Find Academic Supervisors

Jerry ErikssonView profile

Associate Professor

• Computer Science
• Computational Science
• Medical Imaging
• +5 more

Jerry Eriksson is an Associate Professor at the Department of Computer Science , Umeå University. He is also affiliated with the High Performance Computing Center North (HPC2N) at the same institution, focusing on computational methods and applications. Academic Rank : Associate Professor Primary Affiliation : Department of Computer Science, Umeå University Secondary Affiliation : High Performance Computing Center North (HPC2N) Email : jerry.eriksson@umu.se Dr. Eriksson's research spans interdisciplinary domains where computer science intersects with applied physics and network engineering . His work includes: Medical Imaging : Fluorescence optical tomography and electromagnetic shape tomography using advanced mathematical models Computational Methods : Implicit radial basis function techniques, Gauss-Newton optimization, and regularization schemes Network Engineering : Collision detection algorithms in wireless networks and peer-to-peer streaming protocols 3D Measurement : Bundle adjustment formulations for photogrammetry and robotics applications Article trends show a focus on applied inverse problems (2003-2017) with emphasis on: Medical imaging reconstruction algorithms Wireless network optimization techniques Geometric computing for 3D modeling Mathematical methods in computational engineering

• Network Resilience
• Optical Networks
• 5G/6G Systems
• +4 more

Jacek Rak is a Professor at the Department of Communications and Computer Networks within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His research career spans over two decades with continuous contributions to network resilience engineering, evidenced by over 50 publications in top-tier IEEE and Springer journals. He serves as a leading authority in optical network design, vehicular communications, and disaster-resilient systems, frequently collaborating with international institutions including Hungarian Academy of Sciences, University of Lisbon, and Polish-Japanese Academy of Information Technology. Professor Rak's research focuses on network resilience engineering across multiple domains. His work establishes foundational frameworks for protecting communication systems against disasters, with significant contributions to optical network survivability, 5G/6G fronthaul optimization, and vehicular network security. His research methodology integrates mathematical modeling with practical implementation, particularly evident in his development of the κ-Penalty approach for disjoint path calculation and eFRADIR disaster resilience framework. Recent work emphasizes the convergence of wireless and optical technologies for next-generation networks, addressing critical challenges in energy efficiency and cost modeling for 6G infrastructure. His publication portfolio reveals a clear evolution from VANET security (2010-2015) to comprehensive disaster resilience frameworks (2016-present), culminating in the 2020 Springer monograph Guide to Disaster-Resilient Communication Networks co-edited with David Hutchison. Current research examines optical fronthaul optimization for 6G systems, with multiple 2023-2025 publications establishing new methodologies for cost-energy tradeoff analysis in next-generation wireless infrastructure. IEEE Communications Society Distinguished Lecturer (2018-2020) Editor-in-Chief, Optical Switching and Networking special issue on Disaster-Resilient Optical Networks (2021) Technical Program Committee Chair, International Workshop on Reliable Networks Design and Modeling (RNDM 2012-2015) Professor Rak actively mentors next-generation researchers through collaborative projects like RECODIS (Resilient Communication Services Protecting End-user Applications from Disaster-based Failures) and coordinates international research efforts through EU-funded initiatives. His work demonstrates consistent leadership in establishing resilience metrics and validation methodologies for critical communication infrastructure, with increasing focus on climate change adaptation and sustainable network design principles in recent publications.

