Find Academic Supervisors

Vivy SuhendraView profile

Associate Professor

• Software Assurance
• Security and Privacy
• Cybersecurity
• +1 more

Vivy Suhendra serves as Associate Professor of Practice and Programme Director for Master Programmes at the National University of Singapore's School of Computing, while also holding the position of Assistant Dean for Graduate Studies. Previously, she led the Singapore Cybersecurity Consortium (SGCSC) as Executive Director from 2016 to 2022, driving collaborative cybersecurity research between academia, industry, and government agencies. Her career at NUS spans over two decades, beginning as a Research Assistant before advancing to her current leadership roles. Her academic credentials include: Ph.D. in Computer Science, National University of Singapore (2009). Thesis: "Memory Optimizations for Time-predictable Embedded Software". Advisors: Abhik Roychoudhury and Tulika Mitra. B.Comp. (Honors) in Computer Science, National University of Singapore (2004). Dr. Suhendra's research integrates Software Assurance, Cybersecurity, Security and Privacy, and Embedded Systems domains. Her work bridges theoretical foundations with practical applications, particularly in national cybersecurity ecosystem development, smart grid security protocols, denial-of-service mitigation techniques, and real-time embedded system optimization. This interdisciplinary approach enables innovative solutions for critical infrastructure protection and time-predictable software execution in multi-core environments. Her 14 selected publications (2004-2020) demonstrate an evolving research trajectory from foundational embedded systems timing analysis to applied cybersecurity solutions. Early work focused on memory optimization for predictable execution in multi-core embedded systems, which naturally transitioned into cybersecurity applications for smart grids, cloud environments, and national infrastructure. This progression highlights her ability to translate low-level system expertise into high-impact security frameworks for complex real-world systems. Scientific recognition includes: Microsoft Research Asia Fellowship (2006) Valedictorian at NUS School of Computing Ph.D. Commencement (2010) While specific graduate student advising details aren't provided, her leadership as SGCSC Executive Director involved extensive mentorship across academic-industry partnerships. She has also contributed significantly to the research community through roles including Conference Chair for ESEC/FSE 2022 and Workshops Committee Member for ICSE 2024. Dr. Suhendra established the Singapore Cybersecurity Consortium as a national platform for collaborative R&D during her directorship (2016-2022). Though no personal laboratory is specified, her research leadership manifests through cross-institutional teams focused on cybersecurity innovation, particularly in critical infrastructure protection and embedded systems security where she maintains active publication records.

Alireza PoshtkohiView profile

Researcher

• Computer Science
• Neuroscience
• Computational Biology
• +7 more

Alireza Poshtkohi is an interdisciplinary researcher at the University of Hertfordshire , affiliated with the School of Physics, Engineering & Computer Science and the Department of Computer Science . He applies computational and mathematical approaches to neuroscience, physics, and engineering challenges, focusing on modeling the human nervous system and brain diseases at the cellular level using supercomputing technologies. Education: PhD in Neuroscience (Ulster University, 2023), MSc in Parallel Simulation of Electronic Systems (Shahed University, 2011), BSc in Embedded Systems and Computer Networks (2006). His research spans computational neuroscience , mathematical modeling , and high-performance computing , with publications on microglia dynamics, P2X receptors, and parallel system modeling. Recent work includes a 2024 study on the PI3K/Akt pathway and a 2023 book on distributed systems. He collaborates with experimental neuroscientists from the University of Reading and Michigan State University , integrating molecular neurobiology with computational frameworks. His technical expertise includes grid computing, cybersecurity, and simulation environments.

