Magnus Boman is a Professor of AI and Health at the Department of Medicine, Solna, Karolinska Institutet (KI), where he leads the AI@KI initiative to support researchers in AI integration. He is affiliated with the Chronic Inflammatory Disease Epidemiology research group under Johan Askling. His research focuses on AI applications in precision medicine, multimodal prediction, ethical norms in AI systems, energy-efficient computing, and quantum sensor data interpretation. Research Interests: Artificial Intelligence in healthcare and precision medicine Multimodal data analysis for disease prediction and treatment Machine learning for clinical decision support systems Ethical and societal implications of AI Grants: Swedish Research Council: Improving breast cancer histology image classification (2024-2026) Scalable Federated Learning (2022-2025) Ai in sustainable cities (VINNOVA, 2019) Advising & Students: Supervised over 50 PhD and Master's students across KI, KTH, and Stockholm University, focusing on AI applications in healthcare, machine learning, and computational epidemiology. Notable projects include predictive modeling for mental health outcomes and variant filtering in genetic data. Labs & Teams: Leads AI@KI, fostering AI adoption in medical research. Collaborates with the Johan Askling group on epidemiology and chronic disease studies.
David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.
Richard Zemel is a Professor in the Department of Computer Science at the University of Toronto, where he has been since 2000. He holds an Industrial Research Chair in Machine Learning and co-founded the Vector Institute for Artificial Intelligence. His research focuses on machine learning, including unsupervised learning, deep learning, and ethical AI, with contributions to probabilistic models, fairness, and representation learning. Zemel has developed influential systems like the Toronto Paper Matching System and holds awards such as the NVIDIA Pioneers of AI Award and multiple NSERC grants. Education: B.Sc. in History & Science from Harvard University (1984), Ph.D. in Computer Science from the University of Toronto (1993). Postdoctoral work at the Salk Institute and Carnegie Mellon University. Research Interests: Machine Learning (unsupervised/deep learning), probabilistic models, fairness in algorithms, computer vision, natural language processing. He emphasizes ethical AI and practical applications like recommendation systems and causal inference. Awards & Affiliations: Fellow of CIFAR, member of the Neural Information Processing Society (NIPS) Executive Board, and advisor to the Creative Destruction Lab. His work is funded by NSERC, CIFAR, Google, Microsoft, and DARPA. Grants & Labs: Active in grants supporting machine learning research, including projects on fairness and invariant learning. Collaborates with industry partners and leads teams at the University of Toronto and Vector Institute.
Ke Yang serves as Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), College of Sciences. He founded and leads the Cohort for AI REsponsibility (CAREAI) initiative, while also holding core faculty positions in UTSA's School of Data Science and MATRIX (AI Consortium for Human Well-being). Education: Ph.D. from New York University (supervised by Prof. Julia Stoyanovich) Research Focus: Dr. Yang's work centers on AI trustworthiness and responsibility , with specialized expertise in algorithmic fairness, data ethics, and human-centered data science. His research addresses critical challenges including Large Language Model hallucinations, explainable AI frameworks, and algorithmic accountability mechanisms. He actively develops open-source tools like Ranking Facts and FairDAGs to implement these principles in practical systems. Publication Trends: Recent work (2020-2025) demonstrates evolving focus from foundational fairness in ranking systems toward generative AI safety and medical applications. His publications show strong theoretical grounding combined with real-world implementation, particularly in privacy policy analysis and medical question-answering systems using causal inference techniques. Scientific Recognition: Pearl Brownstein Doctoral Research Award (NYU Tandon School of Engineering) CDS Postdoctoral Fellowship (University of Massachusetts) Professional Development: Dr. Yang has secured significant research funding including the CDS Postdoctoral Fellowship at UMass. His graduate work at NYU and Drexel University was fully supported by research assistantships, demonstrating consistent funding acquisition throughout his career. He actively contributes to academic community building through conference tutorials and educational initiatives. Research Ecosystem: He directs CAREAI at UTSA while collaborating across institutional boundaries through MATRIX and the School of Data Science. Previously, he contributed to the Data systems Research for Exploration, Analytics, and Modeling (DREAM) lab and Center for Data Science at UMass Amherst, maintaining continuity in his responsible AI research trajectory.
