Random Graphs Professors and Supervisors in Germany

Wojciech Samotijمشاهده پروفایل

دانشیار

Max Planck Institute for MathematicsGermany

Wojciech Samotij is an Associate Professor at the School of Mathematical Sciences, Tel Aviv University. His research focuses on extremal and probabilistic combinatorics, Ramsey theory, large deviation theory, and additive number theory. He has held positions including Junior Research Fellow at Trinity College, University of Cambridge (2010–2014), and Post-doctoral researcher at Tel Aviv University (2010–2011, 2012–2013). PhD in Mathematics (2010), University of Illinois at Urbana-Champaign, supervised by Jozsi Balogh M.Sc. in Mathematics and Computer Science (2007), University of Wrocław His recent work explores large deviation principles in random graphs, hypergraph containers, and extremal problems in probabilistic settings. Publications span journals like Annals of Probability , Duke Mathematical Journal , and Transactions of the American Mathematical Society , with key contributions to Ramsey-type theorems and entropy-based combinatorial analysis. He has supervised graduate students in topics ranging from additive combinatorics to random graph theory, and taught courses including Probabilistic Methods in Combinatorics, Graph Theory, and Discrete Mathematics. His email contact is samotij@tauex.tau.ac.il and samotij@post.tau.ac.il .

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Inbar Seroussiمشاهده پروفایل

مدرس ارشد

Max Planck Institute for MathematicsGermany

Dr. Inbar Seroussi is a Senior Lecturer at Tel Aviv University, with dual appointments in the Department of Applied Mathematics and the Department of Computer Science, under the School of Mathematical Sciences and School of Computer Science. Her research focuses on high-dimensional stochastic systems, bridging machine learning, statistical physics, and probability theory. B.Sc. in Electrical Engineering and Physics from the Technion (2014) M.Sc. in Physics from the Weizmann Institute (2016) Ph.D. in Applied Mathematics from Tel Aviv University (2019) Her work explores complex systems in high dimensions, with applications to optimization in machine learning, data science, physics, and epidemiology. She leverages insights from probability theory and statistical physics to enhance machine learning algorithms. Dam postdoctoral scholarship (2023) Weizmann postdoctoral scholarship (2019)

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Matthias Löweمشاهده پروفایل

استاد

University of MünsterGermany

Matthias Löwe is a Professor at the Institute of Stochastics within the Faculty of Mathematics and Computer Science at the University of Münster. His academic career spans several decades with continuous contributions to probability theory and statistical physics. His research focuses on deep theoretical investigations of complex stochastic systems, particularly in the context of disordered systems and random structures. Löwe's research interests center on Probability Theory , Statistical Physics , and Random Matrix Theory . His work examines phase transitions in spin systems, fluctuation phenomena in random graphs, and the mathematical foundations of neural network models. His research program consistently bridges theoretical probability with applications in statistical physics, particularly in understanding the behavior of complex interacting particle systems at critical points. His publication record shows consistent output in top probability journals including Annals of Probability , Electronic Journal of Probability , and Annales de l'Institut Henri Poincaré . His most recent work (2020-2023) focuses on propagation of chaos in mean-field models, fluctuations in Ising models on random graphs, and exact recovery problems in block spin systems. Löwe frequently collaborates with researchers including Zakhar Kabluchko, Kristina Schubert, and Jonas Jalowy. Löwe has supervised multiple doctoral students including Raphael Meiners, Mirko Ebbers, Jens Ameskamp, and Sarah Behrens. His research group has included postdoctoral researchers and doctoral candidates working on various aspects of probability theory and its applications.

