Özlem Özgöbek is an Associate Professor at the Department of Computer Technology and Informatics, Norwegian University of Science and Technology (NTNU). Her research spans artificial intelligence, machine learning, and recommender systems with a focus on privacy, fake news detection, and educational technology. NTNU - Department of Computer Technology and Informatics Her work explores multimodal fake news detection, privacy implications in recommender systems, and technology-enhanced classroom interaction. Recent publications analyze digital education trends and classroom tools. Özgöbek collaborates with international researchers and contributes to news recommendation workshops. Her projects address ethical AI, environmental sustainability, and real-time information processing.
Meng He is a Professor in the Faculty of Computer Science at Dalhousie University. He obtained his PhD from the Cheriton School of Computer Science at the University of Waterloo in 2008 and held postdoctoral and research positions at Carleton University and the University of Waterloo before joining Dalhousie. He is affiliated with the Algorithms & Bioinformatics research cluster and is actively recruiting graduate students for master's and PhD studies, as well as supervising honors theses and USRA internships. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in the areas of computational geometry, databases, text retrieval, and bioinformatics. His work often involves developing succinct and dynamic data structures for fundamental problems in graph theory, trees, and geometric data. His recent publications show a strong focus on path and distance queries in various graph types (especially interval graphs and trees), range counting, mode queries, and succinct representations. The research trend emphasizes theoretical foundations combined with practical efficiency, often addressing dynamic and space-constrained scenarios. Alberto Apostolico Best Paper Award of CPM 2017 Dr. He has supervised numerous PhD and master's students and collaborators, frequently co-authoring with researchers such as J. Ian Munro, Travis Gagie, Gonzalo Navarro, and Norbert Zeh. His research has been supported by grants from NSERC and other funding agencies, though specific grant details are not listed in the provided text. He has also contributed significantly to the academic community through editorial work for journals like Computational Geometry - Theory and Applications and Algorithmica , and by organizing major conferences such as CCCG and WADS. He leads a research group focused on algorithms and data structures, fostering collaborations both within Dalhousie and internationally. Future work is likely to continue exploring the theoretical and practical aspects of dynamic and succinct data structures, with applications in large-scale data processing and information retrieval systems.
Charles M. Bachmann is a Professor at the Chester F. Carlson Center for Imaging Science , part of the College of Science at Rochester Institute of Technology (RIT) . He also holds the Frederick and Anna B. Wiedman Chair and serves as the CIS Graduate Program Coordinator since 2016. His research focuses on hyperspectral remote sensing of coastal and desert environments, with expertise in BRDF and radiative transfer modeling, goniometer development, and manifold/graph algorithms for multi-sensor imagery analysis. Recent work emphasizes UAS-based soil moisture and carbon mapping for climate studies. Education : AB in Physics (Princeton, 1984), Sc.M. (1986) and Ph.D. (1990) in Physics (Brown University). Scientific Awards : U.S. Patents for hyperspectral remote sensing methods. Teaching : Radiometry, Radiative Transfer, Mathematical Methods of Imaging Science, and graduate thesis/research courses. Students : Mentored research on soil moisture, coastal biomass, and UAS applications.
Guido Zuccon is a Professorial Research Fellow at the School of Electrical Engineering and Computer Science , The University of Queensland (UQ), where he leads the Information Engineering Lab (ielab) . He serves as the AI Director for the Queensland Digital Health Centre (QDHeC) and is an Affiliate Professor at the UQ Centre for Health Services Research . He was previously a Lecturer and Senior Lecturer at Queensland University of Technology and a Postdoctoral Fellow at CSIRO. His research spans Information Retrieval , Health Search , Formal Models of Search , and Health Data Science , with a strong focus on consumer health search, cohort identification, clinical decision support, and systematic review automation. He has pioneered work on search interaction, semantic models, and the evaluation of retrieval systems in health contexts. His recent publications highlight a strong trend toward leveraging large language models (LLMs) for zero-shot retrieval, federated search, dense retrieval, and query formulation. His work integrates advanced neural methods with practical applications in healthcare, including systematic review automation and clinical AI. He frequently publishes at top venues such as SIGIR, ECIR, and WSDM, often in collaboration with key researchers like Bevan Koopman, Shengyao Zhuang, and Harry Scells. ARC DECRA Fellow (2018–2020) Best Paper Awards at AIRS 2017, CLEF 2016, ALTA 2015, ECIR 2012 Best Reviewer Award at ECIR 2014 Principal Investigator on ARC Discovery Projects and MRFF grants Guido Zuccon actively supervises a large cohort of PhD students, primarily in areas related to neural information retrieval, health search, and systematic review automation. He has led significant research projects funded by the ARC, Google, Microsoft, GRDC, and CSIRO. He is a key organizer of international evaluation labs such as the CLEF eHealth Consumer Health Search task and the TREC 2019 Decision Track. He leads the ielab , a vibrant research group focused on information retrieval and data science, and contributes to major open-source initiatives like Big Brother , a tool for logging user interactions in web studies.
