Find Academic Supervisors

Robert L. MullenView profile

Professor

• Computer Modeling of Engineering Systems
• Biomechanics
• Mechanical Behavior of Micro Electro-Mechanical Systems
• +2 more

Robert L. Mullen is a Professor in the Department of Civil and Environmental Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. His work bridges computational mechanics, uncertainty quantification, and interdisciplinary applications in biomechanics and transportation systems. Ph.D. in Applied Mechanics, Northwestern University (1981) M.S. in Structural Mechanics, University of Illinois (1977) B.S. in Structural Engineering, University of Illinois (1976) Research focuses on: Computer modeling of engineering systems Biomechanics Mechanical behavior of micro electro-mechanical systems Coupled field problems Interval methods for uncertainty Key trends in his 2018-2025 publications include: Advancements in interval finite element methods for structural analysis Machine learning integration in transportation infrastructure monitoring Uncertainty quantification across mechanical and civil engineering domains Innovations in data fusion for traffic management Biotechnological applications (vaccine stabilization) Durability studies in transportation assets Scientific recognition includes: Fellow, American Society of Civil Engineers Professional contributions extend to patents in material science, biotechnology, and structural systems.

Tyler SummersView profile

Associate Professor

• Control and Optimization
• Power and Energy Networks
• Distributed Robotics
• +3 more

Tyler H. Summers is an Associate Professor of Mechanical Engineering at the University of Texas at Dallas (UT Dallas), with an affiliate appointment in Electrical Engineering. He holds a PhD in Aerospace Engineering from the University of Texas at Austin and conducted postdoctoral research as an ETH Postdoctoral Fellow at ETH Zurich. His research focuses on control and optimization in complex dynamical networks, with applications to power grids, distributed robotics, and cyber-physical systems. Education: PhD, Aerospace Engineering, University of Texas at Austin (2010) MS, Aerospace Engineering, University of Texas at Austin (2007) BS, Mechanical Engineering, Texas Christian University (2004) Research Interests: Summers' work emphasizes theoretical tools and computational methods for control and optimization in large-scale networks. Key areas include stochastic optimal power flow for energy systems, distributed formation control for robotic teams, and robust control under uncertainty. His lab, the Control, Optimization, and Networks Lab, explores cyber-physical systems integration, resilient coordination in robotics, and smart grid technologies. Awards: He has received notable honors such as the Air Force Office of Scientific Research Award (2019), Army Research Office Young Investigator Award (2016), and NSF grants for research in water networks and distributed systems. His work bridges foundational control theory with practical applications in energy and robotics. Advising & Grants: Summers advises PhD students and postdocs in systems, control, and optimization. His research is supported by funding from the NSF, Air Force Office of Scientific Research, and industry collaborations. Notable projects include optimizing power grid resilience with renewable energy integration and developing spoof-resilient robotic networks. Labs & Teams: The Control, Optimization, and Networks Lab at UT Dallas focuses on interdisciplinary research, combining insights from control theory, optimization, and computer science. Collaborations span academic and industrial partners, advancing applications in autonomous systems and smart infrastructure.

Mark HolmesView profile

Professor

• Applied Mathematics
• Biomechanics
• Mathematical Biology
• +3 more

Mark Holmes is a Professor at Rensselaer Polytechnic Institute (RPI), affiliated with the Center for Modeling, Optimization and Computational Analysis (MOCA). His research focuses on applied mathematics, particularly in mechanoreception (e.g., tactile sensing in skin and auditory systems) and the mathematical modeling of the sleep-wake cycle. He integrates biomechanical principles with nonlinear dynamics and numerical analysis to address problems in biological systems. Dr. Holmes has authored influential textbooks including Introduction to the Foundations of Applied Mathematics (2009), Introduction to Perturbation Methods (2013), and Introduction to Scientific Computing and Data Analysis (2016). His work bridges theoretical mathematics with real-world applications in engineering and biology, emphasizing interdisciplinary collaboration. His recent publications explore optimization methods, numerical techniques for differential equations, and biomechanical models of sensory systems. He has contributed to understanding the cochlear amplification mechanism and the role of neurotransmitters in sleep regulation. His research often employs perturbation methods and computational modeling to dissect complex biological phenomena. Dr. Holmes’ academic contributions are further evident through his editorial roles, conference presentations, and involvement in curriculum development at RPI. He remains active in both foundational mathematical research and its applied extensions in health and engineering.

