Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au
Philippe Jouvet is a Clinical Professor in the Department of Pediatrics at Université de Montréal's Faculty of Medicine. He is affiliated with CHU Sainte-Justine, a leading pediatric healthcare institution in Montreal, Canada. His clinical and research activities focus on advanced technologies for critical care environments, particularly in respiratory assistance and diagnostic automation. Dr. Jouvet's research spans pediatric intensive care innovations, including machine learning applications for clinical decision-making, respiratory distress detection, and safety protocols for aerosol transmission. His work addresses key challenges in mechanical ventilation, hypoxemia triage, and artifact detection in physiological signals, with a strong emphasis on cross-national collaborative studies. His recent publications demonstrate expertise in integrating artificial intelligence with clinical workflows, evidenced by studies on LoRA adapters for LLMs, vision transformers for rPPG estimation, and hybrid neural networks for signal processing. His work also includes critical contributions to international guidelines for pediatric ventilator liberation and acute respiratory distress syndrome management.
Dora Blanco Heras is a Full Professor in the Department of Electronics and Computer Engineering at the University of Santiago de Compostela. She holds a BS in Physics (1993) and a PhD cum laude from her current university. Research Focus: High Performance Computing, Computer Vision, Remote Sensing Projects: Rapid Digital Monitoring of River Ecosystems, High Performance and Cloud Computing for Demanding Applications Her work emphasizes GPU-accelerated algorithms for multispectral/hyperspectral image processing, anomaly detection, and human-computer interfaces for sustainability indices. She has contributed to technical committees like GRSS Earth Science Informatics and organized summer schools on geospatial AI.
Mohamed Abouelenien is an Associate Professor in the Department of Computer and Information Science at the College of Engineering and Computer Science, University of Michigan - Dearborn . He leads the Affective Computing and Multimodal Systems Lab (ACMS) , focusing on human behavior modeling and multimodal system development. Research Interests: Applied machine learning, computer vision, natural language processing, affective computing, and multimodal interaction. Applications: Healthcare, driver safety, building climate control, and educational assessment. Funding Sources: Ford Motor Company, Educational Testing Service (ETS), Toyota Research Institute (TRI), and Procter & Gamble (P&G). Publication Trends: Recent work emphasizes thermal imaging analysis, multimodal deception detection, driver alertness monitoring, and deep learning frameworks like PyraSegNet and PyraMoT for facial thermal processing. His research combines physiological signal extraction, circadian rhythm modeling, and safety-critical systems. Methodologies: Utilizes computer vision, data mining, machine learning, and natural language processing in healthcare applications.
Petar Vračar is an Assistant Professor affiliated with the Laboratory for Machine Learning and Language Technologies. His work spans interdisciplinary domains, integrating AI with healthcare, education, and computer science. Research interests include: Risk stratification and early disease detection using AI Reliable prediction models for imbalanced datasets Machine learning applications in bioinformatics and medical diagnostics Game-based e-learning systems leveraging mobile technologies Computer modeling for coronary artery disease and ischemia detection Currently leading the STRATIFYHF project (2023-2027) focused on heart failure detection, his career demonstrates sustained collaboration through bilateral research initiatives since 2010.
Professor Peter Österholm at Åbo Akademi University's Faculty of Natural Sciences and Engineering specializes in Environmental Geology with a focus on Acid Sulfate Soils . His work addresses critical environmental challenges through interdisciplinary approaches. 2023-2025 : Active principal researcher in two major projects, including EU BIONEER for post-mining waste management Research Themes : Geochemical remediation, microbial interactions, water quality impacts Recent publications highlight: 2025: Microbial responses to limestone/peat treatments in hypermonosulfidic sediments 2024: Machine learning applications for acid sulfate soil mapping 2023: Innovative macropore targeting to reduce acid-metal release Collaborations include European Regional Development Fund , Kiertokaari , and Finnish Transport Agency . He organized the 2024 GeoDays conference and serves as co-investigator in multiple international projects.
