Saara Repo is a visiting researcher at the University of Helsinki's Clinicum and works with the Centre for University Pedagogy (HYPE). Her work focuses on university pedagogy, online education, and mindfulness in academic contexts. Research Interests: Peer mentoring, mindfulness-based interventions, critical thinking in open university students, and workplace well-being for educators. Projects: Principal investigator for mindfulness training programs (2018-2025), contributor to studies on digital competence in distance teaching (2020), and involved in comparative research on open university models (OPULL) across four countries. Teaching: Delivers lectures and workshops on community-building, peer support, and mindfulness for teachers and postgraduate students. Media Engagement: Participated in expert discussions on mindfulness and self-compassion (2019-2020).
Om P. Damani is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He serves as Faculty In-Charge of the Sustainable Development unit of the Center for Policy Studies and is also associated with the Centre for Technology Alternatives for Rural Areas (CTARA). His work bridges computer science with social development challenges, focusing on practical applications for rural communities. Dr. Damani's research interests span Technology for Development of the bottom 80%, System Dynamics: Modeling and Simulation for Social Development, System Architecture, and Data Science. His work demonstrates how computational approaches can address complex development challenges through projects like GramDrishti (for detecting rural infrastructure in satellite images), JalTantra (for optimizing water distribution networks), and FAI (Farm Assessment Index for holistic farming practice evaluation). His publications reveal a consistent focus on applying computer science to solve real-world problems in water management, agricultural systems, and rural infrastructure. His research has been recognized with significant awards including the IIT Bombay Industrial Impact Award 2010, IIT Bombay Impactful Research Award 2019, and Best Poster Award at Agriculture Science Congress 2017. Dr. Damani has successfully translated theoretical research into practical tools that address development challenges, particularly in water resource management and agricultural systems. As an educator, he has mentored numerous PhD students including Chintan Tundia, Shreenivas Kunte, Nikhil Hooda, Sivamuthu Prakash Murugan, Dipak L. Chaudhari, Prateek Kapadia, and Manoj K. Chinnakotla. His teaching portfolio includes courses on System Dynamics: Modeling and Simulation for Development (CS 752), Program Derivation (CS 420), and ICT for Development. Dr. Damani's educational background includes a Ph.D. in Computer Sciences from the University of Texas at Austin (1994-1999), B.Tech. in Computer Science and Engineering from IIT Kanpur (1990-1994), and prior professional experience at IBM T J Watson Research Lab and Akamai Technologies.
Dr. Hajk-Georg Drost is a Senior Lecturer and Principal Investigator in the Division of Computational Biology at the University of Dundee's School of Life Sciences. He leads the Digital Biology Group, focusing on integrating machine learning and high-performance computing with biological research to advance healthcare innovation. Previously, he established a Computational Biology group at the Max Planck Institute for Biology Tübingen (2019-2024) and conducted postdoctoral research at the University of Cambridge's Sainsbury Laboratory. His research explores: Evolutionary transcriptomics and phylotranscriptomic patterns across species Machine learning applications in genomics and proteomics Development of bioinformatics tools (DIAMOND, myTAI) for tree-of-life scale analyses Gene regulatory networks and transposable element dynamics His publications demonstrate a consistent focus on evolutionary constraints in development, with recent work expanding into single-cell resolution analyses of developmental diseases. Awards include: Royal Society Wolfson Fellowship (2024) Fellow, Cambridge Philosophical Society Postdoctoral Affiliate, Trinity College Cambridge He currently supervises PhD students including Stefan Manolache and leads projects funded by the Royal Society and others, focusing on protein alignment infrastructure and developmental disease research. His lab develops open-source software for genomic analyses and maintains active collaborations across Europe.
Professor Jen Ross is a Professor of Digital Culture and Education Futures at the University of Edinburgh, affiliated with the Moray House School of Education and Sport. She serves as Co-Director of the Centre for Research in Digital Education and Associate Dean (Research Cultures) in the College of Arts, Humanities and Social Sciences. Her research focuses on digital education futures, cultural heritage engagement, and the ethical implications of emerging technologies like AI in higher education. She has supervised 17 doctoral projects, contributing to fields such as online learning, open education, and surveillance studies. Dr. Ross holds a PhD from the University of Edinburgh (2012) and is a Senior Fellow of the Higher Education Academy. Her teaching includes leading courses on Education Futures and Digital Education at the Edinburgh Futures Institute. Notable projects include co-developing the E-learning and Digital Cultures MOOC and leading initiatives like the 'Higher Education After Surveillance' network. Her research publications explore postdigital education, speculative methods in pedagogy, and the intersection of technology with cultural heritage. Recent work addresses AI integration in schools and surveillance practices in academia. Key grants include the AHRC-funded 'Responsible AI in Education' project and the ESRC Impact Accelerator grant on AI futures. She has collaborated with institutions like the National Science and Media Museum, World Bank, and Scotland’s Futures Forum. Her book Digital Futures for Learning: Speculative Methods and Pedagogies (2023) synthesizes her work on forward-thinking educational strategies.
