Heikki Haario is a Professor in Computational Engineering at the LUT School of Engineering Sciences, LUT University, Lappeenranta. His research focuses on robust Bayesian inference, parameter estimation, and uncertainty quantification in chaotic and stochastic systems. Broad research areas: Bayesian Statistics, Chaotic Dynamical Systems, Gaussian Processes, Machine Learning The 15 most recent publications (2025-2023) demonstrate expertise in computational modeling, data-driven methods, and interdisciplinary applications spanning finance, biology, and engineering. Articles emphasize Bayesian techniques, kernel flows, and neural network integration for solving inverse problems and optimizing predictions in uncertain environments.
Federico Bumbaca serves as an Assistant Professor of Marketing at the Leeds School of Business, University of Colorado Boulder. His academic journey spans engineering, operations research, and business disciplines. Education Highlights: PhD in Marketing - Paul Merage School of Business, University of California Irvine MBA - Sloan School of Management, Massachusetts Institute of Technology MS in Operations Research - Virginia Tech MS & BS in Electrical Engineering - University of Toronto Bumbaca's research pioneers big data methodologies in marketing, specializing in Bayesian hierarchical models and distributed computing techniques for target marketing optimization. His work bridges complex statistical modeling with practical marketing applications, particularly in loyalty programs and customer segmentation. The evolution from his early computer vision research to contemporary marketing analytics demonstrates interdisciplinary expertise. His publication portfolio reveals a distinct transition from 1980s engineering research (laser rangefinders, color vision systems) to modern marketing analytics, with recent focus on scalable algorithms for Bayesian modeling. This trajectory reflects his unique ability to apply computational rigor to marketing challenges. Bumbaca teaches Marketing Research and Analytics in undergraduate programs and Data and Decisions in MBA courses, leveraging his industry experience from technology startups in the San Francisco Bay Area where he developed expertise in marketing, business development, and sales.
Lassi Aarniovuori serves as an Associate Professor (Tenure Track) in Electrical Engineering at LUT School of Energy Systems, LUT University in Lappeenranta, Finland. His academic career includes a Doctor of Engineering in Electrical Drives Engineering from LUT University (2010) and a Marie Curie Research Fellowship at Aston University (2017-2019). As an IEEE Senior Member, he maintains active contributions to the field of electrical engineering with particular expertise in power electronics and electric drive systems. Master of Science in Electrical Engineering (2005), LUT University Doctor of Engineering in Electrical Drives Engineering (2010), LUT University Marie Curie Research Fellow (2017-2019), Aston University, Birmingham Professor Aarniovuori's research spans electric vehicles, power electronics, modulation methods, electric drives simulation, energy efficiency measurements, and calorimetric measurement systems . His work focuses on improving the efficiency and performance of electrical machines and power conversion systems, with special attention to loss measurement techniques and thermal management. The research has significant implications for sustainable transportation and industrial applications where energy efficiency is paramount. Analysis of his recent publications (2023-2025) reveals a strong focus on next-generation power electronics, particularly silicon carbide technology for high-speed drives, advanced loss measurement techniques, and optimization of electric machines. His work increasingly addresses practical challenges in electric vehicle charging infrastructure, high-power systems, and the transition to electrified heavy-duty transportation, demonstrating both theoretical depth and practical application. IEEE Senior Member Marie Curie Research Fellowship (2017-2019) While specific grant information isn't detailed in the provided text, Professor Aarniovuori's Marie Curie Fellowship indicates successful competitive funding acquisition. His extensive publication record suggests ongoing research projects and collaborations focused on advancing power electronics and electric machine technologies. His work appears to bridge academic research with industrial applications, particularly in the electric vehicle sector. Professor Aarniovuori's research environment at LUT School of Energy Systems likely includes specialized laboratories for electric machine testing, power electronics development, and thermal measurement systems. His focus on calorimetric measurement systems suggests dedicated facilities for precise efficiency determination of electrical machines and power converters, supporting both fundamental research and industry collaboration.
