جست‌وجوی استاد و پژوهشگر

Silvia Calderazzoمشاهده پروفایل

پژوهشگر

• Bayesian Statistics
• Clinical Trial Design
• Markov Models
• +۵ مورد دیگر

Dr. Silvia Calderazzo is a researcher specializing in Bayesian statistics and its applications in clinical and biomedical research. Her work centers on statistical methodology for clinical trial design, dose-response modeling, and inference in complex biological systems. Research Interests: Her primary areas include Bayesian clinical trial design, Markov models, and statistical modeling of dose-response relationships. She applies advanced statistical techniques to problems in toxicology, oncology, and public health, with a strong methodological focus. Publication Trends: Her recent publications (2018–2019) show a consistent emphasis on Bayesian methods, particularly in quantifying prior information, incorporating external data in trials, and modeling biological and toxicological responses. These works appear in top journals such as JAMA , Biometrics , and Archives of Toxicology , reflecting both methodological rigor and clinical relevance. Education: PhD in Statistics, University of Warwick, UK (2017) Master degree in Statistics, University of Padua, Italy (2012) Bachelor degree in Statistics, population and society, University of Padua, Italy (2010) Scientific Awards: Advising and Grants: No information is available regarding students advised or research grants received. Labs and Teams: While specific lab affiliations are not mentioned, her collaborative publications suggest involvement in interdisciplinary research teams focused on biostatistics and translational medicine.

Navin Kartikمشاهده پروفایل

استاد

• Game Theory
• Political Economy
• Information Economics
• +۱۰ مورد دیگر

Navin Kartik is a Professor in the Department of Economics at Columbia University . His research focuses on strategic communication, information design, electoral competition, mechanism design, and behavioral economics. He has contributed to theories of cheap talk, Bayesian updating, and social learning, with applications to political economy and market mechanisms. His recent work examines test-optional admissions policies ( 2025 ), information revelation in elections ( 2019–2025 ), and the interplay between preferences and information in learning environments ( 2024 ). Kartik has collaborated with leading scholars such as Alex Frankel, Andreas Kleiner, and Richard Van Weelden, producing influential papers on veto bargaining ( 2023 ), convex choice environments ( 2024 ), and optimal contracts for experimentation ( 2016 ). His publications appear in top journals like American Economic Review , Econometrica , and Review of Economic Studies . Kartik teaches graduate-level microeconomic theory (GR6212) and has advised PhD students in game theory and political economy.

Raquel Garrido Alhamaمشاهده پروفایل

استادیار

• Computational modelling of language learning
• Language acquisition
• Language evolution
• +۷ مورد دیگر

Dr Raquel Garrido Alhama is an Assistant Professor of Artificial Intelligence for text and human interaction at the Institute for Logic, Language and Computation (ILLC) , University of Amsterdam. She also co-directs the /k/omputation and Language Lab and previously served as Assistant Professor at the Cognitive Science and Artificial Intelligence department of Tilburg University. She earned her PhD from the ILLC under the supervision of Jelle Zuidema, Remko Scha and Carel Ten Cate, with a dissertation entitled Computational Modelling of Artificial Language Learning . Prior post-doctoral positions include the Max Planck Institute for Psycholinguistics and the Basque Center on Cognition, Brain and Language. Research Interests: Computational modelling of language learning and evolution Language acquisition, word learning and sentence processing Neural-symbolic integration and connectionist architectures Visual and auditory word recognition, syntactic parsing and part-of-speech tagging In recent years her work has converged on validating developmental milestones—such as the onset of productive determiner–noun combinations—using large-scale child corpora and neural models. She also investigates how multi-agent referential games can lead to emergent compositional languages, moving from image to richer graph representations. Scientific Awards: Best Article Award (2019) from Psychonomic Bulletin & Review for the review on computational models of rule learning Best Poster Award (2015) at the International Conference on Cognitive Modeling Teaching & Supervision: Dr Alhama has designed and lectured courses on advanced programming for cognitive science, computational linguistics, linear algebra and language acquisition at Tilburg University, University of Amsterdam, Radboud University and the University of the Basque Country. She currently supervises eleven MSc and BSc students in AI, Logic and Cognitive Science. Labs & Teams: She is the founding co-director of the /k/omputation and Language Lab together with Phong Le, where they use Language Emergence frameworks to explore the cognitive prerequisites for displacement and compositionality in human language.

