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Fatih BayView profile

Professor

• Neutrino Physics
• Particle Physics
• Detector Development
• +3 more

Fatih Bay serves as Full Professor and Chair of the Department of Mechanical Engineering at Antalya Bilim University since 2020, leveraging his physics expertise in an engineering leadership role. With a Ph.D. in Physics from the University of Bern (2012), he has driven major contributions to neutrino physics through international collaborations at CERN, Fermilab, and KEK, currently focusing on the DUNE experiment's quest to explain matter-antimatter asymmetry. His academic journey features: B.Sc. in Physics, Kocaeli University (2004) M.Sc. in Physics, Middle East Technical University (2008) Ph.D. in Physics, University of Bern, Switzerland (2012) Dr. Bay's research centers on experimental neutrino physics and advanced detector development, specializing in liquid argon time projection chambers, emulsion detectors, and iron-scintillator calorimeters. His current work with DUNE integrates deep learning for neutrino interaction reconstruction while exploring supernova neutrino detection capabilities. This builds upon foundational contributions to OPERA, T2K, and MicroBooNE experiments. Analysis of his 2023-2025 publications reveals dominant focus on DUNE detector systems, with emerging trends in AI-driven event reconstruction, liquid argon purity optimization, and multi-detector synergies for supernova neutrino studies. His work bridges particle physics methodology with cutting-edge computing techniques to enhance detection precision in large-scale experiments. His exceptional contributions earned recognition through: Breakthrough Prize in Fundamental Physics (2016) for T2K experiment leadership Dr. Bay secured significant research funding including a TÜBİTAK 'Returning Researchers' fellowship (2015) and leads international collaborations requiring substantial project resources. While student advising details aren't specified, his department chair role implies graduate mentorship in mechanical engineering contexts. His laboratory work spans CERN (Switzerland), KEK (Japan), and Fermilab (USA) facilities, with current focus on DUNE's vertical drift technology development and ProtoDUNE-SP calibration systems.

• Machine Learning
• Computer Vision
• Emotion Recognition
• +5 more

Marina Marjanovic is a faculty member at Singidunum University in Belgrade, Serbia, affiliated with the Faculty of Informatics and Computing. She maintains an active research profile with numerous publications spanning machine learning, computer vision, and signal processing applications. Her work demonstrates strong interdisciplinary connections between theoretical AI development and practical implementations in healthcare, security, and digital government services. Dr. Marjanovic earned her Doctoral degree in Signals and Systems from Universidad Politecnica de Madrid in 2007, following a Master's degree in the same field from the same institution in 2005. Her foundational education includes basic studies in Telecommunications at the Faculty of Electrical Engineering in Belgrade, and secondary education at the third Belgrade high school with a focus on natural sciences. This strong technical background has enabled her transition from traditional signal processing to contemporary AI research. Her research interests center around machine learning and computer vision, with significant contributions to emotion recognition, sign language processing, and explainable AI. She has demonstrated particular expertise in developing robust AI models that address real-world challenges such as demographic bias in age estimation systems, vulnerability to adversarial attacks in deep learning models, and accurate medical diagnostics through imaging analysis. Her recent work shows increasing focus on healthcare applications, security implications of AI systems, and making AI more transparent and interpretable. Analysis of her publication trajectory reveals a clear evolution from signal processing foundations toward contemporary AI applications. Her recent work (2022-2024) shows strong emphasis on healthcare AI, security applications, and explainability, often combining traditional machine learning approaches with novel optimization techniques. She frequently collaborates with international researchers across multiple institutions, demonstrating the global reach of her work. Dr. Marjanovic has established herself as a productive researcher with consistent publication output across reputable journals and conferences including IEEE transactions, Springer publications, and specialized AI conferences. Her collaborative approach is evident through her extensive co-authorship network spanning multiple Serbian and international institutions. She actively contributes to the academic community through conference organization (including Sinteza conferences) and participation in research projects at Singidunum University's research institutes. Her work bridges theoretical advancements with practical implementations across multiple domains, demonstrating versatility and impact across the AI research landscape.

