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Fredrik Lundvall Wollbraaten is a Research Fellow at the University of Oslo, affiliated with the Department of Mathematics within the Faculty of Mathematics and Natural Sciences. His PhD research, supervised by Professor Geir Olve Storvik and in collaboration with Professor Ingrid Glad and Associate Professor Morten Stakkeland (ABB), focuses on sequential learning and decision-making from maritime sensor data, including failure detection and maintenance rule optimization. His work combines methodological development in model building, inferential tools, and computational methods. Education: Master's in Statistics from the University of Oslo (2020), Bachelor's in Mathematics from NTNU (2018). Prior to his PhD, he worked at RagnaRock Geo (AI startup), SpareBank 1 Markets (equity research analyst), and Nordea Markets (Data Scientist). His academic interests span applied Bayesian methods, parallelized MCMC simulations, and statistical learning techniques. He is part of the Big Insight group and contributes to projects on statistical and machine learning methods for sensor data analysis. Fredrik is also actively involved in the Oslo Sports Student Association (OSI).

• Bayesian Statistics
• Computational Statistics
• Data Science
• +3 more

Louveton Nicolas is an Assistant Professor at the University of Poitiers, affiliated with the Center for Research on Cognition and Learning (CeRCA) and the Maison des Sciences de l'Homme et de la Société (MSHS). His research focuses on cognitive ergonomics, human-computer interaction, and virtual/augmented reality applications. 2024 - HDR (Habilitation à Diriger des Recherches) obtained 2021-2024 - Leads the Web, Editorial and UX Strategy master's degree 2018-2022 - Co-directed UX/UI Ergonomics course 2017-2020 - Carnot cognition correspondent Research interests center on usability evaluation, visual attention mechanisms, and cognitive workload management in complex environments. His work spans automotive interfaces, cyber crisis management, and accessibility solutions through smart glasses technology. Recent publications analyze human-system collaboration in cyber crisis simulations, VR-based robot control evaluation, and anthropomorphic representation in autonomous vehicle interfaces. Key projects include RESISTECC (cyber crisis management), INTEROPS (industrial VR ergonomics), and CMI (AI trust in self-driving cars). University of Poitiers faculty Collaborations with Tohoku University (Japan), IMS Bordeaux, and Luxembourg Institute of Science and Technology Thesis supervision in progress: Marine Desvergnes and Deslande Liboutchi Pepe

• Human-Computer Interaction
• Cognitive Ergonomics
• Virtual and Augmented Reality
• +4 more

Dr. Theodore Tomeny serves as Associate Professor of Psychology and Director of Undergraduate Studies at the University of Alabama, with affiliations spanning the Center for Innovative Research in Autism, Center for Youth Development and Intervention, and Alabama Research Institute on Aging. His work bridges clinical practice and academic research within the autism spectrum disorder (ASD) community. Dr. Tomeny earned his Ph.D. from The University of Southern Mississippi followed by a pre-doctoral internship specializing in autism at Indiana University School of Medicine. His educational background established the foundation for his dual focus on clinical application and research innovation in developmental disabilities. His research program centers on autism spectrum disorder and intellectual developmental disabilities across the lifespan, with particular emphasis on family dynamics, sibling relationships, and long-term care planning for aging families. He investigates quality-of-life factors, cross-neurotype interactions, stakeholder perceptions of ASD, and systemic barriers faced by rural and underserved populations. Current projects examine undergraduate attitudes toward autistic peers, social interaction experiences of autistic adults, and rural caregivers' knowledge of autism. Analysis of Dr. Tomeny's publication trajectory reveals consistent thematic focus on family systems surrounding ASD, with increasing attention to lifespan transitions and rural healthcare disparities. His work demonstrates methodological diversity spanning systematic reviews, mixed-methods studies, and naturalistic interventions, all oriented toward improving service delivery and family outcomes. Dr. Tomeny mentors a dynamic research team within the Research on Autism and Families (RAF) Lab, supervising graduate students including Allison Birnschein, Rachel Harmon, Jong-Woo Suh, and Olivia Ward alongside undergraduates like Amaya McClain. His trainees have secured prestigious positions at institutions including Indiana University School of Medicine, Children's Hospital Colorado, and Cook Children's Medical Center. The RAF Lab operates as a multidisciplinary hub conducting eight active research initiatives including long-term care planning for autistic adults, social interaction studies for autistic adults, and investigations into rural teachers' knowledge of autism. The lab maintains strong clinical ties with the University of Alabama ASD Clinic and UA-ACTS program to ensure research translation into practice.

