جست‌وجوی استاد و پژوهشگر

Damien Ernstمشاهده پروفایل

استاد

• Energy Systems
• Operations Research
• Remote Renewable Energy Hubs
• +۵ مورد دیگر

Prof. Damien Ernst is a faculty member at the Montefiore Institute, Department of Electrical Engineering and Computer Science, University of Liège (Belgium). His research focuses on energy systems, remote renewable energy hubs, reinforcement learning, and optimization algorithms. He actively contributes to decarbonization strategies for hard-to-abate sectors like shipping and aviation. Academic Rank: Professor University: University of Liège School: Montefiore Institute Department: Electrical Engineering and Computer Science Research interests include: Design and techno-economic analysis of remote renewable energy systems Reinforcement learning applications in energy and optimization Energy transition policy and sustainability modeling Machine learning techniques for energy system transparency Integration of nuclear, hydrogen, and battery technologies His recent publications demonstrate expertise in: Remote Renewable Energy Hub (RREH) design Small Modular Reactor (SMR) integration Asymmetric Actor-Critic algorithms Energy system optimization and modeling Grid stability and hosting capacity analysis Explainable AI for closed-domain question answering Prof. Ernst leads PhD projects at the intersection of Operations Research and Energy Systems, focusing on energy systems models' transparency and optimization techniques.

• Internet of Things
• Business Process Management
• Model-Driven Development
• +۷ مورد دیگر

Prof. Estefanía Serral Asensio is an Associate Professor at the Faculty of Economics and Business (FEB) at KU Leuven , with a primary affiliation to the Information Systems Engineering Research Group (LIRIS) in Brussels. She holds a highly international and interdisciplinary academic profile, having previously served as an Assistant Professor at Eindhoven University of Technology (2018), led the Semantic Knowledge Representation and Integration research group at the Technical University of Vienna (2012–2014), and contributed to the ProS Research Center at the Technical University of Valencia (until 2012). PhD in Computer Science (2011) Master in Software Engineering, Formal Methods, and Information Systems (2008) 5-year Bachelor in Computer Science (2006) Her research focuses on Internet of Things (IoT) , Business Process Management , and context-adaptive systems , with methodological expertise in Model-Driven Development , Conceptual Modeling , and ubiquitous systems . Key projects include Novel Process Mining Techniques for Discovering IoT-enhanced Business Processes (2022–2025), Novel Sustainability-Driven IoT Prescriptive Analytics for Improving Irrigation Practices in Fruit Trees (2021–2024), and foundational work on Runtime Evolution of IoT Processes (2018–2020). Her publications span top-tier venues like CAiSE , ER , SOSYM , and Internet of Things Journal . She teaches courses in ICT Strategy and Architecture , ICT Management , and Research Methodologies in Business Information Systems Engineering , contributing to academic programs at KU Leuven.

• Machine Learning
• Data Mining
• Big Data
• +۴ مورد دیگر

Jesse Davis is a Professor at the Department of Computer Science , KU Leuven , actively contributing to the Machine Learning group and the Sports Analytics Lab . He is part of the Faculty of Engineering Science and the Leuven.AI Institute . Ph.D. in Computer Sciences from University of Wisconsin-Madison (2007) M.S. in Computer Sciences from University of Wisconsin-Madison (2005) B.A. in Computer Science from Williams College (2002) His research focuses on machine learning, data mining, big data analytics, and sports analytics, with significant work in: Transfer learning and Markov logic networks Anomaly detection and semi-supervised learning Medical NLP and biomechanical data analysis Soccer performance metrics and tactical analysis His recent work explores spatio-temporal data analysis in sports and explainable AI for medical applications, with collaborations spanning finance, healthcare, and semiconductor manufacturing. Notable scientific awards include: Best Paper Award (Applied Data Science Track) at KDD 2019 Best Technical Paper Award at Intelligence Analysis Workshop He advises numerous PhD and Master's students in areas like: Football analytics Tree ensemble compression Medical question-answering systems Biomechanical load prediction His lab develops tools such as: GSSL for Markov network structure learning TODTLER for transfer learning Alchemy system for Markov logic networks

