جست‌وجوی استاد و پژوهشگر

Berrak Sismanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Speech Processing
• Machine Learning
• +۳ مورد دیگر

Berrak Sisman is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). She leads the Speech & Machine Learning Lab (SmILe Lab), focusing on AI-driven speech technologies. She received her PhD from the National University of Singapore in 2020 and was previously a tenure-track faculty member at the University of Texas at Dallas (2022–2024). Research Interests: Her work spans artificial intelligence, speech synthesis, voice conversion, emotion analysis in speech, medical speech applications, and secure speech technology. She develops neural models for expressive and adaptive speech processing. Publications: Her recent articles (2024–2025) emphasize speech emotion recognition, zero-shot prosody control, accent conversion, and disentangled representations in TTS, reflecting a focus on cross-modal learning, robustness, and real-world applications. Awards & Grants: NSF CAREER Award (2024) Amazon Faculty Research Award (2022) Singapore Ministry of Education Award (2021) A*STAR Singapore International Graduate Award (2016–2020) Leadership: She directs the SmILe Lab, recruiting PhD/Master’s students for projects in neural speech modeling. Her grants include NSF and Amazon funding for voice conversion and emotion synthesis research.

Susan Robertsonمشاهده پروفایل

استاد

• Sociology of Education
• Globalization and Education
• Education Governance
• +۴ مورد دیگر

Susan Robertson holds the Chair in Sociology of Education at the Faculty of Education, University of Cambridge, where she serves as Director of the Doctoral Programme and Editor-in-Chief of Globalisation, Societies and Education . She joined Cambridge in 2016 after academic appointments at the University of Auckland and University of Bristol, bringing over three decades of international expertise in critical education studies. Her educational foundation includes doctoral research in Sociology/Policy at the University of Calgary (1990), preceded by professional experience in the Research Division of Western Australia's Ministry of Education. This trajectory established her cross-sectoral perspective on education systems and policy implementation. Robertson's research critically examines state-education relationships through governance frameworks that determine educational equity and outcomes. She investigates how global and regional actors mediate the social contract between states and citizens, with particular focus on geopolitical shifts, OECD influence, and neoliberal transformations in higher education. Her work bridges sociology, political economy, and decolonial perspectives to address social justice imperatives in education. Analysis of her 2024-2026 publications reveals sustained engagement with higher education geopolitics, digital transformation challenges, and global governance mechanisms. Key thematic clusters include critiques of OECD-driven policy frameworks, analyses of ordinal citizenship in education systems, and examinations of how digitalization reshapes university missions while exacerbating existing inequalities. As Director of the Doctoral Programme, Robertson oversees comprehensive PhD training while convening the Culture, Politics and Global Justice research cluster that fosters interdisciplinary collaboration on transnational education challenges. Her service on the ESRC funding council positions her at the nexus of research strategy and resource allocation in UK social sciences. The Culture, Politics and Global Justice cluster under her leadership functions as a dynamic hub for critical scholarship, hosting regular seminars and collaborative projects that connect Cambridge researchers with global partners to address emergent issues in education governance and social transformation.

Jörg Spenkuchمشاهده پروفایل

دانشیار

• Political Economy
• Applied Microeconomics
• Behavioral Economics
• +۴ مورد دیگر

Jörg Spenkuch is an Associate Professor of Managerial Economics & Decision Sciences at Kellogg School of Management, Northwestern University, where he has been since 2013. He holds a Ph.D. in Economics (2013) and M.A. (2009) from the University of Chicago, and dual B.A. degrees in Economics and Business Administration (2007) from the University of St. Gallen, Switzerland. Education: Ph.D., 2013, Economics, University of Chicago M.A., 2009, Economics, University of Chicago B.A., 2007, Economics, University of St. Gallen B.A., 2007, Business Administration, University of St. Gallen Professor Spenkuch's research bridges political economy and applied microeconomics , with a focus on ideological behavior, strategic decision-making, and social dynamics. His work examines topics like: Political Economy: Campaign finance, electoral accountability, and ideological sorting. Behavioral Economics: Satisficing behavior, memory-driven choices, and strategic voting. Public Policy: School desegregation effects, bureaucratic performance, and immigration-crime linkages. His recent publications analyze: Long-term ideological shifts from 1975 school desegregation (2025 working paper). Memory premiums in decision-making (2025 working paper). Complexity's role in chess strategy (2024 Review of Economic Studies ). Political accountability during natural disasters (2025 American Economic Journal ). Scientific Recognition: MinE Best Paper Award (European Economic Association) Chair's Core Teaching Award Deutschlands "Top 40 unter 40" (Capital magazine) FEEM Award (European Economic Association) Best Paper Award, RGS Doctoral Conference At Kellogg, he teaches Leadership and Crisis Management (PACT-440-5) and Business Analytics (DECS-435-0). His work has been published in top journals like Econometrica , American Economic Review , Quarterly Journal of Economics , and Review of Economic Studies , covering topics from partisan spatial sorting to expressive vs. strategic voting behavior.

