Find Academic Supervisors

Alex TownsendView profile

Associate Professor

• Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +6 more

Alex Townsend is an Associate Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds the Stephen H. Weiss Junior Fellowship and has been recognized for both research and teaching excellence. His research focuses on numerical analysis, scientific computing, and theoretical aspects of deep learning, with contributions to spectral methods, low-rank techniques, and computational algebraic geometry. Education: Townsend earned a DPhil (PhD) in Mathematics from the University of Oxford in 2014. Research Interests: Townsend's work spans several areas: novel spectral methods for differential equations, low-rank matrix and tensor techniques, theoretical foundations of deep learning, and computational algebraic geometry. His research emphasizes developing fast, accurate, and robust numerical algorithms with applications in science and engineering. Teaching & Mentoring: Townsend is a dedicated educator, having taught courses at MIT and Cornell on topics ranging from linear algebra and numerical analysis to advanced graduate-level subjects like kernel-based learning and top-ten algorithms of the 20th century. He has mentored numerous PhD students and postdocs, many of whom now hold academic and industry positions. Awards & Honors: 2022 Stephen H. Weiss Teaching Award 2022 Simons Fellowship in Mathematics 2018 SIAG/LA Early Career Prize 2015 Leslie Fox Prize in Numerical Analysis Grants & Funding: Townsend has secured significant funding, including an NSF CAREER grant (2021), to support his work on operator learning and spectral methods. Labs & Collaborations: While not tied to a specific lab, his research frequently intersects with computational mathematics and machine learning communities. He collaborates widely, contributing to open-source tools like Chebfun and Diskfun.

Alan EdelmanView profile

Professor

• Julia Programming
• High Performance Computing
• Linear Algebra
• +3 more

Alan Edelman is a Professor at the Massachusetts Institute of Technology (MIT), renowned for his work in high-performance computing, linear algebra, and random matrix theory. He is a co-creator of the Julia programming language, which emphasizes efficiency and versatility for scientific computing. His research integrates mathematics and computer science, focusing on numerical methods, parallel computing, and applications in fields like quantum control and climate modeling. Edelman leads projects such as Oceananigans.jl for geophysical fluid dynamics and Circuitscape for connectivity analysis in conservation biology. His academic contributions span theoretical advancements in matrix theory and practical implementations of computational tools. Edelman collaborates across disciplines, bridging scientific computing with machine learning and quantum physics. His work on differentiable programming frameworks and GPU acceleration has impacted both academic research and industry applications. Key Projects: Julia programming language, Oceananigans.jl, Circuitscape Research Themes: High-performance computing, numerical linear algebra, random matrices, scientific machine learning Affiliations: MIT’s Theory of Computation Community of Research, PI of multiple NSF-funded projects Edelman’s recent work emphasizes interdisciplinary applications, including climate policy modeling and automated materials discovery. His publications reflect a blend of foundational mathematics and cutting-edge computational techniques.

Hédi HadijiView profile

Associate Professor

• Online Decision-Making
• Adaptivity in Bandits
• Online Learning
• +3 more

Hédi Hadiji is an Associate Professor at the Laboratoire des Signaux et Systèmes (L2S) at CentraleSupélec. His research focuses on the mathematics of online decision-making, particularly adaptivity in bandits and online learning. Prior to this role, he was a postdoctoral researcher at the University of Amsterdam under Tim van Erven. He holds a PhD in Mathematics from Université Paris-Saclay, supervised by Gilles Stoltz and Pascal Massart. Education: PhD in Mathematics, Université Paris-Saclay (2020) Masters in Probability and Statistics, Université Paris-Saclay (2017) Part III Mathematical Tripos, University of Cambridge (2016) École Polytechnique (2012–2016) Research Interests: Hédi’s work addresses theoretical foundations of online learning, including bandit algorithms, reinforcement learning, and optimization. He explores adaptive strategies in dynamic environments, with applications to stochastic and adversarial settings. Key themes include minimax optimal algorithms, regret analysis, and the interplay between exploration and exploitation. Recent Publications: His recent work includes minimax optimal algorithms for linear bandits, tracking solutions in time-varying systems, and diversity-preserving strategies in K-armed bandits. These contributions advance theoretical understanding of adaptive decision-making in complex systems. Teaching: He teaches courses on the theoretical principles of deep learning, reinforcement learning, and mathematical foundations at the graduate level. His lectures emphasize rigorous analysis of generalization, optimization, and the neural tangent kernel. Labs & Affiliations: His primary affiliation is with L2S, where he engages in collaborative research groups such as MODESTY, COMEDY, and SYCOMORE. His work intersects signal processing, control systems, and communication networks.

