Find Academic Supervisors

Ebenezer Ekow EsselView profile

Assistant Professor

• Turbulence
• Aerodynamics
• Flow Control
• +6 more

Dr. Ebenezer Ekow Essel is an Assistant Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on turbulence, aerodynamics, flow control, and computational fluid dynamics, with a particular emphasis on synthetic jet actuators, boundary layer interactions, and machine learning applications in bioassay analysis. He holds a B.Sc. from Kwame Nkrumah University of Science and Technology (Ghana), and M.Sc. and Ph.D. degrees in Experimental Fluid Mechanics from the University of Manitoba. His career includes postdoctoral research at the University of Windsor (NSERC PDF recipient) and the University of Toronto’s Turbulence Research Laboratory. Education: B.Sc. Mechanical Engineering (KNUST), M.Sc./Ph.D. Experimental Fluid Mechanics (University of Manitoba) His research explores advanced flow control techniques, computational methods, and experimental fluid mechanics. Key areas include synthetic jet dynamics, turbulent boundary layers, and the integration of machine learning for bioassay analysis. Recent work emphasizes GPU-native adaptive mesh refinement for lattice Boltzmann simulations and flow interactions in complex geometries. Dr. Essel’s publications span topics like jet actuator modeling, wake interference in cylinder arrays, and turbulent flow characterization in automotive and hydraulic contexts. He leads the Turbulence Research Lab at Concordia and has received the NSERC Postdoctoral Fellowship (2019). Notable Awards: NSERC Postdoctoral Fellowship (2019) His advising focus includes thesis supervision in fluid dynamics and flow control. Current research projects involve active flow control strategies, aerodynamic optimization, and high-performance computing for fluid simulations.

Theodosios PapathanasiouView profile

Senior Lecturer

• Computational Mathematics
• Computational Mechanics
• Applied Mechanics
• +8 more

Dr Theodosios Papathanasiou is a Senior Lecturer in Structural Engineering at the Department of Civil Engineering, Aston University, since September 2021. He holds a PhD in Computational Mathematics/Mechanics from the National Technical University of Athens (NTUA), along with MSc degrees in Applied Mechanics and Computational Mechanics, and a Mechanical Engineering diploma from NTUA. His research focuses on computational mechanics, finite element analysis, hydroelasticity, and coupled field problems. Key areas include thermal conductivity in composite materials, wave propagation in ocean environments, and structural analysis of floating systems. He has contributed to over 30 peer-reviewed publications and collaborated on projects involving metamaterial design, coastal engineering, and fracture mechanics. Teaching activities include Engineering Mathematics, Structural Analysis, and Numerical Methods in Mechanics. He has held academic positions at Brunel University London (2019-2021) and postdoctoral roles at NTUA and the University of Trento (Marie Curie Fellow, 2014-2016). Recent research highlights include optimizing parameter-free PML-FEM methods for ocean wave scattering and studying resonant hydroelastic behavior of ice shelves. His work bridges computational modeling with practical applications in civil and environmental engineering.

David C. CollinsView profile

Assistant Professor

• Astrophysics
• Magnetohydrodynamics
• Star Formation
• +3 more

David C. Collins is an Assistant Professor in the Department of Physics at Florida State University (FSU), specializing in astrophysics and magnetohydrodynamic (MHD) simulations. His research focuses on simulating magnetized turbulence in molecular clouds, its role in star formation, and observational signatures of collapsing systems. PhD in Physics (2009), University of California, San Diego BS in Physics and BA in Mathematics (2001), University of Cincinnati Collins develops and utilizes the adaptive mesh refinement code Enzo , including contributions to its MHD package, and employs the analysis tool yt . His work spans topics such as: Galaxy cluster magnetic fields and turbulence Thermonuclear supernovae (Type Ia) and deflagration dynamics Collisions of giant molecular clouds as triggers for star formation Density and magnetic field distributions in self-gravitating turbulence Recent publications highlight his focus on supersonic turbulence, shock interactions, and statistical modeling of astrophysical fluids. He also contributes to software development for cosmological simulations and observational data analysis.

