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Mine ÇağlarView profile

Professor

• Probability
• Stochastic Processes

Mine Çağlar is a Professor in the Department of Mathematics at Koç University, specializing in probability theory and stochastic processes with applications in mathematical finance and risk analysis. Her work addresses fundamental problems in Markov additive processes, Lévy processes, and Brownian motion, contributing to both theoretical advances and practical financial modeling. Her academic credentials include: PhD in Statistics and Operations Research from Princeton University (1997) Master’s in Industrial Engineering from Bilkent University (1991) B.A. in Industrial Engineering from Middle East Technical University (1989) Professor Çağlar’s research centers on extreme event analysis in stochastic processes, particularly maximum drawdown, maximum loss, and optimal stopping problems. She investigates path properties of spectrally negative Lévy processes and develops mathematical frameworks for degenerate market models. Her work bridges abstract probability theory with real-world financial applications, including risk management and hedging strategies. Recent publications demonstrate sustained innovation in stochastic analysis, with a focus on long-time behavior of complex processes and boundary-crossing phenomena. Analysis of her 15 most recent publications (2018–2024) reveals a cohesive research trajectory emphasizing Markov additive processes (40% of articles), Lévy process extremes (30%), and financial applications (20%). Key methodological trends include path decomposition techniques, Monge-Ampère equations on Wiener space, and stochastic flow modeling. Her work increasingly integrates fluid dynamics concepts like Çinlar models for turbulence simulation, reflecting interdisciplinary expansion into applied mathematics. Her scholarly recognition includes: Hayri Körezlioğlu Research Award (2013) Parlar Foundation Research Incentive Award (2005)

Adam J RothmanView profile

Professor

• High-Dimensional Statistics
• Covariance Estimation
• Multivariate Analysis
• +2 more

Adam J Rothman is a Professor in the Department of Statistics at the University of Minnesota, Twin Cities campus, specializing in high-dimensional statistical methodologies. His research focuses on covariance estimation, multivariate analysis, and developing innovative regression frameworks for complex data structures. His primary research interests include High-Dimensional Statistics, Covariance Estimation, Multivariate Analysis, and Statistical Machine Learning. Rothman develops penalized likelihood methods and shrinkage estimators to address challenges in matrix-valued predictors, categorical responses, and large covariance matrices, with applications spanning scientific domains requiring scalable high-dimensional analysis. Rothman's recent publications (2019-2024) demonstrate consistent innovation in high-dimensional regression and classification. Key trends include covariance matrix regularization, sufficient dimension reduction techniques, and likelihood-based approaches for categorical multivariate responses. His work emphasizes computational efficiency and theoretical guarantees for datasets where variables exceed sample sizes. He has secured major National Science Foundation funding as Principal Investigator for two projects: Sufficient Dimension Reduction of High-Dimensional Data (2011-2015) and New methods for multivariate analysis in high dimensions (2015-2021). These grants supported foundational work in dimension reduction and covariance estimation, advancing methodologies for modern statistical challenges.

Eunsuk KangView profile

Assistant Professor

• Software Engineering
• Formal Methods
• Software Security
• +4 more

Eunsuk Kang is an Assistant Professor at Carnegie Mellon University's Software and Societal Systems Department within the School of Computer Science. His research focuses on enhancing software system safety, security, and reliability through formal methods and rigorous modeling. Ph.D. in Computer Science, MIT Software Engineering, University of Waterloo Research spans cyber-physical systems , software security , formal verification , and ML resilience . Key areas include robust system design against evolving environments, specification engineering, and scalable automated reasoning. Recent publications highlight advancements in CPS safety , formal verification , and ML-based error mitigation . Collaborative projects span conferences like ICSE , CAV , and FMCAD . Scientific Awards CyLab 2025 Seed Funding CyLab 2023 Seed Funding Carnegie Bosch Institute Grant Advises doctoral students in software engineering and formal methods, with affiliations to the Software Design and Analysis (SoDA) Group and CyLab Security and Privacy Institute.

