Find Academic Supervisors

• Machine Learning
• Deep Learning
• Cognitive AI
• +11 more

Volker Tresp is a Professor at the Ludwig-Maximilians-Universität München (LMU) and a leading researcher in machine learning for relational structured domains . His work bridges cognitive AI , knowledge graphs , and quantum computing . He is a PI in the Munich Center for Machine Learning (MCML) and co-director of the ELLIS program on Semantic, Symbolic, and Interpretable Machine Learning . His research interests focus on temporal knowledge graphs , foundation models , multimodal learning , and quantum machine learning . Recent projects include WebPilot (multi-agent web task execution) and FedBiP (federated learning with diffusion models). His work on PyKEEN and RESCAL has advanced knowledge graph embeddings . Volker Tresp's scientific contributions are recognized through ELLIS Fellowship (2020) , Siemens Inventor of the Year (1996) , and Best Paper Awards at ISWC 2021 and IEEE ICHI 2020 . His students have published extensively at top AI venues like AAAI , CVPR , and ECCV . Awards and Honors: ELLIS Fellow (2020) Siemens Inventor of the Year (1996) Best Paper Award, ISWC 2021 Student Best Paper Award, ISWC 2017 Best Paper Runner-up, PKDD 2005

• Computational Biology
• Systems Biology
• Medical Biochemistry
• +3 more

Dr. Marcel Friedrichs is a Researcher at the Faculty of Engineering , Bielefeld University , Germany. He is affiliated with both the Algorithmic Cheminformatics Group and the Center for Biotechnology (CeBiTec) , contributing to interdisciplinary research at the intersection of computational methods and biomedical systems. Researcher in Algorithmic Cheminformatics Group Center for Biotechnology (CeBiTec) member Focus on graph-based data integration and biomedical networks Research Interests : Computational biology, systems biology, bioinformatics tool development, and medical biochemistry. His work emphasizes automated data integration, modeling of regulatory networks, and analysis of gene-disease comorbidities. Article Trends : Publications span 2018–2025, highlighting graph-based data integration, stem cell differentiation (cardiomyocytes), miRNA databases, clinical trial analysis, and pharmacological network modeling. Key keywords include Computational Biology , Medical Biochemistry , and Graph Theory . Labs & Teams : Algorithmic Cheminformatics Group (Faculty of Engineering) Center for Biotechnology (CeBiTec) Contributor to projects like BioDWH2 and GenCoNet

• Digital Signal Processing
• Wavelet Transform
• Remote Laboratories
• +3 more

Dr. Đorđe Damnjanović is an Assistant Professor at the Department of Computer and Software Engineering within the Faculty of Technical Sciences in Čačak , part of the University of Kragujevac . He specializes in digital signal processing, wavelet transform applications, and remote laboratory development for engineering education. Born: July 17, 1986, Belgrade Education: B.Sc. (2010), M.Sc. (2011), Ph.D. (2022) in Electrical and Computer Engineering His research focuses on audio signal analysis using wavelets, human-computer interaction , and remote experimentation systems. He has led projects like the NeReLa Tempus Network and contributed to blockchain consensus mechanisms . Publications span biomedical signal processing, noise reduction, and educational technology. Recent work includes 2025 studies on vehicle vibration analysis and 2024 papers on mobile earthquake detection. He teaches Signals and Systems , Digital Signal Processing , and Human-Computer Interaction at graduate and postgraduate levels.

Branko MarkovićView profile

Assistant Professor

• Speech Processing
• Whispered Speech Recognition
• Computer Engineering
• +3 more

Dr. Branko Marković serves as an Assistant Professor at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. His academic career spans over two decades, transitioning from industry experience in telecommunications to dedicated academic work following the merger of the College of Technical Vocational Studies with the Faculty in 2020. Dr. Marković earned his Bachelor's degree from the Faculty of Electrical Engineering in Belgrade in 1992, followed by a Master's degree in 2004 and a Doctorate in 2018, all focusing on telecommunications and speech processing. His doctoral research centered on speech processing and recognition, establishing the foundation for his subsequent research trajectory. His primary research focuses on speech processing , with groundbreaking work in whispered speech recognition - a challenging domain due to reduced acoustic energy in whispered speech. His research explores sophisticated methodologies including data augmentation techniques, wavelet transformations, inverse filtering, and various feature extraction approaches. He has made significant contributions to Serbian speech database development, notably the "Phonemes_1.0" database. His work bridges theoretical research with practical applications in assistive technologies, security systems, and human-computer interaction, demonstrating consistent innovation through 2024 publications. Analysis of his publication trends reveals an evolution from fundamental techniques like Dynamic Time Warping (DTW) and Mel-Frequency Cepstral Coefficients (MFCC) toward more advanced approaches involving deep learning and multimodal systems. His recent work emphasizes data augmentation for model robustness and wavelet-based feature extraction, reflecting the field's progression toward more sophisticated machine learning methodologies while maintaining focus on the unique challenges of whispered speech. Dr. Marković teaches across multiple academic levels, covering essential computer engineering subjects including Computer Assembly and Service, Internet Technologies, Computer Networks, Distributed Software Systems, Web Programming, and Computer Systems Design. His educational contributions extend to several textbooks for vocational studies in computer engineering fields. His professional journey includes valuable industry experience at the Mihailo Pupin Institute in Belgrade and Canadian telecommunications companies Bell Sygma, Architel, and Nortel Networks, followed by founding his own company Big engineering before transitioning to academia. This industry-academia trajectory enriches his practical teaching approach and research relevance.

