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Anders RantzerView profile

Professor

• Control Systems Theory
• Scalable Control
• Adaptive Control
• +7 more

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

• Higher-order finite element methods
• Numerical linear algebra
• Multigrid methods
• +3 more

Prof. Dr. Martin Kronbichler is a faculty member at the Faculty of Mathematics , Ruhr University Bochum , leading the Numerics group. His research focuses on higher-order finite element methods, multigrid techniques, and high-performance computing for complex fluid and solid mechanics problems. Key Research Areas: Higher-order finite element methods, iterative solvers, multigrid algorithms, exascale mathematical software, and computational fluid dynamics. Notable Projects: EU-funded dealii-X (exascale digital twins), BMBF PDExa (optimized PDE solvers for exascale), and DFG grants for cut-discontinuous Galerkin methods and geometric multigrid. Publications Trends: Recent works emphasize matrix-free operators for hyperelasticity, diffuse-interface models for additive manufacturing, and multigrid smoothers for higher-order elements. Scientific Awards: Recipient of the Humboldt Research Award for his contributions to numerical methods and HPC. Team: Collaborates with researchers like Dr. Shubham Kumar Goswami, Dr. Richard Schussnig, and Natalia Nebulishvili.

• Combinatorics
• Algebraic Combinatorics
• Log-Concave Polynomials
• +13 more

Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo. His research lies at the intersection of combinatorics, optimization, and theoretical computer science, with a focus on log-concave and Lorentzian polynomials and their applications in discrete and continuous settings. Assistant Professor, University of Waterloo (2022–present) Dirichlet Postdoctoral Fellow, TU Berlin (2020–2022) Postdoctoral Fellow, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoctoral Fellow, KTH, Stockholm (Fall 2019) James H. Simons Fellow, Simons Institute, UC Berkeley (Spring 2019) His research explores the deep connections between algebraic structures and combinatorial phenomena, particularly through polynomial capacity and Lorentzian polynomials. He applies these tools to problems in optimization, sampling, and representation theory. His work often involves developing new algebraic and analytic techniques to tackle longstanding conjectures and algorithmic challenges. The recent publications highlight a consistent focus on Lorentzian polynomials, capacity bounds, and their applications in combinatorics, optimization, and theoretical computer science. Key themes include matroid theory, log-concavity, sampling algorithms, volume approximation, and connections to Lie theory and representation theory. The research spans both theoretical developments and algorithmic applications, often in collaboration with leading researchers in the field. Dirichlet Postdoctoral Fellowship, TU Berlin Postdoc Fellowship in Algebraic and Enumerative Combinatorics, Institut Mittag-Leffler James H. Simons Fellowship, Simons Institute, UC Berkeley Jonathan Leake has advised or collaborated with several researchers, though formal advisees are not listed in the provided text. His work has been supported by prestigious fellowships and collaborations with institutions such as the Simons Institute and TU Berlin. He has taught courses including CO 250: Introduction to Optimization, MATH 239: Introduction to Combinatorics, and CO 739: Lorentzian Polynomials at the University of Waterloo and TU Berlin. While specific lab or research group names are not mentioned, Leake's collaborative work with researchers like Petter Brändén, Nisheeth Vishnoi, and Leonid Gurvits suggests active participation in research teams focused on algebraic combinatorics, optimization, and theoretical computer science. His publicly shared code for sampling from HCIZ densities and verifying positivity in Lie-theoretic contexts indicates an active computational research component.

