جست‌وجوی استاد و پژوهشگر

Paula M. Poindexterمشاهده پروفایل

استاد

• News Engagement
• News Literacy
• Diversity in News
• +۳ مورد دیگر

Paula M. Poindexter is a Professor at the School of Journalism and Media, University of Texas at Austin. She holds a Ph.D. from Syracuse University’s S.I. Newhouse School of Public Communications and has served as President of AEJMC (2013-2014), where she founded News Engagement Day and initiated the Scholarsourcing book-publishing venture. Her research focuses on news engagement, diversity in media, and voting behavior, with notable works including Millennials, News, and Social Media (2018) and News for a Mobile-First Consumer (2016). She has chaired 10 Ph.D. dissertations and co-chairs the Maxwell McCombs Graduate Student Research Publishing Award. Awards include the Eleanor Blum Distinguished Service Award (2016) and the Lionel C. Barrow Jr. Award (2009). She spearheaded AEJMC’s Ethical Research Guidelines and the Tankard Book Award. Her advocacy extends to mentoring, with the MAC Division naming a research grant after her. Key contributions include curating the Millennials and News resource and advancing media diversity through grants and pedagogical innovations. She has created seven courses and contributed to over a dozen scholarly publications across books, journals, and encyclopedias. Her work bridges academic research with practical journalism ethics and audience engagement strategies.

Joshua I Glaserمشاهده پروفایل

استادیار

• Computational Neuroscience
• Neural Control of Movement
• Interpretable Machine Learning
• +۷ مورد دیگر

Joshua I Glaser is an Assistant Professor at Northwestern University, holding dual appointments in the Department of Neurology (Feinberg School of Medicine) and the McCormick School of Engineering. His research focuses on computational neuroscience, particularly the neural control of movement and interpretable machine learning. Glaser earned a BS in Physics and Math from the University of Illinois Urbana-Champaign (2011), a PhD in Neuroscience from Northwestern University (2018), and completed postdoctoral training at the University of Pennsylvania and Columbia University. Research Interests: Glaser’s lab develops interpretable machine learning tools to understand neural computations underlying movement and behavior. Key areas include neural population interactions, flexible behavior control, and disease-related neural dynamics. Projects include interpretable dimensionality reduction, decoding models linking neural activity to behavior, and multi-population neural interaction analysis. Lab & Collaborations: The Glaser Lab collaborates with experimental neuroscientists and statisticians. Current projects involve developing novel methods for analyzing neural data while maintaining interpretability. They host a team of PhD, master’s, and undergraduate students. Grants & Awards: While no specific awards are listed, his work reflects sustained funding in computational neuroscience and engineering. Code & Resources: Lab-developed tools include Neural_Decoding (machine learning for neural decoding), SSM (state-space models), and Sparse Component Analysis (interpretable latent factor analysis). These are publicly available on GitHub.

Vadim Linetskyمشاهده پروفایل

استاد

• Financial Engineering
• Mathematical Finance
• Stochastic Modeling
• +۶ مورد دیگر

Vadim Linetsky is a Professor of Industrial Engineering and Management Sciences at Northwestern University. His research focuses on financial engineering, mathematical finance, and stochastic modeling. He holds a Ph.D. in Theoretical and Mathematical Physics from the P.N. Lebedev Physical Institute of the Russian Academy of Sciences (FIRAN), an M.S. in Electrical Engineering from the Moscow State Institute of Radio Engineering, and a B.S. in Electronics and Automation from the University of Technology. His work emphasizes applications in financial markets, including interest rate modeling, credit risk, derivatives pricing, and algorithmic trading. Notable contributions include advancements in spectral methods for pricing financial instruments and the development of models addressing the zero lower bound in interest rate dynamics. Linetsky has also explored long-term risk frameworks and stochastic spectral theory, bridging econometrics and financial mathematics. His publications span journals such as Finance and Stochastics , Econometrica , and Mathematics and Financial Economics , with a focus on practical computational methods and theoretical rigor. Collaborative research projects include high-performance algorithms for Markov processes and interdisciplinary applications of stochastic modeling. Linetsky’s research has been supported by grants, including work on customer default risk management and computational methods in financial engineering. His contributions have influenced both academic theory and industry practices in quantitative finance.

