جست‌وجوی استاد و پژوهشگر

• Low-Resource Language Processing
• Natural Language Processing (NLP)
• Machine Translation
• +۷ مورد دیگر

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.

• Artificial Intelligence
• Natural Language Processing
• Data Analytics
• +۴ مورد دیگر

Professor Xiaohui Tao is a leading academic at the University of Southern Queensland (UniSQ) within the School of Mathematics, Physics and Computing. He serves as Computing Discipline Team Leader and Chair of the ICT Programs' Governance and Leadership Committee. With a PhD in Information Technology from Queensland University of Technology (2009), he has over 200 publications in top-tier journals and conferences like TKDE, AAAI, and SIGIR. His research spans Artificial Intelligence, Natural Language Processing, Machine Learning, Health Informatics, and Data Mining. He leads a prominent research group focused on real-world applications of innovative algorithms and systems. Recent articles include works on automatic summarization, brain computing, and machine unlearning His 2024 publications demonstrate leadership in Explainable AI, Recommender Systems, and Healthcare Applications Awarded the Australia Research Council Grant and Endeavour Research Fellowship, he actively contributes to academic governance as acting Head of School and editorial roles in journals like Elsevier's Natural Language Processing Journal. His mentorship has guided 10+ doctoral candidates to completion.

• Natural Language Processing
• Text Analysis
• Speech Processing
• +۴ مورد دیگر

Mark Cieliebak is a Professor for Speech and Text Processing at the Zurich University of Applied Sciences (ZHAW), School of Engineering, where he leads the Natural Language Processing (NLP) research group at the Centre for Artificial Intelligence. He has held this position since December 2018, following prior roles as a lecturer at ZHAW (2012-2018). His educational background includes a Dr. sc. techn. in Computer Science from ETH Zürich (1999-2003) and a Diplom in Computer Science from Universität Dortmund (1990-1999). His research focuses on Natural Language Processing , Text and Speech Analysis , and Machine Learning applications . Primary domains include dialogue systems evaluation, low-resource speech recognition (particularly Swiss German), automated text generation assessment, and NLP for social media analysis. He also explores educational applications through flipped classroom methodologies and machine learning operations. Cieliebak's publications (2020-2024) demonstrate strong emphasis on NLP evaluation frameworks, speech corpus development for dialects, and generative AI validation. Recurring themes include robustness testing, bias mitigation in automated metrics, and multilingual processing challenges. His work frequently involves large-scale dataset creation and ensemble learning techniques. Scientific Awards: SwissNLP Award 2023 Best Paper Award - Honorable Mention at EMNLP 2020 ZHAW Teaching Award 2015 He leads numerous research projects including: Unified Model for Text Generation Evaluation End-to-End Swiss German Speech Translation Holistic Analysis of Misinformation in Social Networks Automated News Processing (AutoNews) Chatbot Development for Language Learners He co-founded SpinningBytes AG (2015-present) and maintains extensive industry collaborations. As head of the NLP research group, he oversees doctoral candidates and master's students, though specific advisee names aren't listed.

Jacob Laurelمشاهده پروفایل

استادیار

• Static Analysis
• Programming Languages
• Probabilistic Programming
• +۳ مورد دیگر

Jacob Laurel is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. His research focuses on static analysis of programming languages, particularly in probabilistic and differentiable programming. He actively recruits PhD students starting Fall 2025 for work in these areas. Research interests include static analysis techniques for probabilistic/differentiable languages, compiler optimizations, and formal methods for ensuring correctness in AI systems. His work spans topics like abstract interpretation, automatic differentiation, and uncertainty quantification in distributed systems. Notable contributions include the Diamont framework for uncertainty monitoring in distributed programs, the Statheros compiler for low-precision probabilistic inference, and foundational work on higher-order automatic differentiation analysis. He has served on program committees for OOPSLA, SAS, ECCV, and WFVML. Teaching includes a special topics course on probabilistic/differentiable programming in Spring 2025.

Sanket Tavarageriمشاهده پروفایل

استادیار

• Big Data
• Machine Learning
• High Performance Computing
• +۲ مورد دیگر

Sanket Tavarageri is an Assistant Professor in the Computer Engineering Department at San José State University. He holds a Ph.D. in Computer Science and Engineering from The Ohio State University and a B.Tech from National Institute of Technology Karnataka. His research encompasses big data systems, machine learning infrastructure, and high-performance compiler technologies. Research areas include: Polyhedral compilation techniques for deep learning workloads Automatic parallelization and optimization frameworks Hardware-software co-design for computational efficiency Scalable machine learning systems His publication portfolio demonstrates consistent innovation in compiler architecture and parallel systems from 2013-2021. Recent work focuses on AI-enhanced compilation, automatic parallelism for deep learning models, and hardware-aware optimizations. Publications appear in ACM TACO, IEEE Big Data, IPDPS, and PLDI conferences. Tavarageri maintains an active industry connection as a researcher at Google. Tools developed through his BRIGHT laboratory are available on GitHub and Bitbucket. His teaching covers compiler technology, parallel computing, and systems design with emphasis on practical implementation.