• Computational Mechanics
• Numerical Methods (Meshfree/XFEM/Isogeometric)
• Mixed Variational Methods
• +۳ مورد دیگر

Jack HALE is a Research Scientist at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), Department of Engineering. He joined Prof. Stéphane Bordas' team in 2013, focusing on computational mechanics and numerical methods. His work integrates advanced techniques like meshfree methods, XFEM, and isogeometric analysis to address challenges in solid mechanics and high-performance computing. Education : PhD in Aeronautics, Imperial College London (2009-2013), supervised by Dr. Pedro M. Baiz Villafranca. MEng in Engineering, University of Bristol (2004-2008). Research exchange at Rice University (2006-2007) on cross-flow filtration processes. Research Interests : Implicit boundary methods for medical image-based simulations. Development of scalable meshfree/XFEM/isogeometric analysis frameworks. Mixed variational methods to resolve locking phenomena in solid mechanics. High-performance computing for distributed parallel systems. Publications & Software : His 50+ publications emphasize open-access research via ORBilu, with a focus on FEniCSx-based tools (e.g., DOLFINx, FEniCS-shells). Recent work addresses Bayesian model selection, melt instability identification, and SAR data assimilation in aquifer modeling. Collaborations : Open to academic/industrial partnerships in computational mechanics, material science, and biomedical engineering.

Paul Marriottمشاهده پروفایل

استاد

• Geometric Statistics
• Differential Geometry
• Mixture Models
• +۶ مورد دیگر

Paul Marriott is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research focuses on integrating geometric principles, particularly differential and convex geometry, into statistical methodologies, with a recent emphasis on mixture models and information geometry. He has published extensively across diverse journals such as Biometrika, Annals of Statistics, and Psychological Medicine, bridging theoretical and applied statistics. Education: PhD, University of Warwick (1989) MA, University of Oxford (1984) Research Trends: His work explores geometric frameworks for statistical inference, mixture model parameterization, and robustness analysis. Recent publications highlight causal modeling, neural spike train analysis, and high-dimensional data applications. Contact: Office: Mathematics & Computer Building (M3) 4204, Phone: 519-888-4911 x35545, Email: pmarriot@math.uwaterloo.ca

• Digital Rock Physics
• Computational Pore-Scale Physics
• Tomographic Imaging
• +۹ مورد دیگر

Professor Christoph Arns is a full Professor at the University of New South Wales (UNSW) School of Engineering, specializing in Mineral and Energy Resources Engineering. He has been at UNSW since 2008 and has held the position of full Professor since 2014. Currently, he leads the Geoenergy and Geostorage discipline within the school. Prior to joining UNSW, he was a research fellow at the Australian National University from 2001-2008. Professor Arns obtained his Dipl. Phys. from RWTH Aachen, Germany in 1996 and his PhD in Petroleum Engineering from UNSW Sydney in 2002. His academic background combines physics and petroleum engineering, creating a unique interdisciplinary perspective for his research. His primary research focus is on Digital Rock Physics, where he has pioneered computational pore-scale physics based on tomographic images. He specializes in integrating 3D tomographic imaging technology with NMR techniques for petrophysical applications, with particular emphasis on heterogeneity analysis. His work spans multiple sub-disciplines including pore-scale modeling, digital core analysis, rock physics, reservoir characterization, and Minkowski functionals for structural analysis. Professor Arns has developed the MPI-parallel software package 'morphy' for computational physics operating on segmented tomographic images, which includes sophisticated NMR response modeling, electrical property calculations, permeability estimation, elastic moduli determination, and morphological property analysis. His research has substantial industry relevance, as evidenced by his role as a founding member of Digital Core Pty Ltd, an ANU/UNSW spin-off that was sold to FEI for $76 million in 2014 and is now part of ThermoFisher. He has received significant research support through three successive Australian Research Council (ARC) fellowships. His scholarly contributions are reflected in numerous publications spanning from 2000 to 2025, with recent work focusing on advanced computational methods, machine learning applications in rock physics, and detailed analysis of fluid-rock interactions at the pore scale. Professor Arns maintains active leadership roles in multiple professional societies including Interpore (lifetime member, former council chair 2016-2019), Society of Core Analysts (Australasia Regional Director since 2016), Society of Petrophysicists & Well Log Analysts, Society of Exploration Geophysicists, and Society of Petroleum Engineers.

