Find Academic Supervisors

R. ManmathaView profile

Adjunct Professor

• Computer Vision
• Information Retrieval
• Image and Video Retrieval
• +8 more

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.

Torsten ZeschView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Educational Technology
• +6 more

Prof. Dr.-Ing. Torsten Zesch serves as Deputy Scientific Director, Member of the Executive Board, and Head of the Research Professorship for Computational Linguistics at FernUniversität in Hagen since March 2022. He previously held W2 and W1 Professorships for Sprachtechnologie (Language Technology) at Universität Duisburg-Essen from 2014-2022. Zesch is also Spokesperson of the Advisory Board of the German Society for Computational Linguistics and Language Technology (GSCL) since 2024, having previously served as GSCL President from 2018-2024. Dr. Zesch completed his dissertation (Dr.-Ing.) in Computer Science at Technische Universität Darmstadt in 2009. His research spans robust language processing systems, analysis of non-standard language structures, and educational applications of language technology, with particular focus on automatic content scoring and spelling error correction for learner language. Zesch's recent publications reveal a strong focus on integrating language technology with educational applications. His work demonstrates expertise in developing practical NLP solutions for educational assessment, including transformer-based spelling error feedback systems, hierarchical automatic scoring methods, and leveraging LLMs for educational applications despite cold-start problems. He also maintains significant research in hate speech detection, particularly in multimodal contexts and visio-linguistic models. As leader of the Computational Linguistics research professorship within the CATALPA research center, Zesch directs a team investigating how language technology can support educational processes. His projects DAKODA and KISS-Pro focus on language acquisition and educational applications. He has contributed significantly to the field through numerous publications in top NLP and educational technology venues, with over 100 publications spanning from 2006 to present.

• Natural Language Processing
• Machine Translation
• Speech Processing
• +3 more

Popescu-Belis Andrei serves as an Associate Professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO), specifically within the Institute of Information and Communication Technologies (IICT) at HEIG-VD campus in Yverdon-les-Bains. His academic work spans teaching computer science fundamentals and advanced natural language processing concepts across both undergraduate and graduate programs. His research interests focus on Natural Language Processing , Machine Translation , and Human-Machine Dialogue systems, with particular emphasis on low-resource language scenarios. His work addresses challenges in speech-to-speech translation, subword tokenization, neural machine translation optimization, and creative applications like poem generation. Recent publications through 2025 demonstrate his ongoing contributions to the field, particularly in multilingual NMT training schedules and low-resource language processing pipelines. Analysis of his 15 most recent publications reveals consistent focus on practical solutions for language barriers, with increasing attention to reinforcement learning applications and creative language generation. His work on GPoeT demonstrates the intersection of traditional literary forms with modern language models, while his research on low-resource translation pipelines addresses real-world community interpreting needs. He has led significant research projects including Digital Lyric (2019-2020), a collaboration with UNIL (University of Lausanne) to develop systems for aiding poem generation for a public exhibition at Morges Castle. This project demonstrates his interdisciplinary approach bridging computational linguistics with creative applications. His academic contributions include: Development of specialized training pipelines for low-resource neural machine translation Innovative approaches to speech-to-speech translation for community interpreting Novel methods for constrained language model applications in creative writing Significant contributions to understanding subword tokenization mechanics Popescu-Belis maintains active research collaborations across institutions, as evidenced by his work with UNIL and various international conference publications. His teaching portfolio includes courses in natural language processing, data preparation, unsupervised machine learning, and computer science fundamentals, demonstrating his commitment to both theoretical and practical aspects of the field.

