Xiuzhen Jenny Zhang is a Professor of Data Science at RMIT University , affiliated with the School of Computing Technologies. Her research bridges artificial intelligence, machine learning, and social media analysis, with a focus on text mining and trustworthy data science. Her recent publications highlight expertise in point-of-interest recommendation , misinformation detection , multi-task learning , and transformer-based NLP . Key trends include applications of large language models for social good, fairness in recommendation systems , and adversarial learning for robustness. Best Paper Award at TrustCom’12 Best Short Paper Award at ADCS’2009 She leads the Text And LanguagE (TALE) research group and has supervised over 20 PhD students. Research grants include Australian Research Council and Victoria state government funding.
Pablo Calleja is a Research Fellow at the Faculty of Computer Science, Polytechnic University of Madrid (UPM), where he has been a member of the Ontology Engineering Group (OEG) since March 2014. His research focuses on Natural Language Processing (NLP), medical terminology mapping, and legal domain applications. He holds a degree in Computer Engineering from San Pablo CEU University (2013) and has prior industry experience as a software developer at IECISA (2008–2011) and a collaboration grant at the Open Access Classroom, San Pablo CEU University (2011–2013). Key contributions include projects like Drugs4covid for pandemic drug discovery, TermitUp for terminological enrichment, and esT5s , a Spanish text summarization model. His work spans legal knowledge graphs, multilingual compliance systems, and NER techniques for academic content analysis. He has also explored accessibility multimedia services and semantic graph applications in tourism ( DBtravel ). Professional roles include collaboration grants at UPM and active participation in interdisciplinary projects such as SNOMED-CT annotation for medical technical sheets. Research trends emphasize cross-domain adaptation (e.g., K-Flares), data augmentation (Widaug), and multilingual NLP solutions. Advising and grants: His current position is supported by a collaboration grant at OEG. Earlier grants include work at San Pablo CEU University. No formal advisees are listed, but he contributes to collaborative research teams. Labs and teams: Core member of the Ontology Engineering Group (OEG), focusing on knowledge representation, NLP, and applied informatics in healthcare and law domains.
Dr. Mo El-Haj is a Reader (Associate Professor) in Natural Language Processing (NLP) at the College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam, and holds a visiting role at Lancaster University. He specializes in NLP with a focus on Financial NLP, Arabic NLP, and multilingual systems for under-resourced languages. Education: PhD in Computer Science (University of Essex, 2012), MSc in Information Systems (University of Jordan, 2008), BSc in Computer Information Systems (University of Jordan, 2005). Awards include the FHEA Fellowship (2021) and the 2016 BBC NewsHack Best Tool award. Research interests include text summarization, financial narrative processing, biomedical NLP, and corpus linguistics. He leads the VinNLP research group and has supervised/co-supervised over 20 PhD students. Notable projects include the Welsh Automatic Text Summarisation tool (ACC) and the FreeTxt bilingual analysis toolkit, funded by the Welsh Government and AHRC. Publications span 92+ works in top journals/conferences like Computational Linguistics and LREC. Active in organizing workshops (e.g., WACL-4, FinNLP) and has served as external/internal PhD examiner at UK universities.
Ahmed AbuRa'ed is a Researcher at the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra (UPF), Barcelona. He is affiliated with the TALN research group and the Large-Scale Text Understanding Systems Lab. His work focuses on advancing knowledge in scientific text summarization, information extraction, and machine learning. Education: PhD in Computer Science (2020), UPF, Barcelona, Spain M.Sc. in Computer Science (2015), University of Trento, Italy B.Sc. in Computer Information Systems (2007), An-Najah University, Nablus, Palestine Research Interests: Natural Language Processing (NLP), Machine Learning/Deep Learning, Semantic Web, Information Extraction, Data Mining, and Scientific Document Summarization. His projects include developing systems for automatic generation of state-of-the-art reports, scientific text summarization, and cross-document relation discovery. Publications Focus: His 15 most recent articles (2016–2021) emphasize advancements in scientific literature analysis, including citation detection, text simplification, and cross-document summarization. Notable works involve systems like LaSTUS/TALN for scientific text processing and OlloBot for Arabic health dialogue agents. Labs & Teams: Active member of the TALN research group and the Large-Scale Text Understanding Systems Lab at UPF's DTIC department. Open to collaborations in NLP, Machine Learning, and related fields via email or Skype.
