Find Academic Supervisors

• Business Intelligence
• Big Data
• Data Warehousing
• +7 more

Juan Carlos Trujillo Mondejar is a Full Professor in the Department of Languages and Computer Systems at the University of Alicante's Higher Polytechnic School, where he leads the Lucentia Research Group. He has maintained active teaching responsibilities through 2025, instructing courses such as Business Intelligence and Process Management, Project Management of Information Technologies, and Database Technology. His academic credentials include: Doctor of Computer Engineering, Higher Polytechnic School of Alicante, University of Alicante (2001) Doctorate in Recognition, Interpretation and Machine Translation (2001) Computer Engineer, Polytechnic School of Alicante, University of Alicante (1995) Technical Engineer in Management Information Technology (1995) Professor Trujillo's research spans Business Intelligence, Big Data, Data Warehousing, OLAP, data mining, and strategic planning. His work integrates computer science with practical business applications, particularly in healthcare contexts where he applies EEG analysis and deep learning for ADHD diagnosis. He has directed 18 doctoral theses and supervised 35 final degree/master's projects in the last five years, emphasizing internationalization of students. His publication record demonstrates remarkable impact with over 200 conference papers at high-impact venues (ER, UML, DAWAK, CAiSE) and more than 60 JCR-indexed journal articles in top publications like DKE, DSS, IS, and InfSci. His work has earned him recognition as one of the most cited authors in Business Intelligence, with multiple papers ranking among the most downloaded in journals like Data & Knowledge Engineering. Notable achievements include: Co-editing 11 special issues for JCR journals Serving as PC-Chair for major international conferences (ER'18, ER'13, DOLAP'05) Senior Editor of Decision Support Systems (Q1 journal) until 2017 Articles with 152, 128, and 98 citations Multiple papers ranking among top downloaded articles in their respective journals Professor Trujillo has secured substantial research funding as Principal Investigator for national, regional, and Horizon 2020 projects, totaling 33 public research projects in the last five years. His technology transfer activities include 7 Intellectual Property Registrations and co-founding Lucentia LAB, S.L. in 2015, a spin-off company focused on Business Intelligence and Software Engineering technology transfer. He maintains active international collaborations with leading researchers including S. Rizzi, P. Vassiliadis, M. Golfarelli, Il-Yeol Song, and J. Mylopoulos. His current projects include a Smart City initiative with Elda City Council (2025) and an LLM-based data integration project for digital transformation at the University of Alicante (2024-2025).

Jing YangView profile

Academic

• Computer Vision
• Deep Face Analysis
• Model Compression
• +5 more

Jing Yang is a Research Associate at Core LAB and an Affiliated Lecturer in the Department of Computer Science and Technology at the University of Cambridge. Her research focuses on Computer Vision, particularly deep face analysis, model compression, and the intersection of 3D Computer Vision with generative models. She holds a PhD from the University of Nottingham under Prof. Georgios Tzimiropoulos. Research Interests: Her work spans facial action unit detection, knowledge distillation techniques, 3D head reconstruction, and generative adversarial networks (GANs). Notable contributions include advancements in neural avatars, robust facial expression analysis, and compact representation learning for image retrieval. Publications: Recent work includes high-impact publications in ICLR, IJCV, CVPR, and ECCV, focusing on generative models, facial recognition, and efficient neural network training. Examples include 'FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection' (WACV 2023) and 'Gaussian Head & Shoulders' (ICLR 2025). Affiliations: Active in Core LAB (GenAI research) and affiliated with the University of Cambridge's Computer Science department. Engages in collaborative research with industry and academia, contributing to projects like the Cambridge Ring initiative.

• Educational Data Mining
• Artificial Intelligence in Education
• Learning Analytics
• +7 more

Steven Moore is a prolific researcher in educational data mining, AI in education, and learning analytics. He has co-authored 43 indexed works from 2011 to 2025, collaborating extensively with scholars such as John C. Stamper and Huy Anh Nguyen. His research addresses crowdsourcing in education, automated question generation, knowledge component extraction, and the application of generative AI to instructional design. Research Interests: Educational Data Mining & Learning Analytics AI-driven Assessment & Question Generation Crowdsourcing & Learnersourcing at Scale Generative AI for Curriculum and Content Design Human-Computer Interaction in Learning Systems Across his recent publications, Moore explores how large language models and automated toolkits can enhance the quality and scalability of educational content. His work increasingly integrates advanced NLP techniques—such as GPT-4-based evaluation—with rule-based systems to support both formative and summative assessment processes.

