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Stephen John EglenView profile

Professor

• Computational Neuroscience
• Neurodevelopment
• Retinal Development
• +3 more

Stephen John Eglen is a Professor in Computational Biology at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge. He holds the position of Reader, focusing on computational neuroscience with expertise in retinal and visual pathway development. His research explores structural and functional aspects of neural circuit formation, including positional information in retinal neurons and synaptic connectivity mechanisms. Eglen is affiliated with the Computational Biology research group and has contributed to open science initiatives like CODECHECK and DeepClean. His academic career includes roles as a Wellcome Trust Fellow (1997–2000), Lecturer at the University of Edinburgh (2000–2001), and Senior Lecturer at DAMTP (2006–2015). His work bridges computational methods with experimental neuroscience, emphasizing reproducible research practices and computational tools for data analysis. Eglen's research interests span computational modeling of neural development, open science advocacy, and neurophysiological data analysis. He has published extensively on topics like retinal mosaics, neural network topology, and Bayesian statistical methods. His lab develops tools for analyzing neuronal activity in vitro and promoting transparency in scientific workflows.

Kelsey AllenView profile

Assistant Professor

• Cognitive Science
• Machine Learning
• Robotics
• +4 more

Kelsey Allen is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Research Scientist at DeepMind. Her research bridges cognitive science, machine learning, and robotics, focusing on understanding and replicating human-like problem-solving, tool use, and physical reasoning. She holds a PhD from MIT (2016) under Josh Tenenbaum and a B.Sc. in Physics from UBC (2010). Research Interests : Allen investigates computational mechanisms underlying human complex behaviors, particularly tool use and design. Her work emphasizes endowing machines with flexible problem-solving abilities. Key themes include lifelong learning, embodied cognition, and integrating symbolic and neural approaches. Awards & Recognition Best Paper Award at Robotics: Science and Systems (RSS) 2018 Oral Presentation at Cognitive Science Society 2019 Spotlight at NeurIPS 2018 and ICLR 2019 Key Projects : Includes developing graph network simulators for rigid body dynamics, tools for physical design optimization, and studies on human tool-use learning. Her work often combines empirical experiments with machine learning models to bridge human and artificial intelligence.

Shruti TopleView profile

Researcher

• Systems Security
• Privacy
• Machine Learning
• +3 more

Shruti Tople is a Principal Researcher at Microsoft Research in the Azure Research – Security and Privacy group. She holds a Ph.D. from the School of Computing at the National University of Singapore (NUS) , where she received the Dean's Graduate Research Excellence Award . Her work focuses on quantifying and mitigating information leakage in machine learning models while preserving their utility. Key Research Areas include: Systems Security Privacy in Machine Learning Differential Privacy Causal Learning Transfer Learning for Vision & Language Models Recent Publications address challenges such as membership inference attacks, federated backdoor defense, and privacy-enhanced deep learning. Her open-source projects like Analyzing PII Leakage and RobustDG provide practical tools for differential privacy and domain generalization. She collaborates with interns from institutions like NUS , University of Waterloo , and Imperial College London . Scientific Awards : Dean's Graduate Research Excellence Award (NUS)

Konstantin ZuevView profile

Teaching Professor

• Statistical Data Analysis
• Network Data Analysis
• Machine Learning
• +7 more

Konstantin Zuev is a Teaching Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. He holds dual Ph.D.s in Mathematics (Lomonosov Moscow State University) and Civil Engineering (Hong Kong University of Science & Technology). Zuev is also the Undergraduate Option Representative for Information and Data Sciences and Graduate Minor Advisor for the same field. His research focuses on statistical data analysis, network science, machine learning, and computational methods for complex systems. His teaching includes courses like Applied Linear Algebra, Probability Models, and Statistical Inference. Zuev has received multiple prestigious awards, including the 2023 ASCIT and GSC Teaching Awards, the 2021 Humboldt Research Fellowship, and the 2019 Northrop Grumman Prize for Excellence in Teaching. His work spans interdisciplinary topics such as financial market dynamics, epidemiological modeling, and academic curriculum analysis through network theory. Zuev’s research has been published in journals like *Proceedings of the National Academy of Sciences*, *Scientific Reports*, and *Physical Review E*. He is actively involved in consulting projects with organizations like Virtualitics, Inc., focusing on applications in healthcare and network analytics. Beyond academia, he advises student groups like the Caltech Chess Club and Karate Club, reflecting his passion for fostering community engagement and mentorship.

