Furkan Eren Uzyildirim is a Research Fellow at the Department of Computer Engineering, Izmir Institute of Technology (IYTE), where he has worked since 2015. He earned his B.Sc. (2014), M.Sc. (2016), and Ph.D. (2022) in Computer Engineering from IYTE, graduating as a high honors student. His research focuses on Computer Vision and Deep Learning , with a particular emphasis on image segmentation, object recognition, and keypoint matching. He contributes to projects like Safe and secure autonomous driving technologies and Smart agricultural technologies using unmanned vehicle systems , both funded by TÜBİTAK 1512. His recent publications explore unsupervised learning for outdoor plane estimation, advanced keypoint matching algorithms, and 3D scene analysis. These works intersect with subfields such as Autonomous Driving , 3D Scene Understanding , and Feature Extraction . He teaches courses including Numerical Computing and Programming & Data Structures.
Selma Tekir is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology. She completed her undergraduate studies in Computer Engineering at Ege University in 2001, followed by a master's degree from Izmir Institute of Technology in 2004, and earned her PhD from Ege University in 2010. In 2009, she worked as a visiting researcher at the University of Konstanz, Germany. Her educational background includes: B.Sc.: Computer Engineering, Ege University (2001) M.Sc.: Computer Engineering, Izmir Institute of Technology (2004) Ph.D.: Computer Engineering, Ege University (2010) Dr. Tekir's research spans multiple areas of natural language processing and computational linguistics, with a particular focus on Turkish language processing. Her work explores text mining, news analysis, information warfare, and the application of deep learning techniques to linguistic problems. She has made significant contributions to counterfactual detection in Turkish, multi-modal language models, and the integration of symbolic reasoning with neural approaches. Her research often bridges theoretical advances with practical applications, particularly in the Turkish language context which presents unique challenges as a morphologically rich agglutinative language. Analysis of her recent publications reveals a strong trend toward advancing natural language understanding systems for Turkish, developing methods for counterfactual detection, enhancing question answering systems with knowledge graphs, and applying graph neural networks to biological sequence analysis. Her work demonstrates growing interest in combining symbolic reasoning with neural approaches and exploring self-reflection capabilities in language agents. Dr. Tekir has received research funding for projects including: Consistency and Reliability Assessment in News Chains (TÜBİTAK ARDEB 3501) Application of Data Analysis and Visualization Techniques on Historical Sources (BAP) She teaches courses including CENG 381 - Stochastic Processes, CENG 613 - Scientific Research Methods in Computer Science, and SEDS 501 - Introduction to Data Science.
Gregory Jefferis is a Professor and Research Leader at the MRC Laboratory of Molecular Biology (MRC LMB), University of Cambridge, where he heads a group investigating olfactory processing in Drosophila . His work bridges neural circuitry, behaviour, and computational neuroanatomy within the Cambridge neuroscience ecosystem. Jefferis' research focuses on how odour information transforms into behaviour through higher olfactory centres, particularly examining third-order neurons in the lateral horn that integrate specific olfactory channels. His lab employs cutting-edge techniques including genetic labelling, in vivo whole-cell patch clamp recording, and high-resolution computational neuroanatomy to decode innate and learned behavioural responses to general odours and sex pheromones. Recent work demonstrates strong trends in connectome mapping and computational analysis, with 2024 publications in Nature and Cell detailing the complete adult fly brain connectome and neurotransmitter classification via machine learning. His group actively contributes to open-source neuroanatomy tools like the natverse while exploring fundamental questions about sensory processing and memory retrieval in olfactory circuits. Jefferis leads a collaborative team including Isabella Beckett, Sebastian Cachero, Shahar Frechter, Florian Kampf, Myrto Mitletton, Markus Pleijzier, Gerald Rubin, Philipp Schlegel, Valeria Silva-Moeller, Tomke Stuerner, and Catherine Whittle, with extensive international partnerships evident in recent multi-institutional publications.
