Find Academic Supervisors

Pekka ToivanenView profile

Professor

• Artificial Intelligence
• Healthcare Systems
• IoT and Smart Grids
• +3 more

Pekka Toivanen is a Professor at the School of Computing, Faculty of Science, Forestry and Technology at the University of Eastern Finland. His research focuses on artificial intelligence, healthcare systems, IoT, cybersecurity, and machine learning. He has contributed extensively to fields like medical image analysis, smart grids, and robotics, with over 100 peer-reviewed publications. His work bridges theoretical advancements and practical applications, such as improving healthcare diagnostics via AI, enhancing IoT security, and optimizing energy management in microgrids. Research Interests: Dr. Toivanen’s primary areas include AI-driven healthcare solutions, cybersecurity for IoT devices, deep learning applications in medical imaging, and energy-efficient smart grid systems. He has pioneered methods for tuberculosis risk prediction and developed novel encryption algorithms for Bluetooth security. Publications: His recent work emphasizes AI in healthcare (e.g., tuberculosis prediction models) and energy systems (e.g., reinforcement learning for microgrid management). He also explores vulnerabilities in wireless technologies like Bluetooth and ZigBee, proposing robust countermeasures. Grants/Teams: Leads projects on AI distribution platforms for healthcare and secure IoT architectures. Collaborates with interdisciplinary teams in medical and engineering domains.

Michael MeisterView profile

Professor

• Wastewater Treatment Plant Modeling
• Numerical Flow Simulation
• Smoothed Particle Hydrodynamics (SPH)
• +3 more

Michael Meister is a Professor at MCI – The Entrepreneurial School, holding a position in the Department of Environmental, Process & Energy Technology. He has held roles including Junior Professor and Post-Doc researcher at Universität Innsbruck and teaching positions at Imperial College London. His research focuses on wastewater treatment plant modeling, numerical flow simulation using SPH, and hydraulic simulations. Education: PhD in Environmental Technology (University of Innsbruck), MSc in Physics (Imperial College London), BSc in Physics (University of Innsbruck). Research interests include optimizing anaerobic digester mixing efficiency, CFD applications in wastewater processes, and developing SPH-based models for environmental systems. His work bridges computational fluid dynamics with practical engineering solutions for sustainable energy and water management. Key projects include OPTIFAUL (energy-efficient mixing optimization) and SPHAUL (SPH-based digester modeling). He has received multiple teaching awards from MCI and recognition for his doctoral and undergraduate work. Advised over 15 students on topics ranging from biogas potential estimation to CFD-based energy optimization in wastewater treatment plants. Active in peer review for journals and conferences, and serves on evaluation committees for international research programs. Labs/Teams: Leads the OptiFaul research initiative and collaborates with institutions like the University of Leeds and the Austrian Research Promotion Agency.

Antonio CarzanigaView profile

Professor

• Distributed Systems
• Software Engineering
• Content-Based Networking
• +7 more

Antonio Carzaniga is a Full Professor and founding member of the Faculty of Informatics at Università della Svizzera italiana (USI), where he has been active since 2004. Previously, he served as an Assistant Research Professor at the University of Colorado at Boulder from 2001 to 2007. He holds a Ph.D. in Computer Science and a Bachelor’s degree in Electronic Engineering from Politecnico di Milano. Full Professor, Faculty of Informatics, Università della Svizzera italiana (2004–Present) Assistant Research Professor, Department of Computer Science, University of Colorado at Boulder (2001–2007) Ph.D. in Computer Science, Politecnico di Milano Bachelor’s in Electronic Engineering, Politecnico di Milano His research spans distributed systems and software engineering, with a strong focus on content-based addressing networks, publish/subscribe systems, middleware, software fault tolerance, and verification. He has pioneered work in information-centric networking and developed the Siena project, a scalable publish/subscribe service. His recent work extends into programmable networks, GPU-accelerated matching, and performance annotations for cloud systems. The 15 most recent publications highlight a consistent trajectory in scalable, high-performance networking and adaptive software systems. Key themes include content-based communication, packet subscriptions, information-centric networking, and leveraging redundancy for fault tolerance and testing. His work bridges theoretical foundations with practical implementations, often involving system-level software and performance evaluation. Best Paper Award, ACM SIGCOMM Workshop on Information-Centric Networking (ICN'13) Carzaniga has advised multiple graduate students, including Michele Papalini, Koorosh Khazaei, and Daniele Rogora, and has collaborated on funded research projects in distributed systems and networking. He has contributed to software development through projects like the Siena Fast Forwarding engine and the Synthetic Workload Generator. His service includes organizing workshops and contributing to major conferences in software engineering and computer systems. He leads research initiatives such as Siena and Content-Based Networking, focusing on scalable, decentralized communication infrastructures. His lab has developed key tools for evaluating publish/subscribe performance and implementing high-speed forwarding algorithms.

