Find Academic Supervisors

• Adaptive and robust control
• Nonlinear control
• Precision control
• +4 more

Bin Yao is a Professor of Mechanical Engineering at Purdue University, located in West Lafayette, Indiana. He holds positions within the School of Mechanical Engineering and has affiliations with Purdue's broader Engineering College. His research focuses on adaptive and robust control systems, nonlinear control, precision mechanical systems, vehicle control, and robotics, with applications in human-machine interaction, transportation, and advanced robotics. Yao earned his B.Eng. from Beijing University of Aeronautics & Astronautics (1987), M.Eng. from Nanyang Technological University (1992), and Ph.D. from UC Berkeley (1996). He has received numerous awards, including the 2010 Changjiang Chair Professorship and the 2007 ASME Outstanding Young Investigator Award. His work emphasizes theoretical advancements in control systems and their practical implementation in industrial and robotic applications. Key research trends in his publications include precision motion control of gantry systems, adaptive strategies for hydraulic manipulators, and teleoperation systems with haptic feedback. His articles often bridge theoretical control methodologies with real-world mechanical systems, emphasizing robustness and adaptability. Awards: Over 10 prestigious awards spanning from 1985 to 2010, including international recognitions in control systems and academic excellence. Grants: Secured major funding through NSF CAREER Award (1998) and other industrial collaborations. His research lab focuses on advanced control systems integration in mechanical and robotic platforms, with ongoing projects in precision manufacturing and autonomous vehicle dynamics.

• 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 .

Sergei ShabanovView profile

Professor

• Mathematical Physics
• Quantum Field Theory
• Wave Scattering Theory
• +7 more

Sergei V. Shabanov is a Professor of Mathematics and Affiliate Professor of Physics at the University of Florida, College of Liberal Arts and Sciences. He holds offices in LIT 464 (Mathematics) and NPB 2180 (Physics), with phone contacts (352) 392-0281 and (352) 392-8714. His research focuses on mathematical physics, including bound states in the continuum, quantum field theory, Navier-Stokes equations, and laser-induced plasma dynamics. He co-authored monographs on Hamiltonian mechanics of gauge systems and textbooks on calculus. Research Interests : • Quantum field theory (Yang-Mills theories, path integrals) • Wave scattering and plasma physics (laser-induced plasmas, tomography) • Mathematical methods (partial differential equations, operator theory) His work bridges theoretical physics and applied mathematics, with emphasis on constrained systems and interdisciplinary applications. Recent Publications : Shabanov’s recent work includes studies on elastic wave scattering (2024), bound states in elasticity (2021), and plasma chemistry modeling (2019-2016). His research often explores theoretical frameworks with practical implications in spectroscopy and material science. Academic Contributions : - Director of the John R. Klauder Memorial Conference - Associate Director of the Institute for Fundamental Theory (Physics) - Adviser to the University Math Society - Taught advanced courses like Mathematical Methods for Physics and Partial Differential Equations.

Kevin F. KellyView profile

Associate Professor

• Optics and Photonics
• Nanotechnology
• Imaging and Spectroscopy
• +7 more

Kevin F. Kelly is an Associate Professor in the Department of Electrical and Computer Engineering at Rice University. He was formerly the Chair of the Applied Physics Program and is affiliated with the Smalley-Curl Institute. Additionally, he has been a member of the Penn State Center for Nanoscale Science and the Mid-Infrared Technologies for Health and the Environment (MIRTHE) Center at Princeton University. Dr. Kelly co-founded Inview Technology Corporation as its Chief Scientist, focusing on commercializing compressive imaging technologies. He has also consulted for the Baker Institute for Public Policy regarding photovoltaics and taught courses in anthropology and history at Rice. Dr. Kelly holds a B.S. in Engineering Physics from the Colorado School of Mines (1993), followed by an M.S. (1996) and Ph.D. (1999) in Applied Physics from Rice University. His postdoctoral work included fellowships at the Institute for Materials Research in Sendai, Japan, and the Chemistry Department at Penn State University. His research interests span Optics and Photonics, Imaging and Spectroscopy at the nanoscale, and the role of mathematics in image acquisition. He develops Scanning Probe Microscopy techniques and studies Electronic Materials such as graphene and topological insulators. A major focus is Compressive Hyperspectral Imaging systems, including single-pixel camera innovations and advanced microscopy methods. He also pioneers molecular machines like the Nanocar and investigates charge transport in polymer photovoltaics. Over recent years, his work emphasizes interdisciplinary applications, such as integrating compressive sensing with neural networks for machine vision and exploring technological disaster analysis through history courses. His contributions have been recognized with awards like the IEEE Fellow (2022) and Technology Review’s Top 10 Emerging Technologies (2007). In addition to academic roles, Dr. Kelly has co-founded Inview Technology and contributed to grants and collaborations through his involvement in the MIRTHE Center and other institutes. While no formal advisees are listed, his teaching includes courses on nanotechnology since 2009 and he actively engages in policy consultations for photovoltaic commercialization. His lab at Rice and collaborations with the Smalley-Curl Institute drive advancements in nanotechnology and imaging, with a particular emphasis on practical applications of compressive sensing and molecular-scale devices.

