جست‌وجوی استاد و پژوهشگر

Sergey Nadtochiyمشاهده پروفایل

استاد

• Financial Mathematics
• Probability Theory
• Partial Differential Equations
• +۲ مورد دیگر

Sergey Nadtochiy serves as Professor of Applied Mathematics within the College of Computing at Illinois Institute of Technology. His research bridges theoretical mathematics with financial applications through rigorous probabilistic and analytical frameworks. His academic foundation includes: Ph.D. in Operations Research & Financial Engineering from Princeton University (2009), advised by R. Carmona M.A. in Operations Research & Financial Engineering from Princeton University (2008), advised by R. Carmona Specialist (M.Sc.) in Mathematics, summa cum laude, from Moscow State University (2005), advised by A. Shiryaev Nadtochiy's research program centers on Financial Mathematics, Probability Theory, and Partial Differential Equations with applications to market microstructure, optimal contract design, and systemic risk. His work demonstrates deep integration of Stochastic Control and Game Theory to model complex financial phenomena like price impact and limit order book dynamics, while recent publications extend into mathematical physics through Stefan problem analysis. Analysis of his 15 most recent publications reveals a strategic evolution from pure financial mathematics toward interdisciplinary connections with mathematical physics. The 2022-2024 works show increasing focus on the Stefan problem and diffusion-limited aggregation, while maintaining core expertise in market microstructure through studies on price impact concavity, brokerage contracts, and liquidity effects. His scientific recognition includes: Award for excellence (honorific fellowship) from Moscow State University (2001-2005) SIAG/FME Junior Scientist Prize from SIAM (2012) Charlotte Elizabeth Procter Honorific Fellowship from Princeton University (2008-2009) Gordon Y.S. Wu Honorific Fellowship from Princeton University (2005-2009) Nadtochiy has secured significant research funding as sole Principal Investigator on two NSF awards: a CAREER grant (DMS-1855309, 2017-2022) and earlier grant DMS-1411824 (2014-2017). While no formal student advisees are listed in the source materials, his collaborative publications with researchers like M. Shkolnikov and S. Shreve demonstrate active mentorship within research teams focused on probabilistic methods and financial modeling.

Ted Juhlمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Nonparametric Models
• +۳ مورد دیگر

Ted Juhl is the O. Maurice Joy Professor and Area Director of Finance at the University of Kansas School of Business. His research focuses on econometric methodologies, particularly in time series analysis, nonparametric models, panel data, and quantile regression. He has contributed to understanding slope heterogeneity in panel data models and testing for structural stability in econometric frameworks. His work bridges theoretical econometrics with practical applications in finance and policy analysis. His research interests emphasize advancing methodologies for analyzing complex economic and financial datasets, including cointegration analysis, predictive regression, and model diagnostics. Notable contributions include addressing issues of heteroskedasticity in fixed effects models and evaluating the impact of R&D funding on scientific productivity in academic chemistry. Recent publications explore topics like rolling regression estimator distributions, policy heterogeneity in corporate finance, and the implications of cross-sectional correlation in panel data. While no specific awards are listed, his extensive publication record reflects sustained scholarly impact in econometric theory and applications. Teaching focuses on econometrics at the graduate level, aligning with his research expertise. His professional address is Capitol Federal Hall, Room 4113, Lawrence, KS. No advising or grant details are explicitly stated in the provided materials.

Jakob Huberمشاهده پروفایل

پژوهشگر

• Operations Research
• Machine Learning
• Supply Chain Management
• +۳ مورد دیگر

Dr. Jakob Huber is a Researcher at the University of Mannheim's School of Business Informatics and Mathematics, affiliated with the Data and Web Science Group. His research focuses on operations research, machine learning, and data-driven decision-making in supply chain management and inventory systems. He also explores probabilistic reasoning with ontologies and semantic web technologies. Huber's work addresses challenges such as perishable goods inventory optimization, demand forecasting incorporating calendar effects, and real-time decision support systems. His contributions bridge theoretical advancements in machine learning and practical applications in retail and logistics. Selected research highlights include developing cluster-based hierarchical demand forecasting models and infrastructure for probabilistic semantic web reasoning. His publications span journals like the International Journal of Production Economics and the Semantic Web. He holds a PhD from the University of Mannheim, and his expertise spans data science, artificial intelligence, and combinatorial optimization for data integration.