Julien LangouView profile

Professor

• Computational Mathematics
• Numerical Linear Algebra
• Parallel Algorithms
• +3 more

Julien Langou is a Professor and Department Chair in the Department of Mathematical and Statistical Sciences at the University of Colorado Denver, part of the College of Liberal Arts and Sciences. His expertise lies in Computational Mathematics, focusing on numerical linear algebra, parallel algorithms, and high-performance computing. He has contributed significantly to the development of software libraries such as PLASMA and MAGMA, advancing dense linear algebra computations on modern architectures. His research emphasizes communication-avoiding algorithms, fault-tolerant numerical methods, and optimization of data movement in large-scale systems. Langou's work includes foundational contributions to matrix factorization techniques, such as QR and Cholesky algorithms, as well as innovations in distributed memory systems. He has led projects like the BALLISTIC initiative to modernize linear algebra libraries for exascale computing. His research has been supported by grants from NSF and other agencies, addressing challenges in parallel computing efficiency and algorithmic robustness. Key awards include the NSF CAREER award for foundational work in numerical linear algebra and leadership in collaborative research frameworks. Langou has authored numerous publications on topics ranging from error handling in numerical libraries to optimization of I/O-bound algorithms. His academic leadership includes roles in curriculum development and fostering interdisciplinary collaboration in computational science.

Mikhail BelkinView profile

Professor

• Machine Learning
• Deep Learning
• Feature Learning
• +5 more

Mikhail Belkin is a Professor at the Halicioglu Data Science Institute and the Computer Science and Engineering Department at the University of California San Diego (UCSD), as well as an Amazon Scholar. His research focuses on foundational aspects of machine learning, particularly deep learning and large language models, emphasizing understanding phenomena like feature learning, over-parameterization, and generalization. He serves as editor-in-chief of the SIAM Journal on Mathematics of Data Science (SIMODS) and was elected an ACM Fellow in 2023. Research Interests: Generalization theory in deep learning Optimization dynamics and convergence Feature learning mechanisms in neural networks Kernel methods and their connections to neural networks Double descent phenomenon and model complexity Key Contributions: Belkin's work has advanced understanding of interpolation in over-parameterized models, revealing how zero-training-error models can generalize well despite classical statistical concerns. His analyses of optimization landscapes and loss surfaces have clarified why gradient-based methods succeed in deep learning. He introduced the Average Gradient Outer Product (AGOP) framework to explain feature learning in neural networks, bridging classical kernel methods and modern architectures. Publications: His recent work explores feature learning in neural networks, convolutional architectures, and the theoretical underpinnings of LLMs. Over 15 recent papers are listed, spanning topics like kernel regression, matrix completion, and the mathematical foundations of deep learning. Awards & Grants: ACM Fellow (2023) NSF/Simons Collaboration on Theoretical Foundations of Deep Learning PI in the TILOS AI Institute (NSF-funded) Advising & Teams: Belkin collaborates with interdisciplinary teams at UCSD and institutions like the Broad Institute’s Eric and Wendy Schmidt Center, focusing on AI’s societal implications and scientific applications. His work emphasizes bridging theory and practice, with algorithms like Recursive Feature Machines (RFM) and EigenPro showcasing scalable, interpretable machine learning solutions.

Katharine LongView profile

Professor

• Scientific Computing
• Mathematical Modeling
• Applied Mathematics
• +9 more

Katharine Long is a Professor in the Department of Mathematics and Statistics at Texas Tech University (TTU), where she has served since 2007. Her research focuses on scientific computing, mathematical modeling, numerical methods for partial differential equations, uncertainty quantification, and high-performance computing. She has held prior positions at Sandia National Laboratories, SUNY Brockport, and UMass Amherst. Her work spans interdisciplinary applications in physics, biology, engineering, and medicine, with recent emphases on inverse problems in medical imaging and implicit Runge-Kutta methods for PDEs. Education: PhD in Astrophysics from Princeton University (1990), BS in Astronomy from the University of Maryland (1986), and coursework at Prince George's Community College (1981-82). Research interests include numerical optimization, structural sensitivity analysis, astrophysical dynamics (e.g., barred galaxies), and ecological modeling. She collaborates extensively, including with Victoria Howle on computational methods for PDEs and interdisciplinary teams on biomedical applications of infrared thermography. Teaching spans graduate and undergraduate courses in numerical analysis, mathematical methods for physics, and computational mathematics. She has advised numerous PhD, MS, and undergraduate students, many of whom now hold academic or industry positions. Labs/Teams: Co-developer of the Sundance and Trilinos software frameworks for PDE-constrained optimization. Active in professional organizations such as SIAM and AWM.

