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Alexei VernitskiView profile

Senior Lecturer

• Artificial Intelligence
• Knot Theory
• Algebra
• +3 more

Dr. Alexei Vernitski is a Senior Lecturer in the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex. His research focuses on applying artificial intelligence (including reinforcement learning and deep learning) to mathematical problems in knot theory, algebra (e.g., braid theory), and universal algebra. He also explores mathematics education, particularly enhancing student motivation. Previously, he worked in the financial sector as a programmer and as a computer science lecturer. His research interests span AI-driven knot theory, algebraic structures (semigroups, groups), and mathematics education. Notable areas include the application of neural networks to braid untangling, automated reasoning in knot diagrams, and cognitive studies on math anxiety using EEG. He has supervised PhD students in mathematics education, universal algebra, and computer science applications of mathematics. His recent work demonstrates trends in combining machine learning with topological and algebraic problems, emphasizing practical AI solutions for abstract mathematical challenges. His articles reflect interdisciplinary approaches, merging computer science techniques with pure mathematics. Dr. Vernitski has advised multiple PhD students, contributing to diverse fields from knot theory to educational technology. His work bridges theoretical mathematics with real-world applications, such as optimizing data transmission and enhancing learning systems through neuroadaptive methods.

Julia WolfView profile

Professor

• Arithmetic Combinatorics
• Harmonic Analysis
• Analytic Number Theory
• +3 more

Julia Wolf is a Professor of Pure Mathematics at the University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS) and Trinity College. Her research focuses on arithmetic combinatorics, harmonic analysis, and analytic number theory, with interdisciplinary connections to model theory, discrete geometry, and theoretical computer science. She holds an EPSRC Open Fellowship and has organized events like the Warwick-Oxbridge-Manchester-Bristol-London (WOMBL) meetings. Wolf teaches advanced courses such as 'Higher-Order Uniformity' and 'Analytic Number Theory,' emphasizing structure and applications. Her work bridges combinatorial, analytic, and algebraic techniques, addressing problems like polynomial configurations in primes, extremal hypergraph theory, and Ramsey multiplicity. Recent research includes structural stability in finite abelian groups and applications of model theory to additive combinatorics. Wolf actively promotes open-access publishing and has mentored numerous postdoctoral researchers and students through initiatives like the Philippa Fawcett Internship Programme. She also contributes to academic equity efforts, such as gender-inclusive hiring in mathematics. Professional activities include editorial roles, conference organization (e.g., the Simons Institute's Pseudorandomness program), and leadership in collaborative projects like the 'Combinatorics Meets Model Theory' workshop. Her grants and fellowships underscore her contributions to advancing discrete mathematics and fostering international academic networks.

Ivan Z. MilentijevicView profile

Professor

• Computer Science
• Electrical Engineering
• Digital Signal Processing
• +3 more

Professor Ivan Z. Milentijevic is a full professor at the Faculty of Electronics in Niš, University of Niš, within the Department of Electrical Engineering and Computer Science. He earned his PhD in Computer Science from the same institution in 1998, following a Master's (1994) and undergraduate degree (1989) in the same field. His research focuses on systolic arrays, digital signal processing architectures, and project-based learning methodologies. He has published 8 papers in journals with impact factor and is currently involved in 1 domestic and 1 international research project. Research interests include: Optimal design of systolic arrays for matrix operations FIR filter architectures and error-tolerant systems Reconfigurable hardware and parallel processing Educational technologies in project-based learning His recent work (2002–2008) emphasizes configurable architectures for signal processing and collaborative learning systems. Earlier contributions (1996–1998) focused on systolic array optimization for linear algebra operations. Advising/grants: No explicit student/advisor relationships listed. Current projects involve 1 domestic and 1 international collaboration. Grants and funding details not specified. Labs/teams: Affiliated with the Faculty of Electronics' research groups in computer engineering and signal processing, though specific lab names are not mentioned.