Wei GaoView profile

Associate Professor

• Cyber-Physical Systems
• On-Device AI
• Mobile and Embedded Computing
• +7 more

Wei Gao is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on the design, deployment, analysis and measurement of on-device AI architectures and algorithms on mobile, embedded and networked systems. He has strong interests in unveiling analytical principles underneath practical AI deployment problems, and designing systems based on these principles. The developed AI and system solutions are widely applied to various application scenarios, including Internet of Things, edge computing and smart health. Dr. Gao received his PhD from Pennsylvania State University in 2012 and his B.E. from the University of Science and Technology of China in 2005. Dr. Gao's research spans across Cyber-Physical Systems , Infrastructure Security , High Performance Computing , and the Distributed Governance of Information . His work particularly emphasizes on-device AI architectures and algorithms for mobile and embedded systems. He explores how to deploy AI efficiently on resource-constrained devices, with applications in Internet of Things, edge computing, and smart health. His research aims to bridge theoretical principles with practical system implementations, focusing on creating efficient, secure, and reliable AI solutions for real-world deployment scenarios. His recent work has increasingly focused on bringing Large Language Models to edge devices while maintaining performance and security. Analysis of Dr. Gao's recent publications (2021-2025) reveals a strong focus on on-device AI, particularly around Large Language Models for resource-constrained environments. His work addresses critical challenges including model personalization, security against illegal adaptation, sparse activation techniques, and physics-grounded generation. Much of his research targets making AI more efficient, secure, and practical for deployment on edge devices with limited computational resources, while also exploring applications in health monitoring and power systems. Dr. Gao has received significant recognition for his research, including: NSF Faculty Early Career Development (CAREER) Award (2016) Dr. Gao mentors numerous graduate students who contribute to his research in mobile computing, embedded systems, and on-device AI. His research has been supported by various grants, most notably the NSF CAREER award, enabling his team to explore innovative approaches to mobile and embedded AI systems. His lab investigates how to optimize AI for resource-constrained environments while maintaining performance and security, with particular focus on balancing computational efficiency with model accuracy. Dr. Gao leads a research group focused on mobile and embedded AI systems, with particular emphasis on making AI practical for deployment on everyday devices. His team explores novel techniques for model compression, efficient inference, and secure deployment of AI models on edge devices, with applications ranging from health monitoring to smart infrastructure.

Ioan RaicuView profile

Professor

• Distributed Systems
• Cloud Computing
• Grid Computing
• +3 more

Ioan Raicu is a Professor in the Department of Computer Science at Illinois Institute of Technology (IIT) and a guest research faculty at Argonne National Laboratory's Math and Computer Science Division. He leads the Data-Intensive Distributed Systems Laboratory (DataSys) at IIT, focusing on distributed systems, cloud computing, and high-performance computing. His work is primarily funded by the NSF and DOE. Research interests include distributed systems, many-task computing, and data-intensive applications. Over 140 peer-reviewed publications have yielded a H-index of 46, with top papers addressing cloud vs. grid computing comparisons, Globus GridFTP, Swift workflow systems, and Falkon frameworks. Recent projects explore fine-grained parallelism, scalable indexing, and energy-efficient blockchain algorithms. Awards include NSF grants and recognition for lab innovations. Advised PhD students include Alexandru Orhean and Poornima Nookala. The DataSys lab has won the Grainger Computing Innovation Prize and leads initiatives like the BigDataX REU program. Active in conferences such as SC and IEEE IPDPS, Raicu's work bridges theory and practice in extreme-scale computing systems.

Xuming HeView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +9 more

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Maozhen LiView profile

Professor

• High Performance Computing
• Big Data Analytics
• Artificial Intelligence
• +13 more

Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.

Wai Kiong Oswald ChongView profile

Associate Professor

• Artificial Intelligence
• Engineering Systems
• Optimization
• +10 more

Dr. Wai Kiong Oswald Chong is an Associate Professor at Arizona State University's School of Sustainable Engineering and the Built Environment, with a dual affiliation as Senior Global Futures Scientist at the Global Futures Scientists and Scholars program. He holds a PhD in Civil Engineering from the University of Texas-Austin, MSc and BSc in Building from the National University of Singapore, and focuses on integrating artificial intelligence with sustainable engineering systems. PhD (2005): Civil Engineering, University of Texas-Austin MSc (1999) & BSc (1997): National University of Singapore His research bridges lunar construction with Earth-bound sustainable systems, covering topics like: Space habitat modularization Resource circularity systems AI-enhanced building codes Climate-resilient infrastructure Advanced energy modeling Construction supply chain optimization Publications demonstrate consistent focus on: Semiconductor facility HVAC optimization Building energy consumption anomalies Life cycle assessment frameworks Construction risk management Deconstruction and material reuse AI-driven system modeling Current research projects include: Lunar MVI (Moon Village Initiative) Semiconductor fab design optimization Human-AI knowledge interfaces Thermal insulation systems for extreme environments Smart grid energy modeling

