Find Academic Supervisors

• Deep Learning
• Audio/Video Content Analysis
• Biomedical Data Analysis
• +3 more

Dr. Markus Mühling is a Research Fellow in the Department of Mathematics and Computer Science at Philipps-Universität Marburg, working under Prof. Bernd Freisleben. His research focuses on deep learning applications for audio/video content analysis and biomedical data interpretation. He joined the group in 2015 and has contributed to projects involving wildlife monitoring, medical imaging, and heritage digitization. His work spans sensor networks, neural network architectures, and edge computing solutions for real-world challenges. Key research areas include: Deep learning for bioacoustic and visual species recognition Automated analysis of biomedical and histological images Edge-based AI systems for resource-constrained environments Content-based retrieval in video and image archives Publications highlight advancements in biodiversity monitoring (e.g., acoustic sensor networks), medical imaging (e.g., blood cell counting), and cultural heritage (e.g., organ tablature transcription). His work bridges theoretical machine learning with practical ecological and biomedical applications.

Christo BuizertView profile

Associate Professor

• Paleoclimate
• Ice Core Analysis
• Abrupt Climate Change
• +3 more

Christo Buizert is an Associate Professor at Oregon State University's College of Earth, Ocean, and Atmospheric Sciences. His research focuses on reconstructing past climate change through ice core analysis, with expertise in paleoclimate, abrupt climate events, and firn processes. He leads international projects like the West Antarctic Ice Sheet (WAIS) Divide and Roosevelt Island Climate Evolution (RICE) ice core initiatives. Education: BSc (Physics, Delft University of Technology, 2004), MSc (Physics, Delft, 2007), PhD (Geophysics, University of Copenhagen, 2012). Research interests include Dansgaard-Oeschger cycles, firn air transport modeling, and Antarctic ice core chronology. His work integrates field studies, lab analysis, and climate models to explore mechanisms linking oceanic and atmospheric climate signals. Recent projects investigate Miocene/Pliocene ice cores and Southern Ocean influences on CO2 variability. Collaborations span institutions worldwide, including the Center for Oldest Ice Exploration (COLDEX). He advises graduate students on paleoclimate and ice core research. His lab employs advanced techniques like noble gas extraction and continuous flow analysis.

Frank FerraroView profile

Associate Professor

• Natural Language Processing
• Probabilistic Modeling
• Unsupervised Learning
• +5 more

Frank Ferraro is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). His research focuses on natural language processing, probabilistic modeling, and multimodal systems. He holds a PhD in Computer Science from Johns Hopkins University (2017), an MSE from the same institution (2013), and dual BS degrees in Computer Science and Mathematics from the University of Rochester (2011). His research interests include event modeling, unsupervised learning strategies, and the application of large language models (LLMs) to cybersecurity, robotics, and knowledge graph consistency. Recent work explores malware detection via Yara rules, ethical AI in robotics, and interactive dashboards for multimodal situation awareness. Ferraro’s contributions span foundational NLP techniques and applied systems like the PASTA dataset for narrative analysis and the MUMOSA interactive visualization tool. He has published extensively on topics ranging from grounded language learning to distributed classification algorithms. His work bridges theoretical advancements and practical applications, often emphasizing scalability and explainability. Ferraro’s lab develops systems for human-robot interaction, virtual environments, and collaborative AI frameworks. For more details, visit his website .

Charles NicholasView profile

Professor

• Cybersecurity
• Machine Learning
• Data Science
• +3 more

Charles Nicholas is a Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a Ph.D. in Computer and Information Science from Ohio State University (1988), an M.S. from the same institution (1982), and a B.S. in Computer Science from the University of Michigan–Flint (1979). His research focuses on applying data science and machine learning to cybersecurity challenges, particularly in ransomware analysis, malware detection, and threat intelligence. He previously served as Chair of UMBC’s Department of Computer Science and Electrical Engineering (2004–2010) and has chaired the Conference on Information and Knowledge Management five times. His work spans cutting-edge topics such as quantum algorithms for malware classification, knowledge graph generation, and efficient feature extraction methods for malware detection. Notable contributions include frameworks for evaluating malware datasets, improving classifier robustness, and leveraging antivirus scan data for large-scale analysis. Dr. Nicholas is a Senior Member of both ACM and IEEE, reflecting his longstanding contributions to the field. His research emphasizes practical applications of machine learning to cybersecurity, with a focus on scalable solutions for real-world threats.

