جست‌وجوی استاد و پژوهشگر

• Nonlinear Control Theory
• Dynamic Optimization
• Distributed Control Systems
• +۳ مورد دیگر

Thulasi Mylvaganam is a Senior Lecturer in Control Engineering at the Department of Aeronautics, Imperial College London. They specialize in nonlinear control theory, dynamic optimization, and applications to robotics and renewable energy systems. Mylvaganam holds an M.Eng. in Electrical and Electronic Engineering from Imperial College London (2010) and a Ph.D. in Control and Power (2014). They have held roles including Postdoctoral Research Associate (2014–2016), Research Fellow (2016–2017), Lecturer (2017), and Senior Lecturer (2021). Research interests include distributed control, data-driven control, and optimal control strategies for complex systems. They teach courses such as Mechatronics and Computing and Numerical Methods 2 for Aeronautics students. Their work spans robotics, renewable energy systems, and multi-agent systems. Affiliations include the Computational Methods and Mathematical Modelling group and the Robotics Forum. Mylvaganam actively supervises PhD students focusing on advanced nonlinear control topics and emphasizes rigorous academic preparation for prospective candidates.

• Optical Solitons
• Quantum Optics
• Nonlinear Chromatic Dispersion
• +۳ مورد دیگر

Dr. Anjan Biswas is a Professor in the Department of Math & Physics at the College of Arts & Sciences. His research focuses on analytical and computational approaches to quantum optics and nonlinear wave propagation. Education: Ph.D. in Mathematics from the University of New Mexico. Metrics: Erdos Number 4, Hirsch Index 111. Research interests center on optical solitons in fibers with linear and nonlinear chromatic dispersion, Lie symmetry analysis for quiescent solitons, and stochastic modeling in photonic systems. His work explores fractional temporal evolution, polarization-mode dispersion, and Kudryashov’s power-law structures. Recent publications highlight advancements in cubic-quartic soliton modeling, dispersion triplet systems, and noise-resilient optical communication frameworks. Studies span Boussinesq-type models, magneto-optic waveguides, and metamaterial applications. Key methodologies include Adomian decomposition, Sardar sub-equation techniques, and Lie symmetry analysis. Applications range from quantum fluid dynamics to internet bottleneck suppression via exotic self-phase modulation structures.

• Epidemiological Modeling
• Control Theory
• Network Science
• +۳ مورد دیگر

Baike She is a Postdoctoral Fellow at the School of Electrical and Computer Engineering, Georgia Institute of Technology. Their research focuses on interdisciplinary topics at the intersection of control theory, network science, and epidemiological modeling. Key areas include epidemic spread analysis, distributed systems optimization, and privacy-preserving algorithms for networked models. Research interests emphasize mathematical frameworks for analyzing complex systems, including compositional control approaches (e.g., LQR analysis via category theory), robust epidemic control strategies, and leveraging differential privacy in sensitive data computations. Work spans both theoretical developments and applied methodologies for real-world systems such as SIR/SIS epidemic models and infrastructure networks. Recent publications (2022-2025) highlight contributions to distributed reproduction number computation, optimal epidemic mitigation under uncertainty, and the integration of opinion dynamics with vaccination strategies. Methodologies include Gaussian process regression, dissipativity theory, and model predictive control frameworks. No specific awards or grants are explicitly listed in the provided texts. Advising roles and laboratory affiliations remain unspecified based on available information.

• Control Theory
• Optimization
• Machine Learning
• +۹ مورد دیگر

Andrea Iannelli is a Tenure-Track Assistant Professor at the Institute for Systems Theory and Automatic Control (IST) , University of Stuttgart, Germany. He also serves as a faculty member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and participates in the Cluster of Excellence Data-Integrated Simulation Science (SimTech) . His research focuses on reconciling model-based and data-driven approaches for robust and adaptive control of uncertain dynamical systems. Ph.D. : Control and Dynamical Systems, University of Bristol (UK), 2019 Postdoctoral Researcher : ETH Zürich (Switzerland), 2019–2022 Harnessing the intersection of control theory, optimization, and machine learning , Iannelli’s work addresses data-driven modeling, uncertainty quantification, and robust control with applications in energy systems, intelligent transportation, and industry 4.0 . His recent publications highlight trends in LPV frameworks, online convex optimization, and hybrid control systems , emphasizing safety and efficiency. He contributes to the academic community as an Associate Editor for the International Journal of Robust and Nonlinear Control and as a member of international conference IPCs. His group, Trustworthy Autonomy for Smart Adaptive Systems (TASAS) , mentors PhD students in projects spanning adaptive control, uncertainty quantification, and reinforcement learning .

