جست‌وجوی استاد و پژوهشگر

Masashi Toyodaمشاهده پروفایل

دانشیار

• Web Mining
• User Interfaces
• Information Visualization
• +۴ مورد دیگر

Masashi Toyoda is an Associate Professor at the Institute of Industrial Science, University of Tokyo. He leads the Kitsuregawa and Toyoda Laboratory and is affiliated with the International Research Center for Strategic Information Fusion. His research focuses on web mining, user interfaces, and information visualization. Education: Ph.D. in Computer Science, Tokyo Institute of Technology (1999) M.S. in Computer Science, Tokyo Institute of Technology (1996) B.S. in Computer Science, Tokyo Institute of Technology (1994) His research interests span web mining, user interfaces, information visualization, and visual programming. He has conducted significant work in spatiotemporal web analysis, web spam detection, evolution of web communities, and visualization techniques including zooming interfaces and 3D web graph visualization. Recent publications (2008-2011) demonstrate strong focus on web mining, information visualization, and web security. Research trends include 3D visualization of time-series web data, analysis of blog archives and CGM images, and novel methods for detecting and classifying web spam. The work consistently emphasizes large-scale web data analysis and interactive systems. Scientific Awards: 1st DBSJ Paper Award Winner (2003) FIT Paper Award and Funai Best Paper Award (2002) Best Presentation Award at DEWS2002 Presentation Award at DEWS2000 Best Paper Award at DEIM2011 Conference Excellence Award at IPSJ 72nd National Convention As head of the Toyoda Laboratory at the University of Tokyo, he leads research in web mining, information fusion, and visualization. The lab has developed notable projects including WebRelievo for web structure evolution analysis and KLIEG visual programming environment.

Eva Cernadas Garcíaمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Image Segmentation
• +۶ مورد دیگر

Eva Cernadas García is an Associate Professor at the Escola Técnica Superior de Enxeñaría of the University of Santiago de Compostela (USC), where she conducts research at the intersection of machine learning, computer vision, and practical applications in medicine, food technology, and marine resource management. Her academic position is firmly established within the Computer Science department, where she has developed significant expertise in image segmentation, texture analysis, and shape recognition. Dr. Cernadas' research interests span multiple domains of artificial intelligence with a strong emphasis on practical implementations. She specializes in developing computer vision algorithms for medical diagnostics, including software tools like CystAnalyser for polycystic kidney disease detection, STERapp for stereological analysis, and PDApp for dental eruption prediction. Her work extends to food technology applications such as MarblingPredictor for ham quality assessment and Govocitos for fish fecundity estimation. Notably, she integrates gender perspective into her technical work, having been awarded by USC in 2021 for introducing gender perspective in machine learning teaching. Analysis of her recent publications reveals a strong focus on applying machine learning techniques to solve real-world problems across diverse fields. Her work demonstrates consistent innovation in developing efficient algorithms for classification and regression tasks, with increasing attention to gender issues in STEM education. The publications show progression from fundamental algorithm development to practical implementations in medical diagnostics, mental health assessment, and sustainable resource management. Awarded by the University of Santiago de Compostela for the introduction of the gender perspective in machine learning teaching in 2021 Dr. Cernadas actively promotes gender equality in STEM through various initiatives including designing workshops for primary education in the program 'Unha enxeñeira ou cientifica en cada cole', early childhood education programs, and promoting the 'INFORMATIZATE' video contest for non-university students. Her research includes significant grant-funded projects related to service robots, intelligent control scenarios, and development of new technologies for studying reproductive ecology in fishery. She collaborates extensively with researchers across multiple disciplines, particularly with Manuel Fernández-Delgado, with whom she has co-authored numerous publications. Her laboratory work focuses on developing practical software tools that bridge the gap between theoretical machine learning algorithms and real-world applications. The CITIUS research center at USC appears to be her primary research base, where she contributes to projects involving image analysis, pattern recognition, and development of user-friendly interfaces for domain experts in medicine and biology.

