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Qi HanView profile

Professor

• Networking
• Distributed Systems
• Cyber-Physical Systems (CPS)
• +5 more

Qi Han is a Professor in the Department of Computer Science at Colorado School of Mines since 2005. She leads the Pervasive Computing Systems (PeCS) research group, focusing on algorithms and systems for pervasive/mobile computing applications. Her work bridges networking, distributed systems, and robotics with applications in cyber-physical systems (CPS), IoT, environmental monitoring, energy-efficient infrastructure, and mine safety. She holds a Ph.D. in Computer Science from the University of California-Irvine (2005). Education : - Ph.D. Computer Science, University of California-Irvine (2005) Research Interests : Dr. Han's research spans robotics, mobile sensing, and CPS, emphasizing interdisciplinary projects like underground mine safety, smart buildings, and oil refinery inspection. Recent efforts include drone-UGV collaboration, AR systems, and edge computing optimizations. She collaborates globally with institutions like CSIRO (Australia) and Chinese Academy of Sciences. Grants & Collaborations : - Multiple NSF grants secured - Active international partnerships in CPS/IoT research Labs & Projects : - PeCS Group: Develops systems for mobile/robotic sensing applications - Current projects include energy-aware path planning, AR pose estimation, and multi-drone coordination

Li ChouView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Dr. Li Chou is an Assistant Professor of Computer Science in the College of Engineering at West Texas A&M University. He holds a B.S. in Computer Science and an M.B.A. from Southern Methodist University, and an M.S. and Ph.D. in Computer Science from The University of Texas at Dallas. Prior to joining WTAMU, he was a Postdoctoral Research Associate at Rice University's Department of Computer Science. His research focuses on artificial intelligence , machine learning , data science , and scientific computing , with applications in recommendation systems, genomic data analysis, and federated learning. His work emphasizes efficient algorithms, model compression, and optimization for large-scale data processing. Notable contributions include the Random Offset Block Embedding (ROBE) technique for recommendation systems and diversified sampling methods for genomic streams. Dr. Chou teaches courses such as Programming Principles II, Data Structures and Algorithms, Computer Organization, and Artificial Intelligence. He is affiliated with the university's ECORE Lab and Human-Machine Teaming Laboratory , contributing to interdisciplinary research initiatives in engineering and computer science. His publications span topics like neural network optimization, federated learning frameworks, and bioinformatics applications, demonstrating a commitment to advancing both theoretical and applied computing.

Yasaman AmannejadView profile

Associate Professor

• Machine Learning
• Data Analytics
• Cloud Computing
• +7 more

Yasaman Amannejad is an Associate Professor in the Department of Mathematics and Computing at Mount Royal University (MRU), Faculty of Science. She holds a PhD in Software Engineering from the University of Calgary (2017) and an MSc/BSc in Computer Information Technology from Amirkabir University of Technology (2011/2008). Her research focuses on applying machine learning to healthcare diagnostics, performance analysis of cloud and edge computing systems, and addressing social challenges like domestic violence and homelessness. Her work has been funded by NSERC, Petro-Canada, and the New Frontiers in Research Fund. Education: PhD in Software Engineering, University of Calgary (2017) MSc in Computer Information Technology, Amirkabir University of Technology (2011) BSc in Computer Information Technology, Amirkabir University of Technology (2008) Research Interests: Machine Learning for Healthcare (e.g., tropical disease diagnosis, Multiple Myeloma cancer) Performance Optimization in Cloud/Edge Systems Resource-Constrained Device Learning (wearables, IoT) Social Impact Technologies (domestic violence detection, homelessness solutions) Awards & Grants: NSERC Discovery Grant (2020) New Frontiers in Research Fund-Exploration (2020) Petro-Canada Young Innovator Award (2019) Multiple Teaching Awards (2015-2016) Teaching & Industry: Incorporates industry experience into courses, emphasizing real-world applications Outstanding Teaching Performance Award (2016) Over 5 years of industry experience in software engineering Labs/Teams: Part of interdisciplinary teams at MRU's Faculty of Science Collaborates on NSERC-funded projects

