Ju Yeon (Julia) Park is Associate Professor in the Department of Political Science at Ohio State University, specializing in American political institutions and data science. She holds a PhD from NYU and previously held positions at University of Essex and University of Pittsburgh. Her research examines: Legislative and communication strategies in Congress Information dynamics in committee hearings Interbranch relationships between legislators and bureaucrats Textual analysis of political discourse Methodologically, Park employs machine learning, causal inference models, and experimental designs to study how institutional constraints shape political communication. Recent publications analyze argumentation patterns in legislative debates and witness testimony. As faculty affiliate of the Center for Effective Lawmaking, she investigates determinants of legislative success. Her book 'Hearings on the Hill: The Politics of Informing Congress' is forthcoming from Cambridge University Press.
Ryan Browne is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a BMath (2004), MMath (2006), and PhD (2009) from the same institution. His research focuses on model-based clustering , classification , and measurement system quality assessment , with applications in multivariate analysis and statistical inference. He is particularly known for contributions to mixture models and their computational optimization. Education: BMath in Statistics, University of Waterloo (2004) MMath in Statistics, University of Waterloo (2006) PhD in Statistics, University of Waterloo (2009) Ryan’s work emphasizes flexible statistical methodologies , including advancements in skewed distributions, high-dimensional data analysis, and robust algorithms for clustering and classification. He has received the prestigious 2011 W.J. Youden Award from the American Statistical Association for his PhD research on measurement system evaluation. His research trends span computational statistics (e.g., sketching algorithms for big data) and model-based clustering innovations (e.g., mixtures of generalized hyperbolic distributions). Recent work explores parsimonious models, nested Gaussian structures, and efficient parameter estimation for complex datasets. Key Awards: 2011 W.J. Youden Award (American Statistical Association) Ryan collaborates on applied projects, including industrial ecology and sensory data analysis. He has developed R packages like mixture and MixGHD , which implement his methodological contributions.
Marco Valtorta is a Professor and Graduate Director in the Department of Computer Science and Engineering at the University of South Carolina’s Molinaroli College of Engineering and Computing. He specializes in Artificial Intelligence, with a focus on normative reasoning under uncertainty, Bayesian networks, causal models, and computational complexity. His work includes developing algorithms for structure learning in graphical models, causal inference, and applications in multiagent systems. Education: Ph.D., Computer Science, Duke University (1987) M.A., Computer Science, Duke University (1984) Laurea, Electrical Engineering, Politecnico di Milano (1980) Research Interests: Dr. Valtorta’s work integrates logical and probabilistic reasoning, with contributions to causal models, chain graphs, and adversarial machine learning. His funded projects include collaborations with the Office of Naval Research (ONR), IARPA, and the U.S. Department of Agriculture (USDA). Notable collaborations include applying Bayesian networks to healthcare and developing frameworks for trustworthiness assessment in AI systems. Grants & Collaborations: Multi-institution IARPA project on Wigmorean/Bayesian networks for argumentation ONR-funded research on Markov properties of directed hypergraphs with Dr. Linyuan Lu Causal analysis for performance modeling of configurable systems His recent publications emphasize causal inference in AI, automated evaluation of text and sentiment analysis systems, and robustness of foundation models. He has pioneered algorithms for learning chain graphs and addressing adversarial attacks in probabilistic models.
Hyunwoong "Woody" Chang is an Assistant Professor of Statistics in the Department of Mathematical Sciences at The University of Texas at Dallas. He holds a B.S. in Business/Mathematics from Seoul National University (2019) and a Ph.D. in Statistics from Texas A&M University (2024). His research focuses on structure learning of DAG models, convergence of Markov chains, and Bayesian learning methodologies. His work bridges statistical theory and computational methods, with applications in high-dimensional data analysis and model selection. Education: Ph.D. - Statistics, Texas A&M University (2024) B.S. - Business/Mathematics, Seoul National University (2019) Chang's research explores topics such as informed MCMC samplers, complexity analysis of Bayesian models, and Lipschitz continuous autoencoders for anomaly detection. His recent publications emphasize methodological advancements in DAG structure learning, regularization techniques, and rapid convergence algorithms. He currently holds no stated academic awards but actively contributes to statistical theory and computational efficiency in complex models. No advising or grant details are explicitly provided in the text. His affiliation with the School of Natural Sciences and Mathematics suggests involvement in interdisciplinary research teams, though specific lab affiliations are not mentioned.