• Nonlinear Control Theory
• Geometric Control
• Optimal Control
• +3 more

Frédéric JEAN is a Professor and Director of the Unité de Mathématiques Appliquées (UMA) at ENSTA Paris. His expertise lies in nonlinear control theory and geometric control, with applications in neurophysiology modeling. He holds an HDR (Habilitation à Diriger des Recherches) and advises doctoral students across multiple domains. Research Interests: Frédéric’s research focuses on geometric control theory, sub-Riemannian geometry, optimal control, and nonholonomic systems. His work bridges theoretical advancements with practical applications in neurophysiology and aerospace engineering. Recent studies include trajectory planning for planetary landings and robust control strategies under uncertainty. Teaching: He teaches courses on applied differential geometry, dynamical systems analysis, and control systems at ENSTA Paris. Publications: His recent work emphasizes sub-Riemannian geometry, stochastic control, and aerospace applications. Key trends include optimal control in constrained systems and geometric approaches to robotic and biological movement. Students: Co-supervised over 10 doctoral theses, including those by Clara Leparoux (2023), Meryem Kafnemer (2022), and Sofya Maslovskaya (2018). Labs/Teams: Active within the Optimization and Commande team at UMA, contributing to interdisciplinary research in control systems and applied mathematics.

Matthew PeltonView profile

Professor

• Nanophotonics
• Quantum Optics
• Semiconductor Physics
• +2 more

Matthew Pelton is Professor and Department Chair in Physics at the University of Maryland, Baltimore County's College of Natural and Mathematical Sciences. His research focuses on experimental nanophotonics, investigating light-matter interactions at the nanoscale using optical techniques to study quantum phenomena in materials. Key areas include quantum photonics, nanoscale energy transfer, and semiconductor quantum dot physics. His work examines fundamental processes in quantum-confined systems, aiming to advance technologies in quantum information processing, solar energy conversion, and nanoscale sensing. Research combines materials physics with quantum information science to probe dynamical processes in nanoscale materials through advanced spectroscopy. Recent publications demonstrate a consistent focus on quantum light sources, silicon photonics integration, and nanocavity engineering. Trends include developing telecom-band quantum emitters, optimizing color-center LEDs, and controlling excitonic coupling in bioinspired systems. Work frequently combines experimental spectroscopy with computational modeling of nanoparticle optical properties. As director of the Laboratory for Optical Studies of Nanoscale Physics, he maintains rigorous safety protocols for laser operations, chemical handling, and data management. The lab emphasizes collaborative research principles and provides training opportunities for graduate and undergraduate students in nanophotonics experimentation.

Burak SarsilmazView profile

Assistant Professor

• Distributed Control
• Adaptive Control
• Trajectory Optimization
• +2 more

Dr. Burak Sarsilmaz is an Assistant Professor in the College of Engineering at Utah State University, specializing in Electrical and Computer Engineering. His research focuses on distributed control of multi-agent systems, adaptive control, and trajectory optimization for aerospace applications. Education: PhD in Mechanical Engineering, University of South Florida (2020) MA in Mathematics, University of South Florida (2020) MS in Aerospace Engineering, Middle East Technical University (2016) BS in Aerospace Engineering, Middle East Technical University (2013) Research: His work bridges control theory and practical applications in autonomous systems, with emphasis on robustness under uncertainty. Recent publications explore novel approaches to cooperative control and optimization in constrained environments. Students: Actively mentors graduate students including Akalu Desta Teklu and Kursad Metehan Gul. Leads the Autonomy, Robotics, Control, and Optimization (ARCO) research group. Awards: Holds a Canada Research Chair in Translational Cancer Research.

