جست‌وجوی استاد و پژوهشگر

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +۳ مورد دیگر

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.

Joel Sheltonمشاهده پروفایل

استادیار

• Noncommutative Algebra
• History of Mathematics
• Abstract Algebra
• +۳ مورد دیگر

Joel Shelton is an Assistant Professor of Mathematics at Tusculum University, affiliated with the College of Math & Science and the Division of Math & Science department. He joined Tusculum in Fall 2022 and was promoted to his current rank in Spring 2024. Education B.S. in Pure Mathematics, East Tennessee State University (2013) M.S. in Applied Mathematics, East Tennessee State University (2016) Prof. Shelton’s research focuses on noncommutative algebra and the history of mathematics , with recent work exploring hypercomplex number systems and algebraic structures. His earlier research bridged applied mathematics and computer science , particularly through tensor-based Fourier analysis for image reconstruction. His publications reflect interdisciplinary trends in mathematics, spanning abstract algebraic theory and computational applications. Notable works include studies on hypercomplex numbers, R-algebras, and tensor-based image processing algorithms. Joel Shelton teaches a range of courses, from foundational subjects like college algebra to advanced topics in linear algebra and calculus. He emphasizes integrating abstract beauty with real-world problem-solving in his pedagogy.

Dominik Stögerمشاهده پروفایل

استادیار

• Mathematical Foundations of Data Science
• Non-convex Optimization
• Machine Learning Theory
• +۴ مورد دیگر

Dominik Stöger is an Assistant Professor (tenure-track) in the Department of Mathematics at KU Eichstätt-Ingolstadt since 2021, affiliated with the Mathematical Institute for Data Science and Machine Learning (MIDS). His research bridges mathematical theory and data science applications. His educational background includes: B.Sc. in Mathematics, Technical University of Munich (2013) M.Sc. in Mathematics, Technical University of Munich (2015) Ph.D. in Mathematics, Technical University of Munich (2019) Stöger's research centers on mathematical foundations of data science, with emphasis on non-convex optimization in machine learning, theoretical analysis of overparameterized models, and low-rank matrix recovery. He combines optimization theory and high-dimensional probability to develop rigorous guarantees for modern algorithms, addressing critical challenges in deep learning theory. His recent publications (2020-2025) demonstrate consistent output in top venues including COLT, NeurIPS, and ICLR, with particular focus on implicit regularization phenomena and non-convex recovery guarantees. The 2025 pipeline shows active work extending theoretical boundaries in matrix sensing and neural network analysis. Stöger has received significant recognition: NeurIPS 2021 Spotlight Paper (top 3% of submissions) COLT 2025 paper presentation As a tenure-track faculty member, he maintains active collaborations across institutions (USC, TUM) and likely advises graduate students. His research program shows strong momentum with multiple concurrent projects advancing theoretical machine learning. He contributes to the research ecosystem through affiliation with MIDS, fostering interdisciplinary work in mathematical data science at KU Eichstätt-Ingolstadt.

Alexander Alexandrovich Molozhavenkoمشاهده پروفایل

پژوهشگر

• computational mathematics
• artificial intelligence
• matrix computations
• +۲ مورد دیگر

Alexander Alexandrovich Molozhavenko serves as a Research Intern at the Research and Educational Laboratory of Matrix and Tensor Methods in Machine Learning within the Institute of Artificial Intelligence and Digital Sciences at the National Research University Higher School of Economics (HSE). He also holds a teaching position in the Department of Big Data and Information Retrieval at HSE's Faculty of Computer Science. He began his employment at HSE in 2025. His educational background includes a Bachelor's degree from Moscow Institute of Physics and Technology (2023) in Applied Mathematics and Physics. His primary research interests focus on computational mathematics and artificial intelligence, with specific expertise in matrix and tensor computations. Molozhavenko teaches several courses including Fundamentals of Matrix Computations for both Bachelor's and Master's programs, as well as Fundamentals of Tensor Computing. His published research includes work on optimization techniques for tensor train manifolds, demonstrating his specialization in advanced mathematical approaches to machine learning problems. His professional experience prior to HSE includes work at the Laboratory of Advanced Combinatorics and Network Applications at MIPT, and roles as Junior Lecturer in Reinforcement Learning and Optimization.