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Michel AlexisView profile

Assistant Professor

• Harmonic Analysis
• Nonlinear Fourier Analysis
• Quantum Signal Processing
• +3 more

Micel Alexis is an upcoming Assistant Professor of Mathematics at Clemson University, starting January 2026. Currently, he is a postdoctoral researcher at the Hausdorff Center for Mathematics, University of Bonn, working with Christoph Thiele. His research focuses on Harmonic Analysis, particularly Two-Weight Norm Inequalities for Singular Integrals, Nonlinear Fourier Analysis, and applications to Quantum Signal Processing. He completed his PhD at the University of Wisconsin-Madison under Sergey Denisov and held postdoctoral positions at McMaster University and the University of Bonn. Education: PhD in Mathematics, University of Wisconsin-Madison (2016-2021) BA in Mathematics, Northwestern University (2012-2016) Research Interests: His work spans Weighted Norm Inequalities, Orthogonal Polynomials on the Unit Circle, and Quantum Computing applications of Nonlinear Fourier Analysis. He actively collaborates with researchers like Lin Lin, Gevorg Mnatsakanyan, and Ignacio Uriarte-Tuero. Awards: Departmental Outstanding TA Award (2021) Multiple 'Superior TA' ratings from CTAPP (2016-2021) 'TA of the Month' Award (2016) Teaching & Service: Taught courses at UW-Madison and McMaster University, including Calculus and PDEs. Co-organized seminars at Bonn and UW-Madison, and served on academic committees such as the Graduate Program Committee and Town-Hall Committee.

• Numerical Analysis
• Uncertainty Quantification
• Computational Mechanics
• +5 more

Prof. Raul Fidel Tempone is a renowned expert in Numerical Analysis and Uncertainty Quantification (UQ) at RWTH Aachen University, where he established the Lehrstuhl für Mathematics for Uncertainty Quantification . His research focuses on developing efficient numerical methods for stochastic models and differential equations, driven by applications in computational mechanics, quantitative finance, biological/chemical modeling, and wireless communication. Research emphasizes a posteriori error estimation, adaptive algorithms, Bayesian model calibration/validation, and optimal experimental design. Key contributions include multilevel Monte Carlo (MLMC) methods, hierarchical/sparse approximation, stochastic optimization, and machine learning integration for UQ. Recent work includes advancements in: MLMC for PDEs/SDEs and McKean-Vlasov equations Bayesian experimental design with nuisance parameters Uncertainty quantification in porous media and wireless networks Machine learning-based segmentation and filtering techniques He has advised numerous PhD/Master’s students and collaborates widely, producing over 150+ publications in top journals/conferences. His applied research bridges theoretical developments with real-world challenges in engineering, finance, and data science.

• Machine Learning
• Statistics
• Econometrics
• +3 more

Dr. Georg Keilbar is a Professor at the Chair of Statistics within the School of Business and Economics at Humboldt University of Berlin. His research integrates modern econometrics with machine learning, particularly focusing on structured biomedical data analysis, systemic risk modeling, and cryptocurrency dynamics. He leads the DesBi initiative, which explores the fusion of deep learning and statistical methods. His research interests span econometric theory, panel data analysis, quantile estimation, and interdisciplinary applications in finance and biomedical fields. Recent work emphasizes nonparametric methods, causal inference, and software development for statistical challenges. Georg’s publications (2021–2025) reflect a strong focus on bridging econometrics and machine learning, with contributions to panel data models, recursive quantile methods, and cryptocurrency dynamics. His work often addresses practical applications such as systemic risk assessment and model interpretability. He is reachable at georg.keilbar@hu-berlin.de and located in SPA1, Room 400, Humboldt University.

