Nicolas Gutehrlé is a Post-doctoral researcher at the ANR InSciM project (2024–Present), focusing on modeling uncertainty in scientific articles across disciplines. He also serves as a Teaching Assistant at Université de Bourgogne and Université de Franche-Comté, instructing courses in Computational Linguistics, Natural Language Processing, and related fields. His research interests include Digital Humanities, Information Extraction, and Knowledge Management, with a focus on semantic annotation and archival document analysis. **Research Projects** ANR InSciM : Developing linguistic models to identify uncertainty expressions in scientific papers (SSH/STM disciplines). EMONTAL Project (PhD): Automated processing of heterogeneous archival collections for heritage enhancement via NLP and discourse analysis. **Teaching**: Delivers courses in Computational Linguistics, Phonetic Modeling, Cognitive Methods, and Software Engineering for NLP across master’s programs. Guides workshops on LaTeX and scientific writing. **Popularization**: Actively participates in events like European Researchers’ Night and Science Festival, promoting understanding of historical document analysis and semantic technologies through articles and public engagement.
Abbas Akkasi is a Research Fellow at the School of Computer Science, Carleton University. His research focuses on advancing artificial intelligence, machine learning, natural language processing (NLP), and accessibility technologies. He has contributed to projects like TactileNet, which uses AI to create tactile graphics for visually impaired individuals, and has explored biomedical informatics applications in clinical text analysis and symptom recognition. His work bridges multiple disciplines, including multimodal learning, semantic analysis, and generative AI. Notable contributions include developing methods for job description parsing, reference-free summarization evaluation, and improving chemical named entity recognition through undersampling techniques. He has also engaged with interdisciplinary efforts in telecommunications (e.g., LTE-D2D for connected cars) and grid computing optimization. Key research themes include leveraging large language models (LLMs) for biomedical NLP tasks, improving accessibility through assistive technologies, and enhancing machine learning algorithms for imbalanced datasets. His publications reflect a commitment to both theoretical advancements and practical applications of AI across healthcare, education, and industry. Abbas has collaborated with institutions such as the TakeLab (as indicated in his SemEval-2018 participation) and has authored over 20 peer-reviewed articles since 2015. His work emphasizes innovation in NLP, computer vision, and interdisciplinary problem-solving.
Dr. Vimal Kumar Kumar serves as a Research Fellow at the University of Limerick's Department of Computer Science and Information Systems, affiliated with Lero – the Irish Software Research Centre. His academic career includes prior faculty positions as Assistant Professor at Jaypee University of Information Technology (2009-2022) and Lecturer at SRM Institute of Science and Technology (2007-2009), followed by a Postdoctoral Researcher role at University College Dublin (2022-2023). His educational background includes: PhD in A Novel sense based Hindi to Tamil machine translation system from Jaypee Institute of Information Technology (2019) Masters from Anna University Chennai (2005-2007) Bachelor from PSG College of Technology (2000-2004) Dr. Kumar's research centers on Natural Language Processing with specialized focus on machine translation for Indian languages (Hindi, Tamil), text summarization, and sentiment analysis. He extends this work into computer vision applications for medical imaging and activity recognition, plus deep learning implementations for IoT-based agricultural monitoring. His methodology frequently addresses challenges in low-resource language processing and develops optimized neural network architectures for mobile deployment. Analysis of his publication trends reveals consistent interdisciplinary innovation, particularly in healthcare applications (retinal disease detection), agricultural technology (crop yield prediction), and social media analytics. His work demonstrates strong emphasis on practical AI solutions for resource-constrained environments and underrepresented linguistic communities, often leveraging generative models and contextual embeddings. Dr. Kumar actively contributes to Lero's research initiatives, aligning his technical work with UN Sustainable Development Goals through software solutions addressing global challenges in health, food security, and digital inclusion.
