Find Academic Supervisors

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +5 more

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Bingyao LiView profile

Assistant Professor

• Computer Architecture
• High-Performance Computing
• Multi-GPU Systems
• +4 more

Bingyao Li is an incoming tenure-track Assistant Professor at the University of California, Riverside's Computer Science and Engineering Department, starting Fall 2025. She is currently a fourth-year Ph.D. student at the University of Pittsburgh, advised by Dr. Xulong Tang. Her research focuses on computer architecture, particularly multi-GPU systems, virtual memory management, and address translation for high-performance computing. Educational Background: She holds a B.E. (2017) and M.S. (2020) from Tianjin University's College of Intelligence and Computing. Her Ph.D. work at Pitt emphasizes GPU architecture and system-level optimizations for large-scale applications. Research Interests: Her work spans advanced computer architectures, multi-GPU optimization, and parallel computing systems. She explores solutions like object-aware page management (OASIS), TLB enhancements (STAR), and dynamic page placement (GRIT) to improve multi-GPU performance. She also investigates system-level optimizations for large language models and GPU ecosystems from hardware to applications. Awards: She has received the CS50 Outstanding Research Fellowship (2022, 2023) and the SCI Fellowship from the University of Pittsburgh. Her contributions include internships at NVIDIA's Architecture Research Group and talks on multi-GPU computing at NVIDIA and Tianjin University. Future Work: As a faculty member, she plans to mentor PhD students in Winter/Spring/Fall 2026, focusing on next-generation GPU systems and high-performance computing challenges. Her lab will address scalability, efficiency, and emerging applications in parallel computing.

Garth V. CrosbyView profile

Associate Professor

• Internet of Things (IoT)
• Industrial Internet of Things (IIoT)
• Cyber-physical Systems Security
• +5 more

Garth V. Crosby is an Associate Professor at Texas A&M University's Department of Engineering Technology and Industrial Distribution within the College of Engineering. He is also affiliated with the Multidisciplinary Engineering program. His research focuses on IoT and IIoT security, cyber-physical systems, and STEM education innovation. Crosby holds a Ph.D. in Electrical Engineering from Florida International University (2007), an M.S. in Computer Engineering, and a B.S. in Electronics (Applied Physics) from the University of the West Indies. His work spans cybersecurity frameworks for emerging technologies, including blockchain-based federated learning, post-quantum cryptography, and IoT threat mitigation. He has contributed to educational advancements through online lab design and faculty efficacy studies in hybrid learning environments. Crosby's recent publications emphasize securing robotic IoT systems, supply chain blockchain applications, and ransomware evasion techniques using generative AI. His research portfolio includes over 40 peer-reviewed articles in journals like IEEE Transactions and conferences such as ASEE and FiCloud. Key themes include volunteer cloud reliability models (ProTrust), edge computing security, and educational technology evaluation. Crosby's interdisciplinary approach bridges engineering systems with pedagogical innovation, addressing both technical and human factors in modern technological challenges.

Ruth BreuView profile

Professor

• Quality Engineering
• Model Engineering
• Security Engineering
• +12 more

Ruth Breu is a Full Professor and Dean of the Faculty of Mathematics, Computer Science and Physics at the Universität Innsbruck, where she also leads the Quality Engineering research group. She has been a key figure in the Department of Computer Science since 2002 and served as its Head from 2013 to 2024. Her academic journey began with a PhD summa cum laude from the University of Passau in 1991, followed by a habilitation at the Technical University of Munich in 1999. Her research interests include: Quality Engineering Model and Security Engineering Requirements and Software Development Processes Enterprise Architecture Management Threat Intelligence and Digital Twins Her recent publications reflect a strong focus on model-based systems, automated programming assessment, security engineering, and digital twins in construction and energy systems. She frequently collaborates with researchers such as Michael Felderer, Clemens Sauerwein, and Philipp Zech, contributing to advancements in software testing, threat intelligence sharing, and cyber-physical systems. Notable awards include her PhD awarded summa cum laude. She has also been actively involved in national research governance, serving on the board of the Austrian Science Fund (FWF) from 2011 to 2020. She co-founded Txture GmbH in 2017 and holds advisory roles at Universität Passau and FH OST. Ruth Breu advises several students and leads a vibrant research group. Her leadership extends to organizing workshops and contributing to major conferences in software engineering and enterprise modeling. She is deeply engaged in both academic and applied research, bridging theory and practice in IT quality and security.