Mitch Marcusمشاهده پروفایل

استاد

• Statistical natural language processing
• Cognitively plausible models
• Linguistic structure acquisition

Mitch Marcus is a Professor Emeritus in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He earned his Ph.D. from MIT in 1978 and joined Penn in 1987 after working at AT&T Bell Laboratories. His research focuses on statistical natural language processing and cognitively plausible models for automatic acquisition of linguistic structure. Dr. Marcus created and led the Penn Treebank Project, which revolutionized natural language parsing accuracy. He has served as PI on multiple major projects including DARPA LORELEI and GAILA AIX, focusing on unsupervised morphology acquisition and child language acquisition simulations. Awards: Fellow of American Association for Artificial Intelligence (1992) Founding Fellow of Association for Computational Linguistics (2011) Advising: His former PhD students hold positions at institutions including MIT, Johns Hopkins, Google, and Microsoft Research. Research groups: Directed the Penn Treebank Project and co-led the OntoNotes Project. Currently collaborates on natural language understanding for human-robot interaction.

François Jacquenetمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Natural Language Processing
• +۵ مورد دیگر

François Jacquenet is a Professor of Computer Science at the University of Saint-Etienne, where he is a member of the Machine Learning Team at the Hubert-Curien Laboratory. His research focuses on machine learning and data mining applications for natural language processing, with significant contributions to privacy-preserving systems including Hippocratic Multi-Agent Systems and Automata-Based Sequence Mining. His research interests span Machine Learning , Data Mining , Natural Language Processing , and Privacy-Preserving Systems . Jacquenet has led multiple research projects including the PASCAL II Network of Excellence (2008-2012), the Bingo2 project (2008-2010), and the Web Intelligence project (2006-2008), where he focused on ethical web design and privacy protection techniques. His work bridges theoretical foundations with practical applications in areas like fraud detection, meeting summarization, and video tag correction. Analysis of his recent publications reveals a consistent research trajectory in deep learning applications , privacy-preserving techniques , and cross-modal learning . His work shows a progression from foundational research in grammatical inference and automata theory to contemporary applications in neural networks and self-organizing systems. The publications demonstrate strong interdisciplinary connections between computer science, physics, and security applications. Best AI Paper Award at Conference (2006) Professor Jacquenet has supervised numerous PhD students including Maria Galvan, Hoang-Tung Tran, Ludivine Crépin, and Stéphanie Jacquemont. His research has been supported by significant grants including the French Research Agency (ANR), the Rhône-Alpes region, and international networks like PASCAL. He has organized multiple conferences including Privacy on the Web at the ACM Symposium on Applied Computing and the PASCAL Workshop on Teaching Machine Learning. He is actively involved with the Machine Learning Team at Hubert-Curien Laboratory , contributing to the PASCAL Network of Excellence and the REWERSE Network. His research group focuses on developing practical applications of machine learning while addressing fundamental theoretical questions in pattern mining and language learning.

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

Haw-Shiuan Chang is a Research Fellow in the Department of Computer Science at the University of Massachusetts Amherst, based in Room 366 of the Computer Science Building. His research focuses on advancing natural language processing and artificial intelligence through innovative modeling techniques. His primary research interests include: Natural Language Processing (core focus) Machine Learning optimization Large Language Model efficiency Text generation factuality and diversity Computational creativity measurement Recent publications (2021-2025) reveal a strong emphasis on resolving fundamental limitations in language models, particularly addressing entropy extrapolation, softmax bottlenecks, and multi-mode distribution representation. His work bridges theoretical modeling with practical applications in story generation, recommendation systems, and scientific document processing. No scientific awards or mentoring activities are documented in the provided materials. Research is conducted within the Computer Science department infrastructure, with no specific lab or team affiliations mentioned.