Blake JacksonView profile

Research Professor

• Cluster Algebras
• Enumerative Combinatorics
• Representation Theory
• +3 more

Blake Jackson is an Assistant Research Professor at the University of Connecticut's Department of Mathematics. His research focuses on algebraic and enumerative combinatorics, representation theory, and applications of machine learning to mathematical problems. He earned his Ph.D. from the University of Alabama under Kyungyong Lee and is currently mentored by Ralf Schiffler and Kyu-Hwan Lee at UConn. Education and Background: Bachelor's degree from Jacksonville State University, Alabama. Ph.D. in Mathematics from the University of Alabama (Tuscaloosa). Research Interests: Blake’s work spans cluster algebras, symmetric functions, quiver representations, and machine learning. Recent projects include geometric modeling of modules over path algebras, studying Banff/Louise quivers, c-vector geometry for mutation-infinite quivers, and using machine learning to explore quiver mutation-acyclicity. He is actively developing algorithms to find combinatorial bijections on Dyck paths related to q,t-Catalan numbers. Publications: His recent work combines theoretical mathematics with computational methods, focusing on geometric and algebraic structures in combinatorics. Machine learning techniques are increasingly central to his research, driven by promising results in quiver mutation analysis. Awards: No specific awards mentioned in the provided texts. Grants and Advising: While no grants are listed, Blake collaborates with leading researchers in algebraic combinatorics. He mentors students through his research projects, though no formal advisees are noted. Labs/Teams: His current work involves interdisciplinary collaborations, including machine learning applications in mathematical research.

Kenneth R. JacksonView profile

Professor

• Numerical Analysis
• Scientific Computing
• Computational Finance
• +8 more

Kenneth R. Jackson is a Full Professor of Computer Science at the University of Toronto, where he has been active since 1981. He retired in 2020 but continues research in numerical computation, computational finance, and scientific computing. Born in Montreal and raised in Toronto, Jackson earned all his degrees from the University of Toronto: BSc (1973), MSc (1974), and PhD (1978). He held roles as Gibbs Instructor and Visiting Assistant Professor at Yale University before returning to Toronto. Recognized as an NSERC University Research Fellow, he also served as Associate Chair for Graduate Studies (2002–2005) and President of the Canadian Applied and Industrial Mathematics Society (2001–2003). His research focuses on numerical methods for ODEs, parallel computation, validated solutions, and applications in finance, medical imaging, and climate modeling. He has advised over 30 graduate students, many of whom contributed to groundbreaking work in computational finance and scientific computing. Jackson organized the 2001–02 Thematic Year on Numerical Challenges in Science at the Fields Institute and currently advises YetiWare. He teaches advanced courses in numerical methods, optimization, and computational finance, and has published extensively on topics ranging from high-dimensional ODE systems to GPU-accelerated financial modeling.

Twan BastenView profile

Professor

• Embedded Systems
• Cyber-Physical Systems
• Model-Driven Engineering
• +3 more

Twan Basten is a Full Professor in the Electronic Systems group at Eindhoven University of Technology (TU/e). He leads research on embedded and cyber-physical systems, focusing on model-driven design, computational models, and system dependability. He holds an MSc (1993) and PhD (1998) in Computing Science from TU/e, advancing from Assistant to Full Professor by 2009, and became the Electronic Systems group chair in 2013. His research spans international projects (FP5-7, H2020, ECSEL) and Dutch initiatives (STW, NWO, RVO), with over 200 publications and seven best paper awards. He has co-supervised 21 PhD students and actively participates in program committees and conferences. His work contributes to UN Sustainable Development Goals through innovations in smart systems. Education: MSc in Computing Science, TU/e (1993) PhD in Computing Science, TU/e (1998) Research Interests: Explores design methodologies for embedded systems, including scenario-based design, real-time scheduling, and performance analysis. Specializes in model-driven engineering and computational models to ensure system dependability. Active in projects like TRANSACT (real-time systems) and SAM-FMS (flexible manufacturing). Key Contributions: Co-author of 1 book and over 200 scientific publications Recipient of seven best paper awards Co-supervised 21 PhD degrees Senior member of IEEE and lifetime member of ACM Labs & Teams: Leads the Model-Based Design Lab and contributes to EAISI High Tech Systems initiatives. Collaborates on tools like TRACE4CPS for execution trace analysis and CReTS for vehicle platooning simulation.

Barbara WohlmuthView profile

Professor

• Numerical Simulation
• Partial Differential Equations
• Discretization Techniques
• +4 more

Prof. Barbara Wohlmuth is a full professor in Numerical Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. She leads the International Graduate School of Science and Engineering at TUM and has held professorships at Stuttgart, Darmstadt, and Berlin universities. Her research focuses on numerical simulation of partial differential equations, multiscale solvers, and coupled multi-field problems with applications in engineering. Education: Studied mathematics at TUM and Université Joseph Fourier in Grenoble, received her doctorate from TUM in 1995, and completed habilitation in Augsburg. Visiting professorships in USA, France, and Hong Kong. Research interests include discretization techniques, predictive modeling, and interdisciplinary collaboration with engineering disciplines. Notable achievements: 2012 Gottfried Wilhelm Leibniz Prize (Germany’s highest academic honor in sciences), 2005 Sacchi-Landriani Prize. Publications emphasize advanced numerical methods in fluid dynamics, geophysics, and biomedical engineering. Active in editorial roles for international journals and scientific committees across Europe and USA. Elected member of Bavarian and European Academies of Sciences. Key contributions include: Development of robust numerical algorithms for exascale simulations Pioneering work in coupled multi-physics modeling Innovative methods for computational contact mechanics Leadership in graduate education initiatives