Hideyuki SuzukiView profile

Professor

• Nonlinear Dynamics
• Hybrid Systems
• Many-Body Dynamics
• +5 more

Hideyuki Suzuki is a Professor at the Department of Information and Physical Sciences , Graduate School of Information Science and Technology , Osaka University , where he has been employed since April 2016. His research spans nonlinear dynamics , hybrid systems , and many-body dynamics , with applications to power systems , brain modeling , and epidemic networks . 2001 : Ph.D. in Mathematical Engineering and Information Physics, University of Tokyo 1998 : M.Eng. in Mathematical Engineering and Information Physics, University of Tokyo 1996 : B.Sc. in Mathematics, University of Tokyo His research interests focus on nonlinear dynamical systems , particularly those with discontinuities or large-scale interactions , such as chaotic billiards , hybrid systems , and spatio-temporal chaos . He explores computational applications in machine learning (e.g., chaotic Boltzmann machines ), epidemiology (e.g., vaccine allocation models ), and power grid stability . The 15 most recent articles highlight his work in photonic computing , nonlinear sampling algorithms , and chaotic dynamics in engineering and biology . Key trends include interdisciplinary applications of nonlinear mathematics to renewable energy , neuroscience , and epidemic spread . Scientific awards include: 2022 Osaka University Prize 2018 JSIAM Best Paper Award for Hamiltonian Monte Carlo (2017) He leads the Nonlinear Mathematics Course laboratory, which accepts graduate and undergraduate students. His team investigates hybrid dynamical systems , chaotic computation , and real-world modeling in fields like traffic dynamics and epidemic networks . Research is supported by grants from JST CREST and ALCA-Next programs.

Renaud DetcherryView profile

Lecturer

• Low-dimensional Topology
• Quantum Topology
• 3-manifolds
• +5 more

Renaud Detcherry is a Lecturer (Maître de Conférences) at the University of Burgundy, affiliated with the Institute of Mathematics of Bourgogne (UMR 5584 CNRS) within the Faculty of Science and Technology. He has held this position since September 2020 and is part of the Geometry, Algebra, Dynamics and Topology (GADT) research team. His office is located in the Mirande Building (Office 314, Wing A) at the university's Dijon campus. His research focuses on low-dimensional topology and quantum invariants, including: 3-manifold topology, colored Jones polynomials, Witten-Reshetikhin-Turaev TQFT, quantum representations of mapping class groups, skein modules, and connections between quantum invariants and classical topology through various conjectures (Witten, volume, AJ, AMU). He also investigates Dehn surgeries, cosmetic surgery conjecture, and homological representations of surface diffeomorphism groups. Detcherry's recent publications demonstrate a consistent focus on quantum topology invariants and 3-manifold theory, particularly skein modules and their relationships with geometric structures. His work frequently explores connections between quantum algebra and classical topological concepts, with significant contributions to understanding the representation theory aspects of mapping class groups and the geometric properties of quantum invariants. He actively organizes academic events, including co-organizing the upcoming 'New Perspectives on Skein Modules' conference at CIRM (2025) and previously co-organizing the GDR Tresses meeting on 'Geometric and Quantum Topology' (2021) and a working group on modular surface groups (2021-2022).

Jieyang ChenView profile

Assistant Professor

• High-Performance Computing
• Big Data Analytics and Visualization
• Distributed Machine Learning
• +1 more

Jieyang Chen is an Assistant Professor in the Department of Computer Science at the University of Oregon's School of Computer and Data Sciences, where he leads research in high-performance computing and data-intensive scientific applications. His work bridges theoretical computer science with practical solutions for large-scale computational problems. Research Focus: Developing energy-efficient algorithms for CPU-GPU heterogeneous systems Creating fault-tolerant frameworks for scientific computing Designing advanced data compression techniques with error control Optimizing distributed machine learning workflows Dr. Chen's research portfolio demonstrates a consistent focus on performance, reliability, and energy efficiency in scientific computing. His recent publications show increasing sophistication in handling scientific data through techniques like multigrid frameworks, progressive retrieval methods, and adaptive compression algorithms that preserve critical features in climate and other scientific datasets. Education: PhD in Computer Science, University of California, Riverside (2019) MS in Computer Science, University of California, Riverside (2014) BE in Computer Science, Beijing University of Technology Dr. Chen previously worked as a Computer Scientist at Oak Ridge National Laboratory before joining the University of Oregon faculty. His collaborations span national laboratories and industry partners, contributing to real-world applications in scientific computing infrastructure.

