جستجوی استاد راهنمای دانشگاهی

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.

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
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Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.

• Statistical Machine Learning
• Bayesian Nonparametrics
• Scalable Optimization
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Daniel Kráľ is an Alexander von Humboldt Professor for Discrete Mathematics at Leipzig University and an affiliated member of the Max Planck Institute for Mathematics in the Sciences (MPI MiS). Previously, he held the Donald Ervin Knuth Professorship at Masaryk University in Brno and is also an honorary professor at the University of Warwick, where he was a professor of mathematics and computer science and a member of the Centre for Discrete Mathematics and its Applications (DIMAP). His research addresses various topics at the interface of mathematics and computer science, with primary focus on structural and extremal graph theory, discrete algorithms, and combinatorial limits. The theory of combinatorial limits is an emerging area that provides analytic methods to study large graphs such as social networks, establishing new links between analysis, combinatorics, ergodic theory, group theory and probability theory. His work has been supported by prestigious ERC grants including the CCOSA Starting grant and LADIST Consolidator grant. Dr. Kráľ's scholarly output includes over 150 journal research papers and numerous conference contributions. His recent publications demonstrate continued leadership in extremal combinatorics, graph limits, and structural graph theory, with significant contributions to understanding quasirandomness, Turán densities, and the coloring of complex graph structures. Scientific Recognition: SIAM Fellow (2024) Fellow of the American Mathematical Society (2020) Philip Leverhulme Prize in Mathematics and Statistics (2014) European Prize in Combinatorics (2011) ERC Consolidator grant LADIST (2015-21) ERC Starting grant CCOSA (2010-15) Professor Kráľ has supervised numerous PhD students and postdoctoral fellows throughout his career. His editorial service includes Editor-in-Chief of SIAM Journal on Discrete Mathematics (2017-2022), Co-Editor-in-Chief of Journal of Combinatorial Theory (since 2025), and Managing editor of Advances in Combinatorics (since 2018). He has organized multiple international workshops and conferences including Oberwolfach workshops on Graph Theory and the European Conference on Combinatorics, Graph Theory and Applications (EUROCOMB'23).

• Discrete Mathematics
• Graph Theory
• Combinatorics
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Rose McCarty is an Assistant Professor of Mathematics at Princeton University's School of Arts and Sciences, specializing in graph theory and combinatorics with a focus on structural properties of sparse graphs. Her academic journey includes: Current position: Assistant Professor, Department of Mathematics, Princeton University Oberwolfach Leibniz Graduate Student (2019) US Junior Oberwolfach Fellow (2025) Collaborations with Paul Wollan and James Geelen on graph theory research Professor McCarty's research explores graph coloring, graph minors, and algorithmic aspects of graph classes with bounded expansion. Her work bridges theoretical mathematics and computer science, emphasizing the structural foundations of sparse graph families. As an early-career faculty member, she is developing her research program and actively participates in the international mathematics community through workshops and collaborative projects at institutions like Oberwolfach.

• Graph Theory
• Combinatorics
• Discrete Mathematics
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Mathias Sablé-Meyer is a Postdoctoral Researcher at University College London's Sainsbury Wellcome Centre, working in Tim Behrens's lab. He completed his PhD under the supervision of Stanislas Dehaene at PSL/Collège de France and NeuroSpin, CEA, where he investigated humans' ability to manipulate highly abstract structures, particularly focusing on the perception of geometry. His research spans cognitive neuroscience, computational modeling, and experimental psychology, with a focus on understanding the mechanistic implementation of compositional mental representations. He employs methodologies including MEG, fMRI, behavioral experiments, and computational models to investigate how humans mentally represent abstract structures like geometric shapes, language, and mathematical concepts. Sablé-Meyer's work reveals fascinating insights about human cognitive uniqueness, particularly in geometric perception compared to non-human primates. His recent research examines sequence processing mechanisms and aims to establish foundations for mechanistic implementations of Language of Thought models. He has published in top journals including Cognitive Psychology , Trends in Cognitive Sciences , PNAS , and Journal of Neuroscience . Glushko Dissertation Prize from the Cognitive Science Society (2023) As a supervisor, he co-advises Master's students including Maxime Cauté, who is exploring cross-modal representation of sequences, and is involved with several other students including Svenja Kuchenhoff, Amy Wong, and others. His lab follows open science principles, with code systematically published on the Open Science Framework alongside articles. His research program bridges cognitive science, neuroscience, and artificial intelligence, with implications for understanding both human cognition and developing more human-like AI systems. He maintains active collaborations across institutions including UCL, Collège de France, MIT, and École Normale Supérieure.