Martin Wainwrightمشاهده پروفایل

استاد

• High-dimensional statistics
• Information theory
• Statistical machine learning
• +۳ مورد دیگر

Martin Wainwright is a Professor at the University of California at Berkeley with joint appointments in the Department of Statistics and the Department of Electrical Engineering and Computer Sciences (EECS). His research spans high-dimensional statistics , information theory , statistical machine learning , and optimization theory . He has made significant contributions to understanding computational and statistical trade-offs in high-dimensional settings, as well as developing advanced message-passing algorithms for graphical models. His educational background includes a Bachelor's degree in Mathematics from the University of Waterloo and a Ph.D. in EECS from MIT . His work has been recognized with prestigious awards such as the COPSS Presidents' Award (2014) , IEEE Joint Paper Award (2012) , and Sloan Research Fellowship (2005) . He has advised numerous prominent researchers, including Nihar Shah , John Duchi , and Yuchen Zhang . Publications by Wainwright reflect trends in machine learning , high-dimensional data analysis , and graphical model inference . Notable works include advancements in Markov Chain Monte Carlo algorithms , pairwise comparison models , and distributed computation methods . He has also contributed extensively to signal processing and LDPC codes . COPSS Presidents' Award (2014) IEEE Joint Paper Award (2012) Institute of Mathematical Statistics Fellow (2011) NSF CAREER Award (2006) Okawa Research Grant (2005) Sloan Research Fellow (2005)

Frank Cowellمشاهده پروفایل

استاد

• Inequality
• Poverty
• Income Distribution
• +۵ مورد دیگر

Frank Cowell is a Professor of Economics at the London School of Economics (LSE), where he serves as a CASE (Centre for Analysis of Social Exclusion) and STICERD (Suntory and Toyota International Centres for Economics and Related Disciplines) Associate. His academic work spans several decades with publications dating from the early 1990s through to 2018, demonstrating sustained scholarly contribution to the field of economics. Professor Cowell's research primarily focuses on inequality and poverty measurement, income and wealth distribution, taxation, and social mobility. His work combines theoretical economic analysis with empirical applications across different countries and contexts. His research interests include methodological approaches to measuring inequality, particularly addressing challenges related to top incomes and wealth concentration, as well as examining the relationship between social identity and redistributive preferences. His publication record reveals consistent engagement with methodological issues in inequality measurement, with numerous papers addressing statistical robustness, decomposition techniques, and comparisons of income and wealth distributions across countries. His more recent work has increasingly focused on wealth distribution, inheritance, and intergenerational mobility, reflecting evolving scholarly interest in these areas. He has collaborated extensively with researchers across Europe, particularly on cross-national comparative studies using datasets like the Luxembourg Wealth Study. Professor Cowell has made significant contributions to the theoretical foundations of inequality measurement, developing alternative approaches that address limitations in conventional indices. His work on median-normalized inequality measures represents an important methodological innovation aimed at better capturing changes at the top of the income distribution. Throughout his career, Cowell has examined the relationship between economic theory and empirical measurement, often bridging gaps between abstract welfare economics and practical policy applications. His research has implications for tax policy design, social welfare programs, and understanding the dynamics of economic inequality across different populations and time periods. As a CASE and STICERD Associate, Professor Cowell contributes to LSE's research environment focused on social exclusion, inequality, and public policy analysis. His work has been published in numerous discussion papers, journal articles, and book chapters, establishing him as a significant contributor to the field of distributional economics.

