جست‌وجوی استاد و پژوهشگر

• Optical Networking
• Artificial Intelligence in Networking
• 5G and 6G Networks
• +۹ مورد دیگر

Davide Careglio is a Professor at the Department of Computer Architecture, Faculty of Computer Science of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He is a principal researcher in the CBA - Broadband Communication Systems and Architectures group and the IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group. Academic Rank: Professor Department: Department of Computer Architecture School: Faculty of Computer Science of Barcelona University: Universitat Politècnica de Catalunya Email: davide.careglio@upc.edu His research focuses on next-generation networking technologies, particularly in the domains of 5G/6G, AI-driven network automation, optical and elastic networks, energy efficiency, and the Recursive Internetwork Architecture (RINA). His work integrates machine learning and artificial intelligence to enhance network performance, security, and sustainability. He has made significant contributions to multicast routing, QoS assurance in RINA networks, and delay modeling in multidomain environments. The recent publications highlight a strong trend toward integrating AI and machine learning into network control, particularly for 6G services and secure distributed AI. His work spans modeling, simulation, and real-world experimentation, with applications in smart buildings, vehicular networks, and telecom infrastructure. Key themes include latency estimation, energy optimization, network programmability, and time-sensitive networking. Davide Careglio has been involved in prestigious EU-funded projects such as HORIZON 2020 and HORIZON EUROPE, including initiatives like ALLIANCE, SUNSET, and PRADIOT. He has also contributed to the Erasmus+ HEDY project, focusing on AI in education. He has advised PhD students, including D. Sembroiz, whose thesis focused on smart building IoT platforms. He actively collaborates with leading researchers such as Luis Velasco, Josep Solé-Pareta, and Salvatore Spadaro. He serves on the scientific committees of major conferences including IEEE GLOBECOM, IEEE ICC, and ICTON. He is a member of the CBA and IDEAI-UPC research groups at UPC, which are at the forefront of innovation in optical networking, AI for networks, and sustainable telecom infrastructures. These groups lead projects in programmable, knowledge-defined, and self-managed networks.

Xubo Yueمشاهده پروفایل

استادیار

• Federated Data Analytics
• Bayesian Optimization
• Continuous Optimization
• +۵ مورد دیگر

Xubo Yue is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Northeastern University. His research focuses on federated data analytics, Bayesian optimization, continuous optimization, Gaussian processes, and deep learning. He holds a PhD in Industrial & Operations Engineering from the University of Michigan, Ann Arbor (2023). His work bridges theoretical advancements with practical applications in advanced manufacturing, predictive maintenance, and sustainable materials discovery. Key affiliations include the Institute of Industrial and Systems Engineers (IISE), INFORMS, and the American Statistical Association (ASA). Recent research emphasizes scalable federated learning frameworks for distributed systems, causal inference in sensor networks, and sharpness-aware optimization techniques to enhance generalization. His methodologies are applied to interdisciplinary domains such as materials science, IoT systems, and renewable energy simulations. Research trends reveal a focus on: Federated learning architectures for privacy-preserving analytics Bayesian optimization for high-dimensional design spaces Integration of causal reasoning with machine learning systems Autonomous experimentation for accelerated materials discovery No scientific awards are explicitly listed in the provided information. His academic advising and grant activities are not detailed in the current data.

• Formal Methods
• Satisfiability (SAT)
• Satisfiability Modulo Theories (SMT)
• +۱۱ مورد دیگر

Enric Rodríguez Carbonell is a faculty member at the Department of Computer Sciences within the Faculty of Computer Science at Universitat Politècnica de Catalunya (UPC). He is a key member of the LOGPROG - Lògica i Programació research group, focusing on formal methods, automated reasoning, and combinatorial optimization. Research Interests: His work centers on satisfiability (SAT), satisfiability modulo theories (SMT), and their applications in program verification, constraint solving, and optimization. He investigates techniques such as conflict-driven learning, invariant generation, and Max-SMT for proving termination and safety. His research bridges theoretical foundations with practical applications in software analysis and industrial problem-solving. Publication Trends: His recent publications (2020–2024) show a sustained focus on enhancing SAT and SMT solvers, particularly in pseudo-Boolean reasoning, integer linear programming, and multi-conflict analysis. Earlier works established contributions in non-linear arithmetic, termination proofs, and efficient encodings for cardinality constraints, reflecting a long-term commitment to foundational and applied aspects of automated reasoning. Scientific Awards: Best student paper award at SAT 2024 Advising and Grants: Rodríguez Carbonell has contributed to multiple competitive R+D+i projects under Spain’s State Research Plans, indicating active grant involvement. He has co-authored doctoral theses and educational initiatives like Jutge.org, demonstrating engagement in academic supervision and pedagogical innovation. Labs and Teams: He is a core researcher in the LOGPROG group at UPC, which specializes in logic and programming, with strong collaborations across formal methods, verification, and constraint technologies.