Hernisa KacorriView profile

Associate Professor

• Human-Computer Interaction
• Assistive Technologies
• Accessibility Research
• +5 more

Hernisa Kacorri is an Associate Professor in the College of Information (iSchool) at the University of Maryland with an appointment in the University of Maryland Institute for Advanced Computer Studies. She is a member of the Human-Computer Interaction Lab (HCIL) and the Institute for Trustworthy AI in Law & Society. Her research focuses on building technologies that address real-world problems by integrating data-driven methods and human-computer interaction, with particular emphasis on assistive technologies for people with disabilities. Her work includes developing sign language avatars, technologies for independent mobility for the blind, and math-to-speech systems. Dr. Kacorri's research primarily addresses accessibility challenges through technological innovation. Her work spans multiple domains including American Sign Language animation where she develops systems for synthesizing syntactic facial expressions, technologies for people with visual impairments focusing on object recognition and navigation, and accessibility solutions for mathematical content. Her approach combines data-driven methods with rigorous user-centered evaluation involving disabled communities. Her publication record shows a consistent focus on accessibility technologies, particularly in American Sign Language processing and visual impairment solutions. Her work demonstrates strong interdisciplinary connections between computer science, linguistics, and human factors research, with a clear emphasis on practical applications that address real accessibility barriers. Best Paper Honorable Mention at CHI 2017 Best Paper Finalist at ASSETS 2016 NSF Award #1506786 for Syntactic Facial Expression Synthesis research Dr. Kacorri collaborates with researchers at Boston University and Rutgers University on sign language processing projects. Her research methodology emphasizes involving disabled users early in the design process and developing rigorous evaluation frameworks that account for demographic factors and user experience. She has contributed to the development of automatic metrics for evaluating sign language animations and has explored innovative approaches like gamification to improve video captioning through crowdsourcing. Her laboratory work focuses on the Human-Computer Interaction Lab at the University of Maryland, where she leads projects on assistive technologies, sign language animation, and accessibility solutions for mathematical content. Her research group develops technologies that model communication and behavior to benefit people with disabilities.

Yuning DingView profile

Researcher

• Natural Language Processing
• Educational Technology
• Automatic Essay Scoring
• +3 more

Yuning Ding is a PhD Student and Research Assistant in the junior research group 'EduNLP' at the Research Center CATALPA (Center of Advanced Technology for Assisted Learning and Predictive Analytics), FernUniversität in Hagen, since January 2022. Her work focuses on Natural Language Processing applications for educational technology, specifically developing systems for automatic essay scoring and generating formative feedback for learners and summative feedback for teachers. Her educational background includes: M.Sc. in Applied Cognitive and Media Science with Specialization in Cognition & Artificial Intelligence at University of Duisburg-Essen (2017-2019) B.Sc. in Applied Cognitive and Media Science at University of Duisburg-Essen (2014-2017) B.A. in Communications at University of International Relations, Beijing (2009-2013) Ding's research centers on leveraging NLP to enhance writing education through AI-driven assessment and feedback systems. Her work bridges computational linguistics and pedagogy, with particular emphasis on argument mining, cohesion analysis, and cross-lingual content scoring. She investigates how transformer models and multi-task learning can improve the reliability and educational value of automated writing evaluation, while addressing critical issues like fairness and adversarial vulnerability in scoring systems. Her research demonstrates how NLP can provide actionable insights for both students and educators in writing development. Analysis of her publication trends reveals a strategic progression from foundational work on content scoring and error analysis toward sophisticated integrated systems. Recent work emphasizes multimodal feedback generation, argument-cohesion integration, and cross-lingual transfer, with increasing focus on real-world implementation challenges including fairness, robustness, and user experience. Her research spans multiple languages and educational contexts, reflecting a commitment to globally applicable educational technology. Within CATALPA, Ding actively collaborates across disciplines through the center's vibrant knowledge-sharing culture. She participates in project presentations and colloquia that facilitate cross-pollination of ideas between computer science, linguistics, and educational theory. Her work on the DARIUS corpus and FEAT-writing system demonstrates tangible contributions to educational resource development and interactive learning environments.

