جست‌وجوی استاد و پژوهشگر

Trenton Mizeمشاهده پروفایل

دانشیار

• Social Psychology
• Methodological Innovation
• Gender Studies
• +۳ مورد دیگر

Trenton Mize is an Associate Professor in the Department of Sociology at Purdue University, specializing in social psychology and methodological innovation. He holds a Ph.D. and M.A. in Sociology, an M.S. in Applied Statistics, and a B.A. in Sociology and B.S. in Psychology from Indiana University and the University of Georgia, respectively. Education: Ph.D. in Sociology (Indiana University) M.S. in Applied Statistics (Indiana University) M.A. in Sociology (Indiana University) B.A. in Sociology & B.S. in Psychology (University of Georgia) His research combines social psychological theories of status, identity, and stereotyping with methodological advancements in categorical data modeling and experimental design. Key themes include gender and sexuality dynamics, parental leave policy evaluation, mental illness stigma, and statistical tool development. Recent publications focus on gendered parental leave perceptions, sexual identity discrepancies, and Stata-based methodological tools for social scientists. He actively promotes open science by sharing research data, code, and materials. Mize contributes to graduate education through the Advanced Methodologies at Purdue (AMAP) initiative, developing new courses and leading short workshops on advanced statistical techniques. He joined Purdue in 2017 and has published 12 peer-reviewed articles, one book chapter, and one undergraduate research journal piece.

• Data Visualization
• Visual Analytics
• Big Data Analysis
• +۱۰ مورد دیگر

Yun Jang is a Professor in the Department of Computer Engineering at Sejong University , South Korea. His research spans Data Visualization , Visual Analytics , and Artificial Intelligence applications, with a focus on Spatiotemporal Analysis , Volume Rendering , and Human-Computer Interaction . Ph.D. in Electrical and Computer Engineering, Purdue University (2007) M.S. in Electrical and Computer Engineering, Purdue University (2002) B.S. in Electrical Engineering, Seoul National University (2000) His research interests include: Visual Analytics for Big Data Causal Analysis in Urban Traffic Virtual Reality and Cybersickness Measurement Interactive Volume Rendering EEG and Gaze Data Analysis Recent publications emphasize causal modeling in traffic analysis, volume rendering techniques with CNNs, and AI-driven applications for smart cities. He holds multiple Korean and US patents in traffic analytics, VR systems, and data visualization technologies. Notable scientific awards include the Daeyang Distinguished Professor Award and a Best Poster Award at PacificVis 2020 . He leads the Data Visualization Lab at Sejong University, supported by grants from Korean government agencies and industry partners.

Clélia de Mulatierمشاهده پروفایل

استادیار

• Statistical Physics
• Information Theory
• Computer Science
• +۵ مورد دیگر

Clélia de Mulatier is an Assistant Professor at the University of Amsterdam , affiliated with both the Institute for Theoretical Physics and the Informatics Institute . She leads research at the intersection of statistical physics, information theory, and computer science , focusing on theoretical and numerical methods for complex systems . Her work spans collaborations with experimentalists in neuroscience and biology , and she actively participates in educational programs across multiple Dutch universities. Research Labs : Computational Soft Matter Lab, Computational Science Lab Affiliations : Dutch Institute for Emergent Phenomena (DIEP), Netherlands Platform Complex Systems (NPCS) Her research develops minimally complex spin models for high-order data analysis , applying exact Bayesian model selection to uncover hidden variable communities in binary datasets. This work has produced open-source tools like MinCompSpin and MinCompSpin_Greedy for different system sizes. Publications demonstrate expertise in tensor networks for dimensional reduction , epidemic modeling , and branching random walks in confined environments . Teaching includes Python programming , complex systems theory , and statistical inference for physics students across multiple institutions. She serves as program committee member for International Conference on Computational Science and organizes academic discussions through initiatives like Behind the CV: story from a Physicist .

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +۶ مورد دیگر

Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +۵ مورد دیگر

Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.

Marenglen Bibaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Marenglen Biba is a Full Professor and Dean of the Faculty of Engineering and Architecture at the University of New York Tirana, where he also chairs the Department of Computer Science. His academic journey includes a Ph.D. in Computer Science from the University of Bari, Italy (2009), and a Laurea degree cum laude from the same institution (2004). His research spans: Core AI/ML : Machine learning algorithms, statistical relational learning, pattern recognition Language technologies : NLP for Albanian, stemming, morphological analysis Applied domains : Computational biology, social network mining, emotion detection Recent publications (2018–2021) focus on NLP for low-resource languages, deep learning surveys, and emotion-aware data mining. Award highlights include: ICT Academic of the Year 2012 (Albania) Best Paper Award at ILP 2008 Multiple University of Bari research grants He has led EU-funded projects like ADRIATinn (€4.6M budget) and maintains collaborations with the University of Washington, University of Bari, and Asian universities. Administrative leadership includes decade-long oversight of UNYT's Computer Science department and current deanship.

Michael Charles Sachsمشاهده پروفایل

دانشیار

• Biostatistics
• Causal Inference
• Survival Analysis
• +۳ مورد دیگر

Michael Charles Sachs serves as Associate Professor in the Department of Public Health, Section of Biostatistics at the University of Copenhagen's Faculty of Health and Medical Sciences. He holds a Doctorate in Biostatistics from the University of Washington (awarded August 15, 2011) and maintains an active research program with publications spanning 2022-2025. His primary research focuses on biomarker signatures for treatment selection , computational methods for deriving symbolic bounds on causal effects , and regression modeling of cumulative estimands with censored event history data . His general areas of interest include reproducible research, computational statistics, R programming, Bayesian nonparametric models, and survival analysis. Professor Sachs teaches the advanced course "Programming and Statistical Modeling in R" for PhD students and health researchers, covering programming principles, loops, functions, and efficient data manipulation. His recent publications demonstrate expertise in causal inference methodology, survival analysis techniques, and statistical software implementation, with multiple articles accepted for publication in 2025. His work spans theoretical methodology development and practical applications in medical research, particularly in gastroenterology and celiac disease prediction. His scholarly output shows consistent productivity with multiple first-author publications in high-impact biostatistics journals including Journal of Statistical Software, Journal of Computational and Graphical Statistics, and Biostatistics. He actively organizes academic activities, including a May 2025 workshop on statistical modeling in R.