Rakotonirainy Andry is a Professor at Queensland University of Technology (QUT), affiliated with the Centre for Accident Research & Road Safety - Queensland (CARRS-Q). His research focuses on transportation safety, automated vehicles, human factors, and intelligent transportation systems (ITS). He leads interdisciplinary projects exploring driver behavior, connected vehicle technologies, and the societal impacts of automation. Key areas include accident prevention, human-vehicle interaction, and equity in transport systems. His work integrates machine learning, simulation studies, and behavioral analysis to address challenges in road safety. Notable contributions include studies on driver stress detection, automated vehicle acceptance, and the Australian Naturalistic Driving Study (ANDS). He collaborates with institutions globally, advancing innovations like connected vehicle pilots and multimodal AI for traffic safety. Research interests span automated driving systems, vulnerable road user protection, and policy implications of emerging technologies. His findings contribute to safer transportation policies and technologies, emphasizing both technical and human-centric perspectives.
Henrik Jeldtoft Jensen is a Professor of Mathematical Physics and leads the Centre for Complexity Science at Imperial College London. His work spans multiple disciplines, focusing on the statistical mechanics of complex systems, with applications in physics, biology, neuroscience, and finance. Professor, Mathematical Physics Leader, Centre for Complexity Science Institution: Imperial College London His research interests lie at the intersection of theoretical physics and complex systems. He is best known for developing the Tangled Nature Model of evolving ecosystems, which has been extended into financial modeling through the Tangled Finance approach. His work in brain dynamics involves analyzing fMRI and EEG data using tools from statistical physics. He has made significant contributions to self-organized criticality and stochastic dynamics of complex systems, particularly in condensed matter and evolutionary contexts. The recent publications reflect a strong trend toward interdisciplinary complexity science, integrating concepts from physics, biology, economics, and neuroscience. Keywords across these works include complexity, statistical mechanics, dynamical systems, and network theory, with subfields ranging from neural avalanches to financial instability and biodiversity modeling. Henrik Jensen is the author of two influential books: Self-Organized Criticality and Stochastic Dynamics of Complex Systems (with Paolo Sibani), which have been widely cited across disciplines. He has supervised numerous PhD and postdoctoral researchers through the Centre for Complexity Science, though specific names are not listed. His research has been supported by grants from UK research councils and international collaborations, particularly in interdisciplinary complexity projects. He is affiliated with the Centre for Complexity Science, a multidisciplinary research hub at Imperial College London that brings together physicists, mathematicians, biologists, and social scientists to study complex adaptive systems.
Włodzimierz Kasprzak is a Professor at the Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology. His research focuses on computer vision, robotics, human-computer interaction, and machine learning. He has contributed to advancements in human action classification, skeleton-based feature analysis, and multimodal interface design. Research Highlights: Development of lightweight classification models for human actions in video using skeleton-based features. Advances in multi-stream fusion techniques for image and video analysis. Design of embodied agent systems for cybersecurity event visualization and control. Awards and Recognition: 2024: Individual First Class Rector's Award for Scientific Achievements (2022-2023) 2021: Medal of the Commission of National Education 2011: Golden Cross of Merit His work integrates theoretical contributions with practical applications in robotics, surveillance systems, and human-centered technologies.
Katharina Kaiser is affiliated with TU Wien's Fachbereich Software Services. She specializes in medical informatics with a focus on computerized clinical guidelines and healthcare system optimization. Her work bridges temporal data analysis, information extraction from clinical texts, and workflow modeling in medical contexts. Key areas: Clinical decision support systems, guideline implementation frameworks, temporal logic in healthcare processes Notable contributions: Development of TimeML-based clinical guideline modeling, heuristic methods for condition-action sentence identification Her research emphasizes semantic enrichment of medical documents and interactive visualization tools for therapy planning and patient data correlation. Collaborations include the PROTOCOL project and ReMINE deliverables in adverse risk management.