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.

Alice KonigesView profile

Researcher

• High Performance Computing
• Computational Science
• Parallel Algorithms
• +5 more

Dr. Alice Koniges is a computational scientist at the University of Hawaii at Manoa and Maui High Performance Computing Center. She specializes in modeling complex physical phenomena using advanced HPC techniques, with research interests spanning emerging architectures, machine learning, and partial differential equations. Her career includes roles at Berkeley Lab and joint appointments with UC Berkeley and UCLA. She leads the ALE-AMR code development team, which models plasma, fluid, and solid interactions in high-energy density regimes. As a DOE Exascale project PI, she achieved milestones in supercomputing performance and secured multimillion-dollar contracts. Previously, she served as Head of Institutional Computing at Livermore Lab, fostering industry partnerships through CRADAs. Education: PhD in Applied and Computational Mathematics from Princeton University (first woman recipient), MSE and MA from Princeton, and BA from UC San Diego. Over 100 technical papers, two HPC books, and 1,500+ citations highlight her scholarly impact. She has mentored >15 postdocs and graduate students, emphasizing HPC's practical applications. Recent work includes coastal aquifer management models and epidemiological studies of pandemic spread in island environments. Her research bridges computational methods with real-world challenges, such as fusion energy simulations and climate modeling. The PISALE codebase and ALE-AMR framework are core to her contributions. Current focus areas include exascale computing readiness, multiphysics integration, and optimizing HPC for next-gen architectures.

Sean P. CarneyView profile

Assistant Professor

• Applied Mathematics
• Computational Mathematics
• Numerical Analysis
• +6 more

Sean P. Carney is an Assistant Professor of Mathematics at Union College , focusing on applied and computational mathematics. His roles include teaching courses in differential equations, partial differential equations, and numerical methods, while leading research in multiscale and stochastic modeling, PDE-constrained optimization, and turbulent flow dynamics. Education: Ph.D. in Mathematics from the University of Texas at Austin (2020), advised by Björn Engquist; B.S. in Mathematics from the University of Michigan, Ann Arbor (2014). Prior Experience: Postdoctoral Research Fellow at George Mason University’s Center for Mathematics and Artificial Intelligence; Hedrick Assistant Adjunct Professor at UCLA (2020–2023); Graduate Research Assistant at Lawrence Berkeley National Lab (2018–2019). Carney’s research centers on multiscale and stochastic modeling , with applications in transport and mixing in turbulent flows , homogenization theory , and optimization under uncertainty . His work combines numerical analysis with physical modeling to solve complex problems in PDE-constrained optimization and fluid dynamics. Recent publications highlight his contributions to numerical homogenization , chaotic electroconvective flow modeling , and liquid-liquid phase separation in confined systems . These studies span fluid dynamics, computational physics, and applied mathematics, often involving collaborations with institutions like UCLA, UT Austin, and Lawrence Berkeley National Lab. Scientific Awards: Editors' Suggestion, Physical Review Fluids (2020) Carney has mentored students in the UCLA Computational and Applied Mathematics REU program and the Cal-State/UCLA Summer Bridge Program. His teaching spans differential equations, numerical methods, and advanced PDE courses at Union College and UCLA.