• Gravitational Wave Astronomy
• Precision Interferometry
• Instrumental Noise Modeling
• +3 more

Dr. Olaf Hartwig is a Senior Scientist at the Albert Einstein Institute (AEI) in both Potsdam and Hannover. His research focuses on precision interferometry and fundamental interactions, specifically for the Laser Interferometer Space Antenna (LISA) project. He holds a PhD in Physics from the University of Hannover (2021) and has held postdoctoral positions at SYRTE - Observatoire de Paris and AEI. His work bridges instrumental modeling, data processing, and noise reduction for space-based gravitational wave detection. Education: BSc and MSc in Physics (University of Hannover), PhD in Physics (University of Hannover via AEI Potsdam) Current Roles: Split post-doctoral position between AEI Potsdam (global fit for LISA) and AEI Hannover (Performance and Operations team) Research Interests revolve around space-based gravitational wave detectors, with emphasis on: Instrumental Modeling - Refining noise models, addressing data gaps, and mitigating glitches in LISA data Data Processing - Developing simulations, performance models, and software tools like PyTDI Detector Optimization - Clock synchronization, light-travel time estimation, and onboard optical delay compensation Publication Trends (15 most recent) show a focus on LISA instrumentation, with key topics including time-delay interferometry (TDI), stochastic gravitational wave background reconstruction, instrumental noise characterization, and intersatellite ranging. His work frequently integrates GPU acceleration, Python-based toolchains, and end-to-end simulation pipelines.

Stuart BarnesView profile

Lecturer

• Computer Vision
• Artificial Intelligence
• Software Engineering
• +4 more

Dr. Stuart Barnes is a Lecturer in Computational Intelligence and Data Analytics at Cranfield University , where he also serves as Course Director for the MSc Computational & Software Techniques in Engineering program. His academic background combines Physics (BSc, MSc from University of Kent) and Computer Vision (PhD, MSc from Cranfield University). Research focuses on Vision-Based Computing with applications in - Human-Computer Interaction (HCI) and Gesture Recognition - Surveillance and Security systems - Autonomous Vehicle Operations Recent publications highlight his work in semantic segmentation (2025), autonomous refueling systems (2023-2024), and historical contributions to laser shearography (2004-2006). His technical expertise spans algorithm development, machine learning models, and industrial software deployment across aerospace and automotive sectors. Key Collaborations : • Jaguar Land Rover Ltd • Airbus SE • Saab UK Ltd (BlueBear) • Thales SA

