Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Department Head at Carnegie Mellon University. She holds a PhD in Statistics from the University of Washington (2006), an MS in Statistics from Stanford (2006), and a BA in Mathematics, Statistics, and Spanish from Rice University (2002). Her research spans clustering methodology , record linkage , educational data mining , public health , and semantic organization , with a focus on high-dimensional data and adaptive learning environments. She leads the Integrated Statistics Learning Environment (ISLE) and Corporate Capstone programs, emphasizing low-barrier data platforms for education and industry collaboration. Academic Roles : Department Head, Carnegie Mellon; Affiliated Faculty, Block Center for Technology and Society Research Grants : NSF (2017-2019), NIH (2018), Carnegie Mellon ProSEED/Simon Initiative (2020, 2018), Berkman Fund (2014) Her 15 most recent publications focus on data science pedagogy, clustering algorithms, record linkage applications in historical and medical data, educational data mining, and semantic organization studies. Awards include the ASA Waller Education Award (2015) and the William H. and Frances S. Ryan Award (2015) . She mentors a diverse group of PhD, Master's, and undergraduate students, with alumni pursuing careers in academia, industry, and sports analytics.
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Joshua Schwartzمشاهده پروفایل
استادیار
Joshua Schwartz serves as Assistant Professor at the Carnegie Mellon Institute for Strategy and Technology (CMIST) within Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His interdisciplinary work bridges political science, security studies, and technology policy through rigorous empirical analysis. Dr. Schwartz's research centers on three interconnected domains: military technology proliferation (particularly armed drones and reconnaissance strike systems), public opinion dynamics regarding military force and weapons of mass destruction, and gendered dimensions of international crisis bargaining. His innovative methodologies include large-N statistical analysis, survey experiments across multiple countries, and creative applications of pop culture to teach complex security concepts. His publication record reveals strong trends in nuclear deterrence theory (evident in 2024's When Foreign Countries Push The Button ), drone warfare effectiveness (2022 drone counter-terrorism study), and gender-politics intersections (2020-2023 work on female leaders). Recent articles increasingly incorporate experimental designs across international contexts, with growing emphasis on climate security linkages. 2022 APSA David Naveh Founders Award for Best Graduate Student Paper 2019 TESS Special Competition for Young Investigators award Publications in International Security , International Organization , and Security Studies Dr. Schwartz actively mentors students through courses like An Introduction to Technology and War (84-274/674) and Popcorn and Politics , while securing research funding through fellowships at Harvard, MIT, and Notre Dame. His current book project Dovish Reputation Theory: When Backing Down Makes Sense challenges conventional wisdom about military credibility.
Gonzalo Menaمشاهده پروفایل
استادیار
Gonzalo Mena is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His research focuses on applying statistical and machine learning methods to life sciences problems, particularly in neuroscience and epidemiology. He has prior experience as a Florence Nightingale Bicentennial Fellow and Tutor at the University of Oxford, a Postdoctoral Fellow at Harvard University, and holds a Ph.D. in Statistics from Columbia University and a B.Sc. in Mathematical Engineering from Universidad de Chile. His work bridges cutting-edge neurotechnology data analysis with large-scale epidemiological surveillance, emphasizing valid inference from biased datasets. He is a leader in statistical optimal transport, developing methodologies to measure distributional distances and inform scientific discovery. His research spans the DELPHI Lab Group and intersects with computational neuroscience, genomics, and public health policy. Key research areas include Alzheimer's disease modeling via multimodal cell atlases, Bayesian methods for neurodegenerative dynamics, and pandemic impact analysis (e.g., socioeconomic disparities in Covid-19 outcomes). His recent publications highlight interdisciplinary applications of optimal transport theory and permutation inference in neuroscience data analysis. Mena has contributed to foundational statistical frameworks for population size estimation and neuronal identification in C. elegans. His work combines methodological innovation with real-world applications in healthcare and public health surveillance.