جست‌وجوی استاد و پژوهشگر

Kyunghyun Choمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +۶ مورد دیگر

Kyunghyun Cho is a Professor of Computer Science and Data Science at New York University (NYU), holding dual appointments at the Courant Institute and the Center for Data Science. He serves as the Glen de Vries Professor of Health Statistics and Co-Director of the Global Frontier AI Lab (with Yann LeCun). His research focuses on machine learning, natural language processing, and their applications in healthcare and biology. Cho received his PhD from Aalto University (2014) and has held positions at Facebook AI Research (2017-2020) and as a postdoc at the University of Montreal under Yoshua Bengio. He has organized major conferences including ICLR, NeurIPS, and ICML, and co-founded the Transactions on Machine Learning Research (TMLR). His awards include the Samsung Ho-Am Prize in Engineering (2021) and CIFAR Fellowship. Current projects include causal inference frameworks, antibody design via AI, and multimodal healthcare systems. Cho's work bridges foundational ML theory and practical applications in biomedicine, with recent focus on generative models, cross-cultural benchmarking (BLEND), and lab-in-the-loop protein engineering.

Nino Kordzakhiaمشاهده پروفایل

مدرس ارشد

• Statistics
• Probability Theory
• Stochastic Processes

Nino Kordzakhia is a Senior Lecturer at Macquarie University's School of Mathematical and Physical Sciences, affiliated with the Transforming Energy Markets Research Centre. Her expertise spans Statistics, Probability Theory, and Stochastic Processes. She has led five research projects between 2007–2014, including studies on Brownian motion applications, credit risk modeling, and commodity futures pricing. Her recent work focuses on parameter estimation in stochastic processes and multi-factor financial models. Key research outputs include advancements in diffusion process modeling, autoregressive error corrections, and uncertainty quantification in heterogeneous media. In 2023, she received the Faculty of Science and Engineering Award for Inter-School Collaboration. Her academic activities include international collaborations, visiting research positions at institutions like the Institute of Statistical Mathematics and Hitotsubashi University, and editorial contributions to academic journals.

Marieke Timmermanمشاهده پروفایل

استاد

• Multivariate Data Analysis
• Psychometrics
• Statistics & Probability
• +۴ مورد دیگر

Marieke Timmerman is a Professor of Multivariate Data Analysis at the University of Groningen, affiliated with the Faculty of Behavioural and Social Sciences and the Department of Psychometrics & Statistics. Her research focuses on explaining complex psychological data through concise models, particularly in multivariate and longitudinal analyses, with emphasis on latent variable models, test construction, and data reduction techniques. She chairs the Department of Psychometrics & Statistics and is a member of COTAN (Collaborative Organization for Test Norming). Her work spans psychometrics, statistics, and applied psychological testing, with notable contributions to continuous test norming using GAMLSS and multilevel models. Key research areas include cognitive functioning in neurological disorders, emotion dynamics, and recovery trajectories in psychosis. Publications highlight methodological advancements in norming psychological tests, dynamic clustering of longitudinal data, and applications in clinical populations. Awards include the 2017 Kowalski Prize and a 2023 grant for norming research. She supervises PhD students and teaches advanced methods like multilevel analysis and repeated measures. Her ancillary roles include statistical consulting and board memberships, reflecting her interdisciplinary impact. Collaborations bridge clinical, developmental, and computational domains, addressing practical challenges in psychological assessment and intervention.

• Financial Econometrics
• High-Frequency Financial Data Analysis
• Market Microstructure
• +۳ مورد دیگر

Nikolaus Hautsch is a Professor at the University of Vienna in the Department of Statistics and Operations Research . He has extensive affiliations with institutions such as Humboldt University of Berlin , VU University Amsterdam , University of Cambridge , and Northwestern University . His research spans financial econometrics, market microstructure, and high-frequency data analysis. Key Research Themes: Price impact, liquidity modeling, volatility estimation, systemic risk networks, and Bayesian learning in financial markets. Methodological Focus: Point processes, stochastic volatility models, multivariate intensity modeling, and high-frequency covariance estimation. Scientific Awards: No explicit awards mentioned in the provided data. Advising and Grants: Collaborated with numerous researchers across Europe and North America, including Dieter Hess, Ruihong Huang, and David Veredas. Research funded through institutional affiliations and working paper series from organizations like CORE , CFS , and SSRN .