Daniel J McAllister is an Associate Professor at the National University of Singapore Business School , Department of Management and Organisation. His research explores interpersonal relationships in organizations , with particular emphasis on social emotions , trust dynamics , and their implications for organizational citizenship behavior and ethical leadership . He has published extensively in top-tier journals such as Academy of Management Review, Journal of Applied Psychology, and Academy of Management Journal. Academic Focus: Organizational Behavior, Trust/Distrust, Workplace Emotions Teaching Interests: Technical Knowledge, Practical Application, Ethical Decision-Making Key Courses: MNO2007 (Undergraduate), BMA5004A (MBA), MNO6012A (PhD) McAllister's research spans workplace underdog trajectories, awe in leadership, abusive supervision, and cross-cultural management in China. His work examines how emotions like contempt, envy, and schadenfreude influence organizational outcomes. He emphasizes creating a safe learning environment that integrates theoretical knowledge ( technical ), real-world application ( practical ), and ethical judgment ( wisdom ). McAllister has received consistently positive student feedback for his engaging teaching style , with recent evaluations highlighting improvements in time management and practical relevance. He distributes course materials post-class and avoids rote memorization, prioritizing conceptual understanding. His 2025 work on workplace underdogs and awe-driven leadership continues to shape contemporary organizational theory.
Rosella Gennari is an Associate Professor in Computer Science at the Faculty of Engineering, Free University of Bozen-Bolzano, where she conducts research and teaches in Human-Computer Interaction (HCI). Her work is centered on designing interactive technologies for children, focusing on physical-digital (phygital) artefacts, Technology-Enhanced Learning (TEL), and inclusive design. She leads the Research Unit Human-Centred Intelligent Systems and is actively involved in institutional leadership, including serving on the Third-Mission Board. Ph.D. : Computer Science, Amsterdam University (2002) Postdoctoral Experience : CWI, Amsterdam (ERCIM Alain Bensoussan Fellow); FBK-irst, Trento Leadership : Scientific & Technological Coordinator of the FP7-EU TERENCE project Her research explores how children interact with and design smart technologies, including IoT and AI, through playful and tangible interfaces. She investigates socio-emotional learning, digital well-being, and responsible design, often employing participatory and action research methods. Her work bridges computer science, education, and social impact, aiming to empower young learners as co-creators of technology. The analysis of her recent publications reveals a strong trend in developing and evaluating toolkits and frameworks for children and pre-teens to engage in designing smart things, IoT systems, and sustainable cities. Her work consistently emphasizes inclusivity, reflection, and responsible design, often in collaboration with teachers and learners. The publications span top HCI venues and journals, demonstrating a focus on practical applications in educational settings and the impact of technology on young users. Scientific Awards and Recognition ERCIM Alain Bensoussan Fellowship for talented young researchers Editorial Board Member, Journal of Child Computer Interaction (Elsevier, Q1) Regular reviewer for top HCI conferences and journals Advising and Grants : While specific advisees are not listed, her leadership role in the FP7-EU TERENCE project and numerous other research initiatives indicates extensive experience in securing and managing competitive grants. She mentors students through her research group and teaching, fostering the next generation of HCI researchers. Her collaborative network is extensive, with frequent co-authorship with researchers such as Alessandra Melonio, Maristella Matera, and Mehdi Rizvi. Labs and Teams : She leads the Human-Centred Intelligent Systems research unit, which serves as her primary lab and team. This group focuses on placing humans at the center of computer science and information engineering research. She is also a core member of the organizing committee for the MIS4TEL international conference series, highlighting her role in building and sustaining a global research community in Technology-Enhanced Learning.
Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.
Christopher T. Middlebrook is a Professor of Electrical and Computer Engineering at Michigan Technological University (MTU), with an affiliated appointment in the Physics department. He holds a visiting faculty research engineer position at Scientific Applications International Corporation (SAIC) supporting the DoD Executive Agent for Printed Circuits and has served as visiting faculty at the Naval Surface Warfare Center Crane (2016–2020). His expertise spans integrated optical devices, electronic substrate manufacturing, and photonics. Middlebrook leads the Plexus Innovation Laboratory, a campus electronics maker space, and has pioneered PCB fabrication education through courses and media contributions. Education: PhD in Optics from the University of Central Florida, MS in Applied Optics from Rose-Hulman Institute of Technology, and BS in Electrical Engineering from MTU. His research focuses on electro-optic polymers, optoelectronic integration, and advanced manufacturing techniques. He has published 49 papers, holds two patents, and secured grants totaling over $970K, including the Michigan Economic Development Corporation-funded 'Back-End Semiconductor Curriculum' initiative (2024). Research highlights include developing UV resin printer methods for PCB prototyping, optimizing polymer waveguides, and advancing quantum communication technologies. Awards include the HKN Professor of the Year (multiple years), Michigan Tech Graduate Mentor Award, and IPC Carano Teacher Excellence Award. His work bridges academia and industry, emphasizing hands-on learning and innovation. Key Grants: Back-End Semiconductor Curriculum for Advanced Substrates: $970K (2024) Mesosphere Observation Mission (MOMBO): $38K (2022–2023) Labs/Teams: Plexus Innovation Lab, MTU's Electronics Maker Space Courses Taught: EE2230 PCB Fabrication, EE3190 Optical Sensing, EE5500 Stochastic Processes, and 15+ others emphasizing photonics and optoelectronics.
Dr. Richard Gault is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on computer vision and deep learning applied to microscopy data, particularly in medicine, health, and life sciences. He leads a team developing novel methods for medical image analysis, including histopathology and digital pathology, with applications in cancer diagnosis and environmental science. He is actively involved in teaching, having received Excellence in Teaching awards from Queen's University Belfast in 2019 and 2022. His work bridges computational intelligence and healthcare, with notable contributions to AI-driven diagnostics, stain normalization in histopathology, and multimodal data fusion. Dr. Gault's research interests include ensemble learning, fuzzy systems, and generative models like diffusion networks. He supervises multiple PhD students and has mentored graduates now working in machine learning engineering and postdoctoral research. His team’s achievements include awards such as the 2023 Best Oral Presentation at the Pan Ireland Ophthalmology Day and a 2021 Best Paper Award from his school. Key contributions include the LymphoSight AI application for detecting lymphoid structures and HistoClean , open-source software for improving CNN development in histopathology. He has been recognized as a Senior Member of IEEE and a Fellow of the Higher Education Academy. His work is supported by grants such as the R5131ECI project on 3D quantifier approximation via 2D video analysis (2019–2025). He actively engages in academic activities, including conference organization and PhD external examinations across Europe.
