جست‌وجوی استاد و پژوهشگر

Karun Adusumilliمشاهده پروفایل

استادیار

• Econometrics
• Causal Inference
• Missing Data
• +۶ مورد دیگر

Karun Adusumilli is an Assistant Professor of Economics at the University of Pennsylvania, affiliated with the Department of Economics within the School of Arts and Sciences. He holds a PhD from the London School of Economics (2018), an MSc in Econometrics and Mathematical Economics (2013), and a B.Tech in Engineering Physics from the Indian Institute of Technology, Delhi (2011). His research focuses on advanced econometric methodologies, including causal inference, missing data analysis, dynamic treatment allocation, and reinforcement learning applications in economics. Education: PhD in Economics, LSE (2018) MSc Econometrics and Mathematical Economics, LSE (2013) B.Tech in Engineering Physics, IIT Delhi (2011) Adusumilli’s research interests span Econometrics , Causal Inference , Dynamic Treatment Regimes , and Statistical Learning . He has pioneered work on optimal policy design in bandit experiments, minimax allocation strategies, and addressing unobserved heterogeneity in econometric models. His contributions bridge theoretical econometrics with practical applications in healthcare, education policy, and sequential decision-making frameworks. He has received notable awards, including the Kravis Award for Teaching Excellence (2021) and the John Hicks Prize for his doctoral research (2019). His publications appear in top-tier journals like Econometrica and Review of Economic Studies . Adusumilli teaches graduate and undergraduate courses in econometrics and statistical learning, including Econometrics I and Econometric Machine Learning . He has served on scientific committees for major conferences such as the NBER-NSF Time Series Conference and the AEA-ASSA Annual Meetings .

Liudas Giraitisمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Quantitative Methods
• +۳ مورد دیگر

Liudas Giraitis is a Professor of Econometrics at Queen Mary University of London, affiliated with the School of Economics and Finance. He holds a PhD from Vilnius University and has extensive research experience at Heidelberg University, Boston University, and the London School of Economics. His research focuses on econometrics and quantitative methods, emphasizing long memory processes, ARCH models, semiparametric inference, and time-varying random coefficient models. He has published extensively in leading statistical and econometric journals. His work bridges econometrics, statistics, and probability theory, with a strong emphasis on time series analysis and asymptotic theory. Recent research explores forecasting under ongoing structural change, heteroscedasticity testing, and dynamic stochastic general equilibrium (DSGE) models with financial frictions. Education: PhD in Econometrics from Vilnius University Key Research Themes: Long memory processes, ARCH/GARCH models, time-varying models, nonstationary time series, and asymptotic theory Affiliations: Queen Mary University of London, School of Economics and Finance Research Interests: Dr. Giraitis’s work addresses theoretical and applied challenges in econometric modeling, including robust inference for time series with general heterogeneity, cyclical time series analysis, and the development of asymptotic frameworks for dependent data. His contributions span both foundational theory and practical applications in finance and macroeconomics. Publications: Over 50 peer-reviewed articles in journals such as Journal of Econometrics , Annals of Statistics , and Journal of Time Series Analysis . Key works include the monograph Large Sample Inference for Long Memory Processes (2012) and recent studies on time-varying volatility and structural change. Lab/Team: Active in the School of Economics and Finance’s research group on econometric theory and applied time series analysis.

Joachim Vothمشاهده پروفایل

استاد

• Cultural Economics
• Long-Run Growth
• Asset Market Volatility
• +۵ مورد دیگر

Joachim Voth is the UBS Foundation Professor of Economics and Scientific Director of the UBS Center for Economics in Society at the University of Zurich. His research focuses on Cultural Economics, Long-Run Growth, Asset Market Volatility, and the interplay between history and contemporary economic phenomena. He holds a Ph.D. from UCLA and has held visiting positions at institutions including Stanford and NYU. Research Interests : Voth’s work bridges historical analysis with modern economic challenges. Key themes include the impact of cultural factors on economic outcomes, the role of historical institutions, and the application of novel data sources (e.g., historical narratives, visual datasets) to understand societal trends. His recent projects analyze cultural convergence via high school yearbook portraits and the legacy of medieval anti-Semitism in Nazi-era violence. Publications : Over 50 peer-reviewed articles and three books, including Lending to the Borrower from Hell (2014) and Prometheus Shackled (2013). His work frequently combines economic theory with historical evidence, such as linking British industrialization to colonial slave wealth or explaining WWII resistance patterns through Allied bombing and radio propaganda. Grants & Awards : Supported by the UBS Foundation, European Research Council, and National Science Foundation. He is a frequent commentator in venues like VoxEU and The Economist , and his research has been featured in podcasts and documentaries. Advising : Supervised 10+ doctoral students now at top institutions like UCLA, NYU, Stanford, and the Norwegian School of Economics.