Luís A. Nunes Amaralمشاهده پروفایل

استاد

• Complex Systems
• Systems Biology
• Science of Science
• +۵ مورد دیگر

Luís A. Nunes Amaral is the Erastus Otis Haven Professor of Engineering Sciences and Applied Mathematics at Northwestern University's McCormick School of Engineering. He also holds courtesy appointments in Physics and Astronomy, and Medicine (Pulmonary and Critical Care). His research focuses on complex systems, systems biology, and the science of science, addressing challenges in healthcare, innovation, and machine intelligence. He has published over 180 peer-reviewed papers in prestigious journals like Nature, Science, and Cell. Education: PhD in Physics (Boston University), M.S. and B.S. in Physics (Universidade de Lisboa). Research Interests: Emergence of complex systems, healthcare analytics, creativity in science, and AI ethics. His work spans interdisciplinary collaborations, including projects on electronic health records, gene expression dynamics, and open justice systems. Selected Awards: NIH CAREER Award, Keck Foundation Distinguished Scholar, HHMI Early Career Scientist, Fellowships from APS, AAAS, and Network Science Society. He received the 2020 Provost's Award for Exemplary Faculty Service. Lab: Amaral Lab focuses on network science, systems biology, and quantitative social science. Current Students: Includes PhD candidates Maalavika Pillai, Feihong Xu, and Huaxia Zhou. Grants: Active funding in healthcare analytics, AI ethics, and systems biology.

• Statistical Learning
• Multivariate Analysis
• Classification
• +۶ مورد دیگر

Yoonkyung Lee is a Professor of Statistics and Computer Science Engineering at The Ohio State University, affiliated with the Department of Statistics in the College of Arts and Sciences. She holds a PhD from the University of Wisconsin-Madison (2002). Her primary research focuses on statistical learning and multivariate analysis, with specializations in classification, kernel methods, and model stability. She has a courtesy appointment in Computer Science and Engineering since 2016 and served as a faculty co-director of the Translational Data Analytics Institute (2020–2022). Her work has been funded by the National Science Foundation (NSF), and she was elected a Fellow of the American Statistical Association in 2015. Her educational background includes a PhD in Statistics from the University of Wisconsin-Madison. Her research interests emphasize developing methodologies for latent structures in multivariate data, computational frameworks for model stability, and predictive modeling. Notable contributions include advancements in kernel discriminant analysis, Bayesian restricted likelihood methods, and sparse logistic tensor decomposition. Prof. Lee serves on editorial boards for journals such as Chemometrics and Intelligent Laboratory Systems , Econometrics and Statistics , and Journal of Machine Learning Research . Her articles span topics like support vector machines, quantile regression, and nonlinear embeddings, reflecting her expertise in bridging statistics and machine learning. Beyond research, she has advised numerous projects and contributed to interdisciplinary initiatives in translational data analytics.

In-Koo Choمشاهده پروفایل

استاد

• Theory of Machine Learning
• Economic Theory
• Game Theory
• +۳ مورد دیگر

In-Koo Cho is the Asa Griggs Candler Professor of Economics at Emory University. He holds a PhD from Princeton University (1986) and a BA from Seoul National University (1981). His research focuses on economic theory, game theory, and machine learning applications in strategic contexts. He has been a key organizer of the Asian School in Economic Theory, a prestigious event hosted by the Econometric Society, since 2013. His work bridges theoretical economics with practical implications in market design, learning models, and policy analysis. Cho’s academic contributions include foundational studies on competitive equilibrium, learning dynamics, and model validation. He has led international collaborations through his involvement in summer schools and conferences, fostering global exchange in economic theory. His research also addresses societal challenges, such as energy policy and market stability, reflecting a commitment to interdisciplinary impact. Key areas of his expertise include signaling games, algorithmic pricing, and the integration of machine learning into economic modeling. His career has spanned institutions like the University of Illinois and Hanyang University, though his current affiliation is with Emory University.