Zoran GajicView profile

Professor

• Control Systems
• Energy Systems
• Wireless Communications
• +4 more

Zoran Gajic is a Professor of Electrical and Computer Engineering at Rutgers University, where he has taught since 1984. He holds academic leadership roles including Graduate Program Director for the Electrical and Computer Engineering Department and President of the Rutgers AAUP-AFT Faculty Union. His expertise spans controls systems, energy systems (including solar, wind, and smart grids), wireless communications, and networking. Education: B.S. and M.S. in Electrical Engineering from University of Belgrade, followed by M.S. in Applied Mathematics and Ph.D. in Systems Science Engineering from Michigan State University (1984). Research focuses on control theory applications for energy systems and communication networks. He has authored/coauthored nearly 100 journal papers and eight books, including best-selling titles like Linear Dynamic Systems and Signals (translated into Chinese) and Lyapunov Matrix Equation in Systems Stability and Control (republished by Dover). His work includes innovations in multirate control systems, singular perturbation methods, and reinforcement learning applications. Professional recognitions include editorial roles across nine journals, five guest-edited special issues, and plenary lectures at international conferences. Ten of his 17 Ph.D. advisees hold faculty positions globally. Beyond academia, he is a chess master with Life Master ranking from the U.S. Chess Federation and World Chess Federation certification. Key contributions include foundational work on optimal control for renewable energy systems, sliding mode control algorithms, and system decomposition techniques. His research has been supported by NSF and industry partners like AT&T Bell Labs. He leads the Rutgers Center for Systems and Controls (SYCON) as Associate Director and actively contributes to standards through IEEE and IEC initiatives, particularly in power system protection and control system reliability.

Victor LazzariniView profile

Professor

• Computer Music
• Digital Signal Processing
• Electroacoustic Music
• +3 more

Victor Lazzarini is a Professor of Music at Maynooth University, Ireland, specializing in computer music and audio signal processing. He holds a BMus from Universidade Estadual de Campinas (UNICAMP), Brazil, and an AMusD from the University of Nottingham, UK. His research spans electroacoustic music, digital signal processing, and music technology. He leads the Csound project, a widely used sound and music computing system, and authored libraries like Aulib and Aurora. Education: Bachelor of Music (BMus), UNICAMP, Brazil Advanced Music Degree (AMusD), University of Nottingham, UK His research interests include sound synthesis, computer music languages, and the intersection of music with technology. He has authored over 150 peer-reviewed publications and books such as Spectral Music Design: A Computational Approach (Oxford UP, 2021) and co-edited volumes like Ubiquitous Music Ecologies (Routledge, 2020). His work bridges academic and industrial sectors through projects like the Enterprise-Ireland-funded commercialization initiative. Prof. Lazzarini’s contributions to music technology include pioneering tools like Csound, which enable real-time audio synthesis and processing. His articles span topics from digital filter design to historical computer music archaeology, reflecting his expertise in both theoretical and applied domains. Awards: AIC/IMRO International Composition Prize (2006) He advises on interdisciplinary projects such as the BeatHealth initiative, integrating ubiquitous computing and music. His creative output includes electroacoustic compositions performed globally and released on labels like FarPoint Recordings. Lazzarini actively collaborates with international teams, contributing to conferences like DAFx and ICMC, and leads initiatives like the Ubimus (Ubiquitous Music) research network.

Jamey JacobView profile

Professor

• Aerodynamics
• UAV Design & Flight Testing
• Vortex Dynamics
• +5 more

Jamey Jacob, Ph.D., P.E., is a Professor and John Hendrix Chair in Mechanical and Aerospace Engineering at Oklahoma State University, leading the Oklahoma Applied Research Institute (OAIRE). He holds a Ph.D. from UC Berkeley (1995), with earlier degrees from the same institution and the University of Oklahoma. His research focuses on aerodynamics, UAV design, vortex dynamics, geophysical flows, and autonomous systems. Notable projects include solar balloon flight dynamics, eclipse observations, and advanced air mobility (AAM) weather systems. Jacob has pioneered UAV-based weather sensing and developed innovative aerostructures, including inflatable systems. He has received over 15 prestigious awards, including the Regents Distinguished Teacher (2011) and Oklahoma Innovator of the Year (2010). His recent work emphasizes urban wind field mapping and stratospheric balloon applications for planetary science. Jacob’s lab, OAIRE, integrates aerospace engineering with environmental and operational challenges, advancing both academic and applied frontiers.

