Richard Zemel is the Trianthe Dakolias Professor of Engineering and Applied Science in the Department of Computer Science at Columbia University. He leads the NSF AI Institute for ARtificial and Natural Intelligence (ARNI). His research focuses on machine learning, artificial intelligence, statistics, neuroscience, and cognitive science with recent emphasis on robust AI, few-shot learning, algorithmic fairness, and continual learning. Education details are not explicitly listed, but his career includes roles as director of an NSF institute and extensive academic contributions. His research group includes active and former students/postdocs now in academia and industry roles at institutions like Google, DeepMind, NVIDIA, and universities globally. Key Research Themes: Fairness in AI, causal inference, generative models, and neural network architectures. Labs/Teams: ARNI Institute (NSF-funded), Zemel Research Group at Columbia. Advising and grants include supervision of over 40 students/postdocs and leadership in multi-institutional AI initiatives. His work spans theoretical foundations to real-world applications in healthcare, ethics, and computational neuroscience.
Luc Rey-Bellet is a Professor and Honors Coordinator in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. He has been a faculty member at UMass since 2002, progressing from Assistant Professor to Associate Professor in 2008, and to Full Professor in 2013. His office is located in LGRT 1423K, and he maintains regular office hours on Tuesdays and Fridays. Dr. Rey-Bellet received his Dipl. Phys. from Eidgenössiche Technische Hochschule Zürich (ETH Zurich) in 1994 and his Ph.D. in Mathematics from Université de Genève in 1998. Following his doctoral studies, he held postdoctoral positions at Rutgers University (1998-1999) and served as a Whyburn Instructor at the University of Virginia (1999-2002) before joining UMass Amherst. Professor Rey-Bellet's research spans statistical mechanics, applied probability, and their applications across various domains. His work focuses on both theoretical foundations and practical applications, with particular emphasis on non-equilibrium systems, large deviations theory, and computational methods. He has made significant contributions to understanding physical and mathematical properties of non-equilibrium steady states, developing coarse-graining strategies for complex systems, and creating numerical schemes for lattice spin systems and stochastic processes. His research also extends to evolutionary game theory, mathematical economics, Monte-Carlo methods, information theory, uncertainty quantification, and machine learning. Recent publications reveal a strong trend toward interdisciplinary work at the intersection of probability theory, statistical mechanics, and machine learning. Rey-Bellet has been particularly active in developing mathematical frameworks for generative modeling, with focus on Wasserstein distances, divergence measures, and gradient flows. His work on structure-preserving generative models, group-invariant networks, and uncertainty quantification demonstrates how classical statistical mechanics concepts can inform modern machine learning theory. The consistent theme across his recent work is developing rigorous mathematical foundations for understanding complex probabilistic systems and their computational representations. Rey-Bellet has secured significant research funding throughout his career, with grants totaling $101K in 2003, $106K in 2006, $99K in 2010, $280K in 2015, $370K in 2020, and $300K in 2023. Notably, he was awarded larger collaborative grants of $900K in 2019, $1.950M in 2021, and $900K in 2016, reflecting the significance and collaborative nature of his research. While specific teaching awards aren't detailed in the available information, his faculty profile notes "Award-winning teaching," suggesting recognition for his pedagogical contributions. His role as Honors Coordinator further indicates his commitment to undergraduate education and academic excellence. Professor Rey-Bellet maintains an active research group, frequently collaborating with colleagues including Markos A. Katsoulakis, Jeremiah Birrell, Panagiota Birmpa, and others. His research spans theoretical developments in probability and statistical mechanics while maintaining strong connections to computational methods and applications in machine learning and data science. Current projects appear focused on developing mathematically rigorous frameworks for generative modeling, uncertainty quantification, and understanding the statistical properties of complex systems.
