جستجوی استاد راهنمای دانشگاهی

Sharon-Lise Normandمشاهده پروفایل

استاد

• Biostatistics
• Health Services Research
• Medical Device Evaluation
• +۷ مورد دیگر

Sharon-Lise Normand is the S. James Adelstein Professor of Health Care Policy (Biostatistics) at Harvard Medical School and Professor of Biostatistics at Harvard T.H. Chan School of Public Health. Her research focuses on statistical methodologies for health services and outcomes research, particularly Bayesian approaches applied to medical device evaluation, causal inference, provider profiling, and evidence synthesis. She leads the Medical Device Epidemiology Network’s Methodology Center and previously directed Mass-DAC, a cardiac surgery quality monitoring initiative. Normand’s work spans cardiovascular disease, severe mental illness, and medical technology diffusion. She has held leadership roles in professional organizations like the American Statistical Association and received multiple awards for her contributions to biostatistics and healthcare research. Dr. Normand holds a PhD from the University of Toronto and completed a postdoctoral fellowship at Harvard Medical School. Her honors include Fellowships in the American Statistical Association, American College of Cardiology, and American Association for the Advancement of Science. She has advised federal agencies such as the FDA and CMS and contributed to policy developments in healthcare quality and equity. Her research team includes experts in biostatistics and data analysis, focusing on topics like telemedicine adoption disparities, racial/ethnic healthcare quality gaps, and the evaluation of medical device safety. Recent work examines pandemic impacts on healthcare access and methodological innovations in causal inference and composite outcome analysis. Normand’s scientific awards reflect her impactful contributions, including the 2017 American Heart Association Lifetime Achievement Award and the 2018 Mosteller Statistician of the Year. She has authored over 200 peer-reviewed publications, with recent studies addressing opioid medication availability, telemedicine trends, and disparities in mental healthcare access under value-based payment models.

Jerome P. Reiterمشاهده پروفایل

استاد

• Data Confidentiality
• Missing Data Analysis
• Causal Inference
• +۲ مورد دیگر

Jerome P. Reiter is Professor of Statistical Science at Duke University, with additional affiliations at the Duke Population Research Institute and Duke-Margolis Institute for Health Policy. His research focuses on statistical methods for data confidentiality, missing data analysis, and causal inference. Reiter's work addresses challenges in integrating information across multiple data sources, particularly for social sciences and public policy applications. His recent publications demonstrate expertise in privacy-preserving analytics and environmental epidemiology. Bass Fellow at Duke University

Guadalupe Tuñónمشاهده پروفایل

استادیار

• Comparative Politics
• Political Economy
• Politics and Religion
• +۴ مورد دیگر

Guadalupe Tuñón is an Assistant Professor at Princeton University's Department of Politics and School of Public and International Affairs. Her research focuses on comparative politics and political economy, with a regional emphasis on Latin America. She investigates how religious ideas influence electoral dynamics and policy outcomes, particularly regarding inequality and redistribution. Tuñón holds a PhD in Political Science from UC Berkeley (2019) and has held prestigious fellowships, including at Harvard's Weatherhead Center for International Affairs and the Identity & Conflict Lab (University of Pennsylvania). Her academic work has been recognized with major awards: the Mancur Olson Prize (APSA 2020) for best political economy dissertation and the Aaron Wildavsky Award (APSA 2020) for religion and politics scholarship. Recent accolades include the 2024 David Brian Robertson Best Paper Award. Research interests span electoral behavior, judicial politics, and policy design. She employs experimental methods and causal inference techniques to study governance challenges such as tax compliance (Uruguay), campaign finance reforms (Brazil), and housing policy impacts. Tuñón's work bridges theoretical rigor with applied policy analysis, emphasizing Latin America's institutional diversity. Teaching focuses on political economy of Latin America, with courses such as the 2024 Spring seminar on the region's political economy dynamics. Her research agenda includes ongoing projects on religious actors' political influence and institutional responses to corruption cycles in Argentina.