Dr. Arno Onken is a Lecturer (Assistant Professor) in Data Science for Life Sciences at the School of Informatics, University of Edinburgh, where he is also affiliated with the Institute for Adaptive and Neural Computation. He leads a research group focused on developing machine learning and statistical methods for modeling neural activity and analyzing large-scale neuroscience data. His work bridges artificial intelligence and computational neuroscience. His research interests lie at the intersection of machine learning, statistics, and neuroscience. He develops flexible probabilistic models such as copulas and Gaussian processes, deep learning architectures like Vision Transformers for brain activity prediction, and matrix/tensor factorization techniques for dimensionality reduction in neural datasets. His group aims to uncover interpretable structure in complex neural recordings and understand how behavior and cognition are encoded in population activity. The recent publications reflect a strong trend in combining modern deep learning with classical statistical modeling to analyze large-scale neural recordings. His work spans from foundational methods in copula modeling and information theory to applications in predicting visual cortex responses and modeling brainstem-hippocampus interactions across sleep states. The research has been published in top venues including NeurIPS, CVPR, eLife, and PLoS Computational Biology. Dr. Onken actively supervises PhD students and has developed several open-source scientific software packages, including the Mixed Vine Toolbox and Population Spike Train Factorization Toolbox. He teaches core courses in Machine Learning and Pattern Recognition and Data Mining and Exploration at the University of Edinburgh.
Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.
Dr. Jacek Kudera is a post-doctoral researcher in the Department of Phonetics at the University of Trier, coordinator of the LODinG project at the Trier Center for Digital Humanities, and adjunct faculty at WSB Merito University in Wrocław. His work bridges phonetics, Slavic linguistics, digital humanities, and forensic speech science. Education 2022 – PhD, Department of Language Science and Technology, Saarland University, Germany 2019 – MA (Linguistics), Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Denmark 2015 – Magister (Slavic Philology), Institute of Slavic Studies, University of Wrocław, Poland Research Interests His research focuses on phonetic and prosodic aspects of Slavic languages , cross-linguistic speech perception , forensic automatic speaker recognition , and human-robot interaction . He employs experimental methods such as eye-tracking, articulatory measurements (EMA), and large-scale digital corpora to investigate how speakers of closely related languages understand one another and how machines can replicate or support this process. Publication Trends Across more than 25 peer-reviewed articles (2014-2025), Kudera has consistently explored Slavic intercomprehension , speech technology evaluation , and digital humanities infrastructure . Recent work (2024-2025) targets voice cloning security , linked open data for linguistics , and mismatch conditions in forensic speaker recognition . Scientific Awards & Fellowships Visegrad Fellowship, University of Presov (2025) Erasmus+ Fellowships (Ostrava 2025, Zagreb 2014, Rijeka 2012-2013) NAWA Fellowship, Polish Academy of Sciences (2022) Nordlys Fellowship, University of Eastern Finland (2018-2019) CEEPUS & additional Central-European mobility grants (2014-2018) Projects & Funding Coordinator : “Mismatch conditions in machine speaker identification” (University of Trier Research Fund, 2024-2025) Coordinator : LODinG – Linked Open Data in the Humanities (Trier Center for Digital Humanities, ongoing) Coordinator : “Patterns: Linguistic Creativity and Variation” (Trier Center for Language and Communication, 2022-2024) Member : SFB 1102 “Information Density and Linguistic Encoding” (Saarland University, DFG, 2019-2022) Member : Digital Atlas of Dialects of Bosnia and Herzegovina (2017-2018) Member : CLARIN-PL & European Roadmap for Research Infrastructures (2014-2017) Labs & Teams He conducts research within the Phonetics Team at the University of Trier , collaborates closely with the Trier Center for Digital Humanities , and maintains affiliations with the Phonetics Group at Saarland University and the WSB Merito University in Wrocław.
