Find Academic Supervisors

• 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.

Alberto Rodríguez GonzálezView profile

Research Professor

• Quantum Physics
• Condensed Matter Physics
• Statistical Mechanics
• +3 more

Alberto Rodríguez González is a Research Professor at the Albert-Ludwigs-Universität Freiburg, Germany, affiliated with the Quantum Optics and Statistics Group. He holds a PhD in Quantum Physics from the Universidad de Salamanca (2005), with a dissertation on one-dimensional disordered quantum systems. His research focuses on disorder-induced phenomena in quantum systems, including Anderson localization, multifractal analysis, and many-body interactions in cold atoms and condensed matter. Key research areas include quantum chaos, Bose-Hubbard model dynamics, and the interplay between disorder and interactions. He has contributed to understanding critical parameters at Anderson transitions and engineered extended states in disordered systems. His work spans theoretical and computational approaches, with applications to graphene, optical lattices, and ultracold bosons. Publications highlight advancements in multifractal finite-size scaling, symmetry-induced tunneling in disordered potentials, and quantum transport phenomena. His research has been published in journals like Physical Review B, Phys. Rev. Lett., and Ann. Phys. No scientific awards are explicitly mentioned, but his contributions reflect sustained excellence in theoretical physics. Labs/Teams: Active member of the Quantum Optics and Statistics Group at Freiburg, collaborating internationally on quantum materials and nonlinear physics. Research facilities include advanced computational tools for simulating disordered systems and many-body quantum dynamics.

Ofer BusaniView profile

Lecturer

• Statistical Physics
• Probability Theory
• Mathematical Physics
• +3 more

Ofer Busani is a Lecturer at the University of Edinburgh, specializing in probabilistic models with strong ties to statistical physics. His research primarily focuses on the Kardar-Parisi-Zhang (KPZ) universality class, encompassing random growth processes, interacting particle systems, random polymers, and stochastic PDEs. He explores phenomena such as universality, scaling limits, and hydrodynamic behavior in these systems. Busani's work spans theoretical analysis of stochastic processes, including geodesics in directed landscapes, invariant measures in exclusion processes, and correlation decay in Airy processes. His contributions address fundamental questions in non-equilibrium statistical mechanics and stochastic geometry. His publications (2016–2025) reflect a sustained focus on the KPZ universality class, with recent advancements in multi-lane exclusion processes, bi-infinite polymer measures, and semi-infinite geodesic structures. Collaborations with leading researchers such as Timo Seppäläinen and Patrik L. Ferrari highlight interdisciplinary engagement in probability theory and mathematical physics. No scientific awards are explicitly mentioned. His advising record is not detailed here, though collaborations with students like Evan Sorensen suggest active mentorship. Research is centered at the University of Edinburgh with no lab-specific mentions in the provided text.

• Computational Mechanics
• Multiscale Materials
• Gradient Elasticity
• +6 more

Harm Askes is a Full Professor in the Department of Civil and Structural Engineering at the University of Sheffield. His research focuses on computational mechanics of multiscale materials, particularly in gradient elasticity, homogenization, and metamaterials. He holds degrees from Delft University of Technology (MSc, PhD) and a DEng from the University of Sheffield. His work contributes to UN Sustainable Development Goals through advanced material modeling and structural analysis. Key research areas include wave propagation in heterogeneous media, dynamic homogenization, and fracture mechanics. Collaborations span international institutions, with notable contributions to lattice material mechanics and finite element methodologies. His publications reflect expertise in multiscale modeling, with a focus on bridging microstructural details to macroscale behavior. Dr. Askes has an h-index of 20 and over 1,108 citations. His research integrates computational techniques with experimental validation, addressing challenges in material characterization and structural integrity. Ongoing projects explore stochastic material behavior and the design of advanced composite systems. His academic contributions include editorial roles, supervision of postgraduate research, and development of innovative numerical methods for engineering applications. Theoretical frameworks developed by his team, such as gradient-enriched continuum models, are widely applied in computational mechanics.