Carlos CinelliView profile

Assistant Professor

• Causal Inference
• Statistics
• Machine Learning
• +4 more

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington. He is also a Data Science Fellow at the eScience Institute and Affiliate Faculty at the Center for Statistics and the Social Sciences. He obtained his Ph.D. in Statistics from the University of California, Los Angeles, where he was advised by Chad Hazlett and Judea Pearl. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences. Key interests include: Causal inference challenges in social and health sciences Intersections of causality with machine learning and AI Sensitivity analysis for omitted variable bias Robust statistical methods for observational studies Instrumental variables and Mendelian randomization Generalizability of experimental findings His publications demonstrate a consistent focus on developing practical sensitivity analysis tools and advancing causal methodology, with recent work emphasizing applications in machine learning and econometrics. Honors include: Best Paper Award at SBE 2024 in Econometrics UCLA Dissertation Year Fellowship (2020) He actively advises PhD students and has received research funding from: NSF/MMS Royalty Research Fund He leads development of several open-source software packages for sensitivity analysis and maintains active collaborations with researchers at UCLA and other institutions.

Armeen TaebView profile

Assistant Professor

• Selective Inference
• Multiple Testing
• Graphical Models
• +7 more

Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).

• Psychometrics
• Structural Equation Modeling
• Bayesian Statistics
• +5 more

Prof. Dr. Holger Brandt serves as Professor of Psychometrics at the Methods Center within the Department of Social Sciences, Faculty of Economics and Social Sciences, Eberhard Karls University of Tübingen since August 2021. Previously, he held Assistant Professor positions at the University of Zurich (2019-2021) and University of Kansas (2016-2019), following a postdoctoral fellowship at Tübingen's Hector Institute for Empirical Educational Research (2013-2016). His educational background includes a PhD (Promotion) from Goethe University Frankfurt's Institute of Psychology in 2013. Brandt's research pioneers advanced methodological frameworks at the intersection of psychometrics, statistics, and machine learning. He specializes in developing dynamic models for intensive longitudinal data, Bayesian estimation techniques, causal mediator analysis, and identification of inattentive response behaviors in surveys. His work rigorously addresses challenges in measurement invariance, structural equation modeling, and handling complex dependencies in social science data. Analysis of his recent publications reveals a dominant focus on Bayesian approaches for latent variable modeling, particularly spike-and-slab priors and latent class methods. His research consistently targets data quality issues in survey methodology while advancing causal inference techniques that relax traditional no-unmeasured-confounder assumptions. Applications span educational research, psychological assessment, and therapeutic alliance dynamics. As a core member of Tübingen's Methods Center, Brandt provides critical methodological infrastructure for social science research across the university, supporting researchers through statistical consulting and advanced methodology development.

Sofia MorelliView profile

Researcher

• Latent mediation analysis
• Causal models
• G-estimate
• +6 more

Sofia Morelli serves as a Research Associate at the Methods Center within the Department of Social Sciences at the University of Tübingen's Faculty of Economics and Social Sciences. She works on the DFG Project BR 5175/2-1 since August 2024, based at Hausserstr. 11 in Tübingen. Her academic background spans physics and quantitative data science, with specialized expertise in advanced statistical methodologies. Her research focuses on cutting-edge statistical methods including latent mediation analysis, causal modeling, and techniques for handling unmeasured confounding in intensive longitudinal data. She specializes in G-estimation approaches and deconfounding methodologies, bridging theoretical statistics with practical applications in social sciences research. Morelli's recent scholarly output demonstrates significant contributions to methodological statistics and environmental applications. Her work shows a clear trajectory toward developing robust causal inference techniques applicable to complex real-world data structures, particularly in scenarios with unobserved confounders. Her academic journey reflects a strong interdisciplinary foundation, transitioning from physics to advanced quantitative methods with applications spanning environmental science, social sciences, and machine learning.

• Artificial Intelligence
• Logic
• Human-centered Artificial Intelligence
• +2 more

Dr. Natasha Alechina is an Associate Professor at Utrecht University's Faculty of Science , specializing in the Intelligent Systems subdomain. Her research focuses on multi-agent systems, formal verification, and logic-based AI, with particular emphasis on resource constraints and human-centered applications. Research Themes : Specification and verification of multi-agent systems Resource-bounded reasoning in coalitions Temporal and probabilistic logic for AI Causal modeling in autonomous agents Article Trends show consistent contributions to multi-agent systems , formal methods , and responsible AI . Her 2023-2025 work explores causal dependencies, intention progression under uncertainty, and ethical frameworks for group responsibility. Contact : n.a.alechina@uu.nl at Buys Ballot Building, Utrecht.

