جست‌وجوی استاد و پژوهشگر

Jonathan Zhangمشاهده پروفایل

استادیار

• Health Economics
• Public Finance
• Mental Health
• +۳ مورد دیگر

Jonathan Zhang is an Assistant Professor at Duke University's Sanford School of Public Policy and a Faculty Research Fellow at the National Bureau of Economic Research (NBER). He is also affiliated with the US Veterans Affairs Office of Mental Health. Previously, he served on the faculty at McMaster University and was a Postdoctoral Scholar at Princeton University. His academic journey includes a PhD in Economics from Stanford University (2020) and a BSc from UBC. Dr. Zhang's research spans health economics and public finance, with particular focus on physician behavior, health policies, and safety net programs. His work demonstrates deep interest in mental health and substance use issues, examining how healthcare systems impact vulnerable populations. His research methodology often leverages quasi-random assignment and natural experiments to establish causal relationships in complex healthcare settings. His publication record reveals significant contributions to understanding healthcare disparities, particularly regarding racial differences in medical testing accuracy, socioeconomic status impacts on mental health access, and veteran healthcare outcomes. His work frequently appears in prominent working paper series, with numerous 2025 publications indicating active and ongoing research programs. Dr. Zhang maintains active professional engagement through his Google Scholar profile and personal website (jonathanzhang.net). He will be visiting the University of Copenhagen from August 1 - November 15, 2025, suggesting international collaboration and scholarly exchange.

Steven Neil Evansمشاهده پروفایل

استاد

• Stochastic processes
• Probability on algebraic structures
• Random matrices
• +۱۲ مورد دیگر

Steven Neil Evans is a Professor in the Departments of Statistics and Mathematics at the University of California, Berkeley, with a joint appointment since 1999. His research spans stochastic processes, probability on algebraic structures, and applications in population biology, phylogenetics, and computational biology. BSc (Hons I & University Medal) in Statistics, University of Sydney (1983) PhD in Mathematics, University of Cambridge (1987) Research Interests: Evans works on random matrices, Lévy processes, measure-valued stochastic processes, coalescent models in biology and chemistry, phylogenetics (including invariants), biodemography, mutation-selection balance, and stochastic models in population genetics. His recent work connects probability theory with computational biology, focusing on metagenomics and transcriptional regulation. He also explores computational algebra in modeling biological systems. Articles Trends: His publications reveal a trajectory from foundational work in stochastic processes and Lévy processes to interdisciplinary applications in phylogenetics, population genetics, and computational biology. Key subfields include mutation-selection models, random tree structures, stochastic differential equations, and algebraic probability. Recent work addresses phylogenetic networks and Frechet mean sets in metric spaces. Scientific Awards: Rollo Davidson Prize (1990) Presidential Young Investigator Award (1991) Alfred P. Sloan Foundation Fellowship (1993) G. de B. Robinson Prize (1997) Miller Research Professor (2002) Fellow, American Mathematical Society (2012) Member, National Academy of Sciences (2016) Advising and Grants: Evans has advised over 30 PhD/Master's students since 1993. He has received continuous NSF grants (1988-2019), NIH funding (2016-2018), and international fellowships. His academic service includes editorial roles at major journals and organizing conferences in probability and mathematical biology.

Nikhil Srivastavaمشاهده پروفایل

دانشیار

• theoretical computer science
• algebraic graph theory
• spectral graph theory
• +۵ مورد دیگر

Nikhil Srivastava is an Associate Professor of Mathematics at the University of California, Berkeley, and a Senior Scientist at the Simons Institute. He received his PhD in Computer Science from Yale University in 2010 under the supervision of Daniel Spielman, following his undergraduate studies at Union College. After postdoctoral positions at the Institute for Advanced Study (Princeton), MSRI, and a research position at Microsoft Research India, he joined UC Berkeley in 2015. His research spans theoretical computer science and mathematics, with particular focus on spectral graph theory, random matrices, the geometry of polynomials, asymptotic convex geometry, and numerical analysis . His work often bridges the gap between pure mathematics and theoretical computer science, developing deep connections between polynomial methods, linear algebra, and combinatorial structures. Srivastava's research has been recognized with several prestigious awards including the SIAM Polya Prize , the NAS Held Prize , and the AMS Foias Prize , highlighting his significant contributions to the field. His most recent work focuses on numerical linear algebra, spectral theory, and computational mathematics, with publications spanning from algorithmic foundations to applications in mathematical physics. He actively mentors graduate students, currently advising Rikhav Shah, Zack Stier, and Isabel Detherage. His past students include Jorge Garza Vargas (co-advised with Dan Voiculescu), Theo McKenzie, Jess Banks, Satyaki Mukherjee, Archit Kulkarni, Nick Ryder, and Aaron Schild (co-advised with Satish Rao). His research has been supported by the NSF and the Sloan Foundation. At Berkeley, Srivastava has been actively involved with the Simons Institute, participating in numerous programs including Complexity and Linear Algebra (Fall 2025), Sublinear Algorithms (Summer 2024), and several others dating back to 2013. He regularly teaches courses in mathematics, including Discrete Mathematics (Math 55) and Multivariable Calculus (Math 53), and runs a seminar on Discrete Analysis in Evans Hall.