Thomas G. Hansfordمشاهده پروفایل

استاد

• Judicial politics
• Interest group involvement in courts
• Supreme Court precedent
• +۴ مورد دیگر

Thomas G. Hansford is a Professor of Political Science at the University of California, Merced, where he also currently serves as Vice Provost for Academic Personnel. He is affiliated with the School of Social Sciences, Humanities and Arts and the Department of Political Science. Hansford earned his Ph.D. in 2001 from the University of California, Davis and his B.A. in 1993 from St. Mary's College of Maryland. Hansford's research focuses on American politics with particular emphasis on judicial politics, interest group involvement in courts, Supreme Court precedent, public opinion on courts and law, and elections and voting behavior. His work examines how courts interact with other political institutions, how public perceptions of courts are formed, and how legal precedent evolves within judicial hierarchies. He has developed innovative methodologies for measuring interest group ideology through amicus brief analysis and has explored the dynamics of judicial agenda setting across court levels. His research reveals that precedent dynamics operate not just top-down but also bottom-up, with lower court implementation of precedent providing valuable information to higher courts. Hansford's publications span top political science journals including the American Political Science Review, American Journal of Political Science, Journal of Politics, and many others. His research reveals important insights about bottom-up influence in judicial hierarchies, the role of interest groups in Supreme Court decision making, and how public perceptions of the judiciary are shaped by various factors including state court structures. Hansford is the coauthor of "The Politics of Precedent on the U.S. Supreme Court" (Princeton University Press) and is a creator of the Amici Space Project, which develops spatial models of interest group ideology based on amicus briefs filed with the Supreme Court. Throughout his career, Hansford has conducted extensive research using survey experiments to understand public perceptions of courts and has employed sophisticated statistical methods to analyze judicial behavior and precedent dynamics. His work on the informational dynamics of vertical stare decisis has provided new insights into how lower court implementation of precedent informs Supreme Court decision making.

Michael Kunzingerمشاهده پروفایل

استاد

• Generalized Functions
• Differential Geometry
• Partial Differential Equations
• +۳ مورد دیگر

Michael Kunzinger is a Professor at the Department of Mathematics, Faculty of Mathematics, University of Vienna. His research spans generalized functions, differential geometry, and mathematical physics, with a focus on Colombeau algebras, Lorentzian length spaces, and non-smooth geometric structures. His recent work includes stochastic PDEs on manifolds, synthetic curvature bounds, and causality in Lorentzian geometry. Articles highlight applications to conservation laws, sectional curvature analysis, and rigidity theorems. Notably, he explores connections between generalized functions and smooth manifold theory, advancing geometric regularization techniques. 2024: 9 publications on singular limits, Ricci curvature, and synthetic geometry. 2022: Studies on null distance and singularity theorems in low-regularity spacetimes.