Professor Peter Watkinson serves as Professor of Intensive Care Medicine at the University of Oxford and is an NHS consultant in intensive care at the Oxford University Hospitals NHS Foundation Trust. He leads the Critical Care Research Group based at the Kadoorie Centre for Critical Care Research & Education at the John Radcliffe Hospital, Oxford. His work bridges clinical practice with academic research in the field of critical care medicine through the Nuffield Department of Clinical Neurosciences. Professor Watkinson's research primarily focuses on the identification of deteriorating patients in hospital settings. His work encompasses: Design and implementation of studies on wearable monitoring devices Exploration of non-contact monitoring technologies Analysis of standard electronically-recorded patient descriptors Pattern recognition in vital signs data to predict clinical deterioration Development of electronic monitoring systems Application of human factors techniques for technology integration in healthcare Assessment of long-term effects of critical illnesses on patient quality of life The Critical Care Research Group maintains a strong collaborative link with the University of Oxford Institute of Biomedical Engineering. Using data collected from thousands of patients' vital signs both in Oxford and elsewhere, the multi-disciplinary team investigates patterns that precede and predict clinical deterioration in hospitalized patients. Recent publications indicate a strong focus on early warning scores, patient monitoring technologies, and the application of machine learning approaches to critical care data. Professor Watkinson's research output demonstrates consistent productivity with numerous 2024-2025 publications spanning systematic reviews of early warning systems, development of novel monitoring technologies, and analytical approaches to predicting patient deterioration. His work frequently employs rigorous methodology including systematic reviews, meta-analyses, and innovative study designs to address critical questions in intensive care medicine. As leader of the Critical Care Research Group, Professor Watkinson oversees a multi-disciplinary team investigating vital sign patterns and developing predictive algorithms that have direct clinical applications. The group's research has significant implications for improving patient safety through earlier recognition of clinical deterioration and more effective resource allocation in hospital settings.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Daniel E. Horton is an Associate Professor in the Department of Earth, Environmental & Planetary Sciences at Northwestern University's Weinberg College of Arts and Sciences, where he leads the Climate Change Research Group (CCRG). His interdisciplinary work bridges atmospheric science, environmental health, and geoscience through advanced modeling techniques. Education: Ph.D. in Geological Sciences from University of Michigan B.S. in Atmospheric Science from Texas A&M University B.S. in Physics from Tulane University Horton's research spans extreme weather events, near-term societal impacts of climate change, geologic climate evolution, and exoplanet habitability. His group integrates atmospheric, biospheric, cryospheric, hydrospheric, and lithospheric processes using numerical models, environmental observations, and machine learning. Recent work emphasizes environmental justice through neighborhood-scale air quality analysis and transportation electrification impacts. Analysis of his 10 most recent publications (2022-2023) reveals three dominant research thrusts: (1) urban air quality modeling with WRF-CMAQ systems focusing on racial disparities in pollution exposure, (2) transportation electrification impacts on public health and equity, particularly for heavy-duty vehicles, and (3) hydrological extremes including precipitation variability and post-wildfire debris flows. Machine learning applications are increasingly prominent across all research areas. Scientific Awards: NSF CAREER Award (2023) for research on air quality, public health, and equity implications of transportation electrification Horton secured a $600,000 NSF grant supporting both research and educational initiatives, including after-school sustainability programming for Chicago middle schools. His Climate Change Research Group actively recruits graduate students for projects at the intersection of climate science and societal impacts, with strong collaborations across environmental health, urban planning, and engineering disciplines. Current work emphasizes equity-centered climate solutions through fine-scale spatial analysis of pollution and health outcomes.
Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich , leading the Interactive Visual Data Analysis (IVDA) Group . He is associated with the Digital Society Initiative (DSI) and holds a PhD in Computer Science from Technische Universität Darmstadt (2015) with a focus on time-oriented data analysis. His academic journey includes postdoctoral research at TU Darmstadt and the University of British Columbia. Education : Diploma in Computer Science (2009, TU Darmstadt) PhD in Computer Science (2015, TU Darmstadt) Research Interests : Dr. Bernard specializes in interactive visual data analysis , explainable machine learning , and human-centered AI . His work explores time series analysis , multivariate data exploration , and user-driven preference elicitation . He develops visual analytics systems for domains like healthcare , digital humanities , and industrial applications , with a particular focus on responsible AI and transparency in algorithmic systems . Research Trends : His publications emphasize interactive machine learning workflows , visual analytics for healthcare , and time-stamped event sequence analysis . Recent work includes LLM validation frameworks (Human-Data-Model Interaction Canvas) and personalized ranking systems funded by the Swiss National Science Foundation. He integrates temporal data with multivariate analysis across applications from medical manufacturing to chronic disease management . Scientific Recognition : EuroGraphics Young Researcher Award (2022) EuroVis Young Researcher Award (2021) Best Paper Awards at IEEE VIS (2021), EuroVA (2021, 2025) Dirk Bartz Prize (2017), Hugo-Geiger Preis (2016) Teaching & Grants : He teaches Interactive Visual Data Analysis (6 ECTS), Digital Health Seminars , and People-Oriented Computing . Currently leads a SNF Grant on Personalized Visual Analytics for multi-criteria decision support (2024-2028) with ETH Zurich's Prof. M. El-Assady.
