Find Academic Supervisors

Lizhong ChenView profile

Professor

• Computer Architecture
• Machine Learning Accelerators
• GPU Architecture
• +6 more

Lizhong Chen is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University and a core AI faculty member in the Collaborative Robotics and Intelligent Systems (CoRIS) Institute. He leads the STAR Lab which focuses on computing systems and AI applications with emphasis on computing efficiency across various computing platforms from embedded devices to supercomputers. Ph.D., Computer Engineering, University of Southern California, 2014 M.S., Electrical Engineering, University of Southern California, 2011 B.S., Electrical Engineering, Zhejiang University, 2009 Chen's research focuses on efficient computer systems (GPUs, accelerators, HPCs, IoT devices) and their applications in machine learning and natural language processing, especially large language models. His work spans machine learning accelerators, GPU architecture, AI-assisted design for computer architecture, and energy-efficient computing systems. He has made significant contributions to NoC (Network-on-Chip) power-gating research and developed the Agate simulator for simulating NoC power-gating. His recent publications (2023-2025) show a strong focus on large language models, particularly for simultaneous translation tasks, Kolmogorov-Arnold networks, and efficient model architectures. His work bridges computer architecture design with AI applications, creating synergies between hardware efficiency and machine learning performance. Scientific Awards: NSF CRII Award (2016) NSF CAREER Award (2018) Best Paper Nomination at IEEE NAS (2018) Best Paper Runner-up Award at HPCA (2020) Chu Kochen Award from Zhejiang University IEEE HPCA Hall of Fame (2020) Chen has served as an Associate Editor of IEEE Transactions on Computers and as program committee member for top computer system and machine learning conferences. He is the founder and organizer of the Annual International Workshop on AIDArc (AI-assisted Design for Architecture). His research is supported by multiple grants from NSF, NIH, Department of Energy, and the Northwest-AI-Hub supported by the CHIPS and Science Act. He teaches courses in computer architecture, high-performance computing, and specialized topics in AI accelerators and GPU architecture. As director of the STAR Lab, Chen leads research on computing efficiency across the spectrum from embedded and mobile devices to supercomputers and data centers. The lab's recent focuses include machine learning accelerators, GPU architecture, applications of AI in architecture designs, and improving the computing efficiency of machine learning and natural language processing models.

Giulia BovolentaView profile

Lecturer

• Language Learning
• Cognitive Psychology
• Second Language Acquisition
• +3 more

Giulia Bovolenta is a Lecturer at the School of Arts, Culture and Language, Bangor University. Her research focuses on cognitive mechanisms underlying adult second language learning, particularly how individual differences and input properties affect language processing and encoding. She is PI of the 2023-2025 project 'Investigating the nature of difficulty in language learning with EEG' and has received the Leverhulme Trust Early Career Fellowship (2022) and the 2024 Learned Society of Wales Best Poster Prize. Her work explores memory utilization in L2 morphology learning, declarative memory's role in generalization, and prediction errors' impact on syntactic acquisition. She has published widely on topics like verb surprisal effects, artificial language learning, and vocabulary assessment methodologies. Bovolenta actively engages in public outreach, including talks at the Hidden Worlds Science Festival and academic poster presentations. Current research investigates declarative memory's role in explicit vs. item-based learning through pilot studies and EEG-based experiments. She collaborates with institutions like the Learned Society of Wales and mentors PhD students in psycholinguistics and language acquisition.