• Security
• Embedded Systems Security
• Cyber-Physical Systems Security
• +13 more

Francesco Regazzoni is a Senior Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), and affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). His work bridges embedded systems, cybersecurity, and artificial intelligence, with a focus on securing hardware and cyber-physical systems. Research Interests: His expertise spans embedded and cyber-physical systems security, side-channel attacks, post-quantum cryptography, hardware trojans, random number generators, and the security of AI and approximate computing. He also contributes to hardware/software co-design and operating systems security. The analysis of his recent publications reveals a consistent focus on hardware and system-level security , particularly in resource-constrained environments like IoT and embedded devices. His work integrates machine learning for attack detection and applies formal methods to ensure trust in hardware. A growing emphasis is placed on securing AI systems from physical and adversarial threats. Scientific Contributions: Over 100 peer-reviewed publications One book and one patent Extensive international collaboration (Belgium, Netherlands, USA, Switzerland, Singapore) Advising and Grants: While specific advisees and grants are not listed, his leadership in funded research projects and involvement with ALaRI and IDSIA suggest active mentorship and project coordination. His work has been supported by industry (e.g., ST Microelectronics, HP), the Swiss National Foundation, and the European Union. Labs and Teams: He is part of the Graph Machine Learning Group (GMLG) at IDSIA, which evolved from the Advanced Learning and Research Institute (ALaRI). This group focuses on graph machine learning, reinforcement learning, and dynamical systems, particularly in non-stationary environments.

Xiaoyu XiaView profile

Lecturer

• System Privacy and Security
• AI Privacy
• Parallel and Distributed Computing
• +4 more

Dr. Xiaoyu Xia is a Lecturer (equivalent to Assistant Professor in North America) in Cybersecurity & Software Systems at RMIT University's School of Computing Technologies. He received his PhD with the prestigious Alfred Deakin Medal from Deakin University, Australia, and has established himself as a leading researcher in distributed systems and cybersecurity with over 50 peer-reviewed publications in top-tier venues including IEEE S&P, ACM WWW, and IEEE Transactions. Dr. Xia's research spans critical areas at the intersection of computing and security: System Privacy and Security Distributed Systems and Edge Computing AI Privacy and Machine Learning Systems Sustainable Computing Cybersecurity and Privacy-Preserving Technologies His recent work demonstrates a clear trajectory toward developing practical privacy-preserving frameworks for emerging technologies, particularly in edge computing environments and large language models. Dr. Xia has made significant contributions to machine unlearning, secure data management in distributed systems, and energy-efficient edge computing solutions that balance performance with sustainability concerns. Dr. Xia has received notable recognition for his scholarly impact: World's Top 2% Scientists by Stanford University (2022-2024) Alfred Deakin Medal for PhD research excellence (2021) Teaching Excellence Award from Swinburne University of Technology (2021) As an active researcher, Dr. Xia currently leads multiple funded projects including an ARC Discovery Project grant worth over $500,000 for developing privacy-aware intelligent digital twins for secure critical infrastructures. He is open to supervising motivated PhD students with interests in system security and privacy, and distributed ML systems. Dr. Xia serves the academic community through editorial roles as Associate Editor for IEEE Transactions on Dependable and Secure Computing and as a Review Board Member for IEEE Transactions on Parallel and Distributed Systems, and regularly participates in program committees for major conferences including ACM WWW and IEEE ICDCS.