• Network Security
• Cybersecurity
• Cloud Computing Security
• +4 more

Martine Bellaïche is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. With a B.Sc., M.Sc., and Ph.D. from Montreal, she has established herself as a leading researcher in network security and cybersecurity. She is affiliated with the Mobile Computing and Networking Research Laboratory (LARIM), where she contributes to cutting-edge research in communications systems, networks, and algorithms. Dr. Bellaïche's research spans multiple critical areas of modern cybersecurity, with particular focus on network security, prevention and detection of attacks, performance evaluation of defense systems, and defense against Denial of Service (DDoS) attacks. Her work extends to specialized domains including sensor network security, vehicular network security, cloud computing security, and Internet of Things security. Her research approach often combines theoretical foundations with practical applications, addressing real-world security challenges across various technological domains. Analysis of her recent publications reveals a strong trend toward AI-driven security solutions, particularly using deep learning and federated learning approaches for intrusion detection in IoT environments. Her work increasingly addresses emerging security challenges in blockchain technology, metaverse applications, and industrial IoT systems, demonstrating adaptability to evolving technological landscapes while maintaining focus on fundamental security principles. Supervised 5 PhD students to completion, including Alfalqi (2022), Alghamdi (2022), Abusitta (2018), Halabi (2018), and Benbrahim (2016) Mentored 6 Master's students, with research spanning DDoS detection, VANET security, and graph coloring algorithms Active participant in multiple research grants, including Discovery Grants from Polytechnique Montréal Dr. Bellaïche teaches several foundational computer science courses including procedural programming, object-oriented programming, microcomputer architecture, and data structures and algorithms, bridging theoretical knowledge with practical security applications in her teaching.

• Control Systems
• Fog Computing
• Artificial Intelligence
• +3 more

Pietro D'Agostino is a Ph.D. candidate in Computer and Control Engineering at the Polytechnic University of Turin, Department of Control and Computer Engineering (DAUIN), currently in his 38th cycle (2022-2025), while serving as an external lecturer and teaching assistant for Model-based software design courses in the Mechatronic Engineering program. His educational background includes a Bachelor's degree in Electronic Engineering (2020) and a Master's degree in Mechatronic Engineering (2022), both from the Polytechnic University of Turin. Research focuses on fog computing and artificial intelligence integration for industrial IoT, developing real-time edge-processing architectures with sandboxing mechanisms for third-party extensibility and AI-driven predictive maintenance systems that anticipate equipment failures to reduce downtime. This work bridges control systems engineering with embedded IoT technologies to enhance industrial reliability. His 2023-2025 publications demonstrate consistent innovation in industrial predictive maintenance, featuring vibration-based anomaly detection, scalable fog computing solutions, and optimized AI algorithms for resource-constrained embedded deployment, emphasizing low-latency processing and customizable industrial applications. No scientific awards were mentioned in the source material. As a Ph.D. candidate, he does not currently advise students, and research grant details were not provided. He actively contributes to the CAD - Electronic CAD & Reliability Group (DAUIN), focusing on electronic design automation and system reliability in industrial contexts.

Juan David Guerrero BalagueraView profile

Research Fellow

• AI Accelerators
• GPU Reliability
• Fault Tolerance
• +7 more

Juan David Guerrero Balaguera is a Research Fellow at Politecnico di Torino's Department of Automatic Control and Computer Science (DAUIN), affiliated with the CAD group. He holds a Ph.D. in Computer and Control Engineering from Politecnico di Torino (2024), advised by Prof. Matteo Sonza Reorda and Prof. Ernesto Sanchez. His research focuses on dependable hardware for safety-critical systems, including GPU reliability, fault tolerance, AI accelerators, and functional in-field testing. Prior to his Ph.D., he earned a Master's (2017) and Bachelor's (2013) in Electronics Engineering from Universidad Pedagógica y Tecnológica de Colombia, where he taught digital design, embedded systems, and FPGA-based image processing from 2014 to 2020. His research interests span advanced FPGA design, computational arithmetic for AI, fault effects analysis in GPUs, and reliability assessment of neural networks. Notable contributions include methods for generating self-test libraries (STLs) for GPUs, evaluating fault impacts on TCUs, and enhancing CNN robustness via dropout layer optimization. Education: Ph.D. in Computer and Control Engineering, Politecnico di Torino (2024) M.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2017) B.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2013) He has received the Ph.D. Quality Award (2023 and 2024) from Politecnico di Torino and Best Paper recognitions at DATE 2023 and DDECS 2021. His work bridges theoretical fault models with practical GPU testing methodologies, emphasizing real-world applications in AI and edge computing. Current teaching roles include collaborating on GPU programming (Master's level) and computer sciences courses (Automotive Engineering). Research collaborations involve exploring reliability trade-offs in split-computing DNNs and developing fault-aware design flows for AI accelerators.