Georgina HallView profile

Assistant Professor

• Polynomial Optimization
• Semidefinite Programming
• Convex Relaxations
• +3 more

Georgina Hall serves as Assistant Professor of Decision Sciences at INSEAD, holding the prestigious Patrick and Valentine Firmenich Fellowship for Business and Society. She officially joined the institution in September 2019 after completing a postdoctoral position at INRIA's DYOGENE team from January to September 2019. Her educational background includes a PhD from Princeton University's Department of Operations Research and Financial Engineering (2018), where she was a Gordon Y.S. Wu fellow under Professor Amir Ali Ahmadi's supervision. She earned both her Bachelor of Science (2011) and Master of Science (2013) from Ecole Centrale Paris, where she was valedictorian. Dr. Hall's research focuses on optimization theory, particularly polynomial optimization , semidefinite programming , and convex relaxations of NP-hard problems . Her work bridges theoretical mathematics with practical applications in business decision-making. She has made significant contributions to shape-constrained regression using sum of squares polynomials, demonstrating how semidefinite programming hierarchies can effectively fit shape-constrained polynomials to noisy data. Her recent publications reveal a research trajectory that increasingly connects optimization theory with machine learning applications and supply chain decision-making. The 2025 Operations Research paper on shape-constrained regression represents her theoretical contributions, while the Management Science paper demonstrates practical business applications of her optimization framework. Her scientific recognition includes: Médaille de l'Ecole Centrale from the French Académie des Sciences Princeton School of Engineering and Applied Sciences Award for Excellence 2016 Informs Computing Society Prize for Best Student Paper Multiple teaching awards from Princeton University Patrick and Valentine Firmenich Fellowship for Business and Society Dr. Hall teaches Probability and Statistics at the PhD level and has received significant recognition for her teaching excellence, including the Princeton University's Engineering Council Teaching Award and the Excellence in Teaching Award from the Princeton Graduate School. Her research has practical applications in areas including optimal transport maps for color transfer tasks and estimating optimal value functions for conic programs, with real-time applications in inventory management contract negotiation.

Philip TarantoView profile

Lecturer

• Quantum Information Theory
• Open Quantum Systems
• Quantum Thermodynamics
• +7 more

Philip Taranto is a Lecturer (Assistant Professor) at The University of Manchester's Physics & Astronomy department, where he leads the Quantum Information & Spatiotemporal Phenomena (QuISP) research group. He also serves as an editor for the Quantum journal. Originally from Melbourne, Australia, Taranto completed his undergraduate studies and Masters at Monash University under Dr. Kavan Modi and Dr. Felix A. Pollock, focusing on memory effects in open quantum systems. He then earned his PhD at the University of Vienna under Dr. Marcus Huber, studying quantum thermodynamics and complex temporal correlations. Following this, he held a JSPS Postdoctoral Fellowship at the University of Tokyo in Dr. Mio Murao's group before joining the University of Manchester. Taranto's research centers on quantum complexity, exploring how quantum systems' intricate behaviors can be harnessed for computational advantages. His primary focus areas include quantum information theory, open quantum dynamics, quantum thermodynamics, quantum foundations, correlations & entanglement, stochastic & complex processes, and quantum computation & simulation. His methodological approach heavily relies on the framework of higher-order quantum operations—transformations that act upon transformations themselves—which has proven valuable for developing optimal quantum interactive strategies, clarifying memory effects in open quantum processes, and analyzing foundational notions like causality. He also employs tensor networks, graphical calculus, and semidefinite programming in his research. His recent publications reveal a strong focus on quantum thermodynamics, higher-order quantum operations, and quantum memory effects. Taranto has made significant contributions to understanding the relationship between Landauer's principle and Nernst's unattainability principle in quantum cooling, developing protocols for efficient quantum system cooling with finite resources, and characterizing multi-time quantum processes with classical memory. His work on the quantum switch and higher-order quantum operations has advanced our understanding of quantum causality and indefinite causal order. JSPS Postdoctoral Fellowship (2022-2025) Editor of Quantum Journal (since June 2024) Taranto actively collaborates with multiple research groups globally, including the Murao group at the University of Tokyo, the Huber group at TU Wien, and the Modi group at SUTD Singapore and Monash University. He has worked with prominent researchers such as Simon Milz, Jessica Bavaresco, Marco Túlio Quintino, Felix Binder, Martí Perarnau-Llobet, Patryk Lipka-Bartosik, and Andrea Smirne. He is currently accepting PhD students and encourages collaboration with researchers sharing similar interests. Taranto is also committed to social responsibility, advocating for open science, climate justice, and empowering historically excluded and marginalized groups.

