Find Academic Supervisors

Gabriel DANCIUView profile

Lecturer

• Artificial Intelligence
• Big Data Analysis
• Intelligent IoT Systems
• +3 more

Gabriel DANCIU is a Lecturer at the Department of Electronic and Computers, Faculty of Electrical Engineering and Computer Science, Technical University of Banat. His research focuses on Artificial Intelligence, Big Data Analysis, and Intelligent IoT Systems, with particular emphasis on machine learning applications in healthcare, embedded systems, and hardware verification. His work spans disciplines such as automated ML for medical diagnostics (e.g., arrhythmia and pneumonia detection), optimization techniques in FPGA and digital design verification, and IoT-driven environmental monitoring systems. He has contributed to advancements in functional verification using genetic algorithms, adaptive image sensor scaling, and noise pollution tracking via mobile crowd sensing. Publications highlight methodological innovations in blob separation for image segmentation, RGB-D camera scene recognition, and erythema assessment for dermatological evaluation. His research bridges computer science with practical engineering challenges, emphasizing interdisciplinary problem-solving. No scientific awards or grants are explicitly mentioned in the provided texts. He advises no formal students but collaborates on projects involving verification methodologies, AI-driven debugging, and medical imaging systems.

Wei JinView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +7 more

Wei Jin serves as an Associate Professor in the Department of Computer Science and Engineering within the College of Engineering at the University of North Texas. His office is located at Discovery Park F292 with contact details including phone 940-369-7172 and email wei.jin@unt.edu. His academic profile reflects active engagement in both core computer science research and interdisciplinary collaborations. Professor Jin's primary research interests span artificial intelligence, machine learning, and natural language processing, with significant contributions to efficient memory design for data-intensive applications and Wikipedia-empowered search interfaces. His work extends into interdisciplinary domains including environmental engineering and materials science, where he applies computational methods to sustainable energy systems, electrochemical processes, and agricultural sustainability. This dual focus manifests in publications addressing both algorithmic innovation and real-world environmental challenges. Analysis of his 15 most recent publications (2024-2025) reveals two distinct research streams: computer science contributions in AI-driven memory optimization and natural language interfaces, alongside collaborative work in electrocatalysis for seawater splitting, metal recovery from battery waste, and methane emission reduction. The interdisciplinary publications demonstrate strong integration of computational modeling with experimental validation in sustainable technology development. No information regarding student advising, research grants, or laboratory facilities was provided in the source materials. Similarly, details about scientific awards or educational background remain unavailable in the current dataset.

• Bioinformatics
• Artificial Intelligence
• Computer Science
• +3 more

Jie Hao is a researcher at the Information Security Center of Beijing University of Posts and Telecommunications , with a focus on interdisciplinary applications spanning Bioinformatics , Artificial Intelligence , and Medical Informatics . His work bridges computational methods with real-world challenges in healthcare, ecology, and network optimization. Recent publications highlight his contributions to Single-cell RNA sequencing deconvolution (2025) AI-driven intergenerational communication in VR (2025) Digital health applications for COPD management (2025) Deep reinforcement learning for vehicle routing (2025) His methodological innovations include adaptive attention mechanisms for object detection (2025), memory-efficient DNN accelerators (2025), and bilevel optimization algorithms with unbounded smoothness (2024). Collaborations span institutions like University of Melbourne and Chinese Academy of Sciences , reflecting his cross-disciplinary impact.

Yi-Chang James TsaiView profile

Professor

• Smart Cities Infrastructure
• Transportation Systems Engineering
• Pavement Preservation and Management
• +5 more