Bruce A. WadeView profile

Professor

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

Bruce A. Wade is a Professor & Department Head at the University of Louisiana at Lafayette, holding the C.B.I.T. TC/LEQSF Regents Professorship. He is also an Emeritus Professor at the University of Wisconsin-Milwaukee (UW-M). His academic journey includes a Ph.D. in Mathematics (1987) from UW-Madison, followed by a post-doctoral fellowship at Cornell University. He served 29 years at UW-Milwaukee as a faculty member, holding roles such as Associate Chair and Director of the Center for Industrial Mathematics, before transitioning to Louisiana in 2017 as Department Head. Dr. Wade’s research focuses on numerical analysis, computational mathematics, and partial differential equations (PDEs). He specializes in developing algorithms for reaction-diffusion systems, including models like the Black-Scholes, Fitzhugh-Nagumo, and Kuramoto-Sivashinsky equations. His work emphasizes efficient numerical schemes that preserve PDE properties and handle low regularity or mismatched boundary conditions. Recent projects include machine learning applications, missing data imputation, and Ridge Functions. He has authored/co-authored over 70 publications, including influential works on exponential time differencing (ETD) methods, fractional calculus, and parameter estimation. His editorial roles include serving on journals like International Journal of Computational Mathematics and co-founding the Computational and Mathematical Methods in Science and Engineering (CMMSE) conference series. Dr. Wade’s professional service includes organizing conferences, advising graduate students (though specific names are not listed), and securing grants for interdisciplinary research. His academic leadership extends to departmental administration and fostering collaborations between academia and industry.

Spencer BryngelsonView profile

Assistant Professor

• Computational Physics
• Fluid Dynamics
• High Performance Computing
• +3 more

Spencer Bryngelson is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. He specializes in computational physics, numerical methods, fluid dynamics, and high-performance computing. His research focuses on developing scalable simulation tools for multiphase flows, quantum algorithms for fluid dynamics, and Bayesian optimization techniques. Education: Ph.D. and M.S. in Theoretical and Applied Mechanics from University of Illinois at Urbana-Champaign (2015–2017); B.S. in Mechanical Engineering and Mathematics from University of Michigan–Dearborn (2013) Affiliations: Previously senior postdoc at Caltech, visiting researcher at MIT, and postdoc at XPACC Labs/Teams: Leads the Comp-Physics Group (https://comp-physics.group) His research interests include quantum computing applications to fluid mechanics, high-performance computing methodologies, and multiphysics simulation frameworks like the MFC flow solver. Recent work explores hybrid quantum-classical algorithms for PDEs and GPU-accelerated combustion kinetics modeling through tools like Pyrometheus. Notable trends in his articles include: Quantum computing integration in fluid dynamics simulations Development of exascale-ready multiphysics solvers Bayesian optimization for material characterization Advances in WENO schemes and turbulence modeling His work emphasizes portability and scalability in HPC environments, with contributions to libraries like QBMMlib and RoseNNa for neural network integration in CFD.

Khosro ShahbaziView profile

Associate Professor

• Computational Fluid Dynamics
• Multiphase Flows
• Nanoparticle Applications
• +4 more

Dr. Khosro Shahbazi is a tenured Associate Professor of Mechanical Engineering at South Dakota Mines. His expertise includes computational fluid dynamics, multiphase flows, and nanophysics applications in energy and medicine. He completed his Ph.D. at the University of Toronto and held postdoctoral positions at Brown University and University of Wyoming. Research interests focus on novel numerical methods for compressible flows, nanoparticle optoacoustics for cancer therapy, and turbulence modeling. His work bridges theory, computation, and experimentation in renewable energy, inertial confinement fusion, and biomedical systems. Recent publications emphasize high-order finite difference methods, compressible multiphase flows, mining ventilation systems, and photoacoustic diagnostics. Teaching includes Thermodynamics, Fluid Mechanics, Heat Transfer, and specialized graduate courses in computational transport phenomena. Education: B.S. (Sharif University), M.A.S. (Toronto), Ph.D. (Toronto) Laboratory: Computational Transport Phenomena Pedagogy: Emphasizes constructivism and rigorous challenge with support