Poonam YadavView profile

Senior Lecturer

• Internet of Things (IoT)
• Wireless Networks
• Machine Learning (TinyML)
• +4 more

Dr. Poonam Yadav is a Senior Lecturer (equivalent to Associate Professor) in the Department of Computer Science at the University of York, UK, and a visiting research fellow at the University of Cambridge's Computer Laboratory. Her research focuses on resilient IoT and edge computing systems, addressing challenges in distributed systems, privacy, and interoperability. She leads the SYSTRON Lab, exploring distributed systems and network technologies. Her work integrates machine learning, wireless networking, and bio-inspired computing to enhance system reliability. Education: PhD in Computing Research (Distributed Systems & Networking), Imperial College London (2011) M.Tech in Intelligent Systems (AI/ML), IIIT, Allahabad, India Postdoctoral Research at Imperial College London, CERN, and the University of Cambridge Research Interests: Her interdisciplinary research spans IoT, wireless networks, TinyML, citizen science, and complex systems. She emphasizes collaborative trust mechanisms (M2M, M2H, H2D interactions) and system design under resource constraints. Recent work includes resilient 6G architectures, quantum key distribution, and energy-efficient edge computing frameworks. Awards & Recognition: UKIERI Fellowship (PhD funding) Advising & Grants: Dr. Yadav has collaborated on EPSRC, TSB, and NERC projects such as Databox, OpenShare, and FUSE. She leads the SYSTRON Lab and contributes to initiatives like MedPerf (medical AI benchmarking) and the Hyperledger Fabric platform for secure data sharing in autonomous vehicles. Labs & Teams: Founder and Director of the SYSTRON Lab , focusing on distributed systems, interoperability, and network innovation.

Jin U. KangView profile

Professor

• Optical Imaging
• Biomedical Engineering
• Fiber Optic Devices
• +5 more

Jin U. Kang is the Jacob Suter Jammer Professor of Electrical and Computer Engineering at Johns Hopkins University, with a joint appointment in the Department of Dermatology at the Johns Hopkins School of Medicine. He is affiliated with the Kavli Neuroscience Discovery Institute and the Laboratory for Computational Sensing and Robotics (LCSR). His research focuses on optical imaging, sensing, and robotic systems for biomedical applications, including OCT-based surgical tools and image-guided interventions. Kang has pioneered real-time 4D OCT systems and autonomous robotic devices for microsurgeries, such as retinal and vascular procedures. His work also involves developing smart surgical tools through startups like LIV Med Tech Inc. Education: B.S. in Physics (Western Washington University, 1992); M.S. and Ph.D. in Optical Science and Electrical Engineering (University of Central Florida, 1993–1996). Prior to JHU, he worked at the U.S. Naval Research Laboratory. Research interests include fiber optic sensors, medical imaging systems, and surgical robotics. His recent work emphasizes real-time OCT-guided procedures, autonomous robotic surgery, and innovations in corneal and retinal surgeries. Over 20 patents and key awards such as the ONR Young Investigator Award reflect his contributions. Scientific Awards: Recipient of the ONR Young Investigator Award, Australian Institute of Advanced Studies Fellowship, NASA Faculty Fellowship, and Brain Korea Distinguished Faculty Fellowship. He is a Fellow of the Optical Society of America, SPIE, and the American Institute for Medical and Biological Engineering. Advising & Grants: Kang’s research is supported by grants and collaborations with industry (e.g., LIV Med Tech). He has advised numerous projects on surgical robotics and imaging systems, though specific student names are not listed here. His work spans academic and translational efforts, including ventures to commercialize medical technologies. Labs & Teams: Active in the Kavli Neuroscience Institute and LCSR. His lab develops cutting-edge imaging and robotic tools, with recent focus on autonomous systems for microsurgery and intraoperative guidance.