• Biostatistics
• Statistical Methodology
• Epidemiology
• +۶ مورد دیگر

Hegang Chen, PhD, serves as Professor in the Department of Epidemiology and Public Health at the University of Maryland School of Medicine. He is a lead statistician for the General Clinical Research Center (GCRC) and member of the Hormone Responsive Cancers Program within the Marlene and Stewart Greenebaum Cancer Center, with extensive collaborative experience in clinical trials and epidemiological research since joining in 2002. His academic credentials include: Ph.D. in Statistics from the University of Illinois M.S. in Mathematics from the University of Mississippi Dr. Chen's research centers on statistical methodology development—including optimal experimental design, generalized mixed linear models, and machine learning applications for molecular biology and real-time clinical decision support (e.g., predicting blood transfusion needs)—and biomedical collaborations spanning cancer, infectious diseases, trauma, public health, and pharmacogenomics. His work has been published in premier journals like Nature, JAMA, and Annals of Statistics. Analysis of his recent publications (2020-2025) reveals a dominant focus on predictive analytics for traumatic brain injury outcomes, blood transfusion prediction, and lung cancer diagnosis using real-time physiological monitoring and machine learning. This trend demonstrates a translational shift toward operationalizing statistical methods in critical care decision support systems. No scientific awards were mentioned in the provided text. While no specific advisees are listed, Dr. Chen's leadership in the GCRC and extensive collaborative network across trauma, oncology, and global health indicate active mentorship within multidisciplinary teams. His grant involvement is evidenced by NIH-funded GCRC work and high-impact publications in clinical domains. Dr. Chen maintains key affiliations with the General Clinical Research Center as lead statistician and the Hormone Responsive Cancers Program, where he integrates advanced statistical methodologies into translational cancer research and clinical trial design.

• Natural Language Processing
• Automatic Text Summarization
• Sentence Generation
• +۵ مورد دیگر

Juan-Manuel Torres Moreno is an Associate Professor (Maître de Conférences HDR HC) at the University of Avignon (UAPV), where he conducts research in Natural Language Processing at the Laboratoire Informatique d'Avignon (LIA). His academic position includes the HDR (Habilitation à Diriger des Recherches), a post-doctoral qualification in France that enables supervision of PhD students. His primary research interests focus on Natural Language Processing, with particular emphasis on automatic text summarization, sentence generation, and phrase compression algorithms. His work spans both theoretical and applied aspects of NLP, incorporating machine learning techniques and artificial intelligence approaches. His research has significant applications in multilingual processing, text mining, and information extraction systems. Torres Moreno's publication record demonstrates a consistent trajectory in advancing text summarization techniques, with recent work exploring cross-lingual approaches, multimedia content processing, and deep learning applications. His research often bridges the gap between theoretical linguistic concepts and practical implementation, with publications spanning from fundamental NLP algorithms to applied systems for video summarization, speech processing, and multilingual document analysis. He actively collaborates with researchers across multiple institutions including École Polytechnique de Montréal (with 50 joint publications), Laboratoire Informatique d'Avignon (83 publications), and Universidad Nacional Autónoma de México. His work appears in reputable journals such as Computer Speech and Language, Data and Knowledge Engineering, and Pattern Recognition Letters. Within the Laboratoire Informatique d'Avignon, Torres Moreno contributes to the Language Processing research theme, working with colleagues on projects related to multilingual information access, opinion mining, and text analysis. His research group has participated in several evaluation campaigns including DEFT (Défi Fouille de Textes) challenges, focusing on information retrieval and sentiment analysis tasks.

• Bayesian statistics
• robust estimation
• information-assisted hypothesis testing
• +۳ مورد دیگر

Jordan Bryan is an Assistant Professor of Data Science at the University of Virginia's School of Data Science, specializing in multivariate statistical methods with applications spanning environmental monitoring, high-energy physics, and cancer genomics. He earned his Ph.D. in Statistics from Duke University in 2023 and a B.S. in Mathematics from Stanford University, and currently serves as Secretary of the junior section of the International Society for Bayesian Analysis. Education: Ph.D. in Statistics, Duke University B.S. in Mathematics, Stanford University Research Interests: Dr. Bryan develops advanced methodologies in Bayesian statistics , robust estimation , and information-assisted hypothesis testing , with particular emphasis on integrating auxiliary data sources to enhance statistical power. His work bridges theoretical rigor with real-world applications in genomics, environmental systems, and physics, recently expanding to incorporate large language model-derived information for genomic analysis. Publication Trends: Analysis of his 15 most recent publications (2020-2025) reveals a dual trajectory: foundational contributions to statistical methodology (e.g., multirank likelihood frameworks, subscedastic estimation) and high-impact applications in cancer genomics (functional screens, drug repurposing) and environmental science (source apportionment). A notable 2025 publication pioneers LLM integration for genomic hypothesis testing, signaling a strategic expansion into AI-augmented statistics. Scientific Awards: No specific awards, prizes, or fellowships were documented in the source materials. Advising and Grants: Supported by National Institute of Environmental Health Sciences (NIEHS) and National Heart Lung and Blood Institute (NHLBI) training grants during his UNC Chapel Hill postdoctoral fellowship, Dr. Bryan now leads independent research at UVA. While formal student advising isn't detailed in current records, his extensive publication record suggests active mentorship in collaborative projects.