Andrea AlbarelliView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Andrea Albarelli is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Informatics and Statistics. His research focuses on Artificial Intelligence, Machine Learning, and Computer Vision, with a particular emphasis on Explainable AI, Pattern Recognition, and Data Analytics applications. He has contributed to numerous peer-reviewed publications, including studies on hierarchical text classification, fruit ripeness analysis, and embedded vision systems. His teaching activities include courses on Data Analytics for Business and Society, emphasizing practical applications of AI and informatics. Albarelli's work spans theoretical frameworks and applied research, addressing challenges in both academic and industrial contexts. Research Interests: Machine Learning algorithms, computer vision techniques, and their applications in agriculture, edge computing, and multi-camera systems. Recent Contributions: Over 90 publications, including high-impact journals like Electronics and Expert Systems with Applications . His academic contributions reflect a balance between foundational research and real-world problem-solving, with notable projects on model explainability, quantization-aware neural networks, and calibration techniques for vision systems. Albarelli serves as a faculty member in both undergraduate and graduate programs, integrating cutting-edge research into curriculum design.

Tobias StraußView profile

Lecturer

• Mathematics Education
• Historical Document Analysis
• Machine Learning
• +5 more

Dr. Tobias Strauß is a Lecturer at the Institute of Mathematics, Faculty of Mathematics and Natural Sciences, University of Rostock. He teaches courses such as Elementary Algebra and Number Theory and Analytical Geometry, focusing on foundational mathematical concepts and their applications. His research interests span historical document analysis, machine learning, and neural networks, with a particular emphasis on handwritten text recognition (HTR) and computational solutions for cultural heritage digitization. He actively contributes to the Mathematics Society RHO eV, supporting students in mathematics competitions and enrichment programs. Dr. Strauß’s research combines computer vision and machine learning to address challenges in document analysis, including text line detection, cursive script recognition, and keyword search in historical manuscripts. His work also explores semi-supervised learning techniques and neural network architectures tailored for HTR tasks. Through RHO eV, he promotes mathematics education through weekend seminars, district clubs, and game-based learning activities, fostering student engagement and problem-solving skills. His publications highlight advancements in HTR systems, such as the CITlab Recognition & Retrieval Engine, and address topics like regular expression-based decoding and Arabic handwriting recognition. These contributions underscore his expertise in bridging theoretical mathematics with practical applications in digital humanities and education. Dr. Strauß can be contacted via tobias.strauss@uni-rostock.de or through the university’s chat platform. No formal awards or grants are noted in the provided materials, though his involvement in international competitions (e.g., ICFHR, ICDAR) reflects his scholarly engagement.

Zhichao XuView profile

Researcher

• Natural Language Processing
• Information Retrieval
• Deep Learning
• +3 more

Zhichao Xu is a researcher at AWS Bedrock AI, specializing in understanding language models' mechanisms and applications. He holds a PhD in Computer Science from the University of Utah, advised by Prof. Vivek Srikumar and Prof. Bei Wang. His work focuses on deep learning methods in NLP and Information Retrieval, including retrieval-augmented generation, in-context learning, and decoding techniques. Education: PhD in Computer Science, University of Utah (2020-2024) MS in Computer Science, Rutgers University (2018-2020) BS in Applied Statistics, Shanghai University of Finance and Economics (2014-2018) Research Interests: Zhichao explores the intersection of NLP and IR, emphasizing robust and interpretable systems. Key areas include: retrieval-augmented generation, LLM compression, ethical AI, and explainable recommendation. His work bridges theoretical advancements with practical applications in search, dialogue, and fairness. Papers Trends: Recent publications highlight innovations in model architectures (e.g., CSPLADE for sparse retrieval), safety evaluation of compressed LLMs, and novel applications of state space models for text reranking. He also contributes to foundational surveys like the A Survey of Model Architectures in Information Retrieval . Awards: Distinguished Paper Award at ISSTA 2023 Honorable Mention at SIGIR-AP 2023 Teaching & Service: Taught Machine Learning, Data Mining, and Discrete Structures at the University of Utah. Served as a reviewer for top conferences (ACL, EMNLP) and journals (TOIS). Active in organizing workshops on LLM interpretability and fairness.

