Dr. Wenqi Shi serves as an Assistant Professor at the Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center. Her research focuses on the integration of artificial intelligence with healthcare, particularly advancing algorithms and systems for precision medicine. She specializes in working with multi-modal patient data including EHRs, medical notes, imaging, and genomics, with dedicated applications in pediatric healthcare, cancer, and rare diseases. Her research interests include developing large language models for translational medicine, creating agentic AI and generative models for biomedical discovery, and establishing responsible AI practices to enhance clinical outcomes. Publication trends show extensive work in explainable AI, clinical decision support systems, and multi-modal data integration, with recent emphasis on retrieval-augmented language models and causal inference methodologies. Dr. Shi obtained her Ph.D. from the Georgia Institute of Technology prior to joining UT Southwestern.
Felix Naumann is a Professor at the Hasso Plattner Institute in Potsdam, Germany, specializing in data management and database systems. His research focuses on data quality, data profiling, entity resolution, functional dependencies, and database optimization. He has authored over 300 publications in top-tier conferences and journals, including VLDB, SIGMOD, and ICDE. His work bridges theoretical foundations with practical systems, such as TASHEEH for data cleaning and PRISMA for privacy-preserving schema matching. He serves as an editor for the ACM Journal of Data and Information Quality and has led initiatives on hybrid conference formats and inclusive computing. Key contributions include: Data Quality: Pioneered studies on data quality's impact on machine learning and developed tools like AutoTSAD for anomaly detection. Data Dependency Discovery: Advanced functional and inclusion dependency mining algorithms, including Hitting Set Enumeration methods. Schema Matching & Integration: Created systems like BrewER and Frost for entity resolution and schema alignment. Educational Impact: Led massive open courses in data engineering with over 10,000 participants. His work emphasizes practical applications in cultural heritage data (ReCLAIM), Wikipedia table analysis, and cross-platform data systems (RHEEMix). He collaborates extensively with industry and academia, addressing challenges in dynamic datasets and data governance.
Ioannis Stamos is a Professor of Computer Science at Hunter College, City University of New York (CUNY), within the School of Arts and Sciences. His research focuses on Computer Vision, Robotics, Computer Graphics, and 3D Visualization, with emphasis on 3D modeling using range and image data. He earned his Ph.D. in Computer Science from Columbia University (2001), followed by an M.S. and M.Phil. from Columbia's Computer Science Department, and a Diploma of Engineering from the University of Patras, Greece. Dr. Stamos has received prestigious awards including the NSF CAREER Award (2003) and Google Research Awards (2014, 2017). His work integrates 2D images and 3D range data for urban scene modeling, sensor fusion, and real-time object detection. Notable contributions include advancements in 6DoF pose estimation, LiDAR-based curb detection, and Kronecker product models for repeated patterns in urban imagery. He leads the Computer Vision & Robotics Lab and teaches graduate courses in 3D Computer Vision and Photorealistic Modeling. His research is supported by NSF grants, including MRI awards for mobile robotics and large-scale 3D modeling. He serves as Area Editor for the Journal of Computer Vision and Image Understanding and has co-chaired conferences like 3DV 2013. His lab collaborates on projects involving procedural modeling of urban environments and online classification of 3D point clouds.
Dr. Gady Agam is an Associate Professor in the Department of Computer Science at Illinois Institute of Technology (Illinois Tech), affiliated with the College of Computing. His primary research focuses on Computer Vision, Machine Learning, and Artificial Intelligence, with applications in medical imaging, remote sensing, and security systems. He leads the Visual Computing Lab, which explores topics such as deep learning, geometric modeling, and computational methods for data analysis. Dr. Agam teaches courses including CS584 (Machine Learning), CS512 (Computer Vision), and CS577 (Deep Learning). His research has resulted in over 100 peer-reviewed publications and collaborations with organizations like SPIE. He has held leadership roles in conferences such as Document Recognition and Retrieval and has contributed to industry-relevant projects like the MuscleX software. His academic contributions include advancements in image registration, feature detection, and automated medical diagnostics. He actively supervises graduate students in his lab and maintains partnerships with academic and industrial entities to drive innovation in visual computing technologies.