Nicolas BallierView profile

Professor

• Corpus prosody
• Neural machine translation
• Automatic analysis of learner corpora
• +5 more

Nicolas Ballier is a Professor at Université Paris Cité (formerly Université Paris Diderot), where he teaches and conducts research in linguistics and digital humanities. Previously, he taught at the Université de Rouen and Paris 13. His work focuses on the intersection of linguistics, computational methods, and language learning technologies, with particular expertise in neural machine translation and speech processing. His primary research interests include: Corpus prosody Neural machine translation Automatic analysis of learner corpora Digital humanities Epistemology of linguistics (third revolution of grammatisation) Interpretability of neural machine translation systems Representation of speech in Whisper audio models Dr. Ballier's recent work explores how computers transform linguistic data (the 'third revolution of grammatisation'), with a focus on neural machine translation interpretability and speech analysis using large language models like Whisper. His research bridges theoretical linguistics with practical applications in language learning and translation technologies, particularly focusing on how these technologies can be made transparent and useful for translators and language learners. His 15 most recent publications (2022-2024) demonstrate a strong focus on neural machine translation interpretability, Whisper applications for language assessment, and learner corpus analysis. The publications span multiple prestigious venues including EAMT, ACL, LREC-COLING, and specialized journals in speech technology and computational linguistics, showing consistent productivity and impact in his fields. Dr. Ballier has been PI or team member on numerous European-funded projects including DOKTORAND (2012-2016), KVARK project (2014-2026), PHC Ulysses (2019), multitraiNMT (2021), and LT-LIder project (2024-Nov 2026). He has developed platforms like PAPTAN for neural machine translation experiments and MAKE-NMT VIZ for investigating machine translation interpretability. He has supervised PhD students through collaborative projects and has been involved in research initiatives like DLLA (Deep Learning for Language Assessment) exploring CEFR levels with keylog data, Neuroviz (2021-2022), and SPECTRANS (2020-2022) focusing on specialized neural translation and probing information flow in neural networks.

Kalogeraki VanaView profile

Professor

• Distributed Systems
• Real-Time Systems
• Big Data Systems
• +8 more

Prof. Vana Kalogeraki is a Faculty Member at the Department of Informatics , Athens University of Economics and Business (AUEB) , and serves as the Dean of the School of Information Sciences and Technology and Director of the Computer Systems and Communications Laboratory . She has held academic positions at the University of California, Riverside and was a Research Scientist at Hewlett-Packard Labs . PhD: University of California, Santa Barbara M.S. & B.S.: University of Crete, Greece Her research focuses on Distributed and Real-Time Systems , Big Data Systems , Cloud Computing , Human-Centered Systems , and Crowdsourcing . She has published over 200 papers at top journals (IEEE TPDS, ACM TOS, etc.) and conferences (RTSS, DSN, ICDCS, VLDB, MDM), including co-authoring the OMG CORBA Dynamic Scheduling Standard . Her 2023-2025 publications address AI pipelines in serverless environments, edge computing for trauma detection via eye-tracking, and fairness in resource scheduling. She has received prestigious awards including an ERC Starting Grant , Best Paper Awards (DEBS 2017, IPDPS 2009), a Best Poster Award (EuroSys 2024), and multiple UC Research Awards . Her research is funded by the European Union (ARISTEIA, THALIS), NSF, and industry partners like SUN and Nokia. Advising Legacy : Supervised 10 PhD graduates (now at Google, Amazon, IBM, Apple) and over 60 MS/PhD committees Labs & Teams : Leads the Computer Systems and Communications Laboratory at AUEB, focusing on mobile human-centered systems and urban data analytics