Aurélie Labbeمشاهده پروفایل

استاد

• Data Science
• Statistical Learning
• Machine Learning
• +۷ مورد دیگر

Aurélie Labbe is a Full Professor in the Department of Decision Sciences at HEC Montréal, holding the prestigious FRQ-IVADO Chair in Data Science. Appointed as Co-Scientific Director – Academic Partnerships at IVADO in October 2023, she plays a key leadership role in establishing connections between IVADO and partner universities. Her academic journey includes a PhD in Statistics from the University of Waterloo, a Master's degree in Statistics from the University of Montreal, and dual Bachelor's degrees in Applied Mathematics and Social Sciences from Paris-Dauphine University and Pure Mathematics from Versailles-St Quentin University. Her research spans multiple interdisciplinary domains with a focus on developing advanced statistical and machine learning methodologies for big data analysis. Labbe's work bridges theoretical statistics with practical applications across diverse fields including genomics, neuroscience, transportation systems, and health informatics. She has made significant contributions to kernel methods, matrix factorization techniques, random forest applications, and spatiotemporal data analysis, with publications appearing in top journals across multiple disciplines. Analyzing her recent publications reveals a clear trend toward methodological innovation applied to complex real-world problems. Her work demonstrates expertise in handling high-dimensional data from diverse sources including neuroimaging, transportation networks, and genomic studies. The interdisciplinary nature of her research connects statistical theory with applications in healthcare, transportation safety, and biological sciences, reflecting her ability to develop methods that address domain-specific challenges while advancing statistical methodology. Holder of the FRQ-IVADO Chair in Data Science Member of the Center for Mathematical Research Training Professor Labbe actively mentors the next generation of data scientists, supervising numerous doctoral and master's students. Her supervision portfolio includes 1 doctoral thesis (2023), 4 master's theses (2022-2024), and 32 supervised projects spanning 2019-2025. Her students' work covers diverse applications including transportation safety, healthcare analytics, financial modeling, and environmental analysis. Through her leadership of the FRQ-IVADO Chair in Data Science, she coordinates research activities that integrate mathematical, statistical, and computer science expertise with domain knowledge from various data-generating fields. As Co-Scientific Director at IVADO, Professor Labbe leads efforts to establish connections with faculties and departments across five partner universities, integrating them into IVADO's research and knowledge transfer activities. Her leadership role positions her at the forefront of advancing data science research and applications in Quebec's academic ecosystem.

Matt Weinbergمشاهده پروفایل

دانشیار

• Algorithmic Mechanism Design
• Economics and Computation
• Cryptocurrencies
• +۵ مورد دیگر

Matt Weinberg is an Associate Professor of Computer Science at Princeton University , specializing in Algorithmic Mechanism Design . His research focuses on domains where user incentives are critical, such as auctions , cryptocurrencies , and social good applications. Prior to joining Princeton's faculty in 2017, he spent two years as a postdoc in Princeton’s CS Theory group and was a Research Fellow at the Simons Institute in 2015 (Economics and Computation) and 2016 (Algorithms and Uncertainty). Weinberg earned his PhD in Computer Science from MIT in 2014 , advised by Costis Daskalakis , and received his BA in Mathematics from Cornell University in 2010 , where he collaborated with Bobby Kleinberg . His research spans Algorithmic Game Theory , Economics and Computation , and Theoretical Computer Science , with recent trends emphasizing blockchain economics and multi-item auction theory . His scientific awards include Simons Institute Research Fellowships. He advises both PhD and MSE students , with a structured philosophy emphasizing independent problem-solving and technical rigor. Weinberg also co-teaches courses like COS 445: Economics and Computing and COS 521: Advanced Algorithms , often collaborating with faculty such as Mark Braverman . His NSF grant CCF-1954927/1955205 supports a crowdsourced list of TCS-focused Master’s programs.