Ritu Chaturvedi is an Associate Professor at the School of Computer Science, University of Guelph. Her research focuses on data mining and predictive modeling with applications in educational technology, e-commerce, and social media analysis. She holds a faculty position emphasizing interdisciplinary approaches to computational challenges in learning systems and digital environments. Her research interests span Intelligent Tutoring Systems (ITS) Educational Data Mining Sentiment Analysis Recommendation Systems Natural Language Processing Machine Learning Methodologies Recent work includes developing algorithms for personalized learning systems, analyzing social media sentiment during global health crises, and enhancing cross-platform e-commerce recommendations. Her publications demonstrate expertise in data-driven solutions for instructional design, consumer behavior modeling, and privacy policy optimization. Advising focuses on graduate students exploring adaptive learning technologies and data integration strategies. Her lab's projects often involve open-source implementations of recommendation engines and educational analytics frameworks.
Haopeng Zhang is an Assistant Professor in the Department of Information and Computer Sciences at the University of Hawaii at Mānoa, leading the ALOHA Lab. He holds a Ph.D. from the University of California, Davis (advised by Dr. Jiawei Zhang), dual M.S. degrees from Georgia Tech (Electrical Engineering and Computational Science), and a B.S. from UIUC (Electrical Engineering). His research focuses on NLP, Generative AI, AI4Science, and Graph Mining, emphasizing biomedical applications, multimodal reasoning, and workflow generation. Recent achievements include organizing workshops at EMNLP and ACL, receiving grants from NSF and OpenAI, and developing tools like StrucSum and MermaidFlow. Education: Ph.D., Computer Science, UC Davis (2024) M.S., Electrical Engineering & Computational Science, Georgia Tech B.S., Electrical Engineering, UIUC Research interests span structural summarization, LLM applications in healthcare and linguistics, and graph-based NLP systems. His work bridges theoretical advancements with practical tools, including the ALOHA Lab's contributions to reliable summarization systems and multimodal benchmarks. Notable contributions include the DomainSum benchmark and Wi-Chat wireless sensing framework. Key awards include UH travel grants, OpenAI credits, and NSF computing allocations. He actively serves on conference committees (ACL, KDD) and reviews for top journals (ACM TKDD, IEEE transactions). Teaching responsibilities include Advanced AI and Machine Learning courses at UH Mānoa. Lab activities focus on AI-driven solutions for scientific domains, with ongoing projects in biomedical event extraction and low-resource language support.
Da Chen is a Lecturer in the Department of Computer Science at the University of Bath, affiliated with the Bath Institute for the Augmented Human and the Centre for Sustainable Energy Systems. He focuses on advanced computer vision and machine learning techniques, particularly in few-shot learning, incremental learning, video understanding, and their applications to urban planning and sustainability. His work bridges AI with real-world challenges like solar energy prediction, urban mobility optimization, and environmental monitoring. Education: He holds a PhD titled 'The Visual Analysis of Complex Natural Phenomena' (2017), supervised by Prof. P. Hall and Prof. M. Brown. Research Interests: Chen’s research spans generative AI, video object detection, solar radiation modeling, and urban scene analysis. He explores how machine learning can address sustainability goals, such as optimizing bike-sharing systems and predicting energy demands through satellite imagery. His methods often involve GANs, convolutional neural networks, and hybrid sequence encoders. Grants & Collaborations: He co-leads a Royal Society-funded project (2024–2026) on improving transient heat transfer experiments using Bayesian statistics and neural networks. His collaborations span institutions globally, focusing on urban-scale AI applications and environmental science. Labs/Teams: Active in the Bath Institute for the Augmented Human, advancing human-centric AI, and the Centre for Sustainable Energy Systems, integrating computational methods for energy efficiency.