Jeeseop Kimمشاهده پروفایل

استادیار

• Robotics
• Autonomy
• Control Theory
• +۳ مورد دیگر

Jeeseop Kim is an Assistant Professor in the Department of Aerospace and Mechanical Engineering at The University of Texas at El Paso (UTEP), College of Engineering, specializing in robotics, autonomy, and control theory. His research focuses on safety-critical planning and control, with emphasis on bipedal/quadrupedal locomotion, hybrid dynamical system control, and whole-body planning and control. Education: B.S. in Mechanical and Aerospace Engineering, Seoul National University (2014) M.S. in Intelligence and Information (Robotics), Seoul National University (2017) Ph.D. in Mechanical Engineering, Virginia Tech (2022) Postdoctoral Scholar, Mechanical and Civil Engineering, Caltech (2022–2025) His research spans safety-critical control systems for legged robots, including obstacle-aware nonlinear model predictive control (MPC), control barrier functions, and distributed coordination algorithms. Recent work explores adaptive delay estimation, tactile sensing for robotic grasping, and hardware-software co-design for humanoid robots. Key article trends highlight advancements in autonomous inspection robotics, hybrid control architectures, and real-time planning for quadrupedal systems. His work integrates control theory with practical applications in industrial and healthcare domains. Awards: ASME DSCD Rudolf Kalman Best Paper Award (2022) IEEE ICRA Outstanding Paper Award (2023) Jeeseop teaches MECH 4332: Mechanical Computational Applications in Vision and Robotics (Fall 2025). He actively recruits Ph.D. students for Spring/Fall 2026 and seeks motivated undergraduates/MS students with skills in robotics kinematics, programming (C/C++, Python, MATLAB), and CAD design. The AIGIS Lab welcomes applicants with interests in robotics, controls, and autonomous systems.

Mireille E. Brouckeمشاهده پروفایل

استاد

• Mathematical System Theory
• Systems Neuroscience
• Reach Control Problems
• +۳ مورد دیگر

Mireille E. Broucke is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto, where she is a member of the Systems Control Group within the Faculty of Applied Science and Engineering. She teaches various undergraduate and graduate courses including Adaptive Control and Reinforcement Learning, Robot Modeling and Control, and Introduction to Nonlinear Systems, demonstrating her commitment to education in control systems engineering. Professor Broucke's research focuses on mathematical system theory with particular emphasis on Systems Neuroscience, Reach Control Problems, and Patterned Linear Systems. Her work bridges theoretical control theory with applications in neuroscience and robotics. She has developed theoretical frameworks for understanding neural adaptation through control theory principles and has applied reach control theory to robotics problems including motion control of quadrocopters. Her research demonstrates how control theory can provide insights into biological systems while also advancing engineering applications. Her recent publications show a clear trend toward applying control theory to neuroscience, particularly in understanding adaptive internal models in the brain. The publications span from theoretical reach control problems on simplices and polytopes to practical applications in robotics and neural systems. Her work increasingly focuses on the intersection of control theory and neuroscience, examining how the brain implements adaptive control mechanisms for motor functions. This represents a significant shift from her earlier work which was more focused on pure control theory problems. Professor Broucke has advised several PhD students including Fatima Ghadieh, Erick Mejia Uzeda, and Mohamed Hafez. Her research has been supported by various grants that enable her work in control theory and its applications to neuroscience and robotics. She maintains an active research program with numerous publications in top control theory journals including IEEE Transactions on Automatic Control, Automatica, and Systems and Control Letters.

Jiamin Jianمشاهده پروفایل

استادیار

• Stochastic Control
• Mean Field Games
• Optimal Control Theory
• +۳ مورد دیگر

Jiamin Jian is a Postdoctoral Assistant Professor in the Department of Mathematics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts (LSA). His research focuses on stochastic control, mean field games, and optimal control theory, with applications to nonlinear systems and mathematical finance. Jian holds a Ph.D. from Worcester Polytechnic Institute (2024), an M.S. from City University of Hong Kong (2019), and dual B.S./B.M. degrees from Nankai University (2018). His work explores long-time behaviors of stochastic systems, convergence rates in mean field games, and numerical methods for McKean-Vlasov equations. Recent studies include ergodic properties of control systems and hybrid LQG frameworks. Jian’s research bridges theoretical analysis and practical applications in stochastic processes and game theory. No scientific awards or grants are explicitly listed in the provided materials. His advising responsibilities and team affiliations are not detailed here.