Kai LiuView profile

Assistant Professor

• Machine Learning
• Optimization
• Data Mining
• +4 more

Kai Liu is an Assistant Professor in the Computer Science Division at Clemson University . He holds a Ph.D. in Computer Science from the Colorado School of Mines (adviser: Dr. Hua Wang). His research focuses on the intersection of Machine Learning and Optimization , particularly in designing robust, efficient, and scalable algorithms for data mining applications. Key areas include non-negative matrix factorization, tensor completion, and healthcare analytics such as Alzheimer's disease analysis. Teaching: Liu has taught advanced courses including Optimization for Machine Learning , Advanced Machine Learning , and Applied Data Science at Clemson University. He also contributed to undergraduate courses like Discrete Structures . Research Contributions: His work emphasizes theoretical guarantees for algorithms, with applications in bioinformatics (e.g., HIV-1 drug resistance prediction), computer vision, and healthcare. Notable methods include spherical principal component analysis and accelerated tensor completion techniques. Professional Service: Liu serves on program committees for top conferences including ICML, NeurIPS, AAAI, and CVPR. He is a recognized expert in first-order optimization methods and high-dimensional statistics. Personal Interests: Beyond academia, Liu engages in traditional Chinese culture (calligraphy, painting, poetry), enjoys hiking, and is an avid reader of biographies. He is also a marathon runner and long-time supporter of Real Madrid.

Aneka KļaviņaView profile

Lecturer

• Occupational Health
• Environmental Health
• Indoor Air Quality
• +13 more

Aneka Kļaviņa is an Acting Lecturer at the Department of Occupational and Environmental Medicine, Faculty of Medicine, Riga Stradins University. Her work integrates environmental science and public health, with a strong focus on indoor air quality, occupational exposures, and sustainable materials such as sapropel. She actively contributes to EU-funded research projects including PARC under Horizon Europe and national grants supporting doctoral research. Acting Lecturer, Department of Occupational and Environmental Medicine, Riga Stradins University Expert, European Partnership for the Assessment of Risks from Chemicals (PARC), Horizon Europe Participant, Occupational Health and Safety Risks during 3D Printing, RSU Grant Expert (PhD student), Support for Involving Doctoral Students in Scientific Research, EU Structural Funds Her research interests center on occupational and environmental health , particularly indoor air quality assessment , exposure to pollutants in emerging technologies (e.g., 3D printing) , and the therapeutic potential of natural bioactive substances like humic and fulvic acids from sapropel. She investigates both environmental monitoring and human health impacts, aligning her work with UN Sustainable Development Goal 3: Good Health and Well-being. Her recent publications reflect a multidisciplinary approach spanning environmental toxicology , occupational hygiene , green chemistry , and dermatological applications of natural extracts . Trends show increasing focus on real-world exposure assessment, policy-relevant guidelines for indoor air certification, and the development of sustainable, bioactive materials for health applications. Scientific Awards and Recognition No formal awards listed in the provided text. Advising and Grants : While no direct list of students is provided, Aneka Kļaviņa has participated as an expert (PhD student) in a large-scale EU-funded project supporting doctoral education, indicating active engagement in academic training. She has secured and contributed to multiple competitive grants, including Horizon Europe (PARC), RSU internal grants, and EU Structural Funds, demonstrating strong grant-writing and collaborative research capabilities. Labs and Teams : She is affiliated with the Institute of Occupational Safety and Environmental Health at Riga Stradins University and collaborates closely with researchers such as I. Pavlovska, I. Mārtiņsone, Ž. Martinsone, and L. Paegle. Her work involves interdisciplinary teams integrating environmental science, medicine, and policy development.