Maria Chiara BrambillaView profile

Associate Professor

• Algebraic Geometry
• Commutative Algebra
• Moduli Spaces
• +3 more

Maria Chiara Brambilla is an Associate Professor in the Department of Industrial Engineering and Mathematical Sciences (DIISM) at Università Politecnica delle Marche, Faculty of Engineering. Her research specializes in algebraic geometry, with emphasis on moduli spaces, vector bundles, and secant varieties. She investigates fundamental structures in birational geometry and interpolation theory, contributing to advancements in the classification of geometric objects and their deformations. Research Focus: Her work explores: Birational properties of Mori dream spaces and blowups Defectivity and non-defectivity in Segre-Veronese varieties Terracini loci and their minimality conditions Geometric invariants of flag manifolds and hypersurfaces Algebraic boundaries in tensor rank theory Publication Trends: Recent articles (2021-2025) demonstrate deepening work on Terracini loci, twistor geometry, and movable divisors, frequently employing combinatorial and deformation-theoretic methods to resolve problems in higher-dimensional algebraic geometry.

• Environmental Economics
• Resource Flows
• Circular Economy
• +5 more

Hanspeter Wieland is a Researcher at the Institute of Social Ecology within the University of Natural Resources and Life Sciences, Vienna (BOKU). He has been affiliated with BOKU since 2023, following his position at Vienna University for Economics and Business (2013-2022). He holds a Master of Science in Social Ecology from the University of Klagenfurt (completed 2016). His research focuses on environmental economics and resource flows, with expertise in: Material flow analysis and industrial ecology Carbon and environmental footprinting methodologies Global resource inequalities and circular economy transitions Sustainable resource policy and input-output modeling His 21 publications (2016-2025) demonstrate consistent focus on resource flow accounting, with recent work emphasizing: Global material stock dynamics and recycling systems Methodological advances in multi-regional input-output analysis Ecological unequal exchange between Global North/South Policy applications for EU resource efficiency transitions He contributes as peer reviewer for leading journals including Nature Geoscience , Ecological Economics , and Journal of Industrial Ecology . Current research projects include: Resilience and Malleability of Social Metabolism (FWF, 2024-2029) EEA: Investigating material stocks in Europe (2024-2025) Fostering federal climate policies via carbon footprints (FFG, 2024-2026) Developing circular pathways for EU low-carbon transition (EC, 2022-2026)

• Diophantine Approximation
• Geometry of Numbers
• Parametric Approximation
• +4 more

Leonhard Summerer is an Associate Professor at the Faculty of Mathematics, Department of Mathematics . His research primarily focuses on Diophantine Approximation , Geometry of Numbers , and Parametric Approximation . His work explores the Approximation Property in parametric settings, Lattice Theory , and Linear Dependence in number theory. Recent publications include studies on Jarník’s identity, simultaneous approximation to multiple reals, and geometric interpretations of number-theoretic problems. He has authored numerous peer-reviewed articles and contributed chapters to educational books such as 77-mal Mathematik für Zwischendurch , emphasizing mathematical outreach and pedagogical innovation . Active in academic discourse, he has delivered talks on topics like Packings and Tilings in Z and Simultane Approximation m reeller Zahlen since 2006.

Necdet AybatView profile

Associate Professor

• Design of new optimization algorithms
• Federated learning
• Distributed multi-agent optimization
• +1 more

Necdet Aybat is an Associate Professor at the Department of Industrial and Manufacturing Engineering, Pennsylvania State University, College of Engineering. His research focuses on the design of new optimization algorithms, federated learning, distributed multi-agent optimization, and matrix decomposition for image-video processing applications. Research Interests: Spanning convex optimization, distributed consensus methods, and stochastic gradient techniques. Externally Funded Projects: Includes ONR, NSF, and ARO grants totaling over $1.3 million for work on robust optimization algorithms and decentralized power flow optimization. His recent publications highlight advancements in primal-dual algorithms, accelerated gradient methods, and quasi-norm minimization. While no scientific awards are listed, his collaborative projects and teaching contributions (e.g., IE/EE 585 Convex Optimization and IE 597 Modern Data Analytics) underscore his academic impact.

