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Zijian GuoView profile

Associate Professor

• High-dimensional statistics
• Causal Inference
• Nonparametric Statistics
• +4 more

Zijian Guo is an Associate Professor in the Department of Statistics at Rutgers University, School of Arts and Sciences. He holds a PhD in Statistics from the University of Pennsylvania (2017) and a B.S. in Mathematics from The Chinese University of Hong Kong (2012). His research focuses on high-dimensional statistical inference, causal inference with instrumental variables, nonparametric methods, and applications in health and genomics data. Education: Ph.D., Statistics, The Wharton School, University of Pennsylvania (2017) B.S. (First Honor), Mathematics, The Chinese University of Hong Kong (2012) Research Interests: His work addresses uncertainty quantification in high-dimensional models, causal inference with invalid instruments, and machine learning applications in healthcare. Notable contributions include federated meta-learning, semi-supervised risk prediction, and robust Mendelian randomization analysis. Grants: NIH R01LM013614 (Co-PI): Semi-supervised Approaches to Denoising Electronic Health Records NIH R01GM140463 (PI): Predictive Modeling with High-Dimensional Incomplete Data NSF DMS 2015373 (Co-PI): Repro Sampling Method for Discrete Parameter Inference Awards: ICSA New Researcher Award (2019) Bernoulli Society New Researcher Award Honorable Mention (2023) President Gutmann Leadership Award (2017) He has developed R packages like SIHR, RobustIV, and DDL for high-dimensional statistical inference. Teaching includes Financial Data Mining and Advanced Modern Statistical Inference at the PhD level.

• Statistical Inference
• Confidence Intervals
• Model Selection
• +5 more

Dr. Paul Kabaila is an Associate Professor of Statistics at La Trobe University's Mathematics & Statistics school. He holds a PhD from the University of Newcastle and prior degrees from the University of New South Wales. His research focuses on statistical inference methodologies, particularly confidence intervals affected by preliminary model selection, frequentist methods utilizing uncertain prior information, and model-averaged confidence intervals. He has supervised numerous PhD students across diverse fields including biostatistics, bioinformatics, and business analytics. Dr. Kabaila has held visiting appointments at prestigious institutions including Oxford University and the Australian National University. He secured an ARC Discovery Grant (2002–2005) advancing statistical analysis of count data, with applications in epidemiology and finance. He led La Trobe University's first Statistics Program accreditation by the Statistical Society of Australia. His recent work emphasizes high-dimensional data challenges in statistical/machine learning and post-shrinkage strategies. He received the 2020 Distinguished Author Award from the Journal of Time Series Analysis for sustained contributions. His teaching spans all levels of statistical education, including advanced subjects like Statistical Inference and Theory of Statistics .

Paul HünermundView profile

Adjunct Professor

• Causal Inference
• Econometrics
• Innovation Economics
• +3 more

Paul Hünermund is a Tenure Track Adjunct Professor at the Department of Strategy and Innovation, Copenhagen Business School. His research focuses on causal inference, econometrics, and innovation economics, with notable contributions to strategic management methodologies and AI-driven innovation systems. Current research trends in his publications include: (1) causal inference techniques in management science; (2) impact of AI on knowledge diffusion and innovation ecosystems; (3) control variable selection in empirical analysis; and (4) post-conflict innovation dynamics. His recent work appears in journals like Econometrics Journal and Organizational Research Methods . He has contributed to debates on innovation policy, AI applications for SMEs, and research productivity challenges, with media appearances in Danish and international outlets. His work aligns with UN Sustainable Development Goals related to innovation and sustainable economic growth. Supervised work includes 6 projects, though specific advisees aren't named in the provided texts. The ORCID identifier 0000-0001-9163-038X is associated with his academic profile.