• Artificial Intelligence
• Health Informatics
• Data Analytics
• +۹ مورد دیگر

Femke Ongenae is an Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Information Technology . She leads research at the IMEC postdoctoral level in areas bridging eHealth, predictive healthcare, and knowledge graph technologies . Her work focuses on context-aware systems, stream reasoning, and hybrid AI for healthcare and smart infrastructure applications. Key research domains: Artificial Intelligence , Health Informatics , Knowledge Graphs Leadership roles: Digital Innovation for Man and Society research unit, eBehaviourChange group Her recent publications (2023-2025) highlight advancements in: Semantic rule mining for decision support systems Anomaly detection in healthcare and water networks Context-aware machine learning for COPD and migraine monitoring Knowledge graph embeddings for industrial process monitoring Collaborative projects involve: Developing INK framework for knowledge graph rule mining Building DIVIDE system for adaptive IoT querying Creating MASSIF platform for semantic IoT services Advancing stream reasoning for real-time healthcare applications

• Digital Transformation
• Smart City
• Digital Governance
• +۷ مورد دیگر

Anthony SIMONOFSKI is an Associate Professor at the Department of Management Sciences within the Faculty of Economic, Social and Management Sciences at the University of Namur. He is actively involved in the Namur Digital Institute (NADI) and leads the Management of INformation and DIgital Transformation (MIND IT) research group. His academic background includes dual doctorates in Business Economics from KU Leuven and Computer Science from UNamur. His educational background demonstrates exceptional interdisciplinary training: Doctor in Business Economics, KU Leuven Doctor in Sciences (Computer Science), UNamur Master in Business Engineering - Information Management, UNamur Master in Handelsingenieur in de Beleidsinformatica, KU Leuven Bachelor in Business Engineering - Information Management, UNamur SIMONOFSKI's research focuses at the intersection of digital technology and public administration, with particular expertise in digital transformation, smart cities, and citizen participation. His work bridges information systems theory with practical governance challenges, developing frameworks for more responsive public services. His recent publications reveal a strong trend toward AI applications in citizen engagement, with increasing focus on ethical implementation and practical frameworks for public sector use. His scientific contributions have been recognized with several prestigious awards: Best Paper Award at the 19th IEEE Conference on Business Informatics (2017) Publica Award for Methodological Guidance of La Louvière (2019) Nomination for the De Woot Award for Master's Thesis (2018) As Principal Investigator, SIMONOFSKI leads seven major research projects with significant funding, including PARIS2 (2025-2029) on proactive digital public services and Di-Fic (2025-2030) on digital citizenship education. He has supervised numerous graduate students and contributes to the academic community through committee service, including serving on the Faculty Council of Economic, Social and Management Sciences. His work with the ALMEN Alumni Association Committee (as Member 2016-2017 and President 2017-2020) demonstrates his commitment to academic community building. He directs the MIND IT research group which focuses on practical applications of digital transformation in public administration, with particular emphasis on citizen-centered design and implementation challenges in real-world governance contexts.

Coen De Rooverمشاهده پروفایل

استاد

• Program Analysis
• Software Quality
• Static and Dynamic Analysis
• +۷ مورد دیگر

Coen De Roover is a Professor at the Software Languages Lab of the Vrije Universiteit Brussel , actively leading research in program analysis, software quality, and security. He chairs the Bachelor in Computer Science program and supervises a dynamic research group. Research Focus: Static/dynamic analysis, automated testing, software maintenance, AI for SE, infrastructure as code security. Projects: Bugatti (2025-2028), Cracy (2024-2027), CRPF (2024-2028), BaseCamp Zero (2022-2026), EcoPipe (2023-2025), APAX (2022-2024). Scientific Awards: MSR 2025 Distinguished Dataset Award IEEE TCSE Distinguished Paper Award (SANER 2022) ICSE 2022 Best Artifact Award SCAM 2022 Best Artifact Award Conference Leadership: General Chair of SCAM 2024, Program Co-Chair for GPCE 2024, and active in organizing summer schools on security testing.

Adrien BIBALمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Dimensionality Reduction
• +۴ مورد دیگر

Adrien BIBAL holds a Doctor of Sciences degree and is actively engaged in research at the intersection of Machine Learning , Dimensionality Reduction , and Data Visualization . His work emphasizes model interpretability , constraint integration , and physics-informed algorithms , with applications in radiation oncology and nonlinear dimensionality reduction . Education: Bachelor's in Computer Science (UCL, 2011) Master's in Computer Science (UCL, 2013) Master's in Philosophy (UCL, 2015) His research includes advancements in embedding quality assessment , rotational invariance in neural networks, and clinical AI implementation . Key projects like VeriLearn (2018–2022) highlight his focus on verifying artificial intelligence systems. Collaborations span interdisciplinary domains, including physics , clinical medicine , and stochastic processes . Keywords from his work include Machine Learning , Data-Model Dependency , Rotational Invariance , Physics-Informed ML , and Embedding Quality . He has published in venues such as IEEE Transactions on Visualization and Computer Graphics , NeurIPS , and Physics in Medicine and Biology .