Mohit Bansal is the John R. & Louise S. Parker Distinguished Professor and Director of Graduate Admissions in the Computer Science Department at the University of North Carolina Chapel Hill. He leads the MURGe-Lab (UNC-AI Group) and serves as Lead (Core AI) for the ENGAGE NSF-AI Institute. Previously, he was a Research Assistant Professor at TTI-Chicago. Dr. Bansal earned his Ph.D. from UC Berkeley in 2013 under Dan Klein and his B.Tech. from IIT Kanpur in 2008. His research spans Natural Language Processing and Multimodal Machine Learning , with specific expertise in multimodal generative models, grounded and embodied semantics (language with vision/speech for robotics), faithful language generation, reasoning and planning agents, and interpretable deep learning. He employs techniques from structured prediction, reinforcement learning, and model editing to address challenges in compositional generalization and robustness. His recent work focuses on multimodal understanding, vision-language navigation, model merging, and evaluating/factuality in generative models. Trends show increasing emphasis on trustworthy AI, with projects addressing hallucination reduction, cultural bias diagnosis, and safe generation. His publications span top venues including ACL, CVPR, NeurIPS, and ICML, with significant contributions to multimodal foundation models and parameter-efficient learning. AAAI Fellow (2025) Presidential Early Career Award for Scientists and Engineers (PECASE) (2025) IIT Kanpur Young Alumnus Award (2023) DARPA Director's Fellowship (2019) NSF CAREER Award (2019) Microsoft Investigator Fellowship (2019) Outstanding Paper Awards at ACL, CVPR, EACL, COLING, and CoNLL Dr. Bansal has advised numerous PhD students who now hold positions at top institutions including UT Austin, NTU Singapore, JHU, Meta, and Adobe. His lab secures substantial funding from NSF, DARPA, NIH, and ONR, including the $20M NSF-AI Institute on Engaged Learning where he serves as Core AI Lead. Current projects include DARPA's Environment-driven Conceptual Learning (ECOLE) and ONR's Science of Artificial Intelligence program. The MURGe-Lab (Multimodal Understanding, Reasoning, and Generation) develops foundational models for multimodal tasks, with recent work on VideoTree for long video reasoning, SELMA for skill-specific text-to-image experts, and LASeR for adaptive reward model selection. The lab collaborates extensively with industry partners including Google, Meta, and Microsoft.
Anum Afzal is a Ph.D. candidate and Researcher at the Chair of Software Engineering for Business Information Systems (sebis) within the Faculty of Informatics at Technical University of Munich . Her work focuses on improving efficiency and domain adaptation of Large Language Models (LLMs) , particularly in business contexts through collaborations with SAP and Holtzbrinck Publishing Group.
Professor Rob Gaizauskas is a faculty member at the University of Sheffield , serving as Co-Director of the UKRI Centre for Doctoral Training in Speech and Language Technologies and leading the Natural Language Processing (NLP) research group . His academic journey began with a DPhil in Cognitive and Computing Sciences from the University of Sussex (1992), preceded by degrees in Philosophy from Carleton University and a Diploma in Information Processing. Education : DPhil (University of Sussex, 1992), MA (Carleton University, 1978), BA (Carleton University, 1975) His research focuses on NLP , particularly information extraction from texts, temporal/spatial information processing , automatic image description , argument mining , and evaluation of NLP systems . He has pioneered work in multi-document summarization , dialogue analysis , and comparable corpora for machine translation. Notable grants include: UKRI Centre for Doctoral Training in Speech and Language Technologies (2019–2027, £5.5M) VisualSense (2013–2016, £310k) SENSEI Project (2013–2016, £459k) ACCURAT (2010–2012, £268k) Scientific contributions : Co-developer of the GATE framework for text engineering Led biomedical NLP projects like BioWSD and PASTA Pioneering work in temporal relation identification (TempEval)
