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Daniel E. Ho holds multiple prestigious positions at Stanford University: William Benjamin Scott and Luna M. Scott Professor of Law Professor of Political Science Professor of Computer Science (by courtesy) Senior Fellow, Stanford Institute for Economic Policy Research Senior Fellow, Stanford Institute for Human-Centered Artificial Intelligence Faculty Fellow, Center for Advanced Study in the Behavioral Sciences He serves on the National Artificial Intelligence Advisory Commission (NAIAC), as Senior Advisor on Responsible AI at the U.S. Department of Labor, and as a Public Member of the Administrative Conference of the United States (ACUS). Ho earned his J.D. from Yale Law School and Ph.D. from Harvard University, completing a clerkship with Judge Stephen F. Williams on the U.S. Court of Appeals for the District of Columbia Circuit. His research bridges artificial intelligence, law, and public policy with emphasis on: Regulatory governance frameworks for AI systems Fairness and bias mitigation in algorithmic decision-making Environmental enforcement using satellite imagery and computer vision Methods for estimating racial disparities without direct demographic data Legal AI reliability and statutory research systems Analysis of his 2025 publications reveals a consistent focus on practical AI governance tools addressing real-world regulatory challenges. Key themes include developing benchmarks for legal applications, mitigating hallucination in legal AI tools, and creating systems for statutory research. His work demonstrates strong integration of technical AI methods with policy implementation, particularly in environmental enforcement and fairness assessment. As Director of the Regulation, Evaluation, and Governance Lab (RegLab), Ho leads interdisciplinary research partnerships with government agencies. While specific grant details aren't provided, RegLab's operational model indicates substantial research funding for policy-relevant AI projects. No student advisees are mentioned in available materials. Ho's leadership extends to national advisory roles where he shapes federal AI policy through evidence-based recommendations, particularly regarding environmental protection and civil rights enforcement mechanisms.

• Artificial Intelligence Policy
• Regulatory Governance
• Fairness in AI
• +3 more

Professor Nikos Tzavidis is a Professor of Statistical Methodology at the University of Southampton . He has held academic positions at institutions including University College London and the University of Manchester, and currently leads research initiatives integrating statistical methodology with geospatial and survey data. His work focuses on Small Area Estimation, official statistics, and poverty mapping, with applications across global development and public policy. Research interests: Small Area Estimation and Official Statistics Outlier Robust Inference Quantile and M-quantile Models Geospatial Data Analysis Poverty Mapping Machine Learning Applications in Statistics Recent research trends emphasize machine learning integration, geospatial data utilization, and advanced quantile modeling for poverty and demographic studies. His publications span journals like the Journal of Official Statistics and the Journal of the Royal Statistical Society . Scientific awards: ISI Fellow (2013) American Statistical Association (2004) Vice Chancellor's Teaching Award (2013) Johann von Spix Professor (2023) He supervises PhD students in social statistics, demography, and geography, and serves on editorial boards for journals including the Journal of Official Statistics . Externally, he has held leadership roles in the International Statistical Institute and contributed to United Nations and World Bank projects.

• Small Area Estimation
• Official Statistics
• Outlier Robust Inference
• +7 more

Dr. Xinan Zhang is an Associate Professor in the School of Engineering at The University of Western Australia (UWA), specializing in Electrical, Electronic, and Computer Engineering. He holds a BEng from Fudan University (2008) and a PhD from Nanyang Technological University (2014). Before joining UWA in 2019, he held roles as a Lecturer and Research Fellow in Singapore and Australia. His research focuses on power electronics, electrical machine drives, and renewable energy, with over 60 top-tier publications. He is the Portfolio Lead for Industry Engagement in UWA's School of Engineering and co-leads the Power and Clean Energy (PACE) research group. Education: BEng in Electrical Engineering, Fudan University (2004–2008) PhD in Electrical Engineering, Nanyang Technological University (2010–2014) Research Interests: Dr. Zhang’s work spans power electronics, renewable energy systems, energy storage, and smart grid technologies. He emphasizes practical applications, such as battery management systems for vanadium redox flow batteries and adaptive control strategies for microgrids. His contributions address challenges in energy efficiency, grid stability, and sustainable power solutions. Articles & Trends: Recent publications focus on advanced control algorithms for inverters, battery modeling, and renewable energy integration. His work combines data-driven methods with traditional control theory to enhance system efficiency and reliability. Notable areas include DC microgrid control, vanadium redox flow battery optimization, and model predictive control for power electronics. Awards: Listed in Stanford University’s Top 2% Scientists (2020–2022) Grants & Collaborations: He leads or co-leads projects funded by the Australian government and industry partners, including the GenX Betavoltaic Battery Pilot Manufacturing Process and Mine Electrification . These projects aim to advance clean energy technologies and industrial applications. Labs & Teams: As co-lead of the PACE group, he fosters interdisciplinary collaboration to tackle global energy challenges, aligning with UN Sustainable Development Goals for affordable and clean energy (SDG 7).

