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David B. Grusky is the Barbara Kimball Browning Professor and Edward Ames Edmonds Professor in the School of Humanities and Sciences at Stanford University. He serves as Director of the Stanford Center on Poverty and Inequality, co-editor of Pathways Magazine , Senior Fellow at the Stanford Institute for Economic Policy Research, and Faculty Fellow at the Center for Population Health Sciences. His research examines structural inequality, mobility, and poverty, with an emphasis on data-driven policy solutions. Grusky holds a B.A. in Sociology from Reed College (1980) and an M.S. (1983) and Ph.D. (1987) in Sociology from the University of Wisconsin-Madison. His research addresses late-industrial inequality through multiple lenses: rent-seeking and market failure in income inequality; economic mobility; essentialist barriers to gender equality; cross-class interaction patterns; and the decline of traditional social classes. He pioneers projects leveraging administrative data and qualitative methods to monitor poverty and inequality, including the American Opportunity Study and American Voices Project. Recent publications (2019–2024) focus on economic mobility, COVID-19 impacts, gender segregation, and poverty reduction. These works employ interdisciplinary approaches spanning sociology, economics, network science, and policy analysis, consistently emphasizing empirical rigor and actionable insights for reducing inequality. Major honors include: Member, National Academy of Sciences Member, American Academy of Arts and Sciences Fellow, American Association for the Advancement of Science Max Weber Award (2004, corecipient) Founder, Cornell University Center for the Study of Inequality Former Presidential Young Investigator As Faculty Director of the Stanford Center on Poverty and Inequality, Grusky oversees the annual State of the Union poverty reports and leads initiatives to integrate big data into social policy. The center collaborates with interdisciplinary teams to address contemporary challenges in inequality measurement and intervention.

• Social Stratification
• Poverty
• Economic Inequality
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Anthony Bloch is the Alexander Ziwet Collegiate Professor of Mathematics and Professor of Mathematics at the University of Michigan, serving as Chair of the Department of Mathematics. He is affiliated with the Center for the Study of Complex Systems (CSCS) in Weiser Hall. His research focuses on: Geometric Mechanics : Hamiltonian/Lagrangian mechanics, symplectic geometry, and integrable systems including Toda lattices and rigid body dynamics Nonlinear Dynamics : Nonholonomic systems with nonintegrable constraints, stability analysis, and continuous-discrete flow relationships Control Theory : Nonlinear and optimal control applications extending to quantum dynamics and astrophysical systems Recent publications demonstrate geometric methods applied to nonholonomic control, virtual constraints, and stabilization, with growing interdisciplinary work in network dynamics, quantum control, and cosmological models like cosmic reheating. Through the Center for the Study of Complex Systems, Professor Bloch collaborates across disciplines to investigate complex phenomena in natural and engineered systems, leveraging geometric frameworks to address fundamental questions in mechanics and dynamics.

• Geometric Mechanics
• Nonlinear Dynamics
• Hamiltonian Mechanics
• +10 more

Corbin E. Covault serves as Professor and Chair of the Department of Physics at Case Western Reserve University (CWRU), leading experimental research in particle astrophysics and cosmic ray physics through major international collaborations including the Pierre Auger Observatory and Cherenkov Telescope Array (CTA). B.A., Massachusetts Institute of Technology (1985) Ph.D., Harvard University (1991) Covault's research centers on experimental particle astrophysics with emphasis on ultra-high energy cosmic rays, gamma-ray astronomy, and advanced instrumentation. His group develops photodetection systems, GPS timing solutions, and wireless communications for cosmic ray detectors. Key projects include investigating macroscopic dark matter using Auger fluorescence telescopes, designing a 100+ meter diameter 'flat' telescope for exoplanet detection, and pioneering spectral CT imaging with quantum dot X-ray detectors. His instrumentation work directly impacts next-generation observatories like CTA, where his team deploys silicon photomultiplier camera systems. Analysis of Covault's 15 most recent publications (2016-2019) reveals dominant research trends in cosmic ray mass composition, anisotropy studies, and multi-messenger astrophysics. His work consistently bridges particle physics and astrophysics through ultra-high energy cosmic ray observations, neutrino follow-ups of gravitational wave events, and development of novel detection methodologies. Key thematic areas include hadronic interaction modeling at extreme energies, radio-based cosmic ray detection, and searches for exotic particles like magnetic monopoles. Covault leads the CWRU group in the Pierre Auger Collaboration as a member of its 15-person Technical Board, overseeing instrument performance and data integrity. His group received MRI grant funding for CTA camera development starting August 2018, focusing on GPS timing synchronization and rapid trigger processing. Collaborative projects include spectral CT imaging with Philips and Wayne State University, optical SETI telescope development, and macro dark matter investigations with Glenn Starkman. His laboratory activities center on the Auger and CTA observatories, with instrumentation specialties in photodetection systems, GPS timing networks, and wireless data transfer. The CWRU group maintains active roles in Auger's science analysis teams and CTA's prototype deployment at Mt. Hopkins Observatory, Arizona.