Annemarie FriedrichView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Computational Syntax
• +7 more

Annemarie Friedrich is a tenured University Professor for Natural Language Understanding (Computational Linguistics) at the Faculty of Applied Computer Science, University of Augsburg. She also holds membership in the Faculty of Philology and History. Previously, she worked as a Senior Expert on Natural Language Processing and Computational Linguistics at the Bosch Center for Artificial Intelligence. Currently, she serves as president of the German Society for Computational Linguistics (GSCL), the primary scientific association for NLP research in German-speaking regions, and is a member of the ACL Special Interest Group for Annotation (ACL SIGANN). University: University of Augsburg School: Faculty of Applied Computer Science Department: Institute of Computer Science Position: University Professor (tenured) for Natural Language Understanding Professor Friedrich's research focuses on computational linguistics and natural language processing with emphasis on semantics and information extraction from text. Her work spans both machine-learning oriented approaches to text mining for scientific text, syntactic and semantic parsing, and uncertainty in deep learning for NLP, as well as corpus-linguistic research on syntax-semantics interface, discourse, pragmatics, aspect, genericity, and modal verbs. She has particular expertise in annotation and corpus creation, recognizing that machine learning models depend fundamentally on underlying data quality. Her research group at Augsburg actively contributes to computational linguistics through numerous publications and datasets. Analysis of Professor Friedrich's recent publications reveals a strong focus on table question answering, patent text processing, uncertainty modeling, and multimodal scientific document understanding. Her work consistently bridges theoretical linguistics with practical NLP applications, with increasing emphasis on robust evaluation methodologies and domain-specific adaptations of language models. Notably, her research group has produced significant resources including the AnnoCTR dataset for cyber threat reports, PAP2PAT for patent generation, and FREB-TQA for evaluating table QA robustness. Professor Friedrich actively mentors multiple PhD students working on diverse topics including document-level patent processing (Valentin Knappich), temporal processing (Timo Schrader), document-level text modeling (Wei Zhou), and topic modeling for digital forensics (Jenny Maria Felser). Her collaborative approach is evident in co-supervision arrangements with researchers from institutions including Bosch Center for Artificial Intelligence, TU Dresden, and Hochschule Mittweida. She has also successfully guided previous PhD students including Sophie Henning (Uncertainty Modeling), Stefan Grünewald (Syntactic Dependencies), and Subhash Pujari Chandra (Neural Patent Classification). Her teaching responsibilities include courses such as Introduction to Natural Language Processing, Introduction to Python Programming, and specialized seminars on Natural Language Understanding for both Bachelor's and Master's students. These courses reflect her commitment to both theoretical foundations and practical implementation skills in computational linguistics.

Laura DietzView profile

Associate Professor

• Information Retrieval
• Natural Language Processing
• Knowledge Graphs
• +3 more

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire. She leads the TREMA lab and focuses on text-based machine learning, information retrieval, and environmental data science. Her academic journey includes postdoctoral roles at Mannheim University and the University of Massachusetts, alongside a PhD from the Max Planck Institute for Informatics. Education: PhD in Computer Science (Max Planck Institute, 2011), followed by postdoctoral research at UMass (2012–2015) and Mannheim (2015–2016). Tenure-track appointment at UNH since 2016. Research Interests : IR+NLP: Integrates knowledge graphs, entity retrieval, and machine learning to automatically generate comprehensive articles. This work is supported by an NSF CAREER Award (2019–2023). Watershed Data Science: Analyzes storm event impacts using time-series forecasting, with grants from the Northeast Big Data Hub. Machine Learning: Develops methods for sequential data analysis across domains like social media and environmental science. Grants & Awards : NSF CAREER Award (Utilizing Fine-Grained Knowledge Annotations) Elite Postdoc Program of BW-Stiftung (2016–2019) Best Paper at JCDL 2018 for Entity-Aspect Linking Service & Leadership : Organized HIPstIR Workshop on non-mainstream IR research Coordinated TREC Complex Answer Retrieval Track (2017–2019) Keynote speaker at ECIR, NAACL, and SPIRE Labs & Teams : Head of TREMA Lab at UNH, focusing on query-driven knowledge synthesis and environmental data science. Collaborates with faculty in Natural Resources and the Environment Department.