• Power Electronics
• Renewable Energy Systems
• Electrical Machine Drives
• +3 more

Professor Alexandra M. Schmidt is a leading academic in Biostatistics at McGill University, holding an endowed University Chair. She specializes in spatial and spatio-temporal modeling, particularly in epidemiology and environmental health. Previously, she served as a Full Professor at the Federal University of Rio de Janeiro (2012–2016). Her research focuses on Bayesian methodologies for analyzing complex processes, including disease spread, environmental hazards, and socio-economic disparities. She has authored influential books such as Spatio-Temporal Methods in Environmental Epidemiology with R (2023) and contributed to over 150 peer-reviewed articles. Key awards include the ISBA Fellowship (2024), ASA Fellowship (2020), and the Abdel El-Shaarawi Award (2008). Education: PhD in Statistics (2001, University of Sheffield, UK), MSc and BSc in Statistics (Federal University of Rio de Janeiro, Brazil). Research interests span Bayesian inference, spatial statistics, and environmental epidemiology. She has advised numerous PhD/MSc students and collaborated on projects linking statistical methods to public health challenges, such as modeling dengue outbreaks and air pollution impacts. Active in academic service, she has chaired major conferences (e.g., 2022 ISBA World Meeting) and serves on editorial boards of top journals like Bayesian Analysis and Canadian Journal of Statistics . Teaching includes advanced courses on generalized linear models, spatial epidemiology, and Bayesian analysis. Her work bridges theoretical statistics with practical applications, addressing global health issues through innovative spatio-temporal modeling techniques.

• Biostatistics
• Bayesian Inference
• Spatial Statistics
• +3 more

Kristofer Pister is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He co-directs the Berkeley Sensor and Actuator Center (BSAC) and the Ubiquitous Swarm Lab. His career spans groundbreaking innovations in Micro/Nano Electro Mechanical Systems (MEMS), Control Systems, and Low-Power Circuits, with a focus on Smart Dust and synthetic insects. Education: Ph.D. and M.S. in EECS from UC Berkeley (1992, 1989); B.A. in Applied Physics from UC San Diego (1986). His research areas include MEMS , Control Systems , Robotics , and Integrated Circuits , with recent work on self-powered micro-sensors, crystal-free radios, and interplanetary swarm networks. Key awards include the ISA Albert F. Sperry Founder Award (2009) , Alexander Schwarzkopf Prize (2006) , and the NSF CAREER Award (1996) . He has authored numerous influential publications in wireless sensor networks and microrobotics. His lab, Ubiquitous Swarm Lab , explores distributed robotics and swarm intelligence. Pister emphasizes open collaboration in research, ethical conduct in academia, and efficient resource utilization for graduate students.

