Georg Loho is a Professor at Freie Universität Berlin (FU Berlin), acting head of the Discrete Geometry and Topological Combinatorics Group. Previously, he held an assistant professorship at the University of Twente (on leave since 2023). He specializes in discrete geometry, optimization, and algebraic combinatorics, with notable contributions to tropical geometry and machine learning. His research integrates geometric and combinatorial methods with applications in optimization and data science. Education: PhD in Mathematics (2017) from TU Berlin Diploma in Mathematics (2012) from Universität Würzburg Research Interests: Focuses on tropical geometry, discrete optimization, and their applications in machine learning. Explores geometric structures like oriented matroids, polytopes, and their connections to neural networks and algorithm design. Advocates for sustainability in research and education. Teaching: Leads courses on discrete geometry, mathematics & sustainability, and optimization. Active in educational innovation, including free open-source course materials and the MatchTheNet educational game on polytopes. Grants & Collaborations: Participated in the HIM Trimester Program (Bonn, 2021), substitute professorships (Kassel, 2020–2021), and multiple ERC-funded projects. Collaborates with institutions like the London School of Economics (LSE) and EPFL. Labs/Teams: Coordinates the Discrete Geometry and Topological Combinatorics research group at FU Berlin, fostering interdisciplinary projects in geometry, combinatorics, and optimization.
جستجوی استاد راهنمای دانشگاهی
برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۳۱-۳۵ از ۳۵ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Amey Bhangaleمشاهده پروفایل
استادیار
Amey Bhangale is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, Riverside. Prior to this, he held positions as a post-doctoral fellow at the Weizmann Institute of Science under Irit Dinur and a research fellowship at the Simons Institute. His research focuses on Approximation Algorithms , Probabilistically Checkable Proofs , Hardness of Approximation , and Analysis of Boolean Functions . Research Trends His recent work explores inapproximability bounds for constraint satisfaction problems, parallel repetition theorems, and additive combinatorics in finite fields. Notable collaborations include Subhash Khot, Dor Minzer, and Yang P. Liu. Teaching CS219: Advanced Algorithms (2025) CS141: Intermediate Data Structures and Algorithms (2024) CS218: Design and Analysis of Algorithms (2023) CS215: Theory of Computations (2021-2023)
Frank Vallentin is a full professor of applied mathematics (computer science) at the Mathematical Institute of the University of Cologne, Germany. He has held academic positions at Technische Universiteit Delft, Centrum Wiskunde & Informatica (CWI), and the Hebrew University of Jerusalem. His research spans optimization, discrete geometry, harmonic analysis, and computational mathematics. Research Interests: His primary mathematical interests include semidefinite programming, combinatorial optimization, harmonic analysis, discrete geometry, combinatorics, geometry of numbers, special functions, computational complexity, and coding and information theory. These areas reflect a deep integration of theoretical mathematics with algorithmic and computational techniques. The 15 most recent publications reveal a strong focus on geometric optimization, lattice problems, energy minimization, and semidefinite programming bounds. Key themes include chromatic numbers of lattices, symplectic capacities, polarization phenomena, and algorithmic solutions to geometric problems. His work often involves recursive SDP hierarchies, extremal configurations, and computational verification of theoretical bounds. Scientific Awards and Grants: SIAG/Optimization Prize (2011, with Christine Bachoc) NWO VIDI Grant (2010–2015): Semidefinite programming and harmonic analysis DFG Project: Symplectic capacities of polytopes (2017–) EU Horizon 2020 MINOA Project: Optimization with limited quantum resources (2017–) DFG Project: Spectral bounds in extremal discrete geometry (2019–) Advising and Grants: Vallentin has advised numerous PhD and master’s students at TU Delft and the University of Cologne, covering topics in discrete geometry, optimization, coding theory, and quantum information. He has secured major research funding from NWO, DFG, and the EU, supporting interdisciplinary projects in algorithmic optimization and mathematical physics. He is actively involved in organizing workshops and summer schools. Labs and Teams: He leads a research group at the University of Cologne focusing on optimization and discrete geometry, with strong collaborations with CWI Amsterdam, TU Delft, and international institutes. His team works on theoretical and computational aspects of geometric optimization, often using symmetry reduction and harmonic analysis.
