Find Academic Supervisors

Pantelis SopasakisView profile

Lecturer

• Model Predictive Control
• Embedded Numerical Optimization
• Stochastic Optimal Control
• +5 more

Dr. Pantelis Sopasakis is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast, Northern Ireland. His research focuses on developing efficient numerical optimization algorithms and model predictive control (MPC) methodologies for uncertain systems, with applications in autonomous vehicles, smart infrastructure networks, and advanced manufacturing. He leads projects on embedded optimization solvers, GPU-accelerated MPC, and stochastic control for systems like water networks and microgrids. His work emphasizes real-time implementation and safety-critical applications in robotics and energy systems. He teaches postgraduate and undergraduate courses in control theory and signals, and is actively involved in supervising PhD students in areas like parallel algorithms and MPC for uncertain systems. Key achievements include the development of the Open-Source Optimization Engine , widely used for embedded MPC. Research interests span distributed embedded intelligence, intelligent uncertain-aware MPC, and biomedical applications of control systems. He collaborates internationally on projects involving risk-averse control, multi-agent systems, and circular economy applications. His interdisciplinary work bridges optimization theory, robotics, and energy systems, with a focus on scalable and real-time solutions. Teaching includes modules on control systems fundamentals and advanced MPC concepts, supported by his textbook Control Systems: An Introduction . Dr. Sopasakis has contributed to over 50 publications, with recent work on conformal prediction for stochastic control, distributed collision avoidance, and thermodynamical material networks. He participates in conferences and editorial activities, and has organized events like the 2025 IEEE UK and Ireland Robotics Conference. His research group is part of the Energy, Power, and Intelligent Systems and Control clusters at Queen's.

Anna-Sophie Liebender-LucView profile

Research Fellow

• Digital Innovation
• Artificial Intelligence
• Machine Learning
• +1 more

Anna-Sophie Liebender-Luc is a Doctoral Student and Research Fellow at the Max Planck Institute for Innovation and Competition , focusing on Digital Innovation , Artificial Intelligence , and Entrepreneurship . She is actively engaged in Innovation and Entrepreneurship Research , combining academic rigor with practical applications. Postgraduate Studies in Business Research (MBR) at LMU Munich (2020–present) Management Team Member at Centre for Digital Technology & Management (CDTM) (2020–2022) Her research examines the interplay between AI , human capital , and innovation policy . Recent work includes analyzing how GPU expertise drives AI patents and startups, and exploring AI’s role in climate change mitigation through robotics and IoT. Earlier projects address digital gender divides and elderly care technologies . Her publications span topics like neural networks , sustainable business models , and ocean economy forecasting . She has received accolades such as the Rachel Carson Center Best Idea Award (2013) and the Bronnbacher Scholarship (2010–2011). 2024: AI innovation policy and GPU expertise 2022: AI-driven climate action frameworks 2020: Elderly care technologies 2019–2018: Digital gender equality in education 2016: Ocean economy and sustainability

Scott GriffithView profile

Associate Professor

• Electrical Engineering
• Computer Engineering
• Embedded Systems
• +3 more

Scott Griffith is an Associate Professor in the Department of Math/Computer Science at Whitworth University since 2017. He is located in the Eric Johnston Science Center and can be reached at sgriffith@whitworth.edu. M.S., Electrical and Computer Engineering, Carnegie Mellon University B.S., Computer Engineering, Gonzaga University His research focuses on Electrical Engineering, Computer Engineering, Embedded Systems, GPU Processing and Optimization, Additive Manufacturing, and Technology Outreach initiatives. No academic awards, grants, or published articles were explicitly listed in the provided text.