• Classics
• Linguistics
• Digital Humanities
• +3 more

Maria Papadopoulou serves as an Associated Researcher at the LISTIC laboratory of University Savoie Mont Blanc, France, specializing in the intersection of philological traditions and computational methods. Her work bridges Classics, Linguistics, and Digital Humanities through innovative applications of Semantic Web technologies for cultural heritage documentation. Her research focuses on ontology and terminology development for material culture studies, particularly ancient Greek dress and Chinese ceramics. She pioneers humanist-centered approaches to knowledge representation, creating tools like Tedi for multilingual terminology ontologization. Her methodology emphasizes accessibility for non-technical scholars while maintaining computational rigor in semantic modeling. Analysis of her publication trends reveals consistent innovation in domain-specific ontologies, with increasing emphasis on cross-cultural applications (Greek/Chinese) and pedagogical frameworks for digital humanities. Her work demonstrates how formal ontologies can resolve ambiguities in historical terminology while preserving cultural context. Scientific recognition includes: Best paper award at SEMAPRO 2018 for advancing multilingual terminology platforms Papadopoulou actively mentors through post-graduate instruction across international institutions including University Savoie Mont Blanc, Liaocheng University, and Nanjing University NUAA. Her teaching materials integrate AI applications with classical philology, reflecting her commitment to training next-generation digital humanists. She operates within the LISTIC laboratory's interdisciplinary framework, collaborating with computer scientists and domain experts to develop the TAO CI project for cultural heritage terminology. Her current work focuses on scaling ontology-based dictionaries for global humanities research while addressing challenges of semantic interoperability across linguistic traditions.

Salar MohtajView profile

Researcher

• Transfer Learning for Natural Language Processing
• Text complexity/readability prediction
• Evaluation of Large Language Models
• +1 more

Dr. Salar Mohtaj is a senior researcher at the German Research Center for Artificial Intelligence (DFKI) and affiliated with TU Berlin's Quality and Usability Lab. He holds a PhD from TU Berlin (2024), specializing in transfer learning for harmful content detection. His work focuses on NLP applications in fake news/hate speech detection, LLM evaluation, and text complexity analysis. Education: Bachelor's: Information Technology, Shahrood University of Technology (Iran) Master's: Information Technology, Amirkabir University (Iran) PhD: TU Berlin (2024), supervised by Prof. Sebastian Möller Research Interests: Transfer learning for NLP, LLM evaluation, text complexity prediction, and detection of harmful content. He leads projects on multimodal fake news detection and semantic text similarity models. Recent Contributions: Developed the Hamtajoo plagiarism detection system, contributed to the KIALOPRIME dataset, and led EU-funded projects on AI ethics and disinformation. His work bridges NLP techniques with societal challenges like misinformation. Awards & Grants: Received Software Campus funding (2019-2024) for projects on sentiment analysis in fake news. Collaborates with Holtzbrinck Publishing Group and Deutsche Telekom. Labs & Affiliations: Speech & Language Technology group at DFKI and Quality and Usability Lab at TU Berlin. Teaches Natural Language Processing courses at TU Berlin and KI Campus.

• Low-Resource Language Processing
• Natural Language Processing (NLP)
• Machine Translation
• +7 more

Dr. Ignatius Ezeani is a Research Fellow in the Department of Computing and Communications at Lancaster University, specializing in Low-Resource Natural Language Processing (LowResNLP). His work focuses on developing tools and methods for under-resourced languages, particularly African languages such as Igbo, and minority languages like Welsh. He leads projects such as the Igbo-English Machine Translation initiative and the Welsh Summary Creator, advancing computational linguistics in these domains. His research interests include machine translation, part-of-speech tagging, named entity recognition, and corpus-based approaches to language processing. He is affiliated with the University Centre for Computer Corpus Research on Language (UCREL), contributing to data science and linguistic resource development. Ezeani supervises PhD student Chiamaka Chukwuneke and collaborates on initiatives like the National Corpus of Contemporary Welsh (CorCenCC). His projects emphasize participatory research, ensuring technologies address real-world linguistic needs in underrepresented communities. Key contributions include the IgboAPI Dataset, IgboBERT models, and frameworks for spatial narrative analysis. Ezeani’s work bridges computational linguistics with societal impact, fostering multilingualism through technology.

