Find Academic Supervisors

Philipp HungerländerView profile

Associate Professor

• semidefinite optimization
• conic optimization
• polynomial optimization
• +4 more

Philipp Hungerländer is an Associate Professor at the Institute of Mathematics, University of Klagenfurt. He is a member of the combinatorial optimization working group led by Prof. Franz Rendl, focusing on advanced optimization techniques for real-world industrial applications. Research Interests: His work spans three core areas: (1) semidefinite, conic, and polynomial optimization for nonlinear discrete problems, (2) quadratic programming and active-set algorithms for convex/nonconvex models, and (3) exact and heuristic methods for combinatorial optimization in routing and scheduling. Additionally, he explores game theory and dynamic games. Recent Publications: His 15 most recent articles reflect expertise in railway logistics, facility layout, home delivery services, and medical pandemic response. Topics include integrated freight routing, dial-a-ride optimization, and applications of mixed-integer programming. Contact: Email: Philipp.Hungerlaender@aau.at

• Statistics
• Operations Research
• Data Analysis
• +3 more

Florian Frommlet holds positions in two faculties: the Faculty of Business, Economics and Statistics (Department of Statistics and Operations Research) and the Faculty of Computer Science (Research Group Visualization and Data Analysis). His academic rank is Senior Lecturer. His research focuses on statistical methodologies, optimization, and computational statistics, with applications in genomics and data analysis. Key research interests include statistical properties, high-dimensional data analysis, and genome-wide association studies. He has contributed to projects such as Optimal selection procedures in genome wide association studies (2010–2014), funded by research grants. Notable publications span topics like multiple testing procedures and semidefinite programming bounds in combinatorial optimization. No academic awards or formal advisees are listed. His interdisciplinary work bridges statistics, computer science, and genomics, reflecting his dual affiliation across faculties.

Daniel KrennView profile

Assistant Professor

• Discrete Mathematics
• Asymptotic Enumeration
• Analysis of Algorithms
• +5 more

Daniel Krenn is an Assistant Professor in the Mathematics Department at Paris Lodron University of Salzburg. He holds a Ph.D. in Technical Mathematics from TU Graz (2013) and a Habilitation in Mathematics from Alpen-Adria Universität Klagenfurt (2019). His research focuses on discrete mathematics, including asymptotic enumeration, analysis of algorithms, analytic combinatorics, digit expansions, and graph theory. He has contributed to software development in SageMath, particularly in automata and transducer modules. Education: 2003–2009: Diploma in Electrical Engineering (TU Graz) 2005–2008: Bachelor's in Technical Mathematics (TU Graz) 2008–2010: Master's in Mathematical Computer Science (TU Graz) 2010–2013: Ph.D. in Technical Mathematics (TU Graz) 2019: Habilitation in Mathematics (Alpen-Adria Universität Klagenfurt) Research Interests: Discrete Mathematics Analysis of Algorithms and Data Structures Digit Expansions and Cryptography Combinatorics and Analytic Number Theory Key Awards: Promotio sub auspiciis Praesidentis rei publicae (2013) Austrian Mathematical Society Study Prize (2013) Austrian Federal Ministry of Science and Research Prize (2013) Professional Contributions: Coordinator of the doctoral program “Discrete Mathematics” at TU Graz (2015) Contributions to SageMath software development Over 30 peer-reviewed publications in journals like Algorithmica, Theoretical Computer Science, and Journal of Number Theory