Dr. Yi-Chang James Tsai is a Professor and CISE Group Leader at Georgia Institute of Technology's School of Civil and Environmental Engineering. He specializes in smart cities infrastructure, transportation systems engineering, and pavement preservation using advanced sensing technologies. His work has significantly impacted Georgia’s pavement management systems, including the development of a GIS-based pavement preservation system for GDOT. He holds the prestigious Chinese Chang Jiang Scholar title (2009) and received an innovation award for GIS-enabled ship recognition (2007). His research integrates emerging technologies like 3D laser, LiDAR, and AI to enhance infrastructure resiliency, roadway safety, and asset management. Key projects include predictive safety modeling, autonomous vehicle integration, and sustainability of transportation networks. Dr. Tsai leads the Smart City Infrastructure (SCI) Lab, focusing on infrastructure condition assessment, deterioration analysis, and optimization strategies. Education: Doctorate in Civil Engineering (prior to Georgia Tech faculty role) Grants: Multiple GDOT-funded projects on pavement preservation, HFST implementation, and sensor-based safety systems Dr. Tsai’s work emphasizes data-driven decision-making, cost-effective maintenance, and innovation in transportation infrastructure. His lab has pioneered technologies for automated sign detection, pavement crack analysis, and real-time safety scoring. Key Achievements: Developed GDOT’s pavement preservation system managing 18,000 miles of highways Advanced AI-based crack detection and 3D imaging techniques Leader in roadway safety audits and curve sign optimization The Smart City Infrastructure Lab collaborates with industry partners to address challenges in infrastructure sustainability, resiliency, and smart mobility solutions for aging populations and energy efficiency.

Gilles PerrouinView profile

Research Professor

• Software Engineering
• Testing
• Artificial Intelligence
• +3 more

Gilles Perrouin is a Research Professor in the Faculty of Computer Science at the University of Namur, affiliated with the Research Center on Information Systems Engineering and the Namur Digital Institute. His expertise spans Software Engineering, Testing, Artificial Intelligence, and Machine Learning, with a focus on software product lines, configuration management, and automated testing techniques. He leads projects such as SQUAL.AI (Software Quality through Artificial Intelligence) and SmartTune (Hyper-parameter Optimization for Deep Learning). Perrouin has received awards including the Distinguished Reviewer Award (2023 and 2019) and the VAMOS 2024 ten-year Most Influential Paper Award. He actively participates in organizing conferences like the Belgium-Netherlands Software Evolution Workshop and contributes to initiatives promoting diversity in science, such as Women and Girls in Science events. His research involves advancing testing methodologies for complex systems, exploring energy-efficient neural architectures, and addressing fairness in machine learning. Collaborations span international institutions, reflecting his global impact in software engineering and AI.

• Machine Learning
• Biomedical Engineering
• Human-Computer Interaction
• +4 more

Woojin Kim is a Professor in the Department of Mathematics at Duke University, Durham, NC, USA. His research spans interdisciplinary domains including Machine Learning , Biomedical Engineering , Human-Computer Interaction , and Wireless Sensor Networks . He has made significant contributions to medical imaging analysis , autonomous driving systems , and AI in education , with recent work focusing on fairness evaluation in machine learning and vision-language models for healthcare applications . His publications demonstrate expertise in: AI-driven medical diagnostics (CT scans, MRIs) Exoskeleton and robotic systems Autonomous vehicle transition dynamics Privacy-preserving clinical data processing Memory hardware testing frameworks Knowledge tracing algorithms in education Recent article trends highlight increasing emphasis on AI ethics (fairness evaluation), multimodal models (vision-language), and knowledge graph applications for scientific data organization. Collaborations with interdisciplinary teams across biomedical, engineering, and computer science domains underscore his cross-domain impact.

• Neural Networks
• Medical Image Analysis
• Computer Vision
• +5 more

Zhigang Li is a Professor in the Department of Computer Science at South China University of Technology's School of Computer Science and Engineering. His research spans multiple technical domains with significant contributions to neural networks, medical imaging, computer vision, and sensor technologies. Recent collaborations include work with Northwestern Polytechnical University, Hong Kong University of Science and Technology, and various medical research institutions. Dr. Li's research interests focus on neural network architectures, particularly small-world and feedforward networks for system modeling and medical applications. His work bridges computer science with practical applications in healthcare (EEG analysis, schizophrenia detection, liver transplant allocation), environmental monitoring (wastewater treatment), and engineering systems (CMOS image sensors, UAV networks). His research demonstrates strong interdisciplinary connections between theoretical computer science and real-world problem solving. Analysis of his recent publication trends shows increasing focus on medical applications of AI, with significant work in brain functional network analysis, depression recognition, and schizophrenia detection. His technical contributions include novel neural network architectures, efficient sensor systems, and advanced signal processing techniques. The publications reveal a consistent pattern of high-quality output in top-tier journals across multiple disciplines. Dr. Li has received recognition through publications in prestigious venues including IEEE Transactions, Medical Image Analysis, and Expert Systems with Applications, though specific awards aren't documented in the provided bibliography. His work demonstrates significant impact across multiple fields, particularly in applying computational methods to healthcare challenges. His research program includes collaborations with medical researchers for brain imaging applications, electrical engineers for sensor development, and computer scientists for network architecture design. Current projects appear focused on multi-view brain network analysis, energy-efficient sensor systems, and medical AI applications with potential clinical impact.