Valen Johnsonمشاهده پروفایل

استاد

• Bayesian Statistics
• Biostatistics
• Medical Imaging
• +۷ مورد دیگر

Valen E. Johnson is a University Distinguished Professor and Dean Emeritus of the College of Science at Texas A&M University, where he has been a faculty member since 2012. He previously held professorships at the University of Texas M. D. Anderson Cancer Center (2004–2012), the University of Michigan (2002–2004), and Duke University (1989–2001). He also served as a Technical Staff Member at Los Alamos National Laboratory (2001–2002). Johnson earned his Ph.D. in Statistics from the University of Chicago (1989), M.A. in Applied Mathematics from the University of Texas at Austin (1985), and B.S. in Mathematics from Rensselaer Polytechnic Institute (1981). His research focuses on Bayesian methodology, including hypothesis testing, variable selection in high-dimensional spaces, latent variable models, and applications in medical imaging, clinical trials, and educational assessment. He has contributed to the development of non-local prior densities and Bayesian diagnostics for MCMC convergence. His work bridges Bayesian and classical statistical approaches, emphasizing reproducibility and rigorous evidence assessment in scientific research. Johnson has held editorial roles, including Co-Editor of Bayesian Analysis (2010–2014) and Associate Editor of the Journal of the American Statistical Association (2011–present). He is a Fellow of the American Statistical Association and the Royal Statistical Society and has served on the Board of Directors of the International Society for Bayesian Analysis. His advocacy for revised statistical significance standards has sparked major debates in scientific methodology. Johnson has supervised numerous doctoral students, including those whose theses won prestigious awards like the Savage Award. He has collaborated on grants addressing medical imaging, system reliability, and cancer symptom management, reflecting his interdisciplinary impact in biostatistics and public health.

• Public Contract Law
• Administrative Liability
• Constitutional Principles
• +۴ مورد دیگر

Alessandra Amore is a permanent faculty member at the University of Palermo's Department of Law , holding the academic rank of Researcher (GIUR-06/A). Her research focuses on public contract law, administrative liability, constitutional principles, and anti-mafia legislation. She maintains regular office hours and can be contacted via email at alessandra.amore@unipa.it. Research Interests Amore's work explores the intersection of administrative law with contemporary governance challenges, including: Public contract law anomalies and jurisdictional review Administrative liability for lawful acts Anti-mafia interdiction legal frameworks PNRR implementation in socio-health sectors Privacy vs. transparency in judicial proceedings Human dignity in vaccination-related liability Publications Trends Her publications (2017–2024) reflect deep engagement with Italian administrative law, particularly: Evolution of public procurement regulations Balancing constitutional principles in anti-mafia enforcement State responsibility in health policy Interplay between administrative discretion and judicial review Human rights protection in migration governance Procedural autonomy in EU contexts

Jeremy Gaskinsمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Longitudinal Data Analysis
• Missing Data Methods
• +۳ مورد دیگر

Jeremy Gaskins, PhD, is an Associate Professor in the Department of Bioinformatics & Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS). He joined UofL in 2013 as an Assistant Professor, earning tenure and promotion to his current rank in 2019. His expertise lies in Bayesian statistical methods for complex data structures, including longitudinal analysis, missing data imputation, and joint modeling of mixed data types. He collaborates with researchers across multiple departments, including OB/GYN, Radiation Oncology, and Surgery. Education: Ph.D. (2013) in Statistics, University of Florida B.S. (2007) in Mathematics and Applied Mathematics, Auburn University Research Interests: Development of Bayesian methods for longitudinal and clustered data Computational strategies for complex model inference Applications in medical and public health research Missing data mechanisms and imputation techniques Teaching: PHST 661: Probability PHST 662: Mathematical Statistics Collaborations & Labs: Active collaborations with UofL medical departments on applied health research Focus on translational statistics for biomedical and public health problems

B. Douglas Bernheimمشاهده پروفایل

استاد

• Behavioral Economics
• Public Economics
• Game Theory
• +۷ مورد دیگر

B. Douglas Bernheim is the Edward Ames Edmonds Professor of Economics at Stanford University's Department of Economics. He serves as the Academic Council Faculty Director of Undergraduate Studies, overseeing academic programs and policies for undergraduate economics students. His research spans behavioral economics, public economics, game theory, and financial economics, with notable contributions to rationalizability in game theory, coalition-proofness, and behavioral welfare economics frameworks. Professor Bernheim has received prestigious recognitions including an honorary doctorate from the University of Zurich and the 2022 Exeter Prize for groundbreaking work in experimental economics. His work often bridges theoretical economics with practical policy implications, particularly in areas like financial education and addictive behaviors. Research Interests: - Behavioral Welfare Economics - Social Motives for Economic Choices - Financial Education Policies - Theories of Collusion and Market Structures His recent publications explore interventionist policies in welfare states, peer influence in financial decisions, and the empirical foundations of decision theories like cumulative prospect theory. Bernheim's work emphasizes rigorous experimental methods and real-world policy applications, reflecting his dual focus on academic theory and societal impact.

