Find Academic Supervisors

Gordon ErlebacherView profile

Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +5 more

Gordon Erlebacher is a Professor at the Department of Scientific Computing at Florida State University (FSU), where he also serves as Program Director for Interdisciplinary Data Science . His work bridges artificial intelligence, scientific computing, and educational technology. B.Sc. , Free University of Brussels M.Sc. , Free University of Brussels Ph.D. , Columbia University His research spans from early contributions in GPU acceleration of numerical methods (2010s) to modern applications of transformer-based large language models (LLMs) in education and agentic systems for administrative automation. Key areas include: Neural networks (autoencoders, pruning, graph neural networks) Topic modeling and natural language processing Seismic wave propagation and geophysical simulations Interactive visualization tools for oceanographic data AI-driven educational platforms for classroom engagement His recent publications focus on AI in public health (CV19 SelfDefense) , Alzheimer's disease prediction , and GPU-accelerated seismic modeling . Earlier work includes foundational GPU implementations for seismic wave propagation and turbulence simulations. No scientific awards are explicitly mentioned in the provided text.

Wei XiangView profile

Adjunct Professor

• Internet of Things
• Machine Learning for IoT
• Big Data Analytics
• +3 more

Wei Xiang is an Adjunct Professor and Founding Chair of Electronic Systems and Internet of Things Engineering at James Cook University. He leads the development of Australia's first accredited IoT Engineering program and conducts research at the intersection of IoT, machine learning, and big data analytics. Research encompasses: IoT systems for environmental monitoring Machine learning applications for sensor data Edge computing architectures Wireless communication protocols Smart agriculture technologies Publication analysis reveals focus areas: Smart transportation systems (25%) Agricultural IoT (20%) Medical AI applications (15%) Industrial IoT (15%) Network optimization (25%) Honors include Pearcey Entrepreneurship Award, Engineers Australia Cairns Engineer of the Year, and TNQ Innovation Award for establishing Australia's first IoT Engineering degree. Current projects include closed-loop irrigation systems for sugarcane farms and marine monitoring using distributed deep learning.

Robert F HarrisonView profile

Professor

• Computational Data Modelling
• Machine Learning
• Neural Networks
• +5 more

Robert F. Harrison is Professor of Computational Data Modelling at the University of Sheffield's Department of Electronic and Electrical Engineering. He joined the department in 1987 and has served twice as Head of Department (2008-09, 2011-12). Since 2014, he has directed the Centre for Doctoral Training in Control & Systems Engineering. Between 2009-2012, he acted as Director of Sheffield's Rail Innovation and Technology Centre, a Network Rail-funded initiative. Harrison's research focuses on applying systems engineering methods to biomedical and industrial challenges, with expertise in: Machine learning and neural networks for clinical decision support Bayesian hierarchical models for aerospace prognostics Kernel methods and support vector machines Rail network optimization and energy management Biomedical applications including trauma prognosis and thyroid analysis His research themes demonstrate strong interdisciplinary integration across medical diagnostics, transportation systems, and aerospace engineering. Recent work shows increasing focus on cybersecurity in critical infrastructure and optimization of rail networks using Bayesian and evolutionary approaches. Professional Activities Visiting Fellowship at Princeton University's Department of Mechanical and Aerospace Engineering Fellowship at Butler College Visiting Fellowship at James Cook University Member of editorial boards for multiple international journals Technical committee roles for International Federation of Automatic Control (IFAC) Harrison has secured over £3M in research funding from organizations including Siemens, EPSRC, Network Rail, and Rolls-Royce for projects on rail optimization, energy systems, and aerospace prognostics.

Hendrik WilkaView profile

Researcher

• Numerical Analysis
• Scientific Computing
• Computational Physics
• +2 more

Hendrik Wilka is a researcher at the Department of Mathematics, Technische Universität Darmstadt. His work focuses on numerical analysis and scientific computing, with an emphasis on adaptive algorithms and sparse grid techniques. He has served as a teaching assistant for multiple courses including Numerical Analysis, Mathematical Modelling, and Numerical Linear Algebra under professors such as Jens Lang and Tabea Tscherpel. His recent research includes developing efficient collocation algorithms for piecewise smooth functions with kinks, published in the Journal of Computational Physics (2025). Wilka leads the development of simulation software for pandemic modeling and population dynamics, showcased in projects like the 'Long Night of Mathematics.' His technical contributions include maintaining public resources like PGP keys and academic infrastructure tools. Research interests include advancing computational methods for complex systems, with applications in physics and engineering. His technical activities involve academic software development and educational support through teaching assistance.