Galatia Cleanthousمشاهده پروفایل

مدرس

• Mathematical Analysis
• Probability
• Statistics
• +۶ مورد دیگر

Dr. Galatia Cleanthous is a Lecturer in the Department of Mathematics and Statistics at Maynooth University, Ireland, affiliated with the Faculty of Science & Engineering and the Hamilton Institute. She joined Maynooth in 2020 after postdoctoral positions at Trinity College Dublin, Newcastle University, and University of Cyprus, and holds a PhD in Pure Mathematics from Aristotle University of Thessaloniki (2014). Education PhD in Mathematics, Aristotle University of Thessaloniki, Greece (2014) MSc in Mathematics, Aristotle University of Thessaloniki, Greece Diploma in Mathematics, Aristotle University of Thessaloniki, Greece Research Interests Her research bridges pure and applied mathematics, focusing on Mathematical Analysis , Probability , and Statistics . Specifically, she explores Geometric Analysis , Geometric Function Theory , and Harmonic Analysis on manifolds and metric spaces. In statistics, she works on Nonparametric , Spatial , and Environmental Statistics , developing adaptive estimation techniques and studying Gaussian random fields on spheres and other domains. Publication Trends From 2025 back to 2013, her work has consistently appeared in top journals such as Annals of Statistics , Bernoulli , Journal of Nonparametric Statistics , and Transactions of the American Mathematical Society . A clear trend emerges: early publications concentrate on pure analytic topics like Fourier multipliers and function spaces, while recent outputs integrate these theoretical tools into modern nonparametric statistics, density estimation on manifolds, and stochastic modeling of environmental and seismological data. Scientific Awards Master’s degree ranked first with grade 9.8/10, Aristotle University of Thessaloniki (2011) Diploma ranked first among ~200 students, grade 9.7/10, Aristotle University of Thessaloniki (2009) Undergraduate merit awards for three consecutive academic years (2005-2008), State Scholarship Foundation of Greece National first place in Cypriot high-school mathematics entrance exams (2005), Ministry of Education, Cyprus Advising & Outreach Dr. Cleanthous has supervised BSc and MSc students, including Ultán Doherty (BSc, 1st Class Honors, 2021) and Anush Harish (MSc, 2022). She serves as Chair of the Department PR Committee, Member of the University STEM Promotions Committee, and Member of the departmental Equality, Diversity & Inclusion committee. Beyond campus, she trains young mathematicians at the North Kildare Maths Problem Solving Club and organizes public engagement events for Science Week. Labs & Teams She is associated with the Hamilton Institute at Maynooth University, a multidisciplinary research institute fostering collaboration between mathematics, computer science, and engineering.

Tanya N Turanمشاهده پروفایل

استاد

• Management of Vascular Risk Factors in Stroke
• Intracranial Atherosclerosis
• Secondary Stroke Prevention
• +۳ مورد دیگر

Dr. Tanya N Turan is a tenured Professor of Neurology at the Medical University of South Carolina (MUSC), serving as Vice-Chair of Neurology Faculty Affairs. She is a director of the NIH-StrokeNet Regional Coordinating Center "Southeast Collaborative Alliance for Stroke Trials (SE-CoAST)" and actively oversees multi-center clinical trials including SAMMPRIS, CREST 2, and CAPTIVA. Research Interests: Management of vascular risk factors in stroke Intracranial atherosclerosis Secondary stroke prevention Medical imaging techniques for plaque analysis Hypertensive disorders in pregnancy Hemodynamic assessment in cerebrovascular disease Clinical Trials & Grants: NIH-funded SAMMPRIS (Stenting vs. Aggressive Medical Management) CREST 2 (Carotid Revascularization Endarterectomy/Stenting) CAPTIVA (Anticoagulation vs. Antiplatelet Therapies) National StrokeNet infrastructure leadership Publication Trends: Her recent work focuses on intracranial atherosclerosis, vascular risk factors, and hemodynamic effects in stroke. Articles emphasize clinical trials, imaging biomarkers, and long-term outcomes in cerebrovascular disease.