• Particle Astrophysics
• Gamma-ray Astronomy
• Cosmic Ray Physics
• +3 more

Steve Boker is a Professor of Psychology at the University of Virginia, directing the Human Dynamics Laboratory and the LIFE Academy. His research focuses on quantitative psychology, structural equation modeling (SEM), and dynamical systems analysis for longitudinal and time series data. Dr. Boker has pioneered methods like Differential Structural Equation Modeling (dSEM) , Latent Differential Equations (LDE) , and the Windowed Cross-Correlation (WCC) method. He co-developed the widely used OpenMx SEM software framework and invented the RAMpath method for path diagram analysis. Key Research Areas: Dyadic conversation dynamics, adaptive systems in addiction, motion symmetry in social interactions, maternal-infant coupling, and resilience modeling through longitudinal data. Awards & Honors: 2024 Distinguished Researcher Award (UVA) 2020 Saul Sells Award for lifetime achievement in multivariate psychology Fellow, American Psychological Association Fellow, Association for Psychological Science His methodological contributions span over 150 publications, with recent work emphasizing nonlinear dynamics, surrogate data validation, and complexity metrics like the Tangle index for short time series analysis.

• Quantitative Psychology
• Structural Equation Modeling
• Longitudinal Data Analysis
• +5 more

Klaus Moeltner is a Professor in the Department of Agricultural and Applied Economics at Virginia Tech since 2015. He previously held positions at the University of Nevada, Reno, including Associate and Assistant Professor roles. His expertise lies in environmental economics, natural resource valuation, and applied econometrics. He has secured over $6 million in grants as PI or Co-PI and contributes to interdisciplinary research through the Global Change Center and Remote Sensing Program at Virginia Tech. Education: Ph.D., Economics, University of Washington, 2000 M.A., Economics, University of Washington, 1998 M.A., International Policy Studies, Monterey Institute of International Studies, 1994 M.S., Environmental Planning & Engineering, University of Agriculture and Forestry, Vienna, Austria, 1990 Research Interests: Dr. Moeltner’s work focuses on environmental and natural resource economics, including water quality valuation, wildfire health impacts, urban water use, and coastal flooding risks. His research employs advanced econometric methods, such as Bayesian modeling and meta-regression, to inform policy decisions. Current projects include analyzing the economic benefits of stream restoration, harmful algal bloom forecasts, and offshore wind farm co-location opportunities. Awards: 2023: Distinguished Graduate Teaching Award (AAEA) 2014: Best Paper in Environmental and Resource Economics (EAERE) 2004: CABNR Outstanding Instructor Award Advising & Grants: Advised 6 Ph.D. students and secured over $6 million in grants. Research collaborations include USDA-NIFA, EPA, and the European Union. Labs & Teams: Active in interdisciplinary groups like the Global Change Center and Remote Sensing Program at Virginia Tech.

