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• Optimization
• Convex Analysis
• Nonlinear Analysis
• +3 more

Radu Ioan Bot is a Professor and Dean of the Faculty of Mathematics at the University of Vienna, where he also serves as Head of the Department of Mathematics. His primary affiliations include the Department of Mathematics (Oskar-Morgenstern-Platz 1, 1090 Wien) and the Research Network Data Science (Währinger Straße 29, 1090 Wien). Bot's research centers on optimization theory with emphasis on convex/nonconvex optimization, monotone operators, and dynamical systems. He develops fast algorithms for variational inequalities and monotone inclusions by bridging continuous-time dynamics with discrete optimization methods. His work frequently addresses bilevel optimization, Tikhonov regularization, and second-order dynamics, yielding accelerated convergence rates for complex problems. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in time-scaling techniques, vanishing damping dynamics, and structured splitting methods. Key contributions include unifying Nesterov acceleration with Heavy Ball dynamics, developing reflected forward-backward algorithms for constrained optimization, and establishing strong convergence guarantees for monotone operator flows. These advances demonstrate consistent innovation in accelerating optimization while maintaining theoretical rigor. No scientific awards were mentioned in the provided source material. Details regarding student advising and research grants were not specified in the available information, though his leadership roles as Dean and Department Head indicate significant administrative responsibilities alongside active research. Bot participates in the University of Vienna's Research Network Data Science, suggesting interdisciplinary engagement in data-driven methodologies with potential applications in machine learning and computational mathematics.

• Convex Optimization
• Nonconvex Optimization
• Monotone Operators
• +5 more

Radu Ioan Boț is a Professor at the Faculty of Mathematics at the University of Vienna . Since 2020, he has served as Dean of the Faculty of Mathematics and Head of the Institute of Mathematics . He is also the Speaker of the FWF DK Vienna Graduate School on Computational Optimization and a Founding Member of the Research Platform Data Science@Uni Vienna . Education Ph.D. in Mathematics (2003, Chemnitz University of Technology, Summa cum laude ) M.Sc. in Mathematics (1999, Babeş-Bolyai University, Grade: 10 ) Diploma in Mathematics (1998, Babeş-Bolyai University, Grade: 10 ) Research Interests focus on convex and nonconvex optimization , monotone operators , duality theory , and proximal algorithms . His work bridges continuous and discrete time models , emphasizing fast convergence rates and Tikhonov regularization for problems in image processing , machine learning , and inverse problems . Recent Publications highlight advancements in primal-dual splitting , inertial dynamics , and stochastic optimization , with applications to GANs , phase retrieval , and structured convex minimization . Scientific Awards include the 2003 University Prize of Chemnitz University of Technology , 1998 award for graduating with the best grade from Babeş-Bolyai University, and fellowships during his Ph.D. and pre-Ph.D. research stays. Editorial Roles include Editor-in-Chief of SIAM Journal on Optimization (2026–2029) and editorial board membership for journals like Mathematical Programming , Computational Optimization and Applications , and Journal of Optimization Theory and Applications .

Roman SłowińskiView profile

Professor

• Decision Support
• Multiple Criteria Decision Aiding
• Ordinal Data Mining
• +11 more

Roman Słowiński is a full Professor at the Institute of Computing Science , Poznań University of Technology, Poland, and holds a professorial position at the Systems Research Institute of the Polish Academy of Sciences in Warsaw. He is a Full Member of the Polish Academy of Sciences and served as President of its Poznań Branch (2011–2014). His career spans decades of contributions to decision support systems and computational intelligence. His research focuses on Multiple criteria decision aiding Rough set theory and fuzzy set theory Multiobjective optimization Ordinal data mining Robust decision analysis with applications in medicine, economics, and environmental studies. He is renowned for pioneering the use of rough sets in decision analysis, collaborating with Zdzisław Pawlak and others. His recent work emphasizes robust ordinal regression, dominance-based methods, and monotonicity constraints in classification. Scientific awards include EURO Gold Medal (1991) Edgeworth-Pareto Award (1997) Annual Prize of the Foundation for Polish Science (2005) Doctor Honoris Causa from three international universities He has advised 26 Ph.D. theses and served as Coordinating Editor for European Journal of Operational Research , alongside leadership roles in the International Rough Set Society and the EURO Working Group on Multiple Criteria Decision Aiding. He founded the Laboratory of Intelligent Decision Support Systems at PUT.