Megan Peters is an Associate Professor at the University of California, Irvine, with appointments in both the Department of Cognitive Sciences and the Department of Logic and Philosophy of Science within the School of Social Sciences. She serves as president and co-founder of Neuromatch.io and is a Fellow in the Brain, Mind & Consciousness program at the Canadian Institute for Advanced Research (CIFAR). Her research focuses on the intersection of perception, metacognition, and subjective experience, employing methodologies including fMRI, computational modeling, and artificial intelligence. Peters investigates how humans form metacognitive judgments about their perceptions and decisions, examining the neural and computational mechanisms underlying confidence, uncertainty, and conscious awareness. Her work spans theoretical frameworks in philosophy of science to practical applications in neuroscience methods. Peters' recent publications reveal a strong emphasis on metacognitive processes across various domains, with particular attention to how uncertainty is represented in the brain and communicated through behavior. Her research integrates computational neuroscience with philosophical approaches to consciousness and perception, creating a unique interdisciplinary perspective. She has made significant contributions to understanding the representational geometry of psychological spaces, the dynamics of perceptual decision-making, and the development of novel methods for analyzing neural data. Fellow, CIFAR Brain Mind & Consciousness Program As co-founder and president of Neuromatch.io, Peters has created a global platform for computational neuroscience education that has democratized access to advanced training. Her leadership in organizing large-scale virtual conferences has demonstrated innovative approaches to scientific collaboration across geographical boundaries. Through her work with Neuromatch, she has mentored numerous students and early-career researchers in computational neuroscience methods. Peters maintains active involvement in the consciousness science community, regularly participating in and organizing events such as the Metacognitive Science Satellite meeting and CCN (Cognitive Computational Neuroscience) conferences. Her research laboratory investigates fundamental questions about how the brain generates subjective experiences and metacognitive awareness, with implications for both theoretical understanding and potential clinical applications.
Els Verstrynge is an Associate Professor at the Department of Civil Engineering, Faculty of Engineering Science, KU Leuven. Her work focuses on the durability of brittle construction materials and structural resilience of existing buildings , particularly those of architectural heritage. She leads interdisciplinary research projects utilizing multi-scale numerical modeling, advanced non-destructive testing (NDT), and 4D X-ray tomography to study concrete and masonry degradation. Research emphasizes reinforcement corrosion , fatigue fracture , and climate-induced degradation Develops Bayesian updating techniques for corrosion assessment and AI-driven structural analysis (CHAI project) Supervises PhD research on fracture mechanics and degradation processes Her work bridges experimental and computational methods, contributing to conservation strategies for aging infrastructure and innovative reuse of building materials.
Luming Tang is a Research Scientist at Google DeepMind in New York City. He earned his Ph.D. in Computer Science (2024) from Cornell University under Professor Bharath Hariharan, and a Bachelor's in Mathematics and Physics from Tsinghua University (China). Educational Background Ph.D. in Computer Science, Cornell University (2024) Bachelor's in Mathematics and Physics, Tsinghua University (China) His research spans Computer Vision , Generative Models , Multimodal Learning , and AI Systems , with notable work on image diffusion , 3D content creation , few-shot learning , and visual prompt tuning . Publications highlight advancements in autoregressive visual generation , cosine few-shot learners , and spatial-temporal reasoning . His academic service includes peer-review roles at conferences (CVPR, ICCV, NeurIPS) and journals (TPAMI, IJCV), alongside teaching assistantships at Cornell University for courses like CS 4787 Principles of Large-Scale Machine Learning and CS 6670 Graduate Computer Vision . Personal interests include soccer and gaming (FIFA series), though an ankle injury temporarily sidelines him from play.
Stanley Durrleman is a Researcher at Inria (French Institute for Research in Computer Science and Automation), where he has been actively involved since at least 2015. He is a member of the Aramis project team and has also contributed to the Sierra project team. In 2015, he was awarded an ERC Starting Grant for his work on computational models of brain structure and function. Durrleman specializes in building digital models of the brain using computational approaches that average information from medical images of groups of subjects or patients. His research sits at the intersection of computational neuroscience, medical imaging, and artificial intelligence, with a particular focus on modeling brain development and evolution. His publication record demonstrates a consistent trajectory in computational neuroscience, with recent work focusing on deep learning applications for brain imaging, longitudinal modeling of neurodegenerative diseases, and computational frameworks for early disease detection. His research has evolved from foundational work in brain morphometry and image registration toward more sophisticated approaches involving digital twins and multi-scale integration of brain imaging data. Scientific Awards: ERC Starting Grant (2015) Durrleman's research has significant implications for precision medicine and the early diagnosis of neurodegenerative conditions. His work bridges computer science and clinical neuroscience, creating computational tools that help translate complex brain imaging data into meaningful clinical insights. He has established himself as a leading researcher in the field of computational brain modeling, with his ERC grant recognizing the innovative nature of his work.