Marius PesaventoView profile

Professor

• Sensor Array Processing
• MIMO Communication
• Adaptive Beamforming
• +4 more

Prof. Marius Pesavento is a Full Professor at the Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, leading the Communication Systems Group. His research focuses on sensor array processing, MIMO communication systems, adaptive beamforming, and mathematical optimization in networks. He has held academic and industry roles since 2001, including positions at mimoOn GmbH and FAG Industrial Services. Education: PhD (Doktorate) in Electrical Engineering, Ruhr-Universität Bochum (2001–2005) Master of Engineering, McMaster University (1999–2000) Dipl.-Ing. in Electrical Engineering, Ruhr-Universität Bochum (1992–1999) His research interests span robust high-resolution sensor array processing, 4G/5G mobile networks, and network information theory. Notable projects include developing tensor models for ultrasonic sensor calibration and applying machine learning to anomaly detection in network flows. His work bridges theoretical optimization and practical applications in automotive radar, 6G networks, and medical imaging. Labs/Teams: Leads the Communication Systems Group at TU Darmstadt, focusing on interdisciplinary projects in signal processing and communication systems.

André UschmajewView profile

Professor

• Numerical Analysis
• Tensor Methods
• Low-Rank Approximation
• +7 more

Prof. Dr. André Uschmajew is a full professor and holds the Chair of Mathematical Data Science at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg, Germany. He has held prominent research and academic positions at institutions including the Max Planck Institute for Mathematics in the Sciences (Leipzig), University of Bonn, and EPF Lausanne. 2022–present: Chair of Mathematical Data Science, University of Augsburg 2017–2022: Research Group Leader, Max Planck Institute MiS Leipzig 2014–2017: Bonn Junior Fellow Professorship, University of Bonn 2013: Ph.D. in Mathematics, TU Berlin His research centers on the theoretical and computational aspects of low-rank tensor and matrix approximations, with deep connections to Riemannian optimization, functional analysis, and high-dimensional scientific computing. He investigates the geometry of low-rank varieties, convergence of alternating algorithms, and applications in data science and dynamical systems. His work combines rigorous mathematical analysis with algorithmic innovation. The recent publications (2023–2025) reflect a strong focus on optimization methods for low-rank structures, dynamical low-rank approximation for PDEs like the Vlasov-Poisson equation, randomized SVD, Sinkhorn-type algorithms with overrelaxation, and Kronecker product operator approximation. Key themes include convergence analysis, algorithmic acceleration, and applications in scientific computing and signal processing. Although no specific awards are listed, his publication record in top-tier journals such as Numerische Mathematik , SIAM Journal on Optimization , and Foundations of Computational Mathematics indicates significant recognition in applied mathematics and numerical analysis. He advises students and researchers in mathematical data science and numerical analysis, though specific advisees are not named. He teaches courses such as Kernel Methods and Linear Algebra II. He has collaborated with leading researchers including Bart Vandereycken, Daniel Kressner, and Wolfgang Hackbusch. His work is supported through institutional affiliations and likely research grants, though specific grants are not listed. He is actively involved in the development of numerical methods for high-dimensional problems, particularly using tensor networks and manifold optimization. He is affiliated with research teams at the University of Augsburg and previously led a group at the Max Planck Institute MiS Leipzig, focusing on mathematical aspects of data science and tensor methods.

Tobias WollenbergView profile

Researcher

• Flows in Gas Networks
• Power Supply Systems
• Decomposition Methods
• +2 more

Tobias Wollenberg is a researcher at the Faculty of Mathematics, University of Duisburg-Essen, affiliated with the working group led by Prof. Dr. Rüdiger Schultz. His research focuses on mathematical modeling, simulation, and optimization, particularly in the context of gas networks and power systems. Current research projects (2014–present) center on stochastic optimization in gas transport and AC power flow under uncertainty. Specializes in decomposition methods, semidefinite optimization, and stochastic programming for energy systems. His work bridges theoretical advancements in mathematical optimization with practical applications in energy infrastructure. Key publications address risk-averse stochastic programs for unit commitment in AC transmission systems and two-stage stochastic semidefinite programming frameworks.