Yu Yaoمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Robust Machine Learning
• +۴ مورد دیگر

Yu Yao is a Lecturer in Machine Learning at the School of Computer Science, The University of Sydney. He joined in December 2023 and focuses on developing robust and interpretable machine learning systems. His research emphasizes robustness to data noise, adaptable ML systems, and disentangled representation learning. Yao holds a PhD from The University of Sydney under Professors Tongliang Liu and Dacheng Tao, followed by postdoctoral positions at Mohamed bin Zayed University of Artificial Intelligence and Carnegie Mellon University. Education: PhD in Computer Science (University of Sydney), postdoctoral research at MBZUAI and CMU. Research interests include causal inference in ML, multimodal learning, and label noise mitigation. He has published extensively in top venues like ICML, NeurIPS, and ICLR, and served as an Area Chair for AJCAI 2023, NeurIPS 2025, and ICLR 2025. Awards: Outstanding Reviewer (NeurIPS 2023, ICLR 2023), University of Sydney Research Excellence Prize (2019) Teaching: Advanced Machine Learning (USYD), Guest Lectures on noisy label learning (MBZUAI, China University of Petroleum) Service: Action Editor for TMLR, Area Chair for ICML/ICLR/NeurIPS, reviewer for top journals and conferences His lab focuses on trustworthy AI, with ongoing projects on causal mechanisms in robust learning and interpretable multimodal systems. Current advisees include PhD candidates Ruojing Dong and Jiyang Zheng (co-advised with Prof. Liu), and master's student Kai Lian.

Yu Chengمشاهده پروفایل

استاد

• Dynamic Treatment Regimes
• SMART (Sequential Multiple Assignment Randomized Trials)
• Causal Inference
• +۶ مورد دیگر

Yu Cheng is a Professor and Chair of the Department of Statistics at the University of Pittsburgh, affiliated with the Dietrich School. She holds secondary appointments in Biostatistics and Psychiatry. Her research focuses on dynamic treatment regimes, SMART trials, causal inference, and multiple endpoints analysis. She leads a PCORI-funded method grant on SMART studies and co-investigates projects on cardiovascular disease and maternal/child health outcomes. Her awards include ASA Fellow (2024) and Pittsburgh Chapter Statistician of the Year (2020). Dr. Cheng earned her PhD in Statistics from the University of Wisconsin-Madison (2006), following MS (National University of Singapore, 2001) and BS (University of Science and Technology of China, 1999) degrees. She has held leadership roles including ASA LiDS Section Treasurer (2021–2023), Department Chair (2014–present), and Director of Graduate Studies (2016). Her research integrates methodological innovation with clinical collaboration, addressing topics like HIV, smoking cessation, lupus, depression, and cystic fibrosis. Her 150+ publications span journals like Biometrics , Journal of the American Statistical Association , and Biostatistics . Teaching includes courses in Survival Analysis, Probability, and Statistical Consulting. She actively mentors students through supervised consulting programs.

Bryan E. Shepherdمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Measurement Error
• +۳ مورد دیگر

Bryan E. Shepherd serves as Professor of Biostatistics and Biomedical Informatics and Vice Chair of Faculty Affairs in the Department of Biostatistics at Vanderbilt University. As a primary faculty member, he leads methodological research while overseeing departmental academic operations within the Vanderbilt University Medical Center ecosystem. His foundational education includes a PhD in Biostatistics from the University of Washington, establishing his technical expertise in advanced statistical methodologies. This training underpins his dual focus on theoretical innovation and real-world health applications. Dr. Shepherd's research centers on solving complex data challenges through novel biostatistical frameworks, particularly causal inference under measurement error, two-phase sampling optimization, and longitudinal analysis of error-prone clinical data. His work bridges theoretical statistics and urgent public health needs, with HIV research forming a major application domain where he addresses disparities in treatment access, care continuum outcomes, and comorbid conditions across global populations. Recent methodological breakthroughs include R packages for semiparametric likelihood estimation and rank correlation analysis. Analysis of his 2023-2025 publications reveals a dominant trend toward methodological solutions for data imperfections (measurement error, selective sampling) combined with high-impact applications in HIV epidemiology. His Latin American and African cohort studies consistently examine sex disparities, treatment efficacy, and social determinants of health, while his statistical innovations focus on efficiency gains in causal estimation and correlation modeling for clustered data. No specific scientific awards are documented in the available institutional profile. Though student advising details are unlisted, his extensive publication record—particularly collaborative international studies—implies active mentorship of junior researchers. His leadership as Vice Chair of Faculty Affairs suggests significant administrative responsibilities alongside research. Grant activity is inferred from multi-country HIV studies involving Vanderbilt's Center for Quantitative Sciences and Data Coordinating Center. Dr. Shepherd operates within Vanderbilt's collaborative research infrastructure, notably the Center for Quantitative Sciences (CQS) and Biostatistics Data Coordinating Center (VBDCC). His work leverages these resources for large-scale analyses of HIV cohorts across Latin America and Nigeria, focusing on treatment outcomes, genetic risk factors, and health system barriers. The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat) reflects his commitment to global capacity building in biostatistics.