• Artificial Intelligence
• Natural Language Processing
• Blockchain Technology
• +2 more

Dr. Luca Mazzola is a Lecturer at Lucerne School of Computer Science and Information Technology (HSLU-I) and senior research associate in the Information Systems Research Lab. With a PhD in Technologies for Human Communication (USI, 2014) and MSc in Computer Engineering (Politecnico di Milano, 2004), his research spans artificial intelligence, natural language processing, blockchain applications, and machine learning in distributed systems. Mazzola leads multiple research projects including PrivacyGLUE (privacy policy analysis), blockchain-based healthcare management systems, and personalized event matching frameworks. His recent publications focus on NLP applications for privacy compliance, federated learning architectures, and explainable AI for concept recognition. Mazzola teaches courses in Data Science Fundamentals and Natural Language Processing, emphasizing practical applications of ML and AI technologies. Research collaborations include bilateral projects with South Africa's SARCHi NRF and EU Blockchain Observatory. His technical innovations include developing P2P data marketplaces, blockchain authentication systems, and educational recommendation engines using knowledge graphs.

• Knowledge Representation
• Semantic Web
• Artificial Intelligence
• +4 more

Angelo A. Salatino is a researcher affiliated with the Knowledge Media Institute (KMi) at the Open University. His work focuses on advancing knowledge representation, semantic technologies, and AI applications in scholarly communication. He has contributed to over 70 publications since 2013, with recent work emphasizing large language models (LLMs), knowledge graphs, and their integration in research and industry contexts. Education: PhD from the Open University (KMi). Research interests include ontology engineering, scientific question answering, and AI-driven literature review methods. He has co-edited conference proceedings and contributed to projects like the Computer Science Ontology (CSO) and the AIDA knowledge graph. Key areas of research include: (1) knowledge injection strategies for LLMs in academia, (2) semantic analysis of ESG discourse in news, (3) health informatics applications of knowledge graphs, and (4) optimizing tourism data through NLP and graph technologies. His work bridges computational methods with interdisciplinary research challenges. Co-authored over 60 papers with collaborators like Enrico Motta and Francesco Osborne. His research often addresses practical challenges in organizing scholarly knowledge, improving metadata quality, and enhancing accessibility through neuro-symbolic AI approaches.

• Machine Learning
• Biomedical Engineering
• Artificial Intelligence
• +3 more

Yrd. Doç. Dr. Ghazaal Sheikhi is an Assistant Professor in the Department of Artificial Intelligence Engineering at Final University, within the Faculty of Engineering. Her research focuses on machine learning applications in healthcare, biomedical engineering, and natural language processing. She holds a Ph.D. in Computer Engineering from Eastern Mediterranean University (2020), an M.Sc. in Biomedical Engineering from Amirkabir University of Technology (2007), and a B.Sc. in Biomedical Engineering from the University of Isfahan (2003). Education: Doctoral Degree in Computer Engineering, Eastern Mediterranean University, North Cyprus (2020) Master's Degree in Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran (2007) Bachelor's Degree in Biomedical Engineering, University of Isfahan, Isfahan, Iran (2003) Her research interests span Machine Learning, Biomedical Engineering, and Natural Language Processing. She specializes in applying deep learning techniques to medical image analysis, developing explainable AI models for healthcare diagnostics, and enhancing fact-checking systems using NLP. Her work in biomedical data analysis focuses on feature selection methods and predictive modeling for diseases like diabetes. Additionally, she explores speech processing techniques for language-specific challenges, such as Farsi syllable segmentation using signal processing and fuzzy logic approaches. Recent contributions include breast tumor segmentation (2025), NLP-based claim detection (2023), and novel feature selection methods (2021). Earlier work addressed speech signal analysis (2011–2013) and diabetes cost analysis (2016). Scientific Awards: No awards explicitly mentioned in the provided text. Advising & Grants: Information on students or grants is not provided in the text. Administrative tasks are listed but no details are given. Labs/Teams: No specific lab affiliations or teams mentioned.

• Artificial Intelligence
• Deep Learning
• Applied Mathematics
• +3 more

Yoann VALERO is affiliated with the University of Technology of Troyes (UTT) and teaches at Université de Strasbourg. His academic role includes lecturing on Statistical Process Control (SPC) for Master's students and Process Mining for specialized Master's programs in BDAAD. He has conducted seminars on sequence analysis, process modeling, and Petri nets. His research focuses on artificial intelligence for individual journey modeling, deep learning applications, and predictive process monitoring. Recent work includes applying generative adversarial networks (GANs) to process prediction and reinforcement learning for process action recommendations. Key contributions include papers presented at SHEiC 2021, SDSS 2022, ENBIS Trondheim, JSM 2022, and COMPSTAT 2022. His research bridges process mining with healthcare applications and industrial process optimization. No awards or formal advisees are listed in the provided materials. His work aligns with UTT's focus on technological innovation and applied research.

