty of Artes Liberales, University of Warsaw, specializing in linguistics with a focus on computational linguistics, language documentation, sociolinguistics, and historical linguistics. She has held prestigious research positions including Post-Doctoral Fellowships at the Smithsonian Institution (USA) and Max Planck Institute for Geoanthropology (Germany), as well as visiting researcher appointments at University of Cambridge, University of Groningen, Mahidol University, and University of Strasbourg. Dr. Dolińska earned her PhD in Comparative Altaic Linguistics from the University of Warsaw in 2019, following Master's degrees in Applied Linguistics (2011) and Oriental Studies (2015), and a Bachelor's degree in Oriental Studies (2012). Her academic journey included study periods at the National University of Mongolia, University of Leipzig, and University of Duisburg-Essen. Her research interests center on the application of natural language processing tools to low-resource languages, with particular focus on Mongolic and Tungusic languages, minority languages of Thailand and Alaska, and the Altaic language family. Dr. Dolińska leads the project "Interdependence of multilingualism and biodiversity in the Chiang Mai and Satun provinces in Thailand" at the University of Warsaw, and has been intensively studying Thai language for the past two years. Her scholarly work demonstrates a consistent focus on language documentation, computational approaches to linguistic analysis, and the preservation of endangered languages. She has published extensively on Mongolic languages, particularly Dagur, and has contributed significantly to the debate on the Altaic language family hypothesis through comparative analysis of grammatical structures. Dr. Dolińska has received recognition through research stays at prestigious institutions worldwide, including recent appointments at the Smithsonian Institution (2023-2024), Mahidol University (2023), University of Groningen (2023), University of Strasbourg (2023), University of Cambridge (2022), and Max Planck Institute for Geoanthropology (2020-2021). She actively collaborates with language communities in Thailand, Myanmar, and other regions with linguistic diversity, focusing on community-based approaches to language preservation and the development of digital tools for minority languages. Her work bridges theoretical linguistics with practical applications for language documentation and revitalization.

• Corpus Linguistics
• Vocabulary
• Language Testing
• +7 more

Raffaella Bottini is a Lecturer in Corpus Linguistics at the School of Social Sciences, Lancaster University . She completed her PhD at the ESRC Centre for Corpus Approaches to Social Science (CASS), with prior academic training including an MA in Applied Linguistics (King's College London) and an MA in English Language and Literary Studies (Università degli Studi di Milano, Italy). Research Interests: Her work centers on applying corpus linguistics to language testing and second language acquisition, with a focus on lexical complexity and statistical analysis of learner corpora. She develops quantitative methods and automatic tools for analyzing vocabulary use across proficiency levels in spoken exams (e.g., the Trinity Lancaster Corpus). Her research expands into register analysis, data visualization, and pedagogical applications. Teaching: Bottini leads courses on corpus design, statistics, and vocabulary analysis in Lancaster's distance MA programs in Corpus Linguistics and TESOL. She also contributes to undergraduate modules on corpus-based English studies and summer schools in corpus linguistics. Scientific Awards: PhD Research Award PhD Students: Reem Alojaimi Chris Ott Projects: Includes "Linguistic demands of EMI in Higher Education" (2022-2025), analyzing EMI contexts in China, Italy, and Thailand, and participation in "Culture and Communication: Global Perspectives" and the LAEL-China Network.

Alejandro RussoView profile

Professor

• Computer Science
• Information Security
• Functional Programming
• +3 more

Alejandro Russo is a Professor at Chalmers University of Technology , specializing in Information Flow Control (IFC) , Secure Programming Languages , and Functional Programming . His research bridges theoretical foundations and practical implementations, focusing on mitigating timing channels , covert channels , and data leakage in concurrent systems. Developed novel frameworks for Differential Privacy with provable accuracy bounds Pioneered COWL integration for browser security and instruction-based scheduling to prevent cache timing attacks Led major projects like HIPSTER (hybrid static/dynamic IFC) and AppFlow (practical IFC deployment) His publications reveal expertise in security libraries for Haskell and Python , with a focus on faceted execution , label manipulation , and mechanized security proofs . Students under his supervision have explored topics ranging from secure eDSLs to privacy-preserving compilation techniques . Scientific awards : Google Research Award (2011) for Python taint analysis Advising and grants : Principal Investigator for VR , STINT , and Google Research Award Supervised 12+ PhD and Master’s students in security and functional programming research

• Image Registration
• Image Segmentation
• Image Classification
• +6 more

Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.