• Machine Learning
• Probabilistic Numerics
• Bayesian Inference
• +5 more

Philipp Hennig is a Full Professor (W3) at the Department of Computer Science, Eberhard Karls University Tübingen, heading the Chair for Methods of Machine Learning. He previously held positions at the Max Planck Institute for Intelligent Systems, including Emmy Noether Group Leader and Max-Planck-Group Leader. His research focuses on probabilistic numerics, Bayesian inference, and uncertainty quantification, with contributions to machine learning theory and computational methods. He co-founded the field of probabilistic numerics and authored the textbook Probabilistic Numerics — Computation as Machine Learning (2022). Education: PhD in Physics from the University of Cambridge (2007), MSci in Physics from Heidelberg University (2007). Research support includes DFG Emmy Noether Programme grants, ERC Starting/Consolidator Grants, and Max Planck Society funding. He leads the ELLIS Program on Theory, Algorithms, and Computations of Modern Learning Systems and serves as Dean of Studies for Tübingen's Computer Science Department. Key research directions include probabilistic ODE/PDE solvers, Bayesian deep learning, and uncertainty-aware algorithms. His work bridges computational methods with statistical inference, emphasizing scalable and theoretically grounded approaches. Recent projects address neural operator learning, uncertainty quantification in climate models, and efficient Gaussian process methods. Awards include ELLIS Fellowship and ERC grants. Over 100 publications span top venues like NeurIPS, ICML, and JMLR. Active in academic leadership roles, he co-leads the Tübingen AI Center and Cluster of Excellence for Machine Learning in Science. His lab develops open-source tools like ProbNum for probabilistic numerical computing.

• Formal Methods
• Safety-Critical Systems
• Probabilistic Verification
• +4 more

Sebastian Junges is an Assistant Professor in the Software Science Group at Radboud University, Nijmegen, since 2021. He holds a PhD in Computer Science from RWTH Aachen University (2020), and previously worked as a PostDoc at UC Berkeley (2020-2021) and a Research Assistant at RWTH Aachen (2015-2020). His research focuses on formal methods for analyzing safety-critical systems, particularly using extensions of Markov decision processes (MDPs) to ensure dependability in automated systems like network protocols, hardware architectures, and robotics. Education: PhD in Computer Science, RWTH Aachen University, 2020 M.Sc. in Computer Science, RWTH Aachen University, 2015 B.Sc. in Computer Science, RWTH Aachen University, 2012 His research interests include probabilistic verification, policy synthesis for MDPs, and the intersection of formal methods with artificial intelligence. He has contributed to tools like the Storm probabilistic model checker and led projects on scalable analysis of probabilistic models. His work addresses challenges like ensuring safe decision-making in autonomous systems and certifying compliance with safety standards through rigorous algorithmic approaches. Publications focus on advancing MDP analysis, POMDPs, and robust policy synthesis. Recent trends include integrating uncertainty management, decision-tree-based approaches, and compositional verification techniques. He actively contributes to conferences like CAV, AAAI, and TACAS, and serves on program committees and steering groups for formal methods and AI.

Jiang HuView profile

Professor

• Electronic design automation
• VLSI physical design
• Optimization of large computing systems
• +5 more

**Jiang Hu** is a Full Professor of Electrical and Computer Engineering at Texas A&M University, holding a Hans Fischer Senior Fellowship at the Technical University of Munich (TUM-IAS). He earned his B.S. in Optical Engineering from Zhejiang University (1990), M.S. in Physics from the University of Minnesota (1997), and Ph.D. in Electrical Engineering (2001). Before joining Texas A&M in 2002, he worked at IBM Microelectronics. His research focuses on Electronic Design Automation (EDA), VLSI physical design, and hardware security, with notable contributions to machine learning-driven CAD tools and approximate computing architectures. **Education**: B.S., Optical Engineering, Zhejiang University (1990) M.S., Physics, University of Minnesota (1997) Ph.D., Electrical Engineering, University of Minnesota (2001) **Research Interests**: Jiang Hu's work bridges EDA and computer architecture, emphasizing optimization of large-scale computing systems. His projects include automated power modeling frameworks (e.g., APOLLO), neural network-driven placement algorithms, and security-enhanced dataflow architectures. He also explores plasmonic materials and perovskite solar cells through interdisciplinary collaborations. **Awards**: IEEE Fellow (2012) Alexander von Humboldt Research Fellowship (2011) Multiple best-paper awards at IEEE/ACM conferences (2003–2021) **Grants & Leadership**: He served as Technical Program Chair for ACM International Symposium on Physical Design (2011–2012) and co-chaired the ACM/IEEE Workshop on Machine Learning for CAD (2023). His research has led to 10 patents and supervision of 24 Ph.D. students. **Labs & Teams**: His group collaborates with TUM-IAS on projects like *A New and Scalable Methodology for Fast Machine Learning Accelerator Design*, advancing EDA tools for next-generation hardware systems.