Lars GrasedyckView profile

Professor

• Numerical Analysis
• Hierarchical Matrices
• Tensor Approximation
• +7 more

Lars Grasedyck is a Professor of Numerical Analysis at RWTH Aachen University. His research focuses on hierarchical matrices, tensor approximation, and numerical methods for partial differential equations and matrix equations. He has contributed to applications in biomedical engineering, particularly EEG/MEG inverse problems, and is involved in software development (HLIB, HLIB-pro). Education: Diploma and Ph.D. in Mathematics at Christian-Albrechts-Universität zu Kiel (1998, 2001), Postdoctoral work at Max Planck Institute, Leipzig (2002-2010). Research: Specializes in high-dimensional numerical methods, low-rank matrices, and tensors with applications in PDEs, uncertainty quantification, and biomedical modeling. Projects: Leads DFG-funded initiatives on adaptive tensor networks for parametric PDEs and tumor progression modeling. Advising: Supervises doctoral students including Thong Le, Maren Klever, and Dieter Moser. Software: Developed HLib and HLib-pro for hierarchical matrix computations. Conferences: Active in GAMM Fachausschuss Numerische Analysis, organizing workshops and symposia globally.

Daniel RobertzView profile

Professor

• Differential Algebra
• Difference Algebra
• Computer Algebra
• +5 more

Daniel Robertz is a University Professor of Algebra and Number Theory at RWTH Aachen University, Germany, with his office located at Pontdriesch 14/16, Room 102 in Aachen. He actively participates in several academic seminars including the Joint Algebra Seminar at RWTH Aachen University, the Kolchin Seminar in Differential Algebra (online/New York), and the Diff.-Equations and Singularities Seminar (online). Professor Robertz's research spans multiple mathematical disciplines with primary focus on Differential Algebra , Difference Algebra , Computer Algebra , Discrete Geometry , Simplicial Surfaces , Group Theory , Invariant Theory , and Algebraic Systems Theory . He has developed several influential Maple packages including Janet , Involutive , JanetOre , LDA , and OreModules that have advanced computational methods in algebraic analysis of differential systems. His scholarly output demonstrates a consistent focus on algorithmic approaches to differential equations with increasing interdisciplinary applications, particularly in structural engineering through discrete geometry and origami-inspired designs for carbon-reinforced concrete structures. This research trajectory shows how abstract mathematical concepts can solve practical engineering problems, especially in sustainable construction materials development. Editorial Board of Mathematics in Computer Science Special Issue in Honor of Vladimir Gerdt (2022) Applications of Computer Algebra (ACA 2017, Jerusalem) (2019) Professor Robertz has organized numerous international workshops on computational differential and difference algebra and maintains active research collaborations across mathematics, engineering, and materials science disciplines. His work on algebraic methods for structural design involves partnerships with researchers in civil engineering, architectural design, and materials science. He leads research activities in the Chair of Algebra and Number Theory at RWTH Aachen, where his team develops computational methods for algebraic analysis of differential systems. His group maintains strong international connections with research communities in computer algebra, differential algebra, and mathematical engineering applications.