• Recommender Systems
• Multimodal Learning
• Music Emotion Recognition
• +3 more

Marta Moscati works at the Institute of Computational Perception at Johannes Kepler University Linz , focusing on advanced recommendation systems and multimodal learning. Her research spans emotion-based music recommendation, privacy-preserving machine learning, and graph neural networks. Recent work includes: Developing multimodal single-branch architectures for cold-start scenarios Creating preference obfuscation techniques in implicit feedback systems Advancing music emotion recognition with semi-supervised graph networks Contributing to the FAME Challenge for multilingual face-voice association She has published extensively in top AI venues while maintaining technical expertise in both deep learning and theoretical physics , with early work on lepton universality violation. At JKU, she contributes to: Recommendation algorithms development Multimodal representation learning research Musical affective computing applications Privacy-preserving AI frameworks

• Symbolic Regression
• Genetic Programming
• Multiobjective Optimization
• +2 more

Bogdan Burlacu serves as R&D-Headquarters at the Center of Excellence for Smart Production HEAL at University of Applied Sciences Hagenberg. With an ORCID identifier 0000-0001-8785-2959 and h-index of 10 (619 citations), he maintains active research leadership through 2025. His research focuses on Symbolic Regression and Genetic Programming, with significant contributions to Multiobjective Optimization and Benchmark Problems. Key application areas include Explainable AI systems, hardware acceleration for evolutionary algorithms, and astrophysical modeling. His work demonstrates strong interdisciplinary connections between computer science and physical sciences. Recent publication trends show increasing focus on interpretability frameworks and domain-expert validation in symbolic regression, with notable applications in cosmology and engineering systems. His 2025 publications emphasize practical benchmarking methodologies and hardware acceleration techniques. Burlacu actively supervises research through two documented supervised works and contributes to major collaborative projects. He leads research activities within the Center of Excellence for Smart Production HEAL and participates in the Josef Ressel Center for Symbolic Regression. His work integrates distributed intelligence systems with rapid prototyping methodologies for industrial applications.

• Optimization
• Convex Analysis
• Monotone Inclusions
• +6 more

Radu Ioan Boț serves as a full University Professor (Univ.-Prof.) at the Faculty of Mathematics, University of Vienna, where he also holds the position of Dean since October 2020. He leads the Research Group on Applied Mathematics with Emphasis on Optimization and is affiliated with the Vienna Graduate School on Computational Optimization. His academic leadership extends to editorial roles, including his upcoming appointment as Editor-in-Chief of the SIAM Journal on Optimization (2026-2029) and his current position as Area Editor for the Journal of Optimization Theory and Applications. Professor Boț's research focuses on optimization theory and its applications, with particular expertise in convex and nonconvex optimization, monotone inclusions, variational inequalities, and continuous/discrete time dynamics for optimization problems. His methodological contributions span splitting algorithms, inertial dynamics, Tikhonov regularization techniques, and accelerated methods for minimax problems. His work bridges theoretical foundations with practical applications in signal processing, machine learning, and computational mathematics. The analysis of his recent publications reveals a strong emphasis on developing fast optimization methods with provable convergence rates. His research demonstrates a consistent pattern of connecting continuous-time dynamical systems with discrete optimization algorithms, often establishing novel convergence guarantees. His work frequently addresses challenges in nonconvex optimization, monotone inclusions, and saddle-point problems, with increasing applications to machine learning contexts such as GAN training. Professional Recognition Editor-in-Chief of SIAM Journal on Optimization (2026-2029) Area Editor for Journal of Optimization Theory and Applications (2025) Editorial Board Member of Mathematical Programming (2024) Editorial Board Member of Numerical Functional Analysis and Optimization (2020) Editorial Board Member of Computational Optimization and Applications (2019) Professor Boț actively mentors PhD students and postdoctoral researchers, currently supervising David Alexander Hulett, Rossen Nenov, and Chiara Schindler. His research group has secured significant funding, including projects from the Austrian Science Fund and international collaborations. His research group has organized numerous workshops including the Vienna Workshop on Computational Optimization series and the Vienna Seminar on Optimization. His research group maintains active international collaborations, particularly with institutions in China (Fudan University), Australia (University of New South Wales), and various European universities. Recent projects include work on nonsmooth nonconvex optimization methods in acoustics and smart operation of wind turbines under icing conditions.