Guido Cantelmo is an Assistant Professor at the Technical University of Denmark (DTU) within the Department of Technology, Management and Economics, specifically in the Division of Transport's Section for Transport Systems Modelling. His research leverages big data analytics and machine learning to address complex transportation challenges, with expertise spanning traffic flow modeling, demand estimation, shared mobility systems, and urban network optimization. He maintains active collaboration with international cities including Copenhagen, Munich, and Tel Aviv-Yafo for empirical validation of his models. His research integrates computational techniques such as Graph Neural Networks, meta-learning, and physics-informed AI with transportation theory. Primary domains include: Dynamic traffic assignment using real-time data sources Machine learning for imbalanced mobility datasets Emission impact modeling of urban fleets Behavioral analysis of shared mobility adoption Large-scale simulation calibration frameworks Publication analysis (2022-2025) reveals dominant themes: data-driven demand estimation (37% of recent works), machine learning metamodeling (27%), shared mobility optimization (20%), and urban policy impact studies (16%). Methodological innovations include transfer learning for sparse data and multi-city validation approaches. No scientific awards or student mentoring relationships are documented in available sources. Similarly, no information exists regarding research grants, laboratory affiliations, or educational background.
Laurent Cazor is a Postdoctoral Fellow at the Department of Technology, Management and Economics within DTU Management, Technical University of Denmark. His research focuses on transportation systems modeling with emphasis on behavioral realism in route choice and travel demand analysis. His primary research interests include: Transportation Engineering Route Choice Modeling Travel Demand Modeling Behavioral Modeling Transport Systems Analysis of his publication trends reveals significant advancements in bounded rationality frameworks, addressing heteroscedasticity, overlap effects, and bias reduction in imbalanced datasets. His work bridges theoretical choice modeling with practical applications in sustainable transport infrastructure, particularly bicycle path planning using open data sources. Cazor operates within the Transport Systems Modelling section of the Division of Transport, contributing to large-scale network analysis and behavioral model development for transportation planning.
Manja Gersholm Grønberg is a Postdoctoral researcher at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Statistics and Data Analysis group. Her interdisciplinary research bridges statistics, computer science, and biomedical applications, contributing to several UN Sustainable Development Goals related to health and well-being. Her research interests span statistical genetics, machine learning, biostatistics, neuroscience, and data analysis with particular expertise in ordinal data modeling and single-cell RNA sequencing analysis. Dr. Grønberg's work demonstrates strong methodological innovation in crossed random effects models and neighborhood analysis techniques applied to complex biological datasets. Her recent publications (2024-2025) reveal a strong trend toward interdisciplinary applications, with significant contributions in both bioinformatics (scRNAseq data analysis) and neuroscience (brain stimulation techniques for Alzheimer's disease treatment). These works demonstrate her ability to develop sophisticated statistical methods and apply them to cutting-edge biomedical problems. Expertise in Crossed Random Effects modeling Specialization in Imbalanced Data techniques Research in Intranasal Oxytocin applications Advanced Neighborhood Analysis methods Cell Specification computational approaches Dr. Grønberg has served as an internal examiner for Statistical Genetics courses at DTU, demonstrating her expertise and teaching capabilities in this specialized field. Her completed PhD project on 'Computational methods for ordinal mixed models applicable to online user ratings' established her foundation in advanced statistical modeling. Her research team collaborations span multiple disciplines, connecting statistical methodology with biological and medical applications, particularly in the areas of neurodegenerative disease research and single-cell genomics.
Rong Chen is an Associate Professor in the Department of Diagnostic Radiology and Nuclear Medicine at the University of Maryland School of Medicine. He serves as Associate Vice Chair of AI and leads the Biomedical Data Mining Laboratory, focusing on integrating machine learning, computational neuroscience, and neuroimaging to decode brain-behavior relationships. His work spans clinical and translational research for disorders like Alzheimer’s, Parkinson’s, autism, and HIV, and he develops open-source software (GAMMA suite, Advanced Connectivity Analysis) for neuroimaging data analysis. Education: BS in Biomedical Engineering, Southeast University, China (1996) MS in Electrical Engineering, The Graduate School of Chinese Academy of Sciences (1999) PhD in Electrical and Computer Engineering, Washington State University (2003) Postdoctoral Researcher in Radiology, University of Pennsylvania (2005) MTR in Translational Research, University of Pennsylvania (2012) Research Interests: Computational modeling of neural activity and behavior Development of machine learning frameworks for neuroimaging Brain-inspired AI and therapeutic concepts Longitudinal analysis of brain disorders Distributed data mining for heterogeneous databases Software tools for biomarker detection and functional connectivity Scientific Contributions: 20+ years of advanced modeling and algorithm development Two open-source neuroimaging software packages (GAMMA suite, ACA) NIH and BRAIN initiative-funded research Editorial roles in journals like Frontiers in Computational Neuroscience Honors: Senior Member of IEEE Labs & Collaborations: Dr. Chen collaborates with institutions like NIH and Oracle, and his lab has developed tools used in studies on sickle cell disease, autism, and traumatic brain injury.