Lauren Franzمشاهده پروفایل

دانشیار

• Autism Spectrum Disorder
• Child and Adolescent Psychiatry
• Digital Phenotyping
• +۳ مورد دیگر

Dr. Lauren Franz serves as Associate Professor of Psychiatry and Behavioral Sciences and Associate Research Professor of Global Health at Duke University, with Faculty Network membership at the Duke Institute for Brain Sciences. Her clinical expertise centers on child and adolescent psychiatry, specializing in autism spectrum disorder interventions and digital health applications across diverse global contexts. Her academic credentials include: Honorary Associate Professor, Division of Child & Adolescent Psychiatry, University of Cape Town (2023-2028) Residency and Fellowship in Psychiatry and Child and Adolescent Psychiatry, Duke University School of Medicine (2007-2014) M.B.B.Ch., Stellenbosch University, South Africa (2002) Dr. Franz pioneers the integration of artificial intelligence and digital phenotyping to revolutionize autism detection and intervention. Her research spans telehealth adaptation of caregiver coaching in low-resource settings, computational analysis of behavioral markers, and neurodiversity-affirming clinical frameworks. She actively bridges technology development with implementation science to address global disparities in neurodevelopmental care. Analysis of her recent publications reveals dominant trends in machine learning-driven autism screening, cross-cultural adaptation of interventions, and comorbidity mapping between autism and ADHD. Her work consistently leverages electronic health records, mobile applications, and computer vision to identify biomarkers while prioritizing real-world applicability across socioeconomic contexts. Dr. Franz directs significant research initiatives including: Duke-NCCU Interdisciplinary Postdoctoral Training Program (2024-2029) Electrophysiological biomarkers of social engagement in autism (2024-2028) Caregiver Coaching to Improve Outcomes of Young Autistic Children in Africa (2022-2027) ACACIA hybrid effectiveness-implementation trial for autism intervention in Africa She leads the Duke Institute for Brain Sciences' global mental health initiatives, maintaining active collaborations with the University of Cape Town to develop sustainable autism care models. Her international teams integrate psychiatrists, computer scientists, and implementation specialists to create culturally responsive digital tools, with recent work focusing on bias mitigation in AI language models and telehealth delivery in South African communities.

• Machine Learning
• Explainable AI
• Fairness in AI
• +۳ مورد دیگر

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University, Director of the Stanford AI Lab (SAIL), and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI). He also serves as Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research centers on Machine Learning Methods, Explainability, Fairness & Ethics of AI, and Machine Learning Systems. He develops interpretable and reliable models, addresses algorithmic fairness, and builds efficient large-scale ML systems through frameworks like XGBoost. His work bridges theoretical rigor with real-world applications in healthcare and human-centered AI. His recent publications (2023–2025) demonstrate leadership in generative AI evaluation, model reliability, and ethical frameworks. Key trends include developing live benchmarks for research synthesis, on-device calibration techniques, multi-objective optimization with constraints, and societal impact assessment tools—showcasing a trajectory from foundational ML systems to responsible AI deployment. Honors include: Member of the National Academy of Engineering Details about his advising and grant activities were not provided in source materials, though his leadership roles indicate extensive mentorship and funding oversight. As Director of SAIL, he shapes one of the world’s premier AI research centers, while his HAI fellowship drives interdisciplinary initiatives ensuring AI advances human welfare. His industry roles at Visual Layer and Virtue AI translate academic research into practical AI solutions.

Gerard Laceyمشاهده پروفایل

استاد

• Robotics
• Real-time computer vision
• Human-machine systems
• +۳ مورد دیگر

Professor Gerard (Gerry) Lacey is Professor of Electronic Engineering at Maynooth University within the Faculty of Science and Engineering. He also serves as Head of the Department of Electronic Engineering and is affiliated with the Hamilton Institute and ALL Institute at Maynooth. Education BE in Computer Engineering – Trinity College Dublin PhD in Robotics – Trinity College Dublin MBA – University College Dublin Research Focus Professor Lacey’s research integrates robotics, real-time computer vision and human-machine systems with healthcare applications. His work spans assistive robotics for the elderly and visually impaired, augmented-reality surgical simulation, and technology-enabled infection prevention. Key themes include psychomotor learning in digital training tools, AI-driven hand-hygiene monitoring, and the design of user-centred healthcare technologies. Publication Trends Recent publications (2020-2021) concentrate on leveraging augmented reality and artificial intelligence to improve hand-hygiene compliance among healthcare workers, reflecting a translational trajectory from vision-based algorithms to large-scale hospital deployment. Earlier work (1995-2014) laid foundational contributions in mobility aids for the blind, endoscopic image enhancement, and robotic guidance systems. Awards & Recognition RESNA PVA Design Award for Robotics EU IST Award Irish Software Association Innovation Award Enterprise Ireland Technology Commercialisation Award Trinity College Innovation Award Entrepreneurship & Impact Professor Lacey has founded two university spin-outs: Haptica (2000), which created mobile healthcare robots and AR surgical simulators (acquired by CAE Healthcare in 2011), and SureWash (2010), whose hand-hygiene training systems are deployed internationally in hospitals. These ventures bridge academic research and clinical practice, securing real-world impact for his innovations. Laboratories & Collaborations He leads research activities within the Electronic Engineering Department and participates in interdisciplinary initiatives at the Hamilton Institute (systems modelling) and ALL Institute (assisted living and learning). His teams bring together engineers, healthcare professionals and industry partners to advance assistive and medical technologies.