Faisal QureshiView profile

Professor

• Computer Vision
• Deep Learning
• Camera Networks
• +5 more

Dr. Faisal Qureshi is a Professor of Computer Science in the Faculty of Science at Ontario Tech University, where he leads the Visual Computing Lab. He holds a concurrent role as Guest Professor at Mid Sweden University. His research focuses on visual sensor networks, computer vision, and deep learning, with applications in smart camera networks, video surveillance, and hyperspectral imaging. He earned his PhD in Computer Science from the University of Toronto (2007), following degrees from Punjab University (BSc 1993) and Quaid-e-Azam University (MSc Electronics 1995). Key research areas include multicamera tracking, graphics for vision, and neural networks for computer vision. He has pioneered work on self-organizing visual sensor networks and developed computational models for autonomous visual perception. His teaching includes courses on computer graphics, algorithms, and advanced computer vision. He has authored over 100 peer-reviewed publications, including a Best Paper Award at CRV 2017 for optical flow estimation research. Dr. Qureshi’s lab develops innovative solutions for video summarization, optical flow estimation, and activity analysis. His work bridges theoretical computer science with real-world applications in robotics, aerospace, and environmental monitoring. Recent projects explore adversarial machine learning for satellite communications security and hyperspectral image compression using neural representations.

Charles AudetView profile

Professor

• Optimization
• Operational Research
• Blackbox Optimization
• +4 more

Charles Audet is a Full Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal , with affiliations to the Institute for Data Valorization (IVADO) and Research Group in Decision Analysis (GERAD) . Specializing in optimization and derivative-free methods, his work focuses on algorithm development for complex engineering problems characterized by black-box functions, nonconvexity, and nonsmooth constraints. B.Sc. (Ottawa), M.Sc./Ph.D. (Polytechnique Montréal) Joint supervisor with multiple institutions and researchers His research spans algorithmic improvements in pattern search methods (GPS/MADS), global optimization for structured nonconvex problems, and blackbox optimization applications. Current projects involve multidisciplinary design and categorical variable handling in optimization frameworks. Recent publications emphasize mixed-variable domains and robust algorithm design . The NOMAD software ecosystem, which he co-developed, remains central to derivative-free optimization implementations. Key scientific contributions include: Advances in mesh adaptive direct search algorithms Structural exploitation in bilevel/quadratic programming Parallel decomposition techniques Robustness in noisy function optimization Students under his supervision have explored applications in hydroelectric resource modeling , compressor blade redesign , and telecommunication frequency assignment .

Alex AikenView profile

Professor

• Programming Languages
• Compiler Construction
• Parallel Computing
• +2 more

Alex Aiken is the Alcatel-Lucent Professor of Computer Science at Stanford University and serves as the Computer Science Division Director at SLAC. He has been a faculty member at Stanford since 2003, after previously serving as a Professor in the EECS department at UC Berkeley (1993-2003) and as a Research Staff Member at IBM Almaden Research Center (1988-1993). Dr. Aiken received his Bachelors degree in Computer Science and Music from Bowling Green State University in 1983 and his Ph.D. from Cornell University in 1988. He previously served as chair of the Stanford Computer Science Department. Alex Aiken's research focuses primarily on programming languages, compilers, and parallel computing systems. He is renowned for his work on the Legion programming system for heterogeneous parallel machines, the FlexFlow system for deep learning parallelization, and the development of Moss, a widely-used plagiarism detection system. His research spans both theoretical foundations and practical implementations, with a strong emphasis on translating research into usable software tools. His work has significant applications in high-performance computing, software analysis, and compiler technology. His recent publications reflect a continued focus on parallel and distributed systems, with increasing attention to the intersection of programming systems and machine learning. The 15 most recent articles demonstrate his leadership in task-based programming models, verification techniques, and the application of AI to programming systems. His work spans from low-level GPU optimizations to high-level language design and verification. ACM Fellow Recipient of ACM SIGPLAN's Programming Languages Achievement Award Phi Beta Kappa's Teaching Award Professor Aiken has advised numerous Ph.D. students throughout his career, with eight current advisees including Benjamin Driscoll, Qiantan Hong, and Rupanshu Soi. His research group has received substantial funding for projects related to parallel programming systems, compiler technology, and software analysis tools. The group maintains several active open-source projects including Legion, FlexFlow, and Moss. Aiken leads the Legion research group, which focuses on developing programming models and runtime systems for heterogeneous and distributed parallel machines. The group has made significant contributions to the field of task-based parallelism and has developed several widely-used software systems that bridge the gap between theoretical research and practical implementation.