Olivier FARGES is a Senior Lecturer and HDR (Habilitation à Diriger des Recherches) holder at the University of Lorraine, affiliated with ENSGSI (École Nationale Supérieure de Géologie et Sciences Industrielles) within the Groupe INP. He serves as Director of Industrial Partnerships at ENSGSI and is part of the LEMTA Laboratory (CNRS-University of Lorraine), focusing on multiphysics and multiscale modeling of heat transfer in complex environments. His academic roles include teaching courses such as Heat and Mass Transfer, Fluid Mechanics, Scientific Computing Modeling, and Renewable Energy. Dr. FARGES holds a Ph.D. in Energy and New R&D (2014) and an Engineering degree in Energy Engineering (2010), both from the École de Mines Albi. His research emphasizes coupled conductive-radiative heat transfer in porous media, thermal property characterization of heterogeneous materials, and Monte Carlo-based computational methods for energy systems. He has contributed to advancements in photovoltaic system modeling, solar thermal power optimization, and urban climate studies. His work bridges theoretical and applied thermal engineering, with applications in sustainable energy systems, material science, and industrial partnerships. Key research themes include radiative transfer modeling, multiphysics simulation frameworks, and the development of innovative tools for thermal property measurement and energy performance assessment.
William Roche is a Professor of Philosophy at Texas Christian University (TCU), affiliated with the AddRan College of Liberal Arts. He holds a Ph.D., M.S., and B.S. in Philosophy from The Ohio State University and the University of Utah. His primary research focuses on epistemology, formal epistemology, and philosophy of science, with additional interests in logic, philosophy of language, and philosophy of mind. Roche teaches courses such as Critical Reasoning, Symbolic Logic I, Introduction to Epistemology, and Advanced Issues in Epistemology. His work explores topics like foundationalism, Bayesian confirmation theory, and probabilistic support structures. Recent publications include discussions on the perils of parsimony, evidence screening-off mechanisms, and infinite regresses in epistemological frameworks. His research bridges formal methods with traditional philosophical inquiry, addressing questions in scientific methodology and epistemic justification. Roche’s contributions often engage with debates in cognitive science and the logical underpinnings of knowledge claims.
Scott W. Linderman is an Assistant Professor of Statistics at Stanford University and a Faculty Scholar at the Wu Tsai Neurosciences Institute. He holds courtesy appointments in Computer Science and is affiliated with Stanford Bio-X and the Stanford AI Lab. His research focuses on developing probabilistic models and statistical methods to analyze neural data, bridging computational neuroscience and machine learning. Linderman earned his PhD in Computer Science from Harvard University, with postdoctoral training at Columbia University under Liam Paninski and David Blei. He previously worked as a software engineer at Microsoft and holds an undergraduate degree in Electrical and Computer Engineering from Cornell University. Research Interests : Machine learning, computational neuroscience, state space models, neural data analysis, and probabilistic modeling. His lab develops tools like the SSM and Dynamax packages, applying methods to problems such as neural decoding, behavioral tracking, and understanding latent neural dynamics. Awards : 2023 McKnight Scholar Award, 2022 Sloan Research Fellowship, Leonard J. Savage Award (2016). Linderman has advised over 20 PhD students and postdocs, contributing to breakthroughs in neuroscience and machine learning. His work includes collaborations with experimental neuroscientists like David Anderson and Sebastian Seung. Labs & Teams : Linderman Lab focuses on advancing statistical methods for neuroscience. Key projects include state space models (e.g., rSLDS, Gaussian Process SLDS) and behavioral analysis tools like Keypoint MoSeq. The lab emphasizes open-source software and interdisciplinary collaboration.