Juan-Manuel Torres Moreno is an Associate Professor (Maître de Conférences HDR HC) at the University of Avignon (UAPV), where he conducts research in Natural Language Processing at the Laboratoire Informatique d'Avignon (LIA). His academic position includes the HDR (Habilitation à Diriger des Recherches), a post-doctoral qualification in France that enables supervision of PhD students. His primary research interests focus on Natural Language Processing, with particular emphasis on automatic text summarization, sentence generation, and phrase compression algorithms. His work spans both theoretical and applied aspects of NLP, incorporating machine learning techniques and artificial intelligence approaches. His research has significant applications in multilingual processing, text mining, and information extraction systems. Torres Moreno's publication record demonstrates a consistent trajectory in advancing text summarization techniques, with recent work exploring cross-lingual approaches, multimedia content processing, and deep learning applications. His research often bridges the gap between theoretical linguistic concepts and practical implementation, with publications spanning from fundamental NLP algorithms to applied systems for video summarization, speech processing, and multilingual document analysis. He actively collaborates with researchers across multiple institutions including École Polytechnique de Montréal (with 50 joint publications), Laboratoire Informatique d'Avignon (83 publications), and Universidad Nacional Autónoma de México. His work appears in reputable journals such as Computer Speech and Language, Data and Knowledge Engineering, and Pattern Recognition Letters. Within the Laboratoire Informatique d'Avignon, Torres Moreno contributes to the Language Processing research theme, working with colleagues on projects related to multilingual information access, opinion mining, and text analysis. His research group has participated in several evaluation campaigns including DEFT (Défi Fouille de Textes) challenges, focusing on information retrieval and sentiment analysis tasks.
Albert Gatt is a researcher at the University of Malta , with extensive contributions to Natural Language Generation (NLG) , Vision-and-Language (V&L) models , and evaluation practices in NLP . His work spans multimodal reasoning, data pruning efficiency, and reproducibility challenges in human evaluations. Key collaborations include studies on temporal grounding in image sequences (TempVS benchmark) and automated legal violation detection in cookie banners. Research highlights include bridging linguistic theory with computational models (e.g., VALSE benchmark for multimodal grounding) and improving generation quality through contrastive learning frameworks. Scientific awards are not explicitly mentioned in the provided texts. His work emphasizes rigor in automatic metric validation and cross-modal interpretability , particularly in multimodal model attention mechanisms and logical formula minimization for text generation.
Jie Wang is a Professor of Computer Science at the University of Massachusetts Lowell's R. Miner School of Computer and Information Sciences. He joined UMass Lowell in 2001 as a Full Professor and chaired the department for 9 years from 2007 to 2016. He serves as Director for China Partnership of the US-based Consortium for Mathematics and Its Applications (COMAP) since 2011. Prior to UMass Lowell, he was Assistant Professor and then Associate Professor of Computer Science at the University of North Carolina. Professor Wang's research spans multiple areas including text mining algorithms and systems, data modeling, combinatorial optimizations, network security, wireless sensor networks, and computational complexity theory. His work has evolved from theoretical foundations in computational complexity (1980s-early 2000s) to practical applications in data analysis, intelligent text automation, and AI systems. His recent publications focus on AI-Oracle machines, LLMs, text mining, document engineering, and network security. His research portfolio demonstrates a clear evolution from theoretical computer science to applied research with practical impact. The publications show increasing focus on AI, text mining, and document engineering in recent years, while maintaining foundations in algorithm design and network security. His work bridges theoretical computer science with real-world applications across multiple domains. Honorary Advisor (2013) - NeoUnion Hong Kong Education Science Culture Organization MHE Scholar (2012) - Ministry of Higher Education, China PMYR Award for Major New Initiatives (2010) - University of Massachusetts Lowell Teaching Excellence Award (2002) - University of Massachusetts Lowell Nominee of Board of Governors' Teaching Excellence Award (2000) - University of North Carolina Professor Wang has graduated 18 PhD students and is currently directing 5 PhD students. His research has been funded by the National Science Foundation, IBM, Intel, and other companies totaling approximately $4.8 million. He is active in professional service, including chairing conference program committees, serving as journal editors, and as editor-in-chief of a book series on mathematical and interdisciplinary modeling. His laboratory work focuses on text mining systems, network security applications, and computational models for practical problems.