Slavko Žitnikمشاهده پروفایل

دانشیار

• information retrieval
• information extraction
• natural language processing
• +۷ مورد دیگر

Slavko Žitnik is an Associate Professor and Vice-dean at the Faculty of Computer and Information Science, University of Ljubljana, where he is a member of the Laboratory for Data Technologies. His academic career spans multiple research projects and international collaborations focusing on data technologies and natural language processing. His primary research interests include information retrieval, information extraction, natural language processing, entity extraction, relationship extraction, coreference resolution, data merging, redundancy elimination, and ontologies. Dr. Žitnik's work often bridges theoretical computer science with practical applications in various domains including education, healthcare, and smart city ecosystems. Dr. Žitnik has led and participated in numerous significant research projects including P2-0359 on Ubiquitous Computing (2023-2027), PoVeJMo on Adaptive Natural Language Processing with Large Language Models (2023-2026), and the GOBLIN COST Action for building global networks of large-scale knowledge graphs. His recent work demonstrates a strong focus on adapting natural language processing techniques with large language models and creating practical applications of these technologies. His scientific contributions span multiple domains including: Natural Language Processing and Information Extraction techniques Knowledge graph construction and integration Applications in education, healthcare, and smart city ecosystems Development of practical tools and systems for data processing Dr. Žitnik has established international collaborations with institutions including Harvard University's Department of Biomedical Informatics (where he conducted a research visit from July to October 2022), the University of South Florida, and various European partners through COST Actions and other collaborative frameworks.

Marcelo Byrro Ribeiroمشاهده پروفایل

دانشیار

• Relativistic cosmology
• Observational cosmology
• Econophysics
• +۵ مورد دیگر

Marcelo Byrro Ribeiro serves as an Associate Professor at the Physics Institute of the Federal University of Rio de Janeiro (UFRJ), where he maintains an active research program across multiple interdisciplinary domains. His institutional affiliation includes office space (Room A-319/3) and direct university contact channels. His research program spans three major domains: Observational cosmology : Theoretical and observational aspects of relativistic cosmologies, application of general theory of cosmological observations, modeling large-scale galaxy distribution as fractal structures Econophysics and complex systems : Complexity dynamics in personal income distributions, city population sizes, and industry sectorial revenue using Gompertz-Pareto distribution History and philosophy of science : Epistemological aspects of modern cosmology, Ludwig Boltzmann's theories as images of nature, interface between science and religion Ribeiro actively contributes to specialized research communities including the Econophysics group and maintains scholarly connections through the Google Scholar profile . His academic lineage is documented through the Mathematics Genealogy Project. Professional development includes: PhD in Physics from University of London Academic genealogy documented via Mathematics Genealogy Project (ID 84000) Active participation in International Society on General Relativity & Gravitation Contact information: Physics Institute - UFRJ, Centro de Tecnologia, Bl. A, Ilha do Fundão, CEP 21941-972, Rio de Janeiro, Brazil Telephone: +55 21 3938-7482 Fax: +55 21 3938-7368 E-mail: mbr@if.ufrj.br

Mark Taylorمشاهده پروفایل

پژوهشگر ارشد

• Astronomy
• Data Processing
• Virtual Observatory
• +۳ مورد دیگر

Dr Mark Taylor is a Research Fellow in the School of Physics at the University of Bristol, specializing in astronomical data processing. Previously, he served as a Research Assistant at the University of Cambridge from April 1998 to December 2001. Education: B.Sc. from the University of Bristol Ph.D. from the University of Bristol Taylor's research centers on developing sophisticated software for extracting scientific insights from massive astronomical datasets, particularly catalogues of galaxies and stars. He is the creator of TOPCAT, a widely adopted graphical tool for astronomical data analysis operating within the Virtual Observatory framework—a global ecosystem of interoperable data archives and tools. His work bridges astronomy, computer science, and data engineering to handle modern observational data volumes. His publication record reveals a dual trajectory: core contributions to astronomical data infrastructure (e.g., Gaia mission tools, TOPCAT extensions) and interdisciplinary forays into genetics and psychiatry. The 2025 Nature Human Behaviour paper on twin studies demonstrates expanding applications of his data analysis expertise beyond astronomy, while his foundational work remains concentrated on virtual observatory standards, database integration, and visualization of large-scale astrophysical datasets. No scientific awards are mentioned in the provided text. No information regarding student advising or research grants is available in the provided text. Taylor operates within the Astrophysics research group at Bristol and maintains active international collaborations, evidenced by co-authorship in the Gaia Collaboration and multi-institutional genetics studies. His work shows significant real-world impact with 96 Scopus citations and widespread academic/social media engagement.