Ruiqi YeView profile

Research Fellow

• Visual SLAM Acceleration
• FPGA Acceleration
• Computer Vision
• +5 more

Ruiqi Ye is a Research Fellow at the Advanced Processor Technology group within the Department of Computer Science at the University of Manchester. Previously, they completed a PhD (2019-2024) and MSc (2018-2019) at the same institution, and a BEng in Software Engineering from Shandong University (2014-2018). Education: PhD in Computer Science, University of Manchester, 2019-2024 MSc in Advanced Computer Science, University of Manchester, 2018-2019 BEng in Software Engineering, Shandong University, 2014-2018 Ye's research centers on hardware acceleration for visual Simultaneous Localization and Mapping (SLAM) systems using High-Level Synthesis (HLS) on embedded System-on-Chip platforms, with emphasis on data transfer optimization for sparse kernels. They maintain broad expertise in domain-specific acceleration, heterogeneous computing (GPU/FPGA), and performance optimization using C++, CUDA, OpenCL, and HLS C++. Recent publications (2023-2024) demonstrate a clear trajectory toward efficient embedded robotics solutions, combining visual odometry acceleration with FPGA-based hardware design and radiation reliability analysis for MPSoC systems. Their work bridges computer vision, robotics, and hardware engineering disciplines. Scientific awards: None documented in source materials. Advising and grants: No student advising or grant management details provided in available records. Labs and teams: Active in the Soteria JVMs project and Advanced Processor Technology (APT) group, with affiliation to the University of Manchester's Centre for Robotics and Artificial Intelligence.

Qian LiView profile

Researcher

• Database Systems
• Operating Systems
• Distributed Systems
• +3 more

Qian Li is a researcher working at the intersection of database systems and operating systems, with primary affiliation at DBOS Inc. and academic connections to Stanford University's School of Engineering, Department of Computer Science, and Peking University in Beijing, China. The research focuses on developing the Database Operating System (DBOS) concept, which reimagines operating systems with database technology at their core. Research interests center on database systems, operating systems integration, transaction processing, and serverless computing. The work explores how database principles like ACID transactions can improve system reliability, debugging, and application development, particularly in cloud environments. Key projects include DBOS, Epoxy for cross-data store transactions, and Apiary for transactional serverless computing. The publication record shows a strong trend toward integrating database transaction semantics with modern computing paradigms, particularly serverless architectures. Recent work demonstrates how transactional guarantees can simplify application development, improve debugging, and enable new approaches to cloud-native application design. The research bridges theoretical database concepts with practical systems implementation. As a core contributor to the DBOS project, Qian Li has collaborated extensively with leading researchers including Michael Stonebraker, Matei Zaharia, Christos Kozyrakis, and Peter Kraft. The work has been published consistently in top-tier venues including VLDB, USENIX ATC, and CIDR, reflecting significant impact in the systems research community.

• Program verification
• Software Reliability
• Formal methods
• +4 more

Professor Marieke Huisman is a leading figure in software reliability at the University of Twente, where she chairs the Computer Science department within the Faculty EEMCS and leads the Formal Methods and Tools (FMT) Group. She serves as President and Steering Committee chair of the ETAPS association, Member of the board of IPN (ICT-Research Platform Netherlands), and Fellow of the Netherlands Academy of Engineering (NAE). Her research focuses on program verification, reliability of concurrent, parallel and distributed applications, software specification, and formal methods. She develops verification techniques through the VerCors verifier, with applications spanning concurrent data structures, GPGPU programming, and distributed systems. Her work bridges theoretical formal methods with industrial applications, particularly in safety-critical domains. Professor Huisman's recent publications demonstrate strong activity in deductive verification, with particular emphasis on verification of concurrent and parallel systems, memory models, and practical applications of formal methods. Her research shows consistent evolution from theoretical foundations toward practical industrial applications, with increasing focus on GPU programming verification and automated invariant generation. Netherlands Prize for ICT Research 2013 (50,000 euros) 2023 Athena Award for female researchers who stand out as role models She leads multiple significant research projects including NWO OC M Pallas, WWU Münster - UT collaboration Saves, NWO TTW OTP Cheops, NWO VICI Mercedes, and Interreg North Sea Storm_Safe. Her leadership extends to editorial roles including Associate Editor-in-Chief of the Software Track of Science in Computer Programming and executive board membership of VERSEN, the Dutch National Association for Software Engineering. Professor Huisman directs the FMT Group which develops the VerCors verification toolset for concurrent and parallel software. The group collaborates internationally with institutions including WWU Münster, TU Eindhoven, and NlNet Labs, focusing on making formal verification accessible and practical for real-world software development challenges.