Selim Temizer is an Assistant Professor of Computer Science and Engineering at Nazarbayev University's School of Engineering and Digital Sciences (SEDS). Previously, he worked at Middle East Technical University (METU) for seven years before joining Nazarbayev University in August 2019. He holds a PhD and MS in Computer Science from MIT (2011 and 2001), and a BS in Computer Engineering from METU (1999, summa cum laude). His research focuses on artificial intelligence, robotics, autonomous systems, and dynamic collision avoidance for unmanned vehicles. Recent interests include machine learning applications in agriculture and algorithmic trading. He has developed simulation systems, navigation models, and contributed to POMDP-based collision avoidance frameworks for UAVs. Key projects include work on MIT's CSAIL (formerly AI Lab) under Professors Leslie Pack Kaelbling and Tomás Lozano-Pérez. Notable publications address collision avoidance algorithms for aircraft and POMDP applications in autonomous systems. He is also a software developer with expertise in parallel rendering, meta-language design, and embedded systems. His academic journey includes pioneering roles among Turkish students admitted to MIT's EECS program and being one of few Turkish MIT CS PhD holders. He teaches courses like Algorithmic Trading and maintains a portfolio of hobby projects in network topology design and graphics systems.
Dr. Simos Gerasimou is a Senior Lecturer in the Department of Computer Science at the University of York, holding this position since 2023. Previously, he served as a Lecturer (2019–2023) and Research Associate (2016–2019) at the same institution. His research focuses on engineering trustworthy software for autonomous systems, emphasizing rigorous tool-supported approaches through model-based analysis, testing, and formal verification. His work intersects with trustworthy AI, robotics, and software engineering. Dr. Gerasimou holds a PhD in Computer Science (University of York, 2016), an MSc in Software Engineering (University of York, 2011), and a BSc in Computer Science (University of Cyprus, 2010). He is part of the Automated Software Engineering research group , contributing to advancements in safety assurance and formal methods for critical systems. His research interests include ensuring safety and reliability in autonomous systems, with a focus on model-driven approaches for testing and verification. Recent work explores topics such as compositional safety verification, uncertainty quantification in AI systems, and adaptive human-robot collaboration. His publications address challenges in autonomous driving, UAV systems, and stochastic modeling of software behaviors. Dr. Gerasimou’s contributions span both theoretical advancements and applied solutions, bridging gaps between formal methods and real-world engineering challenges. He emphasizes the importance of rigorous methodologies to address safety and dependability in next-generation autonomous technologies.
Dr Dalia Chakrabarty is a Reader in Statistical Data Science at the Department of Mathematics, University of York. Previously, she held positions as Senior Lecturer at Newcastle University and Lecturer at Lancaster University. Her research focuses on probabilistic methods, Bayesian inference, and machine learning applications in fields such as medicine, astronomy, and materials science. She specializes in kernel methods, random graph analysis, and causal forecasting. Notable contributions include developing methodologies for uncertainty quantification and non-parametric learning. Her academic career includes a Royal Society Dorothy Hodgkin Fellowship and supervision of students like Kane Warrior. Dr Chakrabarty's work bridges theoretical statistics and real-world challenges, with publications spanning journals like Plos One and Artificial Intelligence in Medicine . She also authored the textbook Supervised Learning: Mathematical Foundations & Real-world Applications (2024, CRC Press). Research Highlights: Inter-graph distance metrics for medical data analysis Bayesian state-space modeling of galactic dynamics High-dimensional data applications in oncology and materials science Collaborations: Maintains ties with Brunel Mathematics for PhD supervision and international research networks. Contact: Email dalia.chakrabarty@york.ac.uk , Tel: +44 (0)1904 32 1486
Dr. Bei Xiao is a Provost Associate Professor in the Department of Computer Science at American University, affiliated with the Center for Behavioral Neuroscience and the BCCN graduate program. Her research focuses on human visual perception and its application to computer vision, particularly material property estimation and VR/AR integration. She holds a PhD in Neuroscience from the University of Pennsylvania and a BS in Chemistry from Tsinghua University. Key research interests include material perception, generative AI for graphics, and clinical trial prediction using NLP. Her work combines human psychophysics, machine learning, and AR/VR techniques. Notable projects include NIH/NSF-funded studies on translucency perception and clinical trial phase transition prediction. She teaches courses like Deep Learning in Vision and Computer Vision. Awards include the Provost Associate Professor honorific. Her lab collaborates with institutions like NIH, University of Tokyo, and Virginia Tech. Current openings exist for PhD students in material perception and NIH-sponsored projects in VR-based human studies.