Len Gelman is a Professor and Chair in Signal Processing and Condition Monitoring at the University of Huddersfield's Department of Engineering within the School of Computing and Engineering. He also serves as Director of the Centre for Efficiency and Performance Engineering. His research focuses on advanced signal processing techniques for fault diagnosis in electromechanical systems, vibration analysis, and predictive maintenance. He is actively involved in PhD supervision and has authored over 100 publications, achieving 1499 citations and an h-index of 21. Key research areas include digital twin technology, nonlinear spectral analysis, and machine learning for industrial diagnostics. His work addresses challenges in non-stationary signal processing, motor current signature analysis, and condition monitoring under varying operating conditions. Collaborations include interdisciplinary projects with the Centre for Efficiency and Performance Engineering. Recent studies highlight innovations in fault diagnosis frameworks for rotating machinery, conveyor belt systems, and wind turbines. His contributions bridge theoretical advancements with practical industrial applications, emphasizing explainable AI and adaptive diagnostics. Gelman's research has been presented at major conferences like the World Congress on Engineering and published in specialized journals. Education: Not explicitly stated in the provided text. Awards: High citation count and h-index reflect his significant academic impact. Grants/Advising: Supervised 2 PhD projects; accepting new students in diagnostic engineering and condition monitoring. Labs/Teams: Leads the Centre for Efficiency and Performance Engineering and collaborates with the Department of Engineering's research groups.
Dr. Armin Agha Karimi is a Lecturer in the School of Surveying and Built Environment at the University of Southern Queensland. He holds a BSc in Civil Engineering from Tabriz University, an MSc from Middle East Technical University (METU), and a PhD from the University of Newcastle. His research focuses on spatial data integration, cadastral systems modernization, environmental monitoring using remote sensing, and sea level variability analysis. Key research interests include 3D cadastral boundaries in BIM environments, digital twin applications in built environments, and the impact of hydrological loading on land motion. He has contributed to studies on erosion hotspot mapping in Queensland and the implications of coal seam gas activities on land subsidence. His work on Baltic Sea sea level dynamics and Australian coastal projections has advanced understanding of climate-driven environmental changes. Dr. Karimi is affiliated with the Centre for Sustainable Agricultural Systems and actively publishes on geomatics, climate science, and legal aspects of digital surveying. His recent articles highlight innovations in VR-ready survey data transformation and the legal challenges of electronic cadastral plans.
Ove Daniel Jakobsen is a distinguished academic at Nord University's School of Business specializing in ecological economics and sustainability. He serves as a board member at the Center for Sami and Indigenous Studies and holds leadership positions including Deputy Chairman of NaKUHel Academy. His scholarly work bridges economics, environmental science, and social philosophy, challenging conventional growth paradigms with transformative frameworks for sustainable development. Dr. Jakobsen's educational background includes: Dr. Economics from the Norwegian School of Economics (NHH) in 1989 Cand. Philol. from the University of Bergen (UiB) with a philosophy major in 1991 Master of Science in Economics and Administration from the Norwegian School of Economics in 1987 His research focuses on ecological economics as a paradigm shift from traditional economic thinking. He explores circular economy models, ecopreneurship, and dialogue-based approaches to social change, emphasizing the interconnectedness of nature, culture, and health. His scholarship challenges the growth imperative of mainstream economics, advocating for systems that prioritize ecological integrity and social wellbeing. Dr. Jakobsen's recent work examines utopian thinking as a practical methodology for community development and investigates the spiritual dimensions of sustainable business practices. His publications reveal a consistent focus on transforming economic systems through ecological principles, with growing emphasis on community-based solutions and the integration of indigenous knowledge systems. His research demonstrates a progression from theoretical foundations toward practical implementation, with increasing attention to participatory methods and alternative economic indicators that measure wellbeing rather than mere output. The nature-culture-health interplay represents a central theme in his scholarship, recognizing these as interconnected systems rather than separate domains. Dr. Jakobsen actively contributes to public discourse through his board memberships with organizations including Cultura Bank, Public Health Alliance Nordland, and the Norwegian Non-Fiction Writers and Translators Association. His work with the Utopia Workshop methodology provides communities with practical tools for envisioning sustainable futures through collaborative processes. His educational initiatives include developing ecological economics curriculum for Bodø Upper Secondary School and leading the ERASMUS+ 'Applied Ecopreneurship Methodologies' project (2017-2019), demonstrating his commitment to translating academic research into practical applications that benefit communities and students.
Dr. Lizhen Qu is a Lecturer at Monash University’s Faculty of Information Technology, part of the AIM Lab. His research focuses on robust and privacy-preserving neuro-symbolic methods for NLP and multimodal applications, including causal reasoning in dialogue systems, legal AI, digital health, and social NLP. Previously, he worked at Data61/CSIRO and completed his PhD at Saarland University and the Max-Planck-Institute for Informatics. Education: PhD in Computer Science from Saarland University and Max-Planck-Institute for Informatics. Research interests include integrating deep learning with logical reasoning, causal discovery, and ethical AI applications. He leads projects like TMLGenAI (Trusted Generative AI) and HARNESS (Neuro-Symbolic Systems), addressing model robustness and societal impact. Projects: TMLGenAI (2024–2026), HARNESS (2023–2027), and Accessible Data Exploration for Blind People (2023–2027) Contributions: Developed benchmarks like LazyReview and ACCESS, and co-organized ACL and IJCNLP workshops Research trends span causal discovery in NLP, federated learning for legal systems (e.g., FedLegal), and multimodal security. His work aligns with UN SDGs for innovation and health.