Slavche PejoskiView profile

Professor

• Wireless Communication Networks
• Energy Harvesting
• Federated Learning
• +3 more

Slavche Pejoski is a Full Professor at the Faculty of Electrical Engineering and Information Technologies (FEIT) of the Ss. Cyril and Methodius University of Skopje, specializing in the Department of Telecommunications. He holds a PhD in Telecommunications (2015), Master's (2010), and Bachelor's (2007) degrees from the same institution. His academic roles include Assistant (2014–2016), Assistant Professor (2016–2020), Associate Professor (2020–2025), and Full Professor (since 2025). His research focuses on wireless communication networks , energy harvesting , federated learning , intelligent reflecting surfaces (IRS) , and network optimization . He has pioneered work on optimizing resource allocation in IRS-assisted networks, covert communications, and delay-minimization in federated learning. Publications emphasize ALOHA protocols , UAV-based communication systems , and RF energy harvesting . Recent work (2022–2025) highlights advancements in covert communications and IRS-enhanced networks . He received the Best Paper Award at TELFOR 2015 for MRI imaging research. His grants and collaborations span industry partnerships, emphasizing practical network implementations. Labs/Teams: Active in FEIT's Institute of Telecommunications , leading projects on next-generation wireless systems and AI-driven networking.

• Empirical Software Engineering
• Software Quality
• Requirement Engineering
• +3 more

Deepti Mishra is a Professor at the Department of Computer Technology and Informatics at NTNU, affiliated with the Faculty of Information Technology and Electrical Engineering. She leads the Intelligent Systems and Analytics Research Group and the Educational Technology Laboratory. Previously, she held positions as Associate Professor at Monash University (Malaysia) and Assistant Professor at Atilim University (Turkey). Her research focuses on Empirical Software Engineering, Human-Robot Interaction, and Sustainability. Current projects include collaborative research-based education for wind farms and marine plastic pollution solutions, funded by The Research Council of Norway and DIKU. Her research interests span Human-Computer Interaction, Software Quality, and Requirement Engineering. Recent work emphasizes social robotics applications in healthcare, stress mitigation, and educational environments. She actively contributes to the Excited Centre for Excellent IT Education and the NTNU Active Aging Team. Over 50 peer-reviewed publications demonstrate her expertise in AI-driven robotics, edge computing, and software ecosystem decision-making. Publications highlight innovations in gesture-controlled robotics frameworks, multi-pose action recognition systems, and ROS-based human-like robot actions. Key methodologies include systematic reviews, multi-criteria decision analysis, and deep learning architectures. Ongoing projects aim to bridge technology and societal challenges through interdisciplinary collaboration.

Anthony H. AletrasView profile

Professor

• Cardiovascular Magnetic Resonance Imaging
• Fetal Diagnostics
• Medical Imaging Technologies
• +3 more

Anthony H. Aletras is a dual-affiliated academic serving as a Researcher at Lund University's Lund Cardiac MR Group (Medical Faculty) and as Director/Professor of Biomedicine and Imaging Technologies at Aristotle University of Thessaloniki's School of Medicine. He holds a BSc in Electrical Engineering from Aristotle University and a PhD in Biomedical Engineering from The Ohio State University (1996). His postdoctoral work at NIH's Laboratory of Cardiac Energetics (until 2010) and prior role as Chairman/Professor at University of Thessaly (until 2014) further highlight his academic trajectory. Research focuses on cardiovascular MRI technology, fetal diagnostics, and innovations like DENSE strain imaging and CEST contrast agents. His work contributes to UN SDG goals in health and well-being. Notable achievements include pioneering Area-At-Risk determination via intramyocardial edema and advancing CMR perfusion mapping techniques. Recent projects include an advanced fetal imaging initiative (Swedish Research Council, 2024–2028) and technical developments in exercise cardiac MRI. His 91+ publications span MRI simulation, fetal blood oxygenation measurement, and infarct quantification validation across centers/vendors. Award-winning mentorship includes supervising student T. Lala in MRI research. His labs integrate medical imaging with AI and computational methods, emphasizing cloud-based simulation tools like coreMRI. Active collaborations span global institutions, reflecting his leadership in cardiovascular imaging innovation.