• Cryptographic Voting Systems
• Key Exchange Protocols
• Cloud Cryptography
• +3 more

Kristian Gjøsteen is a Professor at the Department of Mathematical Sciences within the Norwegian University of Science and Technology (NTNU) . He actively contributes to the Algebra Group and specializes in cryptographic systems with a focus on electronic voting , security proofs , and privacy-enhancing technologies . Educational Background: MSc and PhD from NTNU Research Interests: His work spans cryptography , key exchange protocols , cloud security , and formal verification of security mechanisms. Particular emphasis is placed on coercion-resistant voting systems , lattice-based encryption , and blockchain privacy models . Article Trends: Recent publications demonstrate expertise in post-quantum cryptography , machine-checked security , and privacy-preserving voting architectures . Collaborative efforts explore hybrid cryptographic schemes , verifiable decryption , and mix-net implementations for secure elections.

Andreas KugiView profile

Professor

• Control Theory
• Automatic Control
• Complex Dynamical Systems
• +6 more

Andreas Kugi is the Scientific Director at the AIT Austrian Institute of Technology and a full professor of Complex Dynamical Systems at TU Wien (Vienna University of Technology) in the Faculty of Electrical Engineering and Information Technology, Institute of Automation and Control. He has held significant academic and leadership roles across Europe, including professorships at Saarland University and offers from TU Dresden and KIT. His research focuses on the modeling, control, and optimization of complex dynamical systems , with strong applications in mechatronics, robotics, and industrial automation . He has led major research centers such as the Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry and the Center for Vision, Automation & Control at AIT. His work bridges theoretical control design and real-world industrial implementation. The recent publications reflect a consistent focus on nonlinear, hybrid, and distributed parameter systems , with applications in robotics, manufacturing, energy, and process industries. His research integrates advanced control theory with practical engineering challenges, emphasizing real-time optimization, robustness, and system efficiency. Scientific Awards: Mechatronic Systems Outstanding Investigator Award (IFAC, 2022) Goldene Stefan-Ehrenmedaille (OVE, 2023) 16 best paper awards Andreas Kugi has supervised over 50 completed PhD dissertations and has been deeply involved in research leadership, including serving as Editor-in-Chief of Control Engineering Practice (2010–2017) and Vice President of the OVE Austrian Electrotechnical Association (2017–2023). He has secured and led numerous research grants, particularly through industrial collaborations in automation and process control. He leads and contributes to major research initiatives, including the Center for Vision, Automation & Control at AIT and the Christian Doppler Laboratory , fostering interdisciplinary teams focused on industrial digitalization and smart systems.

• Numerical Linear Algebra
• Fast Algorithms
• Structured Matrices
• +4 more

Jianlin Xia is a Professor of Mathematics at Purdue University, with a courtesy appointment in the Department of Computer Science. He joined the university in 2014. Xia holds a Ph.D. in Applied Mathematics from the University of California, Berkeley (2006). His research focuses on numerical linear algebra, fast algorithms for structured matrices, and their applications in computational science and engineering. His work addresses challenges in solving large-scale linear systems, eigenvalue problems, and partial differential equations (PDEs) using innovative methods like fast multipole techniques, hierarchical structures, and randomized algorithms. Key areas of research include: Design and analysis of fast algorithms for structured matrices (e.g., hierarchical, semiseparable, Cauchy matrices) Efficient direct and iterative solvers for PDEs, especially Helmholtz equations in seismic modeling Stability and robustness of numerical methods in high-performance computing Applications in wave propagation, inverse problems, and machine learning Xia’s contributions include advancements in low-rank approximations, divide-and-conquer eigenvalue decomposition, and scalable preconditioning techniques. His work emphasizes both theoretical analysis and practical implementation, often leveraging parallel computing architectures. Contact: xiaj@purdue.edu .

Jordan CotlerView profile

Assistant Professor

• Quantum Information
• Quantum Computation
• Quantum Gravity
• +5 more

Jordan Cotler is an Assistant Professor of Physics at Harvard University, affiliated with the Department of Physics within the Faculty of Arts and Sciences. He holds a BS in physics and mathematics from MIT (2015) and a PhD in physics from Stanford University (2020). Before joining Harvard's faculty, he served as a Junior Fellow at the Harvard Society of Fellows from 2020 to 2024. His research focuses on the intersection of quantum information, computation, and spacetime physics. Key interests include quantum algorithms for analyzing many-body and quantum gravitational systems, information-theoretic frameworks for chaotic dynamics, and non-perturbative methods in quantum cosmology and field theory. Cotler's work has advanced quantum algorithm design for experimental platforms and contributed to understanding black hole microstructure and cosmological spacetimes. He has been recognized with prestigious early-career awards, including his Harvard Society of Fellows Junior Fellowship. His publications span foundational topics such as quantum gravity, holography, computational complexity, and quantum chaos, reflecting a multidisciplinary approach to theoretical physics.