Enrique MalladaView profile

Associate Professor

• Control of Networked Systems
• Power Systems
• Optimization
• +5 more

Enrique Mallada is an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Mechanical Engineering, Applied Mathematics and Statistics, and Computer Science. He leads the Networks, Dynamics, and Learning Laboratory (NetDL2ab) and is a core member of MINDS (Mathematical Institute for Data Science) and ROSEI (Ralph O’Conner Sustainable Energy Institute). His research focuses on control systems, optimization, power systems, and machine learning, with applications in networked systems, energy grids, and distributed coordination. Education: B.S. in Telecommunications Engineering from Universidad ORT (2005), Ph.D. in Electrical and Computer Engineering with a minor in Applied Mathematics from Cornell University (2014). Postdoctoral research at Caltech’s Center for the Mathematics of Information (2013–2015). Research Interests : Networked systems (synchronization, distributed coordination), power systems (frequency control, grid resilience), optimization (time-varying algorithms, reinforcement learning), and machine learning (safety-critical applications, sparse recovery). Key Projects : Real-time optimization for infrastructure networks, voltage collapse stabilization in power grids, control of distributed energy resources, and safety-aware reinforcement learning. His work integrates tools from control theory, optimization, and machine learning to address challenges in large-scale systems. Awards : NSF CAREER Award, Caltech CMI Fellowship, Cornell Jacobs Fellowship, JHU Discovery/Catalyst Awards, and Excellence in Teaching Award. Advising & Grants : Mentors graduate students in ECE and related fields. Active in grants focused on energy systems, control theory, and AI safety. Organizes conferences like CISS 2019 and serves on technical committees for IEEE Smart-GridComm and ACC. Labs & Affiliations : NetDL2ab Lab (directed), MINDS (core member), ROSEI (core member), LCSR (affiliate), and Data Science & AI Institute (member). His research bridges academia and industry, addressing real-world challenges in energy and automation.

James ZhengView profile

Associate Professor

• Cyber-Physical Systems Testing and Verification
• Safety Analysis
• Distributed Learning
• +3 more

Dr. James Zheng is an Associate Professor and ARC Future Fellow at Macquarie University's School of Computing. He leads the Intelligent Systems Research Group (ITSEG.ORG) and oversees international engagement in the School. With a Ph.D. in Software Engineering from the University of Texas at Austin (2015), he previously served as Chief Solution Architect at Menulog Australia (2005–2012). His research focuses on Cyber-Physical Systems (CPS) testing, AI system safety, distributed learning, and IoT. He has secured over $2.4M in ARC and Data61 grants, including a 2024–2028 Future Fellowship for robust CPS autonomy. Education: Ph.D. in Software Engineering, University of Texas at Austin, 2015 Research Interests: Dr. Zheng's work addresses safety-critical CPS challenges, leveraging neurosymbolic AI and large language models. His projects include autonomous vehicle safety frameworks, drone landing systems, and fault-tolerant smart grids. He emphasizes interdisciplinary collaboration, integrating machine learning and formal verification techniques. Grants & Projects: ARC Future Fellowship (2024–2028): $960,412 for robust autonomy in CPS ARC Linkage Project (2022–2025): $459,593 for drone landing systems ARC Discovery Project (2021–2024): $448,958 for autonomous driving validation Awards: Deakin Industry Researcher Award (2016) MQ Early Career Researcher Award (Runner-up, 2020) Labs & Teams: Director of ITSEG.ORG, a hub for CPS and AI safety research. Active in international workshops on trustworthy autonomous systems.

Elizabeth CherryView profile

Associate Professor

• Computational Physiology
• Cardiac Electrophysiology
• High-Performance Computing
• +3 more

Elizabeth Cherry is an Associate Professor in the School of Computational Science and Engineering (College of Computing) at Georgia Institute of Technology, and currently serves as the Associate Dean for Graduate Education. She holds a BS in Mathematics from Georgetown University and a PhD in Computer Science from Duke University. Her research focuses on computational modeling of cardiac arrhythmias, leveraging high-performance computing, numerical methods, and data assimilation to understand reentrant wave dynamics in the heart. Key applications include model development, parameter estimation, and integration with experimental data. She has contributed to tools like CardioFit and Unimapper, advancing patient-specific cardiac modeling. Her editorial roles include serving on the editorial board of Chaos and as a review editor for Frontiers in Physiology . She has organized major conferences such as the SIAM Conference on Applications of Dynamical Systems and Dynamics Days. Her work is supported by the NSF and NIH, addressing arrhythmia mechanisms and therapeutic strategies. Research highlights include studying voltage/calcium alternans, fractional diffusion effects, and rotor-mapping algorithms for atrial fibrillation ablation targets. Advising and grants: Her research is funded by NSF and NIH grants. No awards explicitly mentioned in texts. Her lab focuses on interdisciplinary computational cardiology, collaborating on projects like the Cybercardia initiative for implantable device modeling.