Mareike DresslerView profile

Senior Lecturer

• Real Algebraic Geometry
• Computational Algebraic Geometry
• Polynomial Optimization
• +6 more

Mareike Dressler is a Senior Lecturer (A/Professor, tenured) and ARC Discovery Early Career Research Fellow at the School of Mathematics and Statistics, University of New South Wales (UNSW Sydney). She joined UNSW in February 2022 as a Lecturer (Assistant Professor, tenure-track) and was promoted to Senior Lecturer with tenure in July 2024. Her educational background includes: PhD in Mathematics from Goethe-Universität Frankfurt/Main (2018), supervised by Thorsten Theobald M.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2013) B.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2010) Mareike's research focuses on real and computational algebraic geometry and polynomial and convex optimization. She also works on problems intersecting with convex geometry, matrix and tensor computation, applied algebraic geometry, algebraic and geometric combinatorics, and real analysis. Her work particularly involves nonnegativity of polynomials, optimization methods, and ranks of matrices and tensors, with a special interest in sums of nonnegative circuits (SONCs) for sparse polynomials. Her research has strong applications in data science and machine learning. Mareike has received several prestigious awards including the ARC Discovery Project 2025 grant for "Quantifying Uncertainty of Risk-Aware Optimization for Safe Decision-Making," the J G Russell Award from the Australian Academy of Science, and the Early Career Impact Award from UNSW Science. She actively supervises research students, including PhD student Hongzhi Liao, Master's student Qi Wang, and recently supervised Moritz Schick to completion of his PhD in 2025. Mareike has secured significant research funding, including an ARC Discovery Early Career Researcher Award (DECRA) for 2024-2026. Mareike is an active member of the academic community, regularly organizing workshops and conferences such as "Optimization Days" at UNSW and minisymposia at major conferences like SIAM Conference on Applied Algebraic Geometry.

Volker KaibelView profile

Professor

• Discrete Optimization
• Polyhedral Combinatorics
• Extended Formulations
• +5 more

Prof. Dr. Volker Kaibel is a full-time Professor for Mathematical Optimization at Otto-von-Guericke Universität Magdeburg since 2007. His research focuses on Discrete Optimization , Polyhedral Combinatorics , and Extended Formulations , with applications in combinatorial optimization , quantum computing , and genomic epidemiology . His recent work includes studies on Polytope Extensions with Linear Diameters , Steiner Cut Dominants , and Scale-Free Spanning Trees . Publications span journals like SIAM Journal on Discrete Mathematics , Mathematics of Operations Research , and Journal of Computational Biology , emphasizing theoretical advancements in mathematical programming and polyhedral theory . Key projects include the long-running Mathematical Complexity Reduction (2017–2026) and earlier DFG-funded initiatives on Extended Formulations and Orbitopes . His collaborations extend to institutions like the Zuse-Institute Berlin and DFG Research Center MATHEON .

Felix LeditzkyView profile

Assistant Professor

• Quantum Information Theory
• Quantum Shannon Theory
• Mathematical Methods in Quantum Information
• +4 more

