Jianqi Liu is a Hans Rademacher Research Fellow in the Department of Mathematics at the University of Pennsylvania, affiliated with the School of Arts and Sciences. His research focuses on vertex operator algebras (VOAs) and 2D conformal field theory (CFT), with an emphasis on algebraic structures such as fusion rules, Zhu’s algebras, Borel-type subalgebras, and connections to the classical Yang-Baxter equation. He also explores geometric aspects of VOAs and their physical interpretations. He earned his Ph.D. in Mathematics from the University of California, Santa Cruz (2018–2023), under the supervision of Chongying Dong. Prior to his current role, he held teaching roles at both UPenn and UCSC, instructing courses ranging from advanced linear algebra to complex analysis and number theory. His research publications address topics such as twisted conformal blocks, Rota-Baxter operators, and the interplay between algebraic structures and integrable systems. Liu collaborates with researchers like Angela Gibney and Daniel Krashen, focusing on advancing theoretical frameworks in mathematical physics and algebraic geometry.
Matthew K. Tam is an Associate Professor at the School of Mathematics and Statistics, The University of Melbourne, specializing in Operations Research. He is also an investigator at the Melbourne Centre for Data Science and an associate investigator in the ARC Training Centre OPTIMA. PhD in Mathematics (2016) from University of Newcastle under Jonathan Borwein Postdoctoral research at University of Göttingen with RTG-2088 and Alexander von Humboldt Foundation Junior Professor at University of Göttingen (2017-2020) His research focuses on continuous optimization, monotone operator theory, and variational analysis, with applications in wavelet construction and inverse problems. Key trends include distributed algorithms, resolvent splitting, and convergence analysis for feasibility problems. Discovery Early Career Researcher Award (DECRA) Alexander von Humboldt Fellowship He collaborates with institutions like ANZIAM, Springer, and IEEE, with publications spanning mathematical optimization, harmonic analysis, and computational mathematics. His work emphasizes algorithmic design for complex data systems and real-world applications in imaging and industrial modeling.
Prof. Dr. Peter Müller is a Professor at the Mathematical Institute of Ludwig Maximilian University of Munich (LMU), where he also serves as Dean of the Faculty of Mathematics, Informatics and Statistics. His research group focuses on Analysis, Mathematical Physics, and Numerics. Office: Room 439, Block B, Theresienstr. 39, 80333 Munich. Education: Habilitation in Mathematics, University of Göttingen (2006) Habilitation in Physics, University of Göttingen (2002) Ph.D. in Physics, University of Erlangen-Nürnberg (1996) Diploma in Physics, University of Erlangen-Nürnberg (1991) Research Interests: Müller's work spans mathematical physics, analysis, and probability theory, with emphasis on: Random Schrödinger operators and spectral theory Delocalization phenomena in disordered systems Quasiperiodic structures and ergodic properties Entanglement entropy in quantum systems His research bridges rigorous mathematical analysis with applications in quantum mechanics and statistical physics. Publication Trends: Recent articles (2013–2025) predominantly explore spectral theory in disordered quantum systems. Key themes include localization/delocalization transitions, asymptotic analysis of entanglement entropy, and spectral properties of random operators. Methodologically, his work combines functional analysis, stochastic processes, and operator theory to address fundamental questions in mathematical physics. Student Advising: Extensive mentorship of graduate students: 8 PhD students (e.g., Jakob Stern, Ruth Schulte) 17 Master's students (e.g., Leonard Wetzel, Julian Widl) Research Team: Leads the working group "Analysis, Mathematical Physics and Numerical Analysis" with postdoctoral researchers (e.g., Constanza Rojas-Molina) and PhD candidates. Regularly organizes international conferences on mathematical physics and disordered systems.
Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.
David Renfrew is an Associate Professor in the Department of Mathematics and Statistics at SUNY Binghamton. His research focuses on probability theory, random matrix theory, and their applications to mathematical physics and biological systems. Primary research areas: Probability, Random Matrix Theory, Mathematical Physics Special interest in non-Hermitian matrices and free probability interplay Current work explores dynamics of coupled systems and spectral properties of structured matrices Recent publications analyze singularity degrees of random matrices (2025), fractional free convolutions (2024), and universality phenomena in elliptic random matrices (2023). His methodological work bridges abstract probability theory with concrete applications in network dynamics and differential equations.
Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.
Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.
Dr. Elisha Krieg serves as Group Leader at the Leibniz Institute for Polymer Research Dresden and TUD Young Investigator at Dresden University of Technology, Germany. Her research pioneers programmable biomaterials using DNA nanotechnology for advanced biomedical applications, bridging synthetic polymer science with biological complexity. Her academic foundation includes: Ph.D. in Chemistry, Weizmann Institute of Science, Israel (2013) M.Sc. in Chemistry, Weizmann Institute of Science, Israel (2009) Vordiplom in Chemistry, University of Cologne, Germany (2006) Krieg's work centers on developing DNA-based nanomaterials that translate nanoscale programmability to macroscopic functional materials. Her group designs synthetic polymers functionalized with DNA modules to create reconfigurable platforms for diagnostics, tissue engineering, and nucleic acid purification. This approach leverages DNA's molecular precision to overcome limitations of conventional synthetic polymers, enabling dynamic biomaterials that respond to biological cues. Recent publications reveal a clear trajectory toward clinical translation, with 12 of 15 recent papers focusing on DNA-encoded hydrogels for cell culture matrices and nucleic acid diagnostics. The research spans fundamental supramolecular chemistry to applied biomedical engineering, emphasizing scalability and point-of-care compatibility. Key recognition includes: HFSP Postdoctoral Fellowship during Harvard tenure Krieg actively recruits MSc/PhD students through DIGS-BB and secures competitive funding including the TUD Young Investigator program. Her mentorship emphasizes interdisciplinary training in polymer chemistry, molecular biology, and nanomaterial characterization. Current grants support three core application areas: DNA/RNA purification for sequencing diagnostics, dynamic 3D cell culture matrices, and pathogen detection systems. The Krieg Group operates within the Max Bergmann Center of Biomaterials, utilizing advanced facilities for DNA nanotechnology, polymer synthesis, and nanomaterial characterization including cryo-EM and rheology.
Joseph Paat is an Associate Professor at the Sauder School of Business, University of British Columbia, with a strong focus on discrete optimization and integer programming. He is an associate member of the Computer Science department and affiliated with the Institute of Applied Mathematics. BS, Denison University MA, Wake Forest University PhD, Johns Hopkins University His research interests lie at the intersection of discrete optimization, integer programming, and theoretical mathematics, particularly exploring applications of topology, combinatorics, and number theory to optimization problems. He also integrates machine learning techniques into discrete optimization in his teaching and research. Recent publications highlight advancements in proximity bounds, flatness theorems, and algorithmic efficiency for integer programs, alongside theoretical contributions to unimodular hypergraphs, quadratic-free sets, and block-structured optimization. His work often involves collaborations across disciplines and institutions, including ETH Zürich and Deutsche Bahn. Associate Editor, Discrete Optimization (2022–present) Secretary (2022–2025) and Chair (2025–2026) of the Mixed Integer Programming Society Chair of the Organizing Committee, Mixed Integer Programming Workshop (2024) At UBC, Paat teaches courses such as Logistics and Operations Management , Advanced Topics in Optimization , and Discrete Optimization II , emphasizing modeling practices, integer programming, and machine learning integration. He previously taught at ETH Zürich and Johns Hopkins University. Joseph Paat actively participates in research collaborations, supervises graduate students, and contributes to professional service through editorial and organizational roles in optimization societies.