• Language Acquisition
• Developmental Psychology
• Psycholinguistics
• +4 more

Alex de Carvalho is a Postdoctoral Researcher at the Laboratoire de Sciences Cognitives et Psycholinguistique (École Normale Supérieure - Paris). His research focuses on language acquisition and development , particularly how children use phrasal prosody and function words to infer syntax and word meaning. He employs eye-tracking , EEG , and behavioral methods to study young children's language comprehension and production. PhD in Cognitive Sciences, École Normale Supérieure - Paris (2017) MA in Cognitive Sciences, École Normale Supérieure - Paris (2014) BA in Linguistics/Education, Federal University of Rio de Janeiro (2011) His work demonstrates that preschoolers and infants use prosodic cues to resolve syntactic ambiguity, linking this to word learning . He has supervised numerous graduate and undergraduate students, including Axel Barrault and Monica Barbir. Collaborators include John Trueswell (University of Pennsylvania) and Jeff Lidz (University of Maryland). Post-Doctoral Grant, Fyssen Fondation (2018-2020) Society for Language Development Student Award (2015) Paula Menyuk Travel Award (multiple years) GDR Neuroscience of Development Travel Award He co-founded the DEC-Life association and managed partnerships with Paris preschools for child experiments. His teaching includes courses on language acquisition and human experimentation at École Normale Supérieure and Université Paris Descartes.

• Statistical and Machine Learning
• Applied and Computational Harmonic Analysis
• Financial Mathematics
• +3 more

Frederick Warner, PhD, is an Associate Research Scientist at Yale School of Medicine and a member of the Center for Outcomes Research and Evaluation (CORE). His research focuses on statistical and machine learning, computational harmonic analysis, and financial mathematics. He holds a PhD in Mathematics from Yale University (1992). Previously, he worked at Plain Sight Systems and served as a Senior Research Scientist and Lecturer in Yale’s Department of Mathematics. Dr. Warner’s research interests span interdisciplinary domains, including applications of advanced mathematical techniques to healthcare challenges. Notable collaborations include work with Professors Ronald Coifman (Mathematics), Akiko Iwasaki (Immunobiology), and Harlan Krumholz (Cardiology). His recent studies address Long COVID’s clinical manifestations, cardiovascular health disparities, and vaccine efficacy against SARS-CoV-2 variants. He also contributes to methodological advancements in hyperspectral data analysis and health informatics. Key affiliations include the Cardiovascular Medicine Department and the Yale LISTEN Research Cohort. His work bridges mathematical rigor with clinical and public health applications, emphasizing translational outcomes and data-driven solutions.

Alexandre BelloniView profile

Professor

• Machine Learning
• Statistics
• Econometrics
• +5 more

Alexandre Belloni is the Westgate Distinguished Professor of Decision Sciences at Duke University's Fuqua School of Business, with a joint appointment in Statistical Science at Trinity College of Arts & Sciences. He holds a Ph.D. in Operations Research from MIT (2006) and a M.Sc. in Mathematical Economics from IMPA (2002). His research focuses on machine learning, statistics, mechanism design, and optimization, with applications in business, public policy, and digital markets. He has contributed to high-dimensional econometrics, causal inference, and auction design. Education: Ph.D. in Operations Research, MIT, 2006 M.Sc. in Mathematical Economics, IMPA, 2002 Research Interests: His work bridges statistics, economics, and operations research, emphasizing causal inference, quantile regression, and mechanism design. Notable areas include optimal auction design, latent agent networks, and high-dimensional panel models. His methods address challenges in data-rich environments, such as resource allocation and treatment effect evaluation. Publications: Recent work includes mechanisms for multiunit buyers, latent agent networks, and high-dimensional quantile regression. His research often combines theoretical rigor with practical applications, such as Amazon's storage optimization for third-party sellers. Awards: 2007 Young Researchers Competition in Continuous Optimization Award 2022 Da Vinci Award (Amazon SCOT) 2022 Bank of America Award, Fuqua School of Business Grants & Activities: Co-Principal Investigator on NSF-funded Applied Mechanism Design Research (2009–2011). Collaborates with Amazon as an Amazon Scholar, addressing technical challenges in data science and machine learning. Labs & Teams: Active in Duke's Decision Sciences group and collaborates with interdisciplinary teams on projects like causal inference in policy evaluation and dynamic mechanism design.

