Lukas Nickel is a researcher at the Institute of Mathematical Stochastics within the Department of Mathematics and Computer Science at the University of Münster. His work likely aligns with the institute's focus areas, including numerical analysis, machine learning, and stochastic differential equations. He is associated with the Mathematics Münster research cluster and contributes to projects addressing high-dimensional approximation problems and stochastic algorithms. No specific details about his individual research outputs, awards, or students are provided in the available text.
Dr. Liesel Sommer is a Research Associate at the Institute of Applied Mathematics within the Department of Mathematics and Computer Science at the University of Münster. She works in Prof. Christian Engwer's research group focusing on "Applications of PDEs," specializing in computational methods for fracture mechanics and numerical analysis. Her research interests include Unfitted Discontinuous Galerkin Methods, Gamma Convergence, Skeletonization algorithms, Fracture propagation, and Isogeometric Analysis. She has developed numerical schemes for phase-field approximations of pressurized fractures and has contributed to the understanding of fluid-filled fracture propagation through Gamma-convergence results. Dr. Sommer's publications demonstrate expertise at the intersection of numerical mathematics and mechanical engineering applications, particularly in developing computational methods for complex fracture phenomena. Her work bridges theoretical mathematics with practical engineering problems involving material failure and fluid-structure interactions. She has received her doctoral degree (Dr. rer. nat.) from the University of Münster with a dissertation on unfitted discontinuous Galerkin schemes for phase field approximations of cracks under pressure, supervised by Prof. Dr. Christian Engwer. Since 2014, Dr. Sommer has been actively involved in teaching at the University of Münster, contributing to courses such as Numerical Linear Algebra, Scientific Computing, and Nonlinear Modeling in Natural Sciences. Her teaching portfolio includes both lectures and practical exercises, demonstrating her commitment to academic education alongside her research activities.
Dr. Ir. Anh Vu Doan is a Lecturer at the Technical University of Munich (TUM) under the Chair of Integrated Systems and a Senior Project Leader at Infineon in Neubiberg, Germany. With a Belgian-Vietnamese background and prior residence in Japan, he has held academic roles including postdoctoral fellowships at Keio University and TUM, as well as teaching assistant and stand-in lecturer positions at Université libre de Bruxelles (ULB) and IÉSEG School of Management. Research Interests: Embedded systems design Combinatorial optimization Problem modeling and decision aiding Approximate computing and 3D-stacking memory Machine learning reliability and safety Network-on-Chip (NoC) optimization Recent Publications focus on neuromorphic systems, power optimization for multicore processors, adversarial attacks in ML, and multi-objective design strategies using genetic algorithms. His work bridges hardware-software co-design and sustainable mobility decision frameworks. Scientific Awards: Erasmus-Mundus Grant (EASED program) JST/CREST Research Program Education: MSc in Electrical Engineering (ULB, 2009) PhD in Engineering Sciences (ULB, 2015)
M.Sc. Moritz Weißbrich is a Researcher at the Chair for Chip Design for Embedded Computing , part of the Institute of Theoretical Computer Science at Technische Universität Braunschweig. Holding an M.Sc. in Electrical Engineering and Information Technology from Leibniz Universität Hannover, his academic focus spans high-performance/low-power processor architectures , approximating arithmetic circuits , and stochastic computation techniques for fault-tolerant systems. His research portfolio includes 20+ peer-reviewed publications since 2017, with recent work on Nano-scale controllers for FPGA systems Biomedical sensor design in 22nm FDSOI Energy-aware VLIW processor optimization Stochastic timing analysis frameworks He has been developing ultra-low-power embedded solutions since 2021, following prior research assistant roles at Leibniz Universität Hannover's Institute of Microelectronic Systems (2017-2021). Key technical contributions appear in venues like Springer LNCS , IEEE RFIC Symposium , and Journal of Systems Architecture , focusing on processor customization , energy harvesting systems , and radiation-hardened circuit design . His work addresses challenges in autonomous computing, harsh environment electronics, and precision agriculture applications.
Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam , where he co-directs the AMLab with Max Welling. He holds an Assistant Professor position (on leave) at Northeastern University , continuing to advise students and collaborate remotely. His research focuses on combining probabilistic programming and deep learning to develop models that generalize from limited data. Key areas include inductive biases through physical simulators, causal structures , and symmetries , with applications in robotics , NLP , healthcare , and physical sciences . Recent work includes Variational Flow Matching for graph generation Equivariant neural models for physical systems Entropy coding of complex data structures Goal-contrastive reinforcement learning for robotics Awards NSF CAREER award (2021) Students & Postdocs Robin Walters (Postdoctoral Fellow) Ondrej Biza (Ph.D. Candidate) Babak Esmaeili (Ph.D. Candidate) Sam Stites (Ph.D. Candidate) Hao Wu (Ph.D. Candidate) Xiongyi Zhang (Ph.D. Candidate) Heiko Zimmermann (Ph.D. Candidate) Jered McInerney (Ph.D. Candidate) Eli Sennesh (Ph.D. Candidate)
Stephan Eckstein is a junior professor in the Department of Mathematics at the University of Tübingen and a member of the university's machine learning cluster. His research bridges probability theory and machine learning with particular focus on stochastic optimization and numerical approximation. Research interests include: Optimal transport theory and its computational aspects Regularization techniques for high-dimensional problems Causal models and probabilistic structures Graphical models in machine learning Graph neural networks Recent publications analyze dimensional stability in optimal transport, exponential convergence rates for Sinkhorn algorithms, and causal modeling in financial time series generation. Contact: stephan.eckstein@uni-tuebingen.de
Dr. Mania Sabouri is affiliated with the Department of Mathematics at the University of Kassel, working in Numerics and Mathematical Modelling. Her research focuses on advanced numerical methods for solving partial differential equations with applications in computational mechanics, mathematical biology, and biomedical engineering. Her work emphasizes spectral element methods, high-order approximations, and computational modeling of complex systems such as poroelasticity, predator-prey dynamics, and bioheat transfer. She has contributed to efficient algorithms for nonlinear diffusion equations and structure-preserving numerical techniques. Recent publications (2012–2021) highlight her expertise in applying spectral methods to diverse physical and biological systems. She holds no explicitly listed scientific awards but maintains an active research agenda in computational mathematics.
Andrej Bogdanov is a Professor in the Department of Computer Science at the Weizmann Institute of Science's Faculty of Mathematics and Computer Science. With a prolific publication record spanning over two decades from 2002 to 2025, he has established himself as a leading researcher in theoretical computer science and cryptography. His research interests span multiple areas of theoretical computer science, with a particular focus on cryptography, computational complexity, pseudorandomness, and secret sharing. His work often bridges theoretical foundations with practical cryptographic applications, exploring the mathematical underpinnings of secure computation and cryptographic primitives. His research has evolved to address contemporary challenges in quantum computing security and machine learning evaluation. Bogdanov's publication record shows consistent contributions to top-tier conferences including FOCS, STOC, CRYPTO, TCC, and ITCS. His work demonstrates deep theoretical insights while maintaining relevance to practical cryptographic applications. Recent publications indicate expanding interests into quantum computing security and machine learning evaluation frameworks. Bogdanov has collaborated extensively with leading researchers in theoretical computer science, most notably with Alon Rosen (31 joint publications), as well as Siyao Guo, Yuval Ishai, and Chin Ho Lee. His collaborative work spans multiple institutions and reflects the interdisciplinary nature of modern theoretical computer science research. His academic contributions include foundational work on pseudorandom generators, secret sharing schemes, hardness amplification, and more recently, contributions to post-quantum cryptography and quantum security. His research has been supported by multiple grants that have enabled his team to explore the theoretical boundaries of cryptographic security.
Adam Tauman Kalai is a Professor of Computer Science at the University of Chicago, where he conducts cutting-edge research at the intersection of machine learning, artificial intelligence, and human-computer interaction. His work spans theoretical foundations and practical applications, with a particular focus on the societal impacts of AI systems. Dr. Kalai's research interests encompass a broad spectrum of topics in artificial intelligence, with a strong emphasis on machine learning theory, algorithmic fairness, natural language processing, and human-AI interaction. His work addresses fundamental questions about how AI systems can be made more reliable, fair, and understandable. He has made significant contributions to understanding the limitations of language models, particularly around issues of calibration and hallucination, as evidenced by his influential paper "Calibrated Language Models Must Hallucinate" which explores the inherent tension between model calibration and factual accuracy. Analysis of his recent publications reveals a strong focus on addressing critical challenges in AI development, including fairness in chatbots, multicalibration of neural networks, language model self-improvement, and the societal impacts of algorithmic decision-making. His work increasingly bridges theoretical computer science with practical applications in social contexts, demonstrating a commitment to developing AI systems that are not only technically sound but also socially responsible. Dr. Kalai has established himself as a leading researcher through his extensive collaborations with prominent scholars across academia and industry. His work appears consistently in top-tier venues including NeurIPS, ICML, STOC, and ICLR, reflecting the high quality and impact of his contributions to the field. His research program demonstrates a clear trajectory toward addressing some of the most pressing challenges in contemporary AI development, with particular attention to the ethical and societal implications of increasingly capable AI systems. This focus on responsible AI development positions him at the forefront of efforts to ensure that AI technologies benefit society broadly.
Wayne Huang is a distinguished academic affiliated with the College of Business at Southern University of Science and Technology, with additional ties to institutions like Xi'an Jiaotong University, Ohio University, and Harvard University. His career spans over three decades, marked by contributions to information systems, decision support systems, and e-commerce. He holds a PhD from the National University of Singapore (1998) in Information Systems. Key research areas include group support systems (GSS), cybersecurity, e-government implementation, and IT management. His work bridges organizational behavior with technological innovation, focusing on collaborative decision-making processes, cross-functional project coordination, and the socio-technical dimensions of information systems. Notable contributions include studies on real-time credit prediction models, crowdfunding market dynamics, and privacy-preserving machine learning. Huang has collaborated extensively with researchers like James J. Jiang, Haibing Lu, and Richard T. Watson, producing over 90 peer-reviewed articles across journals like IEEE Trans. Engineering Management and Decision Support Systems. His research has practical implications for organizations navigating digital transformation, cybersecurity challenges, and fintech adoption. Huang's work emphasizes empirical studies grounded in real-world industry partnerships and policy-oriented frameworks.