• Compliant Mechanisms
• Robotics
• Precision Manufacturing
• +5 more

Dr. Guangbo Hao is a Professor of Mechanical Engineering at University College Cork (UCC), where he leads the UCC CoMAR research group and directs the Mechatronics/Robotics Lab. He holds dual PhDs from Northeastern University (China, 2008) and Heriot-Watt University (UK, 2011). His research focuses on compliant mechanisms, robotics, and their applications in precision manufacturing, energy harvesting, and medical devices. He has secured over €1.3M in research funding, including grants from SFI, EU Horizon 2020, and Enterprise Ireland. He is an ASME Fellow and has received multiple accolades, including the ASME Compliant Mechanisms Award (2017, 2018, 2022) and the UCC President’s Excellence in Teaching Award (2023). Education: BSc (2004), MSc (2007), PhD (2008) from Northeastern University; second PhD (2011) from Heriot-Watt University Research interests include compliant mechanisms, mechatronics, and robotics with emphasis on deployable structures, medical devices, and energy harvesting systems. His work has resulted in over 200 peer-reviewed publications and 30 invited talks. He supervises students in PhD, research-master, and taught-master programs, with notable student achievements in international competitions and awards. Professional roles include Associate Editorships at Advanced Equipment , ASME Journal of Mechanisms and Robotics , and IEEE Robotics and Automation Letters . He chairs major conferences like ASME IDETC/CIE 2025 and co-chairs the 2026 IEEE/ASME AIM conference. His labs, including the UCC Engineering Maker Lab, focus on innovation in robotics and mechatronics.

Zhong-Ping JiangView profile

Professor

• Nonlinear Control Theory
• Dynamical Networks
• Data-Driven Optimization
• +7 more

Dr. Zhong-Ping Jiang is a Professor in the Department of Electrical and Computer Engineering and an Affiliate Professor in the Department of Civil and Urban Engineering at the Tandon School of Engineering, New York University. He has been with NYU since 2007, previously serving as Associate Professor (2002-2007) and Assistant Professor (1999-2002) at Polytechnic University (now part of NYU). Dr. Jiang received his MSc degree in Statistics from the University of Paris XI in 1989 and his PhD degree in Automatic Control and Mathematics from the Ecole des Mines de Paris in 1993. He has maintained strong international collaborations with researchers across Europe throughout his career. Internationally recognized as a key contributor to nonlinear small-gain theory, Dr. Jiang is also well-known for his seminal work in robust adaptive dynamic programming and robust reinforcement learning for real-time safety-critical control applications. His research spans dynamical networks, nonlinear control theory, data-driven optimization, and reinforcement learning with applications to mechanical, information and biological systems. Dr. Jiang's publications have had significant impact in the field, with over 500 peer-reviewed journal and conference papers, more than 34,300 citations, and an h-index of 90 on Google Scholar. He has also published six research monographs since 2011. His notable awards and honors include: Election to European Academy of Sciences and Arts (2023) Stanford's Top 2% Most Highly Cited Scientists (2023) Election to Academia Europaea (2021) Highly Cited Researchers by Clarivate Analytics (2018) Fellow of Chinese Association of Automation (2017) Fellow of International Federation of Automatic Control (2013) Fellow of IEEE (2008) National Distinguished Professor, Chinese Ministry of Education (2009) Dr. Jiang serves as Deputy Editor-in-Chief for the IEEE/CAA Journal of Automatica Sinica and has held editorial positions for numerous prestigious journals including IEEE Transactions on Automatic Control. He has delivered more than 20 plenary, semi-plenary and keynote speeches at major international conferences and workshops.

