جست‌وجوی استاد و پژوهشگر

Jannis Vamvasمشاهده پروفایل

مدرس

• Natural Language Processing
• Multilingual Language Models
• Machine Translation
• +۵ مورد دیگر

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.

• modeling of the educational process
• information technology in education
• databases
• +۳ مورد دیگر

Natalia Volodymyrivna Shcherbak serves as a Senior Lecturer in the Department of Computer Systems and Networks at Zaporizhia State Technical University's Faculty of Computer Science and Technology, where she also holds the position of Deputy Dean. With over two decades of academic experience since joining the university in 2001, she has established herself as a dedicated educator and researcher in computer science. Dr. Shcherbak graduated from Zaporizhia State Technical University in 2001 with a specialty in "Computer systems and networks" and a qualification as "systems engineer." Her educational background has provided the foundation for her academic career focused on computer science education and research. Her research interests span several key areas including modeling of the educational process, information technology in education (particularly Learning Management Systems), databases, web services, and Service-Oriented Architecture (SOA). Her scholarly work demonstrates a consistent connection between theoretical computer science concepts and practical educational applications, with particular emphasis on developing methodologies for assessing educational quality and optimizing learning experiences through technology. Analysis of her publication trends from 2014-2024 reveals a strong research trajectory in web services quality assessment, decision-making methods for service selection, and educational process modeling. Her recent work (2022-2024) advances multi-criteria decision-making methodologies for QoS-based web service environments, while her earlier publications (2016-2018) focus on test task quality assessment methods and educational resource development. Quality assessment methodologies for web services Educational process modeling and optimization Test task quality and complexity evaluation Computer science curriculum development Throughout her career, Dr. Shcherbak has taught a diverse range of computer science subjects including Assembly language programming, database organization, algorithms and calculation methods, programming, computer logic, and modern programming methods. Her teaching portfolio demonstrates expertise across both theoretical foundations and practical applications in computer science education.

Kapal Devمشاهده پروفایل

استادیار

• Wireless Networks
• Security and Privacy
• Agentic AI
• +۷ مورد دیگر

Kapal Dev is an Assistant Professor at the Department of Computer Science, Munster Technological University (MTU), Ireland, where he has been serving since 2021. He also holds the position of Funded Investigator at CONNECT, Trinity College Dublin, one of Europe's top research centers funded by Science Foundation Ireland. Previously, he was a Postdoctoral Research Fellow with the CONNECT Centre at Trinity College Dublin and worked as a 5G Junior Consultant and Engineer at Altran Italia S.p.A. in Milan, Italy. Dr. Dev's research focuses on Wireless Networks, Security and Privacy, Agentic AI, and Industry 5.0 applications. His work centers on designing effective and efficient communication network protocols for 5G and 6G networks, with emphasis on computational complexity reduction and energy efficiency improvements. His research also addresses data and model privacy preservation, which is increasingly critical with the proliferation of AI services. His recent publications demonstrate strong trends in next-generation wireless networks, particularly 6G systems, UAV communications, federated learning, quantum networking, and AI security applications. His work bridges theoretical innovation with practical implementations for industry 4.0/5.0 applications. Dr. Dev has received numerous prestigious awards including the IEEE ComSoc EMEA Outstanding Young Researcher 2022, The Tom Brazil Excellence in Research Award 2023, and membership in the Global Young Academy (GYA) 2024 (top 45 researchers worldwide). He has also been recognized with the Best workshop Paper award at IEEE WCNC 2024 and the IEEE CTSoc CCN TC Outstanding Service Award 2024. IEEE ComSoc EMEA Outstanding Young Researcher 2022 The Tom Brazil Excellence in Research Award 2023 Global Young Academy (GYA) 2024 membership Best workshop Paper award at IEEE WCNC 2024 IEEE CTSoc CCN TC Outstanding Service Award 2024 2022 Irish Research Council Research Ally Prize Dr. Dev has supervised more than 25 master's and PhD students and secured over €1.5 million in research funding as Principal Investigator for Horizon Europe MSCA Staff exchange, Erasmus+ International Credit Mobility, and Capacity Building for Higher Education projects. He actively contributes to standardization efforts as a working group member of P1954 and serves as founding chair of the IEEE ComSoc special interest group on Industrial Communication Networks. His editorial service spans numerous high-impact journals including IEEE Transactions on Communications and Nature Scientific Reports.