Sebastian KuckukView profile

Researcher

• High Performance Computing
• GPU Programming
• Parallel Computing
• +7 more

Dr. Sebastian Kuckuk is a researcher and head of training at the Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg. He is affiliated with the Department of Computer Science and contributes to the Chair of System Simulation. His work bridges research, training, and software development in high-performance computing. Education: PhD in Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (2019) His research focuses on enhancing performance portability and programmer productivity using domain-specific languages, code generation, automatic parallelization, and GPU programming. These techniques are applied to develop massively parallel numerical solvers for computational fluid dynamics, particularly for the shallow water equations. He is a core developer of the ExaStencils framework, which enables automated generation of efficient multigrid solvers for structured and patch-structured grids. Analysis of his recent publications (2020–2025) reveals a consistent focus on code generation, GPU acceleration, and solver optimization for fluid dynamics problems. Key themes include heterogeneous computing, block-structured grids, and adaptive methods. His work integrates advanced compiler techniques with numerical mathematics to improve scalability and performance on modern HPC architectures. Scientific Recognition: NVIDIA Deep Learning Institute (DLI) University Ambassador Certified Instructor for DLI courses in GPU programming and CUDA He actively contributes to teaching and training through courses such as Programming Techniques for Supercomputers and High-End Simulation in Practice . He conducts workshops and tutorials on GPU programming and performance optimization. While no formal students are listed, his mentoring role is evident through collaborative research and training activities. He has no recorded grants in the provided text, but his involvement in NHR and KONWIHR projects indicates active participation in funded HPC initiatives. Laboratories and Projects: Lead developer of ExaStencils , a code generation framework for multigrid solvers Contributor to GHODDESS , a module for higher-order discretizations in shallow water modeling Active in NHR@FAU and KONWIHR projects focused on GPU computing and performance optimization

Pin ShuaiView profile

Assistant Professor

• Hydrology
• Groundwater-Surface Water Interactions
• Integrated Hydrologic Modeling
• +7 more

Dr. Pin Shuai is an Assistant Professor in the Department of Civil and Environmental Engineering at Utah State University, affiliated with the Utah Water Research Laboratory (UWRL) within the College of Engineering. He leads the Shuai Computational and Integrated Hydrology (SCI-Hy) research group, focusing on advancing the understanding of complex hydrological systems through computational modeling and data integration. Dr. Shuai holds a PhD in Geology (Hydrogeology) from Texas A&M University (2017), an MS in Water Resources Engineering from Wuhan University (2013), and a BS in the same field from Wuhan University (2011). His academic journey was followed by a postdoctoral and staff scientist position at the Pacific Northwest National Laboratory from 2017 to 2022. His research interests are centered on groundwater-surface water interactions, nutrient and contaminant transport, watershed biogeochemistry, and integrated hydrologic modeling. He employs a model-data integrative approach combining field observations, laboratory data, remote sensing, and numerical models powered by high-performance computing. His group emphasizes open-source and reproducible science, addressing critical environmental challenges such as human-water interactions and the impacts of disturbances like drought and land use change on watershed processes. The recent publications of Dr. Shuai reflect a strong trend toward computational hydrology, with a focus on high-resolution watershed modeling, the role of streambed representation, the impact of meteorological forcing resolution, and the application of machine learning and deep neural networks for model calibration and permeability estimation. His work bridges traditional hydrological modeling with modern data science techniques, aiming to improve predictive capabilities in complex hydrological systems. Dr. Shuai has mentored several graduate students, including Collins Stephenson, Ehsan Ebrahimi, Pamela Claure, and Jihad Othman, guiding them through thesis research in civil and environmental engineering. While no scientific awards are listed in the provided text, his active research program, consistent publication record in high-impact journals, and leadership of a growing research group indicate a strong trajectory in the field. He teaches courses such as Groundwater Engineering, Hydrologic Modeling, and GIS for Civil Engineers, contributing to both graduate and undergraduate education. The SCI-Hy group, under Dr. Shuai’s leadership, is actively engaged in projects that integrate advanced modeling tools like the Advanced Terrestrial Simulator (ATS) and Watershed Workflow to parameterize and simulate watershed processes. The group welcomes passionate students and recently advertised for a postdoctoral position focused on ML/AI applications in hydrology, highlighting its forward-looking research direction.

• Computer Vision
• Machine Learning
• Image Processing
• +3 more

Hui Guo is an Adjunct Assistant Professor in the Department of Civil Engineering. Their research intersects computer vision, machine learning, and image processing, with a focus on advanced restoration and super-resolution techniques. Recent publications highlight contributions to image super-resolution challenges (NTIRE 2024/2025), vision transformers (QID), diffusion models for video restoration, and continual learning frameworks (PCL). Work spans OCR-free visual document analysis, adversarial degradation modeling (AND), and parametric image restoration. Contact: guoh15

Peter OswaldView profile

Professor

• Approximation Theory
• Function Spaces
• Applied Harmonic Analysis
• +7 more

Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.