• Micro/Nano Electro Mechanical Systems (MEMS)
• Control Systems
• Robotics
• +6 more

Benjamin Born serves as Professor of Macroeconomics at Frankfurt School of Finance & Management and Research Director at the ifo Institute. He is a Research Fellow at CEPR and CESifo, advises the European Commission's DG ECFIN, serves on the European Parliament's Expert Group on Monetary Policy, and sits on the CEPR–EABCN Euro Area Business Cycle Dating Committee. Starting in September 2025, he will join the University of Bonn as Professor of Macroeconomics. Education PhD in Economics, 2011, University of Bonn, Germany MSc in Econometrics and Economics, 2007, University of York, UK BA/MA in Economics, 2006, University of Siegen, Germany Professor Born's research focuses on business cycles, fiscal and monetary policy, heterogeneous agent models, and empirical methods in macroeconomics. His work bridges theoretical modeling with empirical analysis, often using innovative data sources including firm surveys and social media data. He has made significant contributions to understanding how monetary policy affects different segments of the economy, how fiscal policy transmits through various channels, and how firms form expectations about the future. His recent publications reveal a strong trend toward analyzing heterogeneous effects in macroeconomics, particularly examining how different groups (firms, workers, consumers) respond differently to economic shocks and policies. His work increasingly incorporates social media data and novel survey methodologies to capture real-time economic behavior. A significant portion of his research addresses policy responses to the COVID-19 pandemic, including fiscal stimulus packages and lockdown effects. Professor Born is actively involved in the academic community, serving on the editorial boards of the Journal of Monetary Economics and the European Economic Review. He regularly organizes major academic conferences including the BASEforHANK Winterschool and the ifo Conference on Macroeconomics and Survey Data. Teaching and Supervision Currently teaches Macroeconomics II (first-year Ph.D. course at BGSE) Has taught Macroeconomics and Econometrics at all levels Supervises theses in macroeconomics and applied econometrics

• Business Cycles
• Fiscal Policy
• Monetary Policy
• +3 more

Gary King is the Albert J. Weatherhead III University Professor at Harvard University and Director of the Institute for Quantitative Social Science. He is based in the Department of Government within Harvard's Faculty of Arts and Sciences. One of only 22 University Professors at Harvard, this represents the institution's most distinguished faculty position. King received his B.A. from SUNY New Paltz in 1980 and his Ph.D. from the University of Wisconsin-Madison in 1984. His academic journey has led him to become one of the most influential scholars in political methodology and quantitative social science. Professor King's research spans numerous areas of methodological innovation in the social sciences. His work focuses on developing and applying empirical methods across various domains. Key research interests include: Ecological Inference - developing methods to infer individual behavior from group-level data Automated Text Analysis - creating techniques for extracting knowledge from massive text collections Causal Inference - methods for detecting and reducing model dependence in causal effect estimation Missing Data and Measurement Error - statistical approaches to handle incomplete or imperfect data Survey Research - developing methods for more accurate cross-cultural survey comparisons Unifying Statistical Analysis - integrating diverse methodological approaches into coherent frameworks King's recent publications demonstrate a continued focus on methodological innovation with practical applications. His work spans political science, public health, and data science, with particular emphasis on privacy-preserving data analysis, maternal health metrics, survey methodology, and media effects. A notable trend is the increasing interdisciplinary nature of his research, bridging political methodology with public health, computer science, and demography. His work on census data privacy, maternal mortality disparities, and media influence represents cutting-edge applications of social science methodology to critical societal issues. His scientific achievements have been recognized with numerous prestigious awards: Fellow of the National Academy of Sciences (2010) Fellow of the American Statistical Association (2009) Fellow of the American Academy of Arts and Sciences (1998) Guggenheim Foundation Fellow (1994-1995) Career Achievement Award (2010) Warren Miller Prize (2008) Multiple awards for research software and methodology King has mentored numerous students and postdocs, many of whom now hold faculty positions at leading universities. His research has been supported by major funding agencies including the National Science Foundation, Centers for Disease Control and Prevention, World Health Organization, and National Institute of Aging. He has collaborated with over seventy scholars on research publications and served on numerous editorial boards and professional organization councils. His work on the Mexican universal health insurance program represents one of the largest randomized health policy experiments to date, demonstrating his commitment to rigorous evaluation of real-world policy interventions. As Director of the Institute for Quantitative Social Science, King leads a vibrant research community focused on methodological innovation. His work has practical applications in diverse areas including legislative redistricting (used by the U.S. Supreme Court), health policy evaluation (including the largest randomized health policy experiment to date in Mexico), Chinese censorship analysis (revealing government fabrication of 450 million social media comments annually), and automated text analysis (through Crimson Hexagon, a company he co-founded).