Hendrik BaierView profile

Assistant Professor

• Artificial Intelligence
• Planning
• Search Algorithms
• +5 more

Hendrik Baier is an Assistant Professor in the Information Systems group at Eindhoven University of Technology (TU/e), where he joined in 2022. His research focuses on creating agents capable of succeeding in complex decision-making tasks to help human users solve real-world problems. His work spans planning for long-term goals, learning in unknown environments, and explainability of AI systems for effective human-AI interaction. Dr. Baier's research interests center on planning and search algorithms, reinforcement learning, and explainable AI systems. His work investigates how AI can think ahead and explain its reasoning process, particularly in sequential decision-making contexts. He applies these techniques to practical domains including logistics and transportation, smart manufacturing, and sustainable energy systems. His research bridges theoretical foundations with real-world applications through collaborative projects with industry partners. Analysis of his recent publications reveals a strong focus on explainability in sequential decision-making, with increasing integration of large language models to enhance traditional planning algorithms. His work spans theoretical foundations of Monte Carlo Tree Search, programmatic policy generation, multi-agent reinforcement learning, and practical applications of these techniques. A notable trend is the growing emphasis on human-AI collaboration, where AI systems must not only perform well but also effectively communicate their reasoning to human users. Dr. Baier actively collaborates with researchers across multiple institutions, including CWI Amsterdam where he maintains an affiliation, and has participated in significant interdisciplinary efforts such as the Dagstuhl Seminar on Explainable AI for Sequential Decision Making. His research group at TU/e works closely with industry partners to translate fundamental research into practical applications. He is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute) and contributes to the Decision Making with Artificial Intelligence educational program at TU/e. His laboratory work focuses on developing benchmark environments and frameworks that enable rigorous evaluation of decision-making algorithms, with recent contributions including MOMAland for multi-objective multi-agent reinforcement learning.

Dewar FinlayView profile

Professor

• Healthcare Technology
• Artificial Intelligence
• Digital Signal Processing
• +5 more

Dewar Finlay is a Professor of Electronic Systems and Head of the School of Engineering at Ulster University . He previously served as Research Director for the School of Engineering and Interim Associate Dean for Research & Impact within the Faculty of Computing, Engineering and the Built Environment. His work bridges healthcare technology and computational engineering. Education: BEng in Electronic Systems, Ulster University PhD in Computing, Ulster University Research Interests: His research focuses on healthcare technology with emphasis on computerised ECG analysis and deep learning applications in cardiology . He explores AI-driven diagnostics , signal processing , and medical device validation through projects like DTNet+ Digital Twin Network and IoT-Driven Cybersecurity Framework for Intrusion Detection in Drones . Scientific Awards: Best Poster (2024) - Calibrated Uncertainty AI in ECG Analysis Early Career Investigators Award (2022) - British Society for Heart Failure Grants & Collaborations: He has secured funding from EU Horizon 2020 , RCUK , DEL , and InvestNI . Current projects include AI-assisted echocardiography for congenital heart defects in Sub-Saharan Africa and federated learning frameworks for cardiac healthcare.

Aydin NassehiView profile

Professor

• Smart Manufacturing Systems
• Agent-Based Manufacturing Planning and Control
• Artificial Intelligence in Manufacturing
• +3 more

Professor Aydin Nassehi is Head of the School of Electrical, Electronic and Mechanical Engineering at the University of Bristol, UK. He holds the academic rank of Professor of Production Systems and actively contributes to research in smart manufacturing, agent-based modeling, and digital twins. Research interests include AI integration in manufacturing, generative design, and systems optimization. He has collaborated extensively with Dr. Ben Hicks on projects like ProtoTwinning and Designing the Future , focusing on digital twin applications and transdisciplinary engineering. His scholarly output spans over 150 publications, with recent work emphasizing explainable AI, sustainability in food supply chains, and biomedical manufacturing innovations. Publications demonstrate expertise in machine learning, digital twin cost modeling, and additive manufacturing optimization. His projects have received funding from EPSRC and other academic institutions. Current research explores Industry 5.0 challenges, balancing automation with human-centric approaches, and advancing interoperability in cloud manufacturing environments.