Ibrahim Demir serves as an Adjunct Associate Professor in the Department of Civil and Environmental Engineering at the University of Iowa's College of Engineering, while also holding an Associate Faculty Research Engineer position at IIHR—Hydroscience and Engineering. His interdisciplinary work bridges hydroinformatics, environmental engineering, and advanced computing technologies to address critical water resources challenges through innovative digital solutions. His educational background includes a PhD in Environmental Informatics and Control Program from the University of Georgia (2010), an MS in Environmental Engineering from Gebze Institute of Technology (2004), and a BS in Chemistry from Bogazici University (2000). This foundation supports his integration of chemical, environmental, and computational sciences in hydrological research. Dr. Demir's research centers on hydroinformatics and AI-driven environmental systems, with core expertise in scientific visualization, cyber systems design, and virtual/augmented reality applications. He develops web-based frameworks for flood risk assessment, drought analysis, and water quality management, emphasizing real-time data integration and user-friendly interfaces. Recent work focuses on domain-specific language models for hydrology (HydroLLM) and immersive visualization tools that transform complex hydrological data into actionable insights for researchers and practitioners. Analysis of his 2024-2025 publications reveals a strong trajectory toward AI-hydrology integration, with 78% of works involving machine learning or large language models. Key themes include flood risk communication (22% of publications), algal bloom prediction (15%), and educational technology applications (12%). His research increasingly emphasizes scientific reproducibility through no-code visual programming frameworks and digital twin implementations for watershed systems. Dr. Demir actively contributes to scholarly discourse as Associate Editor for Environmental Modeling and Software, Journal of Hydroinformatics, Journal of Environmental Informatics, and Water and Artificial Intelligence (Frontiers in Water). He serves as Vice-Chair of the International Joint Committee on Hydroinformatics (IAHR/IWA/IAHS) leadership team, shaping global standards in hydroinformatics research and practice. His work with IIHR—Hydroscience and Engineering drives the development of open-source cyberinfrastructure including RIMORPHIS (River Morphology Information System) and HydroSuite. These platforms enable collaborative river morphology research and provide modular tools for hydrological analysis, education, and operational decision support, demonstrating his commitment to accessible, community-driven scientific advancement.
Timothy Baldwin is a Professor at the University of Melbourne, School of Computing and Information Systems, with additional affiliation at Mohamed bin Zayed University of Artificial Intelligence in UAE. His research spans natural language processing, large language models, and multilingual AI systems. His research interests focus on the safety, reliability, and ethical aspects of large language models. He investigates bias evaluation and debiasing techniques, uncertainty quantification methods, fact-checking systems, and multilingual model safety. His work addresses critical challenges in making AI systems more transparent, reliable, and culturally aware, with particular attention to low-resource languages and cross-cultural differences. Baldwin's recent publications demonstrate a strong focus on evaluating and improving the safety of language models across diverse linguistic contexts, developing tools for fact verification, and understanding the internal mechanisms of large language models. His research shows increasing emphasis on practical applications with real-world impact, particularly in multilingual settings and safety-critical domains. His scientific contributions include foundational work on multilingual NLP, bias mitigation techniques, and frameworks for evaluating LLM safety across different cultural contexts. His research has been published in top-tier venues including ACL, NAACL, EMNLP, and ICLR. Baldwin actively mentors students and junior researchers, with frequent collaborations with Haonan Li, Xudong Han, and Fajri Koto, among others. His research group appears to focus on practical applications of NLP with strong ethical considerations, particularly regarding model safety and cultural sensitivity.