• Political Methodology
• Statistical Inference
• Quantitative Social Science
• +7 more

Leo Schwinn is a Lecturer at the Technical University of Munich (TUM) within the Department of Computer Science (I26), working in the Data Analytics and Machine Learning group supervised by Prof. Stephan Günnemann at the TUM School of Computation, Information and Technology. His research focuses on robust machine learning with particular emphasis on data-efficient learning and robustness vulnerabilities of Large Language Models (LLMs). Dr. Schwinn's research interests span multiple critical areas in contemporary machine learning including: Robustness against adversarial attacks in LLMs Embedding space vulnerabilities and defenses Model unlearning and privacy preservation Efficient training methodologies for large models Time-series forecasting with probabilistic frameworks Graph-based machine learning approaches His work bridges theoretical understanding with practical security implications of modern AI systems. Analysis of his recent publications (2023-2025) reveals a strong focus on LLM security, with multiple papers accepted at premier conferences including ICML, CVPR, ICLR, and NeurIPS. His research demonstrates consistent innovation in identifying novel attack vectors while developing practical defense mechanisms, particularly through embedding space manipulation techniques. The work shows increasing sophistication in handling both theoretical aspects of model robustness and practical deployment concerns. His notable scientific achievements include: Receiving the ATE dissertation price for his PhD work at FAU Securing an oral presentation at ICLR 2025 Organizing the ICLR BlogPost Track Becoming a member of ELLIS (European Laboratory for Learning and Intelligent Systems) Dr. Schwinn has served as review process chair for the 2024 Conference on Lifelong Learning Agents (CoLLAs) and actively collaborates with researchers at Mila Quebec AI Institute. His research group at TUM focuses on addressing fundamental challenges in machine learning robustness, particularly as they apply to real-world deployment scenarios where security and reliability are paramount. He maintains active GitHub repositories related to LLM security research, including circuit-breakers-eval and LLM_Embedding_Attack, demonstrating his commitment to open science and reproducible research in the field of AI security.

• Robust Machine Learning
• Large Language Models Security
• Data-Efficient Learning
• +5 more

Malay Ghosh is a Distinguished Professor in the Department of Statistics at the University of Florida. He holds a B.A. (1962) and M.A. (1964) in Statistics from Calcutta University, and a Ph.D. (1969) in Statistics from the University of North Carolina at Chapel Hill. His research focuses on Bayesian statistics, small area estimation, and survey sampling methodologies. Ghosh has contributed extensively to statistical theory and applications, including foundational work in probability matching priors and generalized linear models for small area estimation. Research Interests : His key areas include advanced statistical modeling, methodological developments in survey sampling, and Bayesian approaches to complex data analysis. His work bridges theoretical rigor with practical applications in diverse fields requiring precise estimation techniques. Awards: Fellow, American Statistical Association Fellow, Institute of Mathematical Statistics Elected Member, International Statistical Institute Recipient of TIP (1994) and PEP (1996) Awards Editorial Roles: Editor of Sequential Analysis (since 1996), Co-Editor of Sankhya (since 2000), and Associate Editor of the American Statistician (since 2000). His editorial contributions reflect his leadership in advancing statistical discourse.

• Statistics
• Bayesian Statistics
• Small Area Estimation
• +3 more

Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.

• Big Data Analytics
• Social Network Analysis and Mining
• Analysis of Human Mobility
• +17 more

Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.

• Information Theory
• Signal and Image Processing
• Remote Sensing
• +4 more

Professor Desheng Liu is affiliated with the Department of Geography at The Ohio State University , within the College of Arts and Sciences . His research focuses on developing spatial and statistical methodologies for environmental monitoring and ecological processes. He holds a Ph.D. in Environmental Science from UC Berkeley (2006) and additional degrees in Statistics, Environmental Science, and GIS. Education: Ph.D., 2006: Environmental Science, University of California, Berkeley M.A., 2004: Statistics, University of California, Berkeley M.S., 2003: Environmental Science, University of California, Berkeley B.E., 2001: GIS, Wuhan University, China Research interests include Remote Sensing , Spatial Statistics , GIScience , and Land Cover Change . His work emphasizes statistical modeling of spatial-temporal dynamics in environmental systems. Recent publications (2008–2012) explore topics like land-cover trajectory reconstruction, thermal infrared downscaling, and object-based classification techniques. No scientific awards are explicitly listed in the provided text. Advising and grants information is not detailed here, though his CV may contain additional details. He is involved in teaching advanced courses such as Quantitative Geographical Methods and Spatial Statistics.