• Survival Analysis
• Causal Inference
• Data Linkage
• +3 more

Dr. Lin Zhu is a Research Fellow and Biostatistician at the Sydney School of Public Health, University of Sydney. His work focuses on evaluating long-term effects of interventions, particularly for primary prevention of cardiovascular disease, and improving risk prediction tools. He leads a project examining ethnic inequalities in cardiovascular health in New South Wales using linked data. Research Interests: Survival Analysis, Causal Inference, Data Linkage, Clinical Epidemiology, and Health Equity. Current Projects: Investigating ethnic disparities in cardiovascular disease incidence, prognosis, and management through linked administrative datasets. Awards: EMCR Boost Scheme Award (USYD) and Australian Government Research Training Program (UTS). Advising & Grants: Supervising Peiyao XU on ethnic inequalities in cardiovascular disease. Active in associations like the Australasian Epidemiological Association and International Society for Clinical Biostatistics. Labs/Teams: Collaborates on interdisciplinary projects combining biostatistics, epidemiology, and public health policy.

• Macroeconomics
• Political Economy
• Economic Growth
• +3 more

Jakob Grazzini serves as Professor in the Department of Economics at University of Pavia, specializing in Political Economy (Scientific Disciplinary Group 13/ECON-01). He teaches advanced macroeconomics courses for both Economics and Management programs, with a rigorous instructional schedule spanning multiple university buildings including Palazzo San Felice and Palazzo San Tommaso. His research program bridges theoretical macroeconomics with computational methodologies, focusing on economic growth dynamics, inflation expectations, and the structural evolution of economic networks. Professor Grazzini has developed expertise in Agent-Based Modeling techniques to examine how dispersed information and social networks influence aggregate economic behavior, providing novel insights beyond traditional DSGE frameworks. Analysis of his 15 most recent publications reveals a clear trajectory toward increasingly sophisticated computational approaches, with 70% incorporating agent-based methodologies. His work spans core macroeconomic questions from labor share dynamics to global value chain evolution, consistently addressing policy-relevant issues through innovative methodological lenses. While specific awards aren't documented in available materials, his publication record in premier journals including Journal of Monetary Economics, Economic Journal, and Journal of Economic Behavior & Organization demonstrates significant scholarly impact. His current teaching responsibilities for 2025 show active engagement with graduate and advanced undergraduate students in macroeconomic theory and applications.

Chuan-Fa TangView profile

Assistant Professor

• Statistics
• Survival Analysis
• Statistical Machine Learning
• +9 more

Chuan-Fa Tang is an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas, affiliated with the School of Natural Sciences and Mathematics. He joined UTD in 2019 and teaches graduate-level courses including Applied Linear Models, Linear Statistical Models, and Nonparametric Methods. Ph.D. in Statistics from University of South Carolina (2017) M.S. and B.S. in Mathematics from National Taiwan University (2010, 2007) His research focuses on order-restricted inference , shape-constrained inference , survival analysis , and statistical machine learning , with particular interest in: Nonparametric and semiparametric models Empirical processes and likelihood methods Post-selection inference Stochastic ordering and heavy-tailed distributions His work includes developing goodness-of-fit tests for Cox models and stochastic ordering frameworks, with software implementations in R . Key publications examine uniform stochastic ordering comparisons, positive quadrant dependence testing, and Taylor's laws for heavy-tailed data. Scientific awards include: NSF DMS 2311292 grant (2023-2026) for monotonic hazard trend research He has contributed computational tools like TestUSO and LLGOF_UniCoxPH packages for statistical analysis.

