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Mehrdad NaderiView profile

Lecturer

• Classification
• Cluster Analysis
• Finite Mixture Models
• +5 more

Mehrdad Naderi is a Lecturer in Statistics at the Department of Mathematics, Physics, and Electrical Engineering , Northumbria University . His academic journey includes a PhD in Mathematical Statistics from Shahid Bahonar University of Kerman (2017) and postdoctoral research at National Chung Hsing University (Taiwan), Ferdowsi University of Mashhad (Iran), and University of Pretoria (South Africa). Education: PhD in Mathematical Statistics, Shahid Bahonar University of Kerman (2017) His research focuses on applied statistical inference with emphasis on classification , cluster analysis , factor analysis , finite mixture models , and EM algorithm for robust estimation. He has contributed to multivariate and matrix-variate analysis, particularly in handling outliers and asymmetrical data structures. Recent work includes three-way data clustering using matrix-variate normal distributions and robust Bayesian inference for censored mixture models. His publications demonstrate expertise in distribution theory, statistical computation, and applications to financial data, environmental modeling, and astrophysics. Current collaborations span multiple institutions, focusing on heavy-tailed distributions and computational methods for complex data structures.

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +6 more

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.

Olivier SerreView profile

Professor

• theoretical computer science
• automata theory
• formal languages
• +7 more

Olivier Serre is a CNRS Research Director working at the Institute for Research in Fundamental Computer Science (IRIF), a joint research unit of CNRS and University of Paris. Since September 2021, he serves as Deputy Scientific Director at the Institute for Information Sciences and Technologies (INS2I) at CNRS. His research focuses on theoretical computer science with particular emphasis on automata theory and formal verification frameworks. Dr. Serre completed his education at ENS Cachan (1999-2003) and earned his PhD in Computer Science from Paris-Diderot University (now University of Paris) in 2004. Following a postdoctoral position at RWTH Aachen, he joined LIAFA (now IRIF) as a research fellow in 2005 and became a CNRS Research Director in 2016. His academic journey reflects deep engagement with foundational theoretical problems and their practical verification applications. His research centers on automata theory and formal languages, with particular expertise in games and logic, infinite structures, and verification of higher-order functional programs. Dr. Serre has made significant contributions to collapsible pushdown systems, recursion schemes, and parity games, bridging theoretical foundations with practical applications in program verification. His work demonstrates exceptional depth in developing decision procedures for complex verification problems previously considered intractable. Dr. Serre's extensive publication record in premier theoretical venues shows consistent contribution to advancing the state of the art. His research trajectory reveals a clear evolution from foundational automata theory toward increasingly sophisticated systems involving higher-order computation and imperfect information, with notable breakthroughs in establishing decidability results for complex verification problems.