Timothy Berkelbach is an Associate Professor in the Department of Chemistry at Columbia University and a Research Scientist at the Flatiron Institute's Center for Computational Quantum Physics. He holds a B.A. from New York University (2009) and a Ph.D. from Columbia University (2014), followed by a postdoctoral fellowship at Princeton University (2014–2016). His research focuses on developing and applying computational methods in quantum chemistry and materials science, particularly excited states and spectroscopy. He has received prestigious awards including the AFOSR Young Investigator Award, Sloan Fellowship, NSF CAREER Award, and PECASE. His work bridges theoretical chemistry and computational physics, with applications to plasmons, excitons, and electronic structure in materials. Berkelbach’s contributions include advancements in coupled-cluster theory, GW/BSE methods, and software like PySCF. His research explores phenomena such as anharmonic vibrations in clathrates, plasmon-exciton coupling, and superconductivity in cuprates. He also investigates quantum transport in organic crystals and catalytic effects of electric fields. Education: B.A., NYU (2009); Ph.D., Columbia University (2014) Labs/Teams: Center for Computational Quantum Physics (Flatiron Institute), Columbia Chemistry Department Grants: NSF CAREER Award, AFOSR funding
Dr. Daniel Janini is a Humboldt Research Fellow at Freie Universität Berlin's Department of Neurocognitive and Experimental Psychology. He holds a PhD in Psychology from Harvard University (with Dr. Talia Konkle) and a BS/BA in Biology/Cognitive Science from Case Western Reserve University. His research focuses on the neural dynamics of visual cognition, exploring how the brain processes visual information through domain-general vs. specialized mechanisms. Education: PhD in Psychology, Harvard University (Advisor: Dr. Talia Konkle) BS Biology & BA Cognitive Science, Case Western Reserve University (Advisor: Dr. Ela Plow) Research Interests: He investigates visual perception algorithms using behavioral experiments, fMRI, and computational models. Key areas include category selectivity emergence in the visual system, fMRI study design optimization for reliability, and domain-general learning algorithms. His work challenges traditional views of neurofunctional specialization by demonstrating how general-purpose models can explain human visual tasks like letter categorization and numerical estimation. Grants & Awards: Alexander von Humboldt Fellowship (2024–2025) ERC Consolidator Grant (TRANSFORM project, 2025–present) Supervision & Collaboration: Currently recruiting a Master’s student/research assistant for fMRI methodological research. Seeks candidates with Python/Matlab skills in neuroimaging. Labs & Teams: Member of the Neurocognitive and Experimental Psychology lab, leading projects on visual system modeling and large-scale fMRI dataset design optimization.
Gregor Gantner is a Professor for Mathematics at the University of Bonn (Bonn Junior Fellow) since November 2023. Previously, he held a tenured Inria Starting Faculty Position in Paris (2022-2023) and postdoctoral roles at TU Wien, the University of Amsterdam, and TU Wien again. He completed his PhD in Technical Mathematics at TU Wien in 2017, supervised by Dirk Praetorius, and received a Diploma in Technical Mathematics from TU Wien in 2014. His research focuses on numerical methods for partial differential equations, including finite element methods (FEM), boundary element methods (BEM), isogeometric analysis (IGA), and adaptive algorithms for optimal convergence. Key areas include space-time methods, a posteriori error analysis, and adaptive mesh-refining strategies. Notable awards include the 2019 Austrian Mathematical Society Dissertation Prize, Promotio sub auspiciis Praesidentis rei publicae (2018), and the Dr.-Klaus-Körper Prize (2018). He has led projects such as the French-German ANR-DFG project on robust adaptivity for nonlinear PDEs and the FWF Erwin Schrödinger Fellowship on optimal adaptivity for space-time methods. Teaching activities include advanced courses on numerical analysis and scientific computing, such as 'Optimality of Adaptive Finite Element Methods' (2025) and 'Boundary Integral Equations and BEM' (2024). His software contributions include the IGABEM2D package.
Prof. László Végh holds the Hertz Chair for Algorithms and Optimization at the University of Bonn, part of the Transdisciplinary Research Area 'Modelling'. His research focuses on mathematical optimization, algorithm design, and computational economics, combining methods from discrete/continuous optimization, computer science, game theory, and economics to develop efficient algorithms. Education: BSc and PhD in Mathematics from Eötvös University, Budapest. Postdoc at Georgia Tech (2011-12), faculty at London School of Economics (until 2024), and currently at University of Bonn. Awards include the 2023 Frontiers of Science Award, 2018 STOC Best Paper Award, and 2010 Danny Lewin Student Paper Award. Research highlights include approximation algorithms for market equilibria, strongly polynomial LP algorithms, and auction mechanisms. His work bridges theory and applications in economics and computation. Active in international conferences and collaborates across disciplines.
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.