• Condensed Matter Physics
• Materials Science
• Quantum Phenomena
• +3 more

Maciej Dendzik is a Researcher at KTH Royal Institute of Technology, affiliated with the Department of Light and Materials Physics within the School of Engineering Sciences. His research focuses on advanced experimental techniques in condensed matter physics, including angle-resolved photoemission spectroscopy (ARPES), ultrafast dynamics, and the study of topological materials, 2D systems, and quantum phenomena. He has contributed to understanding electron-phonon interactions in superconductors, the emergence of Weyl fermions in magnetic materials, and energy transfer mechanisms in nanostructured systems. His work integrates cutting-edge spectroscopic methods with computational tools, such as machine learning for band mapping, to explore electronic structures and dynamics in novel materials. Key areas of investigation include charge density waves in kagome superconductors, ultrafast carrier dynamics in topological insulators like bismuthene, and the development of advanced light sources for time-resolved studies. While no specific awards or grants are explicitly listed, his extensive publication record reflects a commitment to advancing experimental methodologies and unraveling fundamental material properties at the nanoscale. He teaches the Degree Project in Engineering Physics at the undergraduate level, contributing to KTH's educational mission.

Benedict HerzogView profile

Researcher

• Energy-Efficient Systems
• Operating Systems
• Embedded Systems
• +3 more

Benedict Herzog is a Researcher at the Bochum Operating Systems and System Software (BOSS) group at Ruhr-Universität Bochum (RUB). Previously, he was part of the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His work focuses on energy-efficient systems, operating systems optimization, and embedded computing. Herzog holds a Master's degree in Computer Science from FAU, where his thesis explored energy demand estimation using artificial neural networks, and a Bachelor's degree in Energy-aware error correction in wireless sensor networks. His research interests span energy-aware computing, system software design, and machine learning applications for optimizing edge and embedded systems. He actively participates in academic activities, serving as a reviewer for conferences like TECS, SBESC, and EMSOFT. Herzog has taught courses such as 'Systemnahe Programmierung in C' and 'Energy-Aware Computing Systems' at FAU, and advised multiple students on topics ranging from interrupt handling overhead to Meltdown/Spectre mitigation impacts. Key technical contributions include frameworks for energy measurement integration (EnergyBudgets), system-call aggregation (AnyCall), and automated OS configuration optimization. His work bridges hardware-software co-design challenges in energy efficiency, with applications in edge computing and real-time systems.

Shivendra PanwarView profile

Professor

• Wireless Networks
• 5G/6G Technology
• Millimeter Wave and Terahertz Communication
• +4 more

Shivendra Panwar is a Professor in the Electrical and Computer Engineering Department at the NYU Tandon School of Engineering and Director of the New York State Center for Advanced Technology in Telecommunications (CATT). He holds IEEE Fellow and NAI Fellow distinctions. His research focuses on wireless networks (5G/6G, mmWave, terahertz), video distribution over wireless, AR/VR/XR applications, and R&D policy. Education: B.Tech from IIT Kanpur (1981), M.S. and Ph.D. from University of Massachusetts Amherst (1983, 1986). He co-founded the NY City Media Lab and is a member of NYU Wireless. Notable awards include the IEEE Leonard G. Abraham Prize (2004), 2011 Multimedia Best Paper Award, and Sony Research Awards (2016-2017). Recent work addresses challenges in millimeter wave systems, full-duplex cellular networks, and latency-sensitive applications. His contributions span protocol design, network architecture innovation, and economic analysis of telecom systems. He has authored/co-authored influential books on TCP/IP and network management.

Faezeh MarzbanradView profile

Senior Lecturer

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +6 more

Dr. Faezeh Marzbanrad is a Senior Lecturer and Equity Diversity and Inclusion Director in the Department of Electrical and Computer Systems Engineering at Monash University. She holds a PhD from the University of Melbourne (2016) and has expertise in biomedical signal processing, machine learning, and statistical data analysis. Her research focuses on developing affordable medical technologies for maternal, fetal, and neonatal healthcare, with an emphasis on telehealth and AI-driven solutions. Dr. Marzbanrad has led and contributed to projects such as AI-driven low-cost ultrasound systems, neonatal respiratory monitoring, and wearable patches for fetal health. She has received prestigious awards including the Veski Victoria Fellowship and the Women in AI Asia Pacific Award. Her team includes multiple PhD students and postdoctoral fellows working on cutting-edge research in neonatal care and medical device innovation. Her academic background includes postdoctoral research at Monash and international collaborations, with a focus on global health equity. Key achievements include developing NeoSSNet for real-time neonatal chest sound separation and advancing fetal monitoring technologies. Awards highlight her contributions to education and research excellence, including the Dean’s Award for Honours Supervision and recognition in the IEEE conferences. Her work bridges engineering and healthcare, addressing critical challenges in neonatal and maternal health.