Chi-Kwan ChanView profile

Research Professor

• Computational Astrophysics
• Black Hole Physics
• Extreme Astrophysics
• +6 more

Chi-Kwan Chan is a tenure-track Research Professor at the University of Arizona , affiliated with the Steward Observatory and Department of Astronomy. He serves as Associate Astronomer and holds key leadership roles in the Event Horizon Telescope (EHT) Collaboration , including Secretary of the EHT Science Council (2020-2022). Research Interests Extreme Astrophysics & Gravity Quasars and Active Galactic Nuclei Theoretical and Computational Astrophysics GPU acceleration for astrophysics simulations Machine learning in data processing Cloud computing infrastructure Scientific Contributions Dr. Chan pioneered the use of GPUs to accelerate black hole modeling, developed novel algorithms for astrophysical research, and built cloud computing infrastructures for handling large observational datasets. His work focuses on integrating advanced computational techniques with theoretical astrophysics. Awards Data Science Fellow Leadership He leads the Software and Data Compatibility and Gravitational Physics Working Groups in the EHT Collaboration, serves as PI of Black Hole PIRE, and holds leadership roles in the Theoretical Astrophysics Program (TAP) and Research Computing Governance Committee.

Michela TauferView profile

Associate Professor

• Heterogeneous Computing
• Cloud Computing
• Volunteer Computing
• +4 more

Michela Taufer is the David L and Beverly JC Mills Chair of Computer and Information Sciences and an Associate Professor at the University of Delaware. She earned her Masters degrees in Computer Engineering from the University of Padova (Italy) and her doctoral degree in Computer Science from the Swiss Federal Institute of Technology (Switzerland). Her research interests include scientific applications and their advanced programmability in heterogeneous computing (i.e. multi-core and many-core platforms, GPUs); performance analysis; modeling and optimization of multi-scale applications on heterogeneous computing; cloud computing and volunteer computing; numerical reproducibility and stability of large-scale simulations on multi-core platforms; and big data analytics and MapReduce. Dr. Taufer has published research on protein folding, molecular dynamics, and workflow execution on cloud and grid platforms. Her work often focuses on performance characterization and optimization of scientific applications on distributed and parallel systems. From 2003 to 2004 she was a La Jolla Interfaces in Science Training Program (LJIS) Postdoctoral Fellow at the University of California San Diego (UCSD) and The Scripps Research Institute (TSRI) From 2005 to 2007, she was an Assistant Professor at the Computer Science Department of the University of Texas at El Paso (UTEP) She joined the University of Delaware in 2007 as an Assistant Professor and was promoted to Associate Professor with tenure in 2012

• Computer Architecture
• Parallel Processing
• Networking Systems
• +3 more

Javier Verdu Mula is a Professor at the Departament d'Arquitectura de Computadors (Universitat Politècnica de Catalunya - UPC) and a key researcher at the CRAAX - Centre de Recerca d'Arquitectures Avançades de Xarxes . His work focuses on computer architecture, parallel processing, and networking systems. Fields of Research include RISC-V virtualization, multithreaded processor optimization, and performance analysis of stateful networking applications. Scientific Awards include the BDigital Global Congress (2015) and Wayra Barcelona (2012) recognitions. Collaborations span institutions like Barcelona Supercomputing Center and researchers such as Manuel Alejandro Pajuelo, Mateo Valero Cortes, and Mario Nemirovsky. His recent publications address RISC-V hypervisor extensions, deep packet processing in parallel architectures, and statistical thread assignment models. He also holds patents in hardware virtualization and resource control systems.