Corentin Caudronمشاهده پروفایل

دانشیار

• Volcanology
• Geophysics
• Volcanic Hydrothermal Systems
• +۵ مورد دیگر

Corentin Caudron is an Associate Professor & Principal Investigator at the WEL Research Institute, Université Libre de Bruxelles (ULB). His research focuses on volcano monitoring through geophysical approaches, with expertise in hydrothermal systems, magma dynamics, seismic noise analysis, volcanic lakes, infrasound, and hydroacoustics. He collaborates internationally with institutions in Indonesia, Iceland, New Zealand, the Philippines, and Hawaii. PhD in Science (2013) from ULB and Royal Observatory of Belgium MSc in Earth Sciences (2009) from ULB His three primary research routes include: Understanding (volcano-)hydrothermal systems using seismic and lake-monitoring approaches, with a focus on precursors to gas-driven eruptions Developing seismic noise-based methodologies (seismic interferometry, amplitude ratios, tremor analysis) for volcanic system monitoring Investigating low-frequency seismic and acoustic signals for underwater volcano degassing quantification Recent publications highlight trends in fiber optic volcano monitoring, machine learning for eruption forecasting, infrasound-seismic integration, and seasonal influences on volcanic activity. His team employs interdisciplinary techniques combining geochemistry and geophysics. Scientific contributions include: FNRS postdoctoral Fellow (2016-2017) Postdoctoral Fellow at University of Cambridge (2015-2016) Postdoctoral Fellow at Earth Observatory of Singapore (2013-2015) He actively supervises 8 PhD students and has co-supervised 2 former PhDs, with projects funded by FWO, FNRS, Wel-T, Fondation Wiener Anspach, and Catalyst. His lab maintains equipment like DAS/FBG interrogators, seismic arrays, and hydrophones. He serves as Co-Leader of the IAVCEI Commission on Volcanic Lakes and Editor for multiple journals.

• Actuarial Science
• Machine Learning
• Insurance Pricing
• +۳ مورد دیگر

Roel Henckaerts is a Research Fellow at the Insurance Research Group, KU Leuven. His work centers on integrating advanced computational methods with actuarial science to optimize insurance pricing models. Research Focus: Henckaerts specializes in machine learning applications for insurance, including telematics-based dynamic pricing, interpretable AI for regulated industries, and data-driven risk assessment. His methodologies emphasize neural networks, tree-based models, and transparent surrogate systems to balance accuracy with regulatory compliance. Publication Trends: Recent articles (2019–2025) demonstrate a shift toward neural networks and model interpretability, while earlier work (2016–2018) established foundations in tree-based methods and risk factor engineering. Thematic evolution shows consistent focus on bridging machine learning scalability with actuarial rigor. Affiliations: As part of KU Leuven's Insurance Research Group, Henckaerts collaborates on industry-facing projects, though specific lab/team structures are undisclosed.

Lieve Mackenمشاهده پروفایل

دانشیار

• Machine Translation
• Natural Language Processing
• Translation Technology
• +۵ مورد دیگر

Lieve Macken is Associate Professor at Ghent University's Department of Translation, Interpreting and Communication within the Faculty of Arts and Philosophy. With 28 years of experience in language technology, she leads research in machine translation, natural language processing, and translation technology applications. Her work bridges academic research and practical implementation through the Language and Translation Technology Team (LT3). Dr. Macken's research focuses on machine translation evaluation, human-machine interaction in translation workflows, literary translation with MT, and educational applications of translation technology. Her work spans multiple dimensions including quality assessment metrics, eye-tracking studies of translation processes, syntactic difficulty prediction, and specialized applications for asylum seekers and language learners. She has pioneered research on translationese in machine translation output and developed methodologies for assessing translation quality through both process and product metrics. Her recent publications reveal a strong trend toward examining the intersection of large language models with traditional machine translation systems, particularly in literary contexts. She investigates how different translation workflows impact textual characteristics, reading effort, and translator productivity. A significant portion of her recent work addresses social applications of translation technology, particularly through the MaTIAS project serving asylum seekers, and educational applications through projects like WiLMa and IVESS. Guest editor of Special Issue 'Advances in Computer-Aided Translation Technology' in Informatics journal (2019) Extensive experience as reviewer/referee for journals, conferences, and research projects External examiner for seven PhD candidates Dr. Macken has supervised numerous PhD projects including Joke Daems' work on translation robots, Arda Tezcan's research on MT quality estimation, and Bram Vanroy's study of syntactic difficulties in translation. She currently supervises the Horizon Europe Marie Sklodovska Curie Fellowship of Paola Ruffo and multiple PhD projects including Margot Fonteyne's WiLMa project and Jasper Degraeuwe's IVESS project. Her research is funded through diverse channels including Horizon Europe, BOF UGent, FWO, and AMIF grants. She coordinates the postgraduate program Computer-Assisted Language Mediation and co-coordinates the European Master's in Technology for Translation and Interpreting (EM-TTI), teaching courses in Computer-Assisted Translation, Machine Translation, and Dissertation. As coordinator of the Language and Translation Technology Team (LT3), Dr. Macken leads a vibrant research group that regularly presents at major conferences including MT Summit, EAMT, and ICTIC. The team's work spans theoretical research in translation technology and practical implementations addressing real-world challenges in scholarly communication, language education, and social integration.