Lin LinView profile

Associate Professor

• Biostatistics
• Bioinformatics
• Statistical Science
• +7 more

Lin Lin is an Associate Professor of Biostatistics & Bioinformatics at Duke University's Division of Integrative Genomics and an Associate Research Professor of Statistical Science in Trinity College of Arts & Sciences. With appointments dating from 2022 to present, Dr. Lin has established herself as a prominent researcher at the intersection of statistics, bioinformatics, and biomedical applications. Her work spans multiple departments and research centers at Duke, reflecting her interdisciplinary approach to solving complex biological problems. Ph.D. from Duke University (2012) Dr. Lin's research focuses on developing advanced statistical and machine learning methods for analyzing complex biological data, particularly in immunology and transplantation research. Her expertise in single-cell data analysis, cytometry data interpretation, and biomarker discovery has led to significant contributions in vaccine studies, HIV/AIDS research, and organ transplantation. She has pioneered methods for handling small cohort studies, longitudinal data, and multi-modal datasets, addressing critical challenges in modern biomedical research where traditional statistical approaches fall short. Analysis of Dr. Lin's publication record reveals a strong emphasis on developing interpretable computational methods that bridge the gap between complex data and biological insights. Her recent work shows increasing sophistication in handling high-dimensional single-cell data, with a particular focus on creating models that maintain interpretability while achieving high predictive accuracy. The trajectory of her research demonstrates a consistent pattern of addressing methodological challenges in biomedical data analysis, with applications spanning immunology, transplantation medicine, and infectious disease research. Dr. Lin has secured substantial research funding from multiple prestigious sources including the National Institutes of Health, National Institute of Allergy and Infectious Diseases, National Heart, Lung, and Blood Institute, and National Institute of Environmental Health Sciences. Her grants portfolio demonstrates expertise across diverse biomedical domains, from HIV/AIDS research to transplantation immunology and environmental health effects. These projects typically involve developing novel statistical methodologies while addressing pressing clinical questions, showcasing her ability to bridge theoretical statistics with practical biomedical applications. As an educator, Dr. Lin teaches advanced courses in Bayesian statistical modeling and analysis, contributing to the training of the next generation of biostatisticians and data scientists. Her research group likely focuses on developing computational tools that address real-world challenges in biomedical data analysis, with particular emphasis on making complex models interpretable and applicable to clinical settings.

Francesca FallucchiView profile

Associate Professor

• Computer Science
• Information Science
• Semantic Web
• +7 more

Francesca Fallucchi is an Associate Professor at Guglielmo Marconi University in Rome since 2008 and an information scientist at Georg-Eckert-Institut (GEI) since July 2017, working in the Human-Centered Technologies for Educational Media department. Her research focuses on the intersection of computer science and humanities, with particular emphasis on knowledge organization , information retrieval , semantic technologies , and big data management applied to educational media and cultural heritage. Her work bridges theoretical computer science with practical applications in digital humanities. Analysis of her recent publications (2021-2024) reveals a research trajectory spanning multiple domains: from foundational work in semantic web and NLP to emerging applications in metaverse technologies, blockchain for energy monitoring, and explainable AI for healthcare. Her work consistently demonstrates interdisciplinary approaches combining computer science with domain-specific challenges. Dr. Fallucchi has been actively involved in academic service, including organizing the 17th International Conference on Metadata and Semantics Research (MTSR 2023) and serving as editor for Computers trade magazine since 2021. Her professional activities include significant project leadership as Deputy Project Manager for Edumeres Toolbox and contributions to numerous research projects including GLOTREC, GEI-Digital, PalTex, DemoS, PVE-E, and WorldViews.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +2 more

Patrick Schramowski is a researcher at the German Research Center for Artificial Intelligence (DFKI) specializing in foundational aspects of systems AI. He contributes to interdisciplinary research across machine learning and computer vision, with a focus on ethical AI and model reliability. Key collaborations include work with researchers like Kristian Kersting and Manuel Brack Active in conferences such as EMNLP and CVPR His recent publications address critical challenges in AI systems, including: Mitigation of shortcut learning in neural networks Safety mechanisms for diffusion models Evaluation of open-source language translation models