• Numerical Solution of Singular Perturbation Problems
• Adaptive Mesh Methods
• Moving Mesh Methods
• +3 more

Alan Hegarty serves as Associate Professor in the Department of Mathematics and Statistics at the University of Limerick, concurrently holding membership in the Centre for Research Training in Foundations of Data Science and the Mathematics Applications Consortium for Science and Industry (MACSI), which drives industry-academic partnerships in mathematical modeling. His academic credentials include a B.A. in Mathematics from Dublin University (1979), M.Sc. from Dublin (1982), and Ph.D. from Dublin (1986), establishing foundational expertise in computational mathematics. Dr. Hegarty's research specializes in numerical solutions for singular perturbation problems , with pioneering work in adaptive mesh methods and moving mesh techniques to resolve boundary layer phenomena. His methodologies address critical challenges in convection-diffusion systems and elliptic equations where conventional numerical approaches fail, employing specialized finite difference and finite element schemes on layer-adapted meshes to ensure stability and accuracy across thin transitional regions. Analysis of his 2020-2024 publications reveals consistent advancement in algorithmic precision for singularly perturbed systems, particularly through higher-order methods for elliptic problems and extensions to smooth domains. This trajectory demonstrates evolving sophistication in handling characteristic boundary layers and convection-dominated flows, with strong emphasis on achieving uniform convergence through fitted meshes and Shishkin-type adaptations. No scientific awards were documented in the provided materials. He actively supervises PhD candidates as indicated by his recruitment status and 52 cumulative research outputs, with funding channeled through MACSI's industry collaborations and the Centre for Research Training. These frameworks support applied mathematical research addressing real-world industrial challenges through consortium-based projects. Within MACSI and the Centre for Research Training, Hegarty contributes to interdisciplinary teams developing data-driven solutions where singular perturbation methods interface with modern data science applications, particularly in fluid dynamics modeling and industrial process optimization.

Stefano ScanzioView profile

Researcher

• Industrial Communication Systems
• Wireless Networks
• Real-Time Networks
• +7 more

Stefano Scanzio is a Senior Researcher at the National Research Council of Italy (CNR-IEIIT) and teaches computer science courses at Politecnico di Torino . With over 60 publications in wireless networks, real-time communication, and industrial IoT, he serves as Associate Editor for Ad Hoc Networks , IEEE Access , and Electronics journals. Ph.D. in Computer Science (Politecnico di Torino, 2008) Laurea in Computer Science (Politecnico di Torino, 2004) His research focuses on industrial wireless networks , particularly Wi-Fi and IEEE 802.15.4 TSCH, with emphasis on: Energy-saving mechanisms in wireless sensor networks Predictive models for Wi-Fi channel quality Reliable communication through seamless redundancy Machine learning integration for network optimization Clock synchronization under non-Gaussian noise Recent publications analyze Wi-Fi 7's multi-link operation, executable QR codes for IoT, and AI-driven network self-configuration. His work has been recognized with multiple best paper awards .

Zhiqiang CaiView profile

Professor

• Numerical Analysis
• Applied Mathematics
• Finite Element Methods
• +3 more

Zhiqiang Cai is a Professor of Mathematics at Purdue University's Department of Mathematics, part of the College of Science. He specializes in numerical analysis and applied mathematics, with a focus on finite element methods, computational fluid dynamics, and partial differential equations. His work includes developing adaptive numerical techniques and applying machine learning approaches to solve complex mathematical problems. Cai's research spans theoretical advancements in numerical methods and practical applications in areas like biotechnology and environmental engineering. He has authored numerous publications, including contributions to journals like SIAM Journal on Numerical Analysis and Mathematics of Computation. His recent work explores neural network-based methods for PDE solutions and enzyme catalysis optimization. Research highlights include advancements in least-squares finite element methods, fractional calculus applications, and microbial bioremediation. His interdisciplinary projects bridge computational mathematics with biology and environmental science. Cai collaborates extensively, evidenced by co-authored papers on topics ranging from enzyme engineering to meshless methods for fractional Laplacians. His work often addresses challenges in error estimation and algorithm efficiency for complex systems.