• Mechanistic implementation of compositional mental representation
• Geometry perception in humans and non-human primates
• Human reasoning and cognitive fallacies
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Prof. Dr. Olga Kalinina leads the Drug Bioinformatics research group at the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS). She holds an M.Sc. in Mathematics from Moscow State University (2003) and a Ph.D. in Bioinformatics from the Russian Academy of Sciences (2007). Her academic journey includes postdoctoral positions at the European Molecular Biology Laboratory (EMBL) and University of Heidelberg, followed by establishing her independent research group at the Max Planck Institute for Informatics (2012-2018). Her research program integrates cutting-edge bioinformatics and computational approaches to address challenges in drug discovery and resistance mechanisms. Primary research domains include: Development of machine learning tools for predicting functional consequences of genetic variants and drug resistance phenotypes Structural bioinformatics approaches for protein-drug interaction analysis Graph theory-based investigation of drug-binding pockets Protein language models enhanced with 3D structural information Explainable AI for drug-target interaction prediction Her group maintains active research projects on: Predicting molecular mechanisms of genetic variants in cardiac/renal diseases (BMBF-funded) Investigating off-target drug binding mechanisms (BMBF-funded) Antimicrobial resistance discovery using explainable AI (HelmholtzAI-funded) Protein structure-informed drug affinity prediction Prof. Kalinina has received the EMBO Long Term Fellowship and currently supervises seven doctoral students. Her laboratory develops computational resources including novel methods for solvent accessibility estimation (SphereCon), variant annotation in structural context (StructMAn), and protein interaction analysis (DIGGER).

• Bioinformatics
• Drug Discovery
• Machine Learning
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Séverine Bérard is a Lecturer in the Computer Science Department at the Faculty of Sciences, University of Montpellier, where she holds a Habilitation à Diriger des Recherches (HDR) since 2016. Her institutional roles include: Co-director of the Bioinformatics Master's program Elected member of the University Board of Directors (since 2021) Elected member of the Faculty of Sciences Council (since 2017) Appointed member of the University Training Committee (since 2016) Her research at ISE-M's Phylogeny and Molecular Evolution Team bridges computer science and bioinformatics, with core focuses on: Development of text algorithms and graph-based methods for genomic analysis Comparative genomics and ancestral genome reconstruction Quality assessment of genome assemblies and meta-assemblies Evolutionary modeling of chromosome rearrangements Her recent publications (2016-2021) demonstrate strong emphasis on algorithmic innovations for evolutionary genomics, including genome scaffolding techniques, DCJ rearrangement models, and ancestral genome reconstruction methods applied to organisms ranging from mosquitoes to bacteriophages. In education, she teaches across all academic levels (L1-M2) in both practical computer science (networks, programming) and theoretical disciplines (discrete mathematics, text algorithms), while leading curriculum development as co-director of the Bioinformatics Master's program.

• Algorithms
• Bioinformatics
• Comparative Genomics
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Ulrike von Luxburg is Professor of Machine Learning Theory at the University of Tübingen's Wilhelm-Schickard-Institute for Computer Science, serving as spokesperson for the Cluster of Excellence 'Machine Learning: New Perspectives for Science' (EXC 2064) and leading multiple DFG-funded research initiatives. Her research centers on theoretical machine learning with emphasis on graph-based methods, statistical analysis of neighborhood structures, multi-scale graph decomposition, and explainable AI frameworks. She develops rigorous mathematical foundations for learning algorithms while exploring applications in neuroscience and uncertain environments. Her scientific recognition includes: Communicator Award 2024 (DFG) Current research leadership encompasses: Theoretical guarantees for explainable machine learning (DFG research grant) EXC 2064: Machine Learning: New Perspectives for Science (Cluster of Excellence) Forecasting and modeling in uncertain environments (Collaborative Research Center) Previous projects include the Heisenberg Professorship 'Statistical analysis of neighborhood graphs', 'Multi-scale analysis of graphs', and neuroscience-focused 'Analysis and modeling of neuronal interactions in the brain'. As head of the Chair of Machine Learning Theory, she directs research on fundamental algorithmic principles, fostering collaborations across computer science, statistics, and life sciences while maintaining active grant funding for theoretical innovation.