• Electronic Design Automation (EDA)
• Analog/RF Circuit Simulation
• Multi-physics Analysis
• +۲ مورد دیگر

Dr. QUAN Chen is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), holding this position since May 2025 after serving as Assistant Professor (2019-2025) and Research Assistant Professor at the University of Hong Kong (2012-2018). A Shenzhen high-level overseas talent, he earned his PhD from the University of Hong Kong and conducts cutting-edge research in electronic design automation. His academic credentials include: Ph.D. from The University of Hong Kong (2010) Master's degree from The University of Hong Kong (2007) Bachelor's degree from Sun Yat-Sen University (2005) Dr. Chen's research pioneers advanced EDA algorithms for large-scale analog/RF circuit simulation, post-Moore multi-physics analysis, and AI-assisted design technologies. His work addresses critical challenges in nanodevice modeling and quantum computing circuits, resulting in over 50 publications in top venues like IEEE TCAD and DAC, plus four Chinese patents. Analysis of his recent publications reveals dominant trends in exponential integrator methods for transient simulation, model order reduction techniques, and physics-informed machine learning for reliability analysis. His work bridges numerical mathematics with practical EDA applications across analog circuits, quantum hardware, and emerging memory technologies. Key recognitions include: Wu Wenjun Artificial Intelligence Science and Technology Award, Second Prize (2020) ICCAD Best Paper Award Nomination (2012) Dr. Chen actively recruits Postdoctoral Fellows, Research Assistants, and Graduate Students while leading major funded projects including NSFC key/general programs and Guangdong Provincial R&D initiatives. His industry partnerships with Huawei, Empyrean, and Guowei Group translate theoretical advances into real-world EDA solutions. He directs a specialized research group at SUSTech focused on computational methods for next-generation circuit design, fostering innovation in simulation algorithms and multi-physics analysis through academic-industry collaboration.

• Saturated-unsaturated water flow coupling simulation
• Surface-groundwater interaction
• Polluted gas intrusion simulation
• +۳ مورد دیگر

Dr. Liang Xiuyu is an Assistant Professor (Associate Researcher) and doctoral supervisor at the School of Environmental Science and Engineering, Southern University of Science and Technology (SUSTech). He joined SUSTech in February 2018 and has been serving as Assistant Professor since January 2021. Prior to SUSTech, he was an Associate Researcher at Nanjing University's Center for Hydrosciences Research from 2015-2018. His academic journey includes a Ph.D. in Hydrology and Water Resources from Nanjing University (2009-2012), an M.S. in Geological Engineering from Anhui University of Science and Technology (2005-2008), and a B.S. in Resources and Environment from the same institution (2001-2005). Dr. Liang's educational background demonstrates a strong foundation in water resources and geological engineering. His research focuses on saturated-unsaturated water flow coupling simulation, surface-groundwater interaction, polluted gas intrusion simulation, and stochastic theory of groundwater flow and solute transport. He has published over 60 scientific papers, with more than 30 as first or corresponding author in leading journals including Water Resources Research, Environmental Science & Technology, and Water Research. His work addresses critical issues in groundwater resources and environmental protection, particularly the interfacial hydrodynamic processes between different water systems. Analysis of Dr. Liang's recent publications reveals a consistent focus on the fundamental mechanisms of groundwater flow and solute transport. His research demonstrates sophisticated analytical and numerical modeling approaches to understand complex hydrological processes, with particular emphasis on temporal scaling characteristics, coupled flow systems, and interactions between surface water and groundwater. The work spans theoretical developments, field applications, and practical solutions to environmental challenges. 2025 Shandong Science and Technology Progress Second Prize 2024 SUSTech Outstanding Doctoral Dissertation Supervisor Award 2024 SUSTech 8th Young Teacher Teaching Competition Second Prize 2024 SUSTech College of Engineering Outstanding Young Talent Cultivation Program 2023 SUSTech Admissions Advanced Individual 2022 Geological Science and Technology Bulletin Outstanding Young Editorial Board 2018 Shenzhen Overseas High-level Talents 2018 Shenzhen Nanshan Pilot Talent 2018 & 2016 Journal of Hydrology Outstanding Reviewer Dr. Liang has successfully led multiple research projects, including National Natural Science Foundation of China grants and national water pollution control projects. He serves as deputy editor for prestigious journals including Journal of Hydrology (2025-), Vadose Zone Journal (2020-), and Stochastic Environmental Research and Risk Assessment (2019-). His academic service extends to committee memberships with the Chinese Hydraulic Engineering Society and international organizations including the American Geophysical Union. Dr. Liang teaches undergraduate and graduate courses in groundwater hydrology and spatial statistics, contributing to the education of next-generation hydrologists. Dr. Liang maintains an active research group at SUSTech focused on groundwater resources and environmental protection. His team investigates critical issues including surface-groundwater interactions, unsaturated-saturated flow coupling, and gas migration processes in porous media. The research has practical applications for water resource management, pollution control, and environmental protection in China and globally.