Christos Chrysoulasمشاهده پروفایل

دانشیار

• Smart Grids
• IoT
• Machine Learning
• +۴ مورد دیگر

Christos Chrysoulas is an Associate Professor in Software Engineering at Heriot-Watt University's School of Mathematical & Computer Sciences in Edinburgh, UK. He holds a PhD in Electrical Engineering from the University of Patras (2009) and has published over 71 research outputs focusing on Smart Grids, IoT, Machine Learning, Big Data Analytics, and cybersecurity. PhD in Electrical Engineering, University of Patras (2009) Diploma in Electrical Engineering, University of Patras (2003) His research focuses on Smart Grids , Industrial IoT , Explainable AI , and Cybersecurity . Recent work explores generative AI applications in critical infrastructure protection, neurosymbolic learning for cyberattack detection, and cloud-native data platform architectures. Key scientific achievements include: Best Paper Award, IFIPSEC 2024 Leading Light Innovation 2022, Scottish Cybersecurity Awards Best Presentation Award, ICISS 2021 He leads the PROFEEDBACK COST Action project (2021-2025) and co-leads the GLASS EU project (2021-2024) focused on decentralized e-governance. His work contributes to UN Sustainable Development Goals related to industry innovation, sustainable cities, and quality education.

Jakob Fehleمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Text Mining
• Sentiment Analysis

Jakob Fehle is a Researcher at the Institute for Information and Media, Language and Culture within the Faculty of Language, Literature and Cultural Studies at the University of Regensburg. Appointed as Scientific Staff Member at the Chair of Media Informatics in March 2021, he contributes to research spanning natural language processing and human-computer interaction while maintaining an active teaching schedule. His academic credentials include: Master of Science (M.Sc.) in Media Informatics, University of Regensburg (February 2021), with thesis on lexicon-based sentiment analysis for German Bachelor of Arts (B.A.) in Media Informatics, University of Regensburg (February 2019) Fehle's research centers on Natural Language Processing with specialized expertise in Text Mining and Sentiment Analysis for German-language content. He has pioneered systematic evaluations of lexicon-based methods and developed the GERestaurant dataset for aspect-based sentiment analysis in restaurant reviews. His work bridges computational linguistics with political science through analysis of social media discourse during the 2021 German federal election, while recent publications explore large language models for synthetic data generation and aspect sentiment prediction in low-resource settings. This research trajectory demonstrates consistent evolution from foundational lexicon techniques to cutting-edge transformer applications. Analysis of his 13 publications (2019-2025) reveals a dominant focus on German-language sentiment analysis, with 70% of works examining political discourse on Twitter and Instagram during electoral cycles. His methodological progression shows clear movement from traditional lexicon-based approaches (2019-2021) toward neural architectures (2022-2024) and large language models (2025), while maintaining consistent application to real-world domains including politics, hospitality, and legal contexts. The interdisciplinary nature of his work connects computational linguistics with political science, game design, and affective computing. As an educator, Fehle has taught Human-Computer Interaction exercises continuously since Summer 2021, alongside Multimedia Technology and Digital Humanities courses. His teaching portfolio demonstrates progressive responsibility, evolving from exercise sessions to co-leading Master's practical seminars across eight consecutive semesters. He maintains office hours by appointment in Building PT, Room 3.0.30 at the University of Regensburg. Fehle operates within the Media Informatics research group under Prof. Dr. Christian Wolff, collaborating extensively with Nils Hellwig, Thomas Schmidt, and Markus Bink. His work integrates with broader projects analyzing social media during German elections and developing adaptive systems for gaming and virtual reality environments.

• Natural Language Processing
• Computational Linguistics
• Syntax Analysis
• +۴ مورد دیگر

Vojtěch Kovář is a researcher at Masaryk University with dual appointments at the Institute of the Czech Language (Faculty of Arts) and the Center for Natural Language Processing (Faculty of Informatics). He has been affiliated with the university since 2006, holding positions as researcher and lecturer while also working as a software analyst at Lexical Computing CZ since 2012. His research focuses on automatic syntactic analysis of natural languages , particularly Czech and related languages, evaluation of syntactic analysis quality , integration of syntactic tools into downstream applications , and corpus linguistics . His work bridges theoretical linguistics with practical language technology applications. Recent publications reveal a strong trend toward dictionary creation methodologies , lexicographic infrastructure development , and corpus-based language resource compilation . His research increasingly addresses rapid dictionary development for both European and Asian languages, as well as bilingual resource creation in response to emerging language needs. PACLIC 2010 best student paper award Member of eLex conference program committee (2017-present) Kovář supervises bachelor's and master's theses at both the Faculty of Arts and Faculty of Informatics, with recent topics including semi-automatic dictionary creation, web-based proofreading systems, and automatic punctuation correction. He teaches multiple courses including Fundamentals of Mathematics and Statistics for Humanities, Algorithmic Description of Syntax, and Programming Basics for Humanities. His research is supported by projects including LINDAT/CLARIAH-CZ (Digital Research Infrastructure for Language Technologies) and the Web-based spelling, grammar and typographical corrector for Czech language initiative.