Ruwen QinView profile

Associate Professor

• Intelligent transportation systems
• Smart civil infrastructure systems
• Human sensing & analytics
• +8 more

Ruwen Qin is an Associate Professor in the Department of Civil Engineering at Stony Brook University. Her research focuses on integrating data analytics, machine learning, and systems engineering into civil infrastructure systems to develop cyber-physical systems and intelligent automation. She applies these technologies to enhance human-AI collaboration, improve transportation safety, and advance smart infrastructure monitoring. Developing AI models for structural health monitoring Applications in worker safety and transportation systems Specializes in computer vision and sensor fusion Her recent work includes deep learning frameworks for drone-assisted inspections, structural component segmentation using weak annotations, and attention-based networks for traffic risk prediction. She also explores explainable AI for crash anticipation and interactive systems for bridge inspectors. Ruwen Qin's research spans interdisciplinary domains, combining civil engineering with AI-driven analytics to address challenges in infrastructure resilience, transportation safety, and human-centric automation systems.

• Data Visualization
• Visual Analytics
• Big Data Analysis
• +12 more

Professor Jang Yoon is a faculty member in the Department of Computer Engineering at Sejong University, South Korea. He currently holds the position of Daeyang Distinguished Professor and leads the Data Visualization Lab. His academic journey includes postdoctoral research at the Swiss National Supercomputing Center (2007-2009), ETH Zurich (2009-2011), and Purdue University (2011-2012). His educational background includes a Bachelor's degree from Seoul National University in Electrical Engineering (2000), and Master's and Doctoral degrees from Purdue University in Electrical and Computer Engineering (2002 and 2007). His academic progression at Sejong University shows his appointment as Assistant Professor (2012-2016), Associate Professor (2016-2022), and Professor (2022-present). Professor Jang's research spans multiple domains within data science and visualization, with primary focus on data visualization, visual analytics, and their applications in various domains. His work bridges theoretical computer science with practical applications in traffic analysis, healthcare, and smart city infrastructure. He has developed innovative techniques for spatiotemporal data visualization, volume rendering, and causal analysis in complex datasets. His recent publications (2023-2025) demonstrate a strong focus on integrating deep learning with visualization techniques, particularly in traffic analysis, volume rendering, and large language model interpretability. His work shows a clear trajectory toward combining causal inference with visual analytics, applying these methods to urban traffic systems, structural health monitoring, and public relations analysis. Professor Jang has served in numerous leadership roles in major visualization conferences including IEEE VIS, IEEE PacificVis (as General Chair in 2023), EuroVis, and HCI Korea conferences. His service contributions include program committee memberships and chair positions across multiple prestigious conferences in the visualization field. His laboratory work focuses on practical applications of visualization techniques with numerous patents registered in Korea. His research has resulted in multiple practical systems for traffic analysis, VR sickness detection, data quality improvement, and eye-tracking applications. The lab maintains strong industry connections through applied research projects addressing real-world problems.

Laure SoulierView profile

Lecturer

• Design of language models for Information Retrieval (IR) and Natural Language Processing (NLP)
• Data-to-Text Generation
• Search-Oriented Conversational Systems
• +3 more

Laure Soulier is a HDR (Habilitation à Diriger des Recherches) Lecturer at Sorbonne University within the MLIA team at the ISIR (Institute for Intelligent Systems and Robotics) laboratory. Her research focuses on the design of language models for Information Retrieval (IR) and Natural Language Processing (NLP) applications, including data-to-text generation , search-oriented conversational systems , language models for robotics , and continual learning with domain adaptation . Recent publications highlight her work on cross-encoders, co-speech gesture generation, and latent space metrics. She supervises an ANR-funded postdoctoral researcher position (SCAI/BnF program) and collaborates on projects involving multimodal techniques, user interaction analysis, and document vectorization. Her scientific contributions include best paper awards at CORIA 2021, SCAI@EMNLP 2019, CORIA 2015, and AIRS 2013. Laure Soulier’s work spans collaborative information retrieval models, entity ranking in heterogeneous networks, and neural approaches for knowledge-based IR. She has contributed to evaluation frameworks for LLMs in IR and co-speech gesture generation, with applications in e-commerce search, medical information retrieval, and social media-based collaboration. Her research integrates user roles, document representations, and reinforcement learning techniques.

