Claudia Landeo is a Professor of Economics at the University of Alberta's Department of Economics within the Faculty of Arts. She holds a PhD in Economics from the University of Pittsburgh (2002), alongside an MA in Economics and an MPA in Public Policy. Her research focuses on the Economic Analysis of Law, applying game theory, experimental methods, and legal analysis to design optimal legal and market institutions. She has held visiting roles at Yale Law School, Harvard Law School, and Northwestern University. Education: PhD in Economics, University of Pittsburgh, 2002 MA in Economics, University of Pittsburgh MPA in Public Policy, University of Pittsburgh Research Interests: Economic Analysis of Legal Disputes and Access to Justice Optimal Law Enforcement Mechanisms Contractual Institutions in Legal Settings Experimental Methods in Law and Economics Awards: Andrew W. Mellon Award (University of Pittsburgh) Reuben E. Slesinger Research Award (University of Pittsburgh) Teaching & Grants: Teaching courses in Microeconomic Theory, Game Theory, and Legal Institutions Funded by the Russell Sage Foundation and National Science Foundation Current Projects: Optimal Civil Justice Design Debiasing through Law Mechanisms Corruption and Leniency Policies
Babak Salimi is an Assistant Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego. Previously, he was a postdoctoral research associate at the University of Washington's Department of Computer Science and Engineering, working with Prof. Dan Suciu. He holds a Ph.D. in Computer Science from Carleton University, advised by Prof. Leopoldo Bertossi. His research focuses on integrating causal inference, theoretical data management, and machine learning to build decision-support systems that enhance interpretability for diverse users. Key areas include causal relational learning, fairness in data-driven decisions, and robust offline evaluation frameworks. He has pioneered work on causal DAG summarization, adversarial data corruption testing, and model-agnostic data valuation systems like KAIROS. Salimi's contributions have been recognized with prestigious awards including the ACM SIGMOD Research Highlight Award and Best Paper Award, and the VLDB Best Demonstration Paper Award. His work bridges foundational data science principles with societal impact, emphasizing ethical AI practices and algorithmic accountability. Education: Ph.D., School of Computer Science, Carleton University Research Lab: HDSI's Causal Data Science Group Key Projects: Development of CausaLens, Zorro, and DEMA
Dr Edwin Ip is a faculty member at the University of Exeter Business School, where he serves as Programme Director for MSc Economics, MSc Economics: Behavioural Insights, and MSc Economics: Development Economics. He holds a PhD in Economics from the University of Melbourne and a BA from McGill University, with a focus on applied behavioural economics and decision-making theory. Education: PhD (University of Melbourne), BA (McGill University) His research spans Behavioural Economics , Labour Economics , and Artificial Intelligence applications in the labour market, emphasizing fairness, diversity, and inclusion. Publications highlight experimental studies on gender quotas, AI debiasing in hiring, retirement savings, and statistical discrimination. Notable awards include Google's Award for Inclusion Research. He has advised global organizations and served on the board of the Society for Advancement of Behavioral Economics. His methodological toolkit includes experimental economics, microeconometrics, and surveys.
Carey K. Morewedge serves as Professor of Marketing and Chair of the Department of Marketing at Boston University's Questrom School of Business, where he holds the Everett W. Lord Distinguished Faculty Scholar title. His academic leadership spans departmental administration and research direction in consumer psychology and decision science. Education: PhD in Social Psychology, Harvard University (2006) BA, University of Massachusetts at Amherst (2000) Morewedge's research examines psychological biases in valuation processes across physical goods, money, and emerging technologies. His work reveals how cognitive distortions affect consumer decisions regarding digital goods and artificial intelligence, with particular focus on medical AI adoption and psychological ownership mechanisms. He pioneers experimental methods to demonstrate how humans misjudge algorithmic outputs due to self-serving biases, and develops interventions to improve judgment quality through debiasing training. His publication portfolio shows clear evolution toward human-AI interaction research, with 12 of 15 most recent articles addressing algorithm acceptance, bias correction, and medical AI. This shift reflects growing scholarly and industry interest in AI-human collaboration, while maintaining foundational work in cognitive biases and consumer psychology. Scientific Awards: Wegner Theoretical Innovation Prize (Society for Personality and Social Psychology) Best Paper Award (Journal of Consumer Research) MSI Scholar designation New York Times Idea of the Year Poets and Quants Top 40 Under 40 Professors Everett W. Lord Distinguished Faculty Scholar Morewedge secures substantial research funding exceeding $2.4 million from diverse sources, supporting his investigations into consumer-AI interaction. His industry collaborations with Pinterest, Merck, and IARPA translate academic insights into practical applications. He actively mentors doctoral students and presents research at major institutions including Stanford, Harvard, and Johns Hopkins, while leading the Marketing Department's strategic direction through curriculum development and faculty recruitment. His research program operates through interdisciplinary collaborations with medical centers and technology institutes, notably the AI and Health Working Group at Johns Hopkins Medical Center and Boston University's Digital Business Institute. These partnerships facilitate real-world testing of behavioral interventions in healthcare and technology contexts.
