Avinash Kori is a Ph.D. researcher at Imperial College London affiliated with the Safe and Trusted AI Centre for Doctoral Training (CDT). Supervised by Prof. Francesca Toni and Prof. Ben Glocker , his research focuses on Explainable AI (XAI) , causality , and deep learning with applications in medical image analysis and optimization algorithms . His work includes publications on arXiv and conferences like MICCAI , covering topics such as robust segmentation , concept-based explanations , and symbolic reasoning in hyperbolic space . He has also explored stochastic optimization , support vector machines (SVM) , and gradient descent variants , providing theoretical and practical implementations. Recent trends in his publications highlight advancements in robust CNN models , causal logic frameworks , and hyperbolic geometry for hierarchical learning . His research is driven by the need to make AI systems more transparent and reliable for critical domains like healthcare. Scientific Awards: AAAIw Overall Best Paper Award (Feb 2021) for CNN interpretability research. He actively contributes to open-source implementations via platforms like GitHub and shares insights through blogs and paper reviews . His academic journey includes an undergraduate degree in Biomedical Engineering Design with a minor in Machine Learning from Indian Institute of Technology, Madras , followed by research internships at Siemens and Stanford University .
Suren Jayasuriya is an Associate Professor at Arizona State University's The GAME School, with joint appointments in the School of Electrical, Computer and Energy Engineering (ECEE) and the Department of Arts, Media and Engineering (AME). He is also an Affiliate Faculty Member at the Mary Lou Fulton College for Teaching and Learning Innovation. His lab, the Imaging Lyceum, focuses on transdisciplinary research bridging computational imaging, computer vision, sensors, and STEAM education. Education Ph.D. Electrical and Computer Engineering, Cornell University (2017) M.S. Electrical and Computer Engineering, Cornell University (2015) B.S. Mathematics, University of Pittsburgh (2012) B.A. Philosophy, University of Pittsburgh (2012) Research Focus Dr. Jayasuriya's work integrates optics, computational photography, and machine learning to develop novel imaging systems. His research spans: Computational cameras and light transport analysis Atmospheric turbulence modeling and video restoration Neural volumetric reconstruction for sonar/radar STEAM education frameworks for K-12 teachers Philosophical aspects of imaging and representation His lab emphasizes interdisciplinary collaboration across engineering, arts, and humanities. Publication Trends Recent publications demonstrate strong focus on computational imaging (45%), AI/ML applications (30%), and educational technology (25%). Dominant themes include turbulence mitigation in videos, neural rendering for sonar/radar, sensor fusion, and AI curriculum development for middle schools. Work frequently appears in top venues like CVPR, SIGGRAPH, and IEEE Transactions. Awards Image Electronics Technology Excellence Award (IIEEJ, 2021) Best Demo Awards: IEEE ICCP 2019, MIRU 2018 Best Paper Award: IEEE ICCP 2014 ASEE Diversity Paper Finalist (2020) Teaching Honors: Fulton Top 5% Award (2019, 2021), ASU Game Changing Faculty (2021) Teaching & Advising Teaches graduate/undergraduate courses including Machine Vision (EEE 515), Minds and Machines (AME 400), and thesis supervision. Leads NSF-funded projects on computational imaging education and AI teacher training. Mentors students through the Imaging Lyceum lab with projects spanning optics, philosophy, and educational technology. Lab & Collaborations Directs the Imaging Lyceum, emphasizing Aristotle-inspired collaborative research. The lab works on: computational cameras, STEAM education, sensor development, and philosophical inquiries into imaging. Collaborates with Carnegie Mellon Robotics Institute and international partners. Funded by NSF, NEH, and industrial partners for projects in sonar imaging, heat resiliency sensing, and educational AI.