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Bin Gaoمشاهده پروفایل

دانشیار

University of MünsterGermany

Bin Gao is an Associate Professor at the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He holds a Ph.D. in Applied Mathematics (2019, University of Chinese Academy of Sciences) and a B.Sc. in Mathematics (2014, Sichuan University). His postdoctoral experience includes positions at UCLouvain (2019-2021) and the University of Münster (2021-2022). Research Interests: Riemannian optimization, tensor computation, parallel/distributed algorithms for orthogonality constraints, machine learning applications. Key Contributions: Development of retraction-free methods on Stiefel manifolds, preconditioned Riemannian algorithms, and geometric frameworks for symplectic eigenvalue problems. Article Trends: Recent work focuses on overcoming the curse of dimensionality via manifold-based optimization, including distributed algorithms for Stiefel manifolds, graph-regularized tensor completion, and second-order methods for symplectic structures. Keywords span numerical analysis, quantum information, and machine learning. Scientific Awards: 2021 Zhong Jiaqing Mathematics Award 2018 Best Student Paper Award (CSIAM) 2018 CAS Special President Scholarship 2017 National Scholarship for Doctoral Students (China) 2016 Honor Student Award (International Workshop on Modern Optimization and Application) Advising & Collaborations: Collaborates with researchers from UCLouvain, University of Münster, and AMSS. Mentors students in Riemannian optimization and tensor computation. Leads the popman research group.

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Alexander Fuchs-Kreißمشاهده پروفایل

استادیار

Leipzig UniversityGermany

Dr. Alexander Fuchs-Kreiß is a Junior Professor for Statistics at the Institute of Mathematics, Faculty of Mathematics and Computer Science at Leipzig University. He joined the university in April 2022 as a member of the Stochastics research group. He is also a scientific member of the International Max Planck Research School for Mathematics in the Sciences and affiliated with the HKMetrics Network. His research focuses on the statistical analysis of network data, particularly semi- and non-parametric methods for dynamic interaction networks. He has developed methodologies for relational event models using counting processes like Hawkes processes, and has contributed to causal inference, high-dimensional statistics, and quantile regression. His work often bridges multiple statistical areas, creating innovative approaches to complex data structures. His recent publications show a strong trend toward developing statistical methods for network data with applications in economics, epidemiology, and social sciences. His research combines theoretical developments with practical implementations, as evidenced by the R packages accompanying his publications. As an educator, he teaches Mathematical Statistics, Statistical Network Analysis, and Causal Inference at Leipzig University. He supervises PhD students working on network models and advises diploma theses in mathematics and business mathematics. Dr. Fuchs-Kreiß maintains active research collaborations with institutions including Heidelberg University, UC Davis, KU Leuven, and LSE. He co-organizes academic seminars and participates in international conferences on statistical learning and probability.

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Emmanouil Athanasiadisمشاهده پروفایل

پژوهشگر ارشد

Leibniz Institute for Zoo and Wildlife ResearchGermany

Dr. Emmanouil Athanasiadis is a bioinformatician at the University of Cambridge's Department of Haematology, with dual affiliations at the Wellcome Trust Sanger Institute and Medical Research Council (MRC) Stem Cell Institute. His research integrates computational biology with medical applications, focusing on single-cell genomics, cancer biology, and cardiovascular disease mechanisms since 2016. His educational foundation includes: BSc in Biomedical Engineering from Technological Educational Institution of Athens (2004) MSc in Medical Physics from University of Patras (2006), funded by State Scholarships Foundation of Greece PhD in Medical Physics from University of Patras (2010), supported by National State Scholarship Foundation Athanasiadis specializes in developing computational frameworks for biological data interpretation. His work spans single-cell RNA sequencing analysis in haematopoiesis, medical imaging algorithms for cancer diagnostics, and drug repurposing pipelines. Key contributions include SPNsim for pulmonary nodule simulation and ChemBioServer for chemical compound analysis, demonstrating his dual expertise in algorithm development and biomedical application. Publication analysis reveals a trajectory from medical imaging (2007-2012) to genomic network analysis (2013-2016), culminating in current single-cell and spatial transcriptomics research. His work consistently bridges computational innovation with clinical questions in oncology, haematology, and cardiovascular disease, with strong emphasis on open-source tool development. His scientific recognition includes: Computational award from Greek Research and Technology Network for 'GRAND' project Multiple presentation prizes at national medical conferences Continuous academic distinctions from State Scholarships Foundation of Greece K. Karatheodoris research scholarship He has secured EU funding through FP7 projects including 'PIK3CA Oncogenic Mutations' and 'NOISEPLUS', and maintains active collaborations across Cambridge, UCL, and Greek research institutions. His teaching contributions include lecturing in biomedical engineering programs at Technological Educational Institution of Athens (2010-2016). Current work centers on single-cell RNA sequencing analysis within Cambridge's haematology research ecosystem, particularly investigating transcriptional dynamics in blood cell development and cancer evolution through collaborative projects with Sanger Institute.