Mark KrumholzView profile

Professor

• Astrophysics
• Star Formation
• Galaxy Evolution
• +3 more

Professor Mark Krumholz is a leading astronomer at The Australian National University (ANU), affiliated with the Research School of Astronomy & Astrophysics within the ANU College of Science. His research focuses on interstellar medium dynamics, star and galaxy formation, and advanced numerical methods in radiation magneto-hydrodynamics. He holds a PhD in Physics from UC Berkeley (2005). Educations: PhD in Physics (UC Berkeley, 2005) His research interests include: Star formation processes and turbulence in molecular clouds Galactic evolution via numerical simulations Cosmic ray dynamics and gamma-ray emission Development of computational tools like the quokka code Key projects include studying galactic winds, cosmic ray propagation, and the Cherenkov Telescope Array (CTA) collaboration. He supervises students in areas like star cluster demographics and galaxy outflows. Notable contributions include: Unveiling galactic wind structures via numerical models Advancing understanding of molecular cloud lifecycles Pioneering GPU-accelerated radiation hydrodynamics His work bridges theoretical astrophysics with observational data, addressing fundamental questions about galaxy formation and evolution.

Hari BalakrishnanView profile

Professor

• Networking
• Mobile Computing
• Distributed Systems
• +5 more

Hari Balakrishnan is the Fujitsu Professor of Electrical Engineering and Computer Science at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads research in networking, mobile computing, and distributed systems, with contributions to protocols, congestion control, and edge-cloud integration. His work on systems like Cricket (indoor localization) and CarTel (mobile sensing) led to Cambridge Mobile Telematics, a global telematics provider. He holds a PhD from UC Berkeley and BTech from IIT Madras, with accolades including the Marconi Prize and IEEE Fellowships. Education: PhD in EECS, UC Berkeley (1998) BTech in Electrical Engineering, IIT Madras (1993) Research Interests: Networking and Protocol Design Mobile and Edge Computing Sensor Systems and IoT Resilient Distributed Systems Awards and Honors: 2023 Marconi Prize (Top in Communications Technology) IEEE Kobayashi Award (2021) SIGCOMM Lifetime Achievement Award (2021) ACM and IEEE Fellowships (2008, 2020) Advising & Impact: Mentored ~30 PhD students and 10 postdocs Co-founder of Cambridge Mobile Telematics (CMT) Recipient of multiple teaching awards at MIT Labs & Teams: Principal Investigator at MIT CSAIL Leading research groups in networking and mobile systems

Fangxin FangView profile

Research Fellow

• Predictive Modelling
• Machine Learning
• Environmental Modelling
• +3 more

Fangxin Fang is Senior Research Fellow in the Department of Earth Science and Engineering at Imperial College London. His research develops predictive modeling frameworks combining machine learning, data assimilation, and computational fluid dynamics for environmental and engineering applications. Research focuses on: AI-driven air pollution forecasting systems Machine learning for multi-scale environmental modeling Adaptive mesh methods for flood prediction High-performance computing implementations Personalized health impact assessments Fang's work integrates computational mathematics with practical applications in urban air quality management, flood risk assessment, climate modeling, and renewable energy optimization. His publications demonstrate innovations in model reduction techniques, generative adversarial networks for fluid flow modeling, and adaptive mesh refinement methods. He leads the EPSRC Open Fellowship project on AI techniques for urban environmental prediction and contributes to initiatives on climate-health interactions. Fang collaborates internationally on machine learning applications in earth system science and sustainable engineering.