• Computer Architecture
• Vector Processors
• GPU Microarchitecture
• +9 more

Roger Espasa Sans is a senior academic and researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the Department of Computer Architecture. He has made significant contributions to computer architecture, particularly in vector processing, GPU design, and performance optimization. Universitat Politècnica de Catalunya (UPC) Faculty of Informatics of Barcelona (FIB) Department of Computer Architecture His research focuses on advanced computer architecture, including vector and superscalar processors, memory systems, instruction-level and data-level parallelism, GPU microarchitecture, and performance modeling. His work bridges theoretical design and practical implementation, especially in high-performance and embedded systems. The recent articles highlight a strong trend in GPU and shader optimization, memory redundancy elimination, and performance modeling. His publications span prestigious venues such as IEEE Microarchitecture Symposium, IEEE HPCA, and The Visual Computer, indicating sustained impact in the field of computer systems and architecture. Premiada (Awarded activity) Roger Espasa Sans has advised several doctoral students, including Manel Fernandez Gomez and Francisca Quintana Domínguez, contributing to the next generation of computer architects. His collaborative network is extensive, involving key figures like Mateo Valero and international institutions. While specific grants are not listed, his prolific output suggests sustained funding. He has contributed to major research groups such as CAP (High Performance Computing Group). He is a core member of the CAP - Grup de Computació d'Altes Prestacions (High Performance Computing Group), and has also been associated with PM - Programming Models and ARCO - Microarchitecture and Compilers. His work often involves simulation frameworks like JINKS and Asim, indicating a strong focus on performance evaluation and modeling.

Sergei SavitskyView profile

Professor

• Reconfigurable Computing
• Digital Signal Processing
• Hardware Architectures
• +3 more

Sergei Savitsky is a Professor at Wedel University of Applied Sciences, where he serves as Head of the Bachelor of Computer Science program and Senate Chairman. He has been a university lecturer at the institution since October 2008, following professional experience at NXP Semiconductors (2006-2008) and Philips Research Europe (2001-2006). His educational background includes: Doctorate in Engineering (Dr.-Ing.) from Technical University of Dresden (2002) with distinction "summa cum laude" Habilitation (Dr.-Ing. habil.) from Technical University of Dresden (2024) with teaching authorization in "Technical Computer Science" Diplom-Informatiker (equivalent to MSc) in Computer Science from Technical University of Dresden (1998) Savitsky's research focuses on reconfigurable computing systems , with particular expertise in FPGA design, hardware acceleration, and error correction coding. His work bridges theoretical computer science with practical hardware implementation, resulting in numerous patents and publications in top venues. He has made significant contributions to the development of adaptive hardware architectures for forward error correction, which are critical for modern communication and storage systems. His recent publications demonstrate a strong trajectory in optimizing hardware design processes, with particular focus on applying machine learning techniques like self-organizing maps and gradient descent algorithms to improve FPGA placement efficiency. His research spans both theoretical foundations and practical applications, with patents filed in collaboration with industry partners like NXP Semiconductors and ST-Ericsson. Among his recognitions is the Best Paper Award at CENICS 2019 for his work on accelerating FPGA placement algorithms. As an educator, Savitsky teaches courses related to digital system design, including "Computer-aided design of digital systems," where he emphasizes algorithmic aspects of Electronic Design Automation beyond basic digital technology concepts.

• Quantum Computing
• Computer-Aided Engineering
• Gaussian Processes
• +6 more

Oliver Ahrend is a doctoral student and researcher at RWTH Aachen University, affiliated with the Chair of Methods for Model-based Development in Computational Engineering (MBD) and the Institute of General Mechanics (IAM). He holds an M.Sc. in Simulation Sciences from RWTH Aachen and a B.Eng. in Mechanical Engineering from Aalen University. His research focuses on bridging quantum computing and engineering applications, particularly in developing methods to apply quantum algorithms to real-world computational engineering problems. A key area of interest is the use of quantum computing in Gaussian processes and discrete-element simulations. Quantum Computing for Engineering Model-Based Development Simulation and Optimization Quantum Algorithms in Mechanics His recent work centers on quantum annealing techniques for particle matching in quasi-cyclic discrete-element simulations, presented at the 16th World Congress on Computational Mechanics in 2024. This reflects a growing trend in applying quantum computing to classical mechanical simulations. Oliver Ahrend has no listed scientific awards at this time. He has been actively involved in research and development projects, contributing to both academic and applied computational engineering. He has served as a research assistant at MBD and IAM from 2022 to 2024 before advancing to his current doctoral position. There is no mention of formal student advising or grant leadership. He is associated with the Chair of Methods for Model-based Development in Computational Engineering, where he contributes to advancing model-driven approaches in computational systems. His GitHub activity indicates engagement in symbolic quantum simulation, GPU computing, and educational quantum computing projects.