Emily Lines is a forest ecologist affiliated with the University of Cambridge, Department of Geography. She specializes in remote sensing (ground to satellite) and data science techniques to study forest structure, function, and dynamics. Her research addresses ecological and conservation questions, ranging from fundamental forest processes to applied challenges like climate change, deforestation, and forest management effectiveness. Department of Geography, University of Cambridge DLA Supervisor, Cambridge NERC Doctoral Landscape Awards Collaborator with geomorphologists, computer scientists, geneticists, and paleoecologists Her group employs fieldwork using terrestrial and drone laser scanning, photogrammetry, and traditional forest mensuration. Methodologically, she integrates deep learning, computer vision, and computational modeling to analyze forests at multiple scales. Priority topics include biodiversity assessment, structural dynamics, competitive interactions, dieback/disease monitoring, microclimate effects, radiative transfer, and regeneration patterns. Key tools: Terrestrial LiDAR, TLS validation, PlotToSat, FOR-species20K dataset, Deadtrees.earth database Recent work spans 3D remote sensing benchmarking, AI applications in forest monitoring, and climate change impacts on European forests Her publications highlight forest-landscape interactions, scalability of data science methods, and cross-disciplinary integration of ecological and computational approaches. She advocates for environmental sustainability in AI applications through initiatives like the C-CLEAR Doctoral Training Programme.
Serge O. Dumoulin is a Professor of Perception, Cognition, and Neuroscience at Utrecht University and Vrije Universiteit Amsterdam. He leads the Computational Cognitive Neuroscience and Neuroimaging group at the Netherlands Institute for Neuroscience and serves as Director of the Spinoza Centre for Neuroimaging , a collaborative facility involving KNAW, AMC, VUMC, and VU Amsterdam. Education: M.Sc. in Biology (Utrecht University), Ph.D. in Neurology and Neurosurgery (McGill University) Research focuses on the intersection of perception, cognition, and neuroimaging, particularly the human visual system. He employs ultra-high field (7T) fMRI, computational modeling, and behavioral studies to explore neural mechanisms of visual perception, attention, and numerical cognition. His work has applications in clinical disorders and methodological advancements like the pRF method, used globally in 100+ institutions. Publications span neuroscience, neuroimaging, and cognitive processes, with a 2023 Advances in MRI Technology paper emphasizing gray-matter optimized fMRI. His 2018 Neuroimage article discusses systematic pRF variations, while a 2021 PNAS study links divisive normalization to visual hierarchy. Awards: Ammodo KNAW Award (2015), NWO Vidi/Vici grants, Neuroimage Editors' Choice Award (2013) Teaching includes courses at Vrije Universiteit Amsterdam on perception, neuroscience, and fMRI data analysis. He emphasizes coding (Matlab/Python) in student internships.
Dr. Raimon Tolosana Delgado is a Research Fellow at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), affiliated with the Helmholtz Institute Freiberg for Resource Technology. He leads research in predictive geometallurgy and statistical analysis of mineral resources, focusing on translating geological data into processing insights. His research integrates geostatistics , compositional data analysis (CoDa) , and machine learning to model ore behavior and resource potential. Key areas include: Predictive geometallurgy for forecasting ore/waste behavior Bayesian statistics for parameter estimation and uncertainty analysis Development of R-based tools (e.g., compositions and gmGeostats packages) for mineral data analysis Particle-based process modelling for mineral separation optimization Recent publications emphasize machine learning integration (e.g., neural networks for geophysical tensor fields), tailings reprocessing (3D geostatistical assessment of resource potential), and advanced statistical methods for compositional data. A consistent trend involves enhancing predictive accuracy in mineral processing through multi-source data fusion. Dr. Tolosana Delgado coordinates the development of technology platforms for geometallurgical data analysis, including databases and interfaces for industrial applications. His work bridges ore geology, mineral processing, and metallurgy to optimize resource efficiency.