• Discrete Optimization
• Convex Geometry
• Mathematical Optimization
• +3 more

René Brandenberg is a Senior Lecturer at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. His research focuses on Discrete Optimization, Convex Geometry, and Mathematical Optimization, with notable contributions to geometric inequalities, containment problems, and algorithm design. He has extensively supervised theses addressing topics like energy system optimization, linear programming applications, and geometric algorithms. His teaching responsibilities include courses such as Linear Algebra for Engineering students and advanced seminars on topics like Convex Optimization. He has co-advised numerous theses, collaborating with researchers like Peter Gritzmann and Michael Ritter. His work bridges theoretical advancements with practical applications in logistics, energy systems, and computational geometry. Brandenberg’s research group participates in initiatives like the TUM-ICL Mathematical Sciences Hub and contributes to interdisciplinary projects, including optimization models for power markets and algorithmic solutions for geometric containment. His publications reflect a deep engagement with foundational problems in computational geometry and optimization theory.

• Optimal Transport
• Calculus of Variations
• Wasserstein Barycenters
• +4 more

Camilla Brizzi is a Researcher at the Technical University of Munich (TUM), affiliated with the Chair of Global Analysis under Prof. Friesecke. She completed her PhD through a co-tutorship program between the University of Florence and the University of Paris Dauphine. Her research focuses on optimal transport theory, calculus of variations, and related areas such as Wasserstein barycenters, multi-marginal problems, and entropic methods. She has contributed to advancements in non-convex cost optimization and supremal variational problems. Her academic journey includes studies at the University of Florence, followed by doctoral research combining European institutions. She collaborates with leading figures in the field, including Gero Friesecke and Luigi De Pascale, and her work bridges theoretical analysis with computational methods. Her publications span high-impact journals like Nonlinear Analysis and Applied Mathematics and Optimization , addressing both foundational and applied aspects of optimal transport. Brizzi’s research interests also extend to the application of optimal transport in solving complex mathematical problems, such as those arising in physics and data science. She actively contributes to the academic community through seminars, conferences, and interdisciplinary projects, reflecting her commitment to advancing mathematical foundations and their real-world implications.

Christoph PflaumView profile

Professor

• simulation of airships
• computational optics
• high performance computing
• +2 more

Prof. Christoph Pflaum holds the position of Professor of Computational Engineering at the University of Erlangen-Nürnberg's Department of Computer Science. His academic journey includes a PhD (1996) and Habilitation (1999) from Technical University Munich and University of Würzburg, respectively. His research focuses on computational optics, high-performance computing, numerical analysis, and multigrid methods, with applications in laser simulation and energy-efficient systems. Education : 1992: Diploma in Mathematics, Technical University Munich 1996: Dr. rer. nat., Technical University Munich 1999: Habilitation, University of Würzburg Research Interests : PDE solvers on sparse grids, laser dynamics (thermal lensing, solid-state amplifiers), computational fluid dynamics for airships, and optical system optimization. His work bridges theoretical numerical methods with industrial applications in photonics and renewable energy. Key Contributions : Developed simulation tools for high-power lasers and solar cells, including LASCAD and FDFD methods. Advances in multigrid algorithms and finite element analysis for complex optical systems. Teaching : Courses in theoretical computer science, scientific computing, and computational optics, emphasizing practical implementation using advanced numerical libraries and HPC. Grants & Projects : Led research on laser systems optimization (Innonet SOL), solar cell simulations (Photonics Europe), and high-performance computing initiatives (Par-EXPDE project).