• Environmental Economics
• Natural Resource Valuation
• Applied Econometrics
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Jessica J. Fridrich is a Distinguished Professor in the Department of Electrical and Computer Engineering at Binghamton University, part of the State University of New York (SUNY) system. She is affiliated with the T. J. Watson School of Applied Science and Engineering. Her research focuses on steganography, steganalysis, digital forensics, and machine learning, with notable contributions to secure data hiding and patented camera fingerprinting techniques approved for legal evidence. Education: PhD in Electrical and Computer Engineering from Binghamton University Her research interests include steganography and steganalysis of digital images, digital forensics for linking photos to cameras via sensor fingerprints, signal estimation and detection, and applications of machine learning. Earlier work explored chaotic nonlinear dynamical systems and encryption. Her methods have led to over 150 refereed publications and seven successfully commercialized patents. Her articles emphasize advancements in batch steganography, JPEG compatibility, and adaptive embedding strategies. Recent work leverages machine learning for steganalysis and explores security trade-offs in high-dimensional feature spaces. 2006-2007 Chancellor's Award for Excellence in Scholarship and Creative Activities 2002 Chancellor's Award for Outstanding Inventor Narrative on advising and grants: She mentors graduate students and leads projects funded by AFOSR, NSF, and AFRL. Her research addresses challenges in data hiding security, forensic analysis, and optimizing steganographic algorithms. The Digital Data Embedding Lab, which she directs, focuses on algorithmic innovation and empirical validation in steganography and forensics. Labs/Teams: Digital Data Embedding Lab

• Steganography
• Digital Forensics
• Signal Processing
• +4 more

Karin A Dahmen is a Professor in the Department of Physics at the University of Illinois at Urbana-Champaign, affiliated with the Carl R. Woese Institute for Genomic Biology. Her research focuses on disordered systems, avalanche dynamics, and plasticity in metallic glasses. She explores material deformation mechanisms, critical phenomena, and the interplay between structure and mechanical properties in complex materials. Her work bridges condensed matter physics and materials science, with emphasis on slip avalanches in bulk metallic glasses, serration statistics in high-entropy alloys, and nanoscale magnetic ordering dynamics. Recent studies include experimental investigations of muscovite mica micromechanics and novel methods for analyzing compressive ductility in metallic glasses. Key achievements include the discovery of universal avalanche statistics across materials from nano-crystals to earthquakes, and the development of theoretical frameworks explaining memory effects in cyclically deformed glasses. Her honors include the APS Fellowship (2013), Guggenheim Fellowship (2016), and Sloan Research Fellowship (2001). Research trends show sustained focus on critical phenomena in materials under stress, with recent emphasis on seismic analogs in slip events and chemo-mechanical weakening mechanisms. Over 170 publications demonstrate her leadership in understanding deformation dynamics across multiple length scales.

• Materials Science
• Condensed Matter Physics
• Disordered Systems
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Tim Futing Liao is a Professor of Sociology & Statistics at the University of Illinois at Urbana-Champaign, holding the LAS Alumni Distinguished Professorial Scholar title. He serves as President of the Sequence Analysis Association and incoming Editor of Socius . His research focuses on inequality, life course analysis, quantitative methodology, population studies, and collective memory. Liao earned his PhD from the University of North Carolina at Chapel Hill. Research Interests: Dr. Liao's work spans migration patterns (especially Filipino/Indonesian domestic workers), health disparities, sequence analysis methodologies, and social stratification. He combines advanced statistical techniques with sociological theory to explore topics like wage inequality, mental health among migrants, and the role of collective memory in social movements. Awards & Roles: Recipient of the ASA Methodology's Paul F. Lazarsfeld Award (2021), he has held editorial roles at top journals including Sociological Methodology , Demography , and Advances in Life Course Research . He also developed influential software tools for sequence analysis and inequality measurement. Grants & Collaborations: Active in interdisciplinary projects, his work involves collaborations on migrant labor conditions, health policy analysis, and methodological advancements. Notable outputs include R/Stata packages for sequence analysis and inequality decomposition. Labs/Teams: Leads research initiatives on life course trajectories and quantitative sociology methodologies at the University of Illinois, contributing to both theoretical advancements and practical applications in social science research.