• Political Philosophy
• AI Ethics
• Argumentation Theory
• +۵ مورد دیگر

Dr. Michael Hoffmann is the H. Bruce McEver Professor in Engineering and the Liberal Arts and a Professor of Philosophy at Georgia Tech's Jimmy and Rosalynn Carter School of Public Policy. He directs the Reflect! Lab, co-leads ETHICx (Ethics, Technology, and Human Interaction Center), and oversees the VIP Digital Deliberation initiative. His research spans political philosophy (designing democratic institutions), AI ethics (user-centric design for human well-being), and interdisciplinary methods for deliberation/consensus-building. He holds a Dr. phil. habil. from TU Dresden (2003) and a PhD from LMU Munich. Key contributions include developing collaborative software tools like AGORA-net for argument visualization, analyzing framing in conflicts, and applying semiotic theory to interdisciplinary collaboration. Awards include the 2024 McEver Professorship and 2011 Gold Star Award for Research Excellence. His work emphasizes wicked problem-solving, reflective reasoning, and digital tools for public deliberation. Education: Dr. phil. habil., Philosophy (TU Dresden, 2003); PhD, Philosophy (LMU Munich) Labs/Initiatives: Reflect! Lab, ETHICx, VIP Digital Deliberation Tools Developed: Argument mapping software, Navicons collaboration system His publications explore topics like climate ethics, logical argument mapping, and cognitive conditions for diagrammatic reasoning. He bridges philosophy, engineering, and liberal arts through innovative interdisciplinary frameworks.

David Wiensمشاهده پروفایل

دانشیار

• Political Philosophy
• Philosophy of Social Science
• Political Economy
• +۳ مورد دیگر

David Wiens is an Associate Professor of Political Science at the University of California, San Diego, with affiliations in Philosophy, Data Science, and Ethics. He holds a PhD in Philosophy (University of Michigan, 2011) and MA degrees in Political Science (2011) and Philosophy (2006). His research integrates political philosophy, social science, and political economy, focusing on formal models of justice, institutional design, and feasibility analysis. Wiens co-edits the journal Politics, Philosophy, and Economics and has presented at leading conferences globally. Education : PhD, Philosophy, University of Michigan (2011) MA, Political Science, University of Michigan (2011) MA, Philosophy, Texas A&M University (2006) BA, Philosophy and History, Brock University (2004) Research Interests : Wiens explores the intersection of ideal and non-ideal theory, emphasizing how formal models inform ethical and political inquiry. Key themes include justice under constraints, institutional stability, and the critique of epistocracy. His work bridges philosophy and social science, with applications to global justice, resource distribution, and democratic theory. Advising & Grants : Wiens has supervised multiple PhD and MA theses, including Matthew Draper and Shelbi Swanson. His service roles include Political Theory Field Coordinator at UCSD and editorial leadership in PPE. While no explicit grants are listed, his research reflects sustained institutional support. Labs/Teams : Affiliated with the Halıcıoğlu Data Science Institute and Computational Social Science Program, Wiens collaborates on interdisciplinary projects linking political theory with quantitative methods.