• Autism Spectrum Disorder
• Intellectual and Developmental Disabilities
• Family Psychology
• +6 more

Derek Hoiem is a Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School for Computing and Data Science since 2009. He holds a PhD in Robotics from Carnegie Mellon University (2007) and completed a Beckman Postdoctoral Fellowship (2008). As co-founder and Chief Science Officer of Reconstruct, he develops AI-driven tools for construction progress monitoring. His research focuses on computer vision, object recognition, scene understanding, and graphics. Key honors include IEEE Fellow (2022), University Scholar (2022), and the Sloan Research Fellowship (2013). He has received NSF CAREER and Intel Early Career awards, alongside best paper awards at CVPR (2006) and WACV (2015). His teaching excellence is reflected in numerous 'List of Teachers Ranked as Excellent' accolades across multiple semesters. Research interests span 3D reconstruction, neural networks, and multimodal models. His work addresses challenges in visual program generation, neural radiance fields, and construction monitoring through photogrammetry. He leads projects in continual learning, efficient AI systems, and explainable multimodal interactions. Professional contributions include grants from NSF and industry partnerships. He advises on cutting-edge AI applications in construction, graphics, and vision-language integration. His lab’s innovations bridge theoretical advancements with real-world systems like Reconstruct’s Visual Command Center.

• Computer Vision
• Object Recognition
• Scene Understanding
• +7 more

Konstantinos Georgalos is a Senior Lecturer in Behavioural and Experimental Economics at the Lancaster Management School , Lancaster University . He serves as Director of the Lancaster Experimental Economics Laboratory (LExEL) and is affiliated with the Alliance of Excellence for Research and Innovation on Aephoria (AE4RIA) . His research focuses on decision-making under risk and ambiguity , emphasizing dynamic choice , behavioral game theory , and subjective belief modeling through classical and Bayesian structural estimation. Research spans complexity aversion , loss aversion , higher-order risk preferences , and machine learning applications for behavioral prediction. His work explores public goods games , coordination mechanisms , and spontaneous order emergence in strategic settings. He supervises Jess Alam , an Integrated PhD student, and contributes to peer-review for journals like Management Science , Games and Economic Behavior , and Journal of Risk and Uncertainty . Georgalos has published extensively on topics such as ambiguity in games , gender effects in risk attitudes , and heuristics versus cumulative prospect theory , reflecting his expertise in experimental design and econometric modeling of behavioral decisions.

• Decision Making under Risk and Uncertainty
• Behavioural and Experimental Economics
• Game Theory
• +4 more

Mingda Qiao is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, starting in Fall 2025. His research focuses on learning theory, theoretical computer science, and sequential decision-making frameworks. He holds a PhD in Computer Science from Stanford University (2025) and a BEng in Computer Science from Tsinghua University. Before joining UMass, he served as a FODSI postdoc at UC Berkeley and MIT, where he contributed to collaborative federated learning and prediction theory research. He actively reviews for top conferences and journals in machine learning and theoretical computer science.

• Learning Theory
• Theoretical Computer Science
• Machine Learning
• +3 more

Anqi Wu is an Assistant Professor at the School of Computational Science and Engineering (CSE) within the College of Computing at Georgia Institute of Technology. She previously held a postdoctoral fellowship at Columbia University’s Zuckerman Mind Brain Behavior Institute and earned her Ph.D. in Computational and Quantitative Neuroscience from Princeton University, with a graduate certificate in Statistics and Machine Learning. Her research focuses on developing probabilistic modeling approaches and scalable inference algorithms for neural and behavioral analysis, with applications in robotics and real-world problems. Education: Ph.D., Computational and Quantitative Neuroscience, Princeton University (2014–2019) Graduate Certificate in Statistics and Machine Learning, Princeton University Postdoctoral Fellow, Center for Theoretical Neuroscience, Columbia University (2019–2022) Research Interests: Probabilistic Modeling for Neural Latent Discovery: Developing disentangled generative models to extract interpretable neural representations. Behavior Analysis: Extracting multi-layered information from animal/human behavior via hierarchical models and reinforcement learning. Efficient Inference: Advancing Bayesian neural networks, Gaussian processes, and scalable algorithms. Her work bridges machine learning, neuroscience, and robotics, with applications in brain decoding, motor control, and reward learning. Publications Trends: Recent work emphasizes reinforcement learning (e.g., EmoBipedNav for emotion-aware robotics), generative models (diffusion models for neural dynamics), and Bayesian methods (Gaussian processes for multi-region brain analysis). Applications span robotics, neuroscience, and spatio-temporal data modeling. Affiliations: Director, BRAINML Lab (BRAIN Intelligence and Machine Learning) Affiliated with Machine Learning Center, Neuro @ GT, and Bioengineering at Georgia Tech