Lisa Goldbergمشاهده پروفایل

استاد

• Mathematical Finance
• Quantitative Finance
• Risk Management
• +۷ مورد دیگر

Lisa Goldberg is a Professor at the Department of Economics, University of California, Berkeley, and Co-Director of the Center for Risk Management Research (CDAR). She is also Head of Research at Aperio Group, now part of BlackRock. Dr. Goldberg integrates industry and academic practices in her research, focusing on Mathematical Finance, Risk Management, and Causal Inference. Education: PhD in Mathematics, Brandeis University (1984) Research Interests: Dynamics of Financial Markets Portfolio Risk Analysis ESG Investing Sports Statistics Causal Inference Recent Research Trends: Dr. Goldberg's recent publications emphasize quantitative finance, including portfolio optimization, ESG characteristics, and causal inference in financial contexts. Her work spans risk parity strategies, tax arbitrage, and sports analytics, reflecting interdisciplinary applications of mathematical finance. Scientific Awards: Alfred P. Sloan Fellowship NSF Visiting Professorship for Women Advising & Grants: While specific students are not listed, Dr. Goldberg has collaborated extensively and secured grants including the Alfred P. Sloan Fellowship and NSF Visiting Professorship for Women. She has also contributed to editorial boards and patent innovations in quantitative finance. Labs & Teams: Dr. Goldberg co-leads CDAR at UC Berkeley and heads research at Aperio Group (BlackRock), bridging academia and industry in financial risk management.

Jade Benjamin-Chungمشاهده پروفایل

استادیار

• Epidemiology
• Infectious Diseases
• Environmental Health
• +۵ مورد دیگر

Jade Benjamin-Chung is an Assistant Professor in the Department of Epidemiology and Population Health at Stanford University and a Chan Zuckerberg Biohub Investigator. Her research focuses on developing and evaluating scalable interventions targeting the interplay between infectious diseases, environmental factors, and human health in resource-limited settings. Education: PhD in Epidemiology, UC Berkeley (2014) MA in Biostatistics, UC Berkeley (2013) MPH in Epidemiology & Biostatistics, UC Berkeley (2008) Research Interests: Her work employs epidemiologic, computational, and statistical methods (e.g., causal inference, machine learning) to improve health outcomes for vulnerable populations globally. Key areas include malaria, diarrheal diseases, soil-transmitted helminths, and influenza. Projects emphasize interventions such as improved sanitation, housing infrastructure, and mass drug administration. Labs & Teams: She leads the Benjamin-Chung Lab at Stanford, focusing on creating evidence-based health interventions through interdisciplinary approaches. The lab collaborates on studies like the CRADLE trial (concrete flooring impacts) and malaria elimination projects. Grants & Collaborations: Her work is supported by NIH grants and collaborations with institutions like UC Berkeley and global health organizations.

Christiern Roseمشاهده پروفایل

مدرس ارشد

• Applied Health Economics
• Health Econometrics
• Econometrics
• +۴ مورد دیگر

Dr Christiern Rose is a Senior Lecturer in the School of Economics at The University of Queensland (UQ), within the Faculty of Business, Economics and Law. He holds a PhD in Economics from the University of Bristol (2016) and completed a post-doctoral fellowship at Toulouse School of Economics. His research focuses on applied microeconometrics with emphasis on peer effects, high-dimensional econometrics, health economics, and illicit drug markets. He is affiliated with the Centre for Efficiency and Productivity Analysis. Education: PhD in Economics, University of Bristol (2016) Post-doctoral Research, Toulouse School of Economics (2016–2018) Joined UQ in 2017 Research Interests: Dr Rose investigates peer effects in social networks, health economics (including mental health, healthcare utilization, and aging populations), and the economics of illicit drugs. He develops econometric methods for high-dimensional data and network analysis, with applications to policy evaluation and social spillovers. Research Trends: His work bridges theoretical econometrics and empirical policy analysis, often using advanced statistical techniques to address causal questions. Recent studies explore housing insecurity’s mental health impacts, physician networks’ influence on innovation adoption, and the role of social peers in addiction recovery. Grants & Supervision: Leading the ARC Discovery Project Understanding macroeconomic fluctuations with unobserved networks (2022–2025) Available for PhD supervision in health economics, network econometrics, and applied microeconometrics Labs/Teams: Affiliate of the Centre for Efficiency and Productivity Analysis, collaborating on productivity measurement and efficiency analysis methods.