Rafael de Andrade Moralمشاهده پروفایل

استاد

• Statistical Modelling
• Ecology
• Wildlife Management
• +۳ مورد دیگر

Rafael de Andrade Moral is a Professor of Statistics at Maynooth University's Faculty of Science & Engineering (since 2025), with prior roles as Associate Professor (2023-2025) and Assistant Professor (2018-2023). He holds a PhD in Statistics (University of São Paulo, 2014-2017) and dual bachelor's degrees in Biology and Education. His work bridges Statistical Ecology , Computational Biology , and Data Science , focusing on modeling ecological systems, agricultural pest dynamics, and biodiversity-ecosystem function relationships. Key research themes include Bayesian modeling , multivariate ecological forecasting , and machine learning applications . He founded the Theoretical and Statistical Ecology Research Group and serves on committees like the Young-ISA Chair . His recent articles span topics like insect abundance forecasting , weed-crop competition under climate change , and neuroinformatics-based learning analysis , reflecting interdisciplinary engagement. Scientific accolades include the Young Statistician Showcase Prize (2018), A-mu-sing Competition First Place (2021), and Maths Week Award (2022). He has advised three PhD students and contributed to over 50 peer-reviewed publications. Active in teaching innovation (e.g., Teaching Statistics through Music ), he also provides statistical consultancy to organizations like NIBIO and Jomakol .

• Causal Inference
• Bayesian Statistics
• Political Methodology
• +۴ مورد دیگر

Thomas Leavitt is an Assistant Professor in the Department of Public Affairs at Baruch College's Marxe School of Public and International Affairs. His research bridges causal inference, Bayesian statistics, and design-based methodologies, with applications to racial/ethnic politics and comparative policy analysis in the U.S. and South Africa. Ph.D., Political Science, Columbia University M.Phil., Political Science, Columbia University M.A., Political Science, Columbia University M.A., Committee on International Relations, University of Chicago B.A., Political Science, DePauw University His work focuses on developing robust statistical frameworks for causal analysis, including averaged prediction models for pre-post policy evaluation, sensitivity analysis for racial disparity studies, and randomization-based Bayesian inference. He investigates methodological challenges in audit experiments, observational studies, and proprietary data's impact on meta-analysis. Recent publications include advancements in difference-in-differences methodology, causal mediation analysis, and algorithmic auditing techniques. His research integrates design-based rigor with Bayesian flexibility to address counterfactual assumptions and model uncertainty. Leavitt leads courses in advanced quantitative methods, causal analysis, and data-driven policy evaluation at both graduate and undergraduate levels, emphasizing practical applications for public service.

• Bayesian Statistics
• Artificial Intelligence
• Natural Language Processing
• +۷ مورد دیگر

Rens van de Schoot is a full professor of Collaborative Methods in AI and Data Science at Utrecht University in the Netherlands and serves as an extra-ordinary professor at North-West University in South Africa. He directs the AI-LAB on AI-aided Knowledge Discovery and coordinates the open-source ASReview project (Active learning for systematic text reviewing), which has become a significant tool for researchers conducting systematic reviews across various disciplines. His research spans multiple domains with a strong focus on Bayesian statistics, artificial intelligence applications in systematic reviews, and psychological methodology. Van de Schoot's work bridges technical AI development with practical applications in social sciences and healthcare research, particularly in PTSD and mental health studies. His methodological contributions include significant work on measurement invariance, small sample size solutions, and the development of the WAMBS checklist for Bayesian statistics. The analysis of his recent publications reveals a clear trajectory toward increasingly sophisticated AI applications in systematic reviewing, with a strong emphasis on Bayesian methodology. His work demonstrates how machine learning can enhance traditional systematic review processes while maintaining methodological rigor. The interdisciplinary nature of his research connects statistics, computer science, psychology, and healthcare research in innovative ways. APA award for best dissertation of division 5 Recipient of prestigious VENI and VIDI research grants from the Netherlands Organization for Scientific Research Member of the Young Academy (Jonge Akademie) of the Royal Netherlands Academy of Arts and Sciences (KNAW) Elected member of the Society of Multivariate Experimental Psychology (SMEP) Van de Schoot has supervised numerous PhD candidates, as evidenced by his research on PhD delays and academic careers. His research program has been supported by multiple competitive grants, including his VENI project on integrating trauma-related background knowledge into statistical models and his VIDI project on expert knowledge and limited data. His work bridges theoretical methodological advances with practical applications across multiple domains. As director of the AI-LAB, he leads a team developing cutting-edge AI tools for knowledge discovery. His coordination of the ASReview project has created an international community of researchers applying active learning to systematic reviewing. He's also actively involved with the Utrecht Platform for Applied Data Science, fostering interdisciplinary collaboration on data-intensive research projects.