Andrew Pavlo is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on database management systems, particularly in the areas of transaction processing, in-memory databases, and self-driving database systems. He leads a productive research group that has published extensively in top database venues including VLDB, SIGMOD, and CIDR. Pavlo's research interests span database management systems, transaction processing, in-memory databases, non-volatile memory databases, and self-driving database systems. His work often bridges theoretical database concepts with practical system implementation, focusing on performance optimization, query processing, and system architecture. Recent work has explored machine learning applications for database tuning, novel storage techniques, and innovative approaches to transaction processing. An analysis of his recent publications reveals a strong focus on self-driving database systems, with significant work on the Database Gym framework for training machine learning models to optimize database performance. His research also examines columnar storage formats, transaction scheduling, and novel approaches to user-defined function optimization. The work demonstrates a consistent trajectory toward making database systems more autonomous and efficient through a combination of systems techniques and machine learning. Pavlo has been instrumental in mentoring numerous PhD students who have become active contributors to the database research community. His research has been supported by significant grants that have enabled the development of innovative database technologies and frameworks. His research group operates within CMU's vibrant database ecosystem, collaborating with other researchers on projects related to database systems, storage engines, and query processing frameworks. The group maintains close connections with industry partners to ensure practical relevance of their research contributions.
Miriam Stock is Professor of Cultural Studies at the Institute of Humanities at Pädagogische Hochschule Schwäbisch Gmünd, where she also serves as Director of the Cultural Studies Department and Spokesperson for the Master's program 'Interkultur und Integration.' She is a founding member of the Center for Migration and Integration Studies 'Migration – Society – School.' Dr. Stock holds a PhD in Geography from Europa-Universität Viadrina Frankfurt (Oder) and completed her diploma in Geography at Ludwig-Maximilians-Universität München. Her academic journey includes research fellowships, organizational roles in international projects, and advisory work with refugee communities. Her research focuses on migration geographies, cultural theories, postcolonial approaches, urban development from post-migrant perspectives, and qualitative research methodologies. She examines migration and flight between the Arab region and Europe, urban development and consumption, rural perspectives on migration society, anti-discrimination in schools, critical masculinity and family studies, and theoretical-practical approaches. Dr. Stock's recent publications reveal a strong emphasis on transnational migration experiences, particularly Syrian refugee families, with attention to emotional dimensions, gender dynamics, and educational contexts. Her work frequently bridges theoretical frameworks with practical applications through projects like Erasmus Mundus Joint Master Program 'Education, Migration, and Diversity' (2024-2030). Success Story 2020 award for the Enable project As an advisor, Dr. Stock has supervised PhD candidates like Sara Mazzei (University of Calabria/PH Schwäbisch Gmünd) working on Arab-Islamic education systems in migrant experiences. Her grant portfolio includes significant Erasmus+ projects such as Enable (Self-learning for Arab refugee children) and PARENTable (Communicating with parents of newly migrated children), alongside research funded by the Werner-Zeller Foundation. Dr. Stock leads the Institute for Humanities and directs Cultural Studies department activities, including organizing lecture series on topics like 'Right Established - How the New Right Changes Education and Society,' 'Migration and Body,' and 'Emotions in Migration Society.'