• Educational Measurement
• Psychological Testing
• Automatic Item Generation
• +5 more

Dr. Mark J. Gierl is a Professor of Educational Psychology at the University of Alberta's Faculty of Education, holding the Tier 1 Canada Research Chair in Educational Measurement. His research focuses on automatic item generation (AIG), assessment engineering, and psychometric methods for evaluating test translation. He authored the 2021 book Advanced Methods in Automatic Item Generation , co-authored with Hollis Lai and Vasily Tanygin. His work integrates cognitive modeling, automated test assembly, and multilingual automated scoring systems. Education: PhD in Educational Psychology from the University of Illinois Urbana-Champaign (College of Education). Research interests include large language model applications in assessment, AIG for medical and science education, and cross-linguistic scoring frameworks. Key contributions include developing AIG systems for secure testing and validation frameworks for generated items across disciplines. Research highlights: Over 50 peer-reviewed articles on topics like neural semantic analysis for video assessment, automated bone loss measurement in dentistry, and LLM-driven reading comprehension item generation. His work emphasizes ethical AI integration in educational measurement, test security, and multilingual assessment equity. Awards: Holder of the prestigious Tier 1 Canada Research Chair since 2021. His work bridges advanced computational methods with foundational psychometric principles to revolutionize modern testing practices.

• Machine Learning
• Big Data
• Computational Linguistics
• +4 more

Jakob Andersen is a Researcher at the Department of Computer Science, Faculty of Engineering and Computer Science, Hamburg University of Applied Sciences. His work focuses on machine learning, big data, and computational linguistics, with specific emphasis on text classification, uncertainty sampling, and human-in-the-loop systems. His recent publications explore real-time active learning, sustainable text classification, and edge device reliability. He contributes to open-source frameworks like Forum 4.0 and MINTFIT, focusing on user comment analysis and computer science education. Andersen is a member of the Association for Computational Linguistics (ACL) and has published extensively in top-tier conferences like ACL-IJCNLP, EACL, COLING, and IEEE BigData Congress since 2016.

Marie BexteView profile

Researcher

• Automatic Content Scoring
• Image-Based Task Analysis
• Natural Language Processing
• +1 more

Marie Bexte is a Research Assistant at the FernUniversität in Hagen's Computational Linguistics professorship since April 2022. She previously worked at the Language Technology Lab at the University of Duisburg-Essen (2021–2022). Holding an MSc in Applied Cognitive and Media Sciences with a focus on Cognition and Artificial Intelligence (2021), and a BSc in the same field (2018), her work centers on automated content scoring and image-based task analysis. Education: MSc Applied Cognitive and Media Science (Cognition and Artificial Intelligence), 2021 BSc Applied Cognitive and Media Science, 2018 Her research focuses on developing and evaluating methods for automated assessment in educational contexts, particularly examining text similarity approaches, zero-shot learning for content scoring, and visio-linguistic model sensitivity to color naming. She co-developed the LeSpell spelling error benchmark and investigates confidence-based grading systems to reduce manual effort. Key publication trends include: Advancing BERT/S-BERT for educational text scoring Multimodal analysis of image-text interactions Argument mining and prompt sensitivity in writing Spelling error detection in learner language Cold-start problem solutions using LLM-generated data She participates in major NLP/educational application workshops (BEA 2022–2025), LREC-COLING, and ACL conferences. Her work bridges computational linguistics with practical classroom applications through systematic benchmarking and model evaluation.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +12 more