• Wireless Sensor Networks
• Brain-Computer Interfaces (BCI)
• EEG Signal Processing
• +۴ مورد دیگر

Dr. Alexander Bertrand is a Professor at the Faculty of Engineering Sciences , KU Leuven, heading the Dynamic Systems, Signal Processing and Data Analysis (STADIUS) division. He leads the Department of Electrical Engineering (ESAT) and contributes to Leuven.AI institute, with expertise spanning wireless sensor networks, brain-computer interfaces (BCI), and biomedical signal processing. Research Focus : Wireless acoustic/EEG sensor networks, distributed signal enhancement, adaptive filtering, neural decoding of auditory/visual attention, and AI-driven time series analysis. Key Projects : EEG-Linx platform for modular brain recordings (2025-2027) Calibration-free BCI systems (2025-2029) AI quality assessment for time series data (2024-2028) Wireless EEG patches for hearing technology (2024) Publications (2023-2025) demonstrate leadership in distributed signal processing , auditory attention BCI , and scalable sensor architectures , with applications in education, healthcare, and wearable tech. Teaching includes courses on digital signal processing, biomedical data analysis, and medical technology design.

• Industrial Artificial Intelligence
• Digital Twin Technology
• Condition Monitoring
• +۷ مورد دیگر

Pradeep Kundu serves as an Assistant Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Technology (Bruges Campus), where he leads the M-Group Asset Performance Management subdivision. He is an active member of Leuven.AI Institute for Artificial Intelligence and holds governance roles in the Mechanical Engineering Department Council and Faculty Council. His research centers on Industrial Artificial Intelligence integrated with Digital Twin Technology, focusing on three core domains: rotating machinery condition monitoring (fault diagnosis/prognosis), manufacturing quality control (tool wear/surface monitoring), and production process optimization. Key methodological contributions address data scarcity through synthetic data generation (Digital Twin/Generative AI) and enhance model robustness via Physics-Informed Machine Learning, Hybrid Modeling, and advanced Statistical Regression. Analysis of his 15 most recent publications (2024-2025) reveals a dominant focus on predictive maintenance for mechanical systems, utilizing diffusion models for damage imaging, entropy-based domain adaptation for bearing failure prediction, and sensor fusion techniques. His work consistently bridges physics-based modeling with deep learning across rotating machinery, structural health monitoring, and smart manufacturing applications. Dr. Kundu currently leads multiple funded projects including 'Intelligent Prognosis of Rotating Machines in Industry 4.0 using Generative AI' (2024-2025) and 'Digital Twin Framework for Fleet-Level Feed Drive Systems Health Assessment' (2023-2027), addressing critical challenges in data-limited industrial AI deployment. His research directly supports Industry 4.0 transformation through maintenance optimization and quality control innovations. He actively contributes to academic governance as member of the OC Smart Operations and Maintenance in Industry committee and the Mechanical Engineering Department Council, while his research group within the Mecha(tro)nic System Dynamics unit develops practical AI solutions for industrial asset performance management.

