جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Dynamical Systems
• Optimization
• +۳ مورد دیگر

Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.

• Physics
• Computer Science
• Mathematics
• +۱۰ مورد دیگر

Cristopher Moore is a Professor at the Santa Fe Institute, where he conducts interdisciplinary research at the intersection of physics, computer science, and mathematics. His work focuses on understanding phase transitions in computational problems, statistical inference, and network analysis. Moore has made significant contributions to the fields of complex systems, quantum computing, and algorithmic justice. Moore's primary research areas include phase transitions in computational problems and statistical inference, where he investigates how problems suddenly become hard or impossible to solve when certain thresholds are crossed. His work spans social networks, big data analysis, quantum computing, algorithmic transparency, and decarbonization efforts. He is particularly known for applying physics-inspired approaches to computational problems, using techniques from spin glass theory, network theory, and computational complexity. His recent publications reveal a strong focus on community detection in networks, phase transitions in data science problems, algorithmic fairness in criminal justice systems, and quantum computing applications. Moore's work demonstrates consistent patterns across multiple disciplines, with recurring themes of phase transitions, computational limits, and the application of physics concepts to computational problems. Moore actively mentors students and has advised numerous PhD and Master's students who have gone on to successful careers in academia and industry. His work on algorithmic justice has influenced policy discussions in New Mexico and beyond, particularly regarding risk assessment in the criminal justice system.

Raffaella Mulasمشاهده پروفایل

استادیار

• Spectral Theory of Graphs and Hypergraphs
• Discrete Laplacians
• Non-backtracking Operators
• +۷ مورد دیگر

Raffaella Mulas is an Assistant Professor in the Department of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. She previously served as a Group Leader and Minerva Fast Track Fellow at the Max Planck Institute for Mathematics in the Sciences, where she maintains an ongoing affiliation. Her research lies at the intersection of spectral graph theory, discrete mathematics, and network science. Research Interests: Her work focuses on the spectral theory of graphs and hypergraphs, particularly the properties of discrete Laplacians and non-backtracking operators. She investigates extremal combinatorics problems such as graph coloring and the Turán problem, often applying spectral methods to derive sharp bounds. Her research has strong applications in modeling and analyzing complex networks. Recent Research Trends: Analysis of the 15 most recent publications reveals a consistent focus on spectral characterizations of graphs and hypergraphs, including signed and complex unit hypergraphs. She frequently studies the normalized Laplacian and its extremal eigenvalues, develops non-backtracking operators, and explores measure-theoretic and geometric representations of networks. A strong thread connects spectral bounds to combinatorial invariants like chromatic number. VU Startpremie Grant Elected Member, European Mathematical Society Young Academy (EMYA) Elected Member, Elisabeth-Schiemann-Kolleg, Max Planck Society Minerva Fast Track Fellow, Max Planck Institute Advising and Grants: While no formal students are listed, she is an active researcher with significant grant funding, notably the VU Startpremie Grant. She collaborates internationally and supervises research projects in spectral graph theory and network analysis. Her affiliation with both VU Amsterdam and MPI-MiS enables broad academic mentorship and collaborative supervision. Labs and Research Groups: Raffaella Mulas leads research within the Mathematics Department at VU Amsterdam and is affiliated with the research group at the Max Planck Institute for Mathematics in the Sciences. Her work contributes to advancing theoretical foundations in discrete mathematics with applications in data science and network modeling.

• Formal Methods
• Satisfiability (SAT)
• Satisfiability Modulo Theories (SMT)
• +۱۱ مورد دیگر

Enric Rodríguez Carbonell is a faculty member at the Department of Computer Sciences within the Faculty of Computer Science at Universitat Politècnica de Catalunya (UPC). He is a key member of the LOGPROG - Lògica i Programació research group, focusing on formal methods, automated reasoning, and combinatorial optimization. Research Interests: His work centers on satisfiability (SAT), satisfiability modulo theories (SMT), and their applications in program verification, constraint solving, and optimization. He investigates techniques such as conflict-driven learning, invariant generation, and Max-SMT for proving termination and safety. His research bridges theoretical foundations with practical applications in software analysis and industrial problem-solving. Publication Trends: His recent publications (2020–2024) show a sustained focus on enhancing SAT and SMT solvers, particularly in pseudo-Boolean reasoning, integer linear programming, and multi-conflict analysis. Earlier works established contributions in non-linear arithmetic, termination proofs, and efficient encodings for cardinality constraints, reflecting a long-term commitment to foundational and applied aspects of automated reasoning. Scientific Awards: Best student paper award at SAT 2024 Advising and Grants: Rodríguez Carbonell has contributed to multiple competitive R+D+i projects under Spain’s State Research Plans, indicating active grant involvement. He has co-authored doctoral theses and educational initiatives like Jutge.org, demonstrating engagement in academic supervision and pedagogical innovation. Labs and Teams: He is a core researcher in the LOGPROG group at UPC, which specializes in logic and programming, with strong collaborations across formal methods, verification, and constraint technologies.