Behnaz Pirzamanbein is an Assistant Professor at the Department of Statistics , Lund University. She also serves as a Project Manager , Researcher for MERGE: ModElling the Regional and Global Earth system, and Principal Investigator for eSSENCE: The e-Science Collaboration. Her academic roles bridge statistics with environmental, climate, and medical research. Her research focuses on spatial-temporal models , Bayesian statistics , and efficient computational methods , with applications in image analysis , artificial intelligence , and quantifying high-resolution synchrotron images . She has contributed to understanding the Baltic Sea during the Last Interglacial, Holocene land cover changes, and microstructural changes in liver disease. Recent Publications highlight her interdisciplinary work: 2025 : Morphological insights into Baltic Sea environmental conditions via foraminifera. 2025 : Deep learning for 3D microstructural analysis in fibrotic liver disease. 2025 : Holocene land cover trends in North America and vegetation-atmosphere feedbacks. 2024 : Comparative analysis of MASH in mouse models and humans using micro-CT. Scientific Awards include: Outstanding Bayesian research applied to climate science (SBSS, ASA, 2016) Honorary Mention in Statistics and the Environment (ENVR, ASA, 2016) She teaches Bayesian Methods and Data Visualization courses, and has supervised masters and doctoral students. Her work intersects with the UN Sustainable Development Goals in Probability Theory, Climate Science, Environmental Sciences, and Computational Mathematics.
Janna Hastings is Assistant Professor for Medical Knowledge and Decision Support at the Institute for Implementation Science in Health Care (Faculty of Medicine) at the University of Zurich since August 2022, while also serving as Vice-Director of the School of Medicine at the University of St. Gallen. Her research focuses on digitalization in clinical contexts, examining how AI-driven knowledge systems reshape clinical practice, professional identity, and doctor-patient relationships. Education: PhD in Computational Biology (University of Cambridge, 2019), part-time MSc in Computer Science (University of South Africa, 2011), MSc in Philosophy (Open University, 2012) Prior Roles: Group Coordinator at European Bioinformatics Institute (2006-2015), Postdoctoral Researcher at Otto-von-Guericke University Magdeburg (2019-2022), Co-Leader of Human Behaviour-Change Project at University College London (2017-2022) Her research spans ontology development for biomedical domains (ChEBI, Human Behaviour Ontology), AI applications in healthcare decision-making, and behavior change interventions. Key projects include: Building ChEBI molecular ontology Developing Human Behaviour-Change Project knowledge system Ontology-driven mental health frameworks LLM applications in radiation oncology Time-series modeling of metabolism in ageing She explores the capabilities and limitations of clinical AI systems, with publications in JMIR and Lancet Digital Health , covering topics like bias prevention in generative AI and proteomic biomarker discovery. Her work bridges biomedical research with implementation science and digital ethics.