Raphaël Troncy is an Assistant Professor at EURECOM's Data Science Department, specializing in Semantic Web technologies, Knowledge Graphs, and Natural Language Understanding. He teaches courses like 'Human-computer interaction for the Web' and 'Semantic Web technologies.' His research focuses on semantic data integration, knowledge graph applications, and recommender systems. Notable projects include DOREMUS (musical work graph), entity2rec (knowledge graph-based recommendations), and 3cixty (city exploration knowledge bases). He actively contributes to semantic web challenges and conferences, winning multiple awards including the 2018 Best Poster Award at ESWC and 2015 First Prize in the Semantic Web Challenge. Troncy's work spans cultural heritage digitization (e.g., Odeuropa olfactory data modeling), cybersecurity anomaly detection (NORIA-O ontology), and interdisciplinary projects like SILKNOW's silk textile knowledge graph. He leads development of tools like DAGOBAH for semantic table interpretation and KG Explorer for knowledge graph exploration. Education: Not explicitly stated in text Labs/Teams: Active in EURECOM's Data Science group, collaborating on projects involving knowledge graphs, AI, and semantic technologies
Weiran Wang is an Assistant Professor in the Department of Computer Science at the University of Iowa. Previously, he worked as a Staff Research Scientist at Google (2021-2024), Senior Research Scientist at Salesforce Research (2019-2020), and Amazon Alexa (2017-2019). He completed his PhD in 2013 at UC Merced under Miguel A. Carreira-Perpinan and postdoctoral research at Toyota Technological Institute at Chicago (2014-2017) with Karen Livescu and Nathan Srebro. PhD: EECS Department, UC Merced (2013) MS: Computer Science, Chinese Academy of Sciences (2008) BS: Computer Science, Huazhong University of Science and Technology (2005) His research focuses on machine learning algorithms for speech processing , multi-view representation learning , and optimization . Key contributions include advancements in end-to-end speech recognition, stochastic canonical correlation analysis, and deep variational methods for multi-modal data. Notable work includes improving WER metrics for telephony speech and developing GPU/TPU implementations for ASR biasing. The 15 most recent publications span 2024-2018, emphasizing speech recognition (2024 NAACL/Interspeech), multi-view learning (2022 ICLR), self-training (2020 Interspeech), and acoustic modeling (2018). Trends include deep learning, attention mechanisms, and distributed optimization techniques. No scientific awards are explicitly mentioned in the provided texts. Current teaching includes CS4980: Deep Learning (Spring 2025) and CS4420: Artificial Intelligence (Fall 2024).
Dr Andrew Starkey is a Reader in the School of Engineering at the University of Aberdeen, where he also completed his PhD in 2001. He holds an Honours degree in Applied Mathematics from the University of St Andrews. He is actively involved in research and currently accepting PhD students in Engineering. His work bridges academia and industry, with a focus on AI applications in engineering, bioinformatics, and geosciences. University: University of Aberdeen School: School of Engineering Academic Rank: Reader Email: a.starkey@abdn.ac.uk Phone: +44 (0)1224 272801 Dr Starkey's research centers on Explainable AI (XAI) , Green AI , and Autonomous AI , with applications in robotics, econometrics, bioinformatics, seismic data analysis, and virtual reality. He has developed novel methods for feature selection, autonomous learning, and knowledge abstraction from agent-environment interactions. His work emphasizes low computational cost and transparency in AI systems. The most recent publications reflect a strong trend in applying AI to complex real-world problems, including digital rock technology, robotic grasping, real-time event detection, and medical data analysis. His interdisciplinary research combines machine learning with domain-specific knowledge in engineering and life sciences, often resulting in practical, industry-ready solutions. Millennium Product Award John Logie Baird Award for Innovation Enterprise Fellowship from Royal Society of Edinburgh and Scottish Enterprise Dr Starkey has supervised multiple research projects and secured funding from major bodies including EPSRC, BBSRC, and industry partners. His past work on the GRANIT project led to the development of AI-based condition monitoring for ground anchorages, resulting in commercialization through BlueFlow Ltd. He has collaborated with researchers across disciplines, including Dr Alasdair MacKenzie (bioinformatics), Dr Anne Schwab (seismic analysis), and Dr David Hazlerigg (genomics). He leads research in AI-driven engineering solutions and is the CEO of BlueFlow Ltd, a spinout company commercializing AI technologies developed at the University of Aberdeen. His lab focuses on developing autonomous, explainable, and environmentally sustainable AI systems for real-world deployment.