• Machine Learning
• Computer Vision
• Bioinformatics
• +4 more

Adriana Birlutiu is a Lecturer in the Computer Science Department at 1 December 1918 University of Alba Iulia , Romania. Her expertise lies in machine learning, computer vision, bioinformatics, and transfer learning, with a recent focus on porcelain-industry optimisation. Education Ph.D., Radboud University Nijmegen, Netherlands (2011) M.Sc., Babeș-Bolyai University of Cluj-Napoca & University of Lorraine (Erasmus), 2005 B.Sc., Babeș-Bolyai University of Cluj-Napoca, 2004 Research Interests Adriana's research spans machine learning , deep learning , computer vision , and bioinformatics . She has contributed to preference learning, domain adaptation, protein–protein interaction prediction, and automated quality control in porcelain manufacturing. Her recent projects integrate deep neural networks with industrial computer-vision systems to detect defects and recognise characters on ceramic surfaces. Publication Trends Across 15 recent publications (2010-2019), Adriana has consistently explored transfer learning , multi-task learning , and Bayesian methods . Articles cluster around two major axes: biomedical applications (protein networks, cancer relapse prediction, respiratory-motion modelling for radiotherapy) and industrial AI (porcelain defect detection, character recognition). The work shows a clear evolution from theoretical machine-learning foundations to practical, domain-specific implementations. Grants & Projects SIVAP (2016-2018): Intelligent ML & computer-vision system for porcelain manufacturing optimisation, UEFISCDI PN-III-P2-2.1-BG-2016-0333. CMRCC (2017-2018): Computational Models for Reproducing Ceramics Colors, UEFISCDI PN-III-P2-2.1-PED-2016-1835. Student Supervision & Mentoring Adriana has supervised more than 25 undergraduate and master’s theses. Her students have won multiple awards at national conferences such as In-Extenso and SCCSS-IEECC , covering topics from automated defect detection to web applications for academic scheduling. Teaching Responsibilities She teaches courses including Machine Learning , Mathematical Software , Fundamental Algorithms , Object-Oriented Databases , and Modelling and Simulation at both undergraduate and master levels.

Boris BankovView profile

Assistant Professor

• Web Development
• Gamification in Education
• Software Engineering
• +7 more

Boris Bankov is a software developer and academic professional currently serving as an Assistant Professor at the Department of Informatics in the University of Economics - Varna. His work combines web development expertise with educational innovation. PhD in Informatics and Computer Science Deputy Director at Center for Electronic and Distance Learning Laureate of John Atanasoff Award (2022) His research interests focus on web development, gamification in education, and the intersection between game design principles and enterprise web applications. He has extensive experience working with technologies including PHP Laravel, MySQL, Bootstrap, and Unity/C# game development. Key publication areas include: Learning Management Systems in higher education, Gamification frameworks for educational environments, Responsive web design solutions, and NFT technology analysis. His work has been published in various academic forums since 2017. John Atanasoff Award of the President of Bulgaria (2022)

Agata FilipowskaView profile

Assistant Professor

• search systems
• profiling
• information extraction
• +9 more

Agata Filipowska is an Assistant Professor at the Department of Information Systems, Institute of Informatics and Quantitative Economics, Poznań University of Economics and Business. Her work bridges computer science, semantic web technologies, and management studies, with a focus on data quality, profiling, and business process management. PhD in Computer Science (2010) and Economics (2009) Active in semantic technologies, machine learning, and art market analytics Key projects include FP7 LOD2 and USE-ME.GOV . Research Interests: Search systems, profiling, information extraction, business process management, and semantic web applications. Her interdisciplinary approach addresses challenges in data quality, user behavior, and art market analysis. Article Trends: Recent work emphasizes knowledge graph ensembles (2024), art market data enrichment, and mobility-based behavioral biometrics. Earlier studies focus on BPM, social telco applications, and Polish language NLP. Activities: She contributes to semantic interoperability, microgrid energy forecasting, and telecom data analysis. Her collaborations span 50 co-authors, including Anna Fensel, Dominik Filipiak, and Piotr Kałużny.