Tyler SorensenView profile

Assistant Professor

• Concurrency
• Heterogeneous Systems
• Compilers
• +4 more

Tyler Sorensen is an Assistant Professor at the University of California, Santa Cruz in the Department of Computer Science and Engineering. He is currently on leave working with the RiSE group at Microsoft Research . His research focuses on concurrency programming, heterogeneous systems (GPUs, accelerators), compilers , and memory consistency models . PhD in Computer Science, Imperial College London (2018) MS in Computer Science, University of Utah (2014) BSc in Computer Science, University of Utah (2012) His work explores programming models for correctness and efficiency on emerging architectures, particularly GPGPU programming. He contributes to standards evolution with the Khronos Group and has developed testing frameworks for GPU memory consistency. His research has significant implications for parallel programming and GPU architecture design . His recent publications analyze GPU memory behavior, concurrency models, and performance portability across different architectures. Key trends include formal verification of memory models, empirical testing frameworks, and performance optimization for heterogeneous systems. Scientific Awards & Recognitions: ISSTA'23 Distinguished Artifact Award ASPLOS'23 Distinguished Paper & Artifact Awards IISWC'19 Best Paper Award PLDI'18 Distinguished Paper Award FSE'17 Distinguished Paper Award ISPASS'20 Best Paper Nomination Tyler advises a diverse group of students working on GPU programming, memory models, and heterogeneous systems. He has served on numerous program committees including ASPLOS 2024, PLDI 2024, and IWOCL 2019, and has been program co-chair for PLDI 2021 and 2022 Student Research Competition.

Stephane VialleView profile

Researcher

• High-Performance Computing
• GPU Computing
• Quantum Computing
• +3 more

Stephane Vialle is a researcher at CentraleSupélec, leading the Interdisciplinary Laboratory of Digital Sciences. His research focuses on High-Performance Computing (HPC), GPU Computing, Quantum Computing, and Quantum Machine Learning. He has extensive experience in developing scalable fine-grained computing environments and optimizing parallel algorithms for distributed systems. His work spans financial engineering, energy management, and railway infrastructure through digital twin technology. Recent contributions include advancements in GPU cluster energy efficiency, stochastic control algorithms, and hybrid classical-quantum architectures for data clustering. Research interests emphasize optimizing parallel computing frameworks for diverse applications, including financial modeling, material science simulations, and transportation systems. His publications demonstrate expertise in distributed computing, fault-tolerant architectures, and algorithmic innovation across multiple computational paradigms. Current projects explore quantum computing integration with classical systems, GPU-based large-scale data processing, and real-world applications of parallel simulation techniques. Notable contributions include the parXXL development environment for coarse-grained platforms and the MINERVE digital twin for railway infrastructure management. His work bridges theoretical computing advancements with practical implementations in engineering and finance domains.

• Computer Architecture
• Systems & Networking
• Approximate Computing
• +6 more

Weng Fai WONG is an Associate Professor and Deputy Head of the Department of Computer Science at the School of Computing, National University of Singapore (NUS). With over three decades of academic experience at NUS, he has established himself as a leading researcher in computer systems, with particular expertise in the interface between hardware and software stacks. Dr. Wong received his B.Sc. (First Class Honors) and M.Sc. from the National University of Singapore in 1989 and 1991 respectively, followed by a Dr.Eng.Sc. from the University of Tsukuba in 1993. His academic journey began at NUS (then DISCS) in 1985, where he progressed from student to Senior Tutor in 1989, and later returned from Japan as a Lecturer in 1993. Dr. Wong's research focuses on systems and networking, with special emphasis on hardware-software co-optimization. His current research interests include approximate computing , neuromorphic computing , and hardware acceleration for deep learning. His work spans computer architecture , embedded systems , compilers and runtime systems , and programming languages . He has made significant contributions to optimizing software for novel hardware including FPGAs, GPUs, and non-volatile memory technologies. His recent publications (2023-2025) demonstrate a strong focus on energy-efficient AI computing, with particular emphasis on spiking neural networks, large language model acceleration, and FPGA-based solutions for graph processing and machine learning workloads. His research shows a clear trajectory toward green AI through hardware-software co-design that minimizes energy consumption while maintaining computational effectiveness. Dr. Wong is a Member of ACM and a Senior Member of IEEE. His paper "Exploiting half precision arithmetic in Nvidia GPUs" was a Best Paper Finalist at the IEEE High Performance Extreme Computing Conference (HPEC 2017). As Deputy Head of the Department of Computer Science at NUS, Dr. Wong plays a key leadership role in academic administration while maintaining an active research program. His work has been supported by numerous research grants, though specific details are not provided in the available information. Dr. Wong leads research in the Systems & Networking area at NUS, with particular focus on the Hardware-Software Interface Laboratory. His team explores innovative approaches to bridge the gap between theoretical computer science and practical hardware implementation, with applications spanning from edge computing to large-scale data centers.