• Financial Econometrics
• Asset Pricing
• Market Microstructure
• +۵ مورد دیگر

Marcin Zamojski is a Research Fellow at the Centre for Finance within the School of Business, Economics and Law at the University of Gothenburg, Sweden. His research spans financial econometrics, asset pricing, and market microstructure, with particular expertise in liquidity measurement, hedge fund strategies, and high-frequency trading analysis. His research interests focus on Financial Econometrics , Asset Pricing , Market Microstructure , Hedge Funds , Liquidity Analysis , and Time-Series Modeling . Zamojski's methodological approach combines sophisticated statistical techniques with practical financial applications, often involving large-scale collaborative research efforts. Zamojski's publication record shows consistent output in high-impact finance journals, with his 2024 Journal of Finance paper 'Nonstandard Errors' being particularly influential (over 17,500 downloads). His research demonstrates a progression from methodological econometric work to applied finance topics, particularly in hedge fund strategy analysis. Nonstandard Errors (Journal of Finance, 2024) - Methodological contribution to financial research reliability Hedge Fund Strategy Experimentation and Clustering (2024) - Analysis of innovation diffusion in hedge funds Dynamic Trade Informativeness (2022) - High-frequency trading analysis Generalized autoregressive Method of Moments (2018) - Econometric methodology development Hedge Fund Innovation (2022) - Study of strategic innovation in alternative investments Zamojski maintains active research collaborations across multiple institutions including Vrije Universiteit Amsterdam, Universite du Luxembourg, and Singapore Management University. His work often involves coordinating large international research teams, as demonstrated by his Journal of Finance publication with over 200 co-authors from institutions worldwide.

Harold Ruizمشاهده پروفایل

دانشیار

• Green Energy
• Transport Systems
• Electrical Machines
• +۳ مورد دیگر

Harold Ruiz is an Associate Professor in Electrical Machines and Energy Systems at the University of Leicester's School of Engineering, leading the Green Energy and Transport (GrEaT) Research Group. He oversees the Electrical and Electronics Engineering team and integrates facilities at Space Park Leicester with the School's experimental resources. His research focuses on decarbonizing energy systems through green technologies, with expertise in superconductivity, power systems, and renewable integration. He holds a PhD in Electrical Machines and Applied Superconductivity from the University of Zaragoza, preceded by MSc degrees in Condensed Matter Physics (National University of Colombia) and Physical Technologies. His career includes roles at Florida State University and the University of Cambridge, contributing to over £2.5M in research projects across superconductivity and energy systems. His research group addresses EPSRC's Energy Challenge, emphasizing sustainable transport and grid efficiency. Key projects include optimizing superconducting materials for low-energy losses and advancing fault current limiters for power grids. Collaborative efforts span international partners, focusing on HTS cables, aerospace propulsion, and renewable energy optimization. GrEaT Group achievements include developing open databases for superconducting materials and pioneering studies on magnetization losses in advanced conductors. Ruiz’s work bridges theoretical models with practical applications, driving innovations in energy storage, grid stability, and decarbonization strategies.

Yuefeng Hanمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Time Series Analysis
• Tensor Data Analysis
• +۴ مورد دیگر

Yuefeng Han is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, part of the College of Science. He holds a Ph.D. in Statistics from the University of Chicago (2019), an M.Sc. in Statistics from the University of Chicago (2014), and a B.S. in Mathematics from Zhejiang University, China (2012). His research focuses on high-dimensional statistics, tensor data analysis, time series analysis, and statistical machine learning, with a particular emphasis on developing methodologies for complex, high-dimensional data structures and dynamic systems. His work has led to contributions in tensor factor models, statistical inference for stochastic optimization, and applications in econometrics and climate science. Han has been recognized with awards including the Consulting Award (2016) and Senior Consultant distinction (2016) from the University of Chicago Department of Statistics, along with a fellowship during his doctoral studies. He has advised PhD and undergraduate students, including Ke Xu, Rui Sheng (co-advised), and Xianglu Zhu. His publications span top journals like the Annals of Statistics, Journal of the Royal Statistical Society Series B, and the Journal of the American Statistical Association. Han also contributes to software development, notably the tensorTS R package for tensor time series analysis. His service includes reviewing for leading statistical journals and consulting projects addressing interdisciplinary challenges in neurobiology, linguistics, and environmental science.

• Machine Learning
• Deep Learning
• Neural Networks
• +۳ مورد دیگر

Claudio Gallicchio is an Associate Professor of Deep Learning at the Department of Computer Science, University of Pisa, Italy. His research focuses on Machine Learning, Deep Learning, Neural Networks, and Dynamical Systems, with specialization in Dynamical Neural Networks, Reservoir Computing, and Graph Learning. He actively contributes to IEEE Task Forces on Reservoir Computing and Randomized Neural Networks, and is a member of several AI societies including AAAI and ENNS. He holds a Ph.D. in Computer Science from the University of Pisa. His work emphasizes addressing challenges in graph neural networks—such as oversquashing—and advancing energy-efficient neural architectures using memristive systems. He supervises students on topics like Recurrent Neural Networks, Neural ODEs, and Deep Learning for Digital Humanities. Gallicchio's recent publications explore Reservoir Computing applications in industrial processes, non-dissipative graph networks, and federated learning. His research bridges theoretical foundations (e.g., Hamiltonian architectural bias) with practical implementations (e.g., memristor-based models). He has pioneered frameworks like GRAMA for adaptive graph time-series analysis and the TEACHING platform for human-centric autonomous systems.