Ayman El-Hag is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is affiliated with the Outdoor Insulation and Condition Monitoring Research Group, focusing on advancing technologies for high-voltage insulation systems and smart grid infrastructure. His work integrates machine learning, signal processing, and materials science to improve condition monitoring and diagnostic methods for power equipment. Research interests include partial discharge detection using UHF and acoustic sensors, machine learning applications for defect classification in outdoor insulators, and the development of non-invasive sensing techniques for real-time monitoring. He also explores energy management systems leveraging fuzzy logic and smart meter data analysis for residential and grid-level applications. Recent publications highlight advancements in capsule networks for insulator discharge prediction, deep learning-based hydrophobicity classification, and novel antenna designs for partial discharge localization. His work emphasizes practical solutions for power system reliability and environmental resilience of insulation materials under extreme conditions. El-Hag is a Full-time faculty member and holds Adjunct faculty status, contributing to interdisciplinary research projects. He actively engages in promoting condition monitoring methodologies through educational initiatives and industry partnerships.
Joshua Abbott is a Professor and Director of Environmental and Resource Economics at Arizona State University's School of Sustainability. His research focuses on equitable and sustainable governance of natural resources amid environmental change, employing economic modeling and interdisciplinary collaboration. Key areas include sustainable fisheries policy, blue economies, water resource management in arid regions, and valuation of natural capital. Abbott has contributed to NOAA Fisheries and US Fishery Management Council initiatives, emphasizing ecosystem and community resilience. Education : Ph.D. in Agricultural and Resource Economics (Environmental & Resource Economics & Econometrics), University of California, Davis M.A. in Economics, University of Washington, Seattle B.B.A. in Economics, Baylor University Research Interests : Sustainable fisheries governance, water resource valuation, ecological-economic couplings, policy innovation for natural resource management, and green accounting frameworks. Professional Roles : Editor in Chief, Marine Resource Economics Former Co-Editor, Journal of the Association of Environmental and Resource Economists Founding member of the Economics for Sustainability lab group Teaching : Courses include Natural Resource Economics, Statistical Methods, Sustainable Seafood, and the Graduate Certificate in Environmental and Sustainability Economics. His work bridges economic theory and practical policy, addressing challenges such as climate adaptation, biodiversity outcomes, and urban water scarcity. Abbott collaborates with institutions like the Center for Biodiversity Outcomes and the Global Futures Scientists and Scholars network.
Nelson Nicolas Higuera Ruiz is a PreDoc Researcher at the Vienna University of Technology, affiliated with the Faculty of Informatics' Knowledge-Based Systems research group. His work bridges logic programming and deep learning for explainable AI. Research Focus: Neurosymbolic AI, Visual Question Answering (VQA), Answer Set Programming (ASP), and hybrid reasoning systems Projects: Leads optimization research in the LCS (2017–2025) project, developing neurosymbolic approaches for intelligent systems Key Contributions: Pioneering adaptive large-neighbourhood search algorithms for ASP optimization, modular neurosymbolic architectures, and contrastive explainability frameworks for VQA Collaborations: Active in international workshops and conferences including IJCAI, AAAI, and CLeaR, frequently collaborating with researchers like Thomas Eiter and Johannes Oetsch Publications: Focus on neurosymbolic integration, optimization algorithms, and explainability across AI, logic programming, and computer vision domains
Avraam Tapinos is a Researcher at the University of Manchester's Division of Cancer Sciences (L5), part of the Manchester Cancer Research Centre. His work contributes to UN Sustainable Development Goals related to health and innovation. Research focuses include cancer genomics, metagenomics, and bioinformatics methodologies. Affiliations: Manchester Cancer Research Centre, Digital Futures Research Beacon Key expertise: Cancer genomics, germline analysis, metagenomic binning Recent research explores genomic landscapes of testicular germ cell tumors, breast cancer evolution in diverse populations, and respiratory virome dynamics in asthma. His computational methods advance alignment-free genomic analysis. Publications span high-impact journals like Nature Communications and American Journal of Human Genetics, with interdisciplinary collaborations in oncology, microbiology, and bioinformatics.