Stéphane Bonhommeمشاهده پروفایل

استاد

• Microeconometrics
• Econometric Theory
• Labour Economics
• +۳ مورد دیگر

Stéphane Bonhomme is the Ann L. and Lawrence B. Buttenwieser Professor of Economics and the College at the University of Chicago's Kenneth C. Griffin Department of Economics. His research focuses on microeconometrics, econometric theory, and labor economics, with emphasis on latent variable modeling and panel data analysis. He holds a PhD from the University of Paris I, Panthéon-Sorbonne (2005). Key contributions include methodologies for handling unobserved heterogeneity in panel data, nonlinear persistence in consumption dynamics, and bias reduction in econometric models. His work has been published in top journals like Econometrica, Journal of Econometrics, and the Journal of Political Economy. Recent research explores grouped patterns of heterogeneity, firm-worker sorting effects, and functional differencing in networks. He is a Fellow of the Econometric Society (2017) and has developed widely used Stata/Python packages for bias correction and discrete heterogeneity estimation.

Ashesh Rambachanمشاهده پروفایل

استادیار

• Econometrics
• Machine Learning Applications in Economics
• Causal Inference
• +۲ مورد دیگر

Ashesh Rambachan serves as Assistant Professor of Economics at the Massachusetts Institute of Technology (MIT), with a visiting appointment at Stanford University's Department of Economics and SIEPR for the 2025-2026 academic year. His primary institutional affiliation remains MIT's Department of Economics. His research program integrates econometric theory with machine learning to advance causal inference methodologies, particularly in quasi-experimental designs and observational data analysis. Key focus areas include algorithmic fairness, structural model robustness, and the application of foundation models to economic problems. This interdisciplinary approach bridges theoretical econometrics with real-world policy evaluation challenges. Analysis of his publication trajectory reveals increasing engagement with AI-driven methodologies since 2020, marked by a shift toward foundation models and large language models in economic contexts. His work consistently addresses credibility gaps in causal estimation while expanding into behavioral economics and algorithmic decision-making frameworks, reflecting evolving priorities in computational social science. Scientific recognition includes: NeurIPS 2024 Spotlight Paper for "Evaluating the World Model Implicit in a Generative Model" No explicit details regarding student advising, grant funding, or laboratory affiliations appear in current public records. His teaching portfolio includes MIT PhD courses on Algorithms and Behavioral Science (14.163) and Statistical Methods in Economics (14.380), alongside undergraduate instruction in Algorithmic and Human Decision-Making (S.6041).

Paul J. Hangesمشاهده پروفایل

استاد

• Cross-Cultural Leadership
• Organizational Climate
• Research Methodology
• +۳ مورد دیگر

Paul J. Hanges is a Professor of Industrial/Organizational Psychology at the University of Maryland’s Psychology Department and academic director of the MPS in Industrial/Organizational Psychology program. He holds a PhD (1986) and MA (1984) from the University of Akron, and a BA (1980) from New York University. His research focuses on cross-cultural leadership, human resource practices, and measurement methodologies, with notable contributions through the GLOBE project, examining leadership effectiveness in 62 societies. Key awards include the M. Scott Myers Award (2004, 2011) and the 2015 Leadership Book Award. His work has been funded by the Hewlett Foundation, Democracy Fund, and U.S. Army Research Institute. He also served as Professor of Global Leadership at Durham University (2020). Education: PhD (University of Akron, 1986), MA (University of Akron, 1984), BA (New York University, 1980) Research Themes: Cross-cultural leadership dynamics, ethical leadership, organizational climate, and dynamic systems analysis Major Projects: GLOBE Study (global leadership effectiveness), ClimateQUAL framework His publications explore leadership behavior’s impact on organizational effectiveness, with a focus on cultural contingencies. Awards include fellowships in major psychological associations. Current affiliations include the University of Maryland and ongoing GLOBE research.