Dionysios Politis is an Assistant Professor at the Aristotle University of Thessaloniki in the Department of Informatics. He holds multiple degrees including a PhD in Informatics from Aristotle University (1998), a Master's in Computer Science from RMIT Australia (1991), and a Master's in Electronic Physics from Aristotle University (1990). His professional experience spans roles as Lecturer (2002), Special Educational Staff (1993-2002), and EU Expert in Legal Informatics. Research Interests: Politis leads research in interdisciplinary domains including Music Information Technology, Human-Computer Interaction, Mobile Device Interfaces, Legal Informatics, and E-Learning Methodologies. His work integrates computational approaches with cultural heritage preservation and accessibility technologies. Publications: Recent articles focus on computational musicology, biomedical interfaces, digital forensics in media distribution, and cross-cultural music analysis. Key themes include chromaticism modeling, ancient music reconstruction, cochlear implant technologies, and e-justice systems. Laboratory: Affiliated with the Multimedia & Human-Computer Interaction Computer Music Lab, where he explores audio-visual interfaces and digital sound applications.
Ivan Kolev Koichev is a Professor at the Department of Software Technologies within the Faculty of Mathematics and Informatics at Sofia University 'St Kliment Ohridski'. He serves as the Director of the MSc program in Information Retrieval and Knowledge Discovery and Head of the Department of Software Technologies Education. His research spans Artificial Intelligence, Machine Learning, Natural Language Processing, and Information Retrieval. MSc in Applied Mathematics (1988), Sofia University PhD in Computer Science (1998), Bulgarian Academy of Sciences Postdoc Fellowships at GMD Germany (1999-2002) and Robert Gordon University UK (2002-2005) His work focuses on adaptive learning systems, fact-checking, author obfuscation, and community question-answering frameworks. Key publications address concept drift adaptation, political debate analysis, image claim verification, and credible news detection. He collaborates extensively with researchers like Preslav Nakov and Momchil Hardalov. He has contributed to SemEval-2016 Task 3 and developed the LEAF multiple-choice question generation system. His research integrates linguistic analysis, credibility assessment, and algorithmic innovation across multidisciplinary domains.
Irina Nikishina is a postdoctoral researcher at the University of Hamburg's Department of Informatics, working in the Language Technology Group under Prof. Chris Biemann. As a researcher in computational linguistics and natural language processing, she contributes to projects like ACQuA-2.0, focusing on semantics, argument mining, taxonomies, and knowledge graphs. PhD in Computational and Data Science and Engineering (2022), Skolkovo Institute of Science and Technology Bachelor's and Master's degrees from National Research University Higher School of Economics (NRU HSE) Her research spans taxonomy enrichment, comparative question answering systems, and biomedical concept representation. She organizes shared tasks like RUSSE’2020 and RuArg-2022, and co-founded the RusNLP semantic search engine for Russian NLP conferences. She chairs the Network Analysis track at the International Conference on Analysis of Images, Social Networks and Texts (AIST) and has served as secretary for AIST 2020 and 2021. Recent publications focus on large language models' performance in lexical semantics, multilingual comparative argumentation systems, and knowledge graph integration for QA tasks. Her work includes developing tools like TaxFree for candidate-free taxonomy enrichment and exploring cross-modal approaches for taxonomic graph expansion.
Jacopo Staiano is a Senior Assistant Professor (RTDb) at the Department of Economics & Management, University of Trento (Italy). His academic journey includes previous positions as Head of Research at reciTAL.ai, research affiliate at Data-Pop Alliance, senior data scientist at Fortia Financial Solutions, and post-doctoral researcher at LIP6, UPMC - Sorbonne Universités and Fondazione Bruno Kessler. Staiano received his BSc in Computer Engineering from the University of Pisa (2003), an MA in Sonic Arts from Queen's University of Belfast (2005), and an MSc in Human Language Technologies and Interfaces from the University of Trento (2010). He completed his PhD under Prof. Nicu Sebe at the Department of Information Engineering and Computer Science, University of Trento. His academic visits include the Intelligent Systems Lab at University of Amsterdam, Ambient Intelligence Research Lab at Stanford University, Human Dynamics Lab at MIT Media Lab, and Telefonica I+D. Staiano's research spans multiple domains with a strong focus on Natural Language Processing and Human-Computer Interaction. His work ranges from modeling human behavior when interacting with technology to analyzing social network structures and virality dynamics. His recent publications demonstrate a significant shift toward large language models, with applications in sustainability reporting, medical diagnostics, financial analysis, and bias detection. His work shows sophisticated integration of technical AI capabilities with social science perspectives. Staiano has received several prestigious awards throughout his career, including an Honorable Mention at ACM DIS 2012, Best Paper Award at ACM UbiComp 2014, the UMUAI James Chen Award 2016, and most recently the Ten Year Technical Impact Award from ACM ICMI 2024. These awards reflect the sustained impact and quality of his research across multiple domains. His research has been supported by collaborations with major institutions including MIT Media Lab, Stanford University, and Telefonica I+D, as well as industry partnerships. His work on projects like DepecheMood for emotion analysis and SALSA for multimodal group behavior analysis has established him as a significant contributor to affective computing and social signal processing. Staiano maintains active connections with multiple research communities, particularly in the areas of computational social science, natural language processing, and affective computing. His work bridges technical AI development with real-world applications in finance, healthcare, and social good initiatives.