• Machine Learning
• Discrete Probability
• Theoretical Computer Science
• +3 more

Dr. Luca Zanetti is a Lecturer at the University of Bath's Department of Mathematical Sciences and a member of the Institute for Mathematical Innovation (IMI). His research focuses on the intersection of machine learning, discrete probability, and theoretical computer science, with an emphasis on designing efficient network analysis algorithms. He holds a PhD and is affiliated with multiple research institutions. Key research interests include algorithm design for unsupervised/supervised learning on graphs, Markov chain analysis, graph clustering, and spectral methods. His work bridges theoretical foundations with practical applications in network science and computational mathematics. Zanetti's recent publications explore topics like Boltzmann transport algorithms, Elo rating systems via Markov chains, and geometric bounds for mixing times. He leads the EPSRC-funded project 'Balanced Allocation Meets Queueing Theory' (2024–2025), demonstrating expertise in both theoretical and applied research. He is actively supervising doctoral students on network analysis and machine learning topics. His research has been recognized with Open Access publications and collaborations across mathematics, computer science, and engineering disciplines.

Caren TischendorfView profile

Professor

• Differential-Algebraic Equations
• Numerical Analysis
• Mathematical Modeling
• +2 more

Prof. Dr. Caren Tischendorf is a Professor of Applied Mathematics at Humboldt University of Berlin's Institute of Mathematics. Her research focuses on differential-algebraic equations, numerical analysis, and mathematical modeling with applications in electrical circuits, energy networks, and physical systems. She investigates modeling, numerical analysis, and solution methods for differential equations with algebraic constraints, including applications in circuit simulation, gas network optimization, and biological systems. Her work spans theoretical foundations and practical implementations. Recent publications cover numerical methods for differential equations, machine learning foundations, gas network modeling, and uncertainty quantification. Her work demonstrates consistent innovation in computational mathematics with applications across engineering and physics domains. As Dean of the Faculty of Mathematics and Natural Sciences (2022-2025), she provides institutional leadership. She serves as Chair of the MATH+ Council, represents DMV in ICIAM, and contributes to scientific committees for SCEE Foundation and KoMSO.

• Quantum Physics
• Medical Informatics
• Statistical Analysis
• +3 more

Dr. Christopher Joyner is a Postdoctoral Research Fellow at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research bridges quantum physics and medical informatics, focusing on applications of Bayesian networks in clinical decision support and spectral analysis of quantum systems. He holds an email address at c.joyner@qmul.ac.uk and is based in the Peter Landin building (room CS 437). Core research interests include quantum chaos, statistical methodologies for medical data, and algorithmic decision tools for healthcare. His work combines theoretical approaches from random matrix theory with practical implementations in clinical environments, emphasizing tools for low back pain management and spinal pathology assessment. Notable contributions include the BENDi Bayesian network decision support system for pain management and foundational studies on symmetries in quantum graphs. His recent publications (2021–2024) reflect dual expertise in quantum systems and healthcare technology development. No academic awards or grants are explicitly listed in available materials. His research group affiliation is with the School's Electronic Engineering and Computer Science division, with potential collaborations across medical and engineering faculties.

• Applied Mathematics
• Probability and Statistics
• Financial Mathematics
• +2 more

Sergei Balakin is a Research Fellow at Impact Labs, Monash University, with active research output spanning two decades from 2003 to 2025. His work bridges theoretical mathematics with practical applications in finance and computing systems. His research focuses on Applied Mathematics, Probability and Statistics, Financial Mathematics, Operator Theory, and Computer Science. Key contributions include combinatorial analysis of Markov sequences, stochastic modeling for computing systems, and dynamic optimization for energy storage trading, demonstrating strong interdisciplinary connections between pure mathematics and real-world problems. Publication trends show evolution from foundational operator equations (2008-2009) to combinatorial probability (2011) and stochastic computing models (2013), culminating in recent financial mathematics applications (2025). His work consistently applies advanced mathematical techniques to solve complex problems across multiple domains. Based at Impact Labs, Monash University, Balakin contributes to interdisciplinary research initiatives where mathematical modeling intersects with economic and computational challenges.