• Stochastic Processes
• Probability Theory
• Mathematical Modeling
• +۴ مورد دیگر

Professor Vitali Wachtel of Bielefeld University's Faculty of Mathematics specializes in advanced stochastic processes, probability theory, and their applications in mathematical modeling. Since 2021, he holds a W3 Professorship and serves as Principal Investigator in CRC 1283 'Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications' since 2023. Chaired Examination Boards for Bachelor & Master Business Mathematics Member, Bielefeld Graduate School in Theoretical Sciences Research focus: Markov processes, random walks in cones, branching processes Research Trends: His recent work spans critical multitype branching in random environments (2025), asymptotic expansions for conditioned random walks (2024), and invariance principles for integrated processes. He explores connections between stochastic processes, combinatorial structures, and risk modeling with level-dependent premiums. Awards: Feodor Lynen Research Fellowship (2017), Alexander von Humboldt Foundation Teaching: Coordinates modules including 'Stochastic Processes' (24-M-PT-STP) and 'Introduction to Probability Theory' (24-B-EW-5). Active in curriculum development and academic governance through multiple university committees.

• Theoretical Computer Science
• Distributed Algorithms
• Parallel Algorithms
• +۳ مورد دیگر

Yi-Jun Chang is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS). He previously held a Junior Fellow position at the Institute for Theoretical Studies, ETH Zurich (2019–2021), and earned his Ph.D. in Computer Science and Engineering from the University of Michigan (2019). His research focuses on theoretical computer science, particularly distributed, parallel, and sublinear graph algorithms. Ph.D., University of Michigan (2019) M.S., National Taiwan University (2015) B.S., National Taiwan University (2013) Chang’s research explores the complexity and optimization of algorithms in distributed systems, including leader election, graph shattering, expander decomposition, and subgraph detection. His work addresses fundamental challenges in time-energy trade-offs, communication efficiency, and deterministic vs. randomized approaches in models like LOCAL and CONGEST. Recent publications highlight advancements in distributed triangle enumeration, optimal coloring, shortest path computation, and certification in bounded pathwidth graphs. Awards include the PODC 2019 Best Paper and Best Student Paper Awards, followed by the 2020 PODC Doctoral Dissertation Award. PODC 2019 Best Paper Award PODC 2019 Best Student Paper Award 2020 PODC Doctoral Dissertation Award Chang teaches courses such as CS3230 (Design and Analysis of Algorithms) and CS5275 (The Algorithm Designer's Toolkit). He advises Ph.D. students Hung Thuan Nguyen and Haoran Zhou, and has collaborated with postdoctoral researchers including Gopinath Mishra and Dean Leitersdorf.

Joaquim Pintoمشاهده پروفایل

استاد

• Mid-latitude meteorology
• Climatology
• Extreme weather events
• +۷ مورد دیگر

Professor Joaquim Pinto is a leading climate scientist at the Karlsruhe Institute of Technology (KIT), where he serves as Head of the Working Group "Regional Climate and Weather Hazards" and as Spokesperson of the collegial institute management team. He holds the prestigious AXA Research Fund Chair position at the Institute of Meteorology and Climate Research - Troposphere Research (IMK-TRO). His academic background includes a Licenciate in Geophysical Sciences - Meteorology from the University of Lisbon (1990-1996), PhD studies at the University of Cologne (1998-2002), and academic positions at the University of Cologne (2002-2016) and University of Reading (2013-2016) before joining KIT in 2016. He became a Privatdozent (lecturer) at the University of Cologne in 2011 and earned his habilitation with research on extreme European wind storms. Professor Pinto's research focuses on mid-latitude meteorology and climatology, with special emphasis on extreme weather events , climate variability in Europe across multiple time scales, regional climate modeling and downscaling methods , and the diagnostic modeling and quantification of risks associated with extreme events affecting Europe. His work bridges fundamental climate science with practical applications for risk assessment and management. His extensive publication record demonstrates expertise in analyzing European windstorms, heatwaves, and compound extreme events. Recent work examines the impacts of climate change on wind energy potential, extreme precipitation events, and the complex interactions between atmospheric circulation patterns and regional climate extremes. His research often employs high-resolution climate modeling, statistical-dynamical downscaling approaches, and interdisciplinary collaborations to address pressing climate challenges. AXA Research Fund Chair in Regional Climate and Weather Hazards Professor Pinto teaches graduate and undergraduate courses including "Climate Modelling and Dynamics with ICON," "IPCC Assessment Report," "Climatology," "Energy Meteorology," "Methods of Data Analysis," and "Regional Climate and Weather Hazards." His teaching reflects his research expertise in climate modeling, extreme events, and regional climate change impacts.