• Econometrics
• Hypothesis Testing
• Multivariate Models
• +۳ مورد دیگر

Professor Grant Hillier is a Professor of Econometrics within the Department of Economics at the University of Southampton. He is responsible for Senior Recruitment and serves as a Fellow of CeMMAP (Centre for Microdata Methods and Practice), a leading research center in Microeconometrics. Born in Adelaide, Australia, he earned his first degree there before completing his doctoral research at the University of Pennsylvania under a Fulbright Scholarship. His research focuses on inference in structural models, hypothesis testing theory, multivariate models, and financial econometrics, with recent work exploring properties of Wishart matrices. He has held the role of Associate Editor of Econometric Theory (1987–2012) and received the Econometric Theory Plura Scripsit Award in 2008 for significant contributions to econometric theory. His academic career includes extensive contributions to econometric methodology, spatial autoregressive models, and nonparametric testing. He collaborates with researchers such as Raymond Kan and Federico Martellosio on topics like matrix properties and spatial econometrics. His work bridges theoretical advancements and practical applications in econometric analysis. Education: B.A. (Adelaide, Australia), Ph.D. (University of Pennsylvania, USA). Key Roles: CeMMAP Fellow, Editor of Econometric Theory . Awards: Econometric Theory Plura Scripsit Award (2008). Research Areas: Wishart matrices, hypothesis testing, spatial econometrics, structural models. His publications span high-impact journals like Journal of Econometrics and Econometric Theory , addressing foundational econometric challenges and methodological innovations.

George Bissiasمشاهده پروفایل

استادیار

• Distributed Systems
• Digital Currencies
• Network Privacy
• +۳ مورد دیگر

George Bissias is a Research Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst. He holds a Ph.D. in Computer Science from UMass Amherst (2010) and has industry experience at mobile advertising startup Fiksu and distributed database startup Hadapt before returning to academia in 2013. His research focuses on distributed systems, digital currencies, and network/data privacy, with notable work on blockchain security, cryptocurrency protocols, and differential privacy frameworks like DpComp. Education: Ph.D. in Computer Science, University of Massachusetts Amherst, 2010 Research Interests: Statistical inference in peer-to-peer networks Secure and scalable Bitcoin implementations Differentially private algorithm benchmarking (DpComp) Blockchain protocols and consensus mechanisms Privacy-preserving data generation (PSynDB) Key Research Directions (2019–2025): Recent work explores fair course allocation mechanisms, DAG-based mining strategies, and economic models for cryptocurrency security. His blockchain research includes protocols like Graphene and Tailstorm, emphasizing transaction efficiency and security. Labs/Teams: Active contributor to UMass's distributed systems and cybersecurity research groups, focusing on interdisciplinary projects blending cryptography, economics, and distributed computing.

• Political Institutions
• Political Economy
• Media and Politics
• +۳ مورد دیگر

Kevin DeLuca is an Assistant Professor of Political Science at Yale University and a Resident Fellow at the Institution for Social and Policy Studies (ISPS). He is also a Faculty Affiliate at the Center for the Study of American Politics (CSAP). His research focuses on political institutions, the political economy of media, and election laws, particularly examining the role of local newspapers in shaping political outcomes. DeLuca holds a Ph.D. in Political Economy and Government from Harvard University (2023), an M.A. in Economics from the University of Texas at Austin (2015), and a B.A./B.S. in Economics and Psychology from the same university (2014). He teaches courses such as 'Data Analytics for Redistricting and Voting Rights' and 'Regulating Elections: Voting Rights and Election Law.' His work intersects with computational methods in redistricting analysis and the influence of media on electoral processes. Previously, he collaborated with institutions like the MIT Election Data and Science Lab (MEDSL) and the Stanford-MIT Healthy Elections Project. His research emphasizes methodologies like Bayesian geocoding for redistricting validation and explores partisan dynamics in gerrymandering solutions. He has contributed to groundbreaking studies on candidate quality measurement via media endorsements and the impact of top-tier elections on down-ballot races.