• Inequality & Mobility
• Life Course Analysis
• Quantitative Methodology
• +4 more

Mohammad Mahmoody is an associate professor in the Computer Science Department at the University of Virginia. His research focuses on theoretical aspects of cryptography, computational complexity, and machine learning, particularly on understanding barriers such as lower bounds and impossibility results. He has taught courses including Algorithms, Cryptography, and Theory of Computation. His work explores foundational questions in cryptography, adversarial robustness in machine learning, and computational assumptions underlying cryptographic primitives. Education: Ph.D. in Computer Science from Princeton University (2010) Affiliations: University of Virginia, Department of Computer Science Research Interests: His interests span cryptography (e.g., encryption schemes, coin-tossing, and registration-based systems), theoretical computer science (computational complexity, algorithms), and machine learning (adversarial robustness, data poisoning). He emphasizes formal proofs and rigorous analysis in security and learning frameworks. Publications: Recent work includes studies on quantum-resistant cryptography, adversarial machine learning, and cryptographic protocol design. Key themes involve impossibility results, lower bounds for cryptographic assumptions, and the interplay between computational constraints and learning theory. Service & Grants: - Served on program committees for ICML, NeurIPS, CRYPTO, and TCC - Organized workshops on cryptography and lower bounds - Research funded by grants exploring adversarial robustness and cryptographic foundations Labs/Teams: - Collaborations with researchers in cryptography, machine learning, and theoretical computer science - Advises a team of graduate students and postdocs in security and learning theory.

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

Jie Peng, Ph.D., is a Professor and Vice-Chair for Graduate Affairs in the Department of Statistics at the University of California, Davis. Her research focuses on statistical methodologies with applications in genomics, neuroimaging, and functional data analysis. She holds a Ph.D. from Stanford University and is actively involved in advancing statistical theory and its practical implementations in biomedical and computational sciences. Education Ph.D. in Statistics, Stanford University Research Interests Dr. Peng’s work spans several key areas including graphical models, high-dimensional inference, and neuroimaging analysis. She develops innovative statistical tools for analyzing complex biological data, such as tumor genomics and brain connectivity patterns derived from diffusion MRI. Her research emphasizes the interplay between theoretical statistics and real-world applications in healthcare and precision medicine. Professional Contributions She serves as an Associate Editor for the Journal of Computational and Graphical Statistics and has contributed to numerous high-impact publications. Her methods have been applied to identify biomarkers in ovarian cancer and to study brain lateralization using Human Connectome Project (HCP) data. Dr. Peng also leads efforts in developing computational frameworks for spatial transcriptomics and dynamic network modeling.

• Graphical Models
• High Dimensional Inference
• Functional Data Analysis
• +4 more

Narges Mahyar is an Associate Professor at the University of Massachusetts Amherst in the Manning College of Information and Computer Sciences (CICS) . She is currently on sabbatical with the Aviz team at the Inria Center, University of Paris-Saclay . Her research focuses on Human-Computer Interaction (HCI) , Information Visualization , and Digital Civics , aiming to empower communities through technology. Education: She holds a PhD in Computer Science from the University of Victoria, an MS in Information Technology from the University of Malaya, and a BS in Electrical Engineering from Tehran Azad University. She completed postdoctoral fellowships at the University of British Columbia (2014–2016) and the University of California San Diego (2016–2018). Research Interests: Her work addresses complex societal challenges like climate change, urban planning, and healthcare by designing inclusive technologies. Key areas include civic engagement, data visualization for equity, and integrating AR/VR for public participation. Notable projects include CommunityClick and RisingEMOTIONS , which enhance public input in decision-making. Publications & Awards: With over 50 publications, her work has received prestigious awards including Best Paper Awards at CHI 2023 , Eurovis 2022 , and CSCW 2020 . Her research emphasizes ethical design and inclusivity, particularly involving marginalized communities. Grants & Advising: She has secured grants totaling over $1.4 million, including NSF funding for projects like "Mapping Instability" . Advises PhD student Mahmood Jasim and collaborates with Ali Sarvghad and Pari Riahi on interdisciplinary research. Labs & Teams: Leads the HCI-VIS Lab at UMass, focusing on social computing and visualization. Collaborates internationally with teams like Aviz and Inria on civic tech initiatives.

