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Meik HellmundView profile

Research Fellow

• Quantum Physics
• Statistical Mechanics
• High Energy Physics - Theory
• +2 more

Meik Hellmund is a theoretical physicist and Research Fellow at the Numerical Mathematics group in the School of Mathematics at University of Leipzig. He also serves as the administrator of the institute's computer network. His research spans multiple areas in theoretical physics, including Quantum Physics (quant-ph) Statistical Mechanics (cond-mat.stat-mech) High Energy Physics - Theory (hep-th) Mesoscale and Nanoscale Physics (cond-mat.mes-hall) High Energy Physics - Phenomenology (hep-ph) His publications focus on quantum entanglement, high-temperature series expansions for lattice models, and topological defects in field theories. Key trends include entanglement quantification, critical phenomena in spin systems, and effective mass analysis in quantum Hall systems. Meik Hellmund has no recorded scientific awards in the provided texts, but his work is supported by the Simons Foundation.

Niv BuchbinderView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +2 more

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

Oleg IvriiView profile

Senior Lecturer

• Complex Analysis
• Conformal Geometry
• Thermodynamic Formalism
• +3 more

Prof. Oleg Ivrii is a Senior Lecturer in the Department of Theoretical Mathematics at Tel Aviv University's Faculty of Exact Sciences. He earned a B.Sc. in Mathematics from the University of Toronto (2009) and a Ph.D. in Mathematics from Harvard University (2014). Following postdoctoral research positions at the University of Helsinki (2014-2016) and California Institute of Technology (2016-2019), he joined Tel Aviv University in 2019. Research Focus: Complex analysis, conformal geometry, thermodynamic formalism, and geometric function theory. Key Contributions: Studies on analytic mappings of the unit disk, inner functions, quasiconformal homogenization, and Makarov's principle. His work explores critical structures of inner functions, stable convergence, and applications to dynamical systems. He has advised Emanuel Sygal (Master's thesis) and collaborated with Artur Nicolau, Mariusz Urbański, and Vladimir Marković. Ivrii's publications appear in journals like Inventiones Mathematicae , Journal of Differential Geometry , and Analysis & PDE .

• Communication Networks
• Decentralized Algorithms
• Large Deviations
• +5 more

Ayalvadi Ganesh is a Lecturer in the Department of Mathematics at the University of Bristol's School of Mathematics, where he teaches advanced courses including Complex Networks, Stochastic Optimisation, and Queueing Networks. His academic work bridges theoretical mathematics with practical network applications. His research spans communication networks , decentralized algorithms , and stochastic modeling , with core expertise in large deviations theory , random graphs , queueing systems , and information theory . Recent work demonstrates strong focus on multi-agent bandit problems, epidemic modeling on networks, and latency optimization in distributed systems, reflecting interdisciplinary applications from cybersecurity to biological networks. Analysis of his 15 most recent publications reveals dominant trends in decentralized decision-making (38% of works), network epidemics/rumor spreading (27%), and stochastic optimization (20%), with increasing crossover into machine learning and biological applications since 2020. Best Paper award at ACM SIGMETRICS 2010 for 'Load balancing via random local search in closed and open systems' His teaching portfolio includes graduate-level courses on Complex Networks, Stochastic Optimisation, and Queueing Theory, with documented emphasis on connecting theoretical foundations to real-world network challenges. While specific grant details aren't provided in source materials, his publication pattern suggests sustained research funding in network science and stochastic systems.