Pamela Carreno-Medranoمشاهده پروفایل

مدرس

• Human-Robot Interaction
• Robot Learning
• Socially Assistive Robotics
• +۴ مورد دیگر

Dr. Pamela Carreno-Medrano is a Lecturer and Early Career Research Representative in the Department of Electrical and Computer Systems Engineering at Monash University. Her research focuses on Human-Robot Interaction (HRI), robot learning, and socially assistive robotics. She holds a PhD in Information & Communication Sciences (Université de Bretagne-Sud), Master's in Computer Science (École Nationale d’Ingénieurs de Brest), and a Bachelor's in Computer Systems Engineering (Universidad EAFIT). Her work emphasizes human-centered design for intelligent systems, including adaptive navigation algorithms, human-robot collaboration models, and affective computing applications. She leads projects on long-term human-robot interaction and has contributed to interdisciplinary studies on robot ethics and public space integration. Dr. Carreno-Medrano also serves as an Adjunct Lecturer at Universidad EAFIT and collaborates internationally on sustainable aging technologies through the ARC Training Centre for Optimal Ageing. Current research themes include aligning task representations between humans and robots, modeling non-goal-driven human behaviors, and socially aware navigation strategies. She actively supervises postgraduate students in HRI, offering projects on interactive robot learning and embodied AI systems.

Douglas Allaireمشاهده پروفایل

دانشیار

• Multidisciplinary design optimization
• Bayesian optimization
• Multifidelity methods
• +۴ مورد دیگر

Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.

Robert Lundeمشاهده پروفایل

استادیار

• Statistical Network Analysis
• Time Series Analysis
• Resampling Methods
• +۲ مورد دیگر

Dr. Robert Lunde is an Assistant Professor in the Department of Mathematics and Statistics at Washington University in St. Louis. He holds a PhD in Statistics and Data Science from Carnegie Mellon University and completed postdoctoral research at the University of Michigan and University of Texas at Austin. His research encompasses statistical inference for network data, resampling methods, and distribution-free inference. Key areas include conformal prediction for network-assisted regression, bootstrap methods for streaming algorithms, and theoretical analysis of network resampling techniques. His work bridges high-dimensional statistics with computational efficiency in network analysis. Dr. Lunde has taught courses including Mathematical Statistics at Washington University and Probability Theory at Carnegie Mellon. His instructional approach emphasizes foundational theory and practical applications of statistical methods.

• Multivariate Survival Analysis
• Empirical Processes Theory
• Longitudinal Data Analysis
• +۳ مورد دیگر

Changyong Feng, PhD, is a Professor of Biostatistics and Computational Biology, Anesthesiology, and Dentistry at the University of Rochester Medical Center. He serves as Co-Director of the Department of Dentistry and holds a faculty position in the Department of Biostatistics and Computational Biology. He earned his PhD in Statistics from the University of Rochester in 2002. Dr. Feng’s research focuses on multivariate survival analysis, empirical processes theory, longitudinal data analysis, and statistical methods in epidemiology and clinical trials. His work spans interdisciplinary collaborations, including studies on anticoagulant efficacy in cardiovascular surgery, microbiome data analysis, and behavioral predictors of oral health. He has contributed to clinical trials, anesthesiology practices, and veterinary pharmacology. His expertise includes developing statistical models for complex biomedical data. Dr. Feng is affiliated with multiple departments at URMC, reflecting his role in bridging statistical methodologies with clinical and translational research. His publications address diverse topics from drug dosing optimization to influenza antibody dynamics. His research underscores the application of advanced statistical techniques to real-world medical challenges.

Benjamin C. Pierceمشاهده پروفایل

استاد

• Programming Languages
• Type Systems
• Formal Verification
• +۳ مورد دیگر

Benjamin C. Pierce is Henry Salvatori Professor of Computer and Information Science at the University of Pennsylvania, with appointments in the School of Engineering and Applied Science. A Fellow of the ACM, his research spans programming languages, formal verification, and security-privacy technologies. He directs the DeepSpec project on verified systems infrastructure and leads climate computing initiatives. Research interests focus on: Formal methods for reliable software via proof assistants like Coq Bidirectional programming and data synchronization Language-based security and differential privacy Publication trends show consistent contributions to type theory foundations, with recent emphasis on property-based testing methodologies and real-world verification. Articles frequently appear in top PL/SEC venues with practical applications in compilers and secure systems. Scientific Awards: ACM Fellow (systems verification) SIGPLAN Distinguished Educator Award (textbook innovations) Advises graduate students through the Penn PL Club. PI for NSF Expeditions in Sustainable Computing. Leads the VERSE project for verified C code and Unison file synchronizer. Directs the Penn Programming Languages Research Group collaborating with industry partners including Amazon and Microsoft Research.