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Stefan Wirtensohnمشاهده پروفایل

پژوهشگر

University of Applied Sciences KonstanzGermany

Stefan Wirtensohn is a Researcher at the Institute of System Dynamics, Konstanz University of Applied Sciences, where he has served as a research associate since 2012. His work bridges theoretical control engineering with practical marine robotics applications, focusing on autonomous surface vehicle development and advanced control systems. His academic credentials include: Bachelor of Engineering in Mechanical Engineering, HTWG Konstanz (2010) Master of Engineering in Mechatronics, HTWG Konstanz (2012) Wirtensohn's research centers on Control Engineering, Mechatronic Modeling, and Nonlinear Control systems for marine environments. He specializes in parameter identification, motion estimation, and trajectory optimization for autonomous vessels, with significant contributions to docking control, wave filtering, and extended object tracking. His methodology integrates unscented Kalman filters, model predictive control, and sensor fusion techniques to address real-world navigation challenges. Analysis of his 15 most recent publications (2013-2022) reveals consistent focus on marine robotics control systems. Key trends include the application of random matrices for lidar-based target tracking, model predictive path integral control for vessel stabilization, and advanced disturbance estimation techniques. His work demonstrates strong emphasis on experimental validation using autonomous surface vehicles across diverse maritime scenarios. He actively contributes to teaching through Control Engineering 1 exercises and laboratory supervision, emphasizing hands-on implementation of control algorithms. Current projects under his involvement include: Integrated autonomous measuring system for maritime surveying tasks Automated parking systems for truck trailers Collision prevention procedures for inland waterway vessels Wirtensohn operates within the Institute of System Dynamics research team specializing in signal processing, control engineering, and robotics, maintaining close collaboration with Johannes Reuter and other institute members on marine automation challenges.

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Hans-Peter Lenhofمشاهده پروفایل

استاد

Helmholtz Institute for Pharmaceutical ResearchGermany

Prof. Dr. Hans-Peter Lenhof holds the Chair for Bioinformatics at Saarland University since 2000, where he leads research in computational biology and disease mechanism analysis. His career includes: Postdoctoral research at Max Planck Institute for Informatics (1993-1999) Research Group Leader at MPI (1999-2000) Professor at Saarland University (2000-Present) His group develops innovative bioinformatics methods with three primary focus areas: Personalized Cancer Therapy : Creating AI/ML approaches to optimize drug selection from over 200 cancer therapeutics, addressing tumor heterogeneity through predictive modeling of treatment efficacy. Diagnosis & Prognosis : Pioneering blood-based diagnostic methods using autoantibody and miRNA profiles that demonstrate high clinical sensitivity for multiple cancers and neurological disorders through collaborative studies. Pathogenic Mechanism Analysis : Employing graph-based network models to visualize and analyze deregulated biological processes, particularly signaling cascades in cancer progression. The lab maintains active collaborations with Andreas Keller's Clinical Bioinformatics group and Eckart Meese's Human Genetics team. Computational tools developed include BALLView for molecular visualization, GeneTrail for high-throughput data analysis, and specialized frameworks for therapy optimization (MERIDA) and miRNA networks (miRTargetLink).

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Simon Apersمشاهده پروفایل

پژوهشگر

Max Planck Institute for the Science of LightGermany

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.

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