جست‌وجوی استاد و پژوهشگر

Paul von Bünauمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Healthcare Technology
• Machine Learning
• +۴ مورد دیگر

Dr. Paul von Bünau serves as Managing Director (CEO) of IDALab, a strategic advisory firm focused on artificial intelligence in healthcare and biotechnology. He concurrently holds an adjunct academic role at Europa-Universität Viadrina Frankfurt (Oder), where he teaches courses on AI strategy and regulatory compliance in healthcare. His educational background includes a PhD in Machine Learning (TU Berlin), M.Sc. in Pure Mathematics (University of St Andrews), and B.Sc. in Computer Science (University of Potsdam). **Professional Background**: Paul provides strategic AI guidance to multinational healthcare companies, biotech startups, and medical device manufacturers. Key engagements include building AI innovation units for a medtech company, accelerating drug discovery through AI-driven R&D, and developing AI tools for healthcare market access under EU regulations. His approach emphasizes aligning technical solutions with organizational needs and regulatory landscapes. **Research & Teaching**: At Viadrina, he delivers seminars on AI strategy (2024) and regulatory compliance in healthcare AI (2025). His research focuses on non-stationary data analysis, clinical data harmonization, and AI applications in neuroscience. Recent work includes a validated clinical data pipeline for scalable AI deployment (JMIR Medical Informatics, 2023) and algorithms for stationary subspace analysis (Physical Review Letters, 2009). **Key Takeaways from Projects**: Companies with strong engineering cultures often underestimate the cultural and structural changes required for AI integration. Rapid prototyping, talent alignment, and cross-disciplinary teams (e.g., AI + healthcare experts) are critical for success. His work highlights the transformative potential of AI in healthcare while emphasizing ethical and regulatory considerations.

Mike Krebsمشاهده پروفایل

استاد

• Graph Theory
• Algebraic Combinatorics
• Discrete Mathematics
• +۶ مورد دیگر

Mike Krebs is a Professor in the Department of Mathematics at California State University, Los Angeles (Cal State LA). He is affiliated with the College of Natural & Social Sciences. His research focuses on algebraic combinatorics, graph theory, and discrete mathematics, with notable work on Cayley graphs, expander families, and combinatorial trace methods. Krebs has collaborated extensively with undergraduate and graduate students, mentoring over 20 theses and co-authoring numerous papers with them. His academic background includes a Ph.D. from Johns Hopkins University (2005), advised by Dr. Richard Wentworth. Krebs has published widely in journals like Discrete Mathematics , Linear Algebra and its Applications , and Journal of Algebraic Combinatorics , with a focus on topics such as chromatic numbers of Cayley graphs, Kazhdan constants, and expander graph constructions. Research trends in his articles emphasize interdisciplinary applications, including sports analytics (PageRank for baseball teams) and pedagogical innovations in mathematics education. He has authored a textbook, Expander Families and Cayley Graphs: A Beginner's Guide (2011), and contributed to the Handbook of Graph Theory . Krebs’s work bridges pure mathematics with computational tools, as seen in his Mathematica code for chromatic number calculations.

Anthony Aidooمشاهده پروفایل

استاد

• Mathematical Biology
• Medical Imaging
• Epidemiological Modeling
• +۳ مورد دیگر

Anthony Aidoo is a Professor of Mathematical Sciences at Eastern Connecticut State University. He holds a Ph.D. in Mathematical Sciences from the University of Vermont, with research interests spanning Mathematical Biology, Epidemiological Modeling, Medical Imaging, and ACh-AChE analysis. His work integrates applied mathematics with real-world applications in biology and medicine. Education: Ph.D., Mathematical Sciences, University of Vermont M.S., Mathematical Sciences, University of Vermont Bachelor's Degree, Kwame Nkrumah University of Science and Technology (Ghana) Research Interests: Mathematical Biology/Physiology Medical Imaging Techniques (e.g., wavelet-based image enhancement) Modeling of diseases like Hepatitis B and campus drinking behavior Collaborations with institutions in Ghana, Canada, Germany, and others Teaching Philosophy: Emphasizes student engagement and the universality of mathematics, particularly through courses like Real Analysis I. Aims to create an enabling environment where students can explore and apply mathematical concepts beyond textbook theories. Professional Contributions: Frequent presenter at international conferences (Bulgaria, Germany, Spain, etc.), with a focus on interdisciplinary research at the intersection of mathematics and life sciences.