Lauri Lovén is a tenure-tracked Assistant Professor at the University of Oulu's Faculty of Information Technology and Electrical Engineering. As vice-director of the Center for Ubiquitous Computing (UBICOMP) and leader of the Future Computing Group (20+ researchers), he coordinates the Distributed Intelligence strategic research area within Finland's 6G Flagship program. Education: D.Sc.(Tech.) 2021, Docent (Edge Intelligence) 2025, University of Oulu Prior Affiliations: TU Wien (2022), ETH Zürich (2023) Research Focus: Specializing in edge intelligence and distributed AI, his work explores cognitive computing continuums across 6G networks, IoT systems, and industrial metaverse applications. Recent Trends: Recent publications reveal two key directions: 1) AI optimization for 6G wireless networks (handover management, semantic slicing), and 2) intelligent data management frameworks (data fabric, message brokers) for distributed systems. Industry Experience: Combines 20 years of software industry expertise with academic research, having served as founder, CTO, and advisor in AI startups.
İLKAY SİBEL KERVANCI serves as an Assistant Professor in the Department of Computer Engineering at Gaziantep University's Faculty of Engineering. Her academic career spans teaching and research in artificial intelligence, machine learning, and data mining with practical applications across finance and bioinformatics sectors. PhD in Computer Engineering, Çukurova University (2023) MSc in Informatics, Kahramanmaraş Sütçü İmam University (2017) BSc in Computer Engineering, Kocaeli University (2004) Her research centers on machine learning applications for cryptocurrency price forecasting using LSTM and GRU networks, neutrosophic logic implementations in regression problems, and drug-target interaction prediction. Recent work demonstrates expertise in hyperparameter optimization, time series analysis, and handling imbalanced datasets through hybrid feature reduction techniques. She actively contributes to advancing neural network architectures for financial and biomedical challenges. Publication trends reveal consistent focus on Bitcoin price prediction (6 publications since 2017), expanding into drug-target interaction modeling and neutrosophic applications. Her work bridges theoretical machine learning with industrial applications in cement manufacturing, stock markets, and pharmaceutical research through recurrent neural networks and optimization frameworks. Dr. Kervanci teaches graduate courses including Introduction to Artificial Intelligence Methods and Introduction to Data Mining Methods, alongside undergraduate courses such as Artificial Intelligence in Engineering and Discrete Mathematics, demonstrating commitment to computational education across academic levels.
Gözde Özsert Yigit is an Assistant Professor in the Department of Computer Engineering at Gaziantep University, Faculty of Engineering. Her research focuses on applying machine learning and AI to biomedical data analysis, environmental monitoring, and predictive modeling. PhD in Computer Engineering (2016-2022), Çukurova University MSc in Computer Engineering (2014-2016), Çukurova University BSc in Computer Engineering (2007-2012), Çukurova University Her work spans machine learning applications , including water quality monitoring , food image classification , and drug-target interaction prediction . Recent studies emphasize interpretable feature selection methods and handling imbalanced datasets. She has contributed to 5 journal articles and 12 conference proceedings between 2014-2025, covering topics like oxygen uptake prediction, admission decision models, and hybrid AI strategies for biomedical and environmental challenges. Currently supervises academic projects and has participated in national refereeing activities (e.g., Teknofest, 2020). Affiliated with YÖK Academic, ORCID, and Web of Science databases.