Sean Brayton serves as an Associate Professor in the Department of Kinesiology & Physical Education at the University of Lethbridge, where he bridges cultural theory with critical analyses of visual media and embodied practices. His academic credentials include a Bachelor of Arts from the University of Lethbridge, a Master of Arts from the University of Alberta, and a Doctor of Philosophy from the University of British Columbia. Brayton's research interrogates intersections of race, labor, and the body within film and television narratives, with concentrated focus on comedy, science fiction, and melodrama genres. He examines how popular media constructs whiteness, represents migrant labor, and processes economic crises like the Great Recession through horror and reality television frameworks. His scholarship consistently applies critical multiculturalism and visual culture theory to deconstruct power dynamics in contemporary cultural production. His 15 publications from 2005-2013 reveal a cohesive scholarly trajectory analyzing racialized labor, embodiment, and identity in media. Brayton frequently investigates how comedy and horror genres negotiate multiculturalism, with recurring attention to whiteness, masculinity, and economic structures across diverse formats from children's cartoons to Hollywood blockbusters. Scientific Awards: None documented in available records. Advising and Grants: No graduate students or research funding details are specified in current institutional materials. Brayton operates within the Socio/Cultural Lab at the University of Lethbridge, which facilitates interdisciplinary research on cultural representations and social phenomena, as evidenced by his 2009 media appearance discussing zombie culture for CityTV's Your City segment.
Vesa Pursiainen is an Associate Professor at the School of Business and Economics , University of St. Gallen (SBF-HSG). His research focuses on corporate finance, fintech, environmental economics, crowdfunding, and behavioral finance, with a particular emphasis on how technology, social dynamics, and crises impact financial decisions and market behavior.
Kate Bowers is a Professor of Security and Crime Science and Head of Department at University College London's Department of Security and Crime Science. She is also Director of the Jill Dando Institute of Security and Crime Science, with over 30 years of experience in crime science research. Education: Doctor of Philosophy, University of Liverpool (1999) Master of Arts, University of Liverpool (1994) Bachelor of Science, University of Durham (1993) Kate's research focuses on quantitative methods and data analytics in crime analysis and prevention, with special interests in predictive policing, big data approaches, evidence-based crime prevention, and innovative data utilization. Her work explores spatial, temporal and spatio-temporal crime patterns, crime radiation theory, and the relationships between crime and environmental context. Her research has been funded by organizations including the UK Government, US Department of Justice, Police, and UK research councils (EPSRC, ESRC, AHRC). She has contributed to three world-leading REF case studies and served as Editor-in-Chief of the Crime Science Journal. Teaching Contributions: Programme Convener for MSc in Crime Science (8 years) Co-developed many MSc modules including Preventing Crimes, Quantitative Methods and Crime Mapping, Spatial Analysis Involved in curriculum development and internal quality assurance External examiner and advisor at University of Leicester and Abertay University
Marat I. Latypov serves as Assistant Professor in the Department of Materials Science and Engineering at the University of Arizona's College of Engineering. He is also a member of the Applied Mathematics Graduate Interdisciplinary Program and leads the Materials Informatics Lab. His research spans computational materials science, sustainable alloy design, and machine learning applications for materials development. Dr. Latypov holds a PhD in Materials Science and Engineering from Pohang University of Science and Technology (POSTECH, South Korea, 2014) and a Dipl.-Ing. in Engineering Physics from Ufa State Aviation Technical University (Russia, 2011). His postdoctoral training included appointments at Georgia Tech/CNRS in France and the University of California, Santa Barbara. His research focuses on materials informatics , physics-informed machine learning , and sustainable structural alloys . Key methodologies include graph neural networks for polycrystal mechanics, vision transformers for microstructure representation, and adaptive experimental design for materials optimization. Recent work emphasizes circular economy applications through construction waste recycling and copper mine tailings valorization. Analysis of his publication record reveals strong emphasis on computational microstructure-property linkages (35% of recent work), machine learning for materials design (30%), and sustainable materials processing (25%), with growing integration of large language models for materials knowledge extraction. NSF CAREER Award (2025) : For damage control in recycled aluminum alloys ISTI Distinguished Faculty Scholar (2024) : At Los Alamos National Laboratory Novelis Hackathon First Prize (2021) : Computer vision application Acta Materialia Outstanding Reviewer (2018) Young Researcher Award (2017) : NanoSPD7 Conference Dr. Latypov advises PhD students including Herbold Fellow Zhuocheng Huang and leads projects funded by NSF and the Grantham Foundation. Current initiatives include chalcopyrite leaching optimization for copper mining and graph neural network development for fatigue prediction. His Materials Informatics Lab maintains collaborations with Los Alamos National Laboratory, MIT, and industry partners including Novelis. The lab operates at the intersection of metallurgy , machine learning , and high-performance computing , with capabilities spanning deep learning, Bayesian inference, and cloud-based computational infrastructure. Recent news highlights participation in CODAS-HEP summer school and publication of vision transformer work in Acta Materialia.