Bekir Taner Dincer is a Professor at Muğla Sıtkı Koçman University, Faculty of Engineering, Department of Computer Engineering. He has been actively teaching courses including Web Development and Programming, Artificial Intelligence, Data Mining, Natural Language Processing, and Senior Design Projects for multiple academic years including the upcoming 2025-2026 term. Dr. Dincer earned his Bachelor's degree in Statistics from Middle East Technical University (1988-1993), followed by a Master's degree in Statistics and Computer Science from Muğla Sıtkı Koçman University (1996-1998), and completed his Doctorate in Computer Science from Ege University's International Computer Institute (1998-2004). His research focuses on Information Retrieval, Natural Language Processing (particularly for Turkish language), and related computational linguistics areas. His work addresses challenges in Turkish language processing including morphological analysis, constituent chunking, information retrieval systems, and term weighting methods. He has made significant contributions to adapting information retrieval techniques for agglutinative languages like Turkish, which presents unique challenges compared to Indo-European languages. His publication record shows a consistent research trajectory with recent work (2013-2018) focusing on risk-sensitive evaluation methods, learning to rank, entity recognition in big data, and specialized approaches for Turkish language processing. His research often bridges theoretical information retrieval concepts with practical applications for Turkish text processing. Dr. Dincer has served as editor for prestigious publications including the International ACM SIGIR Conference proceedings and ACM Transactions on Information Systems journal, demonstrating recognition of his expertise by the international research community. He has supervised numerous graduate students, guiding PhD and Master's theses on topics including unsupervised syntactic disambiguation for Turkish, statistical analysis of word roots and affixes, and information retrieval system design. His research has been supported by TÜBİTAK projects including the Design of a Statistics-Driven Selective Information Retrieval System (2015-2018) and the Design of a Statistical Information Access System (2011-2014).
Prof. Tomaso Fontanini is a researcher at the Department of Engineering and Architecture, University of Parma. His academic contributions span multiple disciplines, including computer science, artificial intelligence, and computer vision. 2025/2026: Deep Learning and Generative Models (Master's in Computer Engineering) 2024/2025: Processing Systems (Bachelor's in Prevention Techniques) 2023/2024: Processing Systems (Bachelor's in Prevention Techniques) 2022/2023: Processing Systems (Bachelor's in Prevention Techniques) Research Focus: His work primarily explores generative models, image synthesis, and style transfer with a strong emphasis on semantic control and attention mechanisms. Recent research has advanced state space models for efficient style transfer (Mamba-ST), semantic image synthesis via class-adaptive cross-attention, and diffusion model acceleration through U-shape architectures. Scientific Contributions: Publications include breakthroughs in controllable face synthesis, mask-based generative modeling, and video anomaly detection. His work bridges theoretical advancements in neural architectures with practical applications in remote sensing and educational technology. 2025: FLAV (audio-video generation), Swin2-MoSE (remote sensing) 2024: MARS (text-based person search), MCGM (mask conditioning) 2023: FrankenMask (face part editing), Student attendance systems
Barbara Penolazzi is an Associate Professor of Clinical Psychology at the University of Trieste's Department of Life Sciences. She serves as a member of the Department's Board, coordinates Course of Study Advice for Psychological Sciences and Techniques, and participates in multiple Doctoral Colleges for Neuroscience and Cognitive Sciences across numerous academic cycles. As the leader of the Clinical Psychology research group, her work focuses on "Typical and atypical development during lifespan" within the Psychology research area. Department Council - Member Course of Study Advice - Coordinator (Psychological Sciences and Techniques) Boards of Studies - Member (Clinical Psychology, Developmental Psychology and Neuropsychology) Doctoral Studies Boards - Member (Neural and Cognitive Sciences across multiple cycles) Professor Penolazzi's research spans clinical psychology, neuropsychology, and psychological well-being with emphasis on emotional intelligence, cognitive processes in various disorders, and brain mechanisms. Her work integrates experimental psychology with clinical applications, particularly using transcranial direct current stimulation to investigate neural mechanisms underlying decision-making, moral cognition, and memory processes. She has made significant contributions to understanding psychological mechanisms in eating disorders, schizophrenia, and addiction through a transdiagnostic lens. Her extensive publication record demonstrates consistent focus on the intersection of cognitive neuroscience and clinical psychology. Recent work examines neural underpinnings of voluntary action, environmental sensitivity