Dr. Wei Le is an Associate Professor in the Department of Computer Science at Iowa State University. His primary affiliations include the Program Analysis & AI Lab and collaborative work with Mayo Clinic. He holds a Ph.D. in Computer Science from the University of Virginia (2010). His research focuses on the intersection of AI and software engineering, particularly in trustworthy AI systems, vulnerability detection, and program analysis. Key areas include AI for medicine, analyzing AI models, and improving software reliability through techniques like dataflow analysis and symbolic execution. Recent work emphasizes efficient vulnerability detection using deep learning (e.g., ICSE 2024), numerical instability in ML applications (FSE 2025), and GPU numerical testing (SC 2024). His research has been supported by NSF, Google, and DARPA. Notable awards include the Distinguished Paper Award at FSE 2016 and Best Presentation at FSE 2008. He has advised over 10 students and collaborates with institutions like Columbia University and UCLA. Teaching responsibilities include courses on program analysis (COM S 413/513), principles of programming languages, and advanced AI topics. He actively contributes to conferences as a PC member and area chair (e.g., ICSE, FSE).
Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.
David M. Howcroft is an Advanced Research Fellow in Natural Language Generation at the School of Natural and Computing Sciences, University of Aberdeen . He previously held research fellowships at Edinburgh Napier University, Heriot-Watt University, and Saarland University, contributing to major NLP projects including ASICA, NLG for Low-Resource Domains, and Madrigal. His work bridges computational linguistics, psycholinguistics, and statistical modeling. His research focuses on natural language generation , particularly in low-resource settings . He develops machine learning methods for data-to-text generation, creates novel corpora (e.g., for Scottish Gaelic), and improves human evaluation practices via crowdsourcing and rigorous statistical analysis. A key interest is the application of Bayesian nonparametrics and ordinal mixed-effects models to better understand and evaluate generated text. He also explores readability, referring expressions, and AI planning for rule-based NLG systems. His recent publications reveal a strong trend toward methodological rigor and inclusivity in NLP. He advocates for better evaluation standards, transparency in metric usage, and participatory design in NLP research. His work spans corpus development , evaluation methodology , low-resource language support , and human-centered NLP . He has led efforts to create datasets for under-resourced languages and to standardize best practices in human assessment. He has received small grant funding for projects such as Scottish Gaelic Generation for Exhibits and has contributed to software tools for data collection and evaluation. He mentors and collaborates widely, though no formal advisees are listed. He has developed backend systems and Android apps for healthcare applications, notably in melanoma patient support via the ASICA project. He is actively involved in research labs and teams including: ASICA Project Team (University of Aberdeen) NLG for Low-Resource Domains (Edinburgh Napier University) Madrigal Project (Heriot-Watt University) SFB 1102 Project A4 (Saarland University) Language Science and Technology (LSV, Saarland University) His scientific contributions are widely disseminated through top-tier venues such as ACL, EMNLP, and INLG. He maintains an active online presence with tutorials and technical blog posts on tools like OpenCCG and Treex.
David Martins de Matos is an Associate Professor at Instituto Superior Técnico (IST), Universidade de Lisboa , and a senior researcher at INESC-ID Lisbon within the Human Language Technology Lab . With a career spanning over three decades, he has taught subjects such as Compilers and Object-Oriented Programming since 1993. His research focuses on Natural Language Engineering , Automatic Natural Language Generation , Music Information Retrieval , and Machine Learning Applications in Healthcare . Education: B.Sc. in Electrical and Computer Engineering (IST, 1990) M.Sc. in Electrical and Computer Engineering (IST, 1995) on object-oriented programming in distributed systems Ph.D. in Systems and Computer Science (IST, 2005) on automatic natural language generation Research Interests: His work bridges Natural Language Processing and Computational Music Analysis , with applications in Health Informatics . He investigates semantic frame induction, dialog act recognition, and multimodal systems for chronic pain assessment, Alzheimer's detection, and music generation. His recent articles explore cross-modal retrieval, deep learning for pain narratives, and embodied semantics via fMRI. Scientific Contributions: He has published over 161 works, including 15 recent articles on chronic pain datasets, dialog act recognition, and music-language correlations. His awards include Senior Member status in ACM (SIGMM, SIGIR) and IEEE (Signal Processing Society, Computer Society) , and membership in the Order of Portuguese Engineers . Advising & Collaborations: He has supervised 111 doctoral and master's theses, mentoring students in topics like Visual Story Generation , Music Summarization , and Health Informatics . He collaborates with institutions such as IBM Research , Northwestern University's Feinberg School , and Universidade de Lisboa .