Chris Develderمشاهده پروفایل

دانشیار

• Natural language processing
• Information extraction
• Smart grid
• +۲ مورد دیگر

Chris Develder is an Associate Professor with the research group IDLab in the Department of Information Technology (INTEC) at Ghent University - imec, Ghent, Belgium. He received his MSc degree in computer science engineering and PhD in electrical engineering from Ghent University in Jul. 1999 and Dec. 2003 respectively (as a fellow of FWO). He has held research visitor positions at UC Davis, CA, USA (Jul.-Oct. 2007) and at Columbia University, NY, USA (Jan. 2013 - Jun. 2015). Prof. Develder leads two research teams within the Internet Technology and Data Science Lab (IDLab): one focused on converting text to knowledge (NLP, primarily information extraction using machine learning), and another on data analytics and machine learning for smart grids. His Text-to-Knowledge (T2K) research group has a strong track record in information extraction across various domains including news, human resources, and biomedical applications, as well as text classification tasks such as sentiment analysis. More recently, the group has expanded into conversational agents and generative models for educational applications. With his team, Prof. Develder has published over 200 papers in international journals and conferences including EMNLP, CoNLL, EACL, ACL, ECIR, CIKM, WSDM, WWW, and NIPS. His recent publications demonstrate a growing focus on practical applications of NLP in healthcare (personality style recognition from speech, biomedical adverse drug event extraction), education (question generation, gap-filling exercises), and labor market analysis (career path prediction, skill extraction). His work bridges theoretical advances in machine learning with real-world applications across multiple domains. Prof. Develder actively supervises PhD students and has co-supervised numerous successful doctorates, including Maarten De Raedt (2024), Semere Kiros Bitew (2024), Yiwei Jiang (2024), Amir Hadifar (2023), and Klim Zaporojets (2022). His former students have gone on to work at organizations including EarlyTracks, Zoom, Nokia Bell Labs, Clarivate, and various academic institutions.

Katrien De Graefمشاهده پروفایل

استاد

• Assyriology
• Ancient Near Eastern Studies
• Digital Humanities
• +۶ مورد دیگر

Prof. Dr. Katrien De Graef is a Professor in the Department of Languages and Cultures at Ghent University, specializing in the languages and socio-economic history of 2nd millennium BCE Babylonia and Elam. Her research bridges traditional philology with digital humanities methodologies, creating innovative approaches to ancient Near Eastern studies. Her research focuses on: Prosopography of Babylonian and Elamite societies Sealing practices as social and economic indicators Network analysis of ancient Near Eastern communities Digital methodologies for cuneiform texts (OCR, NLP) Gender studies in Old Babylonian society De Graef's research demonstrates strong interdisciplinary connections between traditional philology and computational methods. Her recent publications show a clear trajectory toward integrating digital humanities approaches with traditional Assyriological research, particularly in developing OCR for cuneiform cursive script and NLP pipelines for annotating cuneiform texts. This work enhances the integration and interpretation of textual, prosopographical, and material data from the ancient Near East. Her notable scientific contributions include: Research on sealing practices of women in Old Babylonian Sippar Analysis of nadītu women's social identity Studies on the Sukkalmah dynasty in Elam Examination of dual power structures in ancient Susa Investigation of matronymics in Sukkalmah Susa As a supervisor, Prof. De Graef has guided several doctoral students to completion, including Hendrik Hameeuw (2020), Astrid Verhulst (2015), and Guido Suurmeijer (2014). She leads multiple research projects including "CUNE-IIIF-ORM" (2021-2026) which aims to create an internationally image interoperable corpus of cuneiform tablets, and "War, trade and diplomacy" examining international relations between Babylonia and Elam. Her laboratory and research team work at the intersection of Assyriology and digital humanities, collaborating with computer scientists and archaeologists to develop new methodologies for studying ancient texts and material culture.

• Chromatography
• Separation Science
• Computational Chemistry
• +۳ مورد دیگر

Leon Ewoud Niezen is a postdoctoral researcher in the Department of Chemical Engineering at Vrije Universiteit Brussel, specializing in chromatographic separation science. His work bridges analytical chemistry and computational methods with a focus on liquid chromatography optimization. His research interests center on automated method development in liquid chromatography , particularly through reinforcement learning applications. Key areas include: Chromatographic response function optimization Retention time modeling in hybrid RPLC-HILIC systems Protein diffusion analysis under high-pressure conditions Integration of machine learning for experimental design His recent publications demonstrate a clear trend toward AI-driven chromatography, with 7 of 9 outputs since 2023 focusing on computational approaches to separation science. Niezen maintains an active research profile with an h-index of 11 (Scopus), primarily collaborating with Gert Desmet and Deirdre Cabooter. His experimental work frequently examines protein behavior in advanced column technologies, particularly using wide-pore core-shell particles under elevated pressure conditions.