Philip Matchett Woodمشاهده پروفایل

استاد

• Probability Theory
• Combinatorics
• Random Matrices
• +۳ مورد دیگر

Philip Matchett Wood is a Professor in the Department of Mathematics at Harvard University. His research focuses on probability theory, combinatorics, and their applications, with particular emphasis on random matrices, Markov chains, structural behavior in sumsets, and computational methods. He has contributed to foundational work in random matrix theory, including spectral analysis of non-backtracking matrices and universality principles for sparse matrices. His work often bridges theoretical insights with computational exploration, as seen in his development of Maple packages for combinatorial bijections and probabilistic algorithms. Research Interests: Probability Theory, Combinatorics, Random Matrices, Markov Chains, Computational Mathematics, Geometry Grants: Supported by NSA grants (H98230-16-1-0301 and H98230-14-1-0149) from 2014–2018, focusing on random matrix theory and spectral analysis. Publications: Over 20 peer-reviewed articles, including work on outlier detection in random matrix products, computational number theory, and universality in spectral distributions. Contributions: Developed Maple tools for combinatorial bijections (e.g., Fibonacci, ZeckFibBijections), advancing algorithmic approaches to enumerative combinatorics. He is actively involved in teaching, such as Math 22A: Vector Calculus and Linear Algebra I in Fall 2024. His interdisciplinary interests include applying probability to clinical studies (e.g., heart rate variability under anesthesia) and recreational mathematics, exploring topics like the mathematics of Rubik's Cube and origami constructions for angle trisection.

• Nonlinear optimization
• Stochastic approximation
• Algorithm design for big data analytics
• +۳ مورد دیگر

Yuyuan Ouyang is an Associate Professor in the Department of Mathematical and Statistical Sciences at Clemson University. He holds a Ph.D. in Mathematics from the University of Florida (2013). His research focuses on nonlinear optimization, stochastic approximation, and algorithm design for big data analytics. His work bridges theoretical foundations with practical applications in machine learning, network flow programming, and convex optimization. Dr. Ouyang teaches advanced courses including Machine Learning I/II, Network Flow Programming, and Nonlinear Programming. His recent publications emphasize gradient sliding methods, decentralized optimization, and saddle-point problem analysis. He has contributed to SIAM Journal on Optimization, Mathematical Programming, and Operations Research Letters. His research trends show a strong emphasis on algorithmic innovation for large-scale systems, with notable work on complexity bounds, convex reformulations, and statistical estimation techniques. He has collaborated widely on topics ranging from variational inequalities to MRI image reconstruction.

Serdar Özyönمشاهده پروفایل

دانشیار

• Electrical Energy and Power Systems
• Artificial Intelligence
• Renewable Energy Production Systems
• +۳ مورد دیگر

Dr. Serdar Özyön is an Associate Professor and Director of the Smart Systems Design Application and Research Center at Kütahya Dumlupınar University's Faculty of Engineering. He holds a PhD in Electrical and Electronics Engineering from the same university, with prior academic roles including Assistant Professor and Research Assistant since 2005. His expertise spans Electrical Energy Systems, Artificial Intelligence, and Renewable Energy, focusing on optimization algorithms for power distribution and energy systems. Education Bachelor's in Electrical and Electronics Engineering (Dumlupınar University, 2000–2005) Master's in Electrical Engineering (Dumlupınar University, 2006–2009) PhD in Electrical and Electronics Engineering (Dumlupınar University, 2015–2018) Research Interests : Dr. Özyön's work integrates optimization algorithms with power systems, renewable energy integration, and AI applications. He has developed methodologies for solving economic dispatch problems using gravitational search, charged system search, and artificial bee colony algorithms. His recent projects include optimizing solar and wind energy systems, and enhancing grid efficiency through smart system designs. Projects : He leads initiatives like the "Optimization Applications for Multidimensional Engineering Problems" (2022–2024) and has contributed to hydrothermal coordination studies. His research often addresses renewable energy integration challenges and algorithmic improvements for energy systems. Awards & Grants : While no specific awards are listed, his extensive publications and leadership in the Smart Systems Center highlight his academic contributions. Administrative roles include Technology Transfer Board Member and Research Committee participation. Labs/Teams : As Director of the Smart Systems Design Center, he oversees R&D in AI, IoT, Big Data, and Industry 4.0 applications, fostering collaboration between academia and industry.