François TrahayView profile

Professor

• High Performance Computing
• Runtime Systems
• Performance Analysis
• +4 more

François Trahay is a Full Professor in the Computer Science department at Télécom SudParis (Institut Mines-Télécom) and a member of the Benagil Inria team. He leads research in high-performance systems, runtime systems, and performance analysis for HPC and distributed systems. He holds an HDR from Institut Polytechnique de Paris and a PhD from University of Bordeaux (2009). His work includes the EZTrace framework for performance analysis and contributions to storage systems optimization. Education: 2021: Habilitation à Diriger des Recherches (HDR), Institut Polytechnique de Paris 2010: PostDoc at Riken, University of Tokyo 2009: PhD in Computer Science, University of Bordeaux 2006: MS in Computer Science, University of Bordeaux Research focuses on runtime system design, HPC trace analysis, and storage efficiency. Recent projects include PALLAS trace format (IPDPS 2025) and GPU performance prediction (Euro-Par 2024). He advises 4 current PhD students and has supervised 2 former students now in industry. Key contributions include: Co-developer of EZTrace performance analysis framework Co-author of 60+ peer-reviewed papers in top venues (IPDPS, IEEE Cluster, ICPP) Technical leadership in Inria's Benagil team and Samovar Lab

Francois TrahayView profile

Associate Professor

• High Performance Computing
• Storage Systems
• Parallel and Distributed Systems
• +2 more

François Trahay is an Associate Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His research focuses on high-performance computing systems, storage optimization, and performance analysis tools for parallel and distributed environments. He completed his PhD at Université Bordeaux I (2009) and Habilitation (HDR) at Institut Polytechnique de Paris (2021). Research Interests: Trahay specializes in optimizing storage systems (SSDs, RAID configurations), developing performance analysis frameworks (e.g., EZTrace, NumaMMA), and enhancing energy efficiency in HPC. Key areas include I/O performance, parallel runtime systems, and adaptive computing for machine learning workloads. Publication Trends: His recent work (2018–2025) demonstrates strong focus on: (1) SSD/RAID management techniques for modern storage hardware, (2) HPC performance tools for tracing and analysis, and (3) optimization strategies for distributed deep learning systems. Laboratory Affiliation: Member of SAMOVAR Laboratory (UMR CNRS), conducting research in distributed systems, networks, and computational efficiency.

• Computer Security
• Privacy Engineering
• Data Science
• +4 more

UnivProf.Dr. Delphine Reinhardt is a Professor of Computer Science at the University of Göttingen, serving as Head of the Computer Security and Privacy group and Head of the Dean's Office of the Faculty of Mathematics and Computer Science. She is a core member of the Institute of Computer Science and the Campus Institute Data Science (CIDAS). Her work focuses on privacy engineering in emerging technologies like smart devices, extended reality (XR), and human-robot interaction. Research interests include privacy-preserving computation, IoT security, and ethical AI applications. Her recent work explores privacy challenges in smart speakers, telepresence robots for hospitalized children, and cross-platform XR privacy solutions. She leads multiple courses on security and privacy, including advanced seminars and lab internships. Her publications analyze user privacy perceptions across cultures and technologies, with a focus on quantifying privacy risks in smart environments. She actively contributes to standards through leadership roles, balancing academic research with institutional governance responsibilities. Lab affiliations include the Computer Security and Privacy research group, which develops tools like PrivXR and SensitivAlert. She collaborates on EU-funded projects addressing privacy in smart workplaces and autonomous systems.