Robert Bassettمشاهده پروفایل

استادیار

• Operations Research
• Applied Mathematics
• Data-Driven Decision Making
• +۵ مورد دیگر

Robert Bassett is an Assistant Professor in the departments of Operations Research and Applied Mathematics at the Naval Postgraduate School (NPS), Monterey, CA. His research focuses on data-driven decision making, optimization, and statistical methods with defense applications. He holds a PhD in Mathematics from UC Davis and previously worked at Sandia National Laboratories and the U.S. Department of Defense. Education PhD in Mathematics, University of California, Davis (Dissertation: Optimization Methods in Statistics) Research Interests His work spans theoretical and computational aspects of decision-making, including optimization algorithms, robust statistical methods, adversarial machine learning, and signal processing. Notable areas include adversarial perturbations of physical signals, robust sensor subspace methods, and fused density estimation. Publications Trends Bassett's recent work emphasizes robust techniques against noise and adversarial attacks, as well as optimization algorithms for distributed systems. His articles often bridge theory and application, with contributions to machine learning security, sensor networks, and acoustic processing. Advising & Collaborations He has advised several students at NPS, including PhD candidate Peter Barkley and MS students Micah Oh and AJ Van Dellen. His collaborations include researchers from academia and defense sectors.

Brian Linsمشاهده پروفایل

استاد

• Mathematics
• Functional Analysis
• Operator Theory
• +۲ مورد دیگر

Brian Lins is a Professor in the Department of Mathematics and Computer Science at Hampden-Sydney College. He teaches courses such as Math 105 - Prep for Calculus , Math 121 - Statistics , and Math 243 - Differential Equations . His research focuses on functional analysis, operator theory, and nonlinear dynamics, with an emphasis on numerical ranges, nonexpansive mappings, and fixed point theory. His courses include rigorous assessment methods such as in-class exams, homework quizzes, and project-based learning. Students engage with topics like statistical inference, probability distributions, and advanced regression techniques in courses like Math 222 (Statistics) and Math 422 (Advanced Probability Theory).

Ed Carrمشاهده پروفایل

دانشیار

• Cybersecurity
• Graph Theory
• Quantum Computing
• +۳ مورد دیگر

Ed Carr is an Associate Professor of Computer Science at the Department of Computer Science & Mathematics, Emory & Henry College. Previously, he held roles at North Carolina A&T State University as Undergraduate Program Director and Graduate Faculty. His research focuses on Cybersecurity (Intrusion Detection, Zero-Knowledge Proofs), Graph Theory (Hamiltonian Cycles, Graph Coloring), and Quantum Computing. He has advised numerous graduate and undergraduate students, contributing to cybersecurity, algorithms, and systems projects. Education: Ph.D./MS Computer Science (NC A&T), MS Applied Mathematics (Western Carolina), BS Mathematics (Wingate). Research interests span cybersecurity frameworks, graph theory applications, and quantum computing paradigms. His work has been supported by grants totaling $1.57M from NSF, DOD, DOE, and others. Notable projects include GenCyber Summer Camps (DOD/NSA) and quantum information science initiatives. Publications emphasize graph cycle existence and algorithmic developments. He teaches advanced courses like Operating Systems, Concurrent Programming, and Data Structures. Awards: None explicitly listed. Advising highlights include key permutation libraries, ZKP authentication systems, and network security tools. His professional experience spans academia and community colleges, with roles at Gardner-Webb University and Edgecombe Community College.

Nick Wintzمشاهده پروفایل

دانشیار

• Ordinary Differential Equations
• Difference Equations
• Dynamics Equations on Time Scales
• +۶ مورد دیگر

Dr. Nick Wintz is an Associate Professor of Mathematics at Lindenwood University, located in St. Charles, Missouri. He holds a BS and MA in Mathematics from Marshall University and a PhD from Missouri University of Science and Technology (formerly University of Missouri-Rolla), with a Graduate Certificate in Financial Mathematics. His research focuses on control theory, differential equations, and dynamics on time scales. He has advised multiple undergraduate research projects, including award-winning studies on Kalman filters and mathematical biology models. Dr. Wintz teaches a range of courses from introductory calculus to advanced topics in differential equations and complex variables. His academic background includes roles as a lecturer at Missouri S&T and a teaching/research assistant at Marshall University. He joined Lindenwood in 2011 and was promoted to Associate Professor in 2015. His work spans theoretical contributions to control systems, optimal estimation, and mathematical biology, with applications in engineering and life sciences. Dr. Wintz collaborates on interdisciplinary projects, including predator-prey modeling and artificial pancreas systems. His research has been recognized through student awards at the Lindenwood Student Research Symposium. He lives in Saint Charles with his family.