Keunhyun (Keun) Park is an Assistant Professor of Urban Forestry at the University of British Columbia (UBC), affiliated with the Department of Forest Resources Management . He also holds an Adjunct Professor position at Utah State University in the Department of Landscape Architecture and Environmental Planning. Education: BSc and MSc in Landscape Architecture from Seoul National University; PhD in Urban Planning and Design from the University of Utah Research Lab: Faculty lead of the Urban Nature Design Research Lab ( under_lab ) His research focuses on designing healthy, just, and resilient cities through urban nature , with particular emphasis on: Environmental justice and equitable access to urban green spaces Human behavior in public spaces using drone/sensor/VR technology Smart growth urban design impacts on public health and ecological systems Recent publications demonstrate expertise in GIS applications , pedestrian behavior analysis , and urban planning across 20+ studies from 2013-2025. Collaborations include the Vancouver Park Board , Metro Vancouver , and Wasatch Front Regional Council .
Irena Koprinska is a prominent researcher at the University of Sydney with over 150 publications from 1996 to 2025. Her work spans multiple interdisciplinary domains with significant contributions to machine learning applications in educational technology, time series forecasting, and health informatics. She maintains strong research collaborations, particularly with Kalina Yacef (38 joint publications), Mashud Rana (26 papers), and Bryn Jeffries (22 papers), indicating leadership in her research group. Her research interests focus on practical applications of machine learning across diverse domains. In educational data mining, she has pioneered methods for predicting student performance in programming courses, analyzing syntax errors, and developing automated hint generation systems. Her work in time series forecasting has made significant contributions to solar power prediction using advanced neural network architectures. Additionally, she has applied machine learning techniques to medical domains, particularly in sleep disorder detection and analysis. The analysis of her 15 most recent publications (2022-2025) reveals a continued focus on educational technology and time series analysis, with increasing attention to interpretable methods and health applications. Her work demonstrates a consistent trajectory of applying sophisticated machine learning techniques to solve real-world problems across multiple domains, with particular emphasis on creating practical tools for education and renewable energy management. Notable Research Contributions: Development of the HINTS framework for automated programming hint generation Innovative approaches to multistep-ahead time series forecasting Applications of deep learning to sleep disorder detection Methods for predicting student performance in programming education Her publication record in top venues including Machine Learning journal, AIED, EDM, and IJCNN demonstrates significant impact in both machine learning and educational technology communities. The consistent output of high-quality research over nearly three decades indicates sustained scholarly productivity and leadership in her fields of expertise.