• Wood Technology
• Renewable Resources
• Nanotechnology
• +3 more

Claudia Gusenbauer (Dipl.-Ing.Dr.) is a Senior Scientist and University Assistant at the Institut für Holztechnologie und Nachwachsende Rohstoffe of the University of Natural Resources and Life Sciences (BOKU), Vienna. Her research focuses on biobased material design , particularly through nanostructural analysis of wood components using atomic force microscopy (AFM) and chemical force microscopy . She has held research positions at the University of Cambridge (2022) and Lehigh University (2019). Research Highlights: Biobased wood coatings using cellulose nanocrystals High-resolution nanoscale characterization of wood surfaces Surface chemistry of lignocellulosic materials Applications of AFM in renewable resource analysis Scientific Recognition: Danubius Young Scientist Award (2021) Knowledge creates the future Prize (2021) BOKU Best Talent Award (2018) Academic Contributions: She has published extensively on wood nanotechnology, presented at international conferences (e.g., EPNOE, ISWFPC), and supervised master's theses on topics like reaction wood characterization and chemical force titration methods.

• Software Security
• Complex Systems
• Artificial Intelligence
• +6 more

Pierre Parrend is a researcher at INSA de Lyon's College of Engineering, focusing on software security, complex systems, and artificial intelligence applications. He has published extensively on topics including OSGi platform security, graph-based attack detection, and biologically inspired computing models. His research bridges cybersecurity with machine learning, emphasizing explainable AI for threat detection. Recent work includes Agentic AI frameworks for offensive security Graph processing methodologies for ML Multilayer architectures in cyber-physical systems Key collaborations include Pierre Collet, Stéphane Frénot, and Julio Navarro. While no formal education details appear in the data, his work spans 2006-2025 with significant contributions to zero-day attack detection and security architecture.

Fabrice MUHLENBACHView profile

Lecturer

• Artificial Intelligence
• Data Mining
• Text Mining
• +4 more

Fabrice MUHLENBACH is a Lecturer in Computer Science at Jean Monnet University of Saint-Étienne, where he has been working since September 2003. He conducts his research within the Hubert Curien Laboratory (UMR CNRS 5516), specifically as a member of the Connected Intelligence research team. His academic background is multidisciplinary, combining computer science with cognitive psychology and cognitive sciences. MUHLENBACH's research focuses on artificial intelligence, particularly in improving recommendation processes from intelligent systems. His work addresses ethical considerations in AI and explores ways to escape the "filter bubbles" commonly encountered in recommendation systems. He has developed innovative approaches to recommendation systems that consider complementary cultural forms when users become saturated with similar content. His research also investigates the potential usefulness of knowledge from scientific disciplines outside one's expertise. His publications demonstrate expertise in data mining, text mining, recommendation systems, and AI ethics. His work spans from theoretical foundations to practical applications, with notable contributions in clustering algorithms, semantic search, and health informatics. His research has practical implications, as evidenced by the industrial patent filed for a method of automatic multimedia content selection. Best Paper Award at ADMA 2017 conference Patent FR3046269 for "Method for automatic selection of multimedia content in a database" (2017) Contributions to Encyclopedia of Social Network Analysis and Mining (2014, 2017) MUHLENBACH has supervised doctoral theses in recommendation systems and data mining, and has been actively involved in teaching various computer science subjects including data analysis, machine learning, and computer ethics. His work bridges technical aspects of AI with human-centered considerations, reflecting his background in both computer science and cognitive psychology.