Regino Criadoمشاهده پروفایل

استاد

• Complex Networks
• Discrete Mathematics
• Cybersecurity
• +۴ مورد دیگر

Dr. Regino Criado is Full Professor of Applied Mathematics at Rey Juan Carlos University (URJC), where he has held academic positions since June 2000. He currently serves as Academic Director of the Data, Complex Networks and Cybersecurity Sciences Institute (since March 2017) and was elected to the Academia Europaea in May 2023. His career spans European research projects (ESPRIT II/III) and interdisciplinary collaborations at the intersection of mathematics, computer science, and cybersecurity. His research focuses on Complex Networks , including hyper-networks, multiplex networks, and mesoscale structures, with applications to cybersecurity, intentional risk management, and synchronization phenomena. Key innovations include: A game theory-based intentional risk model integrating accessibility, anonymity, and value metrics Hybrid network mining algorithms for credit card fraud detection PageRank extensions to multiplex networks Framework for discrete resilience analysis in technological systems His work on the 2014 Physics Reports monograph The structure and dynamics of multilayer networks (3,500+ citations) established foundational concepts in multilayer network theory. Current research explores linguistic pattern analysis through multilayer hypergraphs for automatic text summarization. Member of Academia Europaea (2023) Chaos Journal Best Paper Award (2011) Academic Entrepeneurs Prize (2002) EUROPA-1992 Prize (1992) As director of the DCNC Institute (2017-present) and BBVA-URJC Chair (2017-2018), he has led major academic-industry collaborations. His 180+ publications (6,500+ citations) demonstrate sustained impact across network science, applied mathematics, and cybersecurity.

• Artificial Intelligence
• Human Language Technologies
• Speech and Language Processing
• +۷ مورد دیگر

Ricardo Daniel Santos Faro Marques Ribeiro is an Associate Professor at Iscte - Instituto Universitário de Lisboa, where he is affiliated with the Department of Information Science and Technology (ISTA) within the School of Technologies and Architecture. He is also a researcher at INESC-ID Lisboa, focusing on Human Language Technologies and speech and language processing. He has held leadership roles including Director of the Master's and Bachelor's in Computer Engineering and Coordinator of the Artificial Intelligence scientific area. PhD in Informatics and Computer Engineering, Instituto Superior Técnico (2011) Master's in Electrical and Computer Engineering, Instituto Superior Técnico (2003) Bachelor's in Mathematics/Computer Science, University of Beira Interior (1996) His research centers on artificial intelligence, with a strong emphasis on natural language processing, text mining, computational linguistics, and machine learning. He explores applications in sentiment analysis, hate speech detection, document summarization, information extraction, and generative AI, particularly in domains such as legal texts, social media, and customer feedback. The recent publications reflect a consistent trend in leveraging advanced NLP techniques, especially pretrained language models and deep learning, to solve real-world problems in text classification, information extraction, and domain-specific understanding (e.g., legal, medical, financial). The work spans both Portuguese and multilingual contexts, emphasizing practical deployment and automation. He has successfully co-supervised three doctoral students and over 70 master’s students, with several currently in progress. He has led significant research projects, including European-funded initiatives like RAGE and national projects with the Ministry of National Defense and the Supreme Court of Justice, demonstrating strong grant acquisition and management capabilities. Ricardo Ribeiro leads the Artificial Intelligence scientific area at Iscte and has directed multiple academic programs. He also contributes to the broader scientific community through participation in major conferences (IJCAI, ECAI, LREC) and editorial work in computational processing of Portuguese.

Martin Monperrusمشاهده پروفایل

استاد

• Software Engineering
• Program Repair
• Artificial Intelligence for Software Engineering
• +۵ مورد دیگر

Martin Monperrus is a Professor of Software Technology at KTH Royal Institute of Technology in Stockholm, Sweden. He leads the software engineering research group focusing on program repair, AI-driven software development, and software supply chain security. His work spans theoretical computer science and practical applications in blockchain, testing, and diversity in systems. Teaches courses such as Automated Software Testing, Blockchain Systems, and Advanced Software Engineering. Research centers on inventing tools for modern software engineering, including program synthesis, neural repair, and resilient systems. Pioneers initiatives like Repairnator (automatic bug fixing bot) and open-source tools like Astor and Gumtree. Research interests include: Automated program repair using large language models Blockchain security and decentralized systems Reproducible builds and dependency management Formal methods for smart contracts Recent work emphasizes: Generating mocks and test data with AI Supply chain attacks in Java/Golang Leaderboards for program repair models Labs/Teams: Leads the KTH Software Technology Group , collaborating with PhD/Master's students on cutting-edge projects.

• Artificial Intelligence
• Reinforcement Learning
• Generative AI
• +۱۰ مورد دیگر