John Jasper is an Assistant Professor in the Department of Mathematics and Statistics at the Air Force Institute of Technology (AFIT), Wright-Patterson Air Force Base, Ohio. He holds a PhD in Mathematics from the University of Oregon and has previously served as a postdoc at the University of Missouri, a visiting assistant professor at the University of Cincinnati, and an assistant professor at South Dakota State University. Education: PhD in Mathematics, University of Oregon (2011), advised by Marcin Bownik His research centers on frame theory, operator theory, and packing problems , with strong connections to harmonic analysis, combinatorics, algebra, and discrete geometry. His work explores the structure and construction of equiangular tight frames, Grassmannian codes, and the diagonals of self-adjoint operators, often using group actions and finite field geometries. Many of his recent publications focus on optimal arrangements of lines and vectors in complex and real spaces, with applications in signal processing and quantum information. The 15 most recent publications reflect a consistent research trajectory in mathematical signal processing , particularly in constructing optimal configurations of vectors using combinatorial and algebraic methods. Key themes include equiangular tight frames, Grassmannian packings, operator diagonals, and finite field frame theory. These works frequently appear in high-impact journals such as IEEE Transactions on Information Theory , Applied and Computational Harmonic Analysis , and Linear Algebra and its Applications . Scientific Awards and Honors: No awards explicitly listed in the provided text. Advising and Grants: While no formal list of students is provided, John Jasper collaborates extensively with leading researchers such as Matthew Fickus, Dustin G. Mixon, Marcin Bownik, and Emily King. His research is supported in part by the National Science Foundation. He co-organizes the CodEx Seminar, a pan-university remote seminar on harmonic analysis, combinatorics, and algebra, indicating active engagement in academic community building and mentoring. Labs, Teams, and Research Groups: John Jasper is part of a vibrant research group focused on frame theory and discrete geometry, collaborating with mathematicians across institutions. He contributes to the CodEx Seminar, which serves as a virtual research hub connecting scholars in harmonic analysis and related fields. His work is deeply collaborative, often involving interdisciplinary teams working on theoretical foundations with applications in coding and signal processing.
Ketan P. Detroja is an Associate Professor in the Department of Electrical Engineering at Indian Institute of Technology Hyderabad . With a Ph.D. from IIT Bombay (2006) and a specialization in Multivariable Control, Distributed State Estimation, and AI/ML for Control Applications , his research focuses on ensuring fault-tolerant and economical operation of microgrids , developing distributed Kalman filtering techniques , and applying machine learning to process monitoring and control . Education: B.E., D. D. Institute of Technology, Gujarat, India Ph.D., IIT Bombay, 2006 Research Interests span Advanced Process Control, Fault Detection and Diagnosis, Cooperative Control, Microgrid Optimization and Control , and Hardware-Software Design for Control Systems . His work addresses challenges in data-driven process monitoring , distributed state estimation , and machine learning-based controller design , particularly for large-scale systems. Scientific Contributions include innovative approaches to inverse-free Kalman filters , reinforcement learning-based PI controllers , and data reduction algorithms for fault diagnosis. His publications reflect a focus on control theory, optimization, and machine learning integration , with applications in power systems, wind energy, and fault diagnostics . Scientific Awards: Best Paper Award at ADCONIP - 2011 Best Paper Award at DYCOPS-2013 Advising has been a significant part of his career, with supervision of Ph.D. students working on topics like fault detection in induction motors , microgrid scheduling , and adaptive control using fuzzy logic . His M.Tech students have explored areas such as visual object tracking, battery SOC estimation, and wind farm control , many of whom are now in prominent roles across academia and industry.
Michael Levitin is a Professor of Applied Mathematics at the Department of Mathematics and Statistics, University of Reading. He leads the Pure Mathematics Group and serves on the Departmental Research Committee. His research focuses on spectral theory, spectral geometry, and applications in mathematical physics, numerical analysis, and operator theory. Levitin holds a Candidate of Sciences (Ph.D.) from the Moscow Institute of Physics and Technology (1989), with a thesis on fluid-structure interactions. Prior to Reading, he held roles at Heriot-Watt University, Cardiff University, and Sussex University. His work includes foundational contributions to spectral asymptotics, non-self-adjoint operators, and geometric spectral theory. Key projects involve Pólya’s conjecture for eigenvalues, Steklov problems, and waveguide eigenmodes. He co-authored the textbook Topics in Spectral Geometry (2023) and frequently organizes international workshops, such as the 2026 Modern Applications of Microlocal Analysis conference honoring Dmitri Vassiliev. Levitin’s research integrates pure and applied mathematics, with applications in physics and engineering. Education: BSc+MSc (Applied Mathematics, Moscow Institute of Physics and Technology, 1986), PhD (1989). Employment History: University of Reading (2010–present), Cardiff University (2007–2010), Heriot-Watt University (1993–2007), University of Sussex (1992–1993). Research Interests: Spectral geometry of Laplace/Dirac/Maxwell operators, eigenvalue inequalities, numerical methods, and operator pencils. Collaborations include notable mathematicians like Iosif Polterovich, David Sher, and Matteo Capoferri. Grants and recognitions include EPSRC funding for microlocal analysis applications. Teaching includes Real Analysis and Complex Analysis courses at Reading. Publications span over 50 peer-reviewed papers in journals like Inventiones Mathematicae , SIAM Journal on Mathematical Analysis , and Journal of Spectral Theory . His work emphasizes interdisciplinary approaches, combining analytical techniques with numerical methods. Current projects include spectral asymptotics in linear elasticity and geometric wave propagators on manifolds.