Fraser KingView profile

Assistant Professor

• Climate and Climate Change
• Radiation and Remote Sensing
• Synoptic Meteorology
• +13 more

Fraser King is an incoming Assistant Professor in the Department of Atmospheric and Oceanic Sciences (AOS) at the University of Wisconsin–Madison, starting in Winter 2026. He holds a PhD in Machine Learning and Remote Sensing of Precipitation from the University of Waterloo (2022) and is currently a postdoctoral research associate at NASA Goddard Space Flight Center. His research integrates machine learning with atmospheric physics to advance precipitation and snowfall retrieval, cloud microphysics, and climate modeling. He has held research positions at the University of Michigan and NASA Jet Propulsion Laboratory. His research interests include: Climate and Climate Change Radiation and Remote Sensing Synoptic Meteorology Atmospheric and Cloud Physics Large Scale Dynamics Machine Learning and Model Interpretability Arctic Snowfall Prediction His recent publications reflect a strong trend in applying deep learning (e.g., U-Net, CNNs) and unsupervised methods (PCA, t-SNE, UMAP) to radar and satellite data for precipitation and snow microphysics. Key themes include radar gap inpainting, melting layer detection, and dimensionality reduction for physical interpretation. His work bridges geoscience and AI, aiming for interpretable models that enhance physical understanding. Scientific awards and professional service include: Finalist for the 2023 Governor General's Gold Medal, University of Waterloo Associate Editor, Journal of Atmospheric and Oceanic Technology (AMS) Member, AMS Committee on Artificial Intelligence Applications to Environmental Science Executive Council Member, AGU Precipitation Technical Committee Executive Member, Eastern Snow Conference Research Board Fraser King has mentored students through research projects and led educational initiatives such as a 12-week course on machine learning for land cover classification. He has secured research experience through internships at Aquanty Inc. and multiple NASA-affiliated institutions. He founded MapsByFraser, a company combining cartography and satellite data, and has collaborated with Google's Quantum AI team. His technical skills span Python, deep learning frameworks, and high-performance computing platforms. He leads several major research projects: Towards Interpretable Physical Models : Using sparse autoencoders and nonlinear dimensionality reduction to interpret geoscience models. Microphysical Dimensionality Reduction : Applying PCA, t-SNE, and UMAP to identify physical modes in precipitation data. BlindPaint : A U-Net for radar gap inpainting in spaceborne systems. DeepPrecip : A deep learning model for surface precipitation retrieval. iPhone LiDAR : Using consumer smartphones for snow depth measurement via drones. NRCan Machine Learning Land Cover Classifier : Training ML models on Sentinel-2 data. Climate Model Calibration : Using ML to correct biases in snow-related climate variables. CloudSat Snowfall Validation : Validating high-latitude snowfall estimates. Snow Modelling : A Rust-based physical/temperature-index snow model.

Peter BuiView profile

Teaching Professor

• Systems Programming
• Operating Systems
• Parallel Computing
• +6 more

Peter Bui is a Teaching Professor in the Computer Science and Engineering department at the University of Notre Dame , located within the College of Engineering. He teaches courses such as Data Structures, Systems Programming, and Ethical and Professional Issues, while also managing the core Elements of Computing programming sequence for the Computing & Digital Technologies minor. Education: Ph.D. in Computer Science and Engineering from University of Notre Dame (2012) His research interests span systems programming, operating systems, parallel computing, cloud computing, distributed computing, programming languages, compilers, and web services . He actively integrates these domains into his teaching and extracurricular work with the Linux Users Group. Recent publications highlight his work in distributed computing frameworks , including the development of tools like WorkQueue and Madeup for scalable scientific workflows and 3D printing integration. Projects such as ROARS and Weaver demonstrate his focus on robust data management and workflow automation. Outside academia, he stewards the Linux Users Group , engages with open-source communities, and balances personal interests like gaming in RuneScape with family time.