• Sentence Production
• Writing Process Analysis
• Digital Literacy
• +4 more

Dr. Malgorzata Anna Ulasik is a researcher at Zurich University of Applied Sciences (ZHAW) specializing in sentence production analysis, digital literacy, and computational writing process research. Her work bridges linguistic theory with technological implementations through projects like THETool (Text History Extraction Tool) and SPPC (Swiss Process-Product Corpus). Key affiliations: Zurich University of Applied Sciences, University of Antwerp collaborations, SwissText conferences Research focus: sentence-centric writing models, keystroke logging analysis, automated feedback systems for academic writing Her methodological innovations include: Transforming sequences for tracking text evolution Sentence history reconstruction from intermediate drafts Integrated analysis of writing processes and final products Corpus development for speech recognition (CEASR, SDS-200) Technical contributions include: THETool implementation for writing process analysis Annotation frameworks using INCEpTION Semi-automated feedback systems for thesis writing Swiss German dialect translation models

Venkatesh R. BellamkondaView profile

Assistant Professor

• Healthcare Quality
• Emergency Medicine
• Medical Education
• +3 more

Venkatesh R. Bellamkonda, M.D. is an Assistant Professor of Emergency Medicine and Consultant at Mayo Clinic's Department of Emergency Medicine. He also serves as Chair for Education within the department. His primary affiliation is with Mayo Clinic College of Medicine. Education : MD: Northeastern Ohio Universities College of Medicine (2006) BS in Natural Science/Chemistry: University of Akron (2001) Residencies: General Psychiatry (2007), Emergency Medicine (2010) Fellowships: Emergency Ultrasound, Teaching, and Basic Research Skills Research Interests : Healthcare Quality: Applying Lean Six Sigma and process improvement strategies. Medical Education: Innovations in curriculum design and asynchronous learning. Point-of-Care Ultrasound: Standardizing ultrasound techniques to enhance diagnostic accuracy. Healthcare Innovation: Exploring AI, 3D video, and smart technologies to optimize care delivery. Articles Trends : Recent work focuses on: School learning models' impact on emergency psychological health visits. Pain management communication and opioid-prescribing disparities. Disparities in emergency department wait times based on patient demographics. Virtual recruitment strategies for residency programs. Awards : 2022 Above and Beyond Recognition Program 2019 Leadership Award (ACEP Quality Improvement) 2016 Teacher of the Quarter (Mayo Emergency Medicine) 2010 Chief Resident Award Grants/Advising : Active in quality improvement initiatives and residency education programs. Leads multiple committees focused on medical education and patient safety. Labs/Teams : Involved in the Mayo Clinic Quality Academy and ACEP's Quality and Patient Safety Section committees.

Jesús Vilares FerroView profile

Research Professor

• Natural Language Processing
• Computational Linguistics
• Information Retrieval
• +4 more

Jesús Vilares Ferro is a Research Professor at the Universidade da Coruña , Faculty of Computer Science, Department of Computer Science and Information Technology. His research focuses on Natural Language Processing , Computational Linguistics , and Information Retrieval . Current projects include machine translation, text mining, and semantic analysis. He has led multiple national and autonomous grants since 2007. His teaching includes Programming II , Programming Paradigms , and Advanced Natural Language Processing Techniques across Computer Engineering and Artificial Intelligence degrees. He has directed numerous final-year projects, including adaptive game designs for accessibility and social media analytics systems. His recent publications analyze efficient parsing techniques, multilingual retrieval, and sentiment analysis frameworks.