Arielle Carrمشاهده پروفایل

استادیار

• Applied Linear Algebra
• Numerical Analysis
• Preconditioners
• +۵ مورد دیگر

Arielle Carr is an Assistant Professor in the Department of Computer Science & Engineering at Lehigh University's Rossin College of Engineering. She holds a Ph.D. in Mathematics from Virginia Tech (2021), an M.S. in Mathematics (2015), and dual B.S. degrees in Mathematics with a Computer Science minor and Sociology with an Education minor from Virginia Tech (2012) and Roanoke College (2008). Her research focuses on applied linear algebra, numerical analysis, and preconditioning techniques for large-scale systems, with applications in quantum computing, weather prediction, and science policy advocacy. Dr. Carr’s work emphasizes preconditioner development, Krylov subspace methods, and recycling techniques to enhance solver efficiency. She is particularly known for integrating machine learning into numerical methods and advancing quantum interior point algorithms. Her recent SIAM Science Policy Fellowship (2024) reflects her commitment to STEM advocacy and policy engagement, including collaborations with institutions like the Da Vinci Science Center to promote women in engineering. Her publications span interdisciplinary topics, blending numerical analysis with emerging technologies like quantum computing and diffusion models for ensemble forecasting. Though not explicitly listed, her research likely involves collaborations with computational science and engineering groups. Awards include the 2024 SIAM Science Policy Fellowship.

Andres Marcos Encinas Bachillerمشاهده پروفایل

استاد

• Discrete Mathematics
• Network Analysis
• Linear Algebra
• +۴ مورد دیگر

Andres Marcos Encinas Bachiller is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Faculty of Mathematics and Statistics and the Department of Mathematics. He holds a Licenciado and Doctorado in Mathematics from the same institution. Professional Category: University Titular (equivalent to Professor). Research Groups: MAPTHE (Matrix Analysis and Discrete Potential Theory), OMGRAPH (Optimization Methods on Graphs). His research focuses on discrete vector calculus, boundary and inverse problems on networks, effective resistance, and M-matrix theory. He has contributed extensively to matrix analysis and its applications in network science, particularly in electrical impedance tomography and graph theory. Recent work includes studies on tridiagonal M-matrices, group inverses, and applications in medical imaging through discrete inverse problems. His articles span linear algebra, network analysis, and combinatorial matrix theory, with a focus on theoretical and applied aspects of these fields. He has supervised doctoral research, including Álvaro Samperio Valdivieso's thesis on inverse problems in networks. His projects include developing EIT devices for breast cancer detection and optimizing methods on graphs. Collaborations involve researchers in algebra, engineering, and applied mathematics. He is actively involved in research networks, including the ALAMA network (Linear Algebra and Matrix Analysis), and has led competitive projects funded by Spanish and Catalan agencies.

• Matrix Analysis
• Discrete Mathematics
• Graph Theory
• +۳ مورد دیگر

Margarida Mitjana Riera is a Senior Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Applied Mathematics I and the Faculty of Mathematics and Statistics. She is part of the MAPTHE (Matrix Analysis and Discrete Potential Theory) and OMGRAPH (Optimization Methods on Graphs) research groups. Her work focuses on matrix analysis, graph theory, linear algebra applications in networks, and optimization. She holds a degree in Mathematics and a PhD from UPC. Her research interests include inverse eigenvalue problems, network analysis, resistance distances, and circulant matrices. She has authored over 200 publications, including articles in journals like Linear Algebra and Its Applications and Special Matrices , and contributed to projects on optimization methods on graphs and discrete potential theory. She actively participates in conferences and collaborates on educational initiatives like the Curs 0 preparatory program for engineering students. Her grants include AGRUPS 2024 and prior competitive R+D+I projects. She advises on research in subdivision networks and matrix theory, with contributions to both theoretical and applied mathematics.

• Control Theory
• Linear Systems
• Numerical Analysis
• +۳ مورد دیگر

Jordan Jens is a Lecturer at the Institute of Mathematics , affiliated with the Faculty of Mathematics and Computer Science at University of Würzburg . He holds a PhD in Mathematics (2008) and a Diploma in Mathematics (2000) from the same university. Education PhD in Mathematics (2008), University of Würzburg Diploma in Mathematics (2000), University of Würzburg His research focuses on Control Theory and Numerical Analysis , particularly in the context of Linear Systems , Reachability Analysis , and Algorithm Design . Recent publications highlight his work on random linear systems over finite fields, optimal control of iterative solvers, and observer design for delayed systems. Publications between 2002 and 2016 reveal expertise in Dynamical Systems , Matrix Equations , and Nonlinear Control , with applications to numerical algorithms and system stability.