• Climate Change
• Atmospheric Dynamics
• Renewable Energy
• +6 more

Juan Antonio Añel Cabanelas is a Professor of Earth Physics at the University of Vigo , affiliated with the EPhysLab research group and the Specialized Group on Atmospheric and Ocean Physics of the Royal Spanish Society of Physics . He serves as an Executive Editor for Geoscientific Model Development and an Associate Editor for PLoS Climate . PhD in Physics (2007) from the University of Vigo, thesis: Climatic analysis of the tropopause using radiosonde data Taught courses in Meteorology, Atmospheric Physics, Computational Science, and Renewable Energy at the University of Vigo and international institutions His research focuses on climate change impacts , upper troposphere-lower stratosphere dynamics , renewable energy modeling , and computational reproducibility in climate research . He emphasizes instrumental data recovery and open science , with recent work addressing stratospheric contraction and mercury cycling . Key publications span extreme weather-energy sector interactions , Fortran code quality , and ozone data analysis . He mentors PhD students in Physics and Computer Science, and has collaborated with institutions in Mexico, Portugal, and the private sector. He advocates for free software and has organized workshops on climate intervention and citizen science . His work is funded by public grants from Spain's Government, Xunta de Galicia, and private entities like Naturgy and Acciona, with computing support from Google and Microsoft.

Robson De GrandeView profile

Associate Professor

• Vehicular Networks
• Intelligent Transportation Systems
• Cloud Computing and Networking
• +5 more

Robson E. De Grande is an Associate Professor in the Department of Computer Science at Brock University, Canada. He holds a PhD from the University of Ottawa (2012) and BSc/MSc degrees from the Federal University of São Carlos, Brazil. His research focuses on vehicular networks, intelligent transportation systems, distributed systems, and cloud computing. He serves on program committees for conferences like DS-RT, MobiWac, and MSWiM, and has organized multiple workshops and special sessions. Education: PhD in Computer Science, University of Ottawa, Canada (2012) MSc and BSc in Computer Science, Federal University of São Carlos, Brazil (2006, 2004) Research Interests: Vehicular Networks (5G, Handover Management) Edge Computing and IoT Performance Modeling/Simulation High-Performance Distributed Systems Intelligent Transportation Systems Publications: Over 100 peer-reviewed articles across journals like IEEE Transactions on ITS, Elsevier Internet of Things, and conferences like IEEE ICC and ACM MobiWac. Recent work emphasizes ML-driven vehicular network optimization and distributed simulation frameworks. Teaching: Teaches Advanced Computer Networks (COSC 4P14), Parallel Computing (COSC 3P93), and graduate-level Mobile Cloud Computing courses. Research Team: Supervises PhD/MSc students and undergraduate researchers in topics like vehicular edge computing, traffic prediction, and simulation systems.

Matt LeBlancView profile

Assistant Professor

• Particle Physics
• High Energy Physics
• Jet Physics
• +7 more

Matt LeBlanc is an Assistant Professor of Physics (Research) at Brown University, affiliated with the CMS Collaboration since 2024 and previously a core member of the ATLAS Collaboration from 2010–2023. His research focuses on experimental particle physics, particularly the analysis of hadronic objects and final states at the Large Hadron Collider (LHC). He employs advanced data science techniques, including machine learning and optimal transport algorithms, to study jet physics and search for new particles beyond the Standard Model. LeBlanc has contributed to jet reconstruction, calibration, and novel analysis methods in LHC data. Education: Ph.D. in experimental particle physics from the University of Victoria (Canada). Postdoctoral appointments at the University of Arizona, CERN, and the University of Manchester (UK). Research Interests: Jet substructure, hadronic object reconstruction, dark matter searches, QCD studies, radiation-hard detector development (e.g., MALTA sensors), and applications of AI/ML in physics. His work bridges experimental particle physics with computational science, addressing challenges in data processing for the High-Luminosity LHC era. Key Contributions: Leader of physics analyses in the ATLAS Collaboration, coordinator for hadronic object reconstruction/calibration, developer of jet energy scale algorithms, and pioneer in applying optimal transport and topic modeling to particle physics data. His recent work emphasizes efficient data pipelines and simulations for future collider experiments.