Michael Hipplerمشاهده پروفایل

استاد

• Photosynthesis Research
• Plant Cell Biology
• Proteomics
• +۷ مورد دیگر

Professor Michael Hippler leads a research group at the Institute of Plant Biology and Biotechnology at the University of Münster, Germany. He also serves as a Special Appointed Professor at Okayama University, Japan, from April 2019 until March 2028 (3 months per year) through the Okayama University RECTOR Program. His research focuses on plant cell responses to environmental stresses and the molecular mechanisms involved in photosynthetic machinery. His primary research interests include adaptation to low iron availability, light-harvesting versus light-dissipation mechanisms, hydrogen metabolism, calcium-dependent protein phosphorylation in plants, photosystem function and regulation, N-glycosylation in algae, and bioinformatics and proteomics. He primarily uses the green alga Chlamydomonas reinhardtii as a model system, combining molecular techniques like reverse genetics and proteomics to study these processes. His recent publications demonstrate a strong focus on N-glycosylation in algae, particularly examining protein modifications and their effects on cellular functions. His work also extensively covers photosystem structure and function, employing techniques like chemical crosslinking, mass spectrometry, single particle electron microscopy, and cryo-electron microscopy. The research has significant implications for understanding photosynthetic regulation and adaptation mechanisms. Professor Hippler leads the Mass Spectrometry-based Proteomics Unit Biology of Plants (MSPUB), which provides large-scale proteomic analyses for research groups at the Institute. The laboratory is equipped with a hybrid linear ion-trap mass spectrometer (Q-Exactive plus-Orbitrap) coupled to an Ultimate Nano liquid chromatography system. He supervises numerous PhD, Master's, and Bachelor's students and has developed several bioinformatics tools including GenomicPeptideFinder (GPF), qTRACE, pyQms, SugarPy, and Crosslinx. His research is funded by various sources including the DFG FOR 5573 "Dynamic Regulation of the Proton Motive Force in Photosynthesis" consortium.

Muhsin Menekseمشاهده پروفایل

دانشیار

• Engineering Education
• Science Education
• STEM Education
• +۵ مورد دیگر

Muhsin Menekse serves as an Associate Professor at Purdue University with a joint appointment between the School of Engineering Education and the Department of Curriculum & Instruction. His academic journey includes a Ph.D. in Curriculum and Instruction (Science Education specialization) from Arizona State University, complemented by advanced degrees in Educational Psychology and Physics from the same institution and Bogazici University. Ph.D. in Curriculum and Instruction (Science Education), Arizona State University M.A. in Educational Psychology (Measurement and Statistics), Arizona State University M.S. in Physics (Teaching specialization), Bogazici University B.S. in Physics (Teaching specialization), Bogazici University Menekse's research centers on engineering and science education with emphasis on student cognition, collaborative learning dynamics, metacognition, and technology-enhanced environments. His work bridges cognitive science with practical classroom applications through tools like CourseMIRROR, which leverages natural language processing to analyze student reflections. Current investigations explore AI-driven scaffolding, reflection quality metrics, and large-scale implementation of mobile learning platforms. Analysis of his recent publications reveals strong thematic continuity in reflection-based learning technologies, with increasing integration of NLP and AI methodologies since 2020. His work consistently addresses scalability challenges in engineering education while maintaining rigorous experimental designs across diverse classroom settings. Seed for Success Excellence in Research Awards (2017-2021) William Elgin Wickenden Award for best Journal of Engineering Education article (2014) NSF Fellow for Summer Institute in Advanced Research Methods (2021-2024) School of Engineering Education Excellence in Undergraduate Teaching Award (2020) AAAS scholarship for Next Generation Science Learning Goals workshop (2014) Menekse mentors doctoral students through dissertation committees while leading the CourseMIRROR project adopted across 15+ institutions including Purdue, University of Pittsburgh, and Bogazici University. His research has secured over $1 million in external funding annually since 2017. The CourseMIRROR platform, developed through collaborative grants, serves as both research instrument and pedagogical tool across engineering, computer science, and physics courses. His laboratory work focuses on the CourseMIRROR ecosystem, integrating mobile interfaces with NLP pipelines to analyze reflection data from thousands of students. Current initiatives involve large language model applications for sentiment analysis and adaptive scaffolding, with deployments spanning undergraduate engineering courses and middle school STEM classrooms.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +۲ مورد دیگر