Rosa Cordillera A. CastilloView profile

Assistant Professor

• Peace and Conflict Studies
• Social Justice
• Subjectivity
• +7 more

Rosa Cordillera A. Castillo is an Assistant Professor at the Institute for Asian and African Studies (IAAW), Humboldt-Universität zu Berlin, where she has developed the Philippine studies component since 2017. A sociocultural and public anthropologist with a 2017 summa cum laude PhD from Freie Universität Berlin, her work bridges academic, artistic, and activist practices to address social justice through lenses of violence, subjectivity, memory, and decoloniality. She co-founded the Philippine Studies Series Berlin (2014) and leads the third-party funded Advancing Philippine Studies at HU Berlin project (2019-2021). Education : PhD in Anthropology (Freie Universität Berlin, 2017); MA and BA in Anthropology (University of the Philippines Diliman, 2009 and 2007). Research Interests : Peace/conflict studies, decolonial ethics, critical research praxis, and media/political intersections in Southeast Asia. Her publications focus on Bangsamoro identity, Duterte’s drug war, and decolonial memory work, with articles in Social Anthropology , Medical Anthropology , and Critical Asian Studies . She has held visiting roles at the University of Bremen and Freie Universität Berlin, and her awards include the German Research Foundation’s Excellence Initiative scholarship (2011-2015). As an inaugural member of ALON: Journal for Filipinx American and Diaspora Studies editorial board and co-creator of the Affect and Colonialism Web Lab , she fosters interdisciplinary collaborations.

• Semantic Web
• RDF Data Management
• Knowledge Graphs
• +5 more

Olivier Curé is a faculty member affiliated with Université Paris Est in France, specializing in semantic web technologies and knowledge engineering. His research focuses on distributed RDF processing, knowledge graph management, and edge computing adaptations of semantic technologies. Research Focus: Dr. Curé leads significant work in RDF stream processing systems (Strider engine), knowledge graph compression (LiteMat encoding), and privacy-preserving semantic data management. His recent work explores edge computing adaptations of knowledge graphs, real-time stream reasoning, and distributed SPARQL query processing. Key innovations include privacy frameworks for RDF anonymization and resource-efficient knowledge representation for IoT environments. Publication Trends: His 15 most recent publications (2020-2025) demonstrate a strong focus on: 1) Scalable RDF processing architectures, 2) Knowledge graph optimization for edge devices, 3) Privacy-enhancing techniques for semantic data, and 4) Distributed reasoning systems. This reflects an evolution from foundational RDF storage research toward adaptive, privacy-aware semantic systems for decentralized computing. Research Infrastructure: Dr. Curé contributes to multiple open-source projects including Strider (distributed RDF processing) and ZodiacEdge (Datalog engine), indicating leadership in semantic technology tooling development.

Jialuo DingView profile

Research Fellow

• Additive Manufacturing
• Wire Arc Additive Manufacturing
• Process Modeling and Simulation
• +3 more

Dr. Jialuo Ding is a Principal Research Fellow in Additive Manufacturing at Cranfield University, where she leads the research development of digital aspects of wire-based Direct Energy Deposition Additive Manufacturing (w-DED AM) processes. She earned her PhD from Cranfield University in 2013 and has since established herself as a leading expert in this field. As the Research Programme Lead of the WAAMMat Programme, she is responsible for delivering research and industrial projects focused on providing practical solutions based on Wire Arc Additive Manufacturing processes. Her research focuses on process modeling and simulation, process monitoring and control, and the applications of large-scale DED AM component building. Dr. Ding's current projects include leading the process modeling research area for the NEWAM project (EPSRC), serving as project lead for HPWAAM (Innovate UK), acting as co-investigator for MultiFun (EU), and managing over 10 WAAMMat industrial projects annually. Dr. Ding's recent publications (2023-2025) demonstrate her active research program in advancing additive manufacturing technologies, particularly in applications for large-scale component building. Her work spans diverse areas including plasma arc deposition, digital twin development, process monitoring frameworks, and material property optimization. Her research addresses critical challenges in additive manufacturing such as residual stress management, microstructure control, and process quality assurance. Dr. Ding collaborates extensively with industry partners including Airbus, BAE Systems, Boeing UK, Bombardier Aerospace, GE Avio, Lockheed Martin UK, Safran, Thales, and numerous other aerospace and manufacturing organizations. Her work bridges the gap between academic research and industrial applications, focusing on practical solutions for real-world manufacturing challenges.