Mingli Chenمشاهده پروفایل

دانشیار

• Econometrics
• Machine Learning
• Time Series Econometrics
• +۴ مورد دیگر

Mingli Chen is an Associate Professor of Economics at the University of Warwick’s Department of Economics. She holds affiliations including Turing Fellow at the Alan Turing Institute, External Fellow at the Centre for Panel Data Analysis (University of York), and Warwick-China Coordinator. Her research focuses on econometrics, machine learning, time series analysis, financial econometrics, and empirical industrial organization. She has served as an Associate Editor for the Journal of Econometrics since 2024 and organized workshops on data science and network analysis. Education: Ph.D. in Economics from Boston University (2015), B.A. in Information and Computing Science from Shanghai University (2009). She has held visiting positions at Stanford University, UC Berkeley, and the Federal Reserve Bank of Boston. Research Interests include high-dimensional econometrics, panel data models, social networks, quantile regression, and the integration of AI with econometrics. Key publications cover topics like quantile graphical models for systemic risk, latent panel quantile regression in asset pricing, and sparse β-models for network analysis. Awards include the International Partnerships Fund (2023), Turing PDRA Award (2020), and Co-Winner of the LABOUR Prize (2017). She advises Ph.D. students at Warwick and Cambridge, with placements at leading institutions like the University of Tokyo. Grants include leadership in UK-China partnerships and the Turing Institute. Teaching focuses on advanced econometrics at the Ph.D. level, including causal inference and machine learning. She co-organizes workshops and serves on conference committees, emphasizing data science and policy applications.

• Human-Computer Interaction (HCI)
• Interaction Design
• User-Centered Design
• +۳ مورد دیگر

Dag Svanæs is a Professor of Human-Computer Interaction (HCI) at NTNU’s Department of Computer and Information Science. His research focuses on embodied interaction, emphasizing the physical, bodily, and social dimensions of human-computer interaction. He developed a usability laboratory for simulating multi-user, multi-device scenarios, particularly in healthcare contexts. His theoretical framework draws heavily from Maurice Merleau-Ponty’s phenomenology, exploring how perception and action shape interaction. Education: PhD in HCI from NTNU (2000). His research areas include interaction design, user-centered methods, and healthcare IT systems design. Key projects include the UNITY-Things IoT framework and exergame development for balance training in seniors. Research Highlights: Svanæs pioneered participatory design methodologies in healthcare IT, emphasizing end-user involvement through role-play and prototyping. His work on pandemic policy critiques and lockdown strategies demonstrates interdisciplinary collaboration between HCI and public health. Publications: Over 50 peer-reviewed articles in venues like Computer Supported Cooperative Work , Frontiers in Computer Science , and IEEE Transactions . Recent work addresses exergame design, pandemic response systems, and embodied interaction theory. Grants & Labs: Built NTNU’s Health Informatics Usability and Design Lab. Collaborates with academic and healthcare institutions on projects like the NSEP (Norwegian Center for Electronic Patient Records).

Andrea Rotnitzkyمشاهده پروفایل

استاد

• Causal Inference
• Epidemiological Methods
• Statistical Theory
• +۳ مورد دیگر

Andrea Rotnitzky is a Professor and Chair of Biostatistics at the University of Washington. Born in Argentina with Jewish heritage, she values both Argentine culture and Jewish traditions. She joined UW in 2022 after a global recruitment, attracted by its collaborative research environment and partnerships with institutions like the Fred Hutchinson Cancer Center. Her work focuses on causal inference, missing data analysis, and semiparametric methods to address public health challenges. Rotnitzky holds a Licenciate in Mathematics from the Universidad de Buenos Aires (1982) and a PhD in Statistics from UC Berkeley (1988). Her research emphasizes developing statistically efficient tools to estimate intervention effects from observational or imperfect experimental data. Key areas include causal graphical models, longitudinal data analysis, and survival analysis. Her recent work highlights include articles on efficient adjustment sets in causal inference, debiased regression methods, and applications to post-COVID-19 symptom studies. Awards include IMS Fellowship (2025) and a $1M Rousseeuw Prize (2023) for statistical contributions. She prioritizes mentoring students and translating statistical innovations into real-world health solutions.