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

Pierre LermusiauxView profile

Professor

• Ocean Data Assimilation
• Numerical Ocean Modeling
• Uncertainty Quantification
• +7 more

Pierre Lermusiaux is the Nam Pyo Suh Professor of Mechanical Engineering and Associate Department Head for Operations Research at MIT. His research focuses on numerical ocean modeling, data assimilation, uncertainty quantification, and applications to ocean dynamics and engineering. He holds a B/M.Eng. from University of Liège, M.Sc. and Ph.D. from Harvard University. Recognized with awards like the MIT Doherty Chair and Spira Teaching Award, his work bridges computational science and marine systems. Education: B/M.Eng., University of Liège, 1992 M.Sc., Harvard University, 1993 Ph.D., Harvard University, 1997 Research Interests: Combines oceanographic modeling with advanced computational methods. Specializes in stochastic ocean prediction, autonomous systems optimization, and interdisciplinary applications of uncertainty quantification. His work enables real-time environmental forecasting and marine robotics navigation strategies. Publications: Focus on developing computational frameworks for probabilistic ocean forecasts, acoustic propagation modeling, and machine learning-based environmental prediction systems. Recent work emphasizes adaptive sampling strategies and energy-efficient path planning for underwater vehicles. Awards: Fulbright Foundation Fellowship (1993-96) Ruth and Joel Spira Award for Teaching (2010) MIT Doherty Chair in Ocean Utilization (2009-2011) Advising & Grants: Leads MIT’s MSEAS group and has secured funding from NSF, ONR, and international collaborations. Advises projects on ocean robotics, climate modeling, and underwater acoustics. Key contributions include the MIT Red Sea Modeling System and real-time forecasting platforms. Labs/Teams: Directs MIT’s multidisciplinary ocean modeling efforts, integrating mechanical engineering, computer science, and environmental science. Co-leads initiatives like the Ocean Predictions with Ensembles (OPEN) project and the METEOR autonomous observatory system.

Ngoc Cuong NguyenView profile

Researcher

• Computational Mechanics
• Molecular Mechanics
• Nanophotonics
• +7 more

Ngoc Cuong Nguyen is a Principal Research Scientist in the Department of Aeronautics and Astronautics at MIT and a member of the MIT Center for Computational Engineering. His research focuses on computational mechanics, numerical simulation, and advanced numerical methods such as hybridizable discontinuous Galerkin (HDG) methods for multi-scale and multi-physics problems. Education: PhD in High Performance Computation for Engineered Systems (2005), National University of Singapore BEng in Aeronautical Engineering (2001), Ho Chi Minh City University of Technology Research Interests: Computational Mechanics, Molecular Mechanics, Nanophotonics Numerical Simulation & Optimization, Scientific Computing, Machine Learning Reduced Basis Methods, High-Order Methods (e.g., HDG), Uncertainty Quantification Key Projects: Development of HDG methods for fluid dynamics, structural mechanics, and electromagnetics Plasmonic nanostructure simulations using quantum hydrodynamic models Space weather modeling via GPU-accelerated HDG approaches Optimization of photonic crystals and nanostructured materials Large-eddy simulation (LES) of hypersonic flows and buffet phenomena Labs & Teams: Active contributor to the MIT Center for Computational Engineering, leading projects in numerical methods, computational fluid dynamics, and interdisciplinary applications of advanced simulation techniques.

Andrew D. LewisView profile

Professor

• Geometric Control Theory
• Global Analysis
• Geometric Mechanics
• +3 more

Andrew D. Lewis is a Professor and Associate Head of the Department of Mathematics & Statistics at Queen's University, Kingston, Canada. His research focuses on geometric control theory, global analysis, and geometric mechanics, with applications to mechanical systems and dynamical systems. He holds a Ph.D. from Caltech, along with M.Sc. and B.Sc. degrees from Caltech and the University of New Brunswick, respectively. His research explores the intersection of geometric methods, topology, and algebra in solving structural problems in control theory and mechanics. He actively mentors graduate students and emphasizes mathematical rigor combined with applied perspectives. Lewis teaches advanced courses in control theory, differential equations, and geometric mechanics, and has developed extensive lecture notes and software tools for academic use. He has organized numerous research events, including the CRM Trimester on Control Geometry and Engineering and the Meeting on Nonlinear Control Theory and its Applications. His work spans theoretical contributions to control systems, geometric mechanics, and applied mathematics, with a focus on controllability, stabilization, and system dynamics. Education: Ph.D., California Institute of Technology M.Sc., California Institute of Technology B.Sc., University of New Brunswick Awards/Honors: None explicitly mentioned in the provided texts. Grants/Advising: Supervised numerous graduate students and postdoctoral researchers, fostering interdisciplinary work in control theory and mechanics.