Markus Huff serves as a Full Professor of Applied Cognitive Psychology and head of the Applied Cognitive Psychology group at the Eberhard Karls University of Tübingen since 2020. He concurrently leads the Perception and Action research group at the Leibniz Institute for Knowledge Media (IWM). His research program investigates perception and action processes within digital environments through a perception-oriented framework that examines how media-mediated content influences human cognition and behavior. Dr. Huff earned his academic foundation in psychology, mathematics, and computer science at the University of Tübingen. Following his psychology degree, he completed his doctorate (Dr. rer. nat.) at the IWM and University of Tübingen with groundbreaking research on verbal influence on visual memory, which earned him the prestigious Leibniz Young Researcher Award in 2007. His academic trajectory includes postdoctoral positions at IWM, University of Tübingen, and Washington University in St. Louis, followed by a junior professorship in general psychology at Tübingen before heading the Department of Research Infrastructures at the German Institute for Adult Education. Professor Huff's research spans four primary interconnected domains. His Multimodal Perception research investigates how humans process and mentally organize media content across various formats including texts, images, comics, and videos. The Perception and Interaction in Social Networks focus examines the complex interplay between attitudes, knowledge, and metacognition during information reception and social interaction in digital spaces. His work on Perception and Action with Digital Agents explores human-AI communication dynamics, while his research on Risk Perception of Artificial Intelligence addresses the psychological dimensions of human responses to emerging digital technologies. Additionally, he conducts significant metascientific research examining policy effectiveness in the research process, with notable findings on research data policies. Analysis of Professor Huff's recent publication record reveals a clear trajectory toward increasingly sophisticated investigations at the intersection of cognitive psychology and digital technology. His work demonstrates methodological rigor across visual perception, audio-visual integration, event comprehension, and the cognitive impacts of digital environments. A particularly noteworthy trend is his expanding focus on AI-related perception and risk assessment, reflecting the growing importance of human-AI interaction in contemporary cognitive science. His research consistently bridges theoretical cognitive frameworks with practical applications in educational technology, health communication, and digital media design. Leibniz Young Researcher Award by the Leibniz Association (2007) Professor Huff directs a multidisciplinary research group comprising members from both the Applied Cognitive Psychology group at the University's Department of Psychology and the Perception and Action group at the Leibniz Institute for Knowledge Media. His research program has secured substantial funding, evidenced by his prolific publication record in high-impact journals spanning psychology, cognitive science, human-computer interaction, and educational technology. His grant portfolio reflects strong interest in understanding human cognition in digital contexts, with particular emphasis on educational applications, health communication, and human-AI interaction. His laboratory work employs advanced experimental methodologies including eye-tracking, behavioral experiments, and computational modeling to investigate how people process information across various media formats and interact with increasingly sophisticated digital agents. The research environment fosters collaboration between cognitive psychologists, computer scientists, and educational researchers, creating a rich interdisciplinary context for advancing understanding of human perception and action in our rapidly evolving digital landscape.
Hongxiao Zhu is an Associate Professor in the Department of Statistics at Virginia Tech. She holds a Ph.D. in Statistics from Rice University (2009), an M.S. in Mathematics from the University of Arkansas at Little Rock (2004), and a B.S. in Finance from Wuhan University (2002). Her research focuses on Bayesian methods, functional data analysis, and statistical machine learning, with applications in medicine, bioinspired sensing, neuroimaging, finance, and genomics. She has developed methods for analyzing high-dimensional data, including biosonar signals, genomic loci, and medical imaging data. Zhu has been recognized with awards such as the Travel Award from the Sixth International Workshop on Statistical Analysis of Neuronal Data (2012) and the SBSS Student Paper Competition (2008). Her teaching includes advanced courses on regression, statistical inference, Bayesian statistics, and statistical computing. She is affiliated with the Institute of Mathematical Statistics and the American Statistical Association. Zhu collaborates with researchers in engineering, biology, and computer science, notably in bioinspired sonar sensing frameworks and UAV applications. Her work bridges statistical theory and interdisciplinary applications, emphasizing robust modeling and computational innovation. Zhu's lab explores sonar terrain analysis, functional mixed models, and sensor-based environmental sensing. Recent projects include simulating bat-inspired sonar systems and analyzing DNA methylation patterns in brain development. Her research trends emphasize integrating Bayesian methods with real-world data challenges in healthcare and robotics. Her awards highlight contributions to statistical methodologies and interdisciplinary research. Grants and collaborations are evident through her work with institutions like SAMSI and Duke University. She advises on advanced statistical techniques for diverse datasets, though no specific advisee list is provided here.