Karen Shenمشاهده پروفایل

استادیار

• Health Economics
• Healthcare Labor Markets
• Long-Term Care
• +۷ مورد دیگر

Karen Shen is an Assistant Professor in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health. She is affiliated with the Roger and Flo Lipitz Center to Advance Policy in Aging and Disability, the Hopkins Economics of Alzheimer’s Disease & Services Center, and the Hopkins Business of Health Initiative. Her research focuses on healthcare labor markets and financing policies affecting aging populations and individuals with mental health and substance use disorders. PhD in Economics, Harvard University, 2021 MS in Statistics, Stanford University, 2013 BS in Mathematical and Computational Sciences, Stanford University, 2013 Dr. Shen's research lies at the intersection of health economics and public policy. Her work investigates how labor market dynamics, staffing policies, and public financing mechanisms impact care delivery and outcomes, particularly in long-term care and mental health settings. She employs rigorous econometric methods to analyze large-scale datasets and inform evidence-based policy. Her recent publications span topics such as healthcare worker shortages, Medicaid financing of home care, nursing home staffing and quality, opioid use disorder treatment access, and the impact of the pandemic on healthcare employment. The articles reflect a strong trend toward policy-relevant empirical research using quasi-experimental designs and administrative data to evaluate real-world interventions. Alleviating Worker Shortages Through Targeted Subsidies: Evidence from Incentive Payments in Healthcare (2024) Who benefits from public financing of home-based long term care? Evidence from Medicaid (2024) Health Care Staff Turnover and Quality of Care at Nursing Homes (2023) Do Policies to Increase Access to Treatment for Opioid Use Disorder Work? (2023) Job Flows Into and Out of Health Care Before and After the COVID-19 Pandemic (2024) Dr. Shen has not received any explicitly mentioned scientific awards in the provided texts. She advises students in health economics and policy, though specific advisees are not listed. She has been involved in research grants related to aging, Alzheimer’s disease, and healthcare policy through her center affiliations. Her work has been widely disseminated, with publications in top journals and attention from news outlets and social media. She is part of collaborative research networks focusing on aging, mental health, and healthcare systems.

Minsu Parkمشاهده پروفایل

استادیار

• Computational Social Science
• Human-centered Data Science
• Cultural Consumption
• +۹ مورد دیگر

Minsu Park is an Assistant Professor of Social Research and Public Policy at New York University Abu Dhabi (NYUAD), with affiliate status at the Center for Data Science at New York University (NYU). He holds a PhD in Information Science from Cornell University, where he was advised by Michael W. Macy and Mor Naaman. His academic work bridges computational techniques and social science theory, focusing on cultural consumption, social networks, and human-centered data science. PhD in Information Science, Cornell University His research centers on understanding how individuals form cultural preferences through social and psychological mechanisms, particularly in domains like music, food, fashion, and science. He leverages large-scale digital trace data—including social media, streaming platforms, and physiological signals from smart devices—to model behavior. His interdisciplinary approach draws from sociology, social computing, and data science, aiming to uncover both individual and global patterns in cultural dynamics. Key themes include variety-seeking behaviors, affective preferences, and the interplay between social position and taste. The 15 most recent publications reflect a strong trend toward using computational methods to explore sociocultural phenomena. Topics span affective preference rhythms in music, testing sociological theories like cultural omnivory, imputing user attributes from digital behavior, and enhancing model interpretability. These works are published in high-impact venues such as Nature Human Behaviour and ICWSM, indicating recognition in both computer and social sciences. Minsu Park is deeply committed to responsible data science, addressing issues like algorithmic bias, transparency, fairness, data privacy, and research ethics. He emphasizes mixed-methods approaches and critical reflection on data generation and usage. Published in Nature Human Behaviour Presented at ICWSM Focus on ethical and interpretable AI Advocate for reproducibility and data curation He mentors students through capstone projects in computer science and teaches courses such as Human-Centered Data Science and Textual Analysis for the Social Sciences. While specific grants are not detailed in the text, his research program suggests involvement in interdisciplinary, data-intensive projects likely supported by academic or scientific funding bodies. His technical expertise includes Python, R, machine learning frameworks (TensorFlow, Keras), and tools like Gephi and AWS. He leads or contributes to research initiatives involving smartwatch-based data collection, surname-based ethnicity matching, and global music consumption analysis, as evidenced by his public GitHub repositories. These projects highlight his commitment to open science and collaborative research.