Liu Zhen holds dual faculty appointments as an Adjunct Assistant Professor of Teaching in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences and an Adjunct Lecturer in the Department of Economics within the College of Arts and Sciences. His interdisciplinary work bridges computational methods with economic theory, focusing on information asymmetry, behavioral decision-making, and financial market dynamics. His educational foundation includes a PhD and MA from Stony Brook University and a BA in Economics from Tsinghua University, establishing strong quantitative and theoretical grounding. These qualifications are reflected in his dual-departmental roles where he integrates technical and economic perspectives. Research interests emphasize how information structures influence market outcomes (information economics), psychological factors in economic choices (behavioral economics), and financial market mechanisms, extended through software and IT systems applications. This synthesis enables modeling of complex phenomena like investor behavior and policy impacts using computational frameworks. His publication record shows an evolution from advanced mathematical research—particularly in matrix theory and algebra—to applied economic investigations. The mathematical works provide rigorous analytical tools now leveraged in current economic studies, demonstrating methodological continuity despite disciplinary shifts. Key recognition includes: 2007 Outstanding Doctoral Student Paper Award (American Accounting Association Mid-Atlantic Regional Meeting) Research funding highlights comprise a Michigan Retirement Research Center grant (2008-2009) and a SUNY-Stony Brook Seed Grant for Survey Research (2007-2008), supporting projects on retirement literacy and survey methodologies. While no formal advisees are documented, his teaching roles involve mentoring students across computer science and economics curricula. Current collaborative work connects with University at Buffalo's interdisciplinary research ecosystem, particularly through the Center for Survey Research during his Stony Brook tenure, indicating ongoing engagement with empirical economic analysis teams.
Dr. Troy Lee is an Associate Professor of Quantum Cryptography at the Centre for Quantum Software and Information within the Faculty of Engineering and Information Technology at the University of Technology Sydney . His research focuses on quantum algorithms, computational complexity, and graph theory, with particular emphasis on quantum-classical separations and query complexity. Education: Not explicitly mentioned Research Areas: Quantum algorithms, computational complexity, graph theory, quantum cryptography, and Boolean function analysis Teaching: Supervised the course Data Structures and Algorithms in 2022 Grants: Currently involved in quantum algorithm design and defense optimization projects (2025-2028, 2021-2024) His recent publications highlight advancements in quantum query complexity, graph algorithms, and exact learning techniques. Notably, his work includes quantum speedups for graph connectivity problems and improved bounds for Fourier-sparse function learning. Dr. Lee maintains active collaborations across theoretical computer science and quantum computing domains, contributing to both foundational and applied research in quantum software development.
Lars Karlsson is an Associate Professor at the Department of Computing Science, Umeå University, where he serves as Assistant Head of Department with responsibilities for undergraduate education. His research focuses on developing efficient algorithms for matrix and tensor computations within high-performance computing environments. He is affiliated with the Parallel and Scientific Computing research group at Umeå. Karlsson's primary research areas include: Numerical Linear Algebra : Specializing in matrix factorizations and eigenvalue computations Parallel Algorithms : Designing scalable solutions for distributed and shared-memory systems Tensor Computations : Developing decomposition methods and completion algorithms Performance Optimization : Auto-tuning techniques for modern computing architectures His publications (2010-2025) demonstrate consistent focus on parallel algorithms for numerical problems, particularly matrix reductions, eigenvalue computations, and tensor decompositions. Recent work emphasizes auto-tuning, robustness in numerical methods, and efficient scheduling for high-performance systems. The majority of publications involve collaborative research within European computing consortia like NLAFET. Karlsson contributes to educational research, having explored mastery learning approaches in university settings. His administrative responsibilities include oversight of undergraduate programs at the Department of Computing Science.