Susanne M Furmanمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Cybersecurity
• Usability Engineering
• +۴ مورد دیگر

Susanne M Furman is an Instructor in the Department of Psychology at George Mason University (GMU), concurrently serving as a cognitive scientist in the Visualization and Usability Group at the National Institute of Standards and Technology (NIST). Her roles span academic and federal agency work, focusing on usability engineering and cybersecurity. She holds a PhD in Applied Experimental Psychology (Human Factors) from GMU, with prior experience at IBM, GE, and the U.S. Department of Health and Human Services. Education: B.A. in Psychology, Bloomsburg University of Pennsylvania (1988) M.A. in Applied Experimental Psychology, George Mason University (1990) Ph.D. in Applied Experimental Psychology (Human Factors), George Mason University (1997) Research focuses on usability of cybersecurity tools, biometric systems, and smart home technologies. Key themes include privacy preservation, user mental models of security, and cross-agency collaboration (e.g., with DHS and FBI). Recent work examines consumer trust in smart home ecosystems and lifecycle management of IoT devices. Publications (selected): Her work bridges usability research with federal cybersecurity needs, including foundational studies on contactless fingerprint devices and smart home privacy challenges. Over 25 years of industry and academic collaboration has produced impactful contributions to human-centered cybersecurity standards. Affiliations: Active in NIST’s Human-Centered Cybersecurity projects and GMU’s Psychology department. She contributes to federal usability programs and collaborates with researchers like Julie Haney and Brian Stanton.

Ioana Banicescuمشاهده پروفایل

استاد

• Parallel Algorithms
• Scientific Computing
• Performance Modeling
• +۳ مورد دیگر

Ioana Banicescu is a Professor at Mississippi State University's Department of Computer Science and Engineering. She holds a Ph.D. in Computer Science from New York University and previously directed the Center for Cloud and Autonomic Computing. Her research focuses on parallel algorithms, scientific computing, performance modeling, and autonomic systems. Her education includes: Ph.D. Computer Science - New York University M.S. Computer Science - New York University B.S./M.S. Electronics and Telecommunications - Polytechnic University of Bucharest Banicescu's research explores fundamental challenges in high-performance computing, including dynamic load balancing, performance optimization, and autonomic resource management. Her recent work emphasizes explainable AI for cybersecurity systems and robust resource allocation models for heterogeneous computing environments. Her publication record demonstrates consistent focus on performance optimization in parallel systems, with recent expansion into cybersecurity applications using machine learning approaches. She maintains collaborations with national laboratories and international research forums. Banicescu has received multiple National Science Foundation awards for research excellence and serves on numerous program committees for IEEE/ACM conferences. She contributes to the academic community through editorial roles for Cluster Computing and the International Journal on Computational Science and Engineering.

Abolfazl Safikhaniمشاهده پروفایل

استادیار

• Network Modeling
• High-Dimensional Statistics
• Spatiotemporal Models
• +۵ مورد دیگر

Abolfazl Safikhani is an Assistant Professor in the Department of Statistics at George Mason University. He holds a PhD in Statistics and Probability from Michigan State University and has held prior positions at Columbia University and the University of Florida. His research focuses on network modeling, high-dimensional statistics, spatiotemporal models, and applications in urban planning, neuroscience, and smart cities. He is an Associate Editor for Technometrics , Statistica Sinica , and Data Science in Science . He has contributed to advancements in statistical methodologies for time series analysis, including change point detection, transfer learning, and spatiotemporal modeling. His work bridges theoretical statistics with practical applications in urban growth prediction, healthcare (e.g., cancer drug response modeling), and transportation systems. Recent research trends include leveraging explainable AI for land use modeling, longitudinal omics data analysis, and structural break detection in high-dimensional systems. His publications span theoretical developments and real-world case studies, such as subway ridership during the pandemic and New York City’s taxi demand dynamics. Despite no listed awards, his editorial roles highlight his influence in statistical science. He actively mentors students and collaborates on interdisciplinary projects, emphasizing data-driven solutions for complex societal challenges.