Robert Nowak holds dual distinguished professorships as the Keith and Jane Morgan Nosbusch Professor in Electrical and Computer Engineering and the Grace Wahba Professor of Data Science at the University of Wisconsin–Madison. Based at the Discovery Building (330 N Orchard Street), he leads interdisciplinary research at the Wisconsin Institute for Discovery, bridging engineering with data science applications. His academic foundation includes: BS, MS, and PhD from the University of Wisconsin–Madison Post-doctoral Fellowship at Rice University Nowak's research program spans artificial intelligence, machine learning, and optimization with dual emphases on AI-driven health applications and systems optimization. His work integrates theoretical rigor with practical implementations, particularly in large language model fine-tuning, active learning frameworks, and neural network theory. Recent publications demonstrate strong focus on improving model efficiency, humor comprehension in AI systems, and theoretical bounds for retrieval-augmented generation. Analysis of his 15 most recent publications reveals dominant trends in large language model advancement (particularly humor understanding and task diversity), theoretical neural network analysis (including sparse architectures and multi-task learning), and novel active learning methodologies for open-world scenarios. His work consistently bridges theoretical machine learning with real-world applications in health and recommendation systems. While specific named awards aren't documented in the source material, his appointment to two endowed chairs (Nosbusch and Wahba professorships) represents exceptional institutional recognition of his scholarly impact. Nowak advises graduate students in the Electrical and Computer Engineering department and secures significant research funding, including NSF grants such as CIF: Small: Advanced Understanding and Applications of Deep Learning. His group operates within the collaborative ecosystem of the Wisconsin Institute for Discovery, fostering cross-disciplinary projects that integrate AI with health sciences and engineering systems. Current projects indicate strong momentum in human-AI collaboration frameworks and optimization of language model training pipelines.
Dr. Sabine Graf is a Full Professor at the School of Computing and Information Systems, Athabasca University. She holds a PhD in Computing and Information Systems from Vienna University of Technology (2007) and has been a faculty member since 2009. Her research focuses on user adaptive systems, learning/academic analytics, personalization, and game-based learning, with over $2.3M in external funding and 130+ peer-reviewed publications (cited 8,200+ times). She leads the OMEGA+ educational game project and the User Adaptive Systems (UAS) research cluster. Education: PhD in Computing and Information Systems, Vienna University of Technology, 2007 MSc in Computing and Information Systems, University of Vienna, 2003 Research Interests: Dr. Graf specializes in making learning systems more intelligent through adaptive interfaces, AI-driven recommendations, and data analytics. Her work bridges educational technology, artificial intelligence, and collaborative learning. Recent projects include developing OMEGA+, analyzing student behavior in online courses, and enhancing adaptive learning systems with context-aware features. Grants & Funding: NSERC Discovery Grant ($205,000), 2020 CFI John R. Evans Leaders Fund ($164,573), 2020 AU IDEA Lab Grant ($5,000), 2021 Multiple Mitacs Globalink Internships, NSERC awards, and AU-funded projects. Advising & Team: Dr. Graf has mentored over 30 students, including PhD, MSc, and postdoctoral fellows. Notable advisees include Moustafa Mahmoud (NSERC Scholar) and Mohammad Belghis-Zadeh (3MT Competition winner). The research team collaborates globally, with members from Brazil, Taiwan, Spain, and beyond. Labs & Initiatives: The Academic Analytics Tool (AAT) project and the OMEGA+ game platform are central to her work. The UAS cluster connects researchers worldwide to advance adaptive systems research.
Luis Merino Cabañas is a Professor at the Universidad Pablo de Olavide , affiliated with the Deporte e Informática department and leading the SRL Service Robotics Laboratory . His research focuses on robotics, systems engineering, and automation, with a specialization in human-robot interaction and path planning. Education : PhD in Systems Engineering from the Universidad de Sevilla (2007), where his thesis explored cooperative perception techniques for multiple unmanned aerial vehicles in forest fire detection. Research Trends : Recent work (2023–2025) emphasizes 3D path planning, sensor fusion (LiDAR, radar, inertial systems), neural distance fields for safe navigation, and socially aware robotics. His studies integrate AI, genetic programming, and multi-modal perception for applications in construction, healthcare, and GNSS-denied environments. Labs & Teams : He leads the SRL Service Robotics Laboratory , contributing to projects like the Skyeye team and BIM2ROS integration for construction robotics.
Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.