Mohamed KhabouView profile

Professor

• Image Analysis
• Pattern Recognition
• Machine Learning
• +4 more

Dr. Mohamed Khabou serves as a Professor in the Department of Electrical and Computer Engineering and Dean of the Hal Marcus College of Science and Engineering at the University of West Florida. He holds a Ph.D., M.S., and B.S. in Electrical Engineering from the University of Missouri. His research spans multi-component image analysis, gender equity in engineering, historical artifact digitization, and distance learning impact analysis. He has authored over 30 publications in IEEE Transactions and other journals, and his work includes systems for mosaic image indexing, actigraphy signal analysis, and land mine detection. Dr. Khabou has mentored over 60 engineering capstone projects, many of which achieved regional/national recognition. He teaches Microprocessor Applications, Digital Logic, and Pattern Recognition courses. His 2013 Faculty Excellence in Teaching Award reflects his dedication to pedagogy. Collaborative research areas include underrepresentation of women in engineering and interactive distance-learning laboratory development. Key research themes include spectral geometry for shape recognition, fuzzy logic applications in cultural heritage preservation, and adaptive machine learning systems. His work bridges theoretical foundations (eigenvalue analysis, Laplacian operators) with practical applications in education technology and biomedical signal processing.

Maria IORIZZIView profile

Professor

• Phytochemistry
• Natural Products Chemistry
• Metabolomics
• +3 more

Prof. Maria IORIZZI is a Full Professor at the Department of Biosciences and Territory, University of Molise. Her research focuses on phytochemical analysis of natural products, with emphasis on plant-derived compounds' biological activities, including antioxidant, anti-inflammatory, and antimicrobial properties. She employs advanced analytical techniques such as NMR spectroscopy, UHPLC-HRMS, and GC-MS for metabolomics and chemical profiling. Her work integrates computational approaches (e.g., in silico screening) to explore drug repurposing and natural product applications in medicine. Key areas include cannabis chemistry, essential oils, and nutraceuticals from Mediterranean plants. Prof. IORIZZI collaborates on projects studying plant metabolite variations, microbial interactions, and traditional herbal medicine efficacy. Research interests span plant secondary metabolites, metabolomics, and their roles in health. She has extensively studied species like Laurus nobilis, Phaseolus vulgaris, and Juniperus communis, analyzing their phenolic components, essential oils, and bioactive compounds. Her contributions include identifying novel compounds with neuroprotective, anti-inflammatory, and antifungal effects. Methodologically, she bridges traditional phytochemical analysis with modern computational tools for drug discovery. Publications highlight her focus on cannabinoid neuroprotection, natural product repositioning, and plant metabolite diversity. While awards are not explicitly listed, her prolific output reflects significant contributions to phytochemistry and natural products research in Italy and beyond. She advises on metabolomics-based studies and collaborates with institutions on biodiversity and medicinal plant projects.

• Cloud Computing
• Distributed Systems
• Serverless Architecture
• +4 more

Aleksander Slominski is a prominent researcher in distributed systems and cloud computing, with a focus on serverless architectures, grid computing, and workflow management. His work spans over two decades, contributing to foundational frameworks like Apache Airavata and Triggerflow, as well as influential studies on security mechanisms and performance analysis in distributed environments. He has collaborated extensively with institutions like Indiana University and IBM, addressing challenges in multi-tenant cloud services and hybrid computing models. Key contributions include pioneering serverless computing research, defining scalable cloud service architectures, and advancing the integration of machine learning models into serverless platforms. Recent work explores edge computing benchmarking and the application of large language models in scientific knowledge organization. Research interests include: serverless computing, distributed systems, workflow orchestration, API ecosystems, and future computing trends. Collaborations with NFDI initiatives highlight his commitment to open science and FAIR principles, leveraging knowledge graphs for sustainable research practices.

Ralf LämmelView profile

Professor

• Software Language Engineering
• Model-Driven Development
• Semantic Web
• +2 more

Ralf Lämmel is Professor and Head of the Software Languages group at the University of Koblenz. His research spans software language engineering, model-driven development, and semantic web technologies. He specializes in API analysis, data validation (SHACL constraints), and variability management for software systems. Recent work focuses on AI-assisted workflows for archaeology and trust analysis in large language models. Publications demonstrate growing interest in knowledge graph validation and semantic web applications. He has developed tools like the Virtual Platform for software variability and ProGS for property graph validation. Service includes program committee memberships for software engineering conferences and editorial roles. He leads research groups exploring API evolution and RDF validation techniques.