Professor Lyudmila Mihaylova is a distinguished academic at the University of Sheffield's School of Electrical and Electronic Engineering, where she holds the position of Professor of Signal Processing and Control. She has established herself as a leading researcher in the fields of signal processing, Bayesian methods, and autonomous systems, with significant contributions to particle filtering techniques for intelligent transportation systems. Her work bridges theoretical developments with practical applications across multiple domains including transportation, healthcare, and industrial automation. Prof. Mihaylova's research interests center on nonlinear filtering, sequential Monte Carlo methods, statistical signal processing, and sensor data fusion. Her work spans both theoretical advancements and practical implementations, with particular focus on high-dimensional problems including vehicular traffic flow estimation, image processing, and localization in sensor networks. She has extensive experience with various image modalities such as optical, thermal, LIDAR, SAR, and hyperspectral imaging. Her group actively develops novel methods for autonomous intelligent systems focusing on sensing, tracking, decision making, and machine learning applications. Analysis of Prof. Mihaylova's recent publications reveals a strong trend toward uncertainty quantification in machine learning models, particularly for safety-critical applications. Her work increasingly integrates traditional signal processing techniques with modern deep learning approaches, with applications spanning sewer inspection robotics, medical diagnostics (particularly sleep apnea detection), UAV swarm tracking, industrial manufacturing, and autonomous vehicle systems. A significant portion of her recent research focuses on developing robust methods that can handle incomplete or outlier-corrupted data while providing reliable uncertainty estimates. Among her notable professional achievements: President of the International Society of Information Fusion (ISIF) Senior member of the IEEE Signal Processing Society Associate Editor for IEEE Transactions on Aerospace and Electronic Systems Associate Editor for Elsevier Signal Processing Journal Prof. Mihaylova has successfully mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to prominent academic and industry positions. Her research has been supported by major funding bodies including EPSRC, EU, MOD/DSTL, and industry partners, with recent projects including 'Protecting Environments with UAV Swarms' (InnovateUK, 2022-2024), 'ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems' (NSF-EPSRC, 2019-2023), and 'Confident safety integration for Cobots' (Lloyd's Register Foundation, 2019-2020). Her research group follows a collaborative approach with the philosophy 'We share knowledge, we grow.' Prof. Mihaylova maintains active research collaborations with institutions worldwide and has held previous academic positions at Lancaster University (2006-2013) and University of Bristol (2004-2006), along with research visiting positions at the University of Ghent, Katholic University of Leuven, and the Bulgarian Academy of Sciences.
Marco Morales Aguirre is a Teaching Associate Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign and an Associate Professor at Instituto Tecnológico Autónomo de México (ITAM). He directs research at the Parasol Laboratory and has held significant leadership roles including founding member and former president of the Mexican Federation of Robotics (FMR). His academic journey spans both US and Mexican institutions, reflecting his international impact in the robotics community. Dr. Morales received his educational foundation from prestigious institutions: a Ph.D. in Computer Science from Texas A&M University, an M.S. in Electrical Engineering, and a B.S. in Computer Engineering from Universidad Nacional Autónoma de México (UNAM). His academic path has included positions as Visiting Professor at Texas A&M University and Lecturer at UNAM and the System of Technological Universities in México. His research focuses on motion planning algorithms for robotics, with particular expertise in multi-robot systems where he's pioneered frameworks like Adaptive Robot Coordination (ARC). His work bridges theoretical algorithm development with practical applications in industrial settings, computational biology, and extended reality interfaces. He has made significant contributions to topological guidance methods that improve planning efficiency in complex environments with narrow passages. Analysis of his recent publications reveals a strong trajectory toward more complex multi-robot coordination problems, with increasing emphasis on integrating task and motion planning. His research group has developed innovative approaches that scale to larger robot teams while maintaining computational efficiency, particularly in congested environments where traditional methods struggle. Member of the National System of Researchers of Mexico (level II) Founding member and former president of the Mexican Federation of Robotics (FMR) Member of the Mexican Academy of Computing Editor of multiple Algorithmic Foundations of Robotics (WAFR) proceedings Dr. Morales actively mentors a diverse group of graduate students who frequently appear as co-authors on his publications. His Parasol Laboratory conducts research funded through various academic and industrial collaborations, including significant projects with manufacturing partners exploring collaborative assembly systems. The laboratory has developed several notable frameworks including ARC, K-ARC, and HAS-RRT that have advanced the state of the art in multi-robot motion planning.