Andrej BogdanovView profile

Professor

• Cryptography
• Computational Complexity
• Pseudorandomness
• +6 more

Andrej Bogdanov is a Professor at the University of Ottawa in the School of Electrical Engineering and Computer Science . He earned his B.S. and M.Eng. from MIT and Ph.D. from UC Berkeley . Before joining Ottawa, he held positions at the Chinese University of Hong Kong , ITCS (Tsinghua) , DIMACS (Rutgers) , and the Institute for Advanced Study . He has served as a Visiting Professor at the Tokyo Institute of Technology (2013) and the Simons Institute (2017, 2021). Research Interests : Computational complexity, cryptography foundations, pseudorandomness, one-way functions, property testing, quantum algorithms, and sublinear-time algorithms. Teaching : Courses on Discrete Mathematics, Great Algorithms, Computational Complexity, and Cryptography at University of Ottawa, Chinese University of Hong Kong, and Rutgers University. Publications : 15+ recent works in TCC , CRYPTO , ICALP , RANDOM , and journals like Journal of Cryptology and Theory of Computing . Service : Program co-chair for SAC 2026 , and committee member for major conferences including CRYPTO , TCC , Eurocrypt , and FOCS . Advising : 12 current and former Ph.D./M.Phil. students, with postdoctoral advisees at institutions like IIT Palakkad and Academia Sinica . His work bridges theoretical computer science with applications in cryptography, quantum computing, and network security.

• Soil Chemistry
• Environmental Chemistry
• Surface Electrochemistry
• +3 more

Xu Renkou serves as Director of the Soil Chemistry and Environmental Protection Laboratory at the Nanjing Institute of Soil Science, Chinese Academy of Sciences, and Head of the Soil Interface Processes and Effects Team at the State Key Laboratory of Soil and Sustainable Agriculture. He holds doctoral supervisor status and serves as Deputy Director of the Soil Chemistry Committee of the Chinese Society of Soil Science while acting as Scientific Advisor to the International Foundation for Science (IFS). His research focuses on surface electrochemical properties of variable-charge soils, soil acidification mechanisms, aluminum chemistry, and heavy metal behavior at soil/water interfaces. Current investigations examine interactions between charged surfaces in soil (colloids, organic matter, microorganisms, plant roots), clay mineral evolution during soil formation, acidification control methods, and biochar applications for soil improvement. His group actively pursues six major projects including National Natural Science Foundation key projects on root-soil interactions and red soil acidification control. Xu's publication record includes over 200 Chinese and English papers with more than 80 SCI-indexed articles in top journals like Soil Biology & Biochemistry and Geochimica et Cosmochimica Acta. His research demonstrates consistent innovation in understanding adsorption mechanisms of organic acids on variable-charge soils, aluminum-organic acid interactions, particle surface diffusion layer effects on acidification, and heavy metal remediation technologies. His work bridges fundamental surface chemistry with practical soil restoration applications. Scientific recognition includes the 2010 Zhu Liyuehua Outstanding Teacher Award from the Chinese Academy of Sciences. His group has mentored numerous award-winning students including multiple recipients of Chinese Academy of Sciences President Scholarships and Jiangsu Province Outstanding Master's Thesis Awards. Key projects include National Natural Science Foundation key projects on root-soil interactions 973 Project on red soil acidification control principles Academy Knowledge Innovation Project on tropical soil electrochemistry Multiple NSFC general projects on nanoparticle interactions and paddy soil evolution The research team maintains strong international collaborations with scientists in Canada, Australia, Russia, United States, and India, conducting joint research on surface chemistry and soil acidification. Their laboratory develops patented soil remediation technologies including acidic soil green improvers and biochar-based solutions.

Iliyan GeorgievView profile

Researcher

• Computer Graphics
• Light Transport Simulation
• Monte Carlo Rendering
• +4 more

Iliyan Georgiev is a research scientist at Adobe, specializing in advanced computer graphics and physically based rendering. He holds a Bachelor's degree in Computer Science from Sofia University, Bulgaria, and a Master's degree from Saarland University, Germany, supported by a fellowship from the Max-Planck Institute. His work focuses on improving rendering efficiency through Monte Carlo methods, light transport simulation, and neural rendering techniques. Georgiev's research bridges the gap between theoretical and applied graphics, with contributions to bidirectional rendering algorithms, importance sampling, and 3D scene modeling. His publications highlight innovations in variance reduction, path sampling, and material-aware rendering. He has collaborated with leading institutions and companies, including Intel Visual Computing Institute, Disney Research Zürich, Weta Digital, Chaos Group, and Autodesk. Notable scientific awards include the Best Student Paper Award at ICPRAM 2025 and the Best Paper Award at EGSR 2024.