Felix Leditzky is an Assistant Professor in the Department of Mathematics at the University of Illinois Urbana-Champaign, holding the Lincoln Excellence for Assistant Professor (LEAP) Scholar position in the College of Liberal Arts & Sciences. He is a member of the Illinois Quantum Information Science and Technology Center (IQUIST) and was previously a David Blackwell Scholar (2023-2025). In 2025, he received an NSF CAREER grant for research on symmetries in noisy multipartite quantum systems. His research centers on mathematical and information-theoretic aspects of quantum information theory, with specific interests in quantum channel capacities, superadditivity phenomena, quantum source coding, data processing inequalities, strong converse theorems, second-order asymptotics, group theory applications, representation theory, matrix analysis, and many-body quantum systems. His work bridges theoretical mathematics with practical quantum information processing challenges. Leditzky's recent publications demonstrate strong focus on quantum communication protocols (teleportation/dense coding duality), entanglement characterization through topological methods, unitary designs for quantum computing, and fundamental limits of quantum channel capacities. His research consistently applies advanced mathematical techniques to solve concrete problems in quantum information science, with increasing emphasis on multipartite systems and networked quantum communication. NSF CAREER grant (2025) Lincoln Excellence for Assistant Professor (LEAP) Scholar David Blackwell Scholar (2023-2025) Editor at Quantum journal (quantum information theory) Editorial board member of Illinois Journal of Mathematics As principal investigator of the Leditzky Research Group, he mentors a diverse team of postdocs, graduate students, and undergraduates across Mathematics, Physics, ECE, and Computer Science departments. His group has secured significant funding including NSF CAREER and IQUIST postdoctoral fellowships. Leditzky actively organizes major conferences including TQC 2022 and QIP 2019, and serves on program committees for QIP and other leading quantum information conferences. The Leditzky Research Group operates at the intersection of mathematics and quantum information science, maintaining strong collaborations across departments and institutions. Their work combines theoretical rigor with computational approaches, frequently employing semidefinite programming, representation theory, and topological data analysis to tackle fundamental problems in quantum communication and information processing.

Jose Israel RodriguezView profile

Assistant Professor

• Applied Algebraic Geometry
• Algebraic Statistics
• Nonlinear Algebra
• +5 more

Jose Israel Rodriguez is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison, with secondary affiliations at the Institute for Foundations of Data Science and the Department of Electrical & Computer Engineering. He began his tenure-track position at UW Madison in Fall 2020 after serving as an NSF Postdoc with Jon Hauenstein and a Provost's Postdoctoral Scholar at the University of Chicago with Lek-Heng Lim. PhD in Mathematics from University of California, Berkeley (supervised by Bernd Sturmfels) Bachelor's degree from University of Texas at Austin (McNair Scholar) Dr. Rodriguez's research focuses on applied algebraic geometry, particularly in algebraic statistics, optimization, and engineering applications. His work bridges theoretical mathematics with practical problems in statistics, robotics, and data science. He is known for developing methods to compute Euclidean distance degrees and maximum likelihood degrees, and for applying multidegrees to solve polynomial systems in economics and kinematics. His recent publications reveal a strong focus on singularity theory applications, numerical algebraic geometry methods, and interdisciplinary collaborations. The research shows increasing emphasis on practical applications in robotics, computer vision, and systems biology, while maintaining strong theoretical foundations in algebraic geometry. Sloan Fellowship (2023) Nellie McKay Fellowship Chancellor's Fellowship (UC Berkeley) McNair Scholar (UT Austin) Dr. Rodriguez actively mentors students and postdocs, currently advising six PhD students with several recent graduates. He co-organizes the Applied Algebra Seminar at UW Madison and is a key organizer for the SIAM Activity Group in Algebraic Geometry. His research group receives funding from multiple sources to support graduate students and undergraduate researchers. He is also co-organizing a semester-long program at the IMSI institute in Chicago titled 'Algebraic Statistics and Our Changing World: New Methods for New Challenges' for Fall 2023. He maintains strong connections with the Max Planck Institute for Mathematics in the Sciences in Leipzig, having participated in their summer school on 'Randomness and Learning in Non-Linear Algebra.' His research group includes postdocs, PhD students, and numerous undergraduate researchers working on various aspects of nonlinear algebra and its applications.

• Mathematical Optimization
• Conic Optimization
• Interior Point Methods
• +3 more

Prof. Dr. Florian Jarre is affiliated with the Mathematical Institute at Heinrich-Heine-Universität Düsseldorf, specializing in mathematical optimization. His research spans conic optimization, interior point methods, semidefinite programming, and nonlinear optimization with applications in computational mathematics. Research interests focus on theoretical and applied aspects of optimization algorithms, including convergence analysis of iterative methods, complexity theory for convex problems, and development of efficient computational techniques for large-scale optimization challenges. Work extends to applications in machine learning and systems biology. Publications demonstrate consistent focus on optimization theory advancements, particularly in interior-point methods and convex programming. Recent work explores connections between optimization and machine learning, including SVM training methods and stochastic gradient descent variants.