Duarte Guerreiro Tomé Antunesمشاهده پروفایل

استادیار

• Control Theory
• Optimal Control
• Stochastic Control
• +۳ مورد دیگر

Dr. Duarte Guerreiro Tomé Antunes is an Assistant Professor at the Department of Mechanical Engineering , Eindhoven University of Technology (TU/e), specializing in control theory. He is affiliated with the Control Systems Technology Group and focuses on optimal/stochastic control and networked control systems. Research emphasizes overcoming the curse of dimensionality in large-dimensional systems through approximate control strategies. Explores event-triggered control for networked systems with latency and computational constraints. Teaches Optimal Control and Dynamic Programming , Training Project 4 , and Robotic Seminars . Publications highlight advancements in: Event-triggered methods for linear quadratic control Stability analysis of networked systems with asynchronous links Frequency-domain modeling of control loops with data losses Switched system regulation via informed policies

Tony Stillfjordمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations
• Stochastic Optimization
• +۴ مورد دیگر

Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net

Zhou Zhouمشاهده پروفایل

مدرس ارشد

• Stochastic Control
• Mathematical Finance
• Optimal Stopping Problems
• +۳ مورد دیگر

Zhou Zhou is a Senior Lecturer in the School of Mathematics and Statistics at the University of Sydney. His academic roles include Senior Lecturer (2022–present) and Lecturer (2018–2021) at the University of Sydney, as well as postdoctoral positions at the University of Michigan and University of Minnesota. He holds a Ph.D. in Applied & Interdisciplinary Mathematics from the University of Michigan (2015) and a B.S. in Mathematics from Nankai University (2010). His research focuses on stochastic control, mathematical finance, and game theory, with particular emphasis on time-inconsistent problems, optimal stopping, and equilibrium strategies. Key areas include applications in financial mathematics, stochastic processes, and dynamic optimization. His work has been published in journals such as Mathematical Finance, SIAM Journal on Control and Optimization, and Finance and Stochastics. Zhou has secured grants including the 2023 Faculty of Science Startup Scheme for time-inconsistent control research and the 2022 Australian Research Council grant on green investment impacts. He teaches courses like Arbitrage Pricing in Continuous Time and supervises research students in financial mathematics. His academic contributions span over 50 publications, with notable work on binomial-tree approximations for stopping problems, equilibrium strategies in mean-field games, and policy iteration for stochastic control. Presentations include talks at international conferences and universities worldwide, emphasizing interdisciplinary applications of stochastic analysis.

Alan Kaptanogluمشاهده پروفایل

استاد

• Applied Mathematics
• Scientific Machine Learning
• Nuclear Fusion
• +۳ مورد دیگر

Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.

Aritra Mitraمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Control Theory
• +۶ مورد دیگر

Aritra Mitra is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from Purdue University (2020), an M.Tech. from IIT Kanpur (2015), and a B.E. from Jadavpur University (2013). Before joining NC State, he was a postdoctoral researcher at the University of Pennsylvania. His research focuses on enabling reliable, efficient learning and decision-making in large-scale distributed systems, addressing challenges like computation, communication constraints, and adversarial robustness. Key areas include control theory, machine learning, signal processing, and network science. Education: Ph.D., Electrical and Computer Engineering, Purdue University (2020) M.Tech., Electrical Engineering, Indian Institute of Technology Kanpur (2015) B.E., Electrical Engineering, Jadavpur University (2013) Research Interests: Dr. Mitra’s work bridges theoretical foundations with practical applications in distributed systems. He designs algorithms for federated learning, reinforcement learning, and adversarial robustness, with applications in control systems and networked environments. Recent efforts emphasize finite-time analysis of TD learning, heterogeneous federated systems, and resilient control under communication constraints. His contributions often integrate tools from stochastic approximation, optimization, and signal processing. Publications: His articles explore cutting-edge topics like federated TD learning, robust system identification under heavy-tailed noise, and distributed multi-agent optimization. Recent trends highlight advancements in asynchronous algorithms, delay-adaptive systems, and model-free control under communication bottlenecks. Grants & Labs: While specific grants are not detailed, his research aligns with themes in distributed computing and control, suggesting potential involvement in NSF or industry-funded projects. No lab-specific details are provided in the text.