Justin CampbellView profile

Lecturer

• Geometric Representation Theory
• Algebraic Geometry
• Langlands Program
• +1 more

Justin Campbell is a Dickson Instructor in the Department of Mathematics at the University of Chicago, specializing in geometric representation theory. His research explores connections between algebraic geometry and representation theory, with particular interest in the geometric Langlands program. Current investigations focus on categorical structures in representation theory and their applications to automorphic forms. His work bridges abstract mathematical theories with computational approaches to fundamental problems in algebra and geometry.

Youssef MarzoukView profile

Professor

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +4 more

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

Amneet BhallaView profile

Associate Professor

• Fluid-Structure Interaction
• Multiphase Flows
• Aquatic Locomotion
• +7 more

Dr. Amneet Bhalla serves as an Associate Professor in the Department of Mechanical Engineering within the College of Engineering at San Diego State University (SDSU). His primary contact email is asbhalla@sdsu.edu, with office located in Engineering Building Room 323-G, and phone number (619) 594-2043. Education: Ph.D., Mechanical Engineering, Northwestern University (2013) M.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2009) B.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2004-2008) Postdoctoral Training: University of North Carolina at Chapel Hill (Mathematics Department) and Lawrence Berkeley National Laboratory (Computational Research Division) Research Interests: Dr. Bhalla develops advanced numerical methods and high-performance computing techniques for computational fluid dynamics (CFD) and fluid-structure interaction (FSI) problems. His work spans aquatic locomotion, renewable energy device modeling, multiphase flows, vehicular aerodynamics, and bioengineering applications. He creates mathematical models to interrogate underlying flow physics for engineering design optimization, with emphasis on open-source software development through the IBAMR library. Publication Trends: Recent publications (2023-2025) focus on robust numerical frameworks for multiphase flows with phase change, acoustic streaming, and fluid-structure interaction. Key themes include mass conservation in level set methods, adaptive mesh refinement, and solvers for non-isothermal gas-liquid-solid systems. Applications range from aquatic locomotion and renewable energy devices to microfluidics and biomedical flows, demonstrating commitment to both theoretical advances and practical engineering solutions. Scientific Awards: No awards mentioned in the provided text Advising and Grants: Dr. Bhalla secured an NSF CAREER award (2023) for "Consistent Continuum Formulation and Robust Numerical Modeling of Non-Isothermal Phase Changing Multiphase Flows". As PI of the CFD Lab, he mentors graduate students in computational mechanics, leveraging prior industrial experience at ExxonMobil Upstream Research Company. His research integrates industrial practicality with academic rigor through collaborations with national laboratories. Laboratory and Team: The Computational Fluid Dynamics and Flow Physics Laboratory (CFD Lab) develops the open-source IBAMR software—a distributed-memory parallel implementation of the immersed boundary method with adaptive mesh refinement. The lab emphasizes transparency, community engagement, and reproducibility, establishing cross-institutional collaborations while advancing computational methods for complex flow phenomena in engineering and biological systems.

Claudius ZibrowiusView profile

Professor

• Low-Dimensional Topology
• Categorified Knot Invariants
• Heegaard Floer Theories
• +3 more

Claudius Zibrowius is a Professor of Low-Dimensional Topology at Ruhr-University Bochum's Faculty of Mathematics. He leads the Topology Group, focusing on categorified knot invariants like knot Floer homology and Khovanov homology, leveraging Fukaya categories of surfaces. His work bridges Heegaard Floer theories and quantum invariant categorifications. Current group members include postdoc Dr. Chen Zhang and PhD student Luca Marchiori. Research highlights include ERC Starting Grant (ERC-2024) and DFG funding, as well as contributions to topics like Gordian distances, Conway spheres, and mutation invariance. Notable collaborations involve Artem Kotelskiy and Liam Watson. Teaching activities include Algebraic Topology I and outreach lectures. He co-organizes conferences like 'Categorification in Low Dimensional Topology' (2025). Awards include DFG Individual Grant (2022) and ERC Starting Grant (2024). His lab engages in computational projects like kht++, a C++ program for Khovanov invariants. Beyond academia, he sings in choirs and advocates for Palestinian rights, publicly opposing Israeli-Palestinian conflict complicity.