Stefan ZimmerView profile

Senior Lecturer

• Numerical Simulation
• Computational Science
• Sparse Grids
• +4 more

Stefan Zimmer is a Senior Lecturer and Academic Councillor at the University of Stuttgart, affiliated with the Simulation of Large Systems department under the IPVS institute. His work focuses on numerical simulation, computational science, and algorithm development with applications in sparse grids, multigrid methods, and high-performance computing. Research interests include adaptive algorithms for large-scale systems, model reduction techniques, and interdisciplinary challenges in technical education. He has contributed to foundational works in sparse grid techniques and parallel computing architectures. Publications span from 1993 to 2015, emphasizing advancements in numerical methods, data mining, and computational fluid dynamics. Notable works include the 2015 multigrid method for adaptive sparse grids and a 2009 textbook on modeling and simulation methodologies. No awards or student advisees are explicitly listed in the provided text.

• Quantum Algorithms for Optimization Problems
• Quantum Software-Hardware Co-Design
• Quantum Error Correction
• +2 more

Lilly Palackal is a Researcher and External PhD candidate at the Technical University of Munich (TUM) School of CIT, affiliated with the Department of Computer Science. She collaborates closely with Infineon Technologies AG on quantum computing research. Her work focuses on quantum algorithms for optimization problems, co-design of quantum software/hardware, and quantum error correction/mitigation. Education: BSc (2018) and MSc (2021) in Mathematics from TUM, with thesis topics in quantum error correction and interactive quantum communication. Current PhD research bridges academic and industrial quantum computing challenges. Research interests include developing quantum solutions for logistics (e.g., vehicle routing), optimization (e.g., knapsack problems), and hardware-aware algorithm design. She supervises multiple student projects across mathematics, physics, and engineering disciplines at TUM and partner institutions. Recent publications explore hybrid quantum-classical methods, QAOA applications, and efficient algorithm encodings for quantum systems. Her work emphasizes practical implementations of quantum technologies in real-world scenarios.

• Quantum Computing
• Quantum Walks
• Quantum Simulation
• +4 more

Jingbo Wang is Professor of Physics at the University of Western Australia, where she heads the Physics department and directs the QUISA Research Hub for Quantum Information, Simulation, and Algorithms. Her research pioneers quantum walk applications in complex systems analysis and quantum compiler development. After completing her PhD at the University of Adelaide, she established a leading quantum computation group at UWA. Her work bridges quantum dynamics, algorithm design, and complex network theory. Recent publications demonstrate strong focus on quantum machine learning, optimization algorithms, and experimental quantum computing implementations. Her team consistently develops efficient quantum circuits for practical problem-solving in finance, biology, and material science. Fellow of Australian Institute of Physics (2021) Vice-Chancellor’s Award in Research Mentorship (2022) School Research Mentorship Award (2022)

Nikos GianniotisView profile

Researcher

• probabilistic machine learning
• dimensionality reduction
• Bayesian inference
• +1 more