Ahmet Cüneyd Tantuğ is an Associate Professor in the Department of Artificial Intelligence and Data Engineering at the Faculty of Computer and Informatics, Istanbul Technical University (ITU). He has been a core faculty member since 2007 and is among the founders of the ITU Natural Language Processing Group. He has held key administrative roles, including Vice Dean (2011–2015) and Head of IT Department at ITU (2012–2020). BSc, MSc, PhD: Istanbul Technical University, Computer Engineering Guest Researcher: University of Copenhagen, Centre for Language Technology (2006–2007) Dr. Tantuğ’s research spans artificial intelligence, natural language processing, machine learning, and deep learning, with a focus on Turkish and other agglutinative languages. His work includes named entity recognition, dependency parsing, machine translation, word sense disambiguation, and text normalization. He has developed linguistic resources for Turkish and low-resource languages such as Amharic, and has contributed to financial AI and temporal expression extraction. His recent publications highlight advancements in Turkish NLP, including optimal vocabulary sizing, tokenization granularity for LLMs, and transformer-based models for named entity recognition. He has led multiple research projects funded by TÜBİTAK, the European Union, and the Ministry of Development. Scientific awards include: Bedri Karafakioğlu Special Award (2000) Siemens Excellence Award (2007) TeknoGirişim Sermaye Desteği Ödülü (2010) He has supervised numerous master’s and PhD students, with theses on topics such as dependency parsing, named entity recognition, and text normalization. He has also served as a consultant for private sector projects in banking, software, and telecommunications, and as a referee for TÜBİTAK, TEYDEB, and the Ministry of Industry and Technology. Dr. Tantuğ leads and participates in active research projects including Turkish text normalization from user-generated content and improving dependency parsing with augmented features. He is a founding member of the ITU Natural Language Processing Group, which continues to be a central hub for NLP research in Turkey.

Pablo Gamallo OteroView profile

Professor

• Natural Language Processing
• Information Extraction
• Machine Learning
• +7 more

Pablo Gamallo Otero is a Full Professor at the University of Santiago de Compostela (USC) with extensive experience in Natural Language Processing and Information Extraction. His academic career began with a Linguistics thesis defended in 1998 at the Université Blaise Pascal, followed by post-doctoral work at the Artificial Intelligence Center of the Universidade Nova de Lisboa. Since 2004, he has been affiliated with USC, progressing from Isidro Parga Pondal research fellow to Ramón y Cajal research fellow, and ultimately to his current position as Full Professor. His research focuses on Natural Language Processing and Information Extraction, with current projects centered on semantic relation extraction and the development of hybrid language models (neuro-symbolic) applied to sentiment analysis and disinformation detection. His work spans machine learning, language technologies, and trustworthy AI, with a particular emphasis on Galician language processing. Gamallo has led numerous research projects including iRead4Skills, HYBRIDS, LingUMT, DeepR3.gal, and Big-eRisk, demonstrating his commitment to advancing language technologies for both academic and practical applications. His recent publications reveal a strong focus on Galician language models, neural machine translation, and the development of resources for low-resource languages. The research trends show an evolution from dependency parsing and lexical normalization toward contemporary challenges in large language models, sentiment analysis, and cross-lingual transfer learning, particularly for Romance languages and Galician specifically. As an entrepreneur in the field, Gamallo is the promoter and founding partner of Cilenis, a USC spin-off specializing in language technologies, and serves as the main developer of linguistic software tools including Linguakit and Avalingua. His contributions to the field extend beyond academia into practical applications that support language preservation and technological accessibility.

Jannis VamvasView profile

Lecturer

• Natural Language Processing
• Multilingual Language Models
• Machine Translation
• +5 more