Jorge Alexander Duitama CastellanosView profile

Associate Professor

• Bioinformatics
• Population Genomics
• Computational Biology
• +3 more

Jorge Alexander Duitama Castellanos is an Associate Professor at the Department of Systems Engineering and Computing , University of Los Andes, Colombia. He coordinates the Master's Degree in Computational Biology program and leads research in bioinformatics, population genomics, and algorithm development for biological data analysis. PhD in Computer Science and Engineering, University of Connecticut (2010) Master of Systems and Computer Engineering, University of Los Andes (2004) Systems and Computer Engineer, University of Los Andes (2003) His research focuses on genomic data analysis , including structural variant detection, transposable element identification, and metabolic network visualization. He develops bioinformatics tools like NGSEP and Phallett , with applications in: Long-read sequencing alignment Population genomics of pathogens Plant stress response mechanisms Viral taxonomy profiling Recent publications highlight innovations in entropy-weighted minimizers , LTR retrotransposon detection , and machine learning for medical imaging . His work spans interdisciplinary collaborations in: Computational biology Plant genomics Parasitology Software engineering

Jose Florez-ArangoView profile

Associate Professor

• Health Informatics
• Human-Computer Interaction
• Human Factors Engineering
• +4 more

Jose Florez-Arango is a Colombian physician and Associate Professor at the Graduate School of Medical Sciences, Weill Cornell Medicine, with over 20 years of experience as a clinician, educator, and researcher. His work focuses on human factors and technology-human interaction to improve healthcare outcomes in resource-constrained environments, positioning him as a thought leader in Latin American telemedicine. His research spans Health Informatics, Human-Computer Interaction, and Human Factors Engineering, emphasizing minimal-code solutions for clinical decision support, knowledge transfer, and workload reduction. Key areas include telemedicine implementation, visual knowledge representation, personalized health tools, and curricular innovation for 21st-century healthcare providers, utilizing methodologies like community-based research and participatory design. Publications reveal a trend toward immersive simulations (VR for obstetrics training), NLP-driven maternal health analytics, mobile mental health screening in global contexts (e.g., Fiji), and XML-based decision support systems—all targeting low-resource settings and provider-patient experience enhancement. Scientific distinctions include: ADVANCE Administrative Fellow – Texas A&M University (2021-2022) MEEl Fellow – COM (2021) Schull Institute Scholar (2009) MFA Scholarship – UTHSC (2007) Fulbright Scholar (2005-2009) Dr. Florez-Arango mentors students within Population Health Sciences but specific advisees are unlisted; his current augmented reality mental health project implies grant activity though no funding details are provided. He leads initiatives in rapid-deployment health tech, collaborating with interdisciplinary teams on tools for disaster management and prehospital care. Active lab work includes developing dynamic electronic patient authoring systems and visual knowledge representation frameworks, with a strong emphasis on field evaluation in Latin America and Pacific Island nations to ensure real-world applicability.

Nathalie FriburgerView profile

Associate Professor

• Natural Language Processing
• Named Entity Recognition
• Finite State Automata and Transducers
• +3 more

Nathalie Friburger is an Associate Professor in Computer Science at the University Institute of Technology of Blois, affiliated with the University of Tours. She is a member of the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT) and the BDTLN Research Team since 2003. Her academic work spans teaching in algorithms, programming, web development, and operating systems, while her research focuses on Natural Language Processing with particular expertise in named entity recognition and finite state transducers. Her research interests center on Natural Language Processing, specifically the automatic extraction of named entities using finite state automata and transducers. She has developed significant tools like CasSys for working with cascades of transducers within the Unitex text processing system. Her work extends to Arabic language processing, Renaissance text analysis, and cultural heritage applications, demonstrating both theoretical depth and practical implementation across multiple languages and domains. Analysis of her recent publications reveals a consistent focus on named entity recognition systems, with increasing specialization in Arabic language processing alongside continued work on French NER. Her research integrates rule-based approaches with data-driven methods, showing evolution from foundational work on transducer cascades toward more sophisticated hybrid systems and applications in digital humanities. The development of CasSys and related tools demonstrates her commitment to creating open, reusable infrastructure for the NLP community. She has served as co-supervisor for PhD students Damien Nouvel (2009-2012) and Fatma Ben Mesmia Chaabouni (2014-2019), contributing to the next generation of researchers in computational linguistics. Her research has been supported by multiple projects including ISTEX (2014-2017), Ortolang (2013-2014), Renom (2011-2013), and others focused on named entity recognition and text processing. Friburger has been actively involved in the academic community as Program Committee Member for TALN conferences (2013-2018), director of the TAL magazine on Named Entities (2013), and reviewer for numerous conferences including EMNLP and CIAA. She has also contributed to book editing, notably for TALN 2001 proceedings and the TAL Review on Named Entities (2013).