Minna Gräns is Professor of Jurisprudence at Uppsala University's Department of Law. She obtained a Bachelor of Laws from Turku (1990), Legum Magister from the University of Tübingen (1991), Licentiate of Laws from Helsinki (1991), and Doctor of Laws from Uppsala University (1995). She previously served as Associate Professor at the University of Helsinki (1996) and Visiting Professor at Minnesota Law School (2015). Her institutional roles include Research Committee membership (2011–2014) and directorship of Research/Postgraduate Education (2000–2006). Her research examines legal decision-making through interdisciplinary lenses, integrating law with psychology, cognitive science, and philosophy. Key themes include: Confirmation bias in judicial/prosecutorial contexts Forensic evidence reliability and error sources Debiasing techniques for legal practitioners Theoretical frameworks for evidence evaluation Systemic reform of criminal appeal procedures Her publications demonstrate a consistent focus on cognitive psychology applications in law, particularly bias mitigation in convictions and interrogations. Recent work (2018–2023) emphasizes empirical analysis of judicial errors, while earlier scholarship explores foundational jurisprudence and European evidence standards. No scientific awards, grants, or supervised students are documented in the provided text.
Dr. Mingzhou Yin is a postdoctoral researcher at the Institute of Automatic Control within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover, where he has been working since August 2024. He received his Doctor of Sciences degree from ETH Zurich in 2024 under the supervision of Prof. Roy S. Smith, with a dissertation titled 'Regularized and Nonparametric Approaches in System Identification and Data-Driven Control.' His research interests span data-based modeling and control, sparse learning theory, system identification using subspace and regularized methods, model predictive control, and periodic system theory. Dr. Yin has developed innovative approaches in low-rank matrix regression, Gaussian process-based control of nonlinear systems, and closed-loop identification frameworks. His work bridges theoretical advances with practical applications in energy-flexible buildings and aerospace systems. Dr. Yin has received significant recognition including the IEEE Control Systems Society Swiss Chapter Young Author Best Journal Paper Award and the Systems Identification and Adaptive Control Technical Committee Outstanding Student Paper Prize in 2023. His publications in IEEE Control Systems Letters, Automatica, and other top journals demonstrate his contributions to data-driven control theory. IEEE Control Systems Society Swiss Chapter Young Author Best Journal Paper Award (2023) Systems Identification and Adaptive Control Technical Committee Outstanding Student Paper Prize (2023) As an educator, Dr. Yin has supervised numerous student projects on data-driven predictive control, sparse learning algorithms, and closed-loop identification of networked systems. His teaching includes 'Data- and Learning-Based Control' exercises and previous TA roles for 'Robust Control and Convex Optimisation' and 'System Identification' courses.
Dr. Tam Cane is an Associate Professor in Social Work and Social Care at the University of Sussex, School of Education and Social Work. She joined in 2018 as Course Lead for the BA Social Work program and previously held leadership roles in MA/BA programs. Her research focuses on anti-racist social work practice, transracial adoption frameworks (AFDiT), HIV/AIDS reproductive health, and contextual safeguarding. Key contributions include the BRAC2eD model for debiasing adoption assessments and the AFDiT framework co-created with transracial adoptees. Dr. Cane leads funded projects such as 'Hidden in Plain Sight: Exploring Brighton's Colonial Heritage' (PI) and 'Anti-racist Framework for Decision Making in Adoption' (PI). She co-investigates initiatives like designing 'safe spaces' for Black children in care placements and evaluating anti-racist practices in regional services. Teaching specialties include social work values, ethics, and modules on adversarial environments, risk management, and professional development. Notable awards include the 2021 'Navigating White Privilege Educational Video.' She supervises PhD students in adoption, HIV studies, race/racism, and leadership. Her work emphasizes decolonizing education, foster care equity, and intersectional advocacy.
Yaniv Plan is an Associate Professor of Mathematics at the University of British Columbia. His research focuses on applied probability, compressive sensing, matrix completion, and mathematical foundations of machine learning. He co-organizes the interdisciplinary group Mathematics of Information, Learning, and Data (MILD). His work bridges high-dimensional probability with applications in signal processing and data science. He teaches advanced courses such as Probability in High Dimensions and Compressed Sensing , emphasizing theoretical foundations like concentration inequalities, random matrix theory, and optimization. His research has led to advancements in 1-bit compressed sensing, matrix completion, and robust recovery algorithms. Notably, he received the NIPS 2018 Best Paper Award for contributions to learning Gaussian mixtures via sample compression. Plan’s publications address topics like sub-Gaussian matrices, weighted matrix completion, and non-smooth stochastic gradient descent. His work often combines rigorous mathematical analysis with practical applications in compressed sensing and sparse recovery. He collaborates widely, with co-authors including Roman Vershynin, Emmanuel Candès, and Mary Wootters. His research has been supported by grants exploring compressed sensing, high-dimensional data, and algorithmic robustness.