Ananda Senمشاهده پروفایل

استاد پژوهشی

• Survival Analysis
• Bayesian Statistics
• Correlated Agreement
• +۳ مورد دیگر

Ananda Sen is a Lee A. Green Collegiate Research Professor in the Department of Biostatistics and a Research Professor in the Department of Family Medicine at the University of Michigan School of Public Health . He has over 180 peer-reviewed publications and is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. Education: PhD in Statistics (University of Wisconsin, 1993), MS and BS from the Indian Statistical Institute (1987, 1985). Dr. Sen specializes in survival analysis (competing risks, recurrent events), Bayesian methods , and correlated agreement with applications in cancer prevention, biomarker research, and genetic/spare data. His collaborative work includes dietary interventions for gastrointestinal cancers, women's health, disability research, and diagnostic testing. His methodological publications focus on Bayesian survival models, competing risks, and correlated agreement measures, while applied works emphasize cancer prevention, nutritional science, and clinical trial design. He served as Director of the NCI-funded Biostatistics Core in GI Cancer Research. Scientific Awards : Teaching Excellence Award (School of Public Health), Fellow (American Statistical Association), Elected Member (International Statistical Institute).

Hasan Fallahgoulمشاهده پروفایل

مدرس ارشد

• Econometrics
• Finance
• Statistics
• +۸ مورد دیگر

Hasan Fallahgoul is currently a Senior Lecturer at the School of Mathematical Sciences, Monash University, and a member of the Monash Centre for Quantitative Finance. Prior to this, he held post-doctoral positions at the Swiss Finance Institute (EPFL) and the European Center for Advanced Research in Economics and Statistics (ECARES) at the Free University of Brussels, Belgium. Senior Lecturer, Monash University (2025–present) Post-Doctoral Researcher, Swiss Finance Institute (2024–2025) Post-Doctoral Researcher, ECARES (2023–2024) His research interests span Econometrics, Finance, Statistics, and Machine Learning, with a focus on Tail Risk, Neural Networks, and Fractional Calculus. Recent work integrates interpretable AI into asset pricing, develops significance tests for neural networks, and explores high-dimensional learning in finance. His recent article trends highlight applications of Machine Learning in financial econometrics, including state space models, Lévy processes, and tempered stable distributions. He has contributed to understanding complexity in financial models, significance testing frameworks, and the role of social signals in market dynamics. Hasan is supported by grants from the Australian Research Council (DP250100063) and the National Natural Science Foundation of China (72033002). He actively develops open-source tools like the SSMEfficientInference Python package for state space model analysis. He is affiliated with the Monash Centre for Quantitative Finance , where he collaborates on interdisciplinary projects involving econometrics, machine learning, and financial engineering.

• Forensic Statistics
• Medical Statistics
• Machine Learning
• +۲ مورد دیگر

Giulia Cereda serves as Associate Professor in the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) at the University of Florence since 2025. Her academic journey includes prior roles as Fixed-term Researcher (RTD-b, 2022-2024), Research Fellow (2021-2022), and Swiss National Science Foundation Postdoc Mobility Fellow (2019-2021) at Leiden University and University of Florence. She holds a Joint PhD in Statistics from Leiden University and University of Lausanne (2011-2016), complemented by Master's and Bachelor's degrees in Mathematics from the University of Milan. Her research spans forensic statistics with focus on rare type match problems in DNA evidence evaluation, medical statistics applied to SARS-CoV-2 pandemic modeling, and machine learning implementations for biogeographical ancestry prediction. Recent publications demonstrate methodological innovations in Bayesian approaches for forensic evidence, compartmental modeling of epidemic dynamics, and supervised learning applications in population genetics. Analysis of her 14 most recent publications (2020-2025) reveals dual research thrusts: (1) forensic statistics addressing DNA mixture interpretation and rare haplotype matching through Bayesian frameworks, and (2) epidemiological modeling of smoking dynamics and SARS-CoV-2 transmission using compartmental models with uncertainty quantification. Her work bridges theoretical statistics with practical public health and forensic applications, frequently employing machine learning for complex prediction tasks. Supported by the Swiss National Science Foundation for postdoctoral research (2019-2021), she has contributed to pandemic response through Tuscan regional modeling and school-based screening strategies. Current office hours are Thursdays 3:00-4:00 PM by appointment, with ongoing research in forensic identification systems and epidemic forecasting methodologies.