Mahzarin Banaji is the Richard Clarke Cabot Professor of Social Ethics in the Department of Psychology at Harvard University. She previously served as the Carol K. Pforzheimer Professor at the Radcliffe Institute for Advanced Study (2002-2008) and as the George A. and Helen Dunham Cowan Chair in Human Dynamics at the Santa Fe Institute (2011-2015). Currently, she also serves as Senior Advisor to the Provost at Harvard University. Banaji is widely recognized as a leading researcher in social psychology, particularly in the field of implicit bias and social cognition. Banaji received her PhD from Ohio State University in 1986 and completed a National Institutes of Health postdoctoral fellowship at the University of Washington in Seattle. She taught at Yale University from 1986 to 2001, where she was the Reuben Post Halleck Professor of Psychology, before joining Harvard University. Professor Banaji's research focuses on the subconscious nature of assessments of self and others in social contexts. She is best known for her pioneering work on implicit bias, particularly through the development of the Implicit Association Test (IAT), which measures unconscious attitudes and beliefs. Her work bridges cognitive science and psychology to understand how implicit biases form, persist, and can potentially be changed. Banaji has demonstrated how these unconscious biases can influence behavior in critical domains including healthcare, education, and law enforcement, often without individuals' awareness. She has also extensively studied how biases change over time, showing that while some implicit biases have decreased significantly (particularly regarding sexual orientation), others remain stubbornly persistent. Banaji has received numerous prestigious awards recognizing her contributions to psychology and social science, including: Election to the National Academy of Sciences (2018) Election as Fellow of the British Academy (2015) William James Fellow Award for lifetime intellectual contributions to psychology Gordon Allport Prize for Intergroup Relations Kurt Lewin Award for outstanding contributions to the integration of psychological research and social action Morton Deutsch Award for Social Justice James McKeen Cattell Award Carol and Ed Diener Award for Outstanding Contributions to Social Psychology Herbert A. Simon Fellow of the American Academy of Political and Social Science Fellow of the American Academy of Arts and Sciences Banaji has advised numerous graduate students and postdoctoral researchers who have gone on to become leaders in social psychology and related fields. She has secured significant research funding to support her work on implicit bias, including grants from the National Science Foundation and the National Institutes of Health. Her research has influenced policy discussions around diversity, equity, and inclusion in educational and organizational settings. Banaji has also been active in translating her research for public understanding through her co-authored book "Blindspot: Hidden Biases of Good People" (2013) with Anthony Greenwald, and through her media series "Outsmarting Human Minds." Professor Banaji leads a vibrant research laboratory focused on implicit social cognition. Her team employs a range of methodologies including behavioral experiments, neuroimaging, computational modeling, and large-scale data analysis to investigate the nature and consequences of implicit bias. Recent work has expanded to examine bias in artificial intelligence systems and how large language models reflect and potentially amplify societal biases. The lab collaborates with researchers across disciplines including computer science, neuroscience, education, and public policy to address the complex challenges of implicit bias in society.
David Bourget is an Associate Professor in the Department of Philosophy at Western University and serves as the Director of the Centre for Digital Philosophy . He holds a PhD from the Australian National University (2010) and a BSc in Computer Science from L'Université Laval (2002). His research focuses on consciousness, intentionality, and digital philosophy , with a special emphasis on the intersection of AI and philosophical inquiry through projects like PhilPapers.org. Education: PhD, Australian National University (2010) BSc, L'Université Laval (2002) Key Research Areas: Philosophy of Mind Philosophy of Language Digital Philosophy Infrastructure Metaphilosophy His recent publications explore topics such as phenomenal intentionality, representationalism, and debunking theories . The Centre for Digital Philosophy , which he leads, maintains major resources like PhilPapers, PhilPeople, and PhilEvents, supported by CFI and ORF grants . He actively collaborates with philosophers like Angela Mendelovici on intentionality and representational theories.
Prof. Masaru Shibata is a leading figure in computational relativistic astrophysics, currently serving as Director at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) since 2018 and Professor at Kyoto University's Yukawa Institute for Theoretical Physics since 2009. His career spans multiple prestigious institutions including University of Tokyo and Osaka University. PhD in Physics, Kyoto University (1994) Graduate studies in Physics, Kyoto University (1989-1993) Undergraduate in Science, Tokyo Institute of Technology (1985-1989) As a Professor with primary focus on Relativistic Astrophysics , Shibata's research investigates gravitational wave sources , neutron star mergers , black hole formation , and multimessenger astrophysics . His work combines general relativistic simulations , magnetohydrodynamic modeling , and neutrino radiation studies to understand high-energy cosmic phenomena. Recent publications (2024-2025) demonstrate expertise in supermassive star collapse , binary neutron star merger dynamics , and black hole-torus systems . These studies employ advanced numerical relativity techniques with applications to gravitational wave astronomy and gamma-ray burst modeling . 2025 Japan's Medal of Honor (Shiju-houshou) 2018 Nishina Memorial Prize 2013 International Society of General Relativity and Gravitation Fellow 2010 JSAP Excellent Young Researchers Prize 2008 Physical Society of Japan Outstanding Paper Award 2003 Nishinomiya-Yukawa Memorial Prize Shibata contributes to both theoretical frameworks and computational methodology in relativistic astrophysics, maintaining active collaborations with international research teams while leading computational projects at his dual institutions.