Hunter N. MoseleyView profile

Professor

• Bioinformatics
• Computational Biology
• Metabolomics
• +12 more

Hunter N. Moseley is a Professor in the Department of Molecular and Cellular Biochemistry at the University of Kentucky. He maintains an active research laboratory focused on bioinformatics and computational biology, with a strong emphasis on metabolomics and systems biochemistry. His work bridges computational methodology development with biological applications, particularly in data integration and analysis. Education: B.A. in Chemistry, Computer Science, and Mathematics, Huntingdon College (1992) Ph.D. in Biochemistry, University of Alabama at Birmingham (1998) His research interests center on developing computational methods for analyzing biological and biophysical data, particularly through integration of omics-level datasets and leveraging public scientific databases. Key areas include metabolomics, systems biochemistry, structural biology, and data harmonization. His laboratory has produced numerous software tools for data analysis and database integration. The recent publications highlight a strong trend in developing computational frameworks for metabolite pathway prediction, database harmonization (particularly KEGG, Reactome, MetaCyc), and FAIR data principles. There is a consistent focus on methodological rigor, data quality, and software development for metabolomics data analysis, including tools for GPU utilization tracking, academic publication tracking, and metabolic database access. Dr. Moseley has mentored numerous students and collaborators who appear as co-authors on his publications. His lab has secured research support that enables collaborative work across disciplines, including environmental health, cancer research, and cardiovascular disease. He leads the Moseley Bioinformatics Lab, which develops and maintains several open-source software packages for the scientific community.

• Computational Science
• Climate Modeling
• High-Performance Computing
• +6 more

Mario Cesar Acosta Cobos is a Researcher at the Department of Computer Architecture of the Barcelona School of Informatics (FIB) at the Universitat Politècnica de Catalunya (UPC). His work focuses on optimizing computational performance and energy efficiency in Earth system models, particularly within the EC-Earth3 framework for the Coupled Model Intercomparison Project (CMIP6). He collaborates extensively with the Barcelona Supercomputing Center (BSC) and other international institutions. Key research interests include high-performance computing , climate modeling , parallel computing , and scientific data compression . His contributions address challenges like workflow optimization for HPC platforms, GPU acceleration for atmospheric chemistry models, and minimizing computational costs in Earth system simulations. Recent work highlights include analyzing the computational and energy costs of CMIP6 simulations, improving EC-Earth3 coupling efficiency, and developing optimized GPU implementations for atmospheric chemistry solvers. His publications span journals like Geoscientific Model Development , Computer Physics Communications , and Future Generation Computer Systems . Acosta Cobos has been involved in collaborative projects such as the Joint-Laboratory of Extreme Scale Computing and contributed to HighResMIP, focusing on high-resolution climate models. His research bridges computational science with environmental applications, emphasizing reproducibility, scalability, and sustainable computing practices.

• Distributed Computing
• Heterogeneous Computing
• Programming Models
• +5 more

Rosa Maria Badia Sala is a Research Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Departament d'Arquitectura de Computadors and the Barcelona Supercomputing Center (BSC-CNS). She specializes in distributed computing, heterogeneous systems, and task-based programming models. Her work focuses on advancing high-performance computing (HPC), cloud computing, and workflow management for large-scale scientific applications. She holds a Doctorat en Informàtica and has been actively involved in numerous research projects, including contributions to the COMPSs programming framework and the optimization of HPC workflows. Her collaborations span institutions like BSC-CNS and international initiatives such as JLESC. Recent research includes GPU-accelerated computing, quantum optimization algorithms, and digital twins for power networks. Badia's publications span journals like Future Generation Computer Systems and IEEE Transactions on Parallel and Distributed Systems, reflecting her expertise in parallel computing, distributed systems, and real-time data analysis. She has supervised multiple doctoral theses and contributes to research grants focused on HPC and AI integration.