• Resilience in 5G
• Distributed Artificial Intelligence (Edge AI)
• Information assessment
• +3 more

Tobias Meuser is a Researcher at the Multimedia Communications Lab of Technische Universität Darmstadt, leading the "Adaptive Communication Systems" group since 2020. He holds a PhD (2019) focused on vehicular network data management and has been a central figure in the third phase of the Collaborative Research Center (CRC) MAKI as a principal investigator in subproject B1. His work emphasizes resilient 5G networks, edge AI, and distributed systems. Education: B.Sc. Business Informatics (Fernuniversität Hagen) M.Sc. Informatics (TU Darmstadt) Research Interests: Resilience in 5G and beyond Edge AI and distributed machine learning Information assessment in vehicular networks Collaborative perception systems Hardware acceleration for network functions Key Projects: Principal Investigator in CRC MAKI's B1 (Monitoring and Analysis) Collaborations with Opel (cooperative maneuvering) and Deutsche Bahn (5G resilience) Labs/Teams: Head of Adaptive Communication Systems group at Multimedia Communications Lab Member of Distributed Sensing Systems group (2016–2020)

Neitzke Adamo, LaurenView profile

Associate Professor

• Paleoceanography
• Stable Isotope Stratigraphy
• Geoscience Education
• +4 more

Lauren Neitzke Adamo is an Associate Professor in the Department of Earth and Planetary Sciences at Rutgers University, where she directs the Undergraduate Program (2022–present) and co-leads the Rutgers University Geology Museum (2016–present). Her career includes roles as Assistant Teaching Professor (2016–present), Lecturer (2008–2016), and prior leadership at the museum. Education : Ph.D. (2016), M.S. (2006), and B.S. (2004) from Rutgers University. Her research spans paleoceanography , stable isotope stratigraphy , and innovative geoscience education , focusing on drone-based remote sensing, 3D visualization, and museum outreach. She integrates technology like UAVs and 3D printers to enhance educational experiences and coastal process analysis. Recent publications highlight her work in virtual field trips (Newark Basin), drone-assisted coastal geomorphology, and science communication through comics and interactive museum exhibits. Collaborative projects emphasize democratizing geospatial tools and engaging diverse audiences via digital platforms.

• Internet of Things (IoT)
• Structural Health Monitoring (SHM)
• Microwave Imaging
• +5 more

Igor Bisio is a Full Professor at the Department of Naval, Electrical, Electronics, and Telecommunications Engineering (DITEN) at Università di Genova. His research focuses on IoT-driven structural health monitoring, microwave imaging for biomedical applications, and wireless surveillance systems. Teaches courses on telecommunications, IoT, and machine learning Pioneers low-cost IoT solutions for SHM and post-stroke rehabilitation Develops microwave tomography techniques for pediatric stroke diagnostics Advances privacy-preserving Wi-Fi-based crowd monitoring Recent research trends include edge AI integration, compressive sensing for vibration analysis, and multi-class object tracking in aerial scenes. He also explores UAV-based monitoring systems, WiFi fingerprinting for localization, and Banach space inversion models for electromagnetic imaging. His work spans interdisciplinary domains combining electrical engineering, biomedical applications, and wireless network security.

Ulrike Pestel-SchillerView profile

Researcher

• Hyperspectral Imaging
• Image Processing
• Video Coding
• +2 more

Dr. Ulrike Pestel-Schiller is a researcher at the Institute for Information Processing, Leibniz University Hannover, Germany, where she has been employed since 1996. Her work focuses on hyperspectral and Synthetic Aperture Radar (SAR) image processing, coding, and evaluation, with significant contributions to remote sensing applications. She actively supervises bachelor's and master's theses in these fields. Her academic background includes: Electrical Engineering and Communications Engineering studies at University of Hannover Dipl.-Ing. (Master's equivalent) awarded in 1989 Dr.-Ing. (Doctorate) completed in 1997 with dissertation on filter bank optimization for subband coding Her research centers on hyperspectral image data processing, coding efficiency, and usability evaluation for human interpreters. She investigates how compression techniques (HEVC, JPEG) impact SAR image usability, often finding counterintuitive results where compression improves interpretability. Recent work integrates deep learning, particularly CNNs, for spectral-spatial analysis in hyperspectral data and fruit classification. Her early career focused on HDTV video coding standards development. Analysis of her publication trends reveals a clear evolution from foundational HDTV subband coding research (1990s) to contemporary hyperspectral/SAR applications. A dominant theme is human-centered evaluation of compressed imagery, with 70% of her 2018-2023 publications examining interpreter performance. She increasingly employs deep learning for band selection and semantic segmentation, while maintaining core expertise in image compression algorithms. No scientific awards were documented in the source material. Dr. Pestel-Schiller supervises undergraduate and graduate theses in hyperspectral/SAR processing but no specific grant funding or formal advising records were provided. Her research appears institutionally supported through the Institute for Information Processing. The Institute for Information Processing serves as her primary research base, collaborating on projects involving drone remote sensing, VideoSAR stabilization, and hyperspectral band optimization. Current work emphasizes practical applications where image compression directly impacts interpreter effectiveness in remote sensing scenarios.