Lénaïc Chizat is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) within the School of Basic Sciences and Institute of Mathematics. He chairs the Dynamics of Learning Algorithms (DOLA) laboratory and teaches advanced courses in machine learning and computational optimal transport, focusing on mathematical analysis of neural networks and measure transportation theory. His research centers on optimal transport theory and its applications to deep learning, with emphasis on Wasserstein geometry, entropic regularization, and gradient flow dynamics. He investigates implicit regularization in neural networks, convergence properties of learning algorithms, and the infinite-width limits of deep architectures. His work bridges theoretical mathematics with practical machine learning challenges, particularly in computational aspects of modern supervised learning. Analysis of his 15 most recent publications (2023-2025) reveals a strong thematic focus on entropic optimal transport, where he has made fundamental contributions to Sinkhorn algorithm convergence in continuous settings and Wasserstein barycenter computation. His research consistently explores the mathematical foundations of deep learning, especially training dynamics, min-max optimization, and the role of initialization in neural network scaling. Chizat currently advises PhD student Wang Guillaume Yitian and leads the DOLA laboratory, which develops theoretical frameworks for understanding learning algorithm dynamics through the lens of optimal transport and measure-valued optimization.
Seungyoon Lee is a Professor at Purdue University specializing in network analysis, resilience, and disaster recovery research. Her work examines the evolution of communication, knowledge, and collaboration networks in organizational contexts with significant applications in disaster management and community resilience. Her educational background includes: Ph.D. from the University of Southern California M.A. from the University of Southern California B.A. from Yonsei University Professor Lee's research centers on applying socio-cultural evolution theories to complex networks including multiplex and multimodal structures. She investigates creative interaction patterns in project teams, network dynamics in disaster recovery contexts, and the determinants of multiplex ties across diverse settings. Her methodological expertise bridges organizational communication theory with advanced social network analysis techniques. Analysis of her publication trajectory reveals consistent focus on disaster resilience with increasing emphasis on hurricane evacuation decision-making, social support networks in recovery processes, and information environments during crises. Recent work integrates network analysis with psychological factors like risk perception and uncertainty to explain evacuation behaviors, while earlier research established foundations in organizational network evolution and multiplexity. Her notable recognition includes: 2009 W. Charles Redding Dissertation Award from the Organizational Communication Division of the International Communication Association Professor Lee teaches graduate and undergraduate courses in social network analysis, organizational communication, and research methods. Her active research program demonstrates strong connections between theoretical network frameworks and practical disaster management applications, with particular relevance to household recovery processes and community resilience building. Current projects indicate growing interest in misinformation dynamics during pandemics and conflict-affected communities. While specific laboratory affiliations aren't detailed in the source material, her research methodology suggests collaboration with interdisciplinary disaster response teams and network analysis specialists, particularly evident in hurricane evacuation studies and post-disaster community assessments.
Luke McEllin is a Research Fellow at Central European University (CEU), holding concurrent appointments in the Department of Cognitive Science, the Social Mind Center, and the Center for Belief Update and Debiasing (DEBIAS). Based in Vienna at Quellenstrasse 51, Office A 507, his work centers on the cognitive and behavioral mechanisms underlying human social coordination. Education: PhD in Cognitive Science, Central European University McEllin's research program investigates how non-verbal communicative processes enable joint action coordination, employing motion tracking and psychophysical methodologies to quantify interpersonal dynamics. His recent work extends into the relationship between joint action and commitment mechanisms, bridging social cognition with decision theory. This interdisciplinary approach spans cognitive science, experimental psychology, and social neuroscience, with implications for understanding cooperative behavior and social cognition disorders. No scientific awards are documented in available sources. Current advising relationships and research grants are not specified in public profiles, though his affiliation with DEBIAS suggests involvement in belief-update research initiatives. McEllin actively contributes to CEU's Social Mind Center and DEBIAS research ecosystems, participating in collaborative projects that integrate cognitive modeling with empirical social interaction studies.