Jannis Vamvas is a Lecturer and active researcher in the Department of Computational Linguistics at the University of Zurich, Switzerland. With a PhD thesis titled Model-Based Evaluation of Multilinguality , he specializes in deep learning for natural language processing, with particular emphasis on multilingual language models and their evaluation. Academic Roles Current Lecturer for Large Language Models , Text Generation , and Mathematical Foundations at UZH Postdoctoral Researcher (MUTAMUR Project, 2023) PhD in Computational Linguistics (2019-2023, supervised by Rico Sennrich, Lena A. Jäger, and Martin Volk) Education M.Sc. in Computational Linguistics (major) and Computer Science (minor) at LMU Munich (2017-2019) B.A. in Computer Science and Philosophy from University of Basel (2011-2015) His research focuses on multilingual language models, particularly SwissBERT for Swiss languages, and methods for evaluating translation quality through approaches like Contrastive Conditioning and Translation Cross-Likelihood. He explores how to adapt models to local data, detect translation errors, and improve document translation through source-primed conversation techniques. Recent publications highlight his work on: Translation direction detection Hallucination mitigation in machine translation Minimum Bayes Risk decoding Swiss multilingual language modeling Semantic difference recognition Scientific recognition includes: EAMT 2024 Highly Commended Thesis Best Paper Award at SwissText 2025 Best Scientific Paper Award at SwissText 2024 He teaches advanced courses on Large Language Models, Text Generation, and Mathematical Foundations, and has served as organizer of the Computational Linguistics Colloquium at UZH. Current projects include the InvestigaDiff initiative and machine translation for Romansh idioms.

Tiange XiangView profile

Researcher

• Generative Models
• Artificial Intelligence for Healthcare
• Computer Vision
• +3 more

Tiange Xiang is a Ph.D. student at Stanford University , affiliated with the Stanford AI Lab and Stanford Vision and Learning Lab. His research bridges generative models and AI for healthcare , focusing on 3D human reconstruction, medical imaging, and diffusion model applications. Education : Ph.D. and M.S. in Computer Science at Stanford University; B.S. in Computer Science and Technology (Advanced) at the University of Sydney. Advisors : Prof. Fei-Fei Li, Prof. Scott Delp, and Prof. Ehsan Adeli. His work includes foundational contributions to occluded human rendering (OccFusion, Wild2Avatar), medical anomaly detection (Exploiting Structural Consistency of Chest Anatomy, SQUID), and fMRI vision decoding (Seeing Beyond the Brain). He has pioneered 3D Gaussian splatting and score-distillation sampling techniques. Collaborators include leading researchers from Google DeepMind and Stanford. Scientific Awards : University Medal (University of Sydney), Stanford HAI Fellowship, Qualcomm Innovation Fellowship Finalist. He has served as a conference reviewer for CVPR, MICCAI, ICCV, and others, and as a teaching assistant for courses like CS 231n. His code repositories for BiO-Net and OccFusion are widely shared and implemented in Python.

Hasan OgulView profile

Associate Professor

• Computational Intelligence
• Health Informatics
• Bioinformatics
• +2 more

Hasan Ogul is an Associate Professor at the Department of Computer Science and Communication, Østfold University College. His research focuses on computational intelligence applied to health informatics, bioinformatics, text mining, and sensor data processing. He has held academic roles including a previous professorship at Baskent University's Department of Computer Engineering and a postdoc at Aalto University. His work spans interdisciplinary projects like AI4AfL (Artificial Intelligence for Assessment for Learning) and contributions to machine learning applications in medical diagnostics and bioinformatics. He is the author of multiple peer-reviewed articles and conference proceedings, with a strong emphasis on generative models, sensor data analysis, and computational biology. Education: PhD from Middle East Technical University (Turkey), postdoc at Helsinki University of Technology/Aalto University. Current courses include Digitalisation and Society (PhD level) and Evolutionary Computation (Master's level). Key Projects: AI4AfL aims to enhance learning through AI-driven assessment tools. His research groups focus on machine learning and its societal applications. He has contributed to conferences like IEEE SAMI and CBMS, showcasing work on infection prediction, microRNA targeting, and speech recognition systems. Publications: Over 30 peer-reviewed articles since 2018, covering topics from protein structure prediction to lecture video segmentation and breast cancer drug development. His work frequently appears in journals like Expert Systems with Applications, IEEE Access, and Sensors.