Brady Thomas Westمشاهده پروفایل

استاد پژوهشی

• Survey Methodology
• Applied Statistics
• Public Health
• +۷ مورد دیگر

Brady Thomas West is a Research Professor at the University of Michigan with joint appointments in the Department of Biostatistics at the School of Public Health and the Survey Research Center at the Institute for Social Research. His work bridges survey methodology, applied statistics, and public health research, providing technical expertise globally on research design and statistical analysis. Education: PhD in Survey Methodology, University of Michigan-Ann Arbor, 2011 MA in Applied Statistics, University of Michigan-Ann Arbor, 2002 BS in Statistics, University of Michigan-Ann Arbor, 2001 West's research focuses on critical challenges in survey methodology including measurement error in auxiliary variables and survey paradata, selection bias in non-probability samples, responsive/adaptive survey design, interviewer effects, and multilevel regression models for complex clustered and longitudinal data. His work directly addresses data quality issues in public health and social science research. His publication record (2016-2022) demonstrates sustained contributions to methodological advancements through influential textbooks on linear mixed models and survey data analysis, alongside high-impact journal articles. Key themes include improving survey estimation techniques, developing frameworks for assessing analytic errors, and synthesizing evidence on interviewer effects. His research consistently bridges theoretical innovation with practical applications in aging studies, public health surveillance, and social epidemiology. Scientific Awards: No specific awards mentioned in the provided information. West actively leads and contributes to major funded research initiatives including the Health and Retirement Study (as Associate Director), the Network for Innovative Methods in Longitudinal Aging Studies (as MPI), and the MAPS Center for SUD/HIV research. He also develops educational resources through Coursera specializations in Statistics with Python and Total Data Quality, demonstrating commitment to training the next generation of data scientists. He collaborates extensively through the Survey Research Center and Department of Biostatistics, contributing to teams focused on longitudinal aging studies, public health data infrastructure, and methodological innovation. His leadership in the Health and Retirement Study and NIMLAS network positions him at the forefront of aging research methodology.

Marina Epelmanمشاهده پروفایل

استاد

• Mathematical Programming
• Linear Optimization
• Nonlinear Optimization
• +۵ مورد دیگر

Marina Epelman is a Professor in the Department of Industrial and Operations Engineering at the University of Michigan , where she has been a faculty member since Fall 1999. She also serves as the Associate Chair of Graduate Education . Her teaching includes graduate and undergraduate courses in linear and nonlinear optimization , such as Linear Programming II and Nonlinear Programming. PhD in Operations Research from MIT (1999) Her research focuses on mathematical programming , covering linear, semidefinite, and nonlinear optimization , with applications in healthcare operations (e.g., medical residency scheduling, radiation therapy optimization), automotive scheduling (e.g., crash test and vehicle prototyping), and algorithm design (e.g., simplex methods, sampled fictitious play, column generation). Recent publications highlight her work in stochastic optimization for healthcare scheduling, dose distribution in radiation therapy , and automotive production planning . Her methodologies often bridge dynamic programming , Markov decision processes , and network optimization .