• Computational Fluid Dynamics (CFD)
• Turbulence Simulation
• Wind Farm Optimization
• +5 more

Professor Sylvain Laizet is a faculty member in the Department of Aeronautics at Imperial College London, part of the Faculty of Engineering. He holds a PhD and Habilitation à Diriger des Recherches from the University of Poitiers in France, specializing in Computational Fluid Dynamics (CFD). His research focuses on turbulent flows and their engineering applications, including wind farm optimization, quantum algorithms for fluid mechanics, and machine learning integration in CFD. Key research areas include: Wake-to-wake interaction in wind farms Bayesian optimization for drag reduction and energy efficiency Immersed boundary methods for moving objects Quantum computing for solving PDEs in fluid dynamics His work leverages high-order numerical methods and large-scale simulations, with contributions to frameworks like Xcompact3D and WInc3D. Recent publications emphasize quantum algorithms, turbulence entrainment, and data-driven optimization strategies for renewable energy systems. Scientific contributions span over 50 peer-reviewed articles in top-tier journals, focusing on advancing CFD methodologies and their applications in environmental and industrial fluid mechanics. His team collaborates globally, including partnerships in France and Brazil.

Amer QounehView profile

Associate Professor

• Computer Architecture
• High-performance Computing
• Data Centers
• +2 more

Amer Qouneh serves as an Associate Professor in the Department of Electrical and Computer Engineering at Western New England University, where his research centers on computer architecture, high-performance computing, data centers, Internet of Things (IoT), and embedded systems with emphasis on energy efficiency and practical implementations. Education: Ph.D. in Computer Engineering, University of Florida, 2014 M.S. in Computer Engineering, University of Florida, 2010 M.S. in Computer Science, University of Wisconsin-Milwaukee, 2007 M.S. in Electrical Engineering, Fairleigh Dickinson University, 1988 B.S. in Electrical Engineering, Fairleigh Dickinson University, 1985 Dr. Qouneh's research integrates theoretical innovation with real-world applications across multiple domains. His computer architecture work addresses thermal resilience in photonic networks and processing-in-memory systems, while his data center research focuses on renewable energy integration, resource allocation, and containerization. In IoT and embedded systems, he develops practical solutions like solar array trackers and edge-based machine learning deployments, demonstrating a commitment to sustainable and accessible technology education. Publication analysis from 2009-2022 reveals a strategic evolution from foundational work in transactional memory and containerization toward advanced energy-efficient architectures. His recent output emphasizes IoT educational integration and embedded AI applications, maintaining consistent contributions to top venues like IEEE Transactions on Parallel and Distributed Systems while adapting to emerging computational challenges in green computing and edge intelligence. Dr. Qouneh actively mentors undergraduate researchers, evidenced by multiple co-authored conference papers on IoT implementations and curriculum development. His scholarly impact spans both technical innovation in data center optimization and educational leadership in modernizing engineering curricula for emerging technologies.

Chenchen LiuView profile

Assistant Professor

• High-performance computing for machine learning
• Brain-inspired computing
• Hardware-software co-design
• +4 more