Evdoxia Taka is a Research Associate in the School of Computing Science at the University of Glasgow, UK. She holds a PhD in Computer Science from the same institution (2023), with a thesis titled 'Interactive Animated Visualizations of Probabilistic Models'. Her research focuses on Uncertainty Visualization, Bayesian Modelling, Explainable AI, and AI Fairness. She has led or contributed to projects such as the Fujitsu-funded EFFI initiative (May 2023–Jan 2024), the Leverhulme Institute-supported 'Women, Ageing, and Machine Learning on Screen' (Jan 2023–Jan 2024), and RAI UK's 'Probable Futures' (Jan 2024–present). Her work spans human-centered AI, probabilistic model interpretability, and cultural heritage conservation through simulation. Recent publications emphasize user-centered assessment of AI fairness and interactive visualization techniques for complex models. She is affiliated with the School of Computing Science and maintains a personal academic website. Grants and projects highlight her interdisciplinary approach, combining technical innovation with societal impact in AI ethics and heritage preservation. No scientific awards are explicitly listed in the provided materials.
Will Briggs is a Professor of Computer Science at the University of Lynchburg since 1998. He holds a PhD from the University of Texas at Arlington, an MS from Georgia Institute of Technology, and a BS from Mercer University in Mathematics and Physics. His research interests span Artificial Intelligence, Game Development, Web Development, Planning Algorithms, and Congressional Districting. Recent teaching focuses include Artificial Intelligence, Computer Graphics, and Programming in Python/C++. Education: PhD in Computer Science, University of Texas at Arlington MS in Computer Science, Georgia Institute of Technology BS in Mathematics and Physics, Mercer University His current work emphasizes reactive planning systems and the SSDL graphics library for C++20. Recent publications explore educational tools for programming beginners and optimization in multi-agent planning systems. Over his career, he has contributed to scalable modularity in distributed planning and communication reduction techniques in multi-agent systems.
Yunhui Guo is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on advanced machine learning techniques including multimodal learning, continual learning, audio-visual recognition, and domain adaptation. He explores challenges in model robustness, cross-modal interactions, and efficient training strategies for deep neural networks. Key research areas include: Developing robust multimodal models for video entailment and dynamic 3D human reconstruction Improving audio-visual segmentation and sound separation through novel adaptation frameworks Advancing continual learning methods to handle domain shifts and out-of-distribution data Creating submodular optimization strategies for active learning in 3D object detection His recent work emphasizes real-world applications like medical image analysis (skin cancer sub-typing), robotics (LiDAR segmentation), and secure AI systems (model watermarking). The research also addresses foundational AI topics such as model uncertainty quantification and adaptive predictive systems. Publications focus on cutting-edge areas like multimodal LLM adaptation, hierarchical out-of-distribution detection, and bimodal online adaptation techniques. Current projects explore the intersection of multimodal perception and lifelong learning systems.
Dr. MyungJin Chung Smale is an Assistant Professor in the Department of Marketing at the College of Business, The University of Akron. She holds a Ph.D. in Marketing from The University of Texas at Arlington and contributes to both teaching and research in consumer behavior and marketing research. Education: Ph.D. in Marketing, The University of Texas at Arlington M.S. in Market Research, The University of Texas at Arlington B.S. in Business Administration, Korea University Her research centers on consumer behavior, with a strong emphasis on how psychological factors such as color, power, self-uncertainty, and artificial intelligence influence consumer decisions. She investigates how visual elements like logo darkness affect brand perception and how consumers differentiate between smart and AI-powered products. Her work bridges behavioral science and marketing strategy, offering practical insights for branding and product development. The recent publications reflect a growing focus on AI in consumer contexts and the psychological underpinnings of visual design in marketing. Her studies frequently appear in high-impact journals such as Psychology & Marketing and Computers in Human Behavior , demonstrating rigorous experimental methodology and relevance to contemporary marketing challenges. Scientific Awards: Excellence in Scholarship Award, St. Ambrose University (April 27, 2022) Dr. Smale actively teaches undergraduate courses including Consumer Behavior and Marketing Research, contributing to student development in core marketing disciplines. While no specific grants or advising relationships are listed, her scholarly output indicates active engagement in research and academic service. She has presented at major conferences such as the American Marketing Association Winter Educators' Conference and the AMA Winter Academic Conference. She maintains a professional presence through her LinkedIn profile and university affiliation, supporting knowledge dissemination and academic collaboration.