Wendy K Tam Choمشاهده پروفایل

استاد

• Quantitative Methods in Social Sciences
• Political Science
• Computer Science
• +۵ مورد دیگر

Wendy K Tam Cho is a Professor at the University of Illinois at Urbana-Champaign with appointments in multiple departments: Political Science, Statistics, Mathematics, Computer Science, Asian American Studies, and the College of Law. She also holds roles as Senior Research Scientist at the National Center for Supercomputing Applications (NCSA), Faculty in the Illinois Informatics Institute, and Affiliate of several research centers including the Cline Center for Advanced Social Research and the CyberGIS Center. Her research focuses on quantitative methods in social sciences, AI applications in law and public policy, and computational epidemiology. She has received prestigious fellowships from the Guggenheim Foundation, Society for Political Methodology, and Stanford’s Center for Advanced Study. Her work bridges computational methods and social science, addressing issues like redistricting, health disparities, and causal inference. Recent publications analyze demographic impacts on pandemic outcomes and the role of AI in legal processes. She leads interdisciplinary teams at NCSA and the Illinois Informatics Institute, advancing data-driven solutions for societal challenges.

Whitney Neweyمشاهده پروفایل

استاد

• Econometrics
• Machine Learning
• Causal Inference
• +۷ مورد دیگر

Whitney Newey is the Ford Professor of Economics at the Massachusetts Institute of Technology (MIT) and a Research Associate at the National Bureau of Economic Research (NBER). He holds affiliations with multiple prestigious institutions, including fellowships with the American Academy of Arts and Sciences, the Econometric Society, and the International Association of Applied Econometrics. His research focuses on advanced econometric methodologies, particularly in structural and causal parameter estimation, machine learning integration, and high-dimensional regression analysis. Dr. Newey’s academic career includes roles as co-editor of Econometrica and program co-chair for the 2005 World Congress of the Econometric Society. His work bridges theoretical econometrics and applied policy analysis, addressing topics such as taxable income elasticity, demand modeling, and semiparametric estimation techniques. Notable achievements include the 2020 American Economic Association Distinguished Fellow award and the Sloan Foundation Research Fellowship. His publications span leading journals such as Quantitative Economics , Econometrica , and Journal of Political Economy , reflecting contributions to both foundational econometric theory and practical policy evaluation. His advisory roles include mentoring through MIT’s Department of Economics, though specific advisee names are not listed in available materials. Current research emphasizes debiased machine learning applications and inference methods in complex economic models.

Rajarshi Royمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Optical Systems
• Chaos Theory
• +۳ مورد دیگر

Rajarshi Roy is a Professor at the Institute for Physical Science and Technology (IPST) at the University of Maryland. His research focuses on nonlinear dynamics, chaos theory, and their applications in optical systems. He explores phenomena such as synchronization patterns, machine learning-driven network analysis, and quantum-optical systems. His experimental work includes studies on optoelectronic oscillators, delay-coupled systems, and photonic random number generation. His research interests span nonlinear dynamics , chaos theory , and optical systems . Key areas include synchronization of coupled oscillators, chimera states, and machine learning applications in network inference. He also investigates noise effects in photonics and quantum technologies, such as entanglement quality estimation in fiber systems. His recent work emphasizes combining machine learning with nonlinear dynamics to analyze complex systems. For instance, his studies on delayed dynamical systems and neuromorphic computing showcase innovations in network inference and reservoir computing. Experimental validations using optoelectronic setups highlight his interdisciplinary approach. Roy’s articles explore cutting-edge topics like entropy harvesting in photon-counting systems, topological control of synchronization, and suppression of optical scattering via chaos. His contributions bridge fundamental nonlinear science with technological applications in photonics and information systems.

• Econometric Theory
• Time Series Analysis
• Econometrics
• +۳ مورد دیگر

Dr. Majid Al Sadoon is an Associate Professor and Director of Research at the Business School, Durham University. He holds a PhD from Cambridge University (2011) and has held roles including Assistant Professor at Universitat Pompeu Fabra and Robert Solow Postdoctoral Fellow at Cambridge. His research focuses on Econometric Theory and Time Series Analysis , with contributions to linear rational expectations models, Granger causality, and panel data methodologies. His work bridges theoretical econometrics with practical applications in dynamic modeling and statistical inference. Key research contributions include advancements in spectral approaches to rational expectations models, subspace Granger causality testing, and consistent estimation techniques for panel data. He has published in top journals like Econometric Theory , Journal of Econometrics , and Econometric Reviews . Current research trends emphasize regularization methods for econometric models and identification problems in linear systems. Dr. Al Sadoon advises students on topics such as dynamic panel data and econometric modeling. His academic leadership includes directing research initiatives at the Business School, fostering interdisciplinary collaboration in quantitative economics.