• Machine Learning
• Computational Neuroscience
• Probabilistic Modeling
• +3 more

Mario Ghossoub is an Associate Professor and Sun Life Research Fellow at the University of Waterloo, specializing in Actuarial Science. His research focuses on optimal risk-sharing mechanisms, reinsurance markets, game-theoretic models of insurance, and behavioral economics. He investigates topics such as Pareto optimality, distortion risk measures, and decision-making under uncertainty, particularly in contexts involving heterogeneous beliefs and risk preferences. Key research areas include multi-armed bandit problems under mean-variance frameworks, Nash equilibria in large reinsurance markets, and the design of efficient peer-to-peer insurance systems. His work bridges actuarial science with economics, finance, and decision theory to address challenges in risk management, contractual design, and market efficiency. Recent contributions analyze counter-monotonic risk allocations, robust distortion risk measures, and Stackelberg equilibria in multi-agent systems. His articles often explore the interplay between behavioral biases (e.g., rank-dependent utility) and traditional actuarial principles, with applications to flood risk management and decentralized exchange markets. While no specific scientific awards are listed, his extensive publication record reflects significant contributions to theoretical and applied aspects of risk and insurance. His work is characterized by rigorous mathematical modeling and relevance to practical policy design in financial and insurance sectors.

• Actuarial Science
• Risk Management
• Insurance Economics
• +4 more

José C. Príncipe is a Distinguished Professor at the University of Florida , where he holds the Don D. & Ruth Eckis Endowed Distinguished Professor position in the Department of Electrical and Computer Engineering. His primary research area is Signals and Systems , with a focus on adaptive systems and learning theory. Education Agregado, University of Aveiro (1985) Ph.D., Electrical Engineering, University of Florida (1979) M.S., Electrical Engineering, University of Florida (1974) B.S., Electrical Engineering, University of Porto (1972) Príncipe’s research spans machine learning, computational neuroscience, and biomedical signal processing. He has pioneered work in kernel adaptive filtering, information-theoretic learning criteria, and neural network architectures. His recent publications emphasize uncertainty quantification, spiking neural networks, and robust filtering techniques. His 15 most recent articles reflect trends in machine learning (transformers, predictive coding, reinforcement learning), signal processing (kernel methods, entropy-based filtering), and biomedical applications (electrophysiology, pain prediction). Key subfields include neural interfaces, nonlinear system identification, and statistical dependence measures. Scientific Awards : Fellow, American Association for the Advancement of Science (2021) Fellow, National Academy of Inventors (2021) IEEE SP Society Claude Shannon-Harry Nyquist Technical Achievement Award (2021) Life Fellow, IEEE (2017) SEC Faculty Achievement Award Winner (2016) IEEE Neural Network Pioneer Award (2011) Honorary Doctorate, Aalto University (2010) Gabor Award, International Neural Network Society (2006) Príncipe leads the Computational NeuroEngineering Laboratory (CNEL) at the University of Florida, fostering interdisciplinary research at the intersection of neuroscience and engineering. His work includes grants and collaborations in biomedical systems, brain-computer interfaces, and machine learning theory.

• Signals and Systems
• Adaptive Systems
• Learning Theory
• +6 more

Shujian Yu is an Assistant Professor at the Department of Artificial Intelligence , part of the Faculty of Science at Vrije Universiteit Amsterdam. He is also affiliated with the Network Institute . His research focuses on Information Theory , Machine Learning , and Deep Neural Networks , with applications in causal discovery, brain network analysis, and generalization bounds. Key research interests include: Information-Theoretic Methods for ML interpretability and robustness Transfer entropy and causal inference in complex systems Feature selection and dimensionality reduction techniques Brain network-based psychiatric diagnosis (e.g., schizophrenia analysis) He teaches courses on Data Mining Techniques , Deep Learning , and Introduction to Reinforcement Learning . Recent work explores hierarchical state space models, Cauchy-Schwarz divergence applications, and Granger causality in chemical processes. His collaborations span interdisciplinary fields including neuroscience and industrial engineering. Publications emphasize theoretical foundations while addressing practical challenges in ML generalization, adversarial robustness, and sequential decision-making.