Maunendra Sankar Desarkarمشاهده پروفایل

دانشیار

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +۱۰ مورد دیگر

Dr. Maunendra Sankar Desarkar is an Associate Professor at the Computer Science and Engineering Department of Indian Institute of Technology Hyderabad , India, with affiliations to the AI Department. His research spans Natural Language Processing , Information Retrieval , and Machine Learning , focusing on robust, responsible, and culturally-aware AI systems. Education: B.E. (University of Burdwan), M.Tech (IIT Kanpur), Ph.D. (IIT Kharagpur, 2014) Current work emphasizes zero-shot cross-lingual transfer , dialogue interpretability , and ethical NLP (non-toxicity, empathy). Recent articles explore large language model evaluation , hallucination detection , and tokenization for Indic languages . Collaborations include Microsoft and JICA-funded projects. Scientific contributions include: Microsoft India Ph.D. Fellowship Award Yahoo Key Scientific Challenges Honorable Mention (2012) Students include Suvodip Dey (Ph.D. in Dialogue Systems), Aishwarya Maheswaran , Debolena Basak , Kaushal Kumar Maurya , and Maharaj Brahma . Advises on projects involving disaster response systems , generative AI , and multilingual NLP .

Leila Ghalichiمشاهده پروفایل

پژوهشگر ارشد

• Healthcare Informatics
• Public Health
• Mental Health Services
• +۵ مورد دیگر

Dr. Leila Ghalichi is a Senior Research Fellow in Applied Health Sciences, focusing on equitable healthcare access for injuries in low- and middle-income countries (LMICs). Her work bridges technology acceptance models, artificial intelligence applications, and health systems reform. Active in healthcare informatics and global health policy Key projects: ADLIFE (digital personalized care), ISPF ODA (academic leadership dissemination) Research spans trauma care governance, mental health service revitalization, and diagnostic AI Recent work examines technology adoption in healthcare through the UTAUT framework, AI-driven diagnostic precision, and health equity challenges in LMICs. She leads the 2024-2025 ISPF ODA project on academic leadership dissemination skills. Collaborations include international partnerships in Iran, UK (Research England), and institutions across Europe. Her output trends emphasize digital health platforms, clinical governance, and post-pandemic mental health systems.

• machine translation
• analogy
• multilingual alignment
• +۷ مورد دیگر

Yves Lepage is a Professor at Waseda University's Faculty of Science and Engineering, specifically within the Graduate School of Information, Production, and Systems. He maintains an active research laboratory (lepage-lab.ips.waseda.ac.jp) and teaches courses including Example-based machine translation/NLP, Natural language processing, and Master's/Doctoral thesis supervision for the 2025 academic year. His research focuses on the application of analogical reasoning to natural language processing problems, particularly machine translation. Lepage's work spans formal analogy between strings, sentence-level analogies, morphological analysis, and multilingual systems. His research interests include machine translation, analogy, multilingual alignment, multilingual large language models, and foreign language aids. He has made significant contributions to understanding analogical density in corpora and developing methods to leverage analogies for translation, especially in low-resource scenarios. His publication record shows consistent output through 2024, with research evolving from foundational work on proportional analogy to sophisticated applications with neural networks. Recent work explores masked prompt learning for analogies, fuzzy analogies for translation, and organizing lexica into analogical grids for morphological generation across languages. Waseda University Teaching Award (Spring semester 2016) Lepage has successfully led multiple research projects funded by the Japan Society for the Promotion of Science, including "Theoretically founded algorithms for the automatic production of analogy tests in NLP" (2021-2024) and "Self-explainable and fast-to-train example-based machine translation using neural networks" (2018-2021). His work has involved international collaboration, including a 2023-2024 research period at the University of Montreal. He serves as Concurrent Researcher at the Waseda Research Institute for Science and Engineering (2024-2026) and has been active in professional organizations including the Information Processing Society of Japan and the Japanese Natural Language Processing Association.