• Computational Neuroscience
• Neurophysics
• Brain Oscillations
• +۲ مورد دیگر

Paul Tiesinga is Professor of Neuroinformatics at Radboud University's Faculty of Science, where he directs the Neuroinformatics group at the Donders Centre for Neuroscience. His research focuses on understanding information processing in the brain, particularly the role of oscillations ('brain waves') in cognitive functions and disease mechanisms. He combines computational modeling with experimental approaches including optogenetics to investigate cortical dynamics. As Director of Education for the Institute for Mathematics, Physics and Astronomy, he oversees academic programs while maintaining active research in: Computational modeling of cortical circuits Oscillation mechanisms in neural coding Data sharing policies for neuroscience Information transfer between brain areas His recent publications reflect advances in neurotechnology development, thalamocortical connectivity analysis, and novel methodologies for neural data interpretation.

Lan Wenمشاهده پروفایل

استادیار

• Causal Inference
• Survival Analysis
• Biostatistics
• +۳ مورد دیگر

Lan Wen is an Assistant Professor in the Department of Biostatistics at the University of Waterloo. Their research focuses on causal inference, survival analysis, and missing data methodologies with applications in public health and epidemiology. They specialize in developing statistical methods for estimating causal effects in observational studies and clinical trials, particularly in the presence of confounding variables, censoring, and complex treatment regimens. Lan's work bridges theoretical advancements and practical applications, addressing challenges in longitudinal cohort studies, HIV prevention strategies, and comparative effectiveness research. They have contributed to methodologies such as the parametric g-formula, robust estimation techniques, and meta-analysis approaches for evaluating model performance. Key contributions include studies on grace periods in treatment comparisons, stochastic interventions in HIV prevention, and interpretability in causal inference. Their research emphasizes rigorous statistical modeling to inform healthcare policy and improve population health outcomes.

Yeying Zhuمشاهده پروفایل

دانشیار

• Statistics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Yeying Zhu is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Her research focuses on causal inference, machine learning applications in networks, and statistical methodologies for handling missing data and confounding variables. She holds a Ph.D. in Statistics from The Pennsylvania State University (2013), an M.S. in Statistics & Applied Probability from the National University of Singapore (2008), and a B.S. in Statistics with a minor in International Economics & Trade from East China Normal University (2006). Her work bridges theoretical statistics with practical challenges in causal analysis, including mediation analysis, propensity score modeling, and network treatment effects. Recent research explores UAV-enabled integrated networks using reinforcement learning and anomaly detection in satellite systems through ensemble learning techniques. She has contributed to advancements in high-dimensional causal mediation analysis and semi-parametric estimation techniques. Notable areas of contribution include: Causal inference frameworks for non-ignorable missing data Machine learning-driven solutions for UAV trajectory optimization and satellite anomaly detection Development of robust statistical methods in longitudinal data and instrumental variable models Her articles reflect interdisciplinary collaborations spanning statistics, computer science, and engineering, emphasizing methodological innovation and real-world applicability.

Wei Aiمشاهده پروفایل

استادیار

• Data Science for Social Good
• Machine Learning
• Causal Inference
• +۴ مورد دیگر

Wei Ai is an Assistant Professor at the University of Maryland, affiliated with the College of Information (INFO) and the Institute for Advanced Computer Studies (UMIACS). His research focuses on data science for social good (DSSG), integrating machine learning, causal inference, and experimental design to address societal challenges in education, virtual collaboration, and quantum computing. He leads the Center for Educational Data Science and Innovation (EDSI) and has secured grants from the NSF, Gates Foundation, and Walton Family Foundation for projects like M-Powering Teachers and classroom quality assessment tools. Education: PhD in Information from the University of Michigan (advised by Qiaozhu Mei). Previous academic roles include teaching at the University of Michigan and Peking University in courses like Data Mining and Information Retrieval. Research Interests: Machine Learning and Causal Inference, AI for Education, Virtual Teams and Social Identity, Large Language Models for Social Applications. He has published in venues such as PNAS, Management Science, ACL, and the Web Conference. Grants: Major awards include a NSF grant on middle-grade math instruction analysis (with Min Sun) and a Gates Foundation grant for classroom dataset development (with Jing Liu). Labs/Teams: CLIP Lab member, collaborating on interdisciplinary projects with UMIACS and the Joint Quantum Institute. Prospective students: Open to mentoring PhD students through INFO and Computer Science programs. Actively supervises current students in education technology and quantum computing domains.