Daniel TishView profile

Lecturer

• Design Robotics
• Environmental Systems
• Materials & Material Systems
• +3 more

Daniel Tish is a Lecturer in Architecture at Harvard University's Graduate School of Design (GSD), holding a joint Postdoctoral Fellowship between the Materials Processes and Systems (MaP+S) group and the Lewis Lab in the School of Engineering and Applied Sciences. His research focuses on carbon-negative biocomposites derived from microorganisms, robotic fabrication, and circular economy solutions to reduce architecture's carbon footprint. Supported by the Salata Institute, Center for Green Buildings and Cities, and Joint Center for Housing Studies, his work bridges material science, biology, and design. Education includes a Doctor of Design from GSD, a Master of Architecture (Distinction) from the University of Michigan, and a Bachelor of Science in Architecture from Washington University in St. Louis with a self-designed Sustainable Design major. Research interests emphasize bespoke materiality, digital fabrication, sustainability, and computation. He investigates biomaterial fabrication methods that address unpredictability in materials, aligning with cyber-physical systems research. His work challenges industrialized materials in digital fabrication and promotes eco-conscious technologies. Publications appear in ACADIA, Fabricate, Rob|Arch, IASS conferences, and journals like Construction Robotics and TAD. Exhibitions include Design Miami/ Basel. He previously taught at the University of Michigan and University of Technology Sydney, and led Autodesk's computer-vision research for construction robotics. Grants include Salata Institute funding for biocomposite research and Lewis Lab collaborations. His courses include 'BioFabrication' (Spring 2025) at GSD.

• Computational Mathematics
• Numerical Analysis
• Fluid Dynamics
• +3 more

Delis Anargyros is a Professor of Computational Mathematics at the School of Production Engineering & Management, Technical University of Crete. He holds a B.Sc. in Mathematics from the University of Crete (1993), an M.Sc. in Numerical Analysis and Computing from the University of Manchester (1994), and a Ph.D. in Applied and Computational Mathematics from the University of the West of England (1998). His academic career includes roles as Director of the MSc in Applied Mathematics program (2019–2023) and membership in research groups such as the Turbomachines & Fluid Dynamics Laboratory and the Applied Mathematics and Computers Laboratory (AMCL). B.Sc. Mathematics, University of Crete M.Sc. Numerical Analysis, University of Manchester Ph.D. Applied and Computational Mathematics, University of the West of England His research focuses on computational methods for fluid dynamics (e.g., shallow water equations, Boussinesq-type models), traffic flow modeling, and numerical analysis. Notable contributions include finite volume schemes for hydraulic and traffic systems, and collaborative projects like TRAMAN21 (traffic management) and C-NORA (transport systems control). He has advised on grants involving numerical methods for environmental systems and is active in coastal engineering applications. Recent work includes acoustic streaming simulations, wind turbine optimization, and PDE-based traffic control algorithms. He leads the MSc in Applied Mathematics and collaborates with institutions like IACM-FORTH and the Coastal Information Research Unit (CIRUM). His current projects address multi-lane traffic dynamics, cooperative vehicle systems, and unstructured mesh simulations for coastal and fluid dynamics problems.

Amol MaliView profile

Associate Professor

• Data Science
• Internet of Things
• Artificial Intelligence
• +7 more

Amol Mali is an Associate Professor in the Department of Computer Science at the University of Wisconsin-Milwaukee, currently on sabbatical for Spring 2025. His research bridges theoretical AI with practical applications across diverse domains, leveraging his interdisciplinary background spanning computer science and mechanical engineering. Education PhD, Computer Science, Arizona State University, Tempe, May 1999 MS, Mechanical Engineering with specialization in Robotics, Indian Institute of Technology, Kanpur, India, June 1994 BS, Engineering in Mechanical Engineering, Victoria Jubilee Technical Institute (VJTI), University of Bombay, India, July 1991 Research Interests Dr. Mali's work spans Data Science , Internet of Things , and Artificial Intelligence with specialized focus on Planning , Autonomous Agents , and Computer Game Design . His research extends to Robot Motion Planning and AI in Healthcare , while actively addressing Ethics, Diversity, Inclusion, and Equity implications in technology development and Higher Education practices. Publication Trends His 2016-2023 publications reveal a strong emphasis on AI applications in gaming (machine learning, motion planning, symbolic systems), health-focused AI/IoT implementations, and human-computer interaction innovations like ergonomic keyboard design. Work consistently connects theoretical AI frameworks to real-world problem-solving across healthcare, education, and accessibility domains. Scientific Awards No scientific awards were mentioned in the provided information. Advising and Grants The available documentation does not specify doctoral advisees, grant funding, or research team leadership roles. Labs and Teams No dedicated research laboratories or collaborative teams are referenced in the source material.