Andrei DraganescuView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Multilevel Algorithms
• +4 more

Andrei Draganescu is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). He holds a Ph.D. in Applied Mathematics from the University of Chicago (2004) and a B.Sc. in Mathematics from the University of Bucharest, Romania (1993). Before joining UMBC in 2006, he completed a postdoctoral appointment at Sandia National Laboratories. He currently serves as the Graduate Program Director for the Applied Mathematics program at UMBC. His research focuses on numerical analysis of partial differential equations, particularly multilevel algorithms for PDE-constrained optimization. He has led or co-led multiple grants funded by the National Science Foundation (NSF) and Department of Energy (DOE), including projects on multigrid methods, optimal control of PDEs, and optimization-based domain decomposition. Draganescu has advised several Ph.D. students and postdocs, including Sumaya Alzuhairy (2021), Mona Hajghassem (2017), and Jyoti Saraswat (2014). His publications span topics such as multigrid preconditioning, PDE-constrained optimization, and numerical linear algebra, with contributions to journals like SIAM Journal on Numerical Analysis and Numerical Linear Algebra with Applications . He has organized conferences such as the 2024 Fall Finite Element Circus and the Sayas Numerics Days. His teaching spans graduate and undergraduate courses in numerical analysis, matrix analysis, and differential equations.

• Computational Mechanics
• Structural Analysis
• Finite Element Methods
• +2 more

Francesco Parrinello is Associate Professor in Structural Engineering at the University of Palermo. His research develops computational methods for fracture mechanics, including hybrid equilibrium elements for cohesive crack propagation and delamination analysis. Teaching responsibilities include Computational Mechanics of Structures and Science of Construction courses for Civil and Energy Engineering students. Recent publications focus on adaptive numerical formulations for complex material failure under dynamic loading. Laboratory work emphasizes experimental validation of computational models.

Xuemin TuView profile

Professor

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +3 more

Xuemin Tu is a Professor in the Department of Mathematics at the University of Kansas, serving as the Scheduling Officer. His research focuses on scientific computing, numerical analysis, and applied mathematics, emphasizing domain decomposition methods, BDDC algorithms, and data assimilation. He is affiliated with the College of Liberal Arts & Sciences and actively contributes to interdisciplinary computational science. Research interests include iterative methods for partial differential equations, numerical solutions of fluid dynamics problems, and the development of efficient algorithms for large-scale simulations. His work bridges theoretical analysis and practical applications in engineering and geophysics. Key trends in his recent articles (2018–2025) highlight advancements in BDDC algorithms for fluid and porous media systems, implicit sampling techniques for data assimilation, and machine learning applications in image recognition. He also explores stochastic models for parameter estimation and uncertainty quantification. No scientific awards or grants are explicitly listed in the provided text. Advising roles or student mentorship details are not included, though his research areas suggest contributions to graduate training in computational mathematics. Labs or collaborative teams are not detailed here, but his affiliations imply involvement with computational science initiatives at the University of Kansas.

Andrew SingerView profile

Professor

• Signal Processing
• Underwater Acoustic Communications
• Biomedical Acoustics
• +4 more

Andrew Singer is the Dean of the College of Engineering and Applied Sciences and a Professor in the Department of Electrical and Computer Engineering at Stony Brook University. Previously, he held roles at the Grainger College of Engineering, University of Illinois Urbana-Champaign, including positions as Associate Dean for Innovation and Entrepreneurship. His research spans signal processing, underwater acoustic communications, biomedical acoustics, and entrepreneurship. He leads projects like the MAGIC Automated Acoustics Laboratory and Augmented Listening Technology, focusing on audio signal enhancement, wearable devices, and underwater communication systems. Research interests include adaptive signal processing, turbo equalization, and biomedical applications of acoustics. Notable contributions include high-data-rate ultrasonic communication through biological tissues and real-time video streaming using acoustic channels. His work integrates machine learning and physical models to address challenges in acoustics, communications, and biomedical engineering. Recent articles explore reliable measurement in unreliable systems, underwater source localization, and bio-inspired signal processing techniques. Awards and recognitions are not explicitly listed but his extensive publications and leadership roles highlight his contributions to the field. Advised students and collaborators span academia and industry, including roles at Apple, Amazon, and defense agencies. Current initiatives include developing mechatronic systems for acoustic research and advancing assistive listening technologies.

Xiangmin JiaoView profile

Associate Professor

• High-performance computing
• Numerical methods
• Geometric computing
• +5 more

Xiangmin (Jim) Jiao is an Associate Professor in the Department of Applied Mathematics and Statistics at Stony Brook University, affiliated with the Computer Science Department and the Institute for Advanced Computational Science. He holds a B.S. from Peking University, M.S. from UC Santa Barbara, and Ph.D. in Computer Science from UIUC. His research focuses on high-performance geometric and numerical computing, including algorithms and software for dynamic surfaces, mesh optimization, and multiphysics coupling in applications like computational fluid dynamics and biomedical engineering. He has received awards such as the Outstanding Teacher Award (2010-2012) and the David J. Kuck Outstanding Ph.D. Thesis Award (2001). His teaching spans courses like AMS 527 (Numerical Methods) and AMS 561 (Computational Science). Key research contributions include work on finite element methods, preconditioning techniques, and surface reconstruction. He has led projects on adaptive mesh refinement and multilevel linear solvers, with applications in climate modeling and structural mechanics. His lab, the Numerical Geometry Group (NumGeom), develops open-source software tools for numerical simulations. Collaborations include interdisciplinary work in biomedical computing and climate science. Recent research emphasizes robust numerical methods for singular systems and high-order surface integration techniques.