Prof. Dr. Bastian von Harrach-Sammet is a W3 Professor for Numerics of Partial Differential Equations at Goethe University Frankfurt, where he has served as Dean of the Department of Computer Science and Mathematics since January 2024. He studied mathematics and physics at the University of Mainz as a scholarship holder of the German National Academic Foundation, earned his doctorate in 2006, and held postdoctoral positions at the Johann Radon Institute (Austria) and Yonsei University (South Korea). His academic trajectory includes professorships at TU Munich (2010–2011), University of Würzburg (2011–2013), and University of Stuttgart (2013–2015) before joining Goethe University. His research focuses on inverse problems , numerical analysis of PDEs , and optimization , with applications in medical imaging (e.g., electrical impedance tomography) and computational mechanics. Key themes include monotonicity-based reconstruction, convex optimization for inverse coefficient problems, and stability analysis. Publications emphasize theoretical rigor in inverse problems, featuring advances in Calderón-type problems, fractional Schrödinger equations, and stochastic inclusion detection. Recent work (2023–2025) explores convex reformulations of inverse problems and computational methods for elasticity/tomography. Awards & Honors: Robert Bartnik Visiting Fellowship (2017) MediaV Young Researcher Award (2016) Dissertationspreis der Freunde der Universität Mainz e.V. (2007) General Membership, Institute for Mathematics and Its Applications (2006) He is a liaison lecturer for the German Academic Scholarship Foundation, co-editor of journals ( Inverse Problems , Communications on Analysis and Computation ), and co-founder/treasurer of the Inverse Problems International Association. No specific grants, labs, or student advisees are detailed in the source text.
Giovanni Fantuzziمشاهده پروفایل
استادیار
- Optimization
- Dynamical Systems
- Fluid Mechanics
- +۴ مورد دیگر
Giovanni Fantuzzi serves as a W1 Professor (equivalent to Assistant Professor) in the Department of Mathematics at Friedrich-Alexander University Erlangen-Nuremberg. He leads research within the FAU DCN-AvH Chair for Dynamics, Control, Machine Learning and Numerics under the Alexander von Humboldt Professorship framework, holding office in Room 03.318 with contact details including giovanni.fantuzzi@fau.de and +49 9131 85-67134. His educational background includes a PhD and Master of Engineering in Aeronautics from Imperial College London, supplemented by a research position in Engineering Science at the University of Oxford during his doctoral studies. Key academic milestones are documented through his ORCID, Google Scholar, and LinkedIn profiles. Fantuzzi's research program integrates mathematical analysis with computational optimization to solve nonlinear differential equations, focusing on deriving a priori scaling laws for heat transport and developing provable numerical schemes for PDE-constrained optimization. His methodology bridges convex optimization, polynomial optimization, and dynamical systems theory, with recent applications extending to transformer neural networks and sentiment analysis through hardmax mechanisms. Current teaching includes Data-driven methods for dynamical systems and Polynomial optimization and applications for WS 24/25. Analysis of his 15 most recent publications reveals dominant trends in fluid mechanics (particularly convection and heat transfer), polynomial optimization techniques, and data-driven dynamical systems analysis. His work consistently applies convex optimization frameworks to derive rigorous bounds in physical systems while expanding into machine learning applications like transformer model analysis. Geophysical Fluid Dynamics Fellowship at WHOI (2015) EPSRC Doctoral Prize Fellowship (2018) Imperial College Research Fellowship Fantuzzi's research program is supported by prestigious fellowships including the Imperial College Research Fellowship and EPSRC Doctoral Prize. His academic service includes organizing the FAU MoD Lecture & Workshop on AI for maths and maths for AI (June 2025) and co-hosting the #MLPDES25 Machine Learning and PDEs Workshop. He actively supervises research within the FAU DCN-AvH group, focusing on polynomial optimization applications in dynamical systems and PDEs. As core faculty in the FAU DCN-AvH Chair, Fantuzzi collaborates within a multidisciplinary team specializing in dynamics, control, machine learning, and numerical methods. The group maintains strong international connections through workshops like the Oberwolfach Seminar on Polynomial Optimization for Nonlinear Dynamics and participates in conferences including CIN-PDE and Nečas Seminar on Continuum Mechanics, driving innovation at the intersection of mathematics and computational physics.