Zhihua MaView profile

Senior Lecturer

• Computational Fluid Dynamics
• Wave Hydrodynamics
• Numerical Methods
• +6 more

Zhihua Ma is a Senior Lecturer in the Department of Computing and Mathematics at Manchester Metropolitan University (MMU), UK, and a member of the Centre for Mathematical Modelling and Flow Analysis (CMMFA). He holds a BEng in Aerospace Engineering (2003) and a PhD in Fluid Dynamics (2008) from Nanjing University of Aeronautics & Astronautics, China. His research focuses on developing novel numerical methods for computational aerodynamics and wave hydrodynamics, with over 30 publications in top-tier journals such as the Journal of Fluid Mechanics and Physics of Fluids. **Education:** BEng in Aerospace Engineering, Nanjing University of Aeronautics & Astronautics, China (2003) PhD in Fluid Dynamics, Nanjing University of Aeronautics & Astronautics, China (2008) Advanced Fellow of Higher Education Academy (AFHEA, 2016) Fellow of Higher Education Academy (FHEA, 2018) **Research Interests:** Violent hydrodynamic impacts on rigid/flexible structures Extreme wave simulations under extreme conditions High-performance computing (CPU/GPU) Aerodynamic inverse design and optimization Multiphase flows and compressible meshless methods His recent work emphasizes advancing numerical methods for wave-structure interactions, compressible flow modeling, and offshore renewable energy applications. He has contributed to collaborative projects like the CCP-WSI Blind Test Series, focusing on validating CFD solvers for challenging hydrodynamic problems. **Advising/Grants:** Supervised 7+ PhD/MSc students in computational fluid dynamics and meshless methods Reviewer for EPSRC, Royal Society, and journals like Journal of Computational Physics Editorial roles: Guest Editor for Water (2021) **Labs/Teams:** Active member of the CMMFA and the Engineering & Materials Research Centre at MMU, focusing on mathematical modeling and flow analysis.

Hao ZhengView profile

Assistant Professor

• Computer Architecture
• AI/ML Systems
• Domain-specific Accelerators
• +4 more

Hao Zheng is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Central Florida , specializing in computer architecture and AI/ML systems . He received his Ph.D. from The George Washington University in 2021, advised by Prof. Ahmed Louri. Research Focus: Domain-specific accelerators, wafer-scale integration, hardware-software co-design, and AI-assisted chip design. Teaching: EEL6938 (Computer Architecture for AI/ML) since Fall 2025. His recent work explores graph neural networks , vision transformer acceleration , and secure on-chip communication , with publications at top venues like ISCA, MICRO, and DAC. He serves as an Associate Editor for IEEE Transactions on Computers and IEEE Transactions on Sustainable Computing. Notable honors include the NSF CAREER Award (2025) , GWU ECE Best Dissertation (2022) , and multiple AMD Fellowships for his students. His iCAT Lab focuses on cutting-edge research in computer architecture, supported by grants from L3Harris and NVIDIA .

• Metric Geometry
• Complex Analysis
• Conformal Mapping
• +2 more

Matthew Romney lectures in mathematics at Stevens Institute of Technology's Schaefer School of Engineering. His research explores geometric structures in metric spaces through analytic methods. He holds a PhD from University of Illinois and postdoctoral experience at Stony Brook University. Current NSF-funded work develops uniformization techniques for non-Euclidean surfaces. Romney received the Wolfgang Haken Prize for his doctoral contributions to quasiconformal geometry.