• Conic Linear Programming
• Nonlinear Conic Programming
• Geometry of Convex Sets
• +2 more

Bruno F. Lourenço serves as Associate Professor at The Institute of Statistical Mathematics (ISM) and SOKENDAI (The Graduate University for Advanced Studies), holding dual appointments in the Department of Fundamental Statistical Mathematics and Department of Statistical Science. He concurrently holds a Visiting Associate Professor position at RIMS-Kyoto University through March 2026. His research centers on conic optimization theory, with specialized focus on conic linear programming (including regularization techniques and ill-posedness treatment), nonlinear conic programming (algorithm development and optimality conditions), and the geometric properties of convex sets. His work consistently addresses error bounds in optimization frameworks and extends into nonsmooth optimization methodologies, contributing to both theoretical foundations and computational applications in mathematical programming. Recent publications (2024-2025) reveal concentrated research on specialized cone structures including hyperbolic, copositive, and homogeneous cones. Key thematic trends encompass facial geometry analysis, duality gap resolution in semidefinite programming, constraint qualification-free error bounds, and projection methods for hyperbolicity cones. His work demonstrates strong integration of algebraic geometry with optimization theory, particularly through polynomial representations and symmetry properties of cones. Scientific Awards: No scientific awards were documented in the provided materials. Advising and Grants: The source documentation contains no explicit references to graduate students supervised, research grants administered, or external funding sources. His active publication record and leadership of the Statistical Decision-Making Group suggest ongoing research activity, but specific mentorship or grant details remain unreported in this context. Labs and Teams: Dr. Lourenço leads the Statistical Decision-Making Group at ISM, which focuses on developing optimization frameworks for statistical inference problems. The group's recent output indicates strong emphasis on conic programming applications to statistical modeling, with particular attention to computational tractability and theoretical guarantees in high-dimensional settings.

Sevag GharibianView profile

Professor

• Quantum Computing
• Quantum Complexity Theory
• Quantum Algorithms
• +3 more

Sevag Gharibian is a Professor (W2) at Paderborn University's Department of Computer Science, affiliated with the Faculty of Computer Science, Electrical Engineering and Mathematics, and the Institute for Photonic Quantum Systems (PhoQS). He holds a D. Phil. from the University of Waterloo (2012) and has held academic positions at Virginia Commonwealth University (2014-2018) and postdoctoral roles at UC Berkeley and the University of Illinois at Chicago. His research focuses on quantum complexity theory, quantum algorithms, and quantum Hamiltonian complexity. Education Doctorate (D. Phil., 2012): University of Waterloo, Canada M. Math (2008): University of Waterloo B. Sc. Computer Science (Co-op, 2005): University of Waterloo Research Interests His work bridges quantum computing and computational complexity, exploring topics like quantum Hamiltonian complexity, quantum algorithms, and the limits of quantum computation. He emphasizes theoretical foundations, including the complexity of quantum satisfiability problems and oracle-based separations between complexity classes. Publications Gharibian's recent work addresses quantum algorithmic challenges (e.g., Hamiltonian simulation, variational methods) and foundational questions in quantum complexity theory. His 2023 papers on oracle separations and multi-prover interactive proofs highlight advancements in understanding quantum vs. classical computational boundaries. Awards and Honors Good Practices Digital Teaching Distinction (2020), Paderborn University Teaching Excellence Awards (2016-2017), Virginia Commonwealth University NSERC Banting Postdoctoral Fellowship (2013-2014) Advising & Grants He leads projects like PhoQuant (photonic quantum computing platforms) and chairs major conferences (e.g., ICALP 2023). His editorial roles include Coordinating Editor for Quantum (2021-present). Labs & Teams He heads the Quantum Computation group at Paderborn University and collaborates with the Institute for Photonic Quantum Systems (PhoQS).