• Linguistics
• Romanistics
• Phonetics and Phonology
• +2 more

Snezana Guduric is a Full Professor at the University of Novi Sad, Faculty of Philosophy, Department of Romance Studies. Her work spans linguistics, phonetics, and contrastive studies of French and Serbian languages. She has led significant projects and authored numerous publications. Education: Master of Linguistics (1989), Doctor of Linguistics (1997) Research Interests: Focus on phonetics, phonology, syntax, and cultural interactions in language. She explores vowel systems, contrastive grammar, and lexical borrowings. Publications Trends: Recent works include computational linguistics (AI in translation), phonological contrasts in Romance languages, and historical linguistics in the Balkans. A notable chapter (2024) examines AI's role in poetry translation. Scientific Awards: Pavle Ivić Award (2010) for Serbian phonology research Golden Franco-Romance Award (2021) for promoting French culture Grants & Projects: Current member of the international project DIDAFE (2023-2026). Previously led national projects on language contact and bilaterals with Slovenia. Editorial roles include co-editing thematic collections on linguistics.

AHMET KESMEZView profile

Assistant Professor

• Linguistics
• Second Language Acquisition
• Language Revitalization
• +6 more

Dr. Ahmet Kesmez is an Assistant Professor at Bingöl University's Faculty of Arts and Sciences, Department of English Language and Literature. He holds a PhD in English Language Teaching from Atatürk University's Institute of Educational Sciences (2020), preceded by an MA in English Linguistics (Atatürk University, 2013) and a BA in English Language and Literature (Yüzüncü Yıl University, 2010). His academic career includes roles as a Research Assistant at Bingöl and Atatürk Universities (2010-2012), Lecturer at Bingöl University (2014-2023), and his current position since 2023. His research focuses on sociolinguistics, language revitalization, bilingualism, and applied linguistics, with emphasis on endangered languages like Zazaki. He investigates technology's role in language preservation, EFL pedagogy, and L1 interference in second language acquisition. Recently, he has explored AI-driven solutions for revitalizing endangered languages and EFL learners' perceptions of digital tools. He teaches courses such as Linguistics I, Semantics I, and Speech I. Despite no listed awards, his work bridges theoretical linguistics with practical educational challenges, particularly in multilingual contexts. No specific grants, labs, or teams are mentioned in the provided data.

• Health Literacy
• Behavior Change
• Public Health
• +4 more

Dr. Julie Ayre is a behavioral scientist and researcher in the Faculty of Health at the University of Technology Sydney (UTS). Her work focuses on health literacy, behavior change, and patient-centered care, particularly in diabetes management and vulnerable populations. She holds a PhD from UTS (2020), exploring health literacy's role in diabetes self-management apps. Her research includes developing health-literate action plans, improving communication in primary care, and evaluating virtual care interventions. Leadership: Currently leads the development of the Sydney Health Literacy Lab's online Health Literacy Editor to simplify health materials. Collaborations: Partnered with Western Sydney Diabetes and Diabetes NSW/ACT on health literacy initiatives. Public Health: Conducted studies on pandemic responses (e.g., COVID-19 knowledge in multilingual communities) and AI's role in health communication. Her research interests span health literacy strategies, digital health innovation, and equitable healthcare communication. She frequently collaborates with clinicians, policymakers, and community groups to co-design practical solutions addressing health disparities.

Mike ZhangView profile

Researcher

• Natural Language Processing (NLP)
• NLP for Education
• NLP for Human Resources (HR)
• +7 more

Mike Zhang is a Postdoctoral Researcher in the Data, Knowledge and Web Engineering group at the Department of Computer Science , Aalborg University . Previously, he completed his PhD in Natural Language Processing (NLP) at the IT University of Copenhagen under the supervision of Prof. Barbara Plank and Associate Professor Rob van der Goot, with additional guidance from Prof. Johannes Bjerva and Prof. Euan Lindsay during his postdoc. PhD research focused on Computational Job Market Analysis and NLP for HR Current work centers on NLP for Education and NLP Applications Active in resource creation including datasets and annotation guidelines Part of research groups NLPnorth and MaiNLP His research spans Large Language Models , Multilingualism , and Generative AI with emphasis on practical applications in education and human resources. He has contributed to datasets like HiFi-KPI and presented at venues including IEEE CVPR and ACL workshops.