• Human-Computer Interaction (HCI)
• Information Visualization
• Digital Civics
• +4 more

Gerome Miklau is a Professor of Computer Science at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences (CICS). He leads the DREAM Lab and focuses on privacy, security, and equitable data management, particularly in differential privacy and fair data analysis. His work includes designing algorithms for private data synthesis, privacy-preserving SQL engines, and auditing systems like AuditGuard. He co-founded Tumult Labs to commercialize privacy technology and advised the U.S. Census Bureau on privacy for the 2020 decennial census. Education: Ph.D. in Computer Science from the University of Washington (2005), B.S. in Mathematics and Rhetoric from UC Berkeley (1995). Research Interests: Differential privacy, secure data management, fairness in algorithms, privacy-preserving data synthesis, and forensic database analysis. His lab develops tools like Ektelo and PrivateSQL, addressing challenges in privacy-accurate tradeoffs and scalable private data processing. Awards: 2006 ACM SIGMOD Dissertation Award, 2007 NSF CAREER Award, 2013 ICDT Best Paper Award, and two ACM PODS Test-of-Time Awards (2020 and 2012). Grants & Service: Co-chair of OpenDP Advisory Board, steering committee member for TPDP workshops, and organizer of the 'Data, Responsibly' Dagstuhl workshop. His service includes program committees for SIGMOD, ICML, and FAT*. He teaches courses on databases, privacy, and programming. Labs/Teams: DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and collaborations with the Center for Data Science and Cybersecurity Institute at UMass Amherst.

• Data Privacy
• Database Systems
• Differential Privacy
• +3 more

Karim Oweiss is a Pre-eminent Professor at the University of Florida, with joint appointments in the Department of Biomedical Engineering (Herbert Wertheim College of Engineering), Electrical and Computer Engineering, and Neuroscience (McKnight Brain Institute). He holds a Ph.D. in Electrical Engineering and Computer Science from the University of Michigan (2002). His research focuses on neural mechanisms of sensorimotor integration and the development of clinically viable brain-machine interfaces (BMIs) to restore damaged neurological function. His work spans computational neuroscience, neural decoding, optogenetics, and advanced neurotechnology, with a strong emphasis on closed-loop systems and neural plasticity. 2025 : Chemogenetic stimulation of phrenic motor output and diaphragm activity 2024 : Chemogenetic phrenic motoneuron activation enables increased tidal volume 2023 : Compressive sensing of functional connectivity maps from patterned optogenetic stimulation Oweiss has received the NSF Excellence in Neural Engineering Award (2001) and is a Senior Member of the IEEE. He has published extensively on topics including neural decoding, compressive sensing, and multiscale neural interfacing. As editor of Statistical Signal Processing for Neuroscience and Neurotechnology (2010), he has contributed significantly to the field's methodological foundations. His lab develops tools like NeuroQuest for large-scale neural data analysis and implantable neuroprocessors for wireless BMI applications.

• Neuroscience
• Biomedical Engineering
• Electrical Engineering
• +5 more

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +7 more

Bernard Koch is an Assistant Professor in the Department of Sociology at the University of Chicago. His research focuses on the science of science, culture, cultural evolution, AI ethics, and computational social science. Prior to joining the University of Chicago, he was a Postdoctoral Research Fellow at Northwestern University’s Kellogg School of Management. He earned his Ph.D. in Sociology from UCLA in 2023. His work bridges sociology and computational methods, exploring topics such as machine learning applications in social research, ethical implications of AI, and cultural dynamics. Recent projects include analyzing the role of large language models (LLMs) in social simulations and confronting the misuse of genetics in online contexts. Koch’s articles span themes like data-centric machine learning (DMLR), the historical development of deep learning, and the lifecycle of datasets in research. He also investigates cultural evolution through case studies like music subcultures and material culture diversification. He holds no listed awards but has contributed to interdisciplinary collaborations, including genomics research on the Mnemiopsis leidyi organism. While no formal advisees are listed, his research group likely engages in computational social science projects.

• Science of science
• Culture
• Cultural evolution
• +2 more