Dr. Chenchen Liu is an Assistant Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She is affiliated with the Computing Compass Laboratory and holds a Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) under the focus group Enabling Neuromorphic Computing for Multi-Tenant AI. Her work bridges hardware-software co-design with neuromorphic computing. Ph.D., Electrical and Computer Engineering, University of Pittsburgh (2017) M.S., Electrical and Computer Engineering, Peking University (2013) Her research focuses on high-performance computing for machine learning through novel computer architecture and system designs, brain-inspired computing, machine learning security, non-volatile memory, and VLSI design. Key areas include: Neuromorphic hardware resilience and optimization Memristor-based neural network architectures Runtime scheduling for multi-tenant AI Security in neuromorphic computing Energy-efficient memory systems Recent publications explore memristor defect tolerance, ReRAM-based CNN training efficiency, and spiking network quantization. The work emphasizes hardware-software co-design for AI acceleration. NSF Career Award (2023) Best Poster Award, Machine Learning and Systems Conference (2022) Best Paper Award, IEEE Symposium on VLSI (2014) She contributes to academic service as TPC Chair/Track Chair for DAC, GLVLSI, Cloud Summit conferences and serves as Associate Editor for IEEE Transactions on Circuits and Systems (TCAS-1) and Neurocomputing journal.

• Parallel and Distributed Systems
• Cloud Computing
• Computer and Communication Networks
• +12 more

Deniz Turgay Altılar is a Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), Faculty of Computer and Informatics. He has been an active academic since 1988, progressing from Research Assistant to Associate Professor in 2013 and later achieving the rank of Professor. His work is centered on distributed systems, cloud computing, and computer networks, with expanding interests in AI applications for agriculture and healthcare. Education Bachelor of Science, Control and Computer Engineering, Istanbul Technical University (1984–1988) Master of Science, Computer Engineering, Istanbul Technical University (1988–1992) Research experience at Queen Mary and Westfield College, University of London (1998–2002) His research interests include parallel and distributed systems, cloud computing, secure computation, deep learning, and cybersecurity. He applies these to interdisciplinary domains such as corn yield prediction, radar identification, and medical diagnostics using ECG signals. His recent work emphasizes efficient and secure AI systems. The publication trends show a strong focus on distributed deep learning, straggler mitigation, secure multiparty computation, and hardware-based attack detection. His work bridges theoretical computer science with real-world applications in agriculture, medicine, and national security. Recent articles highlight innovation in lightweight AI models, privacy-preserving computation, and cross-layer system design. Scientific Awards No specific awards are mentioned in the provided text. He actively supervises students and leads major research projects, including those on cache side-channel attacks in multi-tenant clouds, distributed OpenCL platforms, real-time scheduling, and molecular communication in nano-networks. These projects reflect sustained external funding and leadership in cutting-edge computing domains. His research group collaborates across disciplines and institutions, contributing to both national and international scientific communities. Labs and Teams : While not explicitly named, his role as Principal Investigator on multiple projects suggests leadership in a research lab focused on distributed systems, cybersecurity, and applied AI at ITU.

• Medical Imaging
• Computed Tomography (CT)
• X-ray Simulation
• +4 more

Dr. Franck Patrick Vidal is an Honorary Professor at Bangor University's School of Computing and Engineering, with additional affiliations at the Science and Technology Facilities Council. He holds a PhD in Computer Science and has extensive experience in medical imaging, visualization, and simulation. His research focuses on X-ray imaging, computed tomography (CT), and high-performance computing applications in medical physics. He has contributed to developing open-source tools like gVirtualXray for real-time X-ray simulations and has been involved in projects addressing large-scale emergency response visualization (RAMPVIS). Education includes a PhD from Bangor University (2008), a Master's from Teesside University (2002), and a Postgraduate Certificate in Higher Education (2016). He has held roles such as Senior Lecturer and Postdoctoral Research Fellow at institutions like Inria and CEA Saclay. His research interests span medical imaging technologies, optimization algorithms, and the application of artificial intelligence to healthcare. Notable awards include the 'Best Poster Presentation' and the 'David Duce Prize'.