Leia B Bagesteiroمشاهده پروفایل

دانشیار

• Biomechanics
• Motor Control
• Neurological Movement Analysis
• +۶ مورد دیگر

Dr. Leia B Bagesteiro is an Associate Professor in the Department of Kinesiology at San Francisco State University. She specializes in biomechanics and motor control, with expertise in analyzing human movement through kinematics, kinetics, and electrophysiology. Her academic background includes a B.Eng. and M.Eng. in Mechanical Engineering and Biomechanics from the Universidade Federal do Rio Grande do Sul, Brazil, and a Ph.D. in Biomedical Engineering from the University of Surrey, England. She also completed postdoctoral research at Penn State University’s Movement Neuroscience Laboratory and Columbia University’s Motor Performance Laboratory. Her research focuses on interdisciplinary applications of biomechanics and biomedical engineering, including prosthetics control, ADHD diagnosis via EEG signals, and pediatric motor control analysis. She teaches advanced courses such as Biomechanics, Advanced Biomechanics, and independent study modules. Notable research trends include the use of machine learning (e.g., random forests, spiking neural networks) for signal classification and the development of adaptive systems for prosthetics. Her work bridges clinical diagnostics, rehabilitation engineering, and educational methodologies, emphasizing practical applications of biomechanical principles. Dr. Bagesteiro has collaborated on projects involving gait analysis on compliant surfaces, interlimb coordination in children, and the effects of aging on motor control. Her contributions span experimental biomechanics, computational modeling, and clinical validation of movement-related technologies.

Luella Fuمشاهده پروفایل

دانشیار

• Statistics
• Large-Scale Inference
• Multiple Testing
• +۳ مورد دیگر

Luella Fu is an Associate Professor in the Department of Mathematics at San Francisco State University, part of the College of Science & Engineering. Her research focuses on large-scale statistical inference, with expertise in multiple testing, empirical Bayes methods, and heteroscedasticity analysis. She holds a position within the university's Mathematics department and contributes to advancing methodologies in statistical theory and applications. Her work emphasizes developing robust frameworks for handling heterogeneous data and controlling false discoveries in high-dimensional settings. Key research themes include nonparametric estimation, adaptive model selection, and the impact of standardization in hypothesis testing. She has also explored applications of statistical methods in social network analysis, such as studying smoking behavior through Facebook data. Dr. Fu is affiliated with the Department of Mathematics, located in Thornton Hall 937. She can be reached at luella@sfsu.edu, with office hours available via the department's live schedule.

Seung Jae Leeمشاهده پروفایل

دانشیار

• Civil Engineering
• Geotechnical Engineering
• Computational Modeling
• +۶ مورد دیگر

Seung Jae Lee is an Associate Professor and Undergraduate Program Director in the Department of Civil and Environmental Engineering at Florida International University (FIU). He holds a Ph.D. in Civil Engineering from the University of Illinois at Urbana-Champaign (2014). His research focuses on advanced computational modeling of granular materials, structural dynamics, and disaster-resilient infrastructure. Key areas include particle geometry characterization, soil-water interaction analysis, and wind engineering for building systems. He leads efforts in integrating 3D imaging and simulation techniques to enhance material understanding and infrastructure performance. Education: Ph.D., Civil Engineering, University of Illinois at Urbana-Champaign, 2014 Research interests span particle morphology analysis, computational mechanics, and sustainable construction materials. His work emphasizes practical applications such as improving cement-based materials through aggregate morphology optimization and enhancing building envelope systems against extreme winds. Lee's methodologies often combine experimental testing with advanced numerical simulations, such as discrete element modeling (DEM) and finite element analysis (FEA). His articles highlight trends in 3D particle characterization, wind-induced structural behavior, and biotechnology-driven soil improvement. These studies aim to bridge microscopic material properties with macroscopic engineering performance, contributing to safer and more durable infrastructure systems. Notable projects include full-scale testing of curtain wall systems and development of predictive models for material degradation under cyclic loading. His lab focuses on disaster preparedness through computational frameworks integrating imaging and simulation for construction projects.