Dr. Norita Mohd Nasir is a Lecturer in Accounting and Deputy Director of Undergraduate Studies at the School of Business, Monash University Malaysia. She is actively involved in research, teaching, and academic leadership, with a strong focus on sustainability and financial reporting. Her work aligns with the United Nations Sustainable Development Goals, particularly in education and environmental sustainability. Doctor of Philosophy in Management, Monash University Masters in Accounting & Financial Management, University of Essex Dr. Nasir's research centers on environmental sustainability , education sustainability , and financial reporting , with a unique emphasis on Islamic perspectives in corporate environmentalism. She has published extensively on sustainability in business, Shariah-compliant environmental practices, and sustainable finance. Her work bridges religious ethics with modern corporate responsibility, offering innovative insights into green accounting and ethical leadership. Her recent publications and projects reflect a strong trend toward sustainable finance , anti-corruption initiatives , and tax policy reforms , particularly in the Malaysian context. She explores how theological principles can translate into practical sustainability frameworks and how educational approaches can foster environmental consciousness. Her research is interdisciplinary, combining accounting, theology, public policy, and environmental science. Fellow member of CPA Australia (FCPA) Member of the Malaysian Institute of Accountants (CA) Active participant in the Women in Business committee of CPA Australia (Malaysia) Dr. Nasir serves as a chief investigator on multiple research projects, including initiatives on sustainable finance, anti-corruption, and taxpayer behavior during the pandemic. She is actively involved in public engagement, editorial work, and academic events. She teaches core financial accounting courses (ACW1120, ACW2120, ACW3120) and is committed to innovative teaching methods that enhance student engagement and job readiness. She is also a mentor and advisor to PhD students, contributing to the next generation of accounting scholars. She is a key contributor to the Monash Climate-Resilient Infrastructure Research Hub and participates in various sustainability-focused networks and training programs. Her work emphasizes collaboration across disciplines and institutions to promote ethical, sustainable, and inclusive business practices.
Olya Mandelshtam is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo . Her research focuses on algebraic combinatorics, particularly symmetric and quasisymmetric functions, with connections to probability and interacting particle systems. Education: Ph.D. in Mathematics, University of California, Berkeley (2016), advised by Lauren Williams Presidential Postdoctoral Scholar at UCLA (2016–2017) Tamarkin Assistant Professor and NSF Postdoctoral Fellow, Brown University (2017–2021) Research Interests: Her work bridges algebraic structures (e.g., Macdonald polynomials, Koornwinder polynomials) and probabilistic models like the asymmetric simple exclusion process (ASEP) and zero-range processes (TAZRP). She explores combinatorial bijections, multiline queues, and integrable systems to study these connections. Recent Activities: She organizes the Combinatorics Seminar at Waterloo and participates in conferences such as ICERM workshops on Category Theory and Machine Learning, ICECA, and events on integrable systems in algebraic combinatorics. Advising: Current graduate students include Kartik Singh, Jerónimo Valencia Porras, Guilherme Zeus Dantas e Moura, and Harper Niergarth, with past advisee William Chan. Research collaborations include work on multiline queues, non-attacking fillings, and particle system dynamics.
Scientia Professor Richard Bryant is a leading researcher in trauma psychology at the University of New South Wales, where he serves in the School of Psychology. With over three decades of research experience, he has established himself as a world authority on posttraumatic stress disorder (PTSD), prolonged grief disorder, and trauma-related mental health conditions. Professor Bryant received his B.A. (Hons.) from the University of Sydney in 1983, followed by his M.Clin. Psych. and Ph.D. from Macquarie University in 1986 and 1989 respectively. He was awarded a D.Sc. from UNSW in 2016, recognizing his substantial contributions to the field. His clinical training and academic background have positioned him at the forefront of trauma research and treatment development. Dr. Bryant's research primarily focuses on posttraumatic stress disorder, prolonged grief disorder, global mental health initiatives, cognitive behavior therapy applications, and clinical trials methodology. His work spans diverse populations including trauma survivors, refugees, first responders, and individuals affected by disasters. He has pioneered early intervention approaches following traumatic events and developed innovative treatments for persistent trauma reactions across various cultural contexts. An analysis of his recent publications reveals a strong emphasis on global mental health applications, particularly in conflict zones and with displaced populations. His research increasingly incorporates digital health solutions, examines cultural variations in trauma response, and investigates novel treatment approaches including memory specificity training and integration of technology in therapy delivery. His work consistently bridges clinical practice with research evidence to improve mental health outcomes worldwide. Professor Bryant maintains an active research program with numerous ongoing clinical trials and international collaborations. His work has significantly influenced clinical guidelines for trauma treatment globally, particularly through his contributions to World Health Organization interventions and evidence-based practice recommendations. Based at the University of New South Wales in Sydney, Professor Bryant continues to mentor emerging researchers while maintaining a robust publication record that demonstrates both depth and breadth across trauma psychology domains. His Mathews Building office serves as the hub for a productive research team investigating critical questions in trauma response and recovery.