Ting Daiمشاهده پروفایل

استادیار

• Educational Psychology
• STEM Education
• Student Motivation
• +۴ مورد دیگر

Ting Dai is an Assistant Professor in the Department of Educational Psychology at the University of Illinois at Chicago (UIC). Her research focuses on student motivation, engagement in STEM fields, and methodological issues like missing data and measurement invariance. She teaches statistics, research design, and assessment courses. Dr. Dai has been awarded grants from the Institute of Education Sciences (IES) and NIH, including roles as Co-PI/Co-I in studies addressing STEM achievement, biology education, and coping strategies for urban youth. Education: Ph.D. and M.Ed. in Educational Psychology from Temple University. Professional memberships include American Educational Research Association (AERA), American Psychological Association (APA), and European Association for Research on Learning and Instruction (EARLI). Research interests span cognitive-motivational interventions in STEM, measurement validity, and educational technology applications. Current work includes a pilot study on STEM identity for underrepresented minorities. She serves on editorial boards for Journal of Educational Psychology and Journal of Experimental Education . Grants and projects include IES-funded studies on biology reasoning assessments and motivational interventions. Her methodological contributions address missing data techniques and longitudinal research designs.

Huixia Judy Wangمشاهده پروفایل

استاد

• Quantile regression
• Extreme value theory
• Bioinformatics
• +۴ مورد دیگر

Huixia Judy Wang is a Professor in the Department of Statistics at The George Washington University since 2017. Previously, she held positions at North Carolina State University from 2006 to 2017, advancing from Assistant to Associate Professor. Her expertise spans quantile regression, extreme value theory, bioinformatics, and longitudinal data analysis with applications in environmental science, health, and finance. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2006 M.S., Statistics, Fudan University, 2002 B.S., Statistics, Fudan University, 1999 Research focuses on developing robust statistical methods for analyzing extremes, high-dimensional data, and complex dependencies. Key themes include quantile-based approaches for spatial/temporal data, survival analysis with detection limits, and subgroup identification in clinical trials. She has contributed to methodological advancements in copula models, threshold regression, and Bayesian quantile inference. Her publications address applied problems such as climate variability impacts, biomedical data interpretation, and financial risk modeling. She maintains an active record of interdisciplinary collaboration across environmental science, genomics, and public health.

Guo Yuمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Data Science
• +۳ مورد دیگر

Guo Yu is an Assistant Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara (UCSB). His research focuses on high-dimensional statistics, machine learning, and applied probability, with applications to econometrics and healthcare analytics. He holds a PhD from Cornell University. Education: PhD in Statistics, Cornell University Research Interests: Statistical methodologies for high-dimensional data Machine learning algorithms with interpretability Applications in healthcare prediction (e.g., COVID-19 modeling) Econometric models with error control Recent Work Trends: His articles emphasize robust statistical frameworks for high-dimensional inference, including error variance estimation, interaction modeling, and cost-efficient feature selection. He also explores hybrid models combining autoregressive methods with neural networks for time-series prediction. His work often bridges theoretical statistics and practical applications in cloud computing and large-scale data analysis. Awards: None listed. Grants and Advising: No specific grants or advisees listed in the provided texts. His research likely involves collaborations with institutions like Microsoft Azure (as seen in the 2018 Deepview paper).

Alex Roitershteinمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Statistical Models
• +۵ مورد دیگر

Alex Roitershtein is an Instructional Associate Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on probability theory, stochastic processes, and their applications in statistical models, combinatorics, and complex systems. He has contributed to areas including random walks, Markov processes, evolutionary dynamics, and network science. His work often bridges theoretical developments with practical applications in infrastructure resilience, genetic models, and algorithmic combinatorics. Roitershtein’s recent publications explore topics ranging from optimal resource allocation strategies in financial systems to extinction dynamics in evolutionary models. He has also investigated network robustness through Markovian influence graphs and studied combinatorial patterns in permutations and words. His research frequently employs probabilistic methods, generating functions, and stochastic analysis to address both foundational and applied questions. His articles reflect a multidisciplinary approach, with contributions to statistical physics (e.g., avalanche dynamics in excitable networks), computational biology (e.g., Wright-Fisher population models), and discrete mathematics (e.g., enumeration of staircase patterns). While no specific awards are listed, his extensive publication record demonstrates sustained scholarly activity in probability and its applications.