• Operations Research
• Mathematical Optimization
• Game Theory
• +3 more

Werner Schachinger is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Business, Economics and Statistics. His research focuses on optimization theory, mathematical analysis, and game theory, with particular emphasis on completely positive matrices, quadratic optimization, and evolutionary dynamics. He has authored over 18 publications since 2007, exploring topics such as CP-rank bounds, combinatorial asymptotics, and algorithmic methods in optimization. Education background not explicitly stated but inferred through research focus and academic rank. His research interests span operations research, mathematical optimization, probability theory, and combinatorics. Notable contributions include studies on the complexity of optimization models, equilibrium analysis in game theory, and the application of copositive programming techniques. Publications highlight trends in optimization theory, with recent works addressing geometric distribution analysis (2023) and Plancherel averages (2023). Earlier contributions include work on moral play equilibrate (2021) and cp-rank lower bounds (2015–2020). No scientific awards explicitly mentioned in the texts. Advising and grants details are not provided in the text. However, activities include organizing the 2018 Workshop on Optimization, Game Theory, and Data Analysis, and contributing to talks on copositive optimization and duality in conic programming. Labs or research teams are not explicitly mentioned, though collaborations with researchers like Immanuel Bomze and Jörgen Weibull suggest active participation in interdisciplinary projects.

• Applied Mathematics
• Graph Theory
• Combinatorics
• +3 more

Prof. Angelika Wiegele is a full Professor at the Department of Mathematics at Alpen-Adria-Universität Klagenfurt. She serves as a member of the university senate and head of the Institut für Mathematik. Her research focuses on semidefinite optimization, combinatorial optimization, and their applications in graph theory and operations research. She holds a Dipl.-Ing. (2001) and Dr. techn. (2006) from Alpen-Adria-Universität Klagenfurt, with thesis supervision by Franz Rendl. Her academic career includes positions at TU Graz, IASI-CNR Rome, and Universität zu Köln, as well as a visiting professorship at Università di Roma Tor Vergata. Her educational background includes studies at City University London (Erasmus) and TU Eindhoven (Leonardo project). Recent work emphasizes SDP-based methods for graph partitioning, edge expansion, and combinatorial problems, with a focus on high-performance computing solutions. Her research bridges theoretical optimization with practical applications in network design and algorithmic development. Publications from 2021–2024 highlight advancements in semidefinite programming for graph partitioning, SDP-based bounds for cutwidth, and exact solvers for clustering problems. She contributed to the BiqBin and SOS-SDP solvers for quadratic optimization and co-edited special journal issues on mixed-integer nonlinear optimization. Her work often appears in top operations research journals and conference proceedings. Prof. Wiegele has held global faculty positions at the University of Cologne (2022–2024) and is active in international research collaborations through PRACE high-performance computing initiatives. She maintains an ORCID profile and a university webpage with teaching and research materials.

• Discrete Optimization
• Algorithm Design
• Computer Vision
• +3 more

Prof. Vladimir Kolmogorov is a faculty member at the Institute of Science and Technology Austria (IST Austria), specializing in discrete optimization and algorithm design. He holds a Ph.D. in Computer Science from Cornell University and has held positions at Microsoft Research and University College London. His research focuses on combinatorial optimization, MAP inference in graphical models, and applications in computer vision. Educations: M.S. in Applied Mathematics and Physics, Moscow Institute of Physics and Technology Ph.D. in Computer Science, Cornell University Research Interests: Dr. Kolmogorov's work spans algorithmic optimization, including complexity analysis of constraint satisfaction problems, graph algorithms, and machine learning applications. His contributions include foundational work on graph cuts for computer vision and the development of efficient optimization methods for discrete problems. Publications: His recent work includes advancements in parallel algorithms for Gibbs distributions, semidefinite programming, and combinatorial optimization. These contributions highlight his expertise in bridging theoretical computer science with practical applications. Awards: Royal Academy of Engineering/EPSRC Research Fellowship (2006–2011) ERC Consolidator Grant (2014–2020) Best Paper Award at ECCV 2002 Outstanding Student Paper Award (NIPS 2007) Best Paper Honorable Mention (CVPR 2005) Advising and Grants: He has advised multiple PhD students and leads a research team at IST Austria. His grants include significant funding for exploring optimization in machine learning and discrete systems. Labs/Teams: His lab focuses on theoretical and applied discrete optimization, collaborating with institutions globally. Current projects include developing faster algorithms for graph problems and advancing Gibbs distribution analysis.