• Algebra
• Number Theory
• Coding Theory
• +۳ مورد دیگر

Assoc Prof Frederique Elise Oggier is an Associate Professor in the School of Physical and Mathematical Sciences, Division of Mathematical Sciences at Nanyang Technological University (NTU). She holds a PhD from the Swiss Federal Institute of Technology (EPFL) and has held visiting positions at Caltech and the Research Center for Information Security (Tokyo). Her research focuses on algebraic coding theory, lattice-based cryptography, and applications of number theory to secure and reliable communication systems. Education: Bachelor’s and Master’s in Mathematics from the University of Geneva PhD in Mathematics from EPFL Research Interests: Her work bridges abstract algebra with practical coding challenges, emphasizing lattice codes for wiretap channels, distributed storage systems, and security protocols. Specialized in algebraic structures like cyclic division algebras and modular lattices, her contributions advance both theoretical foundations and real-world implementations of secure communication systems. Publications: Her recent work addresses cutting-edge topics such as MDS codes, non-GRS code constructions, and lattice-based security in noisy channels. These contributions highlight her expertise in coding theory and its interdisciplinary applications. Grants/Advising: While specific grants are not detailed, her prolific publication record reflects sustained research activity. She advises on projects related to distributed storage and secure coding, though student names are not listed in the provided texts. Labs/Teams: Affiliated with NTU’s mathematical sciences division, she collaborates with global researchers on projects such as lattice coding for 5G/6G systems and cryptographic protocols leveraging algebraic number theory.

• Model Predictive Control
• Power Electronics
• Renewable Energy Integration
• +۵ مورد دیگر

Ricardo Aguilera Echeverria is an Associate Professor at the University of Technology Sydney (UTS), School of Electrical and Data Engineering . With a Ph.D. in Electrical Engineering from the University of Newcastle (2012), he has held academic positions at UNSW Australia (2014-2016) and UTS since 2016. His research focuses on model predictive control (MPC) applied to power electronics , renewable energy integration , and microgrid control systems . He actively supervises Masters and PhD students and has developed courses such as Control Studio A and Control Studio B . Education: PhD in Electrical Engineering (University of Newcastle, 2012) MSc in Electronics Engineering (Universidad Tecnica Federico Santa Maria, 2007) BSc in Electrical Engineering (Universidad de Antofagasta, 2003) Research Interests: Model Predictive Control (MPC) for power converters Microgrid stability and cybersecurity Second-life battery integration Hybrid DC-AC microgrid solutions Recent Research Trends: Advancements in modular multilevel matrix converters (M3C) for LFAC systems Development of per-phase instantaneous power theories for LVRT compensation Sliding mode observers (SMO) for cyberattack mitigation in AC microgrids Optimal control strategies for delta-connected CHB converters in energy storage Grants & Projects: Lead investigator in HORIZON Europe (2024-2027) on digital solutions for renewable energy systems ARC Discovery Project (DP240102646) on extending second-life battery life (2024-2026) Collaborative grants with Sovereign Propulsion Systems Pty Ltd and NSW Department of Industry for hybrid-electric vehicle control

Ceyhun Eksinمشاهده پروفایل

دانشیار

• Networked Multi-Agent Systems
• Game Theory
• Distributed Optimization
• +۵ مورد دیگر

Ceyhun Eksin is an Associate Professor and the Corrie and Jim Furber '64 Faculty Fellow at the Texas A&M University Industrial & Systems Engineering Department. He is also affiliated with the Electrical & Computer Engineering Department. His research focuses on networked multi-agent systems, integrating game theory, distributed optimization, and control theory to address challenges in autonomous systems, energy systems, and epidemiological modeling. Education: Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2015), followed by a postdoctoral fellowship at Georgia Institute of Technology (hosted by Professors Jeff S. Shamma and Joshua S. Weitz). Research interests emphasize the design and analysis of complex systems, including distributed algorithms for autonomous teams, epidemic dynamics influenced by behavioral changes, and optimization in smart grids. His work bridges theoretical foundations with practical applications in cyber-physical systems and social networks. Notable awards include the NSF CAREER Award (2023) and TAMIDS Career Initiation Fellowship (2023). His research has been published in top journals like Proceedings of the National Academy of Sciences and IEEE Transactions . Lab activities center on the NetMaS (Networked Multiagent Systems) Lab, focusing on theoretical and algorithmic innovations for multi-agent systems. Collaborations span academia and industry, addressing real-world challenges in energy, healthcare, and robotics.