Peer Nowackمشاهده پروفایل

مدرس

• Machine Learning
• Atmospheric Chemistry
• Climate Change
• +۲ مورد دیگر

Dr. Peer Nowack is an Honorary Lecturer at the University of East Anglia's School of Environmental Sciences, specializing in machine learning applications for climate science and atmospheric chemistry. His research addresses climate sensitivity, extreme weather, ozone dynamics, and causal network analysis for model evaluation. Research interests focus on reducing uncertainty in regional climate projections through novel computational methods. His work bridges atmospheric physics with data science to develop efficient Earth system models. Recent publications demonstrate trends in causal inference techniques for climate projections, machine learning-based ozone parameterizations, and observational evidence of cloud feedback amplification. He maintains collaborations across Europe and supervises PhD candidates in machine learning applications.

Lauri Goldkindمشاهده پروفایل

استاد

• Artificial Intelligence in Social Work
• Data Science for Social Services
• Ethical Use of Technology in Human Services
• +۳ مورد دیگر

Lauri Goldkind is a Professor at the Graduate School of Social Service, Fordham University, located at Fordham Westchester. Their work focuses on the intersection of artificial intelligence (AI), technology, and social work, with an emphasis on ethical integration, data justice, and the impact of AI on human services organizations. Education details are not explicitly provided in the available text, but as a Professor in Social Service, it is typical to hold a doctoral degree, likely in Social Work or a related field. Goldkind’s research interests revolve around the ethical and practical implications of AI in social work and human services. Key areas include the responsible integration of AI technologies, data science applications in nonprofit sectors, tele-mental health practices, and promoting social justice through technology. Their work also addresses challenges such as bias in AI, data privacy, and the role of technology in advocacy. Recent publications explore topics such as AI’s impact on clinical social work, ethical considerations in AI implementation, and the use of large language models in mental health. Goldkind also examines the intersection of technology and social justice, including data labor in human services and the platform economy’s influence on social work. No information on scientific awards, advising students, or grants is provided in the available text. Their work often emphasizes interdisciplinary collaboration and the adaptation of social work practices to digital realities, including platform-based advocacy and data-driven decision-making. Labs or teams are not explicitly mentioned in the provided materials, but their research aligns with Fordham’s commitment to social justice and innovation in human services.

Corrado Loglisciمشاهده پروفایل

استادیار

• Temporal Data Mining
• Temporal Learning
• Textual Data Mining
• +۱۳ مورد دیگر

Corrado Loglisci is an Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research focuses on Temporal Data Mining , Machine Learning , and Quantum Computing , with applications in bioinformatics, medical informatics, and cybersecurity. He earned his Ph.D. in Computer Science with a thesis on temporal projection in longitudinal data. Research Highlights : Temporal Learning, Textual Data Mining, Quantum-Classical Hybrid Systems Collaborations : IRSTEA Research Institute (France), Aristotle University of Thessaloniki (Greece) His publications address dynamic network analysis , emotion detection in social media , and quantum-enhanced classification . He contributes to program committees and journal editorial work, including a special issue on Mining Complex Patterns in the Journal of Intelligent Information Systems . Notable contributions include the jKarma framework for change detection and studies on concept drift robustness in intrusion detection systems. His work spans European/National research projects, leveraging machine learning for tasks like mobile crowd sensing trustworthiness prediction (2020) and investor behavior analysis (2023-2025).

• Ageing
• Family Studies
• Digital Divides
• +۵ مورد دیگر

Bruno Arpino is a Full Professor of Social Statistics at the Department of Statistical Sciences, University of Padua, Italy. He serves as an Associate Editor of the European Journal of Population and focuses on research areas including ageing, causal inference, machine learning, and social demography. His work emphasizes methodological rigor in quantitative social research and addresses topics such as loneliness in later life, digital divides, and public health policy. Key professional activities include organizing the 1st Conference of the Sustainable Ageing Working Group (September 2025, Padova) and hosting academic events like the 'What Makes a Rigorous Quantitative Social Research?' workshop with Prof. David Bartram. Arpino has contributed to influential publications on causal validity, control variable selection, and statistical methodologies, including the CMatching R package for clustered data analysis. His research bridges theoretical advancements with practical applications, addressing challenges in social statistics and policy. Arpino actively promotes interdisciplinary collaboration through his academic roles and engagement with professional networks.