Amos LawlessView profile

Professor

• Data Assimilation
• Numerical Weather Prediction
• Meteorology
• +4 more

Professor Amos Lawless is a leading academic in the Department of Meteorology at the University of Reading, specializing in data assimilation and inverse problems. His research focuses on advancing numerical methods for meteorological and oceanographic modeling, particularly in variational data assimilation, error covariance analysis, and coupled atmosphere-ocean systems. He holds a Professorship position and contributes to the development of algorithms for improving forecasting accuracy in numerical weather prediction (NWP) systems. His work integrates computational mathematics, environmental science, and applied statistics to address challenges in climate modeling and predictive systems. Key research themes include the conditioning of data assimilation problems, model bias correction, and the application of optimization techniques to enhance forecast precision. His publications span prestigious journals like the Quarterly Journal of the Royal Meteorological Society and SIAM Journal on Applied Dynamical Systems, reflecting his expertise in both theoretical and applied aspects of data assimilation. Collaborations involve institutions such as the Met Office and international research groups, aiming to bridge the gap between computational methods and real-world environmental modeling challenges. Recent work emphasizes hybrid data assimilation approaches, cross-domain error correlations in coupled systems, and the use of randomized preconditioning to tackle large-scale computational problems. His contributions are pivotal in advancing the mathematical foundations of environmental forecasting systems, ensuring robustness and efficiency in modern NWP frameworks.

Hanna VeselovskaView profile

Researcher

• Phase Retrieval
• Super-Resolution
• Sigma-Delta Modulation
• +4 more

Hanna Veselovska is a researcher in the Data Science Group at the Technical University of Munich (TUM), part of the Faculty of Mathematics and the TUM School of Computation, Information and Technology. Her research focuses on Quantization Theory, Mathematical Perspectives of Machine Learning, Image Processing, and Graph Signal Processing. She holds a PhD from the Institute of Mathematics of the National Academy of Sciences of Ukraine (NASU) under Anatoly Holub and has held postdoctoral positions at TU Braunschweig (Germany) and the University of Lübeck (Germany as a DAAD fellow). Current Position: Researcher at TUM Data Science Group PhD: Institute of Mathematics of NASU (2017) Postdoc: TU Braunschweig (2018–2020), University of Lübeck (DAAD fellowship) Her research interests span Phase Retrieval, Super-Resolution, and Sigma-Delta Modulation, with contributions to numerical analysis and signal processing. She has published extensively on topics such as regularized sampling formulas, graph signal quantization, and non-intrusive surrogate modeling. Her work bridges theoretical mathematics with applied data science, with applications in image processing and crashworthiness analysis. Teaching includes courses on Modelling and Simulation with Differential Equations, Random Matrix Theory, and Foundations in Data Analysis at TUM, as well as engineering mathematics courses at TU Braunschweig. Key contributions include advancements in digital halftoning via sigma-delta modulation and recovery of atomic measures on spheres. Her research emphasizes interdisciplinary approaches to solving complex signal processing challenges.

• Uncertainty Quantification
• Ensemble Kalman Methods
• Bayesian Inverse Problems
• +3 more

Xin T. Tong is Associate Professor in the Department of Mathematics at the National University of Singapore, specializing in uncertainty quantification, machine learning, and operations research. His research develops theoretical foundations and methodologies for structured problem-solving in high-dimensional settings. Current investigations focus on ensemble Kalman methods, Bayesian inverse problems, sampling algorithms, and stochastic optimization with non-i.i.d. data. Research emphasizes mathematical analysis of algorithm efficiency and structure-aware computational methods. Professional background includes postdoctoral work at NYU's Courant Institute and Ph.D. from Princeton University.

Jose A. SotoView profile

Associate Professor

• Combinatorics
• Algorithms
• Combinatorial Optimization

Jose A. Soto is an Associate Professor and Academic Chair of the Mathematical Engineering Department at the University of Chile. He is also an associated researcher at the Center for Mathematical Modeling. His research focuses on Combinatorics, Algorithms, and Combinatorial Optimization. Previously, he was a postdoc at TU-Berlin's Combinatorial Optimization & Graph Algorithms Group. He holds a PhD in Mathematics from MIT (2011) and an undergraduate degree in Mathematical Engineering from the University of Chile (2006). He has led multiple grants, including Fondecyt Project 1181180 on approximation algorithms and Fondef IDeA ID18I10250 for VRP optimization. He has collaborated internationally, including with the Max Planck Institute on fast approximation algorithms for massive datasets. His research spans topics like prophet inequalities, matroid secretary problems, and geometric optimization, with notable contributions in Mathematics of Operations Research, Journal of Machine Learning Research, and Algorithmica. He has advised numerous students and postdocs, including Krzysztof Fleszar and Kevin Schewior during the MPII-UChile collaboration. Teaching includes courses on Approximation Algorithms, Combinatorial Optimization, and Calculability/Computational Complexity. He has organized conferences like IPCO 2013 and participated in program committees for WADS 2021 and LATIN 2018.