Sandro BartoliniView profile

Associate Professor

• High-Performance Computing (HPC)
• Computer Architecture
• Embedded Systems
• +5 more

Sandro Bartolini serves as Associate Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, Italy, where he teaches advanced courses in computer architecture and parallel programming while leading cutting-edge research in high-performance computing systems. His academic journey began with a cum laude Laurea in Computer Engineering followed by a PhD in Computer Science and Engineering from Università di Pisa. Education: PhD in Computer Science and Engineering, Università di Pisa Laurea in Computer Engineering (cum laude), Università di Pisa Research Focus: His work centers on photonic interconnects for chip multiprocessors , energy-efficient software optimization for multi-core/GPU architectures, and performance-portable parallel programming models . Current investigations span cryptographic acceleration, blockchain algorithms, and hardware/software co-design for emerging computing paradigms, with strong emphasis on practical implementations bridging theoretical advances and real-world applications. Publication Trends: Recent publications (2019-2023) reveal three dominant threads: (1) Photonic network innovations addressing energy bottlenecks in chip multiprocessors, (2) The PHAST library ecosystem enabling seamless CPU/GPU programming across domains from autonomous vehicles to UAV navigation, and (3) Hardware accelerator designs for convolutional networks and cryptographic workloads. These works consistently target performance-portability challenges in heterogeneous computing environments. Grants and Collaborations: As principal investigator for the Italian Ministry-funded PHOTONICA project, he established international research partnerships with Murcia University, Columbia University, and Hong Kong University of Science and Technology, while securing industry collaborations with STMicroelectronics, Intel Munich, IBM, and IMEC. He has also managed complex IT system deployments for Siemens Italy, RAI (Italian public broadcasting), and SpaceDys. Academic Leadership: Bartolini serves as Associate Editor for the Eurasip Journal of Embedded Computing and actively contributes to the European HiPEAC network. His research group at Siena maintains strong industry ties for technology transfer, particularly in photonic interconnect validation and parallel programming frameworks for next-generation computing systems.

Ke (Cory) WangView profile

Assistant Professor

• Computer Architecture
• Interconnection Networks
• Machine Learning
• +8 more

Dr. Ke (Cory) Wang serves as Assistant Professor at the University of North Carolina at Charlotte in the Department of Electrical and Computer Engineering, where he conducts research in computer architecture and parallel systems. Ph.D. in Computer Engineering (2022), George Washington University M.Sc. in Electrical Engineering (2015), Worcester Polytechnic Institute B.Sc. in Computer Science (2013), Peking University His research focuses on machine learning-enabled computer architecture with emphasis on network-on-chip design , domain-specific accelerators , and cross-layer optimization for manycore systems. Specific areas include anomaly detection, graph neural network acceleration, and fault-tolerant communication frameworks. Recent publications demonstrate increasing integration of deep learning techniques with traditional architecture design, particularly for graph convolutional networks and heterogeneous systems . His work addresses multi-objective optimization across performance , energy efficiency , and security dimensions . Key scientific recognitions include: National Science Foundation award (CSR: Small: Cross-layer Design, 2023) National Science Foundation award (CRII: SHF: Flexible Design Framework, 2023) Dr. Wang leads the Intelligent Computer Architecture & Systems Laboratory , which develops advanced frameworks for network-on-chip optimization and AI-driven system design.