Nikos Gianniotis serves as a Staff Scientist in the Astroinformatics group at the Heidelberg Institute of Theoretical Studies (HITS), leveraging his computer science expertise to develop advanced machine learning solutions for astronomical data analysis. His role focuses on probabilistic modeling and algorithmic innovation within a dedicated research environment. His academic credentials include: PhD in Computer Science from the University of Birmingham (2007) MSc in Natural Computation (with Distinction) from the University of Birmingham (2003) BSc in Computing Science (First class) from the University of Aberdeen (2002) Dr. Gianniotis specializes in probabilistic machine learning , Bayesian inference , and dimensionality reduction techniques applied to astronomical challenges. His research bridges computational statistics and astrophysics, with emphasis on interpretable models for complex observational data. Recent work demonstrates sophisticated integration of Gaussian processes with time-series analysis for cosmic phenomena. Analysis of his 15 most recent publications (2016-2024) reveals consistent innovation in machine learning for astronomy, particularly in time-delay estimation for active galactic nuclei, spectral analysis, and time-series dimensionality reduction. His methodology frequently combines Gaussian processes, variational inference, and neural architectures like autoencoders to address astronomical data challenges. No scientific awards were documented in the provided materials. While no student advisement or grant details were specified, his publications indicate active collaboration within the astroinformatics community. The Astroinformatics group at HITS provides his primary research environment, focusing on scalable computational frameworks for next-generation astronomical datasets from missions like Kepler.

• Stochastic Analysis
• Partial Differential Equations
• Nonlinear PDEs
• +5 more

Benjamin Gess is a Professor of Mathematics at Technische Universität Berlin and leads the 'Stochastic analysis in the Sciences' research group at the Max Planck Institute for Mathematics in the Sciences (MIS) in Leipzig. He holds an ERC Consolidator Grant (2023) for his project 'FluCo', focusing on fluctuations in continuum systems and conservative SPDEs. His research spans stochastic partial differential equations (SPDEs), nonlinear PDEs, stochastic dynamics, interacting particle systems, machine learning, and fluid dynamics, with an emphasis on conservative SPDEs and their applications. Education: PhD (summa cum laude) from Universität Bielefeld (2011), MSc from University of Warwick (2009). Postdoctoral positions included University of Chicago (2013–2015) and Technische Universität Berlin (2012). He has held academic roles at Universität Bielefeld (W3 Professor from 2019–2024) and the Max Planck Institute since 2015. Key research interests include: regularization by noise, large deviations, stochastic flows, and the interplay between stochasticity and complex systems. His work combines stochastic analysis, nonlinear PDE theory, and kinetic theory to address challenges in singular SPDEs and supercritical phenomena. Recent grants include the ERC Consolidator Grant, and his research has led to advancements in understanding fluctuating hydrodynamics, machine learning dynamics via stochastic gradient descent, and self-organized criticality models. He supervises a research group and collaborates internationally with institutions like the University of Chicago and MPI MIS.

Ali HashemiView profile

Researcher

• Machine Learning
• Statistics
• Optimization
• +3 more

Ali Hashemi is a Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and a Postdoctoral Researcher at Technical University of Berlin. He holds a PhD (Dr. rer. nat.) with distinction from TU Berlin (2022) and an MSc in Electrical Engineering from Sharif University of Technology (2013). His research focuses on machine learning, Bayesian methods, and neuroimaging applications, particularly in inverse problems and explainable AI. He has contributed to advancements in structured noise modeling, robust source localization, and 3D neural networks for brain imaging. His work bridges signal processing and AI to improve biomedical data analysis. Education: PhD in Machine Learning, Technical University of Berlin (2022) MSc in Electrical Engineering, Sharif University of Technology (2013) Research Interests: Machine Learning & Optimization Bayesian Inference Neuroimaging (M/EEG, fMRI) Explainable AI & Diffusion Models Signal Processing for Biomedical Applications Key Achievements: Developed the 'Champagne Algorithm' for noise estimation in brain imaging Published widely in top venues like NeurIPS and NeuroImage Recipient of PhD distinction award for outstanding dissertation Labs & Affiliations: Active in TU Berlin's Uncertainty & Inverse Modeling group and Charité’s Brain and Data Science lab. Collaborates on open-source tools for M/EEG analysis (e.g., contributions to MNE-Python).