Kostas MagoutisView profile

Professor

• Distributed computer systems
• Scalable data stream processing
• Internet of Things
• +3 more

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete and collaborating researcher with FORTH-ICS. His research focuses on distributed systems, scalable data processing, IoT, and cloud computing, with projects including GreenInCities for urban regeneration and STREAMSTORE for stateful stream processing systems. Research interests include: Distributed computer systems architecture Elastic stream processing platforms Quantum-enhanced computing applications Multi-cloud application lifecycle management Recent publications demonstrate strong focus on federated data systems, quantum computing applications, and IoT-enhanced infrastructure, with consistent output in high-impact conferences and journals. Awards and distinctions: Multiple best paper awards from USENIX conferences Grand Challenge Audience Award at DEBS 2022 Marie Curie Fellowship and IBM Research awards Advises over 20 PhD and MSc students in distributed systems research. Leads multiple EU-funded projects and serves on program committees for top conferences including SOSP, EuroSys, and IEEE BigData. Directs research groups in distributed systems and cloud computing at FORTH-ICS.

• Computer Science
• Distributed Computing
• Parallel Computing
• +4 more

Radu-Aurel Prodan is a full Professor in the Department of Computer Science at the University of Innsbruck, Austria. He is actively involved in teaching and research, with office hours from Monday to Friday, 09:00–13:00. His work is centered within the academic and technical framework of one of Austria's leading computer science departments. His research interests span key areas in modern computing systems, including distributed computing, parallel computing, workflow scheduling, high-performance computing, cloud computing, and grid computing. These domains reflect his contributions to scalable and efficient computing infrastructures. The publications in his body of work demonstrate consistent engagement with challenges in system efficiency, resource management, and scientific workflows across distributed environments. Broad themes include optimization, performance modeling, and large-scale data processing in parallel systems. Scientific awards: No specific awards mentioned in the provided text. There is no available information on student advising, research grants, or leadership in specific labs or teams within the provided content. His professional activities appear to be anchored in the Department of Computer Science at the University of Innsbruck.

Renato NegraView profile

Professor

• High Frequency Electronics
• Cyber-Physical Systems
• Neuromorphic Computing
• +7 more

Prof. Dr. Renato Negra is a faculty member at RWTH Aachen University, serving as the Chair of High Frequency Electronics within the Faculty of Electrical Engineering and Information Technology. His research is centered on advanced electronic systems with a focus on reconfigurable and low-power architectures for real-time applications. Research Interests: His work spans high frequency electronics, neuromorphic computing, embedded systems, and cyber-physical systems. He develops FPGA-based and edge-computing solutions for computer vision, robotics, and smart infrastructure, particularly in elderly monitoring and autonomous navigation. His research integrates deep learning with hardware optimization for energy efficiency and real-time performance. The recent publications highlight a strong trend toward event-based vision , neuromorphic sensors , and low-power embedded AI , applied in domains such as smart cities, healthcare, and robotics. There is a consistent emphasis on real-time processing, reconfigurable systems, and the deployment of neural networks on constrained hardware platforms. Scientific Awards: No awards or honors were mentioned in the provided text. Advising and Grants: While no specific students or advising roles are listed, the volume and depth of publications suggest active supervision or collaboration within research projects. Although no grants are explicitly named, involvement in EU-level initiatives (e.g., FitOptiVis ECSEL Project) and national R&D programs (e.g., BIO-PERCEPTION) can be inferred from the research topics and publication contexts. Labs and Teams: Prof. Negra leads the research activities in High Frequency Electronics at RWTH Aachen. While not directly linked to the Computer Vision and Robotics Lab (CVR-Lab) mentioned in the text, his work aligns closely with neuromorphic and CPS research themes, suggesting potential interdisciplinary collaboration.