David Sbarraمشاهده پروفایل

استاد

• Social Relationships and Health
• Stress and Health
• Divorce and Marital Separation
• +۴ مورد دیگر

David Sbarra serves as Professor and EOS Foundation Endowed Chair in Health Psychology at the University of Arizona's Department of Psychology, where he directs the Laboratory for Social Connectedness & Health (LSCH). His research examines the biopsychosocial mechanisms linking social relationships to health outcomes, with particular focus on how divorce, marital separation, and loneliness function as risk pathways for poor health. Dr. Sbarra's academic credentials include: B.S. in Psychology from Cornell University (1996) Clinical Internship at University of Wisconsin Department of Psychiatry Ph.D. in Clinical Psychology from University of Virginia (2004) His research program centers on social relationships and health, stress physiology, and genetically-informed methodologies including twin studies, polygenic risk scores, and Mendelian Randomization. The Laboratory for Social Connectedness & Health investigates how high-quality relationships promote health resilience while relationship disruptions create vulnerability, emphasizing causal inference through advanced quantitative methods and open science practices. Recent publications reveal a pronounced shift toward establishing causal relationships in the social connection-health field, with heavy utilization of genetically-informed designs and multi-method approaches. His 2024-2025 work demonstrates particular innovation in applying Mendelian Randomization to loneliness-depression pathways, examining cardiovascular impacts of marital separation, and developing novel measures for couple therapy processes. Dr. Sbarra holds the prestigious EOS Foundation Endowed Chair in Health Psychology, recognizing his significant contributions to understanding social determinants of health. His co-authored work in the American Psychologist has helped establish social connection as a critical public health priority. While specific advising details aren't documented in available sources, his laboratory direction and course instruction (including PSY 383 Health Psychology and clinical practica) indicate active mentorship of graduate students. His research program likely operates through multiple federally funded grants supporting genetically-informed longitudinal studies. The Laboratory for Social Connectedness & Health employs diverse methodologies including daily diary assessments, physiological monitoring, fMRI, and genetically-informed designs to examine how social connection operates as a fundamental health determinant across the lifespan. Current projects focus on causal mechanisms linking relationship quality to biological aging, intervention development for attachment security, and public health applications of social connection research.

Fengzhu Sunمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Statistical Genetics
• +۶ مورد دیگر

Dr. Fengzhu Sun is a Professor of Quantitative and Computational Biology and Mathematics at the University of Southern California. His research spans computational biology, bioinformatics, statistical genetics, and mathematical modeling, with a focus on metagenomics, protein interaction networks, and genome sequence analysis. Dr. Sun earned his Bachelors in Mathematics from Shandong University, Masters in Probability and Statistics from Peking University, and PhD in Applied Mathematics from USC. He returned to USC in 2000 as an associate professor after serving at Emory University (1995-2000), becoming a full professor in 2006. His research interests encompass protein interaction networks, gene expression, SNPs, linkage disequilibrium, and their applications in predicting protein functions, gene regulation networks, and disease gene identification. He pioneered alignment-free methods for genome and metagenome sequence comparison, with recent work focusing on virus-host interactions in metagenomic data. His publication record shows consistent innovation, with recent work (2023-2025) emphasizing deep learning approaches (DeepMicroClass, DeepDecon, DeepLINK) and novel statistical methods. His research demonstrates strong interdisciplinary integration of computational methods with biological applications. Fellow of American Association for the Advancement of Sciences (AAAS, 2012) Fellow of American Statistical Association (ASA, 2015) Fellow of Institute of Mathematical Statistics (IMS, 2023) Fellow of International Society for Computational Biology (ISCB, 2024) Fellow of Asia-Pacific Artificial Intelligence Association (AAIA, 2025) Member of International Statistical Institute (ISI, 2012) USC Mellon Mentoring award for faculty mentoring (2012) USC Dornsife College senior Raubenheimer Outstanding Faculty Award (2017) Dr. Sun has mentored numerous successful students and postdocs, many now in academic positions or at leading tech and biotech companies. His research group develops computational methods for complex biological data analysis, with current focus on advanced deep learning for metagenomic classification, cancer cell fraction estimation, and virus-host interaction analysis. He has created influential software tools including DeepMicroClass, ImputeCC, DeepDecon, and ViralCC that have become standard resources in computational biology.