William Parnell is a Professor of Applied Mathematics at the University of Manchester's School of Mathematics. His research focuses on continuum mechanics, metamaterials, and industrial composites, with applications in soft tissue mechanics and acoustic wave manipulation. He leads the Mathematics of Waves and Materials (MWM) group and co-founded the Manchester Materials Modelling Centre (M3C). He has held roles including EPSRC Fellowship 'NEMESIS' (2014-2019) and its extension, contributing to transformative materials science. Education: BSc Mathematics (First Class), University of Bristol (1996-1999) MSc Mathematical Modelling and Scientific Computing (Distinction), University of Oxford (1999-2000) PhD in Applied Mathematics, University of Manchester (2001-2004) His research interests span elastic wave propagation, cloaking, and viscoelastic modeling. He has pioneered hyperelastic cloaking techniques and developed mathematical methods for metamaterials. His work contributes to UN Sustainable Development Goals related to advanced materials and digital innovation. Key achievements include the 2019 Whitehead Prize and over 80 publications. His grants include funding for microstructured material design and collaborations with Thales UK and the National Physical Laboratory. Grants & Awards: EPSRC Fellowships (NEMESIS and extension) Whitehead Prize (2019) Labs/Teams: MWM Group (focusing on waves and materials) M3C (Manchester Materials Modelling Centre)
Nick Heard is a Professor and Chair in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on computational Bayesian inference, clustering, and changepoint analysis applied to dynamic networks (e.g., computer networks, social networks) and bioinformatics. He leads the EPSRC-funded NeST project on Network Stochastic Processes and Time Series, collaborating with universities including Bristol, Oxford, and LSE. His work bridges statistical theory with applied problems in cyber-security and neuroscience. Research Interests - Modelling large dynamic networks - Changepoint analysis and anomaly detection - Statistical methods for cyber-security - Bayesian computation and inference - Spectral clustering and graph embeddings Grants & Collaborations - Co-leads the NeST project on Dynamic graph embeddings: procedures and inference - EPSRC Programme Grant (EP/T004870/1) supporting Network Stochastic Processes and Time Series research Software & Tools - Developed open-source packages for Bayesian changepoint analysis (e.g., changepoints ) - Code for p-value combination methods ( standardised_partial_product )
Professor Cleo Kontoravdi is a Professor of Biological Systems Engineering at the Department of Chemical Engineering, Imperial College London, within the Faculty of Engineering. Her roles include Director of Postgraduate Studies (2021–present) and Postgraduate Admissions Tutor (2018–2021). She holds affiliations with key research centers such as the Centre for Process Systems Engineering, Centre for Synthetic Biology, and Future Vaccine Manufacturing Research Hub. Her research focuses on applying systems engineering principles to bioprocessing, integrating model-based tools like sensitivity analysis and optimization with experimental work on mammalian cell cultures and vaccine production. Key areas include metabolic flux analysis, media optimization, and multiscale modeling. Her recent publications highlight advancements in hybrid modeling frameworks, mRNA vaccine process design, and metabolic engineering of CHO cells. She actively contributes to vaccine manufacturing strategies and sustainable biopharmaceutical supply chains. Education: PhD (2007) and MEng (2002) in Chemical Engineering from Imperial College London. Professional experience spans academic roles (since 2007) and industry R&D at Lonza Biologics (2006–2007). Her work bridges computational biology, bioprocess engineering, and systems biology, addressing challenges in vaccine development and production efficiency. Awards: Not explicitly listed in the provided text. Research grants and collaborations are implied through her involvement in high-impact projects like the Future Vaccine Manufacturing Hub. She advises on bioprocess optimization, glycoengineering, and biomanufacturing sustainability.
Keith J. Holyoak is a Distinguished Professor in the Department of Psychology at the University of California, Los Angeles (UCLA), where he conducts foundational research in cognitive psychology. His work centers on human reasoning, learning, decision making, and problem solving, with a specific focus on the psychological mechanisms of analogy and relational knowledge across diverse domains including law, politics, mathematics, and science. He directs the Reasoning Lab at UCLA, integrating experimental, computational, and neuroimaging approaches to investigate cognitive processes. Education: Ph.D. from Stanford University Research Focus: Holyoak's research program systematically explores how analogy facilitates knowledge transfer and learning, examining the neural underpinnings of complex reasoning with emphasis on prefrontal cortex functions. His work bridges theoretical cognitive science with practical applications, investigating causal learning, deductive processes, and the constraints shaping human inference. Through computational modeling and cross-domain studies, he reveals universal principles governing relational reasoning while addressing domain-specific manifestations in scientific, legal, and social contexts. Publication Trends: His scholarly output demonstrates consistent evolution from foundational work on pragmatic reasoning schemas (1980s) toward integrated models of causal learning and Bayesian inference (2000s-2010s). Recent publications emphasize rational analysis frameworks, cross-species comparisons in causal cognition, and the role of invariance principles in knowledge generalization. The corpus reveals deep methodological pluralism—combining behavioral experiments, computational modeling, and neuroimaging—to address fundamental questions about the architecture of human thought. Research Infrastructure: Holyoak leads the Reasoning Lab (https://reasoninglab.psych.ucla.edu), which maintains a collaborative environment for interdisciplinary research. The lab's infrastructure supports advanced experimental paradigms, computational modeling suites, and neurocognitive investigations, facilitating research on analogical transfer, causal inference, and decision-making under uncertainty across the lifespan.