• Hierarchical Bayesian models
• Spatial statistics
• Multilevel data analysis
• +۷ مورد دیگر

Emanuela Dreassi is a Full Professor of Statistics at the University of Florence, where she currently serves as Director of the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) from November 1, 2024, to October 31, 2028. She is affiliated with the School of Economics and Management and has held various institutional roles including Director of the Bachelor's Program in Statistics (2014-2022) and Vice-President of the School of Economics and Management (2019-2022). Her research spans hierarchical Bayesian models and spatial statistics, with methodological advancements in specifying and estimating models for multilevel data, missing data, and latent variable models. She has made significant contributions to robust analysis in small area estimation, compatibility of conditional distributions, semicontinuous data modeling, Bayesian predictive inference, and knockoffs construction. Her work bridges theoretical statistics with practical applications in epidemiology, environmental studies, and medical research. Dreassi's recent publications (2020-2025) reveal a strong focus on methodological innovations in Bayesian statistics, spatial analysis, and knockoff filters for variable selection. She has published extensively in top statistical journals while also collaborating on interdisciplinary research in medical fields including plastic surgery and epidemiology. Her work demonstrates a consistent trajectory of advancing statistical methodology while maintaining strong connections to real-world applications across multiple domains. As an active referee for numerous prestigious journals including Biometrics, Journal of the Royal Statistical Society, and Statistics in Medicine, Dreassi contributes significantly to the scholarly community. She has coordinated research units for PRIN and HORIZON2020 projects and participated in numerous national and international conferences as organizer, scientific committee member, and session chair.

Manolis Zampetakisمشاهده پروفایل

استادیار

• Theoretical Machine Learning
• Statistics
• Optimization
• +۳ مورد دیگر

Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoc at UC Berkeley's EECS Department working with Michael Jordan, and earned his PhD from MIT's EECS Department under Constantinos Daskalakis. His research spans Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, and Mechanism Design. He has received the ACM SIGEcom Doctoral Dissertation Award and a Google PhD Fellowship. Current affiliation: Yale University (Assistant Professor) Prior affiliations: UC Berkeley (Postdoc), MIT (PhD student), NTUA (Undergraduate) His research focuses on algorithmic game theory, robust statistics, and optimization challenges in machine learning. He explores computational complexity in multi-player games, truncated linear regression, and strategy-proof mechanisms. Recent work includes backdoor attacks in neural networks and jailbreaking black-box LLMs, with publications in top venues like NeurIPS, COLT, FOCS, and STOC. Notable scientific contributions have been recognized through awards and special issues. He co-organized workshops at FOCS 2018, WALE 2019, and WALE 2022. His students include Anay Mehrotra, Jane Lee, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis, often co-advised with prominent researchers like Amin Karbasi and Tuomas Sandholm.

Caleb Vatralمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Dr. Caleb Vatral serves as an Assistant Professor in the Department of Computer Science within the College of Engineering at Tennessee State University, where he maintains an office in McCord Hall 005F on the main Nashville campus. His academic appointment positions him at the intersection of artificial intelligence research and educational innovation. His educational foundation includes: Ph.D. in Computer Science from Vanderbilt University, Nashville, TN B.Sc. in Computer Science from Eastern Nazarene College, Quincy, MA Dr. Vatral's research program pioneers human-AI teaming in educational contexts , uniquely merging distributed cognition theory with multimodal learning analytics. His work systematically examines how AI systems can complement human strengths in training environments, with particular focus on nurse education simulations and collaborative STEM learning . By prioritizing stakeholder needs throughout the design process, he develops practical AI integration frameworks that enhance rather than replace human capabilities in experiential learning. Analysis of his recent publications (2022-2024) reveals a consistent trajectory in multimodal learning analytics applied to high-stakes training environments. His research spans healthcare simulation, collaborative problem solving, and confidence assessment, demonstrating methodological sophistication through Bayesian modeling and mixed-reality frameworks. The work consistently targets real-world educational challenges with measurable performance outcomes. Dr. Vatral's research excellence has been recognized through: Best Paper Award, Human Performance Analysis Subcommittee at I/ITSEC (2022) Best Long Paper Award at International Conference on Computer-Supported Collaborative Learning (2024) While current advising details aren't specified, his active publication record suggests ongoing research projects with student involvement opportunities. His teaching portfolio spans core AI and data science courses alongside specialized topics in educational technology. Dr. Vatral maintains productive research collaborations, primarily with Vanderbilt University colleagues, as evidenced by co-authored publications across multiple high-impact venues.