James V. BurkeView profile

Professor

• Convex Optimization
• Non-smooth Optimization
• Variational Analysis
• +7 more

James V. Burke is a Professor of Mathematics at the University of Washington with extensive contributions to optimization theory and its applications. His academic career spans several decades, during which he has developed fundamental theories in nonsmooth and convex optimization, variational analysis, and computational methods for complex optimization problems. Research Focus Burke's primary research centers on convex-composite optimization , where he has established critical theoretical foundations and practical algorithms. His work on weak sharp minima has become foundational in optimization theory, providing essential insights into solution stability and error bounds. He has made significant advances in gradient sampling algorithms for nonsmooth, nonconvex optimization problems, which have broad applications in engineering and data science. More recently, Burke has applied optimization techniques to state estimation problems , particularly developing robust Kalman smoothing methods using Student's t-distributions and other non-Gaussian models. His research bridges pure mathematical theory with practical computational methods, demonstrating consistent innovation across multiple subfields of optimization. Academic Contributions Burke has taught numerous graduate-level courses including Math 509 (Optimal Control), Math 554 (Linear Analysis), and specialized courses on convex analysis and optimization. His research collaborations span multiple institutions, with frequent co-authorship with leading optimization researchers such as Tim Hoheisel, Adrian Lewis, and Michael Overton. He regularly presents his work at major conferences including SIAM Optimization and ICCOPT, with his most recent presentation at the SIAM Conference on Optimization in Seattle (June 2023).

Marc ToussaintView profile

Professor

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Marc Toussaint is Full Professor leading the Learning & Intelligent Systems Lab at TU Berlin's EECS Faculty. His research integrates machine learning, optimization, and AI reasoning to solve fundamental robotics problems like physical reasoning and human-robot interaction. He holds a physics diploma from University of Cologne and PhD from Ruhr-Universität Bochum. Key research themes include: Task-motion planning integration Reinforcement learning for robotics Physical simulation and control Probabilistic inference methods Recent publications focus on efficient kinodynamic planning, belief space planning under uncertainty, and neural policy learning. He develops open-source robotic tools like the 'robotic python package' used in academic courses worldwide. Toussaint collaborates with Amazon Robotics and MIT CSAIL, and has held positions at Max Planck Institute and University of Stuttgart.

Max CohenView profile

Researcher

• Control Systems
• Robotics
• Artificial Intelligence
• +3 more

Max Cohen is a Postdoctoral Scholar Research Associate in the Department of Mechanical and Civil Engineering at the California Institute of Technology (Caltech). His research focuses on safety-critical control systems, adaptive control, and the integration of control barrier functions with reinforcement learning. He explores theoretical frameworks for robust control design in nonlinear systems, emphasizing safety guarantees through formal verification methods. His work addresses challenges in automated vehicle navigation, hybrid systems, and rehabilitation engineering using functional electrical stimulation (FES). Key research directions include uncertainty quantification in adaptive systems, layered control architectures, and safe exploration strategies in model-based reinforcement learning. Publications span topics like control barrier function synthesis, temporal logic-guided learning, and reduced-order modeling for safety-critical applications. Current research trends emphasize bridging formal methods with data-driven control paradigms to ensure safe operation in complex robotic and cyber-physical systems. No scientific awards or grants are explicitly mentioned in the provided information. His office is located in Gates-Thomas Laboratory (Room 300), and he actively contributes to interdisciplinary projects at Caltech's engineering division.

• Numerical Methods
• Partial Differential Equations
• Computational Mathematics
• +3 more

Francisco Manuel Bernal Martínez is an Associate Professor in the Department of Mathematics at Carlos III University of Madrid. His research focuses on numerical methods, partial differential equations, and computational mathematics, with applications in industrial engineering and materials science. He leads projects such as 'Financiación adicional 5º año (2022)' and collaborates on initiatives like 'Clustering Automático de Comportamientos de Invertebrados en Libertad mediante Imagen 3D.' His work emphasizes domain decomposition algorithms, radial basis functions, and stochastic control problems. He has advised at least one PhD thesis and holds grants from regional and national funding bodies. Key research interests include meshless methods, probabilistic domain decomposition, and uncertainty quantification in energy systems. Recent publications highlight advancements in hybrid algorithms for large-scale PDEs and volatility modeling. Bernal Martínez actively participates in academic networks and has presented at international conferences on computational methods and industrial mathematics.