Nianbo Dongمشاهده پروفایل

استاد

• Quantitative Methods
• Educational Evaluation
• Causal Inference
• +۶ مورد دیگر

Nianbo Dong is the Guy B. Phillips Professor in Education at the University of North Carolina at Chapel Hill (UNC-CH). His expertise lies in quantitative methodologies for educational evaluation, focusing on causal inference, multilevel experiments, and program effectiveness analysis. He holds a Ph.D. from the University of Pennsylvania (2009) and prior roles at the University of Missouri-Columbia and Vanderbilt University. Dr. Dong's research develops statistical tools for experimental design and causal analysis, including the PowerUp! software suite for multilevel experiment planning. His work evaluates teacher training programs, early childhood education interventions, and educational policies. He has received $4.3M+ in NSF and IES grants, highlighted by the NSF Early Career Award (2017). His methodological contributions include cost-effectiveness analysis frameworks, moderation/mediation effect designs, and addressing nested data structures. Recent work explores disparities in K-8 academic outcomes and trauma impacts on randomized trials. He advises on grant proposals and serves as Associate Editor for Educational Evaluation and Policy Analysis . Publications span 200+ peer-reviewed articles in journals like Journal of Educational Measurement and Journal of Research on Educational Effectiveness . His work bridges statistical rigor with real-world educational challenges, emphasizing practical tools for researchers and policymakers.

• Financial Markets
• Algorithmic Trading
• Cryptocurrencies
• +۴ مورد دیگر

Andrea Barbon is an Assistant Professor at the University of St. Gallen's School of Finance within the Department of Finance. His research focuses on financial markets, algorithmic trading, cryptocurrency dynamics, liquidity provision, and market microstructure. He has collaborated with institutions like the Swiss Finance Institute and Peak Capital Trading. His work addresses topics such as DeFi, stablecoin monetary policy interactions, and NFT bubble formation. Barbon's research outputs span 12 scholarly papers, with significant contributions to understanding trading strategies (e.g., day trading systems, intraday momentum), cryptocurrency market quality, and liquidity risks in option markets. Notable articles include 'Nonstandard Errors' (Journal of Finance, 2024) analyzing liquidity through multi-analyst studies and 'Gamma Fragility' (2020) exploring option market imbalances. His articles consistently engage with high-frequency data, algorithmic systems, and emerging financial technologies like blockchain. While no awards or grants are explicitly mentioned, his prolific publication record reflects active engagement in financial innovation and policy-relevant research.

• Quantitative Finance
• Portfolio Optimization
• Covariance Matrix Estimation
• +۳ مورد دیگر

Gianluca De Nard serves as a Senior Research Associate at the University of Zurich's Department of Economics, Research Fellow at New York University's Volatility and Risk Institute, and Head of Quantitative Research at OLZ AG in Zurich. His career bridges academic research and practical finance applications, with significant contributions across multiple institutions. Dr. De Nard specializes in quantitative finance with expertise in portfolio optimization, covariance matrix estimation, and financial econometrics. His research focuses on developing robust statistical methods for handling large-dimensional financial data, with particular emphasis on shrinkage techniques and factor models. His work addresses fundamental challenges in modern portfolio theory, including nonstandard errors in financial research, climate risk integration, and AI applications in investment management. Analysis of his recent publications reveals a clear evolution toward integrating machine learning with traditional financial econometrics. His 2024-2025 work shows increasing sophistication in applying AI techniques to portfolio construction while maintaining rigorous statistical foundations. The recurring themes across his research include improving covariance matrix estimation, developing factor models for portfolio selection, and addressing methodological challenges in financial research. Dr. De Nard has established collaborations with leading researchers including Nobel laureate Robert F. Engle, Olivier Ledoit, and Michael Wolf. His work appears in top finance journals including the Journal of Finance, Journal of Financial Econometrics, and Journal of Banking and Finance, with his 2024 paper 'Nonstandard Errors' accumulating over 17,000 downloads. At OLZ AG, he applies his academic research to practical investment challenges as Head of Quantitative Research, demonstrating his ability to translate theoretical advances into real-world solutions. His dual academic-industry roles position him at the forefront of quantitative finance research and application.