Naoki Saito is a Professor in the Department of Mathematics at the University of California, Davis, and the Director of the UC Davis TETRAPODS Institute of Data Science (UCD4IDS). His research lies at the intersection of applied mathematics, signal processing, and data science, with a focus on multiscale analysis and harmonic analysis on graphs and networks. His research interests include Applied and Computational Harmonic Analysis , Graph Signal Processing , Multiscale Transforms , Wavelets , Spectral Graph Theory , and Mathematical Data Representation . He develops theoretical frameworks and practical algorithms for analyzing complex datasets, particularly through the use of Laplacian eigenfunctions and multiscale basis dictionaries. The recent publications reflect a strong trend toward graph-based signal processing , scattering transforms , and topological data analysis . His work emphasizes the construction of natural, adaptive bases for signals on graphs and simplicial complexes, enabling efficient and interpretable data analysis. The integration of harmonic analysis with machine learning techniques is a recurring theme. Although no specific scientific awards are listed in the provided texts, his sustained scholarly output and leadership in the field are evident. Dr. Saito advises a number of students and postdoctoral researchers, including J. Irion, Y. Shao, H. Li, and others. His research has been supported by various grants, though specific funding sources are not detailed in the provided materials. He leads the UCD4IDS, a research institute focused on data science, indicating active involvement in collaborative, interdisciplinary research and academic leadership.
Jürgen Cito is an Associate Professor with tenure at Vienna University of Technology (TU Wien), specializing in software engineering, explainable AI, and performance engineering. He leads research at the IPA Lab (as indicated by his personal website) and maintains a visiting researcher position at Google. His academic journey began with joining TU Wien as an Assistant Professor in Spring 2020, with promotion to Associate Professor announced in April 2024. His research interests span multiple critical areas of modern software development, with particular focus on developer experience, program comprehension, and the intersection of AI with software engineering practices. His work bridges theoretical foundations with practical industrial applications, as evidenced by collaborations with major technology companies. Analysis of his recent publications reveals a strong emphasis on practical tools and methodologies that enhance software quality, performance, and security. His research trajectory shows increasing focus on explainable AI techniques applied to software engineering problems, performance prediction from source code, and automated security testing approaches that leverage large language models. best teaching award for distance learning for Web Engineering (2020) Cito actively contributes to the software engineering community through numerous conference committee roles, including program committee positions at ASE, ICSE, ESEC/FSE, and other major venues. His lab appears to focus on developer tools, program analysis, and AI-assisted software engineering, with connections to both academic and industrial research environments.
Professor Cynthia White serves as Pro Vice-Chancellor of Massey University's College of Humanities and Social Sciences since 2019, previously holding roles as Research Director (2012-2019) and Head of Linguistics Department (2008-2012). Her academic leadership spans editorial boards of seven international journals and governance roles including Marsden Fund Panel Chair (2018-2024). Educational background: Bachelor of Arts, Victoria University (1977) Bachelor of Arts (Honours), Victoria University (1978) Diploma in Teaching English as a Second Language, Victoria University (1978) PhD in Applied Linguistics, Massey University (1993) Her research pioneers emotion, agency and identity in discourse with focus on online language contexts, immigrant/refugee settlement experiences in New Zealand, and narrative conflict analysis. This manifests in two books (Cambridge University Press, Multilingual Matters) and 60+ publications spanning distance language learning, migration linguistics, and identity studies. Her work bridges theoretical discourse analysis with real-world applications in multicultural integration. Scientific recognition: International TESOL Virginia French Allen Award (2004) Professor White drives international research collaboration through projects with Open University UK and Nottingham University, plenary speaking engagements across 10+ countries, and advisory roles including US National Middle East Language Resource Centre. Her governance extends to Massey University Council (2010-2016) and International Applied Linguistics Association Executive Board (2017-2019).
Michał Paweł Michalak is an Assistant Professor at AGH University of Science and Technology in Kraków, affiliated with the Faculty of Geology, Geophysics and Environmental Protection and Department of Geoinformatics and Applied Informatics. His research spans computational geometry in geological modeling, machine learning applications in Earth sciences, statistical epidemiology, and ecclesiological dynamics. Doctoral degree from University of Silesia in Katowice Developed GeoAnomalia software suite combining C++, R, and ParaView Created unbiased risk metrics (WCSIR) for infectious disease surveillance Research focuses on: Subsurface geological modeling using combinatorial algorithms and machine learning; Spatial epidemiology emphasizing testing heterogeneity bias; Ecclesiological dynamics analyzing factional theological interactions. His 2020/37/N/ST10/02504 grant project received 'very good' evaluation for developing angular distance metrics in geological contacts. Scientific contributions include: 2025 Solid Earth paper on fault-related structure detection 2025 Frontiers in Public Health work on global pandemic risk evaluation 2023 AGH Rector award recipient Developer of open-source geological analysis tools Collaboration network includes Harvard University, Technische Universität Freiberg, and University of Texas at Austin. Currently teaching GIS modeling, optimization methods, and geoinformatics systems. Maintains a personal website with open data access.