Halil Kilicoglu is an Associate Professor at the School of Information Sciences (iSchool), University of Illinois at Urbana-Champaign. He holds affiliate appointments at the National Center for Supercomputing Applications , Division of Nutritional Sciences , Personalized Nutrition Initiative , and Center for Health Informatics . His research bridges Natural Language Processing , Biomedical Informatics , and Scientific Reproducibility . Education: PhD in Computer Science (2012), Concordia University Previous Role: Staff Scientist, U.S. National Library of Medicine (NIH) Research Focus: Kilicoglu develops advanced NLP and machine learning techniques to extract and organize knowledge from biomedical texts. His work enhances clinical trial transparency , drug repurposing , literature-based discovery , and scientific communication . Current projects include automated assessment of randomized controlled trials and knowledge graph construction for biomedical domains. Recent Article Trends: His publications emphasize transformer models , retrieval-augmented generation , and multi-label classification applied to citation integrity , diet-microbiome associations , and clinical trial reporting . Scientific Awards: TrustNLP 2023 Best Paper SemEval 2021 Best System Paper IMIA Yearbook Best Paper (2020, 2017) AMIA Distinguished Paper (2016, 2007) Students: Mentors PhD candidates in information science and informatics , including Janina Sarol, Lan Jiang, Mengfei Lan, Shufan Ming, Gibong Hong, Evan Guerra, and Joe Menke. Labs & Teams: Leads a lab at the iSchool focused on biomedical text mining , collaborating with institutions like NIH and SpringerNature. Projects include SemRep extension , MENAGERIE tool , and COMBINI initiative .
Dr. Ahmad Aghaebrahimian is a researcher at the ZHAW School of Life Sciences and Facility Management, affiliated with the Institute of Computational Life Sciences. He specializes in computational methods applied to healthcare, natural language processing (NLP), and bioinformatics. His work integrates deep learning, ontology-based systems, and signal processing to address challenges in healthcare informatics, biomedical research, and security systems. Research Projects: Project Leader: Advancing Information Accessibility in Hospitals (LLMs) Project Leader: Multi-document Patient Records Summarization Project Leader: Plant Cell Cultures with Deep Learning Deputy Leader: Automatic Supply Chain Monitoring Research Interests: His research focuses on AI-driven solutions for healthcare, including ontology-aware relation extraction, medical text mining, and robust signal processing systems. He also explores NLP applications in question answering, entity disambiguation, and parallel corpus creation. Recent work includes drone detection using CNNs in low SNR environments and computational methods for natural products discovery. Publications Trends: Over the past decade, his publications emphasize interdisciplinary approaches combining machine learning with bioinformatics and medical informatics. Key themes include deep learning model optimization, biomedical knowledge graph construction, and practical applications of NLP in healthcare systems. Grants & Collaboration: Leads research initiatives on AI in colorectal cancer classification and supply chain monitoring, demonstrating expertise in securing project leadership roles within academic-industry collaborations.
Dr. Emdad Khan is an Adjunct Professor of Computer Science at Maharishi University of Management. He holds a PhD in Computer Science from the University of California, Santa Cruz, along with MS degrees in Electrical Engineering (University of New Orleans) and Engineering Management (Stanford University), and a BS in Electrical Engineering from Bangladesh University of Engineering & Technology. His research focuses on Natural Language Processing (NLP), Artificial Intelligence (AI), Big Data, Machine Learning, and their applications in Intelligent Internet systems, Biological Systems, and multi-disciplinary education. He has pioneered technologies like netECHO (voice-based Internet access) and the Semantic Engine using Brain-Like Approach (SEBLA), emphasizing brain-inspired algorithms for solving NLU challenges. With 23 patents and over 75 publications, Dr. Khan has authored books including Internet for Everyone: Reshaping the Global Economy (2011) and contributed to academic works on neural networks and fuzzy systems. His work bridges technical innovation with social impact, aiming to drive economic development and global peace through technology. Dr. Khan has presented plenary talks at major conferences such as the 17th International Conference on Artificial Intelligence (AIKED ’17) and the 15th International Conference on Applied Computer Science (2016). His research explores brain-like algorithms for cognitive computing and biological systems analysis, integrating principles from Vedic Science and the Science of Creative Intelligence.