• Remote Sensing
• Spatial Statistics
• GIScience
• +1 more

Dirk Praetorius is a Professor of Numerics of Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Wien) , affiliated with the Institute for Analysis and Scientific Computing (ASC) within the Faculty of Mathematics and Geoinformation . He leads the research group on Numerics of PDEs and has held various leadership roles, including Institute Director (since 2020) and head of the Numerics research area. His work focuses on numerical methods for PDEs, including Finite Element Methods (FEM), Boundary Element Methods (BEM), adaptive algorithms, and computational micromagnetics. Education and Career: Praetorius earned his Diplom in Mathematics (2000) and PhD in Applied Mathematics (2003) from TU Wien, followed by a Habilitation in Numerical Analysis (2005). He has been a faculty member at TU Wien since 2005, progressing from Assistant Professor to full Professor in 2017. He has also held visiting positions at institutions such as the University of Jyväskylä and RICAM (Linz). Research Interests: His research spans numerical analysis, adaptive FEM/BEM, a-posteriori error estimation, matrix compression, and computational micromagnetics. He has contributed to modeling spin dynamics, magnetic skyrmions, and multiscale systems. His work emphasizes efficient algorithms for large-scale problems and optimal computational complexity. Awards and Editorial Roles: Praetorius received the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He serves as Senior Editor for Computational Methods in Applied Mathematics (CMAM) and on the editorial board of Applied Numerical Mathematics (APNUM) . He co-founded the outreach initiative TUForMath to promote mathematics education. Grants and Projects: He leads or co-leads several research projects funded by the Austrian Science Fund (FWF), including the collaborative SFB "Taming Complexity in Partial Differential Systems" (2017–2025) and international collaborations with Germany. His work addresses topics like functional error estimates, nonlinear PDEs, and computational design of magnetic devices. Labs and Teams: He contributes to the ASC Institute and coordinates interdisciplinary projects involving computational physics and engineering. His team develops software tools like MooAFEM and Commics for micromagnetic simulations.

• Numerical Analysis
• Partial Differential Equations
• Finite Element Method
• +3 more

Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.

• Statistical Modeling
• Machine Learning
• Bayesian Methods
• +3 more

Professor Nick Vivyan is a Professor in Political Science at Durham University's School of Government and International Affairs, where he joined as Lecturer in Quantitative Social Research in 2010 and was promoted to Professor of Politics in 2018. He serves as Fellow of the Durham Research Methods Centre and Professor at the Centre for Institutions and Political Behaviour. Education: PhD in Political Science, London School of Economics (2010) Visiting Researcher, Stanford University (2008) Vivyan specializes in quantitative and experimental analysis of political behaviour, accountability, and representation with UK focus. His research bridges British politics, legislative studies, political economy, public opinion, and electoral systems through rigorous methodological innovation. He particularly examines voter-MP relationships, electoral dynamics, and methodological challenges in survey research. His recent publications reveal consistent focus on electoral behaviour measurement (turnout misreporting, constituency opinion estimation), legislative accountability mechanisms, and cross-national polarization trends. Methodologically, he pioneers experimental designs and survey innovations while maintaining historical and comparative perspectives. Supervision & Grants: PhD supervision in British/comparative politics, electoral behaviour, and legislative politics (e.g., Elisabeth de Vega Alavedra) Principal investigator on ESRC, British Academy, and Leverhulme Trust grants including £504k ESRC/AHRC project on electoral violence (1832-1914) Co-investigator on international projects funded by Austrian National Bank and European research councils As founding member of Durham's Centre for Institutions and Political Behaviour, Vivyan contributes to interdisciplinary research on political institutions and citizen behaviour while serving on the Editorial Board of Electoral Studies .

• British Politics
• Legislative Politics
• Political Economy
• +2 more