Alain Simons is a Senior Lecturer in Games Technology Programming at Bournemouth University, Faculty of Media and Communication, within the Department of Games Technology and Games Programming. His work bridges technical innovation and educational application in game development, 3D modeling, and digital imaging. His research focuses on novel imaging technologies such as VectorPixels , which aim to reduce bandwidth usage by representing photographic images through vector-based rendering. He also pioneers Scale Model Games (SMG) , integrating physical miniatures with online multiplayer systems for immersive tactical gameplay, and eScale Model Racing , simulating real-world racing conditions in scaled environments. Additional work includes avatar-based support for online learners and gamification of historical and engineering education. His publications span augmented reality, game engines, architectural visualization, and educational technology. Trends show a consistent focus on blending physical and digital interactivity, optimizing visual data, and enhancing learning through gamification. Senior Fellow of Higher Education Academy (2018) Alain supervises final-year student projects and integrates research into teaching. He has secured multiple grants from EPSRC, HEFCE, HEIF, and the Paul Mellon Centre, supporting projects in immersive simulation, structural behavior gamification, and VR for STEM outreach. He is actively involved in consultancy, including advising on Innovate UK bids. He leads initiatives in broadcasting student work across university screens and developing automated marking tools. His lab activities center on the Center for Games and Music Technology , where students engage in modeling, programming, and historical research for gameplay development.
Dr. Helen Durand is an Associate Professor in the College of Engineering at Wayne State University, where she holds a joint appointment in Electrical and Computer Engineering. With a PhD from UCLA (2017), she leads the Durand Lab focusing on cyberphysical systems for next-generation manufacturing. Current academic roles: Associate Professor of Chemical Engineering and Materials Science (Primary), Secondary Appointments in Electrical and Computer Engineering Education: PhD, MS, BS in Chemical Engineering from UCLA Research Interests span four key areas: Advanced Control Theory for nonlinear systems, including Lyapunov-based economic model predictive control (EMPC) Cybersecurity frameworks for industrial control systems, cyberattack detection/handling Quantum Computing integration in control algorithms and cyberattack prevention Digital Twin Development for dynamic process modeling and virtual testing Recent Publications demonstrate her interdisciplinary approach combining chemical engineering, control theory, and quantum technologies. Key trends include Quantum algorithm applications for control optimization (2022-2024) Image-based control simulation environments using Blender (2024) CFD modeling for semiconductor manufacturing (2023) Directed randomization security architectures (2023) Lyapunov-based attack detection systems (2020-2022) Scientific Recognition Featured in AIChE's 35 Under 35 list (2023) Invited speaker at 12 institutions (2023-2024) 5 keynote presentations at international conferences Teaching includes graduate courses in Advanced Engineering Mathematics and undergraduate Product/Process Design. The lab recruits multiple funded PhD students annually, with active research in Detroit's manufacturing ecosystem.
Dr. Marc Munar Covas is an Assistant Professor at the Department of Mathematical Sciences and Computer Science, University of the Balearic Islands, where he has worked since 2020. He holds a PhD in Information and Communication Technologies (2024), a Master in Intelligent Systems (2020), and a Bachelor in Mathematics (2019) from the same institution. His research focuses on theoretical and applied aspects of fuzzy logic operators, mathematical morphology, and artificial intelligence. He has contributed to abdominal surgery image processing through the development of the clinically validated mobile application Redscar , which detects infection signs using AI analysis of patient photographs and health questionnaires. Currently undergoing multicenter validation, this tool aims to improve postoperative care in Spanish hospitals. He has also developed MIA4LUNG , a web platform for lung region segmentation and pathology detection (e.g., COVID-19, tuberculosis) using deep learning and information aggregation techniques. As a member of the Soft Computing, Image Processing and Aggregation (SCOPIA) research group and collaborator of the Graphics and Computer Vision Unit and Artificial Intelligence (UGIVIA), he has participated in national and regional competitive projects. His international research experience includes a three-month stay at LORIA (Nancy, France). Teaching activities center on mathematical analysis and statistics across multiple degree programs, including dual degrees in Mathematics with Computer Engineering and Telecommunications Engineering. He has taught courses like Integral Calculus in Several Variables , Statistics , and Linear Algebra Methods since 2020.