Nathalie Abadie is a Researcher at the Geographic Information Science and Technology Laboratory (LaSTIG) within the National School of Geographic Sciences at the University of Paris-East. She leads research in geographic information science with a focus on knowledge capture, geohistorical data, and semantic web technologies. Her work bridges historical geography, digital humanities, and artificial intelligence. Her research interests include structured data matching with geographic reference datasets, creation of geohistorical knowledge graphs, and knowledge acquisition for geographic data. She develops methodologies for spatial named entity linking, multimodal image matching, and historical data integration, particularly applied to urban evolution studies of Paris from 1789-1950. Dr. Abadie's recent publications demonstrate trends in geohistorical knowledge graph construction, historical document analysis, and real-time geolocation applications. Her work increasingly integrates machine learning with traditional GIS techniques to address challenges in historical data processing and disaster response. She actively advises PhD students including Solenn Tual, Charly Bernard, and Helen Mair Rawsthorne, and has led major research projects such as SoDuCo (Study of Urban Spatial Structures Evolution) and Mezanno (Collaborative Annotation Tools). Her grants include CNRS-funded initiatives in digital humanities and geospatial AI. Dr. Abadie co-leads the STRUDEL research team and directs multiple national working groups including the CNRS GDR MAGIS commission on Geohistorical Knowledge Graphs. She organizes major conferences including the French Knowledge Engineering Conference and workshops on Digital Humanities and Artificial Intelligence.
Dr Frank Po-Yen Lin is a medical oncologist and clinical informatician based in Sydney, Australia with conjoint research appointments at the NHMRC Clinical Trial Centre, University of Sydney and the Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research. Education: 2022: Fellowship of the Australasian Institute of Digital Health (FAIDH) 2019: Fellowship of the Royal Australasian College of Physicians (FRACP), Internal Medicine (Medical Oncology) 2009: PhD in Medical Informatics, University of New South Wales 2003: MB ChB, University of Otago, New Zealand Dr Lin's research focuses on evidence-based precision medicine and the application of digital health in oncology, with particular emphasis on empowering genomic decision-making, multidisciplinary care, and biomarker development through artificial intelligence and machine learning. He is co-investigator of several clinical trials in early drug development as part of the Australian Genomic Cancer Medicine Program and leads the development of the TOPOGRAPH precision oncology knowledgebase and open source resources for treatment decision-support and electronic medical record analytics. His publication portfolio includes 52 journal articles, 2 book chapters, 6 conference papers, 1 conference poster, 9 conference abstracts, and 6 preprints, demonstrating substantial scholarly output across his research domains. Analysis of his recent work reveals a strong emphasis on integrating AI with clinical oncology practice, developing decision-support tools, and advancing precision medicine implementation. Professional Recognition: Associate Editor of JCO Clinical Cancer Informatics Chief investigator and co-investigator on multiple research grants Fellowship of the Australasian Institute of Digital Health (2022) Fellowship of the Royal Australasian College of Physicians (2019) Dr Lin actively supervises research candidates in medical oncology, precision oncology, clinical trials, artificial intelligence applications in medicine, applied and translational genomics, and medical informatics decision-support systems. His work bridges clinical oncology practice with cutting-edge informatics approaches to improve cancer care delivery and outcomes.