Muskaan Singh is a Lecturer in Data Analytics at the Intelligent Systems Research Centre (ISRC) within the School of Computing, Engineering and Intelligent Systems at Ulster University . A member of the Cognitive Analytics Research Lab (CARL) , her work bridges Natural Language Processing (NLP) , Artificial Intelligence , and Practical Applications in domains ranging from machine translation to biomedical diagnostics. Education: PhD in Machine Translation (Thapar Institute of Engineering and Technology, 2016-2020) Master’s in Machine Translation (IIIT Hyderabad, India) Her research spans NLP and AI with applications in code-switched language modeling , depression detection , social media analytics , and medical diagnostics . She has developed multilingual tools for automatic minuting, including DeepCon and ALIGNMEET , and contributed to EU-funded projects like ROXANNE (criminal network analysis) and ELITR (European Live Translator). Key scientific awards include first prizes in international NLP competitions (EVAL4NLP, LT-EDI, SMM4H) and recognition at EMNLP , ACL , and COLING . She received the Inclusion and Diversity Grant (EMNLP 2021) and GHC Scholarship (2019). Current projects include AI-EPOCMON (AI-Enabled Point-of-Care Monitoring) and T3-NCP (crime prevention for safer communities). Dr. Singh has supervised grants from UKRI and Alzheimer’s Research UK , focusing on AI for health and IT operations . Her team at ISRC collaborates globally with institutions in Switzerland , Czech Republic , and India . She also leads research for the Center for Data Science and Artificial Intelligence at IIIT Lucknow, India.
Cristian Mihaescu is a Lecturer at the Department of Computer Science and Engineering (DCTI), University of Craiova, within the Faculty of Automatic Control, Computers and Electronics. He is actively involved in teaching and research related to machine learning, distributed systems, and educational data mining. Teaching: Data Structures and Algorithms, Parallel and Distributed Algorithms, Machine Learning, Distributed Systems Engineering Research Focus: Machine learning applications in education, social network analysis, and compiler optimization Technological Interests: Microservices, data mining, and intelligent system design
Jackie Chi Kit Cheung is an Associate Professor at the School of Computer Science, McGill University , and holds the Canada CIFAR AI Chair at Mila - Quebec AI Institute. His research bridges Natural Language Processing (NLP) with insights from linguistics and psychology, focusing on system evaluation, automatic summarization , and computational semantics . Key Affiliations : Mila - Quebec AI Institute, Centre for Research on Brain, Language and Music, Centre for Intelligent Machines His lab develops state-of-the-art NLP systems while proposing challenge datasets and evaluation measures. Research emphasizes broader applications in education , health , and language revitalization . Recent work includes the ACL 2024 COSMIC framework for summarization evaluation (SAC Award). He teaches advanced courses like Formal and Neural Models of Pragmatics (Winter 2024) and Evaluation of NLP Systems (Winter 2025). His group includes 10 PhD students, 8 Master's students, and active collaborations with institutions like Mila and SRI International. Scientific Recognition : Canada CIFAR AI Chair SAC Award for ACL 2024 paper Current projects explore representational harms in LLMs , long-context modeling , and mechanistic hallucination mitigation . Students and co-supervised researchers work on fairness, robustness, and commonsense reasoning.
Zain Muhammad Mujahid is a PhD Fellow at the Department of Computer Science , University of Copenhagen (UCPH). His research focuses on Natural Language Processing with emphasis on Large Language Models (LLMs), bias detection, and fact-checking methodologies. Research Interests Factuality and bias prediction in news media LLM evaluation and error analysis Cross-lingual fact-checking systems Arabic-centric language modeling Evidence attribution in summarization AI safety in multilingual contexts Publications Zain's recent work addresses critical challenges in trustworthy AI, including automating error detection in NLG systems, developing cross-lingual bias detection frameworks (SAFARI), and creating benchmarks like Factcheck-Bench for evaluating automatic fact-checkers. His research also explores bilingual safety evaluation in Kazakh-Russian contexts and cultural adaptation of LLMs for Arabic language processing.