Nicholas PippengerView profile

Professor

• Discrete Mathematics
• Probability Theory
• Computational Applications
• +2 more

Nicholas Pippenger is a Professor of Mathematics at Harvey Mudd College. Previously, he held positions at MIT, the University of British Columbia, and Princeton University. He earned his B.S. in Natural Science from Shimer College (1965) and B.S., M.S., and Ph.D. in Electrical Engineering from MIT (1967, 1969, 1974). Research Interests : Focuses on discrete mathematics, probability theory, and their applications to computational problems. His work spans algorithm design, stochastic processes, and formal languages. Recent studies include computational complexity, graph theory, and geometric modeling. Awards : Fellow of the Royal Society of Canada (Academy of Science) Fellow of the ACM Fellow of the IEEE Fellow of the AMS Grants & Projects : Received the NSF grant AF: Small: RUI: Concrete Computational Complexity (2009). Active in research areas such as stochastic systems, cellular automata, and algorithmic art. Labs/Teams : No specific lab affiliations listed, but collaborates across disciplines in mathematics and computer science.

Haifeng WangView profile

Assistant Professor

• Structural Engineering
• Wind Engineering
• Resilience Engineering
• +4 more

Haifeng Wang serves as Assistant Professor in the Department of Civil and Environmental Engineering at Washington State University's Voiland College of Engineering and Architecture since January 2023, following adjunct and postdoctoral roles at WSU and Lehigh University. His educational background includes: Ph.D. in Civil Engineering, University at Buffalo (2021) M.S. in Civil Engineering, Southeast University (2015) B.S. in Civil Engineering, Southeast University (2012) Dr. Wang's research integrates machine learning , computer vision , and drone technology with structural engineering to advance infrastructure resilience. Key focus areas include: Physics-informed surrogate modeling for wind hazards Vision-based structural condition assessment Performance-based wind design of structures Resilience quantification under extreme events His publication trends (2018-2024) reveal a strategic shift toward deep learning-enhanced wind load identification and data-driven catastrophe modeling , with increasing emphasis on physics-guided neural networks and multi-sensor data fusion for structural health monitoring. Notable recognitions include: ASCE SEI Tier 1 Young Professional Scholarship (2023) NSF NHERI Summer Institute Travel Award (2022) Mark Diamond Research Fund (2018) Dr. Wang advises graduate students in civil engineering research while securing competitive funding through awards from ASCE, NSF, and professional societies. His lab develops cutting-edge methodologies combining experimental validation with computational innovation for infrastructure resilience challenges.

Christian KrattenthalerView profile

Professor

• Enumerative Combinatorics
• Algebraic Combinatorics
• Hypergeometric Series
• +3 more

Christian Krattenthaler is a retired Professor at the University of Vienna's Faculty of Mathematics, specializing in combinatorics and algebraic combinatorics. He is a member of the combinatorics group and co-leads the Arbeitsgemeinschaft für Diskrete Mathematik. His research focuses on enumerative combinatorics, hypergeometric series, and number theory, with notable contributions to lattice path enumeration, determinants, and tilings. He has developed Mathematica packages like HYP, HYPQ, and RATE for hypergeometric series manipulation and sequence guessing. Krattenthaler is an editor for journals such as the Séminaire Lotharingien de Combinatoire, Electronic Journal of Combinatorics, and Annales de l’Institut Henri Poincaré D. His work includes advancements in Pfaffian rings, Schubert varieties multiplicities, and combinatorial identities. Collaborations span across institutions like TU Wien and TU Graz within the Special Research Programme (SFB) on Discrete Random Structures. He has advised numerous students, though specific advisee names are not listed here.