Michele Salviمشاهده پروفایل

دانشیار

• Probability Theory
• Random Processes in Random Media
• Random Graphs
• +۴ مورد دیگر

Michele Salvi is an Associate Professor in Mathematics at Università degli Studi di Tor Vergata in Rome. He previously held a Marie Skłodowska-Curie fellowship, conducting research in Berlin, Munich, and Paris. His work focuses on Probability Theory, with emphasis on random processes in random media, random graphs, and statistical mechanics, bridging applications in Physics, Computer Science, and Biology. Random processes in random media Random graphs Mathematics of Neural Networks Stochastic homogenization Mixing times for Markov chains Statistical mechanics Salvi’s recent publications highlight interdisciplinary trends, particularly in the spectral analysis of deep neural networks, scale-free percolation dynamics, and spanning tree geometry in random environments. His collaborations span Europe, with projects involving probabilistic models in epidemiology, reinforcement learning, and stochastic homogenization. He has received the Marie Skłodowska-Curie fellowship, reflecting his international research experience. His work is aligned with the Department of Mathematics at Tor Vergata, which holds the "Department of Excellence" MatMod@TOV 2023-2027 grant.

• Data Mining
• Machine Learning
• Relational Learning
• +۱۲ مورد دیگر

Jennifer Neville is a Senior Principal Researcher at Microsoft Research Redmond and holds the Samuel Conte Chair Professor of Computer Science and Statistics at Purdue University. With over 100 publications and 10K citations, her research spans data mining, machine learning, and AI algorithms for relational and networked domains including social networks, epidemiology, and web analytics. Education: BS in Computer Science, University of Massachusetts Amherst (2000) MS in Computer Science, University of Massachusetts Amherst (2004) PhD in Computer Science, University of Massachusetts Amherst (2006) Her work focuses on relational learning techniques that exploit connections between entities to enhance pattern discovery. Recent research explores large language models (LLMs), emphasizing alignment with user intent through interaction at scale, while addressing statistical biases from graph structures. Selected scientific awards include the NSF Career Award (2012), ICDM Best Paper (2009), and IEEE’s 10 to Watch in AI (2008). She served on the AAAI Executive Council (2015-2018) and chaired multiple conferences including SIAM Data Mining (2019) and ACM Web Search (2016). Contact: neville@cs.purdue.edu jenneville@microsoft.com

Angela Boatmanمشاهده پروفایل

دانشیار

• Higher education policy
• college access and student success
• remedial and developmental education
• +۵ مورد دیگر

Angela Boatman is an Associate Professor in the Department of Educational Leadership & Higher Education at Boston College specializing in higher education policy evaluation, with emphasis on improving access and success for underrepresented student populations through evidence-based interventions. Her educational credentials include: Ed.D. in Higher Education from Harvard University M.P.P. and M.A. in Higher Education from the University of Michigan B.A. from the University of Minnesota Dr. Boatman's research program systematically examines college access barriers and success pathways, focusing on remedial education reform (particularly mathematics), financial aid design, community college transfer policies, and behavioral interventions to reduce loan aversion. She employs rigorous quasi-experimental and experimental methodologies to evaluate policy impacts on student outcomes, with particular attention to equity implications for traditionally marginalized groups. Her work bridges academic research and practical policy implementation through sustained engagement with federal and state education agencies. Analysis of her recent publications (2019-2025) reveals consistent thematic focus on remediation models (corequisite vs. traditional, high school vs. college timing), behavioral economics applications in financial aid, and cross-institutional transfer policies. Her research spans diverse contexts including U.S. community colleges, technical institutions in Chile, and rural-serving universities, demonstrating methodological versatility through large-scale administrative data analysis, randomized trials, and comparative international studies. Dr. Boatman has secured substantial external research funding from the Institute for Education Sciences, Bill & Melinda Gates Foundation, Lumina Foundation, and Peabody College. She actively contributes to national discourse as a faculty affiliate of the Center for the Analysis of Postsecondary Readiness (CAPR) and the Center for Education Policy Research (CEPR) at Harvard University, frequently presenting findings to policymakers and practitioners through congressional briefings, Federal Reserve conferences, and national education forums.