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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.

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

Sonja Wogrin is a University Professor (Univ.-Prof.) at Graz University of Technology (TU Graz), where she has been heading the Institute for Electricity Economics and Energy Innovation since August 2021. She holds a Dipl.-Ing. in Technical Mathematics from TU Graz (2008), a Master of Science in Computation for Design and Optimization from MIT (2008), and a doctorate in Electricity Systems from Universidad Pontificia Comillas (2013). Her educational background includes: Doctorate in Electricity Systems, Universidad Pontificia de Comillas (June 2013) Dipl.-Ing. in Technical Mathematics, Graz University of Technology (October 2008) Master of Science in Computation for Design and Optimization, MIT (June 2008) Professor Wogrin's research focuses on decision support systems in the energy sector, optimization methodologies, and particularly the problem of generation capacity expansion. Her work spans several key areas including bilevel programming, capacity expansion planning, energy storage systems, and time series aggregation for energy system optimization. She has made significant contributions to understanding how to integrate renewable energy sources into power systems while maintaining economic efficiency and grid stability. Her research often addresses the challenges of decarbonizing electricity systems through advanced mathematical modeling and optimization techniques. Her recent publications demonstrate a strong focus on improving the computational efficiency of energy system models while maintaining accuracy, with particular attention to the integration of renewable energy sources, energy storage systems, and the development of resilient energy communities. She has pioneered work on time series aggregation methods that balance computational tractability with model accuracy, which is crucial for long-term energy planning under uncertainty. Professor Wogrin has received several prestigious awards and fellowships including: 4th EASE Student Award for "Co-Optimisation of energy storage technologies in tactical and strategic planning models" (2019) Beca de movilidad para investigadores "NILS Ciencia y Sostenibilidad" (2015) Beca Erasmus "Personal Docente/Investigador" de formación (2016) Beca Iberdrola de ayuda a la investigación en energía y medio ambiente (2020) She leads multiple significant research projects including EU - NetZero-Opt, RINGs, iKlimET, V2G-QUESTS, and CIDEAL, which focus on optimizing energy systems for net-zero emissions, resilient energy networks, climate and energy system modeling, vehicle-to-grid integration, and industrial decarbonization. Her work has substantial practical implications for energy policy and grid operations in Austria and beyond. Professor Wogrin collaborates extensively with industry partners including Austrian Power Grid AG, KELAG, and Netz Niederösterreich, ensuring her research addresses real-world energy challenges. Professor Wogrin leads the research group at the Institute for Electricity Economics and Energy Innovation, which develops advanced optimization models for energy systems. Her team has created the LEGO (Low-carbon Expansion Generation Optimization) model, an open-source tool for energy system optimization that has gained international recognition. The group's work spans from fundamental optimization methods to practical applications in energy system planning and operation, with a strong emphasis on computational efficiency and model accuracy.

• Optimization
• Bilevel Programming
• Capacity Expansion Planning
• +5 more

Yifan Hu is a Professor of Practice at Northeastern University, specializing in AI/ML/NLP and Information Visualization. He held senior roles at Amazon (Senior Manager of Applied Science, 2023-2024) and Yahoo! Research (Senior Director, 2014-2023), leading teams in AI-driven projects. Earlier, he contributed to AT&T Labs, Wolfram Research, and Daresbury Laboratory. His research focuses on graph visualization, machine learning, and data science applications. Research interests include advanced visualization techniques (e.g., SmartGD framework), AI moderation systems, graph algorithms, and federated learning fairness. He has pioneered methods for graph layout optimization, dynamic data visualization, and adversarial NLP defenses. Notable achievements include winning the 2017 IEEE ICDM 10-Year Highest-Impact Paper Award for work on collaborative filtering. He currently teaches Data Mining (CS 6220) and Machine Learning (CS 6140) at Northeastern, emphasizing practical applications of AI. Labs/Projects: Actively develops visualization tools like SmartGD and DeepGD , exploring interdisciplinary applications in healthcare, cybersecurity, and large-scale data analysis. His work bridges theoretical advancements with industrial-scale implementations.

• AI
• Machine Learning
• Natural Language Processing
• +4 more

Dr. Ernö Robert Csetnek is a researcher affiliated with the Faculty of Mathematics , Department of Mathematics . His work spans optimization, dynamical systems, and non-smooth mathematical analysis. Faculty of Mathematics, Department of Mathematics Active in convex and non-convex optimization, variational inequalities, and dynamical systems His recent publications focus on Tikhonov regularization , inertial dynamics , and minimax algorithms for non-smooth and non-convex problems. Key themes include convergence guarantees, differential equations modeling, and operator splitting techniques. Scientific awards: No awards explicitly mentioned in the provided data.

• Optimization
• Applied Mathematics
• Convex Analysis
• +7 more

Ivana Ljubic is Professor of Operations Research at ESSEC Business School in Paris. Her research develops optimization methods for network design problems with real-world applications. Research employs mixed integer programming and metaheuristics to solve combinatorial optimization problems. Applications include telecommunications networks, logistics, and bioinformatics. Current projects address uncertainty modeling and bilevel optimization in distribution systems. Awards include multiple fellowships from the Austrian Academy of Sciences and optimization society honors. She serves on editorial boards of the European Journal of Operational Research, Transportation Science, and Networks.

• Network Design
• Combinatorial Optimization
• Optimization under Uncertainty
• +3 more

Thomas Pock is a Professor at the Institute of Visual Computing at TU Graz. His research focuses on computer vision, optimization methods, and mathematical models in computer vision, with significant contributions to medical imaging and inverse problems. He leads projects integrating deep learning with traditional variational methods for applications in MRI reconstruction, microscopy, and cardiac signal analysis. His work emphasizes efficient sampling techniques, uncertainty quantification, and algorithmic optimization in computational imaging. Education and academic background are not explicitly detailed in the provided texts, but his extensive publication record indicates expertise in interdisciplinary areas such as biomedical engineering, signal processing, and machine learning. Key research themes include variational networks, total variation methods, and generative adversarial networks (GANs) for image reconstruction and segmentation. His research spans collaborations in both academic and clinical settings, addressing challenges in 3D reconstruction, particle flow estimation, and cardiac electrophysiology modeling. Notable projects include Total Deep Variation and inverse Eikonal methods for medical data analysis. He actively contributes to open-source tools and frameworks for variational optimization and deep learning integration.

• Computer Vision
• Optimization Methods
• Medical Imaging
• +5 more