Jonathan Wrightمشاهده پروفایل

استاد

• Time Series Econometrics
• Empirical Macroeconomics
• Finance
• +۴ مورد دیگر

Jonathan Wright is a Professor of Economics at Johns Hopkins University, affiliated with the Krieger School of Arts & Sciences. He holds a PhD from Harvard University, an M.Sc. from the London School of Economics, and a B.A. from Trinity College Dublin. His research focuses on time series econometrics, empirical macroeconomics, and finance, with particular emphasis on forecasting, high-frequency news effects, term structure analysis, and weak identification issues. He has held editorial roles at prominent journals such as the Journal of Applied Econometrics and the Journal of Econometrics. Notable awards include being a Fellow of the International Association for Applied Econometrics (2017) and the Journal of Econometrics (2024). His work spans central bank policies, asset pricing, and macroeconomic modeling, with extensive contributions to understanding monetary policy impacts and financial market dynamics. Education: PhD, Harvard University (1997); M.Sc., London School of Economics (1991); B.A., Trinity College Dublin (1990). Employment highlights include roles as a Research Associate at the National Bureau of Economic Research and Acting Director of the Center for Financial Economics at Johns Hopkins. He has also served as an economist at the Federal Reserve Board and has taught courses in econometrics, macroeconomics, and financial markets. His research interests span a broad range of topics, including inflation dynamics, yield curve analysis, and the effects of unconventional monetary policy. He has authored over 50 peer-reviewed articles and book chapters, with recent work focusing on structural breaks in the Phillips curve, quantitative easing effects, and forecasting methodologies under model uncertainty.

Piotr Fryzlewiczمشاهده پروفایل

استاد

• Time Series Analysis
• Change-Point Detection
• High-Dimensional Statistics
• +۴ مورد دیگر

Piotr Fryzlewicz is a Professor of Statistics in the Department of Statistics at the London School of Economics and Political Science (LSE). He specializes in time series analysis, change-point detection, high-dimensional statistics, and statistical learning. His research includes methodologies like Wild Binary Segmentation for multiple change-points, Haar-Fisz transformation for non-Gaussian data, and multiscale modeling. Fryzlewicz holds a PhD in Statistics from the University of Bristol (2003) and an MSci in Mathematics from Wrocław University of Science and Technology. Affiliations: London School of Economics (since 2011) Former roles: University of Bristol, Imperial College London, Winton Capital Management Research Interests: Focuses on statistical methods for nonstationary data, multiscale techniques, and applications in finance, neuroscience, and social sciences. His work emphasizes practical implementations through R packages like breakfast and nsp . Key Achievements: Recipient of the Guy Medal in Bronze (2013) from the Royal Statistical Society Author of over 50 peer-reviewed articles and book chapters Developed widely cited methods in change-point detection and volatility modeling Teaching & Supervision: Currently teaches Financial Statistics (ST436) . Past PhD students include notable researchers in statistical theory and applications. Advises on doctoral research in time series and machine learning.

• Macroeconomics
• Macroeconomic Policy
• Macroprudential Policy
• +۵ مورد دیگر

Professor Joseph Pearlman holds the position of Professor of Economics at City St George's, University of London. He has a PhD in Control Systems from Imperial College and an MSc in Economics from the London School of Economics (LSE). His research focuses on macroeconomic and macroprudential policy, endogenous growth theories, and the implications of imperfect information on Vector Autoregression (VAR) analysis. His work extends into expectational learning dynamics and the application of DSGE models to real-world economic issues like oil price impacts and financial stability. Key academic roles include research associate positions at the European Central Bank and International Monetary Fund. His publications span over four decades, addressing topics such as monetary policy rules, fiscal-monetary coordination during crises, and global economic integration. Notable contributions include works on robust policy design using prediction pools and validation techniques for DSGE models under uncertainty. His research has explored regional economic disparities in North-South frameworks, the role of innovation cycles in growth, and the mechanics of European monetary integration. He frequently collaborates with institutions like the IMF, Federal Reserve, and academic networks in macroeconomic modeling and policy analysis. His scholarly outputs include edited volumes analyzing the UK economy post-2008, contributions to the Oxford Handbook of the Indian Economy, and methodological guides on DSGE model construction published in the Handbook of Research Methods in Empirical Macroeconomics. Labs/Teams: Active in the DSGE modeling community, collaborating with central banks and international organizations on policy-oriented research. His work bridges theoretical macroeconomics with applied econometric techniques for policy evaluation.