Sujit Biswasمشاهده پروفایل

مدرس

• Blockchain
• FinTech
• Cybersecurity
• +۱۲ مورد دیگر

Dr. Sujit Biswas is a Lecturer in Cybersecurity and FinTech at the Department of Computer Science, City, University of London. He also holds an honorary Research Associate position at the Centre for Blockchain Technologies (CBT), University College London (UCL). His academic journey includes prior roles at the University of East London, University of Surrey, and Faridpur Engineering College, Bangladesh. He earned his PhD in Computer Science and Technology from the Beijing Institute of Technology, China. His research spans blockchain, FinTech, cybersecurity, federated learning, IoT, and AI-driven healthcare systems. He is particularly known for his work on consensus algorithms, blockchain interoperability, privacy-preserving systems, and scalable distributed architectures. His interdisciplinary research bridges theoretical innovation with practical applications in finance, healthcare, and smart cities. The recent publications of Dr. Biswas reflect a strong trend in integrating blockchain with AI and IoT, especially in secure, privacy-preserving frameworks for smart homes, vehicular networks, and e-health. His work increasingly emphasizes explainable AI, zero-trust security models, and sustainable, scalable solutions for 6G and next-generation networks. Beijing Institute of Technology Excellent Student Award 2018-2019 Dr. Biswas actively mentors research students, including Qasim and Tooba, and collaborates with industry partners such as FinTechFish for the UnityAgro project. He serves on the editorial board of the MDPI journal Blockchains , contributing to the advancement of scholarly discourse in distributed ledger technologies. His work is supported by interdisciplinary collaborations across academia and government sectors. He is a member of IEEE and a Life Member of the Bangladesh Computer Society (BCS), reflecting his commitment to professional development and global research engagement. His lab and research team focus on blockchain-AI convergence, with projects targeting real-world deployment in financial technology and healthcare infrastructure.

• Natural Language Processing
• Information Retrieval
• Web Personalization
• +۶ مورد دیگر

Wolfgang Nejdl is a distinguished Professor at Leibniz University Hannover, working within the Faculty of Mathematics and Computer Science and affiliated with the Institute of Information Systems. With an extensive publication record spanning over two decades, he has established himself as a leading researcher in Natural Language Processing, Information Retrieval, and Web Personalization. His work bridges theoretical advances with practical applications, particularly in clinical NLP and semantic web technologies. Nejdl's research interests encompass a broad spectrum of topics including Natural Language Processing, Information Retrieval, Web Personalization, Semantic Web technologies, Machine Learning applications, Clinical NLP, and Recommender Systems. His recent work has focused on critical challenges such as clinical outcome prediction using MIMIC datasets, financial literacy evaluation of large language models, and developing resources for low-resource languages like Tigrinya. He has made significant contributions to understanding data drift in clinical applications and developing interpretable AI systems for healthcare. His publication record shows a consistent trajectory of impactful research, with recent articles demonstrating his ability to tackle emerging challenges in NLP and AI. From his foundational work on web personalization using ODP metadata to his current research on clinical NLP and low-resource language processing, Nejdl has consistently addressed important problems at the intersection of information systems and human needs. His work on stance detection incorporating toxicity and morality analysis represents innovative approaches to understanding social media discourse. Among his notable achievements are the development of the EDUTELLA P2P infrastructure based on RDF, significant contributions to boilerplate detection algorithms, and pioneering work on preventing shilling attacks in recommender systems. His research has been widely cited, reflecting its substantial impact on the fields of web science and natural language processing. Nejdl has supervised numerous students and collaborated extensively with researchers worldwide, particularly with Alexander Loeser, Jens-Michalis Papaioannou, and Paul Grundmann. His work demonstrates a consistent focus on practical applications of theoretical advances, particularly in healthcare and web technologies. He has also worked on important infrastructure projects like the L3S Research Center, contributing to the broader academic and technological ecosystem.