Klara Nahrstedt is the Maybelle Leland Swanlund Endowed Chair and Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. She also serves as Director of the Coordinated Science Lab and holds professorships in Electrical and Computer Engineering, the Information Trust Institute, and the National Center for Supercomputing Applications (NCSA). Her expertise spans cybersecurity, edge computing, IoT, and multimedia systems. Her research focuses on immersive content delivery, network security, and AI-driven optimization for edge computing environments. Notable contributions include frameworks for robust 360-degree video analytics, cybersecurity in industrial networks, and energy-efficient video streaming. Awards include AAAS Fellow, ACM Fellow, and the Humboldt Research Award. Recent work emphasizes adaptive systems for IoT, reinforcement learning in multimodal applications, and generative models for intrusion detection. Key projects include Fire360 (firefighting video benchmarks) and AquaScope (underwater image transmission). Collaborations span academia and industry, addressing challenges in smart grids, telemedicine, and urban IoT. Awards: AAAS Fellow (2019), ACM Fellow (2012), Edward J. McCluskey Technical Achievement Award (2012) Labs/Teams: Coordinated Science Lab, NCSA, Information Trust Institute
Shuang Zhao is an Associate Professor of Computer Science at UC Irvine, co-directing the Interactive Graphics & Visualization Lab (iGravi). He holds a PhD from Cornell University (2014) and a postdoc at MIT. His research focuses on physics-based computer graphics, scientific computing, and inverse rendering, with applications in material science, biomedicine, and robotics. Zhao's NSF CAREER Award (2023) supports his work on Physics-Based Differentiable and Inverse Rendering, enabling automated 3D reconstruction and medical imaging advancements. Education: Ph.D., Computer Science, Cornell University (2014); Postdoc at MIT. Research areas: Inverse rendering, differentiable rendering, Monte Carlo methods, and light transport modeling. Notable projects include Meta's digital twin creation for the Metaverse, non-line-of-sight imaging, and medical imaging applications. His lab develops algorithms for efficient inverse solutions and collaborates with industry (Meta, Nvidia, Adobe). Teaching includes advanced graphics courses like CS 114 and ICS 162. Awards include ACM programming contest championships and Best Paper recognitions. Students supervised include Cheng Zhang (Facebook Fellow), Kai Yan, and Zahra Montazeri. Hobbies include photography and Japanese anime/video games.
Dr. Beate Ehrhardt is a Mathematical Innovation Research Fellow at the University of Bath's Institute for Mathematical Innovation. Her research spans hypothesis testing, Bayesian statistics, machine learning, optimal experimental design, networks, and causality. She completed her PhD in Statistical Sciences at University College London and holds an MSc in Mathematics from Universität Bremen. Her research interests include: Statistical analysis of pharmaceutical and insurance data Design of pre-clinical experiments Community structure analysis in large networks Recent publications focus on electoral systems, digital health technologies, and clinical applications of machine learning. Her work on hip fracture classification demonstrates significant advancements in medical AI applications. Dr. Ehrhardt leads multiple research projects funded by UK Research Councils and health organizations, including studies on chronic pain mechanisms and fetal outcomes following antiepileptic exposure.
Jim Smith is a Professor in the Department of Statistics at the University of Warwick. He actively leads research initiatives in Bayesian statistics, decision theory, and causal modeling with applications to food security, forensic science, and public health. His work bridges theoretical advancements in graphical models with real-world implementations through collaborations with government agencies and industry partners. PhD in Statistics (University of Warwick) Research focus: Bayesian networks, chain event graphs, expert judgment elicitation, causal inference Key collaborations: Martine Barons, Manuelle Leonelli, Ann Nicholson Research Interests Smith's work centers on high-dimensional Bayesian decision analysis, causal discovery in dynamic systems, and integrating expert judgments into formal models. He has developed novel Chain Event Graphs for complex system modeling and focuses on applications in: Food security risk assessment Forensic evidence evaluation Counter-terrorism strategies Medical imaging analysis Crisis management Energy security Recent Publications Trend His recent work demonstrates increasing focus on adversarial risk analysis (2025), secure data modeling for law enforcement (2024), and methodological improvements in Bayesian networks (2023-2024). Key themes include: Polynomial regression-based moment propagation Multi-agent decision frameworks Dynamic graphical model extensions Causal analysis in security contexts Mathematical foundations of Bayesian graphs Scientific Awards Fellow of the Alan Turing Institute (2017-2024) COST European Cooperation award for expert judgment research EPSRC-funded OxWaSP PhD training consortium co-director Grants & Collaborations Currently serving as PI in 6 Alan Turing Institute projects and CI on 7 others. Major grants include: European Food Standards Agency expert elicitation funding EPSRC OxWaSP initiative National Digital Twin Programme research Home Office policy evaluation projects Labs & Teams Co-leads a university research team studying cross-domain expert coherence. Member of the Food Global Research Priority team coordinating UK food security studies. Developed software tools in R/Python for Chain Event Graph analysis.