Yongtao ZhangView profile

Professor

• Computational Physics
• Mathematical and Computational Biology
• Numerical Solution of Differential Equations
• +3 more

Yongtao Zhang is a Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a PhD from Brown University (2003) and an M.S. from Nankai University (1999). His research focuses on developing high-order numerical methods for partial differential equations (PDEs), including weighted essentially non-oscillatory (WENO) schemes, discontinuous Galerkin (DG) methods, and implicit integration factor (IIF) methods. These methods are applied to computational physics (e.g., shock wave dynamics, fluid flows) and computational biology (e.g., morphogenesis, tissue patterning). Dr. Zhang's work emphasizes numerical solutions for hyperbolic conservation laws, stiff advection-reaction-diffusion equations, and Hamilton-Jacobi equations. He has contributed to sparse-grid implementations, fast sweeping methods, and exponential time differencing schemes to enhance computational efficiency and accuracy. His research bridges theoretical advancements with practical applications in high-dimensional systems and complex geometries. He is an editorial board member for Journal of Scientific Computing and Communications on Applied Mathematics and Computation , reflecting his leadership in the field. Teaching responsibilities include numerical analysis, scientific computing, and applied mathematics courses. His work has been published in leading journals such as Journal of Computational Physics and SIAM Journal on Scientific Computing .

Tonatiuh Sanchez-VizuetView profile

Assistant Professor

• Numerical Analysis
• Scientific Computing
• Computational Physics
• +6 more

Tonatiuh Sánchez-Vizuet is an Assistant Professor in the Department of Mathematics at the University of Arizona , where he develops numerical methods for magnetically confined plasma simulations and computational physics problems. Education B.Sc. in Physics, National Autonomous University of Mexico (UNAM) M.Sc. in Mathematics, National Autonomous University of Mexico (UNAM) Ph.D. in Applied Mathematics, University of Delaware Research Interests center on numerical analysis, scientific computing, and computational physics, with specific focus areas including: Hybridizable Discontinuous Galerkin (HDG) methods Boundary Element Method (BEM) for time-domain wave propagation Coupling integral and partial differential equations Uncertainty quantification in plasma physics Transient electromagnetic scattering analysis Thermo-piezoelectric wave interactions Scientific Contributions include 15+ peer-reviewed publications on HDG discretizations, boundary integral formulations, and surrogate modeling. His work spans applications in: Magnetic confinement fusion reactors Wave-structure interaction Transient thermoelasticity Dielectric scattering problems Grants NSF-DMS-2137305 (2021-2023): $250,000 as single PI for HDG methods in plasma confinement Awards Featured by Lathisms.org as prominent Hispanic mathematician (2018) Teaching includes graduate-level real analysis and mathematical statistics courses, with class notes freely available for student use.

Daniela RoscaView profile

Professor

• Wavelet Analysis
• Numerical Analysis
• Geometric Modeling
• +3 more

Professor Daniela Rosca holds a position at the Department of Mathematics, Technical University of Cluj-Napoca, Romania. Her academic career includes a PhD in Approximation with Wavelets (2004) and a Habilitation Thesis on Wavelets on Two-Dimensional Manifolds (2012). She specializes in wavelet theory, numerical analysis, geometric modeling, and grid generation on manifolds. Research Interests: Her work focuses on wavelet transforms on spheres and other manifolds, geometric mappings (area/volume preservation), numerical approximation methods, and applications in material science and computational geometry. Notable contributions include spherical wavelet bases, equal area projections, and optimal point configurations on spheres. Publications: Over 30 peer-reviewed articles in journals like Applied and Computational Harmonic Analysis, Mathematics and Computers in Simulation, and SIAM Journal on Scientific Computing. Key topics include wavelet analysis on tori/spheres, sparse Legendre expansions, and grid generation in 3D rotation spaces. Books: Authored textbooks on discrete mathematics, integral calculus, and wavelet analysis for academic use in Romania.

Eftychios SifakisView profile

Professor

• Computer Graphics
• Physics-Based Modeling and Simulation
• Scientific Computing
• +4 more

Eftychios Sifakis is a Professor of Computer Sciences at the University of Wisconsin-Madison. He joined the department in January 2011 after a postdoctoral appointment at UCLA (2007-2010) and holds a PhD in Computer Science from Stanford University (2007). His research focuses on physics-based modeling, computer graphics, and scientific computing, with applications in biomechanics and virtual surgery. Key projects include surgical simulators, material point methods, and real-time deformable body simulation. Education: PhD in Computer Science, Stanford University, 2007 BS in Computer Science & Mathematics, University of Crete, Greece, 2000-2002 Research Interests: His work bridges computational physics and visual computing, emphasizing algorithms for fluid dynamics, topology optimization, and medical simulation. He develops scalable numerical methods for large-scale simulations on heterogeneous hardware. Teaching: Courses include CS559 (Computer Graphics), CS839 (Physics-Based Modeling), and CS412 (Numerical Methods). He has taught at both UW-Madison and UCLA. Labs/Teams: Leads research on computational biomechanics and fluid-structure interaction within the UW-Madison Computer Sciences Department. Collaborates on projects like the Chrono physics engine and surgical simulation frameworks.