Soukaina Filali Boubrahimi serves as an Assistant Professor in the Computer Science Department within the College of Engineering at Utah State University. Her academic appointment is based in the SER 332 building located at 4205 Old Main Hill, Logan, UT 84322-0001. She maintains a research-active position with a focus on computational methods for complex temporal data analysis. Dr. Filali Boubrahimi's research program centers on time series analysis , machine learning , and space weather prediction , with particular emphasis on solar flare forecasting and counterfactual explanation systems. Her work bridges theoretical machine learning advancements with practical applications in heliophysics, hydrology, and social media analysis. The research portfolio demonstrates significant expertise in handling imbalanced temporal datasets, developing novel data augmentation techniques, and creating interpretable AI systems for critical prediction tasks. Analysis of her recent publication trajectory reveals consistent contributions to counterfactual explanation frameworks for time series data (Info-CELS, M-cels, ACTS), space weather prediction systems (solar flare and energetic particle event forecasting), and generative modeling approaches (AVATAR, ChronoGAN). Her work frequently addresses the challenges of severely imbalanced datasets through contrastive learning and sophisticated preprocessing techniques, demonstrating methodological innovation in handling rare but critical space weather events. While no specific awards are documented in the available information, her research program appears substantial based on the volume and quality of recent publications spanning multiple high-impact domains. The research demonstrates strong interdisciplinary connections between computer science, space physics, and environmental science. Her laboratory activities focus on developing machine learning frameworks for temporal data analysis, with particular attention to space weather prediction systems. The research group appears to specialize in creating robust models for rare event prediction, explainable AI systems for time series classification, and novel data augmentation techniques for imbalanced temporal datasets. Current projects likely include the development of multimodal fusion approaches for solar energetic particle prediction and spatio-temporal modeling for hydrological applications.
Marek Miśkowicz serves as a Professor at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His primary institutional contact is miskow@agh.edu.pl, with physical location in building B-1, room 212. His research spans signal processing, biomedical engineering, and electronics, specializing in event-based sampling methodologies, time-to-digital conversion techniques, and reconstruction of bandlimited signals from nonuniform samples. Key contributions include QRS detection algorithms for ECG monitoring, POCS-based reconstruction frameworks, and event-driven control systems for industrial IoT applications. His work emphasizes resource efficiency in embedded systems and mobile health monitoring through approximate computing and adaptive sampling strategies. Recent publications (2022-2025) demonstrate consistent focus on signal reconstruction from irregular samples, with growing emphasis on spiking neural networks for event classification and industrial IoT optimization. Biomedical applications (particularly ECG analysis) and industrial control systems represent dominant application domains, while methodological innovations center on iterative reconstruction algorithms and temporal accuracy evaluation in noisy environments. No scientific awards were referenced in the source materials. No information regarding student advising or research grants was available in the provided documentation. The source texts contained no details about laboratory facilities, research teams, or collaborative groups associated with Professor Miśkowicz.
Prof. OSMAN ÇULHA is a Professor in the Department of Gastronomy and Culinary Arts at the Faculty of Tourism, Alanya Alaaddin Keykubat University. He has built a distinguished academic career spanning over 15 years in tourism and hospitality education, with expertise in tourism management and gastronomy. His academic journey began as a Research Assistant at Yaşar University, progressed through lecturer positions at Adnan Menderes University, and led to his current professorship at Alanya Alaaddin Keykubat University. His educational background includes a Doctorate in Tourism Management from Adnan Menderes University (2014), a Master's degree in Tourism Management from Dokuz Eylül University (2008), and a License in Tourism Management Education from Gazi University (2005). Prof. Çulha's research primarily focuses on the intersection of tourism management and gastronomy, with particular interest in food festivals, culinary tourism, and the relationship between local foods and tourism development. His work examines how gastronomic experiences contribute to destination branding, place attachment, and tourist satisfaction. He has extensively studied the Didim International Olive Festival as a case study for understanding how local food events can drive regional tourism development. His research also explores workforce issues in the hospitality industry, including employee motivation, organizational commitment, and the impact of training programs. Prof. Çulha has received recognition for his academic contributions, including the prestigious Academic Golden Pen Award Certificate of the Year in 2025 for his work "Collaborations of Tourism Academies in Türkiye: Topics, Stakeholders, Scope, Benefits and Obstacles." His publications span numerous high-impact journals in tourism and hospitality research, covering diverse topics from underwater diving motivation to the quality dimensions of non-formal vocational tourism education. His scholarly work demonstrates a consistent focus on practical applications of tourism theory to real-world industry challenges. He has served in various administrative capacities, including as Farabi Coordinator and Faculty Board Member at Alanya Alaaddin Keykubat University, and as Deputy Head of Department at Adnan Menderes University. His professional development includes specialized training in occupational health and safety, big data applications, and artificial intelligence in research processes. Prof. Çulha is actively involved in academic service as a referee for multiple tourism journals, including Alanya Academic View Journal, Tourism Research Journal, and International Journal of Hospitality Management. His commitment to advancing tourism education is evident through his editorial contributions to publications such as "Traces of Gastronomy from Past to Present: Research-Based Evidence" and "International Tourism Management."