Eloi Martinetمشاهده پروفایل

پژوهشگر

• Shape Optimization
• Numerical Methods for PDEs
• Level Set Methods
• +۳ مورد دیگر

Eloi Martinet is a postdoctoral researcher at the University of Würzburg, affiliated with the Mathematics of Machine Learning team within the Institute of Mathematics. He holds a PhD in Spectral Shape Optimization from LAMA and LJK (2019–2023), an engineering degree from ENSIMAG, and an Agrégation de Mathématiques. His research focuses on variational methods in machine learning, numerical PDEs, and shape optimization using neural networks and graph-based approaches. Education: PhD in Spectral Shape Optimization (2019–2023), LAMA (Chambéry) & LJK (Grenoble) Agrégation de Mathématiques (2020–2021), Institut Fourier (Grenoble) Engineering School in Informatics and Applied Mathematics (2016–2019), ENSIMAG (Grenoble) Research Interests: Shape/topology optimization, numerical methods for PDEs, level set methods, neural networks, and graph-based machine learning. His work explores variational approaches to machine learning and solving PDEs with neural networks. Teaching: Recent roles include lectures on Data Science foundations, Machine Learning on Graphs, and numerical analysis tutorials at JMU and ENSIMAG. Grants & Supervision: Co-supervised MSc internships on phase fields on graphs and PDE spectrum analysis. His research projects include optimizing Neumann eigenvalues on spheres and meshless shape optimization with neural networks. Labs/Teams: Member of the Mathematics of Machine Learning team at the University of Würzburg, collaborating on projects involving FreeFem and Python tools for eigenvalue optimization.

• Calculus of Variations
• Partial Differential Equations
• Spectral Geometry
• +۳ مورد دیگر

Lorenzo Brasco is a Full Professor at the University of Ferrara, specializing in Calculus of Variations, Partial Differential Equations, and Spectral Geometry. His research focuses on functional inequalities, geometric optimization, and nonlinear analysis. He has authored a textbook, Handbook of Calculus of Variations for Absolute Beginners , and contributed to over 70 publications in leading journals. Key research areas include spectral geometry, Poincaré-Sobolev inequalities, fractional Laplacian, and geometric analysis. His work explores topics like topological constraints, symmetry properties, and optimal constants in functional spaces. Recent studies address capacitary inradius, Steiner symmetric sets, and fractional spectral bounds. Dr. Brasco is actively involved in academic activities, organizing workshops and contributing to conferences on variational methods, nonlinear PDEs, and geometric analysis. He has collaborated with institutions worldwide and maintains an extensive publication record across diverse mathematical disciplines.

• Spectral Optimization
• Calculus of Variations
• Partial Differential Equations
• +۳ مورد دیگر

Dorin Bucur is a Professor at Université de Savoie, specializing in spectral optimization, calculus of variations, and partial differential equations. His research focuses on eigenvalue problems, shape optimization, and geometric inequalities, often involving applications to material science and engineering. He has organized numerous conferences and workshops, including the Vito Volterra Meeting in Calculus of Variations (2025) and the Shape Optimization and Isoperimetric Inequalities conference (2016). His work frequently addresses isoperimetric inequalities, free discontinuity problems, and the interplay between geometry and spectral properties. Recent contributions include studies on the fundamental gap conjecture, nonlocal isoperimetric problems, and stability of eigenvalues under domain perturbations. Bucur has collaborated extensively, with notable co-authors like I. Fragalà, A. Giacomini, and B. Velichkov. Bucur's research also explores thermal insulation optimization and the application of phase-field methods to packing problems. He has contributed to foundational works on Faber-Krahn inequalities and the mathematical analysis of Cheeger clusters. His articles frequently appear in top journals such as Calc. Var. Partial Differential Equations and Arch. Ration. Mech. Anal.

• Calculus of Variations
• Optimization
• Partial Differential Equations
• +۳ مورد دیگر

Ilaria Fragalà is an Associate Professor at the Department of Mathematics, Politecnico di Milano. Her research focuses on calculus of variations, geometric analysis, and partial differential equations, with applications to optimization problems involving shapes, partitions, and nonlocal perimeters. She has contributed to fundamental results in optimal partitioning, eigenvalue optimization, and the honeycomb conjecture. Education: PhD in Mathematics (2000) with a thesis on elliptic approximations in Finsler geometry, and earlier academic work on functional inequalities and measure theory. Research interests include nonlocal perimeters, spectral geometry, free discontinuity problems, and the interplay between geometry and PDEs. Her recent work addresses optimal convex partitions, robustness of geometric structures, and symmetry properties in variational problems. She has organized conferences such as the Vito Volterra Meeting in Calculus of Variations (2025) and participated in workshops like the 'Matter of Shapes' (2025). Her publications emphasize rigorous analytical methods and geometric insights.