Ivana LolićView profile

Assistant Professor

• Econometrics
• Behavioral Economics
• Economic Uncertainty
• +3 more

Ivana Lolić is an Assistant Professor at the Department of Statistics, Faculty of Economics & Business, University of Zagreb. She holds a PhD in Economics from the same institution, with a focus on economic uncertainty and consumption modeling, and has additional education in Mathematical Statistics (MSc, Summa Cum Laude) and Financial Analysis (BSc). Her research bridges Behavioral Economics and Econometrics, with emphasis on economic sentiment analysis, uncertainty measurement, and macroeconomic modeling. Her scholarly contributions include developing economic sentiment indicators, analyzing inflation expectations, and exploring nonlinear effects in macroeconomic processes. She has received multiple awards for scientific excellence, including the Mijo Mirković Award (2016) and recognition as Most Productive Assistant Professor (2022). She actively reviews for journals like Financial Innovation and European Journal of Finance. Scientific Awards: 2022 Most productive faculty member among Assistant professors 2016 Mijo Mirković Award 2016 Isaac Kerstenetzky Award 2016 Best paper in Official Statistics session 2013 Best paper at Dubai conference She teaches courses in Applied Machine Learning, Statistical Computer Lab, Business Statistics, and Statistics, while mentoring national statistics competition participants. Her work has been funded by Croatian Science Foundation and University of Zagreb grants, with methodological expertise in machine learning, time series analysis, and Bayesian econometrics.

Philip TetlockView profile

Professor

• Political Psychology
• Organizational Behavior
• Decision Science
• +5 more

Philip Tetlock is the Leonore Annenberg University Professor at the University of Pennsylvania, holding appointments in the Department of Psychology (School of Arts and Sciences) and the Wharton School (Management). He is a leading scholar in political psychology , organizational behavior , and forecasting science , with a career spanning institutions like UC Berkeley, Ohio State University, and Penn. His work bridges social psychology , decision theory , and geopolitical analysis . Education : B.A. (1975), M.A. (1976), University of British Columbia; Ph.D. (1979), Yale University Tetlock’s research focuses on expert judgment , forecasting accuracy , and counterfactual reasoning . He pioneered studies showing that while most experts are no better than chance at predicting political events, a subset termed superforecasters consistently outperform peers. His Good Judgment Project (with Barbara Mellers) demonstrated how team-based aggregation and probabilistic thinking improve predictive outcomes. His recent publications analyze prediction markets , crowd wisdom , and Bayesian betting to identify elite forecasters. Articles emphasize decomposing complex questions , calibrating confidence , and reducing cognitive biases through structured methods. Key Awards : American Political Science Association’s Woodrow Wilson Award (2006), Robert E. Lane Award (2006), Harold Lasswell Award (2008), MacArthur Fellowship (1987-1989), and Andrew Carnegie Fellowship (2015). Leadership Roles : Director of the Institute of Personality and Social Research (UC Berkeley, 1988-1995), Associate Dean at Wharton (2003-2004), and co-founder of the Good Judgment Project .

Lukas LuftView profile

Researcher

• Robot Localization and Mapping
• Causality and Foundations of Quantum Physics
• Probabilistic Methods
• +3 more

Lukas Luft is a postdoctoral researcher at the Autonomous Intelligent Systems group within the Department of Computer Science at the University of Freiburg. His work spans robotics and quantum physics, focusing on probabilistic methods for robot localization, multi-robot systems, and causal inference. Post Doc (2020–present) PhD in Computer Science, University of Freiburg (2020) Master and Bachelor in Physics, RWTH Aachen and University of Freiburg His research in Robot Localization and Mapping includes advanced probabilistic techniques like Bayes filters, decentralized algorithms for multi-robot systems, and change detection in environments using full posterior distributions. He also explores Causality and Foundations of Quantum Physics , applying entropic inequalities and information theory to causal discovery and non-locality. The articles highlight his contributions to robotics, particularly in sensor modeling for Lidar, simultaneous localization and mapping (SLAM), and efficient probabilistic methods. In quantum physics, his work addresses causal structures and entropic information, bridging AI with foundational physics. Luft has collaborated with leading researchers, including Prof. Wolfram Burgard and Bernhard Schölkopf, and contributed to key conferences like Robotics: Science and Systems (RSS) and IEEE IROS.