• Power Systems
• Renewable Energy Integration
• Inverter-Based Resources
• +۴ مورد دیگر

Henrik Johansson is a doctoral student at the Division of Electromagnetic Engineering and Fusion Science (EMF), School of Electrical Engineering and Computer Science (EECS), Royal Institute of Technology (KTH). He focuses on interactions between power system protection algorithms and control strategies of inverter-based resources in grids with high power electronics penetration. B.Sc. and M.Sc. in Electrical Engineering and Electric Power Engineering from KTH (2020, 2022) Development engineer at Scibreak (HVDC auxiliary power supplies) Teaching assistant in mathematics courses at KTH His research intersects power systems, renewable energy integration, and grid protection algorithms. Recent publications analyze impacts of grid-forming inverters on distance protection schemes and adaptive fault detection methods. Articles highlight trends in power electronics integration and protection system design for modern grids. Scientific recognition includes the Teaching Assistant of the Year award from KTH electrical engineering students. He collaborates with Professor Nathaniel Taylor and Professor Xiongfei Wang on advancing protection strategies for grids with high renewable energy penetration.

Herke van Hoofمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Modular Reinforcement Learning
• Hierarchical Structures
• +۵ مورد دیگر

Herke van Hoof serves as Associate Professor at the University of Amsterdam within the Informatics Institute, where he leads research in the AMLab at Science Park (Lab 42, L4.05). His academic journey spans from AI degrees at Groningen University through doctoral studies at TU Darmstadt under Professor Jan Peters to postdoctoral research at McGill University with Professors Joelle Pineau, Dave Meger, and Gregory Dudek. PhD: TU Darmstadt (2016), supervised by Prof. Jan Peters Postdoc: McGill University, Montreal AI Bachelor & Master: University of Groningen Van Hoof's research focuses on overcoming data inefficiency in reinforcement learning through modular and hierarchical approaches. His work explores how structured representations can enable knowledge transfer between tasks, improve learning efficiency, and facilitate applications in domains with complex state and action spaces. The lab investigates symmetry exploitation in multi-agent systems, gradient estimation techniques for discrete variables, and applications to combinatorial problem solving. His publication record shows consistent contributions to top AI venues including ICML, ICLR, NeurIPS, and JMLR over the past seven years. The research trajectory demonstrates evolution from foundational policy search methods toward increasingly sophisticated modular architectures, with recent emphasis on knowledge-assisted AI for critical infrastructure applications through the AI4REALNET project. Third place in ARC prize (Paper award) Van Hoof actively supervises PhD candidates and postdoctoral researchers, with current projects including human-robot collaboration using brain-computer interfaces (with Maryam Alimardani at VU) and interactive robot learning with flexible human input (through The Hybrid Intelligence Centre). His research receives support from multiple funding initiatives including the AI4REALNET project which focuses on applying AI to critical infrastructure systems. As co-organizer of the BeNeRL 2024 workshop and participant in the AI4REALNET consortium, van Hoof maintains strong connections with the broader European AI research community while directing the AMLab's research on modular reinforcement learning approaches.

Yi-Hsin Chenمشاهده پروفایل

استاد

• Educational Measurement
• Psychometrics
• Cognitive Diagnostic Modeling
• +۳ مورد دیگر

Dr. Yi-Hsin Chen is a Professor in the Department of Educational Measurement and Research at the College of Education, University of South Florida (USF), located on the Tampa campus in office EDU 362. Her teaching responsibilities include: EDF 6432: Foundations of Measurement (Online) EDF 6407: Statistical Analysis for Educational Research I (Online) EDF 7439: Item Response Theory EDF 7469: Computer-Based Testing EDF 7436: Rasch Models Her primary research focuses on cognitive psychometric models including GDINA, LCDM, and DINA, which integrate cognitive attributes into measurement frameworks. She specializes in diagnostic classification modeling, cross-cultural validation of educational constructs, and development of computer-based assessments. Her work bridges theoretical psychometrics with practical applications in mathematics and literacy assessment. Analysis of her recent publications reveals consistent innovation in cognitive diagnostic modeling, particularly in mathematics education (TIMSS, van Hiele geometry), orthographic processing, and cross-cultural validation. Her research increasingly incorporates computational methods like Bayesian estimation and R programming while addressing emerging challenges such as AI-generated test items. The interdisciplinary nature of her work spans educational psychology, statistics, and international assessment.