Francesco V. PepeView profile

Associate Professor

• Quantum Imaging
• Quantum Information
• Quantum Optics
• +7 more

Francesco V. Pepe is an Associate Professor in the Department of Physics at the University of Bari Aldo Moro, Italy, where he is affiliated with the Dipartimento Interateneo di Fisica. He leads the Quantum Optical Technologies Laboratory (QuOT Lab) and is the Principal Investigator of the INFN project PICS (Plenoptic Imaging with Correlations), focused on advancing correlation plenoptic microscopy. His research spans quantum imaging, quantum information, and quantum optics, with a strong emphasis on correlation-based imaging techniques and their applications in high-resolution 3D microscopy. PhD in Physics (specific institution and year not specified in text) His research interests lie at the intersection of quantum optics and imaging science, focusing on quantum imaging , correlation-based microscopy , plenoptic imaging , light-field technologies , and quantum information processing . He explores the use of intensity correlations in light to overcome classical limitations in resolution, depth of field, and imaging speed. His work also extends to quantum simulation of many-body systems (e.g., Schwinger model), bound states in the continuum , and light-matter interactions in waveguide QED platforms. The recent articles (2024–2025) reveal a strong trend toward quantum-inspired imaging techniques , particularly correlation plenoptic and hyperspectral imaging, with applications in turbulence-robust and real-time volumetric imaging. There is also a significant focus on quantum simulation of gauge theories using quantum computing platforms, dimensional reduction in field theories , and non-Markovian dynamics in quantum systems. The consistent use of correlation measurements, quantum error mitigation, and GPU-accelerated processing highlights a multidisciplinary approach combining theory, computation, and experimental design. Francesco V. Pepe has supervised Master’s students in Physics and is actively involved in collaborative research projects. While no formal scientific awards are listed in the provided text, his leadership in the INFN PICS project and extensive publication record underscore his prominence in the field. He has also contributed to advancements in quantum decay dynamics , spontaneous emission in dispersive media , and nonexponential decay phenomena , often in collaboration with leading researchers in quantum optics and condensed matter physics. He is associated with the QuOT Lab, which focuses on developing quantum optical technologies for imaging and sensing. The lab engages in both theoretical modeling and experimental implementation, particularly in correlation imaging, plenoptic microscopy, and quantum simulation. The team collaborates widely across Italy and internationally, contributing to projects in quantum 3D imaging, remote sensing, and Earth observation.

• Visual Analytics
• Big Data
• Software Engineering
• +7 more

Adriano Lopes is an Invited Assistant Professor at Iscte - Instituto Universitário de Lisboa, affiliated with the Department of Information Science and Technology within the School of Technologies and Architecture. He is also an Associate Researcher at ISTAR-IUL, the university's research center in Information Sciences, Technologies, and Architecture. His academic work spans software systems engineering, visual analytics, and big data technologies. PhD in Computer Science – University of Leeds, UK (1999) Master’s in Computer Science – University of Coimbra (1993) Bachelor’s in Electrical Engineering (Computer Science branch) – University of Coimbra (1986) Postgraduate studies in Financial Analysis – Technical University of Lisbon, ISEG (2013) Adriano Lopes’ research focuses on visual analytics, big data, software engineering, and computer graphics, with a strong emphasis on data visualization and its applications in domains such as smart tourism and urban planning. His work integrates human-centric design with advanced computational techniques to support decision-making in complex systems. His recent publications highlight a shift toward applied research in digital transformation for sustainable tourism, particularly through spatiotemporal visualization of tourism crowding and carrying capacity modeling. These works demonstrate a consistent trend in leveraging data-driven platforms for real-world societal challenges, especially in the context of post-pandemic recovery and sustainable development. Adriano has supervised numerous Master’s students at both Iscte and Universidade Nova de Lisboa, with thesis topics covering sentiment analysis, GPU-based rendering, anomaly detection, and tourism flow forecasting. He has participated in EU-funded research projects such as RESETTING, which aims to relaunch sustainable tourism models through digitalization. His academic service includes organizing roles in major visualization conferences like EUROVIS 2006 and the Eurographics UK Conference. He has been actively involved in research labs and teams including ISTAR-IUL and CITI (research center at FCT/UNL), contributing to interdisciplinary projects that bridge computer science with architecture, telecommunications, and tourism. His work reflects a strong commitment to collaborative, applied research with societal impact.