• Data Mining
• Pattern Mining
• Database Systems
• +3 more

Arnaud Giacometti is a Professor at the University of Tours, affiliated with the Faculty of Science and Technology and the Computer Science Department. He is also the President of the University and leads research at the Tours Fundamental and Applied Computer Science Laboratory (LIFAT). Research Interests: His work focuses on data mining, pattern discovery, and knowledge graph analysis. He has developed innovative methods for frequent pattern mining, sequential pattern sampling, and constraint-based clustering, emphasizing efficiency and scalability in large datasets. Recent Publications: His research trends include trie-based sampling, reservoir pattern sampling in data streams, and semantic web-driven comparison tables. Key subfields span distributed databases, knowledge graph representativeness, and memory-aware parallelization for big data. Scientific Roles: As President of the University of Tours, he oversees institutional leadership while maintaining active research contributions. His laboratory (LIFAT) serves as a hub for fundamental and applied computer science research.

• 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.

• Geomatics
• Computer Vision
• Artificial Intelligence
• +7 more

Maarten Vergauwen is a Professor at KU Leuven's Faculty of Engineering Technology, where he serves as head of Geomatics and campus chair for Science, Engineering and Technology Group across Ghent and Aalst Campuses. His institutional roles include head of the Division Digital and Sustainable Civil Engineering, Subdivision Geomatics Ghent, and contact person for Geomatics. His research focuses on the intersection of geomatics, computer vision, and artificial intelligence with applications in heritage documentation, 3D modeling, and infrastructure analysis. Key research thrusts include BIM automation, digital twin development for sewer systems, UAV-assisted bridge inspections, and AI-driven cultural heritage preservation. His work leverages advanced techniques in point cloud processing, transformer networks, and semantic segmentation to solve complex spatial problems. Vergauwen actively supervises graduate students on topics ranging from SCAN-to-BIM automation to digital epigraphic documentation of archaeological sites. His research is supported by numerous grants including CHAI: Cracking built Heritage with AI, Deep Sewer, Foundation Models for EO Big Data, and UAV-assisted bridge inspections projects. Member of East and West Flanders Workplace Safety Committee Member of Bureau ADS and Board of Faculty of Engineering Technology Observer of KU Leuven Student Services Council, Brugge He teaches courses including Geomatics 1 & 2, Fotogrammetrie, GIS, Topografie, and 3D-modelleren across KU Leuven's engineering programs.

• Data Mining
• Statistical Association Mining
• Machine Learning

Yinjia Zhang is a Researcher in the Department of Computer Science at the School of Science , with a focus on data-driven methods and computational models. Research Interests : Her work spans Data Mining Statistical Association Mining Machine Learning with applications in healthcare, metabolomics, and web user analysis. She aligns her research with the United Nations Sustainable Development Goals (SDGs) to address global challenges through computational innovation. Research Trends : Between 2023 and 2024, Zhang's publications concentrated on feature selection optimization for imbalanced datasets, taxonomy-based distance metrics for user analysis, and predictive modeling for blood glucose levels. These studies integrate machine learning with healthcare data (e.g., Parkinson’s disease, diabetes) and web user behavior. Conference Activity : In December 2024, she presented her Parkinson’s disease metabolomics study at the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024) .

Hagen LangerView profile

Researcher

• Natural Language Processing
• Knowledge Representation
• Multiagent Systems
• +7 more

Dr. habil. Hagen Langer is a Senior Researcher at the University of Bremen since 2004 and Managing Director of the Collaborative Research Center EASE (SFB 1320) since July 2017. He previously served as Technical Coordinator of the EU-funded RoboHow project until 2016. Education: Doctoral Degree - Georg-August-Universität Göttingen Habilitation - University of Osnabrück His research focuses on natural language processing , knowledge representation , multiagent systems , cognitive robotics , and machine learning . These interests are reflected in his work on semantic interoperability, autonomous logistics, and knowledge-intensive robotic tasks. Key publication trends include: Robotics research with emphasis on cognitive capabilities (2006-2016) Multiagent system architectures for logistics and decision support Knowledge representation frameworks for autonomous processes Natural language processing techniques for semantic understanding Simulation modeling for smart materials and logistics Scientific Awards: Best Student's Paper Award 2009 (Proc. of KDIR 2009) As Managing Director of CRC EASE , he leads research on Everyday Activity Science and Engineering, focusing on knowledge-enabled autonomous systems integration.