Vajira Thambawita is a researcher at the University of Oslo's Department of Informatics, specializing in medical image analysis and AI-driven healthcare solutions. With over 90 publications since 2019, their work spans gastrointestinal endoscopy, reproductive medicine technology, and multimedia systems for clinical applications. Research focuses on medical image segmentation (polyp detection, sperm tracking), anomaly detection in time-series data, and multimodal analysis for clinical decision support. Key projects include the Medico Multimedia Task at MediaEval (sperm tracking), VISEM-Tracking dataset, and ImageCLEFmedical challenges for GI tract analysis. Their work bridges computer vision with clinical practice through collaborations with Oslo University Hospital and Simula Research Laboratory. Recent publications demonstrate strong trends in generative AI for medical data augmentation (SinGAN-Seg, PolypConnect), explainable AI for clinical validation , and multimodal integration (SoccerNet-Echoes). The research consistently targets real-world clinical problems with emphasis on transparency and robust evaluation. Thambawita actively contributes to major benchmark challenges including ImageCLEF, Medico, and Medical AI competitions, serving as organizer and participant in advancing evaluation standards for medical AI systems.
Prof. Dr. Didier Stricker is a distinguished Professor of Computer Science at Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU) and serves as Scientific Director and Head of the Augmented Reality Research Department at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Augmented Vision Group, which comprises approximately 30 researchers working across various domains of computer vision and augmented reality. His work bridges academic research with industrial applications through collaborations with major companies including Sony, Google, and John Deere. His educational background includes electrical engineering studies at the Polytechnic Institute of Grenoble and the Technical University of Karlsruhe. He earned his doctorate from the Technical University of Darmstadt in 2002 with a dissertation on "Computer Vision-Based Calibration and Tracking Methods for Augmented Reality Applications." Prof. Stricker's research spans virtual and augmented reality, computer vision, human-computer interaction, cognitive interfaces, and on-body sensor networks. His work focuses on developing practical applications that enhance human capabilities through advanced visual computing technologies. He has pioneered approaches in video and sensor analytics, particularly in creating cognitive interfaces that respond intelligently to user needs and environmental contexts. His recent publications reveal a strong emphasis on 3D scene understanding, real-time processing for augmented reality applications, and the integration of large language models with spatial reasoning capabilities. There's a clear trend toward more sophisticated multimodal approaches that combine vision, language, and spatial understanding to create more natural and intuitive human-computer interactions. Among his notable achievements: Innovation Prize of the German Society of Computer Science (2006) Organized the first IEEE & ACM International Symposium on Mixed and Augmented Reality (ISMAR) in 2002 Member of the ISMAR steering committee from 2000-2007 Multiple best paper and demonstration awards at major conferences Several registered patents in tracking and augmented reality technologies Prof. Stricker has supervised numerous PhD and Master's students through his leadership of the Augmented Vision Group. His research is supported by significant funding from both European and national research organizations, as well as through industrial partnerships. He serves as an expert reviewer for various research funding bodies and contributes to the academic community through editorial roles for journals and conferences in VR/AR and computer vision. The Augmented Vision Group under his direction maintains strong connections with industry partners and participates in numerous collaborative research projects including LUMINOUS, SHARESPACE, I-Nergy, BIONIC, and VIDETE. These projects span applications in language-augmented XR systems, social experiences in hybrid spaces, AI for energy systems, personalized body sensor networks, and 4D scene analysis.