Kevin S. LaBar is Professor of Psychology and Neuroscience and Professor in Psychiatry and Behavioral Sciences at Duke University's Trinity College of Arts & Sciences. He serves as Associate Director of the Center for Cognitive Neuroscience and maintains affiliations with the Duke Initiative for Science & Society, the Center for Brain Imaging and Analysis, and the Center for Cognitive Neuroscience. His academic journey began with a B.A. from Lafayette College in 1990, followed by a Ph.D. from New York University in 1996. Dr. LaBar's research focuses on understanding how emotional events modulate cognitive processes in the human brain. His laboratory aims to identify brain regions that encode the emotional properties of sensory stimuli and demonstrate how these regions interact with neural systems supporting social cognition, executive control, and learning and memory. His integrative approach utilizes psychophysiological monitoring, functional magnetic resonance imaging (fMRI), machine learning, and behavioral studies in both healthy adults and psychiatric patients. His work spans multiple domains including fear conditioning, emotional memory, emotion regulation, and the neural basis of emotional experience. His recent publications reveal a strong emphasis on emotional memory mechanisms, emotion regulation strategies across the lifespan, neural correlates of anxiety and fear, and developing neuroscience-informed interventions for emotional dysregulation. His research increasingly incorporates advanced neuroimaging techniques, computational approaches, and translational applications for clinical populations. Fellow, Association for Psychological Science (2010) Young Investigator Award, Cognitive Neuroscience Society (2005) CAREER Award, National Science Foundation (2003) Ralph E. Powe Junior Faculty Enhancement Award, Oak Ridge Associated Universities (2001) Young Investigator Award, National Alliance for Research on Schizophrenia and Depression (2000) Scholar of the Year Award, Lafayette College Alumni Association (1990) Dr. LaBar has secured substantial research funding, including multiple NIH grants and VA awards, with projects spanning from basic emotion research to clinical applications for conditions like PTSD, depression, and misophonia. His laboratory has trained numerous graduate students and postdoctoral fellows who have gone on to successful careers in academia and research. His work bridges cognitive neuroscience with clinical applications, particularly in developing neurostimulation-enhanced behavioral interventions for emotion dysregulation.
Dr. Amon Göppert serves as Professor and Chair of the Intelligence in Quality Sensing group at RWTH Aachen University's Laboratory for Machine Tools and Production Engineering (WZL). His work integrates artificial intelligence into manufacturing processes to enhance quality control, production efficiency, and sustainable practices. Göppert's research spans intelligent manufacturing systems with core expertise in AI-driven production engineering, circular economy applications, and advanced assembly systems. He investigates how machine learning optimizes production ramp-up, disassembly processes, and flexible manufacturing while developing sensor-based quality control solutions for industrial applications. His recent publications reveal strong trends toward AI implementation in production planning, with emphasis on worker assistance systems for disassembly, digital twin applications for real-time control, and mobile robotics in line-less assembly environments. Key focus areas include sustainable manufacturing, metrology innovation, and adaptive scheduling systems. Göppert leads multiple high-impact research initiatives: AI-driven Product Development: Machine learning for smart measurement strategies in metrology MetaVision Consortium: Industrial metaverse applications for AI-supported vision systems Generative AI for Non-Destructive Testing optimization Cluster of Excellence Internet of Production participation As Chief Engineer at WZL, he oversees technical implementation of research projects and collaborates with industry partners to translate innovations into practical manufacturing solutions, particularly in adaptive assembly systems and quality sensing technologies.