• Optimization
• Control Theory
• Partial Differential Equations (PDEs)
• +۳ مورد دیگر

Prof. Daniel Walter is a Junior Professor at the Institute of Mathematics, Humboldt University of Berlin, within the Faculty of Mathematics and Natural Sciences. His research focuses on non-smooth optimization, optimal control, and numerical analysis of partial differential equations (PDEs). He specializes in developing advanced methods for sensor placement in inverse problems and stabilizing control systems using computational techniques. His work integrates theoretical analysis with practical applications, addressing challenges in feedback stabilization, convergence of optimization algorithms, and data-driven approaches. Notable contributions include studies on extremal points in optimization norms, linear convergence rates of conditional gradient methods, and semiglobal stabilization using neural networks. Key Research Areas: Non-smooth optimization and sparse methods PDE-constrained optimization and control Inverse problems and sensor placement strategies Numerical methods for parabolic and elliptic equations Recent publications emphasize algorithmic advancements in optimization, with a focus on acceleration and convergence guarantees. His interdisciplinary approach bridges mathematical theory with engineering applications, particularly in stabilization and parameter estimation. Advising & Grants: While no specific grants or advisees are listed, his research indicates active involvement in training through cutting-edge projects in optimization and control.

Trung Tuyen Truongمشاهده پروفایل

استاد

• Several Complex Variables
• Dynamical Systems
• Algebraic Geometry
• +۷ مورد دیگر

Trung Tuyen Truong is a Professor of Mathematics at the University of Oslo, Faculty of Mathematics and Natural Sciences, Department of Mathematics, since September 2023. Previously, he served as an Associate Professor at the same institution from September 2017 to September 2023. His academic journey includes a PhD from Indiana University (2006-2012) under Professor Eric Bedford, followed by postdoctoral positions at Syracuse University (2012-2014), Korea Institute for Advanced Study (2014-2015), and The University of Adelaide (2015-2017). His research spans several complex variables, dynamical systems, and related topics in algebraic geometry. He is particularly interested in applying dynamical systems theory and computational techniques to solve both theoretical and practical problems. His work includes developing optimization algorithms, exploring connections between Weil's Riemann hypothesis and dynamical systems, and investigating the Jacobian conjecture. Truong has made significant contributions to optimization methods, particularly developing Backtracking Gradient Descent and Backtracking New Q-Newton's method, which have applications in deep learning and solving complex mathematical problems. His research has been supported by various grants including a Seed grant from UiO Growth House for developing AI tools for mathematical education, membership in MSCA-Cofund-DP for ERC funding PhD positions, and as Principal Investigator for the Young Research Talents grant from the Research Council of Norway. Truong is actively involved in developing software tools to help students learn mathematics and optimization, as well as creating efficient computational tools. His NUV Optimization solvers have demonstrated superior performance compared to established tools like Matlab and IPOPT in certain optimization problems. He is collaborating with AI companies and medical doctors to use AI for diagnosing from lung data measurements. His publications demonstrate expertise across pure mathematics, computational methods, and interdisciplinary applications, with recent work focusing on dynamical systems approaches to number theory problems and practical optimization algorithms.

Victor Preciadoمشاهده پروفایل

دانشیار

• Network Science
• Dynamical Systems
• Data Science
• +۳ مورد دیگر

Victor Preciado is an Associate Professor at the University of Pennsylvania, holding appointments in the Departments of Electrical & Systems Engineering and Computer & Information Science . He is affiliated with the Warren Center for Network & Data Sciences , the PRECISE Center , and the Applied Math and Computational Science graduate group . His research bridges Network Science , Dynamical Systems , and Data Science , focusing on modeling, analysis, and control of complex networked systems. Ph.D. in Electrical Engineering and Computer Science from MIT (supervised by Prof. George Verghese) Visiting scholar at UC Berkeley, Santa Fe Institute, and Courant Institute (NYU) Postdoctoral researcher at GRASP Lab (working with Prof. Ali Jadbabaie) His research interests include epidemic modeling and control , socio-technical networks , network controllability , and data-driven optimization . Recent work explores contact-aware robotics and operator learning using attention mechanisms. His publications from 2022–2024 highlight advancements in robust control , epidemic tracking , and hypergraph neural networks , with applications in pandemic management , robotics , and temporal network analysis . Scientific awards include: 2024: IEEE Robotics and Automation Society Best Paper Award finalist (for Stabilization of Complementarity Systems ) 2022: IEEE Control Systems Magazine Best Paper Award (retrospectively for Analysis and Control of Epidemics ) 2017: National Science Foundation CAREER Award 2019: IEEE Transactions on Network Science and Engineering Best Paper Award runner-up He has served as Associate Editor for IEEE Transactions on Network Science and Engineering and IEEE Transactions on Control of Networked Systems , and as Guest Editor for special issues on pandemics and control of epidemics in journals like Annual Reviews in Control and SIAM Journal on Control and Optimization .