• Earth Observation
• Remote Sensing
• Data-centric AI
• +9 more

Mariana Belgiu is an Associate Professor at the Department of Earth Observation Science (EOS) within the Faculty of Geo-Information Science and Earth Observation (ITC) at the University of Twente. Her work bridges Earth Observation (EO), data-centric artificial intelligence (AI), and food security, with a focus on addressing environmental and societal challenges through innovative geospatial solutions. PhD in Remote Sensing, University of Salzburg MSc in Applied Geoinformatics, University of Salzburg Her research develops AI methods for analyzing multi-temporal EO data, particularly in hidden hunger (micronutrient deficiencies) and slum mapping. Key themes include: Data-centric AI in scarce-label environments Transferability of EO-driven models Imaging spectroscopy for crop nutrient estimation Citizen science integration for climate vulnerability assessments The 69 research outputs span EO applications for: Global crop nutrient prediction Urban poverty mapping Climate resilience in Sub-Saharan Africa AI fairness and explainability in geospatial contexts Earth observation education frameworks Scientific Recognition Copernicus Masters 2015, T-Systems Big Data Challenge Esri Young Scholar Award (2013) Best Master Thesis in Geoinformatics (2010) As a supervisor of 7 PhD students , she mentors work on deep learning for cloud removal, global crop monitoring, and transferable slum mapping. She also leads the EO4all working group, promoting gender equity in EO science, and serves as Associate Editor for the ISPRS Journal (Impact Factor 12.7). Major grants include the SPACE4ALL project (NWO, 2023–2027) and EO4Nutri (ESA, 2023–2025), alongside contributions to Horizon Europe's ASTRAIOS initiative.

Gaia TavoniView profile

Assistant Professor

• Theoretical Neuroscience
• Computational Neuroscience
• Neural Coding
• +5 more

Gaia Tavoni, PhD, is an Assistant Professor in the Department of Neuroscience at Washington University in St. Louis School of Medicine, where she leads the Tavoni Lab. Her research investigates fundamental principles of brain function through theoretical and computational approaches, focusing on neural coding, sensory processing, decision-making, and memory optimization. Education: BS in Physical Engineering, Polytechnic of Turin (2010) International MS in Physics of Complex Systems (2012) PhD in Physics, École Normale Supérieure, Paris (2015) Post-doc in Theoretical Neuroscience, University of Pennsylvania (2020) Research Interests: Dr. Tavoni develops unified frameworks connecting computational, algorithmic, and implementational perspectives in neuroscience. Key areas include: multimodal sensory coding principles, neuroplasticity mechanisms, economic decision circuits, Bayesian inference theories, and memory optimization in heterogeneous networks. Her lab employs biophysical models, information theory, and statistical mechanics to study how neural networks optimize behavior. Publication Trends: Recent work demonstrates strong emphasis on normative theories of neural computation, with publications spanning sensory integration, memory capacity optimization, and adaptive inference. Articles frequently integrate mathematical modeling with experimental neuroscience to explain cortical feedback mechanisms, decision circuits, and cognitive trade-offs. Awards: Sloan Fellowship (2024) PRX Life Reviewer Excellence Award (2024) Swartz Foundation Fellowship (2017) Computational Neuroscience Postdoctoral Fellowship (2015) Lab & Team: Directs a team including postdoctoral researchers (Mahsa Khoshkhou, Ryan McGee, Kaining Zhang), graduate student Eleonora Bano, and research assistant Timothy Crimmins. The lab focuses on neural circuit optimization and collaborates with groups like the Padoa-Schioppa Lab for economic decision research.