Renita Coleman is an Associate Professor at the University of Texas at Austin's School of Journalism and Media, where she has taught since 2005 after achieving tenure in 2009. With expertise in agenda-setting theory, visual communication, and media ethics, she has authored over 30 peer-reviewed articles and two influential books: 'Designing Experiments for the Social Sciences' (2018) and 'The Moral Media' (2005). Her research examines how visual elements shape ethical reasoning and public perception in news media. Her academic credentials include: Ph.D. in Journalism, University of Missouri (2001) M.A. in Journalism, University of Missouri (1997) B.S. in Journalism, University of Florida (1979) Coleman's research program centers on the intersection of visual communication and moral decision-making in journalism. She investigates how photographs influence ethical reasoning among journalists, the framing of health news through visual elements, and racial dynamics in journalistic ethics. Her work employs experimental methods to analyze affective agenda-setting, where emotional responses to visual stimuli shape public priorities. This approach bridges communication theory with practical journalism ethics. Recent publications reveal intensifying focus on polarization mechanisms, climate change communication, and fact-checking efficacy. Coleman's scholarship increasingly examines how adaptive framing strategies overcome audience resistance, particularly regarding scientific consensus issues. Her methodological rigor in experimental design provides robust evidence for how visual and emotional elements drive media effects beyond traditional textual analysis. Professor Coleman has received extensive recognition for scholarly excellence: Multiple AEJMC top paper awards across divisions (2002-2014) Teacher of the Year at LSU (2002-2004) Ranked #1 for AEJMC convention productivity (1999-2008) She teaches doctoral seminars in qualitative methods and experimental design while mentoring graduate students in visual communication and ethics research. As associate editor of Journalism & Mass Communication Quarterly, she shapes scholarly discourse in the field. Though specific grant funding isn't detailed in available materials, her sustained publication record demonstrates consistent research activity. Coleman's background as a 15-year newspaper professional (including roles at the Raleigh News & Observer and Orlando Sentinel) informs her applied research perspective.
Professor Damian Grimshaw is Professor of Employment Studies at King's College London's King's Business School, where he serves as Associate Dean for Research Impact. He is also London & South Forum Co-Lead for the Productivity Institute. Previously, he was Director of the Research Department at the International Labour Organisation (Geneva, 2018-19) and Professor at the University of Manchester, where he served as Head of the HR and Employment Relations and Law group and Director of the European Work and Employment Research Centre. Professor Grimshaw's research spans multiple disciplines including labour market analysis, comparative employment relations, feminist economics, sociology of work, and management. His work focuses on international comparisons of low-wage labour markets, outsourcing and HRM, technology and the future of work, precarious work, collective bargaining, and gender inequality. His research has strong policy impact across UK, European and international arenas. Recent publications reveal a clear trend toward examining the future of work through multiple lenses: collective bargaining structures, digital labor platforms, gendered impacts of pandemic policies, and regional approaches to decent work. His work consistently addresses how institutional frameworks shape employment outcomes across different national contexts and sectors. Professor Grimshaw has received significant research funding from major organizations including the British Academy, ESRC, European Commission, International Labour Organisation, Leverhulme Trust, Low Pay Commission, OECD, and Russell Sage Foundation. He coordinates a major UKRI research programme on human capital and productivity and leads the Regional Productivity Forum for London and the South alongside Marcela Miozzo and Mario Gruber. His ongoing research projects include precarious work in Europe and the gender inequalities of the Covid-19 pandemic and recession. During 2021-22, he taught 'Globalisation and Employment' on the MSc in HRM and Employment Relations and 'International HRM' to undergraduates. His research team is actively engaged in projects examining automation in knowledge-intensive business services and artificial intelligence's impact on occupations, with particular focus on the UK and US contexts.