Ingenuin GasserView profile

Professor

• Modelling with Partial Differential Equations
• Kinetic and Fluid Dynamic Equations
• Renewable Energy Systems
• +3 more

Prof. Ingenuin Gasser is a Professor of Modelling and PDEs at the University of Hamburg's Department of Mathematics. He holds roles including UHH Coordinator for the Joint Master's Programme 'MathMods' and former Vice Dean for Internationalization and Young Researcher Support (2012-2022). His research focuses on mathematical modelling with PDEs, renewable energy systems, traffic flow dynamics, and semiconductor equations. He leads interdisciplinary projects on urban systems and energy technologies. Education & Career: Born 1965 in Brixen, Italy Diplom in Technical Physics (1989, TU Wien) Diplom in Technical Mathematics (1992, TU Wien) PhD in Mathematics (1996, TU Berlin) Habilitation in Mathematics (2000, UHH) Junior Professor (2002–2003, UHH) Full Professor (W3) since 2010 at UHH Research Interests: Modelling with PDEs, kinetic/fluid dynamics, renewable energy systems (solar updraft towers, osmosis power), traffic flow models, semiconductor equations, and interdisciplinary urban system modelling. Publications: Over 67 publications, recent works focus on PRO power stations, solar energy systems, and urban modelling. Key themes include energy efficiency optimization, nanofluid applications, and mathematical models for sustainable infrastructure. Grants & Projects: Participated in EU-funded networks (e.g., TMR), DFG projects on quantum transport, and international collaborations in applied mathematics and renewable energy.

Maria Christina MarianiView profile

Professor

• Applied Mathematics
• Nonlinear partial differential equations
• Stochastic differential equations
• +5 more

Maria Christina Mariani is a Professor and Department Chair in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP). Her interdisciplinary research bridges mathematics with applications in public health, geophysics, physics, and finance, with a focus on developing novel mathematical models for complex data analysis. Dr. Mariani earned her Ph.D. in Mathematics from the University of Buenos Aires in 1992, where she received an Outstanding dissertation award. She also holds an M.S. in Physics (1996) and an M.S. in Mathematics (1987), both from the University of Buenos Aires with highest honors. Her research interests span Applied Mathematics, Nonlinear partial differential equations, Stochastic differential equations, Machine Learning techniques, Mathematical Finance, Mathematical Physics, and Numerical Methods. She has developed mathematical models for medical data analysis (particularly breast cancer, heart disease, and prostate cancer), seismic and explosive data, and financial markets. Her work emphasizes the development of mathematical models to enhance understanding of medical data and extreme events in various phenomena. Dr. Mariani's recent research focuses on applying machine learning and stochastic models to complex data sets across multiple domains. Her work demonstrates consistent innovation in developing novel algorithms for medical diagnosis and prognosis, analyzing seismic data, and modeling financial markets using Levy processes, Ornstein-Uhlenbeck models, and wavelet techniques. She has mentored numerous students throughout her career, including PhD candidates, MS students, and post-doctoral researchers, demonstrating her commitment to academic development and knowledge transfer. Dr. Mariani has served as Department Chair and holds the Shigeko K. Chan Distinguished Professor title in Mathematical Sciences, reflecting her significant contributions to the field and institution.

• Mathematical Physics
• Differential Geometry
• Dynamical Systems
• +3 more

Alessandro Arsie is a Professor and Graduate Program Director in the Department of Mathematics and Statistics at the University of Toledo's College of Natural Sciences and Mathematics. His research focuses on mathematical physics, differential geometry, dynamical systems, and control theory, with notable contributions to F-manifolds, integrable systems, and geometric mechanics. He has authored over 50 publications in prestigious journals like Communications in Mathematical Physics and Nonlinearity . His work often bridges pure mathematics and applied fields, such as robotics and ecology. Key research interests include the geometry of Hamiltonian systems, bifurcation analysis in predator-prey models, and the interplay between algebraic structures (e.g., F-manifolds) and integrable hierarchies. He has explored topics like collision dynamics in celestial mechanics and optimization in multi-agent robotic networks. Dr. Arsie’s recent articles emphasize geometric approaches to differential equations and the application of advanced mathematical techniques to real-world systems. He collaborates with researchers in robotics and theoretical physics, reflecting his interdisciplinary expertise.