• Optimal Transportation
• Mathematical Economics
• Mathematical Physics
• +۳ مورد دیگر

Brendan Pass is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on optimal transportation theory, with applications in mathematical economics, mathematical physics, and density functional theory. He is a core member of the Kantorovich Initiative, an interdisciplinary group dedicated to advancing optimal transport methodologies and their practical implementations. His work bridges pure mathematics with applied fields, addressing complex problems in multi-marginal transport, economic equilibrium modeling, and geometric analysis. Pass’s academic contributions span theoretical frameworks for optimal transport between spaces of differing dimensions, cyclic cost structures, and robust risk management. He explores intersections with diverse disciplines, including econometrics, financial mathematics, and geometric probability. His publications often address uniqueness conditions for Monge solutions, Wasserstein metrics, and barycenter problems in metric spaces. He has co-authored works on topics ranging from hedonic pricing models to vectorial martingale transport. Recent research trends include leveraging optimal transport for inequality measurement, robust optimization, and data denoising. His methodologies emphasize structural analysis and algorithmic design, with applications in crop root systems, quantum chemistry (via density functional theory), and option pricing. Pass frequently collaborates with institutions globally, contributing to both theoretical advancements and real-world problem-solving. Teaching responsibilities include advanced mathematics courses such as Math 156 (Calculus for Business/Economics) and Math 300 (Boundary Value Problems). His scholarly output includes over 50 peer-reviewed articles, conference contributions, and edited volumes. Current projects explore ODE characterizations of transport problems, distributional robustness, and partial identification in econometric models.

Ilze Kalninaمشاهده پروفایل

استادیار

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

Ilze Kalnina is an Assistant Professor of Economics at the Department of Economics, Poole College of Management, North Carolina State University. She previously held an Assistant Professor position at the University of Montreal. Her research focuses on econometrics, particularly nonparametric estimation and inference for volatility using high-frequency data. Kalnina obtained her PhD in Economics from the London School of Economics in 2009. She is affiliated with the Poole College's Econometrics group and contributes to the Economics Graduate Program. Education: PhD in Economics, London School of Economics (2009) Research Interests: Econometrics methodologies High-frequency financial data analysis Volatility modeling and leverage effect estimation Nonparametric statistical techniques Her publications consistently address volatility dynamics, leverage effect, and high-frequency econometric challenges. Articles span technical innovations like subsampling methods to applied topics such as risk premia and beta estimation. No scientific awards are explicitly listed in the provided texts. Advising and grants: No formal advisees or grant records are mentioned. She has not been associated with any labs or research teams in the available data.

Ivana Komunjerمشاهده پروفایل

استاد

• Econometrics
• Financial Economics
• Macroeconomics
• +۴ مورد دیگر

Ivana Komunjer is a Professor of Economics at Georgetown University's Department of Economics. Her research focuses on econometric theory, financial economics, and macroeconomics, with a particular emphasis on asymmetric power distribution (APD) models and their applications to risk measurement. She has developed widely used MATLAB tools for APD density functions, including pdf, cdf, quantile, and random number generators. Her work also addresses dynamic stochastic general equilibrium (DSGE) models, nonlinear filtering, and quantile methods in economic forecasting. Komunjer teaches advanced courses such as Macroeconometrics and Theory of Financial Markets. Her contributions span theoretical econometrics, financial modeling, and policy analysis, with a strong emphasis on methodological innovation.