• Machine Learning
• Theoretical Machine Learning
• Graph Theory
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Dr. Christoph Fraenz is a researcher at the Leibniz Research Centre for Working Environment and Human Factors (IFADO) in Dortmund, Germany, where he works within the Psychology and Neurosciences department in the Neuroimaging and interindividual differences research group under Prof. Dr. Michael Nitsche. His research focuses on the neural basis of interindividual differences in cognitive functions and personality, utilizing a multimodal approach combining psychometrics, fMRI, DTI and EEG methodologies. Dr. Fraenz's work primarily investigates how variations in brain structure and function relate to individual differences in intelligence, personality traits, and cognitive abilities across the human population. Dr. Fraenz completed his academic training with a Bachelor's degree in Psychology from Westfälische Wilhelms University of Münster (2009-2012), followed by a Master's degree in Psychology from Ruhr University Bochum (2012-2015). He earned his PhD from the Department of Biopsychology at Ruhr University Bochum (2015-2019) and subsequently worked as a Research Associate there (2019-2020) before joining IFADO. His publication record demonstrates a strong focus on understanding the relationship between brain structure/function and cognitive abilities, with recent work particularly examining white matter microstructure, neurite density imaging, functional connectivity patterns, and genetic correlates of brain organization. His research often employs polygenic scoring approaches to understand how genetic factors contribute to individual differences in cognition. Dr. Fraenz has published extensively in high-impact neuroscience journals including Cerebral Cortex, Nature Communications, NeuroImage, and Human Brain Mapping, with numerous collaborative publications featuring researchers like Erhan Genç, Caroline Schlüter, and Sebastian Ocklenburg.

• Neuroimaging
• Cognitive Psychology
• Individual Differences
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Prof. Dr. Erhan Genç serves as a University Professor (W2) for 'Neuroscientific Foundations of Differential Psychology' at TU Dortmund University since 2023, operating under the Jülich model for research activities at IfADo (Leibniz Research Centre for Working Environment and Human Factors). He leads the 'Neuroimaging and Interindividual Differences' research group at the Institute for Occupational Safety and Health of the German Social Accident Insurance in Dortmund, Germany. His primary research focuses on: Neural basis of interindividual differences in cognitive functions and personality Application of multimodal neuroimaging methods (7T, 3T EEG/fMRI, TMS/fMRI, DWI, neurogenetics) Genç's research program investigates how variations in brain structure and function correlate with differences in cognitive abilities and personality traits. His work employs advanced neuroimaging techniques to uncover neural mechanisms underlying human intelligence and cognitive control processes, with particular emphasis on structural and functional brain connectivity. His publication record demonstrates consistent contributions to understanding the relationship between brain microstructure (particularly white matter organization and neurite architecture), brain network properties, and cognitive abilities. His recent work has established important links between polygenic scores, brain network efficiency, and intelligence measures. Methodologically, his expertise spans diffusion MRI for assessing microstructural properties of brain tissue, functional MRI for investigating brain connectivity patterns, and multimodal integration of different neuroimaging techniques to provide comprehensive insights into brain-behavior relationships.

• Neuroscience
• Differential Psychology
• Cognitive Functions
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Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +۴ مورد دیگر

Professor Stephan Doerfel holds a professorship for Data Science at Kiel University of Applied Sciences since 2021, where he is assigned to the Department of Media and serves as head of the Institute for Data Science (IfDS). He teaches Mathematics and Multivariate Statistics, Machine and Deep Learning modules in the Master's Program Data Science (MADS). His primary research interests span Data Science, Machine Learning, and Deep Learning, with significant contributions to recommender systems, social bookmarking systems (particularly BibSonomy), formal concept analysis, and publication analysis. His recent work shows a shift toward applied industrial problems, especially in injection molding quality prediction and agricultural technology. Analysis of his publication trends reveals an evolution from foundational work in formal concept analysis and social tagging systems (2010-2016) to more applied machine learning research in manufacturing and agriculture (2020-2024). His work consistently bridges theoretical computer science with practical applications across diverse domains. Professor Doerfel has been actively involved in numerous academic service roles including peer reviewing for major journals and serving on program committees for prestigious conferences including ECML/PKDD, WebSci, and ICFCA. His extensive conference participation spans over a decade with consistent contributions to the machine learning and data science community. He has supervised various research projects including AVAPS (focusing on injection molding data), PUMA (Academic Publication Management), and Info 2.0 (Informational self-determination in Web 2.0). As a core contributor to the BibSonomy open-source project, he has helped create a scholarly social bookmarking system that serves as a testbed for recommendation algorithms and user behavior studies.

• Data Science
• Machine Learning
• Deep Learning
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Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.

• Process Systems Engineering
• Chemical Engineering
• Optimization
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