Peng Ding is an Associate Professor in the Department of Statistics at the University of California, Berkeley. He holds a B.S. in Mathematics and B.A. in Economics from Peking University, followed by an M.S. in Statistics from the same institution. He earned his Ph.D. in Statistics from Harvard University in 2015 and completed a postdoctoral fellowship at Harvard T.H. Chan School of Public Health. His research focuses on causal inference, missing data, Bayesian statistics, and applied statistical methods in biomedical and social sciences. Ding is particularly known for his work on improving the robustness of causal inference in observational studies and randomized experiments through sensitivity analysis and design-based approaches. His research interests include methodologies to address contaminated data (e.g., missing values, measurement errors), factorial experiments, and sensitivity analysis for unmeasured confounding. He has contributed to theoretical advancements in rerandomization, regression adjustment, and instrumental variable techniques. His work emphasizes practical applications in fields such as epidemiology, social sciences, and public health. Peng Ding teaches courses on causal inference, statistical theory, and linear models. His most recent courses include Data, Inference, and Decisions and Linear Models . He actively mentors graduate and undergraduate students through directed study programs. His research has been published in top-tier statistical journals and presented at international conferences.
Zachary E. Ross is a Professor of Geophysics at the California Institute of Technology (Caltech) and holds the William H. Hurt Scholar distinction since 2021. His research integrates machine learning, computational mathematics, and seismology to analyze earthquakes and fault zones using large seismic datasets. Education B.S., University of California, Davis (2009) M.S., California Polytechnic State University, San Luis Obispo (2011) Ph.D., University of Southern California (2016) His research focuses on high-resolution imaging of fault zones, understanding earthquake sequences in space and time, and applying artificial intelligence to seismic data analysis. He develops scalable algorithms for waveform inversion, ground-motion synthesis, and real-time seismic monitoring. Recent publications highlight his work on neural operators for wave propagation, AI-driven seismicity analysis, and induced earthquake dynamics. He teaches advanced courses including Ge 264 – Machine Learning in Geophysics and Ge 271 – Dynamics of Seismicity . Scientific Awards William H. Hurt Scholar (2021–present)
Paul Milgrom is the Shirley and Leonard Ely Professor of Humanities and Sciences in the Department of Economics at Stanford University , with courtesy appointments in the Department of Management Science and Engineering and the Graduate School of Business . As co-founder and chairman of Auctionomics , he applies auction theory to high-stakes bidding scenarios. Co-recipient of the 2020 Nobel Prize in Economic Sciences for auction theory advancements Distinguished Fellow of the American Economic Association (2020) 2018 John J. Carty Award (with Kreps and Wilson) Milgrom's research spans Auction Theory , Market Design , Game Theory , and Industrial Organization . His work on radio spectrum auctions has reshaped global telecommunications policy. Key award trends: 2018 : CME Group-MSRI Prize, John J. Carty Award 2014 : Golden Goose Award 2012 : BBVA Foundation Frontiers of Knowledge Award 2008 : Nemmers Prize His publications reveal expertise in mathematical economics , organizational theory , and telecommunications policy , with recent focus on AI/ML applications to market design and combinatorial auction complexity .
Ed Chien is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. He specializes in applying differential geometry and topology to graphics, computational engineering, and machine learning. Previously, he was a postdoctoral researcher at MIT's CSAIL and Bar-Ilan University. His work focuses on mathematically rigorous solutions to problems in geometric data processing and optimal transport. Education PhD in Mathematics, Rutgers University (2015) A.B. in Mathematics & Physics, Dartmouth College (2009) Research Highlights Dr. Chien's research includes fundamental studies on hexahedral mesh topology for Finite Element Modeling, optimal transport applications in machine learning, and singularity-free geometric algorithms. His work bridges theoretical mathematics with computational tools for engineering and graphics. Publications span top venues like NeurIPS, Eurographics, and SIGGRAPH, reflecting contributions to geometry processing and machine learning intersections. Program committee roles include Eurographics SGP (2019) and AAAI (2020). Awards & Recognition No specific awards listed, but notable service includes program committee memberships and peer-reviewed contributions to leading conferences. Grants & Advising Active in mentoring through academic positions but no explicit grant details provided. Research focuses on advancing geometric algorithms and optimal transport methodologies.