Daisuke Kawahara is a Professor at Waseda University's Faculty of Science and Engineering and a Visiting Professor at the National Institute of Informatics. He holds a PhD in Informatics from Kyoto University (2005) and has previously served as Associate Professor at Kyoto University and Senior Researcher at NICT. His research spans natural language processing, computational linguistics, and AI infrastructure. Education: Ph.D. in Informatics, Kyoto University (2005) Graduate Studies in Intelligent Informatics, Kyoto University (1999–2002) M.Eng. in Electronic & Communication Engineering, Kyoto University (1997–1999) B.Eng. in Electrical Engineering, Kyoto University (1993–1997) Research Focus: Kawahara specializes in NLP, including syntactic parsing, semantic role labeling, language resource development (e.g., JGLUE benchmark), and multilingual corpus construction. His work integrates machine learning with linguistic theory to improve text understanding systems, error correction tools, and dialogue agents. Publication Trends: His recent articles emphasize Japanese and Chinese NLP, neural network-based parsing, and practical applications like educational tools and pandemic information systems. Common themes include benchmarking, corpus annotation, and cross-lingual adaptation. Awards: 情報処理学会 自然言語処理研究会 優秀研究賞 (2025) 言語処理学会最優秀論文賞 (2024, 2023) 科学技術分野の文部科学大臣表彰 (2017) Multiple Best Paper Awards from NLP conferences (2000–2025) Projects & Advising: He leads JSPS-funded projects like Building General Language Understanding Infrastructure (2021–2025) and Acquisition of Knowledge Frames (2018–2021). No student advisees are listed. Labs & Teams: Collaborates with RIKEN Center for Advanced Intelligence Project and maintains ties to Kyoto University's NLP lab. Focuses on large-scale language modeling and collaborative AI-human intelligence frameworks.

Peter Tuckerمشاهده پروفایل

دانشیار

• Community-based art
• Art as Social Practice
• Creative Placemaking
• +۳ مورد دیگر

Peter Tucker is an Associate Professor in the Art Department at the State University of New York at Fredonia, where he has been teaching since 2009. His academic journey includes an M.F.A. from Massachusetts College of Art, a B.F.A. from the University of Texas, and a B.A. from Oklahoma State University. Professor Tucker's research focuses on Community-based art, Art as Social Practice, Creative Placemaking, and Interactive Sculpture . His work bridges artistic practice with community engagement, creating structured models that generate dialogue about community improvement and perception. He develops projects specifically designed to be implemented by others in different communities, providing comprehensive toolkits to facilitate this process. His artistic output reveals a consistent exploration of interactive systems and community participation. Recent works like the 2023 Yew Gems and Apple Yoke continue his long-standing interest in woodworking and material exploration that began with early pieces like Alter 1 (2003). His projects often incorporate mechanical elements, audience participation, and local materials, demonstrating both technical skill and conceptual depth. Professor Tucker has secured funding through the New York State Council on the Arts, supporting initiatives like the Community Metaphor project in Chautauqua County. His work spans traditional sculpture, digital integration, and large-scale community projects that create multiple 'points of entry' for community dialogue. His studio practice is deeply connected to his teaching philosophy, emphasizing hands-on creation and community involvement. The integration of woodworking techniques with contemporary concepts creates a distinctive approach that blends traditional craftsmanship with innovative social practice.