Roland OlssonView profile

Associate Professor

• Machine Learning
• Automatic Programming
• Artificial Neural Networks
• +3 more

Roland Olsson is an Associate Professor at the Department of Computer Science and Communication, Oslo and Akershus University College of Applied Sciences (HIOF). He holds a doctorate from the University of Oslo (UiO) and previously served as an Associate Professor at Chalmers University of Technology in Gothenburg, Sweden. His research focuses on machine learning, automatic programming, and algorithm design through large-scale combinatorial search, particularly via his ADATE system. Olsson’s work emphasizes meta-machine learning, including the automatic design of neural network architectures. His research group explores applications in image processing, symbolic regression, and optimization. Key areas include improving algorithms for edge detection, oil well event prediction, and real-time CNC machine control through imitation learning. Publications span domains like evolutionary computation, industrial informatics, and smart grids. His work frequently appears in journals like IEEE Open Journal of the Industrial Electronics Society and conferences such as IEEE International Conference on Big Data and Smart Computing. Though no specific awards are listed, his contributions to automatic programming and machine learning algorithms are widely recognized in the academic community. Olsson’s advising and grant involvement is implied through collaborative projects with industry partners (e.g., CNC machine operators, oil well monitoring). He leads the Machine Learning group at HIOF and teaches courses in machine learning. His research extends to interdisciplinary applications, including IoT architectures for smart grids and healthcare activity recognition.

• Information Visualization
• Visual Analytics
• Multidimensional Data
• +5 more

Rafael Messias Martins is a Researcher at Linnaeus University, affiliated with the Department of Computer Science and Media Technology within the Faculty of Technology. He holds an MSc in Computer Science from the University of São Paulo and a PhD in Computer Science from the University of Groningen. His primary research focuses on Information Visualization and Visual Analytics, particularly emphasizing Multidimensional Data and Networks. He is a core member of the Information and Software Visualization (ISOVIS) research group and leads multiple ongoing and completed research projects, including InfraVis (a national research infrastructure for data visualization) and initiatives addressing medication risks and carbon mitigation in forestry. His work bridges theoretical advancements in visualization with practical applications in education, healthcare, and environmental science. Education MSc in Computer Science, University of São Paulo, Brazil PhD in Computer Science, University of Groningen, Netherlands Research Interests His research explores the intersection of visualization techniques with complex data analysis, emphasizing: Interactive visual analytics for high-dimensional data Machine learning interpretability through visualization Educational data analytics for K-12 institutions Applications in healthcare (e.g., medication risk prediction) and environmental science (e.g., carbon footprint reduction) Development of national visualization infrastructures (InfraVis) Recent Trends in Articles His recent work emphasizes: Enhancing trust in machine learning models through visual explanations Optimizing visualization tools for educational stakeholders Algorithmic fairness in urban planning simulations Scalable dimensionality reduction techniques for streaming data Grants & Collaborations He has coordinated projects such as IDEAL (interaction design curriculum development), TimberVis (3D timber structure visualization), and seed projects addressing carbon mitigation and medication risks. Collaborations span academic, industrial, and governmental partners in Sweden and internationally. Labs & Teams He leads the ISOVIS group, which develops open-source tools like SBGTool (student grouping analytics) and FeatureEnVi (feature engineering visualization). The group also contributes to InfraVis, a national platform for visualization resources.

• Fatigue and Fracture Mechanics
• Probabilistic Computational Mechanics
• Risk Assessment and Management
• +7 more

Yongming Liu is a Professor of Aerospace and Mechanical Engineering at Arizona State University's School for Engineering of Matter, Transport and Energy. He leads the Prognostic Analysis and Reliability Assessment Laboratory (PARA) and has been at ASU since 2012. His research focuses on fatigue/fracture mechanics, probabilistic computational methods, risk assessment, multi-physics damage modeling, and uncertainty quantification across scales. Key interests include experimental testing, diagnostics/prognostics, and structural durability. Education: Ph.D. Civil Engineering (Vanderbilt University, 2006); M.S. and B.S. Structural Engineering (Tongji University, Shanghai, 2002 and 1999). Research activity includes NSF-funded projects on fatigue prognosis for coastal bridges, DOE-supported studies on creep-fatigue analysis of alloys, and industry collaborations on pipeline diagnostics. Recent work emphasizes AI-driven approaches for structural health monitoring, aerospace safety, and energy systems. Courses taught span mechanics of materials, probabilistic methods in engineering design, and graduate supervision. Key contributions include physics-informed machine learning frameworks, fatigue life prediction under uncertainty, and multi-agent reinforcement learning for air traffic management. His lab develops advanced diagnostics/prognostics tools bridging structural and biomedical applications.