• Lifeline Earthquake Engineering
• Seismic Design of Buried Pipelines
• Structural Control
• +7 more

Himan Hojat Jalali is an Assistant Professor of Civil Engineering at The University of Texas at Arlington (UTA), specializing in lifeline earthquake engineering and infrastructure resilience. He leads the Structures and Applied Mechanics group and focuses on seismic performance of buried pipelines, metamaterials for vibration control, sustainable materials, and structural reliability. Education: PhD, Structural and Earthquake Engineering, Sharif University of Technology (2015) MS, Structural Engineering, Sharif University of Technology (2008) BS, Civil Engineering, Iran University of Science and Technology (2006) Research Interests: Seismic resilience of buried infrastructure Finite element modeling of pipelines Metamaterial-based seismic mitigation Sustainable construction materials Infrastructure condition assessment Grants & Awards: Over $1M as PI and $4.1M as co-PI in grants from USDOT, TxDOT, and WRF Recipient of multiple paper awards from ASCE/UESI Pipelines division Editorial roles: Associate Editor of Measurements , Guest Editor of Buildings Journal Teaching: Courses include Finite Element Methods, Advanced Mechanics of Materials, and Structural Dynamics. Active in mentoring graduate researchers.

Seulip LeeView profile

Assistant Professor

• Scientific Computing
• Numerical Analysis
• Finite Element Methods
• +3 more

Seulip Lee is a Norbert Wiener Assistant Professor in the Department of Mathematics at Tufts University, School of Arts and Sciences. His research focuses on scientific computing, numerical analysis, and computational fluid dynamics, with an emphasis on multiphysics simulations using finite element methods. He holds a PhD in Mathematics from the University of California, Irvine (2021), and degrees from Yonsei University (M.Sc., 2015; B.Sc., 2013). Lee’s work bridges theoretical analysis and computational experimentation, addressing challenges in partial differential equations and optimization. He has published extensively on enriched Galerkin methods and numerical techniques for fluid dynamics. Teaching responsibilities include MATH 51 (Differential Equations) and MATH 125 (Numerical Analysis). His courses emphasize both analytical rigor and computational implementation, using tools like MATLAB for practical problem-solving. Professional experience includes a Limited Term Assistant Professor role at the University of Georgia (2021–2024), with research collaborations under mentors like Xiaozhe Hu and James Adler. His lab and team activities focus on advancing numerical algorithms for complex physical systems.

Kallol SettView profile

Associate Professor

• Risk and Reliability
• Civil Infrastructure
• Extreme Events
• +5 more

Kallol Sett is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo (SUNY), within the School of Engineering and Applied Sciences. His research focuses on risk and reliability analysis of civil infrastructure under extreme events, with expertise in uncertainty quantification, multi-hazard resilience, and geomechanics. He leads the Risk and Reliability Research Group, which develops computational tools integrating physics-based and data-driven modeling, stochastic calculus, and high-performance computing. Education includes a PhD from the University of California, Davis (2007), an MS from the University of Houston (2003), and a BE from Jadavpur University (1997). His work is funded by NASA, NSF, USDOT, NIST, and industry partners. Key research themes include probabilistic geotechnical site characterization, stochastic simulation of seismic ground motion, and life-cycle cost-benefit analysis of infrastructure systems. Advising includes mentoring over 10 PhD and MS students, with notable alumni now in academia and industry roles such as Assistant Professors at Embry-Riddle Aeronautical University and Tianjin University. His lab’s recent studies address real-time decision support systems for hurricane-impacted infrastructure, resilience deficit indices, and multi-hazard financial risk assessment of integrated infrastructure systems.

Alex GittensView profile

Assistant Professor

• Machine Learning
• Computational Linear Algebra
• Randomized Numerical Linear Algebra
• +3 more

Alex Gittens is an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), joined in 2017. His research focuses on algorithmic trade-offs between computational efficiency and accuracy in large-scale linear algebra and machine learning contexts. He has expertise in kernel methods, randomized numerical linear algebra, and low-rank approximation techniques. Education: PhD in Applied and Computational Mathematics, Caltech (2013) Industry Postdoc at eBay Research Labs (2013-2015) Postdoctoral Scholar at International Institute of Computer Science (2015-2016) His research explores scalable machine learning algorithms, nonlinear and multilinear sketching applications, and sampling for low-rank tensor/matrix approximation. Current technical interests include attention mechanisms for knowledge graph completion, federated learning trade-offs, and causal inference in adversarial settings. Recent publication trends show active contributions in federated learning (privacy-fairness optimization), causal information extraction (financial text analysis), and adversarial machine learning (robustness-security trade-offs). His work emphasizes trustworthy ML systems and computational efficiency in high-dimensional data processing. Teaching includes foundational discrete mathematics (CSCI 2200) and advanced machine learning courses (CSCI 6968/4968). He offers advising through Slack channels and via email, focusing on course selection, research opportunities, and graduate school preparation.