Dr. Shahram Dehdashti is a researcher at the Chair of Theoretical Information Technology (Lehrstuhl für Theoretische Informationstechnik) at the Technical University of Munich (TUM), working under Prof. Boche. His research spans quantum information science, quantum communication, and quantum machine learning, with significant contributions to theoretical frameworks for future quantum networks and practical implementations in optical systems. His primary research domains include: Quantum Information and Communication Quantum Computing Algorithms Quantum Machine Learning Quantum Optics and Photonics Metamaterials and Transformation Optics Quantum Cognition He investigates quantum channel capacities, quantum-classical signal co-propagation, and quantum-enhanced feature spaces, while uniquely applying quantum probability models to cognitive decision-making processes. Analysis of his 2020-2025 publications reveals accelerating convergence between quantum computing and artificial intelligence, particularly in quantum federated learning for edge devices and quantum-inspired sentiment analysis. His work consistently bridges theoretical quantum information with practical optical implementations, while maintaining distinctive contributions to quantum cognition through experimental protocols for decision modeling. No scientific awards were documented in the source material. There is no evidence of student supervision or grant funding in the available information. Dr. Dehdashti operates within TUM's Chair of Theoretical Information Technology, which focuses on fundamental quantum information processing and theoretical aspects of next-generation communication systems.
Cosmin Stoica Spahiu is a Senior Lecturer in the Department of Computer Science and Information Technology at the University of Craiova's Faculty of Automatic Control, Computers and Electronics. He holds a PhD in Computer Science from the University Politehnica of Bucharest (advisor: Prof. Mircea Petrescu) and graduated with a Software Engineering degree in 2004. Courses: Database Design, Human-Computer Interaction, Software Testing Research Focus: Multimedia and image processing, particularly in medical imaging and semantic processing Scientific Recognition: Received two Best Paper awards at international conferences (MMEDIA 2010, ICCGI 2010). Participated in 7 national/international research projects related to medical imaging, distributed databases, and content-based retrieval systems.
Georgios Petropoulos is an Assistant Professor of Geoinformatics at the Department of Geography, Harokopio University of Athens, Greece, where he has been teaching since 2020. His expertise spans Earth Observation technology, Geographic Information Systems, and remote sensing applications for environmental monitoring and geohazard assessment. He has previously held academic positions at Aberystwyth University in the UK and has been involved in numerous research projects related to land surface process modeling and remote sensing. His educational background includes: PhD in Earth Observation Modelling from King's College London, University of London (2008) MSc in Remote Sensing from the intercollegiate program between University College London, King's College London, and Imperial College (2002) BSc in Natural Resources Development & Agricultural Engineering from the Agricultural University of Athens (1999) Dr. Petropoulos specializes in the application of Earth Observation technology for understanding land surface interactions and feedback processes. His research focuses on utilizing remote sensing synergistically with land surface process models to derive key state variables of the Earth's energy balance and water budget. He has extensive experience in using advanced remote sensing technology for land use/cover mapping, vegetation properties retrieval, and geohazard assessment including floods, wildfires, and frost events. His work also involves developing open-source software tools for Earth Observation and implementing comprehensive benchmarking approaches for remote sensing algorithms. Dr. Petropoulos has received numerous prestigious awards and fellowships throughout his career, including: Marie Curie Individual Fellowship (2017) Senior Fellow of the UK Higher Education Academy (2015) Marie Curie Reintegration Grant (2013) European Space Agency fellowship (2010) Excellent Associate Editor Award for journal Environmental Modelling & Software (2021) Best conference poster award at the Asian Conference on Remote Sensing (2018) As an educator, Dr. Petropoulos has taught undergraduate and postgraduate courses in cartography, geoinformatics, remote sensing, and GIS at multiple institutions including Harokopio University, Aberystwyth University, and the Agricultural University of Athens. He has secured competitive research funding from various sources including the European Commission, ESA, and national research councils. His editorial work includes serving as an editor for Elsevier's Book series in "Earth Observation" and as an editorial board member for several international journals. Dr. Petropoulos is actively involved in international research collaborations and has contributed to numerous scientific committees and working groups related to Earth Observation standards and validation approaches. His current research continues to advance methodologies for soil moisture retrieval, land surface temperature estimation, and environmental monitoring using multi-sensor remote sensing approaches.