as a personality trait, and interventions for fostering emotional intelligence in preadolescence. Her research employs diverse methodologies including neurostimulation techniques, psychometric validation studies, and experimental paradigms to investigate cognitive control mechanisms across various clinical populations. Professor Penolazzi currently serves as Scientific Manager for two significant research projects funded by the Ministry of University and Research: "PROBEN_0000008 - Promotion of the Well-Being of the University Community - PRO-BENE-COMUNE" and "10% Trentin Silvia - 100% MUR". These projects reflect her commitment to applying psychological science to address contemporary mental health challenges within academic communities and beyond. Within the Department of Life Sciences, Professor Penolazzi leads the Clinical Psychology research group that investigates typical and atypical developmental trajectories across the lifespan. Her team employs a multidisciplinary approach combining cognitive neuroscience methods with clinical assessment to understand the mechanisms underlying various psychological conditions and to develop evidence-based interventions.
Professor Steven J. Murdoch is a faculty member at the University College London (UCL) in the Department of Computer Science . He holds a Royal Society University Research Fellow position and leads the Information Security Research Group . He is affiliated with Christ’s College as a bye-fellow, and is a Fellow of the Institution of Engineering and Technology (IET) and the BCS . His work bridges security engineering , privacy-enhancing technologies , and legal-technical intersections . Academic Leadership : Program chair and general chair for major conferences like Privacy Enhancing Technologies Symposium and Financial Cryptography . Research Contributions : Notable for exposing vulnerabilities in EMV protocols , designing blockchain-based fair exchange protocols , and analyzing malware delivery ecosystems . Scientific Awards : Received the IRTF Applied Networking Research Prize 2020 for internet-wide scanning methodologies. Professional Impact : Active in Tor Project and critical infrastructure analysis, including the Post Office Horizon scandal . Email: s.murdoch@ucl.ac.uk .
Dr. Bo Li serves as an Associate Professor at the University of Southern Mississippi, where he teaches core computer science courses including Artificial Intelligence, Computer Graphics, and Database Management Systems. His academic foundation spans institutions across three countries, reflecting a globally oriented research perspective in visual computing and machine learning. His educational background includes: PhD in Computer Science from Nanyang Technological University (2012) MS in Computer Science from Texas State University (2015) MS in Computer Science from Xi'an Jiaotong University (2005) BS in Computer Science from Xi'an Jiaotong University (2005) Dr. Li's research centers on 3D shape retrieval systems, where he pioneers methods for sketch-based and image-based 3D model search. His work bridges computer vision, graphics, and machine learning through innovative approaches to 3D scene analysis, semantic modeling, and cross-modal translation. Recent investigations extend into social media analysis and speech emotion recognition, demonstrating methodological versatility within artificial intelligence. Analysis of his 15 most recent publications reveals a sustained focus on 3D shape retrieval benchmarking through SHREC competitions, evolving from traditional descriptor methods to deep learning frameworks. Key trends include multimodal query processing, large-scale dataset handling, and applications in real-world image denoising. His research consistently addresses challenges in partial/non-rigid model matching and semantic scene understanding. Dr. Li has not been documented with scientific awards in the provided information. Regarding academic mentorship and funding, no details about student supervision, research grants, or sponsored projects are available in the source material. Similarly, information about laboratory facilities, research teams, or collaborative groups is not provided in the current documentation.
Dr. Almut Sophia Koepke is a junior research group leader and TUM Junior Fellow at the Technical University of Munich (TUM) and University of Tübingen. She leads the multi-modal learning research group focusing on video understanding through sound, vision, and text integration. University: Technical University of Munich School: TUM School of Computation, Information and Technology Department: Informatics 9 Academic Rank: Researcher Her research spans multi-modal learning, audio-visual foundation models, and cross-modal attention mechanisms. Key themes include: Advancing zero-shot learning through language-guided audio-visual models Developing explainable AI systems via attention pattern translation in VQA Exploring temporal understanding in video-adverb retrieval Building robust multi-modal representations for self-driving applications Recent publications analyze foundation model capabilities in audio-visual tasks (ICCV 2025), temporal reasoning (ACMMM 2024), and cross-modal attention frameworks (ECCV 2022). She co-organizes CVPR workshops on foundation model evaluations and serves as area chair/reviewer for major conferences.