Prof. habil. dr. Gintautas Dzemyda is a leading Lithuanian computer scientist, Professor and Senior Researcher at Vilnius University Institute of Data Science and Digital Technologies (VU DMSTI), and Head of the Cognitive Computing Group . He is simultaneously affiliated with the Institute of Mathematics and Informatics (MII) in Vilnius, where he has built an internationally recognized scientific school in visual data analysis. Education & Qualifications 1984 – Candidate of Technical Sciences (PhD equivalent), thesis on “Problem Structure Analysis – a Tool for More Effective Optimization”. 1997 – Habilitation Doctor of Technical Sciences, dissertation on “Isolation of Necessary Knowledge to Improve Optimization Efficiency”. 1992 – Associate Professor, Institute of Mathematics and Informatics, Vilnius. 1998 – Professor, Kaunas University of Technology. Research Interests Prof. Dzemyda’s research integrates data science, artificial intelligence, optimization, and cognitive computing . Core topics include dimensionality reduction, multidimensional data visualization, neural-network–based analytics, parallel and distributed computing, multi-criteria decision support, and advanced AI applications in medicine (ophthalmology, cardiology, oncology). His work has pioneered Lithuanian capabilities in visual analytics and large-scale data exploration. Publication Trends Across 270+ refereed works and 2 Springer monographs (2013, 2023), recent outputs (2021–2025) emphasize geometric multidimensional scaling for big-data visualization, deep learning for pancreatic-cancer detection on CT images, reinforcement learning for autonomous navigation, and fraud-detection techniques for highly imbalanced financial datasets. These contributions appear in Springer LNCS/LNNS, Informatica, Journal of Global Optimization, Engineering Applications of Artificial Intelligence , and other top venues. Awards & Recognition Lithuanian State Science Prize (2001 & 2021) Honorary Doctor of the University of Latvia (2019) Knight's Cross of the Order “For Merit to Lithuania” (2007) Doctoral Supervision & Committees He has mentored 28 doctoral graduates (15 direct, 13 through academic descendants) and currently supervises: Dalia Breskuvienė – Classifier training-set optimization Modestas Motiejauskas – Emotion recognition in photographs Victor Bulava – Machine-learning methods for cyber-incident early detection He also chairs or serves on doctoral and habilitation committees at VU, KTU, VGTU, VMU and MII. Laboratory & Projects As Head of the Cognitive Computing Group , Prof. Dzemyda coordinates several national and EU projects, including the current Lithuanian Research Council grant “Geometric Method for Multidimensional Scaling” (S-MIP-20-19, 2020-2022) and the SMART programme project “CognitiveSTATS” (2021-2023) focused on combating misinformation during pandemics. His team develops open-access tools for large-scale data visualization and contributes to the MIDAS national research-data archive.
Prof. Dr. Virginijus Marcinkevičius is a Professor at Vilnius University , serving as the head of the Smart Technologies Research Group and the Artificial Intelligence Laboratory within the Institute of Data Science and Digital Technologies . He is also a Senior Researcher , Project Lead Researcher , and Group Leader . Based in Vilnius, Lithuania, he has been instrumental in advancing research in machine learning , artificial intelligence , cybersecurity , and natural language processing . Research Interests: Machine Learning & AI Cybersecurity & Threat Detection Natural Language Processing Hyperspectral Imaging & Remote Sensing Autonomous Systems & Robotics Big Data & Cloud Computing His work spans both theoretical and applied aspects, including IoT security , visual analytics , and intelligent decision support systems . Recent projects include the development of propaganda detection systems , hyperspectral unmixing algorithms , and autonomous driving agents . Doctoral Supervision: He has supervised 19+ PhD students and 5+ consultants , covering topics from machine learning in cybersecurity to neural machine translation and autonomous UAV navigation . Projects & Grants: He has led or contributed to 15+ national and EU-funded projects , including: CognitiveSTATS – COVID-19 data literacy platform Propaganda and Disinformation Research – ML-based detection DAMIS – Data mining system for national research Raštija 2 – Lithuanian language resource integration Publications: He has authored or co-authored 60+ peer-reviewed publications in journals like IEEE Access , Informatica , Frontiers in Psychology , and Machine Vision and Applications . Professional Memberships: He is a member of the Lithuanian Computer Society , Lithuanian Mathematical Society , and Lithuanian Operations Research Society .