• Neural Machine Translation
• Computational Linguistics
• Natural Language Processing
• +۶ مورد دیگر

Antonio Toral is a leading researcher at the University of Groningen, focusing on Neural Machine Translation (NMT) , human evaluation , and parallel corpus curation . His work spans low-resource language modeling, lexical diversity enhancement, and multilingual figurative language detection. Affiliations: University of Groningen, MaCoCu Project, CREAMT Consortium Key projects: MaCoCu (Massive collection of under-resourced language data) CREAMT (Creativity in literary translation) Research interests include: Improving NMT naturalness and lexical richness Document-level evaluation of machine translations Character-level modeling and downsampling techniques Reproducibility challenges in human NLP evaluation Cross-lingual formality transfer without parallel data His recent articles (2021–2025) demonstrate expertise in: Reinforcement learning for naturalness preservation Statistical analysis of translationese effects Dependency-based reordering models Pivot translation for Catalan→Chinese Domain-specific corpus creation for EU Digital Service Infrastructures

• Computer Architecture
• Operating Systems
• Memory Management
• +۵ مورد دیگر

Youngjin Kwon is an Associate Professor at the School of Computing, KAIST, and a member of the Computer Architecture and Systems Lab (CASYS). His work bridges systems research with practical applications in memory management, concurrency debugging, and energy-efficient computing. Recipient of Best Paper Awards at ACM SOSP'24, SOSP'21, and USENIX ATC'18 Honored with KAIST's EWON Endowed Chair Professor (2021) and Soo-Young Lee Teaching Innovation Award (2021) Awarded KAIST Breakthrough (2023) and Technology Innovation Award (2023) Research focuses on: Processing-in-Memory (PIM) and DRAM architectures Concurrency bugs in kernel and hypervisor systems Energy-efficient frameworks for large language models Memory disaggregation and tiered memory systems Trusted Execution Environments (TEE) for secure computing SmartNIC offloading and eBPF-based optimizations His recent publications highlight innovations in speculative decoding, memory safety, and system scalability. Awards reflect both technical excellence and pedagogical impact. He actively mentors graduate and undergraduate students in his lab, emphasizing hands-on research in cutting-edge system design.

• Natural Language Processing
• Computational Linguistics
• Machine Translation
• +۲ مورد دیگر

Rico Sennrich serves as an Honorary Fellow at the School of Informatics, University of Edinburgh, affiliated with the Institute for Language, Cognition and Computation (ILCC). His academic profile reflects deep engagement with computational approaches to human language. Research interests span critical areas in artificial intelligence and linguistics: Natural Language Processing (core methodology) Computational Linguistics (theoretical foundations) Machine Translation (practical applications) Neural Networks (architectural innovation) Multilingual Systems (global language challenges) His work bridges theoretical linguistics with cutting-edge machine learning techniques, particularly in low-resource language scenarios. Professional correspondence is maintained through institutional email channels. Contact details: Email: rico.sennrich@ed.ac.uk

Bing Liuمشاهده پروفایل

استاد

• Sentiment Analysis
• Lifelong Learning
• Chatbot
• +۴ مورد دیگر

Bing Liu is a Distinguished Professor at the University of Illinois at Chicago (UIC) in the Department of Computer Science. He earned his Ph.D. in Artificial Intelligence from the University of Edinburgh and previously held a faculty position at the National University of Singapore (NUS) School of Computing. His research spans Sentiment Analysis , Lifelong Learning , Chatbot development, Natural Language Processing , Data Mining , and Machine Learning . Liu has made significant contributions to the field, exemplified by three Test-of-Time awards from SIGKDD and WSDM, and the prestigious ACM SIGKDD Innovation Award . Education Ph.D. in Artificial Intelligence, University of Edinburgh Research Interests Sentiment Analysis and Opinion Mining Lifelong Machine Learning Chatbot and Dialogue Systems Web Data Mining Knowledge Discovery Selected Publications (2025-2024 Trends) Focus on Continual Learning and Open-world Classification with techniques like Kernel-based Adaptation , Neuro-Symbolic Systems , and Multi-Modal Pre-training . Explores Health Coaching Dialogues using Neuro-Symbolic Goal Summarization and Text Generation in low-resource settings. Advances in Length Generalization for reasoning systems and Class Incremental Learning via likelihood ratio prediction. Scientific Leadership Chair of ACM SIGKDD (2013-2017) Program Chair for KDD, ICDM, WSDM, and other key conferences Associate Editor for leading journals like TKDE, TWEB, and DMKD Fellow of ACM, AAAI, and IEEE Books Authored Lifelong Machine Learning (Morgan & Claypool, 2018) Sentiment Analysis (Cambridge, 2015) Web Data Mining (Springer, 2011)