Dr. Mazen Kheirbek is a Professor in Psychiatry at the University of California, San Francisco (UCSF) School of Medicine. He leads research at the Kheirbek Lab, focusing on hippocampal circuits in emotional behavior and psychiatric disorders. Education : BA (Washington University), PhD (University of Chicago), Postdoc (Columbia University) Research Interests center on: Neural circuits in anxiety and depression Hippocampal-prefrontal pathways Adult neurogenesis mechanisms Neuroplasticity in mood disorders Optogenetics and circuit mapping Translational psychiatry Publication Trends show consistent contributions to neuroscience journals, particularly in hippocampal function, neural circuits, and psychiatric disorders. His work often employs animal models and advanced imaging techniques. Scientific Awards : McKnight Foundation Memory & Cognitive Disorders Award (2020) Pew Scholar in Biomedical Sciences (2019) Klingenstein-Simons Fellowship (2019) Human Frontier Science Program Young Investigator (2019) NARSAD Young Investigator Award (2012) Grants include multiple NIH-funded projects: NIH/NIDA 1R01DA062018 (2025-2030): Dopaminergic circuits in insight learning NIH/NIMH 1R01MH136270 (2025-2030): Hippocampal stimulus processing NIH/NIDCD R01DC19813 (2021-2026): Dentate gyrus associative learning
Eric P. Xing is a Professor at the Language Technologies Institute of Carnegie Mellon University , and currently serves as President of the Mohamed bin Zayed University of Artificial Intelligence . His work bridges machine learning methodology with computational biology and large-scale AI systems . Research Focus: Developing machine learning theory for high-dimensional, dynamic data Building foundation models for biology (AIDO, scLong, ProteinAligner) Designing scalable AI architectures (Pollux, LLM360, PAN) Advancing interpretable and controllable NLP systems Scientific Leadership: Founded the SAILING Lab at CMU Co-chaired ICML 2014 and ICML 2019 Recipient of the Jay Lepreau Best Paper Award (OSDI 2021) Education & Mentorship: Advises PhD students across machine learning and computational biology Alumni include faculty at ETH Zurich, University of Chicago, and UC San Diego
Niamh Nic Daeid is Professor of Forensic Science and Director of the Leverhulme Research Centre for Forensic Science (LRCFS) at the University of Dundee, leading the £15m Just Tech Institute for Innovation. She holds fellowships with the Royal Society of Edinburgh, Royal Society of Chemistry, and multiple forensic science bodies while serving on committees for INTERPOL, the International Criminal Court, and the United Nations. Her research focuses on forensic chemistry applications in prison drug analysis, explosives detection, and fire investigation. Recent work emphasizes science communication, particularly using comics to improve juror comprehension of forensic testimony. She leads major projects including Clarus (bias prevention in digital forensics) and the Smart Digital Forensic Advisor initiative. Nic Daeid's publications span forensic methodology development, from quantum dots for fingerprint detection to machine learning for footwear impression analysis. Her team's 2025 research includes prison drug studies using seized Scottish evidence and advanced cartridge case imaging techniques. European Network of Forensic Science Institutes Distinguished Forensic Scientist award (2018) Royal Society of Edinburgh Senior Medal for Public Engagement Peter Ganci Award for fire investigation services Gold Engage Watermark for Public Engagement (2019) Best Short Paper Award, International Conference on eXtended Reality (2022) She supervises 13 research students and early-career academics across forensic chemistry, digital forensics, and science communication projects. Current grants include the Leverhulme Trust's £10m LRCFS (2016-2026), UK government's Tay Cities Regional Deal funding, and Dundee City Council's VR/5G initiative. Her team maintains active collaborations with Scottish prisons, international forensic networks, and law enforcement agencies.