• Medical Ultrasound Imaging
• Cardiac Imaging
• Blood Flow Dynamics
• +3 more

Lasse Løvstakken is a Professor at the Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU). His research focuses on medical ultrasound imaging, particularly in developing advanced techniques for blood flow analysis and cardiac imaging. Key projects include 3D ultrasound imaging of blood flow in pediatric and adult hearts, leveraging artificial intelligence and deep learning for automated measurements and diagnostic improvements. His work emphasizes the integration of AI into echocardiography, such as real-time guidance systems and automated strain analysis, to enhance reproducibility and reduce variability. Collaborative efforts span clinical validation of new imaging modalities and interdisciplinary applications, such as seabed classification using deep learning. He leads projects funded by institutions like NTNU and collaborates with international teams on innovations in cardiac mechanics, valve timing, and hemodynamic modeling. Løvstakken’s research also addresses translational challenges, including telemedicine applications of handheld ultrasound devices and automated quantification tools for clinical use. His contributions bridge biomedical engineering and clinical cardiology, aiming to improve diagnostic accuracy and patient care through cutting-edge imaging technologies.

• Robotics
• Biomedical Engineering
• Control Systems
• +3 more

Jason Gu is a Professor in the Department of Electrical and Computer Engineering at Dalhousie University, cross-appointed to the School of Biomedical Engineering. His research integrates robotics, control systems, and biomedical engineering to develop innovative solutions for mobile robotics, surgical systems, and rehabilitation technologies. His primary research domains include: Robotics : Mobile robotics, surgical robots, rehabilitation assistive devices, wireless control systems, and multi-sensor data fusion. Biomedical Engineering : Artificial eye implant control, medical robotic devices, and rehabilitation technology design. Control Systems : Real-time intelligent control, nonlinear systems theory, and embedded control applications. Alternative Energy : Development of novel energy technologies and systems. Analysis of his recent publications (2023-2025) reveals a strong convergence of AI with robotics, particularly in vision-language models for human-robot interaction, semantic SLAM for dynamic environments, and medical image processing. His work shows increasing emphasis on lightweight algorithms for UAVs, neural interfaces, and energy-efficient control systems for aerospace applications. His distinguished honors include: IEEE Canada President (2020-2021) and President-elect (2018-2019) Fellow of the Engineering Institute of Canada (FEIC) Fellow of the Canadian Academy of Engineering (FCAE) Professional Engineer (PEng) designation Professor Gu leads a dynamic research laboratory developing advanced robotic platforms including the PA10 Portable General-Purpose Intelligent Arm and B21r Mobile Robotic System. His team actively pursues real-world applications in surgical robotics, terrain perception for legged robots, and alternative energy systems through industry-academic partnerships and competitive research grants.

Yang LiuView profile

Senior Lecturer

• Data Security
• Privacy-Preserving Computing
• Federated Learning
• +3 more

Dr. Yang Liu is a Senior Lecturer in the Department of Computer Science at Swansea University's Faculty of Science and Engineering. He holds a D.Phil. in Computer Science from the University of Oxford and specializes in data security, privacy-preserving computing, and blockchain technologies. Research Focus: Dr. Liu's work centers on secure computation frameworks with specific expertise in: Federated learning systems optimization Privacy-preserving smart contract design Blockchain security and regulation Cryptographic techniques for data protection Secure machine learning applications Recent publications demonstrate methodological innovations in adaptive federated learning algorithms, regulatable blockchain architectures, privacy-enhanced recommendation systems, and security vulnerability analysis in Ethereum networks. His research bridges theoretical cryptography with practical applications in cloud/edge computing environments.