• Information Theory
• Machine Learning
• Deep Neural Networks
• +7 more

Ignacio Monzón is an Associate Professor at the University of Turin (Department of Economics and Statistics, ESOMAS) and Junior Chair at Collegio Carlo Alberto. He holds a Ph.D. in Economics from the University of Wisconsin-Madison (2010). His research focuses on game theory, observational learning, labor economics, delegation, and bargaining, with publications in top journals like the American Economic Review and Theoretical Economics. He teaches Microeconomics, Industrial Organization, and Research Methods at both undergraduate and graduate levels. His current roles include Director of the Master in Economics and Data Analytics at Collegio Carlo Alberto and the University of Turin. He is also a Research Fellow at CEPR. Monzón’s research explores topics such as expert evaluations in decision-making, bargaining dynamics in imperfect markets, and social cooperation through uncertainty. His work bridges theoretical models with applied analyses, including labor market sorting and educational policy.

• Game Theory
• Observational Learning
• Labor Economics
• +3 more

Maryam Tohidi-Moghaddam is a Research Fellow at the University of Hamburg's Faculty of Medicine, affiliated with the Institute of Neurophysiology and Pathophysiology within the Center for Experimental Medicine. Her work focuses on understanding the neurophysiological and cognitive mechanisms underlying decision-making processes, particularly in contexts involving value-learning, context-dependence, and sequential information processing. She holds a Master of Science degree and is based at the N43 building in Hamburg. Her research explores how temporal factors, such as previous decisions and contextual cues, influence human judgment and evidence accumulation. Key areas include rhythmic neural sampling of decision alternatives and the residual effects of prior decisions on current cognitive processes. Her studies bridge neuroscience and cognitive science, contributing to our understanding of neural decision dynamics and behavioral outcomes. Contact: +49 (0) 40 7410-5824 | Email | Located in N43, 4th Floor, Room 411.

• Neuroscience
• Cognitive Science
• Decision-Making Processes
• +3 more

Ibrahim Ekren is an Associate Professor of Mathematics at the University of Michigan, specializing in stochastic control, PDEs, and mathematical finance. He holds a Diplôme d'Ingénieur from École Polytechnique (2009), an M.Sc. from Université Paris VI (2010), and a Ph.D. from the University of Southern California (2014). His research integrates stochastic analysis with applications in finance and machine learning, including market microstructure modeling, online learning algorithms, and high-dimensional optimization. Recent publications demonstrate a focus on quantitative finance (Kyle-Back models, liquidity effects) and machine learning (regret minimization, adversarial prediction). He actively advises doctoral students (including Lu Vy and Liwei Huang) and postdoctoral researchers. His NSF grants include DMS-2406240 (2024-2027) and DMS-2007826 (2020-2024). He serves as associate editor for Finance and Stochastics, Applied Mathematics and Optimization, and Advances in Continuous and Discrete Models.

• Stochastic Control Theory
• Stochastic Partial Differential Equations
• Mathematical Finance
• +3 more

Asim Ansari is the William T. Dillard Professor of Marketing at Columbia University’s Columbia Business School. His research focuses on Internet Recommendation Systems, Digital Customization, Social Network Modeling, and Bayesian Methods for Customer Data. He holds affiliations with the Data, Media and Society; Financial and Business Analytics; and Foundations of Data Science centers. Professor Ansari has been recognized with the Paul Green Award (1994) and the Dean’s Award for Teaching Excellence (2009). His work has been nominated for prestigious awards including the O’Dell Award and Long Term Impact Award. He serves as an Associate Editor for Management Science and Quantitative Marketing and Economics , and is on the editorial boards of Marketing Science and the Journal of Marketing Research . His research explores cutting-edge topics such as generative models for consumer behavior, probabilistic machine learning applications in marketing, and the dynamics of consumer choices. His work bridges statistical methods with real-world marketing challenges, emphasizing data-driven strategies for businesses and consumer insights.

• Internet Recommendation Systems
• Digital Customization
• Social Network Modeling
• +5 more

Salar Ghamat is an Associate Professor in Operations and Decision Sciences at Wilfrid Laurier University's Lazaridis School of Business and Economics, where he holds the Canada Research Chair in Business Analytics in Supply Chain. His research focuses on developing analytical frameworks for supply chain optimization, healthcare operations, and incentive design. Research interests include network coordination in multi-agent systems, game-theoretic approaches to capacity allocation, and behavioral interventions in clinical decision-making. His work frequently examines how information asymmetry affects operational efficiency across manufacturing and service industries. Publications demonstrate strong methodological diversity, combining econometric analysis with optimization models to address sustainability challenges in green supply chains and healthcare policy. Recent work explores blockchain applications for enhancing supply chain transparency while preserving consumer privacy.

• supply chain analytics
• health care analytics
• incentive models
• +2 more