Thomas HuckleView profile

Professor

• Numerical Linear Algebra
• Scientific Computing
• Parallel Computing
• +7 more

Prof. Thomas Huckle is a Professor of Scientific Computing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His research focuses on numerical linear algebra, parallel computing, and their applications in physics and computer science. Key interests include solving linear problems on parallel architectures, image processing, multigrid methods, preconditioning, and tensor-based high-dimensional problem approximation. Education: Studied mathematics and physics at the University of Würzburg (diploma in mathematics, 1985 PhD, 1991 habilitation). Professional History: DFG-funded research at Stanford University (1993–1994), appointed to TUM in 1995, and member of the Mathematics Department since 1997. Research Interests: Prof. Huckle’s work spans numerical methods for large-scale systems, including structured matrices, regularization techniques, and quantum computing applications. He develops algorithms for parallel computing environments and contributes to software tools like ELPA for eigenvalue problems. Grants and Labs: Engaged in projects such as the ELPA-AEO eigensolver and ESSEX-II initiatives. Active in the SCCS (Scientific Computing and Computational Science) group at TUM, focusing on high-performance computing and numerical methods.

Oliver JungeView profile

Associate Professor

• Numerical Mathematics
• Scientific Computing
• Dynamical Systems
• +4 more

Oliver Junge is an Associate Professor of Numerics of Complex Systems at the TUM School of Computation, Information and Technology, Technische Universität München. His research focuses on developing novel numerical methods for dynamical systems, with applications in molecular dynamics, astrodynamics, systems theory, and image processing. He holds a doctorate from the University of Paderborn (1999) and has conducted research at institutions like Georgia Tech and Caltech. Notable awards include the TopMath Supervisory Award (2020) and the Research Prize from the University of Paderborn (2004). Educational Background: He studied mathematics and computer science at the Universities of Darmstadt, Bordeaux, Hamburg, and Bayreuth before completing his PhD in Paderborn. His academic career includes roles as a junior professor in Paderborn and associate professor at TUM since 2005. Research Interests: Junge specializes in numerical mathematics and scientific computing, emphasizing the analysis and implementation of numerical methods for complex systems. His work bridges theoretical rigor and practical applications, particularly in computational fluid dynamics, optimal control, and topological data analysis. Awards: His accolades include the Dilthey Prize (1998) and recognition for his contributions to computational methods in dynamical systems. Grants & Advising: While specific grants or advisee names are not listed, his publications indicate extensive collaborative research and mentorship in computational mathematics and systems theory. Labs/Teams: Collaborates with interdisciplinary teams at TUM and internationally, focusing on numerical methods and their applications in engineering and natural sciences.

Thang N. DinhView profile

Associate Professor

• Graph Machine Learning
• Quantum Computing
• Approximation Algorithms
• +7 more

Thang N. Dinh is an Associate Professor in the Department of Computer Science at Virginia Commonwealth University (VCU), part of the College of Engineering. He holds a Ph.D. in Computer Engineering from the University of Florida (2008–2013) and a B.S. in Information Technology from Vietnam National University, Hanoi (2003–2007). His research focuses on Graph Machine Learning, Quantum Computing, and Approximation Algorithms, with applications in Social Networks, Blockchain, Smart Grids, Network Security, Drug Discovery, and Pandemic Modeling. Key research projects include quantum-inspired state estimation for power grids, quantum computing for healthcare optimization, and privacy-preserving federated learning. He has received numerous awards, including Best Paper Awards at ACM Mobicom and ACM Sensys, the Honda Young Engineer and Scientist Award, and a Microsoft Scholarship. His work bridges theoretical computer science with real-world challenges in cybersecurity, healthcare, and distributed systems. Teaching responsibilities include courses on Graph Neural Networks, Social Network Analysis, Blockchain, and Advanced Algorithms. His research group develops scalable solutions for billion-scale networks, leveraging quantum computing and AI to address problems in security, optimization, and healthcare.

Jens LangView profile

Professor

• Numerical Analysis
• Computational Fluid Dynamics
• Adaptive Finite Element Methods
• +4 more

Prof. Dr. Jens Lang is a Professor at the Numerical Analysis Group within the Department of Mathematics at Technische Universität Darmstadt . His work focuses on adaptive numerical methods for partial differential equations (PDEs), particularly in computational fluid dynamics, uncertainty quantification, and optimal control.