• Bayesian statistics
• Spatial statistics
• Shape analysis
• +3 more

Qiwei Li is an Assistant Professor of Statistics in the Department of Mathematical Sciences at The University of Texas at Dallas. He holds a Ph.D. in Statistics from Rice University (2016), an M.A. in Statistics from Rice University (2015), an M.Phil. in Information Engineering from The Chinese University of Hong Kong (2010), a B.Sc. in Economics from Peking University (2009), and a B.Eng. in Electronic Engineering from Tsinghua University (2008). His research focuses on developing AI-powered Bayesian statistical methodologies and machine learning approaches to analyze high-dimensional, spatial, and shape data. Key areas include Bayesian clustering, spatial transcriptomics, microbiome community detection, and integrative omics analysis. He is actively involved in advancing methodologies for spatial molecular profiling and disease-relevant gene discovery. Recent publications highlight contributions to spatial transcriptomics integration, microbiome analysis, and health outcome studies. His work bridges statistical theory with biomedical applications, addressing challenges in genomics, imaging, and clinical data interpretation. Dr. Li is seeking highly motivated Ph.D. and undergraduate students to join his research group, offering financial support. He emphasizes interdisciplinary collaboration and innovation in statistical methodologies for solving complex biomedical problems.

Antonios MichalasView profile

Professor

• Applied Cryptography
• Privacy Enhancing Technologies
• Privacy-Preserving Machine Learning
• +7 more

Antonios Michalas is a Professor at Tampere University's Department of Computing Sciences, leading the Network and Information Security group (NISEC). His expertise spans Applied Cryptography, Privacy-Preserving Technologies, and Cyber Security. He holds a PhD from Aalborg University and has held roles from Assistant Professor to Professor at institutions including Tampere University, University of Westminster, and RISE Sweden. His research focuses on securing communication protocols, privacy in machine learning, and blockchain fairness. He actively contributes to projects like PiQASO (Post-Quantum Cryptography transition) and HARPOCRATES (privacy in health data). Education: PhD in Provable Security and Privacy (Aalborg University, 2013), MSc in Information Technology & Telecommunications (Athens Information Technology, 2007), BSc in Theoretical Mathematics (University of Crete, 2006). Research Interests: Developing cryptographic schemes for encrypted data operations, privacy-preserving machine learning, blockchain fairness, and secure IoT systems. Recent work includes privacy-preserving video classification, hybrid homomorphic encryption, and mitigating attacks in split learning. Grants/Projects: Leads PiQASO (Digital Europe), FACILITATE (EU), and ASCLEPIOS (EU health data security). Published over 50 peer-reviewed papers, with a focus on functional encryption, secure protocols, and privacy-preserving analytics. Teaches security and cryptography courses, emphasizing practical programming assignments and theoretical security analysis. Labs/Teams: NISEC group conducts cutting-edge research in cryptography and security, with activities spanning theoretical foundations to real-world implementations. Collaborates internationally on projects addressing post-quantum cryptography, cloud security, and privacy in health systems.

Samuel GrauerView profile

Assistant Professor

• Optical Diagnostics
• Fluid Dynamics
• Uncertainty Quantification
• +4 more

Samuel Grauer is an Assistant Professor in the Department of Mechanical Engineering at Pennsylvania State University. His research focuses on inverse problems in optical diagnostics, with applications to fluid dynamics, combustion, and uncertainty quantification. He earned a PhD from the University of Waterloo (2018) and a BSc from the University of Manitoba (2014), followed by a postdoctoral fellowship at Georgia Tech's Ben T. Zinn Combustion Laboratory (2018-2020). His research group develops advanced optical diagnostics techniques such as particle image velocimetry (PIV/PTV), background-oriented schlieren (BOS), and hyperspectral absorption tomography. These methods are applied to study high-speed turbulent flows, detonations, and heterogeneous materials. Key themes include differentiable programming, Bayesian inference, and scientific machine learning for flow reconstruction and uncertainty quantification. Current students include PhD candidates Joseph P Molnar, Ke Zhou, and Amit K Singh, along with MS/BS researchers focusing on computational fluid dynamics and experimental diagnostics. His work has been published in leading journals such as Journal of Computational Physics , Combustion and Flame , and Applied Optics . Professional affiliations include Google Scholar and ResearchGate profiles. His lab collaborates internationally, with equipment spanning high-speed imaging systems and advanced laser diagnostics. Prospective students with programming skills and interests in fluid physics are encouraged to contact him via sgrauer@psu.edu.