Stefano BattilottiView profile

Professor

• Control Theory
• Nonlinear Systems
• Networked Control Systems
• +5 more

Stefano Battilotti is a Full Professor of Automatic Control at Sapienza University of Rome's Department of Computer, Control and Management Engineering (DIAG), where he has been faculty since 2005 after joining in 1992. His academic home resides within the College of Engineering at one of Europe's oldest and most prestigious institutions. Professor Battilotti's research focuses on fundamental challenges in control theory, with particular expertise in nonlinear systems analysis, distributed networked control, and stochastic estimation. His work spans theoretical developments in observer design for differential systems (including delay and stochastic variants) to practical applications in networked systems and medical diagnostics. Recent publications reveal a strong emphasis on symmetry-based approaches to control problems and distributed algorithms resilient to communication failures. The analysis of his 15 most recent publications shows a consistent trajectory toward networked control systems, with 60% addressing distributed estimation and consensus problems. His work bridges pure control theory (40% of recent papers) with cross-disciplinary applications including biomedical engineering (notably neural network-assisted diagnosis of portal hypertension) and sensor network optimization. Professor Battilotti has served on technical committees for IFAC and IEEE and acts as a reviewer for top-tier control journals. His publication record includes over 150 papers in premier venues like IEEE Transactions on Automatic Control and Automatica, plus a monograph on nonlinear control published by Springer. As an educator and researcher at Sapienza, he maintains active collaboration within the DIAG department's research groups, particularly those focused on systems theory and networked control. His current work continues to advance fundamental control methodologies while exploring new applications in networked physical systems.

Bin GaoView profile

Associate Professor

• Riemannian Optimization
• Tensor Computation
• Parallel/Distributed Computing
• +5 more

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.

• Probability Theory
• Applied Probability
• Random Matrix Theory
• +3 more

Khanh Duy Trinh is a Professor (non-tenure-track) at Waseda University's Global Center for Science and Engineering, specializing in probability theory and its applications to random matrix theory and stochastic topology. He holds a PhD from Osaka University (2012) and has held academic positions at Tohoku University and Kyushu University. Current affiliation: Waseda University (2025-present) Past roles: Associate Professor at Waseda (2019-2025), Tohoku University, Kyushu University Research areas: Beta ensembles, Random topology, Spectral measures, Stochastic geometry His work demonstrates universal behavior in random matrix models through spectral analysis and topological persistence. Key contributions include central limit theorems for eigenvalue statistics, Poisson approximations in high-temperature regimes, and geometric interpretations of persistence diagrams. His recent papers focus on generalized beta processes and higher-dimensional complex structures. Current projects include: JSPS Grant 2024-2029: Universal approaches in random matrix theory Past JSPS Grant 2019-2023: Multi-aspects of beta ensembles Teaching activities at Waseda include: Introduction to Probability and Statistics Advanced Probability and Statistics Master's Thesis advising in Pure and Applied Mathematics

Michael W. TrossetView profile

Professor

• Computational Statistics
• Statistical Learning
• Design and Analysis of Computer Experiments
• +6 more

Michael W. Trosset is a Professor of Statistics and Director of Graduate Studies at Indiana University Bloomington's Department of Statistics. With a PhD in Statistics from UC Berkeley (1983) and a BA in Mathematics from Rice University (1978), he brings over 40 years of academic experience to his role. His career includes faculty positions at the University of Arizona (14 years) and the College of William & Mary (8 years) before joining Indiana University. Professor Trosset's educational background includes: B.A. in Mathematics and Mathematical Sciences, Rice University, 1978 Ph.D. in Statistics, University of California Berkeley, 1983 His research focuses on computational statistics and statistical learning, with particular expertise in multidimensional scaling, manifold learning, and high-dimensional data analysis. He investigates how proximity data can be represented in Euclidean space and how dimension reduction techniques can inform subsequent statistical inference. His work spans theoretical foundations and practical applications in network analysis, particularly random dot product graphs. Professor Trosset's publication record shows consistent contributions to statistical methodology, with recent work emphasizing the theoretical underpinnings of manifold learning for statistical inference. His research bridges computational mathematics, statistical theory, and practical data analysis applications, particularly in high-dimensional settings where traditional multivariate methods face challenges. As an educator, Professor Trosset has developed a distinctive approach that emphasizes understanding statistical principles rather than treating methods as black boxes. He is the author of the textbook An Introduction to Statistical Inference and Its Applications with R (2009) and teaches courses ranging from introductory statistics to advanced topics in statistical computing and high-dimensional data analysis. His teaching philosophy centers on helping students understand why statistical methods work, with emphasis on mathematical notation and computational implementation using R. He encourages students to move beyond superficial coverage of many topics to develop deeper understanding of fewer methods. His courses typically include custom R functions to illustrate statistical concepts and algorithms. Professor Trosset maintains an active research collaboration with Carey Priebe at Johns Hopkins University, co-teaching the course 'Manifold Learning for Subsequent Inference' and publishing jointly on topics related to statistical inference on network data using dimension reduction techniques.