• Marketing Analytics
• Data-Driven Design
• Preference Measurement
• +7 more

Ryan Dew is an Assistant Professor of Marketing and the Govil Family Faculty Scholar at the Wharton School of the University of Pennsylvania. He is affiliated with the Wharton AI and Analytics Initiative, Wharton Human-AI Research, and the Penn Eidos LGBTQ+ Health Initiative. His research bridges marketing, data science, and machine learning, with a focus on developing interpretable computational tools for customer analytics and design. Research Interests: His work centers on marketing analytics, data-driven design, preference measurement, and decision support. He leverages Bayesian nonparametrics, deep generative models, and representation learning to analyze unstructured data (e.g., text, images) in marketing contexts such as branding, logo design, and influencer selection. His methodological approach emphasizes flexibility and interpretability in modeling consumer behavior. The recent publications reflect a strong trend toward integrating AI and machine learning into core marketing problems—particularly in customer segmentation, product design, and influencer marketing. There is a clear emphasis on adaptive systems, dynamic modeling, and the use of rich data sources to enhance decision-making. Scientific Awards: 2022 Frank M. Bass Award 2018 INFORMS SMS Doctoral Dissertation Award 2018 ASA Marketing Section Doctoral Research Award Finalist, 2019 Frank M. Bass Award Finalist, 2020 Paul Green Award Finalist, 2022 John D.C. Little Award 2023 MSI Young Scholar Wharton School's 2024-2025 Govil Family Faculty Scholar Wharton Teaching Excellence Award Advising and Grants: Ryan Dew teaches PhD and MBA courses including MKTG9570 (Empirical Models in Marketing) and MKTG7120 (Data & Analysis for Marketing Decisions). He advises PhD students through dissertation supervision (MKTG9950) and independent study. He co-organizes a cross-school virtual reading group on probabilistic ML and Bayesian computation for junior faculty and PhD students, indicating active mentorship and collaboration. While specific grants are not listed, his research awards and scholar designation suggest competitive funding support. Labs and Teams: He is actively involved in the Wharton AI and Analytics Initiative and Wharton Human-AI Research, suggesting participation in interdisciplinary research teams focused on AI applications in business. His collaborations span multiple institutions and disciplines, including psychology, computer science, and statistics.

Brian MillerView profile

Senior Lecturer

• Epistemology
• Formal Epistemology
• Philosophy of Perception
• +4 more

Brian Miller is a Senior Lecturer in the Department of Philosophy at Rice University, where he has taught since completing his PhD at the University of Texas at Austin in 2016. His research focuses on epistemology, formal epistemology, and their intersections, particularly exploring the rational significance of perceptual experience and Bayesian models of belief revision. He also investigates logic's normative role and the history of modern philosophy, with a special interest in Hume. His work bridges philosophical analysis with formal methods, addressing topics like perceptual learning, epistemic undermining, and the structure of justification. Though no specific grants or awards are listed, his publications reflect a deep engagement with foundational questions in epistemology and philosophy of mind. His professional website is available at briantmiller.net .

Yury PolyanskiyView profile

Professor

• Information Theory
• Machine Learning
• Statistics
• +3 more

Yury Polyanskiy is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Laboratory for Information and Decision Systems (LIDS), the Institute for Data, Systems, and Society (IDSS), and the MIT Statistics and Data Science Center. He holds a Ph.D. from Princeton University (2010) and an M.S. from the Moscow Institute of Physics and Technology (2005). His research focuses on information theory, machine learning, statistical inference, error-correcting codes, and wireless communication. He has contributed to fundamental limits of communication systems, finite-blocklength analysis, and applications of information theory to learning and signal processing. Notable awards include the 2020 IEEE Information Theory Society James Massey Award, the 2013 NSF CAREER Award, and the 2011 IEEE Information Theory Society Paper Award. His work spans theoretical advancements and practical applications, including the development of the SPECTRE toolbox for short-packet communication. He is also co-authoring a textbook on information theory. Recent research highlights include studies on quantization techniques for machine learning (e.g., NestQuant), transformer-based empirical Bayes methods, and novel approaches to massive random access in wireless networks (e.g., unsourced multiple access). His contributions bridge information theory and modern data science, addressing challenges in high-dimensional data representation, neural network dynamics, and efficient communication architectures.