• Computer Vision
• Machine Learning
• Sports Analytics
• +3 more

David Lau is a Lab Instructor/Supervisor at the University of Waterloo, specializing in computer vision and machine learning applications across sports analytics and environmental monitoring. His work focuses on developing algorithms for hockey puck localization, player tracking, and sea ice classification using SAR imagery. He contributes to datasets like AI4Arctic and explores synthetic data augmentation techniques for improving model robustness. Research interests include deep learning architectures for multi-task learning, contextual cue utilization in vision systems, and the integration of remote sensing data with climate models. Key areas of exploration involve improving hockey video analysis through homographic projections and leveraging Bayesian methods for uncertainty quantification in environmental predictions. Recent work emphasizes domain-specific adaptations like sports field localization and ice hockey event detection. His contributions span from foundational algorithms (e.g., IceGCN for SAR imagery) to practical systems like PuckNet for puck location estimation in broadcast videos. Advising and grants: No specific grants or advisees listed in available information. Collaborates on projects involving hockey analytics teams and environmental monitoring initiatives, though specific team names are not disclosed.

• Marine Systems Design
• Deep Sea Mining
• Aquaculture Logistics
• +5 more

Bjørn Egil Asbjørnslett is a Professor of Marine Systems Design at the Department of Marine Technology, Norwegian University of Science and Technology (NTNU). He serves as Director of NTNU Ocean and Coast, a strategic research area focusing on marine innovation. Additionally, he manages the Bachelor in Engineering (Aquaculture) and Minor in Aquaculture programs. His research spans deep-sea mining systems , ship design complexity , Arctic shipping , and resilient maritime logistics . He leads projects on vessel design for offshore mining, emergency response optimization, and aquaculture logistics. His recent work emphasizes technology transfer from oil/gas to deep-sea mining , multi-stakeholder design solutions , and analytical methods for vessel performance prediction . He has received no explicitly stated awards but is actively engaged in cross-disciplinary collaborations with industry and academia. His advising focuses on doctoral students in marine systems engineering and design methodologies.

• Cartography
• Geographical Information Science (GIS)
• Terrain Modelling
• +4 more

Terje Midtbø is a Professor in the Department of Civil and Environmental Engineering at NTNU, part of the Faculty of Engineering. His research focuses on Cartography, Geographical Information Science (GIS), and spatio-temporal visualization. He leads the 5-year Master's Programme in Engineering and ICT at NTNU and holds leadership roles in organizations like GeoForum (Norwegian Association for Geomatics) and the International Cartographic Association (ICA). His work explores map animations, web-based geographic information distribution, and indoor positioning systems. Notable contributions include studies on hazard map intuitiveness, cartographic terminology standardization, and uncertainty visualization in geoscience contexts. He frequently publishes in journals like Cartography and Geographic Information Science and Journal of Geovisualization and Spatial Analysis . Midtbø has organized and participated in international cartographic events, contributing to the development of the Cartographic Body of Knowledge. His recent work emphasizes digital innovation in mapping, including applications of machine learning in remote sensing and VR/AR interfaces for spatial data.

Shruti KshirsagarView profile

Assistant Professor

• Data Science
• Deep Learning
• Machine Learning
• +8 more

Dr. Shruti Kshirsagar is an Assistant Professor and MS Data Science Graduate Coordinator at the School of Computing, Wichita State University (WSU), part of the College of Engineering. She holds a PhD in Telecommunications from INRS (Montreal, Canada), specializing in Speech & Deep Learning, alongside industry experience at EERS Global Technologies and Bosch. Education: PhD in Telecommunications, INRS, Montreal (Canada) Master's and Bachelor's in Electronics & Telecommunication Engineering, University of Mumbai Research interests include Data Science, Deep Learning, Affective Computing, Healthcare AI, Medical Image Processing, and Speech Signal Processing. Her work emphasizes robust solutions in emotion detection, assistive technologies, and healthcare diagnostics. Awards: Recipient of the Best Doctoral Thesis Award (INRS). Grants: Secured multiple research grants (specific names not listed). Advising/Grants: Mentor to graduate students, develops Deep Learning curricula, and leads research projects. Professional memberships include IEEE Signal Processing Society and ISCA. Labs: Maintains a lab focusing on Affective Computing and Signal Processing, with a dedicated lab website.