Hongyang SunView profile

Assistant Professor

• High-performance computing
• Cloud/edge computing
• Computational data science
• +2 more

Hongyang Sun is an Assistant Professor in Electrical Engineering and Computer Science at University of Kansas, researching high-performance computing, cloud/edge systems, and computational data science. Focuses on performance, reliability, and energy efficiency in large-scale systems. Research combines algorithmic design, optimization techniques, and applied machine learning. Previously held positions at Vanderbilt University and French research institutes. Leads projects on resilient scheduling, machine learning acceleration, and edge computing resource management.

Dmitry SavostyanovView profile

Lecturer

• Numerical Mathematics
• Tensor Decompositions
• High-Dimensional Problems
• +3 more

Dr. Dmitry Savostyanov is a Lecturer at the University of Essex, School of Mathematics, Statistics and Actuarial Science (SMSAS). His research focuses on developing efficient algorithms for high-dimensional problems using low-rank tensor product approximations, linear algebra, and numerical methods. He holds a PhD in Computational Mathematics from the Russian Academy of Sciences and a PGCert in Higher Education from the University of Brighton. Qualifications: PhD (2006), MSc (2003), BSc (2001), PGCert (2016). Appointments: Senior Lecturer at University of Brighton (2014–2020), Senior Research Fellow at University of Southampton (2012–2014), and Visiting Research Fellow at University of Chester (2011–2012). His research interests span numerical mathematics, tensor decompositions, and computational methods for high-dimensional systems. Recent work includes applications in epidemiological modeling, quantum control, and NMR simulation. He has collaborated on software tools like the TT-toolbox for tensor train formats. Publications highlight contributions to tensor interpolation, quantum algorithms, and efficient numerical solvers. No scientific awards are explicitly listed, but his work reflects sustained innovation in computational mathematics. No student advisees or lab affiliations are detailed in the provided materials. Grants and funding sections exist but lack specific details.

Alfa HeryudonoView profile

Professor

• Radial Basis Function Methods
• Numerical Analysis
• Scientific Computing
• +3 more

Alfa Heryudono is a Professor in the Department of Mathematics at the University of Massachusetts Dartmouth , where he has been since 2008. His academic journey includes a BS in Physics (2000), MS in Mathematics (2002), and PhD in Applied Mathematics (2008). BS: University of Indonesia MS: Southern Illinois University Edwardsville PhD: University of Delaware Marie Curie Visiting Fellow: Uppsala University (2010-2012) His research focuses on Radial Basis Function (RBF) methods , Scientific Computing , and Numerical Methods for PDEs , with applications in Mathematical Modeling and Engineering Problems . Recent publications highlight advancements in least squares RBF-FD , convection-diffusion equations , and meshless collocation techniques . His work is supported by grants from NSF, ONR, and Marie Curie FP7. Scientific Awards: ONR Grant: Data-driven Hydrodynamic Models ($74,409) ONR Grant: Nonlinear System Learning ($438,284) ONR Grant: Marine Technology Data Learning ($435,393) Marie Curie FP7 Fellowship (2010-2012) Heryudono leads the Mathematical and Computational Consulting (MC²) course , a multidisciplinary engagement program where students tackle real-world problems from industries and research groups. His administrative roles include Co-Director of the Data Science MS program and EAS PhD program. He actively contributes to reproducible research by sharing MATLAB codes for adaptive RBF methods and serves as Associate Editor for the Journal of Approximation Software (JAS) and SIAM member.

Yanlai ChenView profile

Professor

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +3 more

Yanlai Chen is a Professor in the Department of Mathematics at the University of Massachusetts Dartmouth and serves as Chief Research Officer. He holds a PhD (2007) and MS (2007) from the University of Minnesota Twin Cities, and a BS (2002) from the University of Science and Technology of China. His research integrates numerical analysis, scientific computing, and machine learning. Research interests focus on numerical PDEs, model reduction, machine learning applications in scientific computing, uncertainty quantification, and high-performance computing algorithms. His work bridges theoretical mathematics with practical computational challenges. His publications demonstrate consistent focus on physics-informed neural networks (2021-2024), model reduction techniques (2019-2024), and computational methods for differential equations. Recent work shows increased emphasis on machine learning integration with traditional numerical methods. He has supervised 6 doctoral dissertations and leads the NSF-funded ACCOMPLISH program supporting STEM education through contextualized computing curricula.