Dr. Andrey Alenin serves as a Senior Lecturer at UNSW Canberra within the School of Engineering and Technology. His extensive academic contributions focus on advanced optical systems, particularly in polarimetry and imaging technologies. With numerous publications spanning over a decade, his work has established him as a significant contributor to the field of optical science and engineering. Dr. Alenin's research interests center on optical polarimetry, Mueller matrix systems, and advanced imaging techniques. His work spans theoretical foundations of linear systems in optics to practical implementations of polarimetric imaging systems. He has made significant contributions to channeled spectropolarimetry, photoelastic modulator systems, and polarization visualization methods. His research bridges fundamental optical theory with real-world applications in remote sensing, satellite imaging, and quantum communications. The extensive list of book chapters he co-authored demonstrates his deep understanding of Fourier optics and linear systems theory. Analysis of Dr. Alenin's recent publications reveals a strong trajectory from fundamental polarimetric theory toward practical implementations. His work increasingly incorporates machine learning techniques for polarization data processing, particularly deep learning for spectral-temporal analysis. The research shows progression toward applications in remote sensing and satellite systems, including tropical cyclone monitoring and intersatellite quantum communications. His co-authorship of the comprehensive reference 'Field Guide to Linear Systems in Optics' with J.S. Tyo has provided valuable educational resources for students and researchers in the field. Dr. Alenin maintains active collaborations with researchers across multiple institutions, as evidenced by his extensive co-authorship network. His work appears in leading optics journals including Applied Optics, Optics Express, and the Journal of the Optical Society of America. His research has practical applications in defense, remote sensing, and quantum communications technologies.
Thierry Artières is a University Professor at Aix-Marseille University, primarily affiliated with École Centrale Marseille (ECM), where he holds multiple leadership positions including Head of the Computer Science teaching unit, Head of the IAAA course of the Computer Science Master's degree, and Head of the IAM course of the 3rd year Computer Science option. He is a key member of the QARMA (Machine Learning) research team within the LIS (Laboratoire d'Informatique et Systèmes) and collaborates with several research institutes including the READ laboratory, Institut de Neurosciences de la Timone (INT), and the ILCB (Institute of Language, Communication and the Brain). His research interests span Machine Learning, Deep Learning, and Artificial Intelligence with applications to neuroscience, medical imaging, and computational biology. His work focuses on understanding brain representations of voice and sound, optimizing MRI acquisition through deep learning, and developing novel machine learning techniques for multi-label classification and generative modeling. He has supervised numerous PhD students including Loris Berthelot, Hamed Benazha, Malek Senoussi, Swetali Nimje, and Charly Lamothe. His recent publications reveal a strong focus on applying deep learning to neuroscience problems, particularly in understanding how the brain processes sound and voice. His work combines theoretical machine learning advances with practical applications in medical imaging and cognitive neuroscience, often through collaborations between computer science and neuroscience laboratories. His research demonstrates a consistent trend toward interdisciplinary work that bridges AI with biological and medical domains. Member of QARMA Machine Learning team at LIS Collaborator with Institut de Neurosciences de la Timone Involved with ILCB Institute (Institute of Language, Communication and the Brain) Supervisor of multiple PhD students and Master's interns Regularly posts about internship opportunities in Machine Learning and AI Professor Artières actively mentors students through PhD positions, Master's internships, and engineering student projects. He has secured funding for multiple research projects, including ANR-funded collaborations with neuroscience institutes. His lab regularly offers 5-6 month internships on cutting-edge topics in machine learning, and he has facilitated numerous research opportunities for students interested in AI and data science careers. He also contributes to understanding the French job market for AI and data science professionals.
Gerlind Plonka is a Professor of Applied Mathematics at the University of Göttingen, specifically within the Institute for Numerical and Applied Mathematics (NAM). Her research focuses on Numerical Fourier Analysis Wavelet Theory Regularization and Nonlinear Diffusion Methods Fast Algorithms and Numerical Stability Signal and Image Processing Applications Her recent publications emphasize structured subsampling in Fourier domains, Prony-type methods for exponential sum recovery, and deep learning integration in medical imaging. She supervises active PhD candidates including Benjamin Kocurov, Anahita Riahi, Yannick Nicola Riebe, and Janina Schmidt, with a legacy of advising over 50 graduates across diverse topics like Sparse FFT Algorithms Phase Retrieval Constraints Wavelet-Based Image Compression Nonlinear Diffusion Filters High-Dimensional Data Approximation