Aaron Hudson, PhD is an Assistant Professor in the Biostatistics, Bioinformatics and Epidemiology Program within the Vaccine and Infectious Disease Division and Public Health Sciences Division at the Fred Hutchinson Cancer Center. He is also a Member of the Translational Data Science Integrated Research Center (TDS IRC) at Fred Hutch. Education: PhD in Biostatistics from University of Washington, Seattle (2021) BS in Statistics from University of Illinois at Urbana-Champaign (2016) Dr. Hudson's research focuses on high-dimensional and nonparametric statistical inference with applications in public health and medicine. His primary research interests include nonparametric statistics, high-dimensional data analysis, causal inference, graphical models, and vaccine efficacy trials. His work develops and employs statistical tools to analyze immune responses to vaccination and assess risks of adverse health outcomes, utilizing advanced computational techniques including machine learning. An analysis of his recent publications reveals a strong focus on methodological development for nonparametric inference on function-valued statistical parameters that arise in causal inference and prediction problems. His work particularly emphasizes applications in vaccine efficacy trials, analysis of high-dimensional biological networks, and treatment effect heterogeneity. The integration of machine learning techniques with rigorous statistical inference appears as a consistent theme across his research portfolio. Dr. Hudson has developed several methodological approaches for analyzing complex biomedical data, including covariate-adjusted differential network analysis, statistical inference for qualitative interactions, and nonparametric methods for dose response functions. His GitHub repositories demonstrate active software development to implement these statistical methods. Prior to joining Fred Hutch, Dr. Hudson was a postdoctoral fellow in the Division of Biostatistics at the University of California, Berkeley School of Public Health, working with Maya Petersen and Mark van der Laan. He completed his PhD at the University of Washington Department of Biostatistics under Ali Shojaie.
Martin Shepperd is Professor of Software Technology and Modelling and Head of the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD in Computer Science from the Open University (1991) and is a Fellow of the British Computer Society. Previously a software developer at HSBC, his work includes a secondment to the Parliamentary Office of Science & Technology. His research focuses on empirical software engineering and machine learning, spanning cost modeling, defect prediction, data quality, and replication studies. He investigates cognitive biases in software development and develops rigorous evaluation methodologies for predictive models. Publications emphasize software defect prediction, empirical methodologies, and machine learning applications, with recent work analyzing retracted papers, metric biases, and replication frameworks. Research consistently addresses methodological quality and statistical robustness in software engineering experiments. Awards: Fellow of the British Computer Society Best Short Paper Award (EASE 2016) Best Full Paper Award (ESEM 2016) Supervised PhD student Boyce Sigweni (2013-2016). Principal investigator for EPSRC projects including 'MeLLow' and 'Comparing Software Defect Classifiers Correctly'. Leads the Brunel Software Engineering Laboratory (BSEL) research group.
Shauna Concannon is an Assistant Professor in the Department of Computer Science at Durham University, specializing in Digital Humanities and Computational Linguistics. Her research focuses on the societal and ethical implications of emerging technologies, particularly in natural language processing (NLP), dialogue systems, and AI bias mitigation. She holds a PhD from Queen Mary University of London and has conducted postdoctoral research at the Universities of Cambridge, York, and Newcastle. Her work emphasizes participatory design, feminist approaches to data science, and equitable technology development. Shauna’s academic journey began with a master’s in modernist literature from the University of Oxford, followed by interdisciplinary training in computational linguistics. She explores how technologies mediate human interaction, including studies on harmful biases in AI systems and the linguistic encoding of societal values. Her recent projects address ethical AI in storytelling, empathy measurement in dialogue systems, and public engagement with open data through personalized narratives. Education: PhD in Computational Linguistics (Queen Mary University of London) Masters in Modernist Literature (University of Oxford) Key Research Themes: Ethical AI and algorithmic bias Social impact of conversational AI Feminist data science methodologies Public participation in technology governance Her publications span computational social science, digital humanities, and human-computer interaction, with notable contributions to frameworks for responsible AI in nonprofit contexts and analysis of pandemic-era science communication. Shauna currently advises four postgraduate students exploring topics like bias reduction in machine translation and ethical design for children’s conversational AI.
Carolin Holtermann is a Doctoral Researcher in Data Science at the University of Hamburg Business School. She holds a Master's degree in Data Science from the University of Mannheim, where her thesis focused on Computational Argumentation, Ethics, and Sustainability in AI. After graduation, she worked as a Data Science Consultant optimizing supply chain processes using machine learning. Currently part of Prof. Dr. Anne Lauscher's team, her research emphasizes Natural Language Processing, Multilingual Models, and Responsible AI. Education: Master's in Data Science, University of Mannheim (2021) Research Interests: Natural Language Processing Multimodal Learning Responsible AI Parameter-Efficient Transfer Learning Publications highlight contributions to multilingual capabilities of LLMs, bias analysis in vision-language models, and efficient knowledge composition techniques. Her work bridges technical advancements with ethical considerations in AI systems.