Álvaro Jesús López López is a researcher at the Instituto de Investigación Tecnológica (IIT) and a professor at the Higher Technical School of Engineering (ICAI), both part of Comillas Pontifical University. He holds the position of Associate Research Fellow and serves as a principal investigator at the Chair of Smart Industry, where he leads applied research on digital technologies such as generative AI, computer vision, and synthetic data generation. He also directs executive programs in generative artificial intelligence for professionals at Comillas Onexed. His research interests include Artificial Intelligence, Reinforcement Learning, Generative AI, Smart Industry, and Railway Systems . His work focuses on applying advanced AI techniques to industrial and transportation challenges, particularly in energy efficiency and automation. He has published extensively in high-impact journals such as Engineering Applications of Artificial Intelligence , Applied Intelligence , and Transportation Research Part C . The recent articles highlight a strong trend toward applied AI in industrial and robotic systems , with a focus on sim-to-real transfer, keyphrase extraction, and reinforcement learning enhanced with semantic knowledge. His work bridges the gap between theoretical AI and real-world industrial applications. Scientific Awards: Honorable Mention from the Industry 4.0 Observatory (2021) Honorable Mention at the 6th Connected Industry Promotion Award Erasmus Teaching/Research Staff Training Grant (2014) He has advised multiple PhD students and leads several industry-funded research projects with organizations such as Ferrovial, Endesa, Metro de Madrid, and the European Commission. He has also organized scientific events and delivered numerous invited talks on AI and digitalization. His research has led to practical applications, including a patent on utilizing regenerative braking energy in railways. He completed a research stay at the Royal Institute of Technology (KTH) in Stockholm and has contributed to books and policy reports on digitalization in industry.

Hui Liمشاهده پروفایل

دانشیار

• Information Retrieval
• Data Mining
• Data Management
• +۸ مورد دیگر

Hui Li is an Associate Professor in the Department of Computer Science and Technology at the School of Informatics, Xiamen University. He is affiliated with the Media Analytics & Computing Laboratory (MAC Lab) and holds a Ph.D. in Computer Science from the University of Hong Kong. Ph.D., Computer Science, University of Hong Kong His research centers on Information Retrieval, Data Mining, and Data Management, with a strong emphasis on Recommender Systems. Key areas include shilling attacks and defenses, bias analysis and debiasing, large language model-based recommendation, generative information retrieval, code retrieval, and multi-modal large language models. His work bridges theoretical innovation with practical applications in real-world platforms. His recent publications span top-tier journals and conferences such as IEEE TKDE, TNNLS, TOIS, KDD, SIGIR, AAAI, ACL, and ICLR. The publications demonstrate a consistent focus on robustness, fairness, and efficiency in recommender systems, increasingly integrating large language models and graph-based methods. Themes include adversarial attacks, self-supervised learning, and data augmentation using LLMs. Hui Li actively supervises graduate students, many of whom have published in CCF-A and CCF-B ranked venues. Notable students include Zhang Sheng, Liu Hao, and Li Lianyun. He has contributed to open science by releasing datasets like the Dianping datasets for sequential and social recommendation research. His work has practical implications in e-commerce, social platforms, and software engineering tools. He collaborates with industry partners such as Alibaba, Tencent, and Pinduoduo, where several of his students have been placed. His research is supported by publications in high-impact journals and participation in leading AI conferences.

• Machine Translation
• Natural Language Processing
• Computational Linguistics
• +۷ مورد دیگر

Dr. Steffen Eger is a professor at the Technical University of Darmstadt, focusing on Natural Language Processing, Machine Translation, and Computational Linguistics. His work spans both theoretical and applied aspects of NLP, including adversarial robustness, evaluation metrics, and creative text generation. Key Contributions: • Pioneering research in LLM-based evaluation metrics • Development of efficient translation quality frameworks • Analysis of social solidarity in historical discourse • Exploration of poetic creativity through neural models His recent publications demonstrate expertise in: - Human-Aware Translation Evaluation - Summarization of Historical Texts - Adversarial Defense Mechanisms - Explainable AI for Generation Metrics While no explicit awards or student supervision details are provided, his collaborative work appears across major NLP venues like ACL, EMNLP, and COLING. His research frequently intersects with human-centric NLP tasks, emphasizing reproducibility and robustness in system design.

Jennifer Fosterمشاهده پروفایل

دانشیار

• Natural Language Processing
• Artificial Intelligence
• Sentiment Analysis
• +۴ مورد دیگر

Dr. Jennifer Foster is an Associate Professor and Associate Dean for Teaching and Learning in the Faculty of Engineering and Computing at Dublin City University. She specializes in Natural Language Processing (NLP) and AI, with a focus on language parsing, sentiment analysis, and Irish language technology. She leads projects funded by Science Foundation Ireland (SFI) and other agencies, supervising 11 PhD students to completion and currently mentoring four more. Her research spans NLP applications including grammar checking, question answering, and neural language model evaluation. She teaches NLP courses for Data Science and Computing programs, as well as introductory Python programming. Foster has authored over 100 peer-reviewed publications and serves on conference program committees, including the Association for Computational Linguistics executive board (2016-2019). Recent work includes developing gaBERT, an Irish language model, and exploring machine-generated story coherence. Her contributions to resources like the Irish Universal Dependencies Treebank and the GenERRate error-generation tool highlight her commitment to advancing NLP for under-resourced languages.