Dr. Stephanie Schwartz is a full-time Professor in the Department of Computer Science at Millersville University since Fall 2003. With a Ph.D. (2006) and M.S. (1993) from the University of Delaware and a B.S. (1991) from Shippensburg University, her academic background is rooted in computer science. Former industry experience at MapQuest, AMP, and Primavera Software bridges theoretical research with practical applications. Education: Ph.D. in Computer Science, University of Delaware (2006) M.S. in Computer Science, University of Delaware (1993) B.S. in Computer Science, Shippensburg University (1991) Her research focuses on machine learning , data science , user modeling , cognitive modeling , and artificial intelligence , with a strong emphasis on information graphics accessibility for visually impaired users. Recent publications highlight her work in cybersecurity (code reuse attacks, phishing detection) and visual analytics (chart interpretation, multimodal document processing). Dr. Schwartz leads cross-disciplinary collaborations through projects like the Software Productization Center , focusing on automated understanding of bar charts, line graphs, and Bayesian network applications. Her research trends since 2010 show sustained innovation in accessible visualization and machine learning for security . Scientific Awards: James Chen Annual Award for Best Journal Article (2007) She has contributed to information graphics accessibility , developing systems like Interactive SIGHT for textual summaries of charts. Her work spans educational robotics , document summarization , and user preference modeling in collaborative environments.
Weiyi (Ian) Shang is an Associate Professor at the University of Waterloo, affiliated with the Department of Electrical and Computer Engineering within the Faculty of Engineering. His research focuses on software engineering, performance testing, and machine learning applications in software systems. He leads the Software Engineering and System Engineering Lab, emphasizing practical solutions for logging, performance optimization, and automated testing. Key research areas include log analysis (privacy leakage detection, log summarization, and logging strategies), performance monitoring (regression detection, workload modeling), API evolution (migration techniques, workaround analysis), and automated code generation (LLMs in bug decomposition, AI code evaluation). His work bridges theoretical advancements with industrial applications, particularly in web systems and mobile app ecosystems. Publications span empirical studies, novel algorithms (e.g., DELA for error detection, CoMSA for configuration testing), and tools like LogAssist and Log4Perf. His research consistently addresses challenges in developer productivity, system reliability, and security across diverse domains like federated learning and DevOps practices. Notable contributions include improving log management through topic models, enhancing performance testing efficiency via microbenchmark optimization, and analyzing privacy risks in mobile app logs. Ongoing work explores AI-driven code evaluation and generalizable code embeddings for software tasks. Shang’s lab collaborates with industry on real-world systems, as seen in case studies involving serverless applications and database-centric systems. His research often involves empirical studies and tool development to bridge gaps between academic research and practical software engineering challenges.
Souvika Sarkar is an Assistant Professor at the School of Computing within the College of Engineering at Wichita State University. Her research focuses on enhancing AI and data science accessibility through interdisciplinary work at the intersection of Natural Language Processing (NLP), Information Retrieval (IR), and AI. She aims to develop context-aware and scalable AI systems capable of semantic understanding of natural language, particularly for broader societal benefit. Dr. Sarkar holds a Ph.D. from Auburn University, where she was honored with prestigious awards including the 100+ Women Strong Outstanding Departmental Annual Graduate Award and Auburn University’s Outstanding Doctoral Student Award. She also earned a master’s in software engineering from Jadavpur University. Prior to academia, she worked as an IT Analyst at Tata Consultancy Services, managing Microsoft SharePoint migrations and enterprise process workflows. Her research interests span NLP applications in education, multilingual AI systems, and ethical AI practices. Notable projects include developing conversational frameworks for K-12 physics education and analyzing annotator bias in hate speech detection systems. She has also explored deploying NLP models on embedded devices to address computational constraints. Publications highlight her work on digital twin security, LLM-driven meta-review systems, and Bangla language processing. Her industry experience bridges academic research with real-world enterprise solutions, reflecting a commitment to practical AI applications.