Chris Donahueمشاهده پروفایل

استادیار

• Generative AI
• Music and Creativity
• Machine Learning for Sequential Data
• +۴ مورد دیگر

Chris Donahue is an Assistant Professor in the Computer Science Department at Carnegie Mellon University and a part-time Research Scientist at Google DeepMind's Magenta team . He leads the Generative Creativity Lab (G-CLef) , focusing on unlocking human creative potential through generative AI for music, programming, gaming, and natural language. Education : PhD at UCSD (advised by Miller Puckette and Julian McAuley), postdoc at Stanford CS (advised by Percy Liang). Research Interests : Controllable generative modeling for sequential data, interactive AI systems for non-musicians, and real-world applications of machine learning in creative domains. Scientific Awards : Best Paper Award (top 1% of submissions) at CHI 2025 Best Paper Award (top 1) at NAACL Student Research Workshop 2025 Advisees : PhD students Wayne Chi, Irmak Bukey, Yewon Kim, Nathan Pruyne, and Satvik Dixit, along with incoming and alumni mentees. Labs & Teams : Leads the Generative Creativity Lab (G-CLef) at CMU and collaborates with Google DeepMind's Magenta team on real-time music generation projects like MusicFX DJ Mode and Piano Genie.

• Digital Economy
• Digital Transformation
• Big Data and Business Innovation
• +۳ مورد دیگر

CHEN Yubo serves as Coca-Cola Chair Professor and Director of the Center for Internet Development and Governance at Tsinghua University's School of Economics and Management. A recipient of the National Science Fund for Distinguished Young Scholars, he ranks among Stanford/Elsevier's World Top 2% Scientists (2022) with extensive publications across premier marketing and business journals. His educational background includes: Ph.D. in Marketing, University of Florida M.Eng. in Systems Engineering, Southeast University B.Eng. in Industrial Management Engineering, Southeast University Prof. Chen's research pioneers digital economy transformation, focusing on big data innovation in networked environments and climate sustainability strategies. His work bridges theoretical marketing frameworks with China's digital evolution, examining platform economies, consumer behavior in social media ecosystems, and sustainable business models. Recent studies analyze mobile commerce dynamics, advertising spillovers in short-video platforms, and offline-online retail integration through advanced spatial analytics. His publication portfolio demonstrates consistent leadership in digital marketing research, with 15+ top-tier journal articles since 2010 spanning Journal of Marketing, Marketing Science, and Information Systems Research. Key thematic clusters include digital platform economics (35%), social media analytics (25%), sustainability marketing (20%), and retail transformation (20%), characterized by rigorous field experiments and large-scale behavioral data analysis. Major scientific recognitions include: National Science Fund for Distinguished Young Scholars Stanford/Elsevier World Top 2% Scientists (2022) INFORMS Frank M. Bass Best Paper Finalist Journal of Marketing MSI/Paul H. Root Award Finalist Journal of Interactive Marketing Best Paper Award Prof. Chen maintains active industry partnerships with Alibaba, JD.com, Baidu, and CITIC Bank through joint research initiatives, while serving on China's National Teaching Advisory Committee for Business Administration and as Editor-in-Chief of Journal of Marketing Science. His Center for Internet Development and Governance operates as a strategic hub for digital economy policy research, leveraging academic-industry collaboration to address China's technological transformation challenges.

• Intelligent information retrieval systems
• Ontology engineering
• Semantic web technologies
• +۴ مورد دیگر

Dr. Ikechukwu Nkisi-Orji is a Research Fellow at Robert Gordon University's School of Computing, Engineering & Technology, where he conducts research at the intersection of artificial intelligence, semantic technologies, and case-based reasoning. He is affiliated with the university's Artificial Intelligence & Reasoning Research Group and has extensive collaboration experience with the British Geological Survey and Oil and Gas Innovation Centre. His research focuses on intelligent information retrieval systems, ontology engineering and alignment, semantic web technologies, natural language processing, and case-based reasoning applications to real-world problems. Dr. Nkisi-Orji has made significant contributions through the development of the CloodCBR platform, a cloud-based CBR framework that received an honorable mention at ICCBR 2020, and the iSee platform for personalized explainable AI experiences. His recent publication trends (2023-2025) show a strong focus on integrating case-based reasoning with modern AI techniques, particularly large language models and retrieval-augmented generation systems. His work increasingly addresses explainable AI challenges, legal question answering systems, and methods for improving the reliability of AI outputs in specialized domains. Honourable mention at ICCBR 2020 for CloodCBR platform Dr. Nkisi-Orji has active supervision availability for PhD students in Semantic Information Retrieval, Natural Language Processing, and Ontology design/alignment/application. His research is supported by collaborations with industry partners including the Oil and Gas Innovation Centre, where he has worked on extracting business intelligence from text and applying case-based reasoning to asset inventory management. As the key architect of the CloodCBR platform and contributor to the iSee XAI platform, Dr. Nkisi-Orji leads research efforts focused on making AI systems more transparent, reliable, and applicable to industrial-scale problems through the integration of traditional AI methods with contemporary approaches.