Roman Słowińskiمشاهده پروفایل

استاد

• Decision Support
• Multiple Criteria Decision Aiding
• Ordinal Data Mining
• +۱۱ مورد دیگر

Roman Słowiński is a full Professor at the Institute of Computing Science , Poznań University of Technology, Poland, and holds a professorial position at the Systems Research Institute of the Polish Academy of Sciences in Warsaw. He is a Full Member of the Polish Academy of Sciences and served as President of its Poznań Branch (2011–2014). His career spans decades of contributions to decision support systems and computational intelligence. His research focuses on Multiple criteria decision aiding Rough set theory and fuzzy set theory Multiobjective optimization Ordinal data mining Robust decision analysis with applications in medicine, economics, and environmental studies. He is renowned for pioneering the use of rough sets in decision analysis, collaborating with Zdzisław Pawlak and others. His recent work emphasizes robust ordinal regression, dominance-based methods, and monotonicity constraints in classification. Scientific awards include EURO Gold Medal (1991) Edgeworth-Pareto Award (1997) Annual Prize of the Foundation for Polish Science (2005) Doctor Honoris Causa from three international universities He has advised 26 Ph.D. theses and served as Coordinating Editor for European Journal of Operational Research , alongside leadership roles in the International Rough Set Society and the EURO Working Group on Multiple Criteria Decision Aiding. He founded the Laboratory of Intelligent Decision Support Systems at PUT.

Dimitris Korobilisمشاهده پروفایل

استاد

• Macroeconometrics
• Time-Series Analysis
• Forecasting
• +۶ مورد دیگر

Professor Dimitris Korobilis holds the rank of Professor of Econometrics at the Adam Smith Business School, University of Glasgow. He previously served as Professor of Finance at the University of Essex. His roles include directing the MSc in Data Analytics for Economics and Finance, co-ordinating the annual ASBS Summer School in Empirical Macroeconomics, and leading the macroeconomics research cluster. Education: PhD in Economics from the University of Strathclyde (2010). Research focuses on applied statistical inference in macroeconomic and financial data, emphasizing Bayesian methods, machine learning, and high-dimensional modeling. Notable contributions include developing algorithms for macroeconomic forecasting and policy analysis, with applications at institutions like the IMF and ECB. His work bridges econometric theory and practical policy tools. Teaching includes courses on Econometrics, Statistical Machine Learning, Time Series Forecasting, and Bayesian Data Analysis. Advises PhD students exploring topics like DSGE models and Bayesian microeconometrics. Publications span over 30 peer-reviewed articles in top journals such as the Journal of Econometrics and Journal of Business & Economic Statistics, with a focus on Bayesian VAR models, factor analysis, and macroeconomic risk monitoring.

Klaus Nordhausenمشاهده پروفایل

استاد

• Multivariate Statistical Methods
• Spatial Statistics
• Blind Source Separation
• +۴ مورد دیگر

Klaus Nordhausen is a Professor in the Department of Mathematics and Statistics at the University of Helsinki. His research focuses on multivariate statistical methods, spatial statistics, blind source separation, and high-dimensional data analysis. He actively contributes to computational statistics and machine learning applications in environmental and biomedical domains. Recent work includes advancements in spatio-temporal modeling, invariant coordinate selection, and variational autoencoders for multivariate data. Nordhausen leads the project Signal recovery in noisy spatial data (2024–2028), funded by the Academy of Finland. He is a frequent invited speaker at international conferences and collaborates with researchers globally, particularly in spatial and computational statistics. His publications span statistical methodology, environmental modeling, and machine learning, emphasizing robust techniques for complex data structures. Key contributions include spatial blind source separation algorithms, signal dimension estimation, and novel applications of independent component analysis. Professional activities include organizing workshops and hosting academic visitors, reflecting his role in fostering international collaboration. Nordhausen maintains an active research agenda with over 130+ peer-reviewed outputs across journals like Environmetrics , Neural Networks , and Annals of the Institute of Statistical Mathematics .