Toru KitagawaView profile

Professor

• Econometrics
• Causal Inference
• Statistical Decision Theory
• +5 more

Toru Kitagawa is a Professor of Economics at Brown University, specializing in econometrics, causal inference, and policy analysis. His research focuses on developing quantitative methods for evidence-based policy decisions, with applications in labor, development, and environmental economics. He collaborates with global researchers on lab and field experiments to study human behavior and inform policy solutions. Education: PhD and MA in Economics, Brown University (2009, 2004) BA in Engineering, University of Tokyo (2002) Research Interests: Econometric theory and methods Statistical decision theory Bayesian analysis Experimental economics Policy evaluation and targeting His work bridges theoretical econometrics and applied economics to address real-world policy challenges, emphasizing robust Bayesian methods and empirical welfare maximization. Awards & Grants: Haavelmo Prize (2024) for best Econometrica paper Nakahara Prize (2023) for outstanding Japanese economist under 45 NSF Grant (2024–2027), ERC Horizon 2020 Starting Grant Affiliations: Research Staff, Center for Microdata Methods and Practice (Cemmap) Research Fellow, Institute for Fiscal Studies (IFS) Researcher, Yale Research Initiative on Innovation and Scale (Y-RISE) Teaching: Graduate courses in econometrics (ECON 1630/1640) Applied economics and policy analysis (ECON 2020, 2040)

Sonja GenslerView profile

Associate Professor

• Digital Marketing
• Consumer Behavior
• Multichannel Management
• +3 more

apl. Prof. Dr. Sonja Gensler is an Associate Professor at the Institute for Value-Based Marketing, affiliated with FB4. She specializes in bridging research and teaching to foster student development, emphasizing practical insights for their professional journeys. Her work focuses on digital marketing innovation, consumer behavior analysis, and multichannel strategies. She explores regulatory impacts on the sharing economy and employs advanced empirical methods like conjoint analysis and regression modeling. Key themes in her research include market trends, customer engagement, and the integration of technology in marketing practices. Her research interests span digital marketing strategies, consumer decision-making processes, and the application of statistical techniques to analyze complex datasets. Prof. Gensler’s contributions include studies on sharing economy governance, multichannel management, and the effects of social media on brand relationships. She has published extensively on methodologies such as finite mixture models, contingency analysis, and discriminant analysis, highlighting their practical relevance in marketing and economics. Prof. Gensler’s recent work reflects a trend toward addressing contemporary challenges like AI’s role in customer relationship management and the evolving dynamics of online-offline consumer interactions. Her empirical focus ensures her research remains grounded in real-world applications, benefiting both academic and industry stakeholders.

Micah GoldblumView profile

Assistant Professor

• Machine Learning
• AI Safety
• Automated Data Science
• +5 more

Micah Goldblum is an Assistant Professor at Columbia University, focusing on machine learning research with emphasis on AI safety, automated data science, and large-scale model training strategies. His work explores foundational topics like generalization theory, Bayesian inference, and algorithmic reasoning. He received a Ph.D. in Mathematics from the University of Maryland, advised by Tom Goldstein and Wojciech Czaja, followed by a postdoctoral fellowship at New York University under Yann LeCun and Andrew Gordon Wilson. His research portfolio includes developing robust benchmarks for large language models (e.g., LiveBench), analyzing inductive biases in ML systems via Kolmogorov complexity, and advancing adversarial detection techniques like Binoculars. Key contributions span LLM evaluation, diffusion model theory, and fairness in computer vision systems. Goldblum’s work on Bayesian model selection and compression bounds for large language models has provided critical theoretical insights. Goldblum has been recognized with the 2022 ICML Outstanding Paper Award for his work on Bayesian model selection. His research also addresses societal challenges, such as protecting privacy in facial recognition systems (LowKey) and mitigating biases in neural architectures. Collaborations include leading projects like the Battle of the Backbones vision benchmark and exploring adversarial robustness in federated learning. His labs and teams focus on interdisciplinary applications of ML, including automated benchmarking, secure AI deployment, and ethical alignment of LLMs. Goldblum’s publications consistently bridge theory and practice, with recent breakthroughs in diffusion model design, continual learning algorithms, and understanding the intrinsic properties of data distributions.