Dick BotteldoorenView profile

Professor

• Acoustic Modeling
• Noise Mapping
• Environmental Sensor Networks
• +8 more

Dick Botteldooren is a full Professor of Acoustics at Ghent University, Belgium, leading the WAVES (Wireless, Acoustics, Environment & Expert Systems) research unit. He serves as I-INCE vice-president for Europe and Africa and previously held leadership roles as Editor-in-Chief of Acta Acustica (2004-2013) and president of the Belgian Acoustical Society (until 2018). His educational background includes: MSc in Electronic Engineering (1986, Ghent University) PhD in Applied Science (1990, Ghent University) Professor Botteldooren's research spans acoustic modeling, noise mapping, environmental sensor networks, computational intelligence, and human-inspired machine listening. His work integrates biomonitoring (particularly EEG), health impact assessment of sound, urban sound planning, and soundscape design with strong policy implications. Recent focus areas include AI-driven analysis of animal vocalizations and therapeutic soundscape design for dementia care. Analysis of his 2021-2025 publications reveals three dominant trends: (1) Machine learning applications in bioacoustics (especially broiler chicken vocalization monitoring for welfare assessment), (2) EEG-based attention detection systems using spiking neural networks and cross-modal transfer, and (3) Soundscape augmentation for dementia care through auditory processing-driven environmental interventions. His work consistently bridges computational methods with real-world acoustic challenges. His scientific recognition includes: Fellow of the Acoustical Society of America Professor Botteldooren has advised national and international health councils and noise policymakers, translating research into practice. His work has received sustained funding from the Belgian National Fund for Scientific Research and other international bodies, supporting both fundamental acoustics research and applied policy development. The WAVES research unit under his leadership conducts interdisciplinary work combining wireless sensor networks, computational intelligence, and human-centered acoustics, with active projects in urban noise monitoring, bioacoustic surveillance, and neuroacoustic applications.

Laurent VanbeverView profile

Associate Professor

• Network Programmability
• Internet Routing
• Network Verification
• +5 more

Laurent Vanbever is an Associate Professor at ETH Zürich and heads the Networked Systems Group (NSG). He specializes in network programmability, Internet routing, and network security, aiming to make networks more performant and manageable. Previously, he held a postdoctoral position at Princeton University and earned his PhD from the University of Louvain. Education: PhD in Computer Science, University of Louvain (2012) Postdoctoral Researcher, Princeton University (2012–2014) MSc in Computer Science, University of Louvain (2008) MSc in Management, Solvay Brussels School (2010) Research Interests: His work bridges theory and practice, focusing on network programmability, routing protocols, and sustainable networking. Recent projects include NetFabric, an ETH spin-off for network observability, and studies on energy-efficient router design (e.g., Fantastic Joules ) and BGP reconfiguration ( Taming the transient ). Key contributions include xBGP, Magnifier, and SP-PIFO. Awards: Best Paper Award at HotCarbon '24 SIGCOMM 2015 Best Paper Award Multiple IETF/IRTF Applied Networking Research Prizes Advising and Labs: Supervises PhD students (e.g., Benjamin, Weiran) and manages the NSG lab. Collaborates with NetFabric, offering student projects in network observability and sustainability. Labs & Teams: Leads the NSG and co-founded NetFabric, focusing on next-gen network observability using AI. Active in teaching Communication Networks and Advanced Topics in Communication Networks .

Matthias JanetschekView profile

Associate Professor

• Computer Science
• Cybersecurity
• Distributed Systems
• +5 more

Matthias Janetschek holds the position of Associate Professor at the Management Center Innsbruck (MCI), Austria, since September 2020. Previously, he served as a Lecturer at MCI (2018–2020) and was a Co-Founder/Senior Developer at Felix Solution UG (2013–2015). He earned a PhD in Computer Science from the University of Innsbruck (2011–2019), alongside earlier degrees in Computer Science (BSc 2002–2007, MSc 2007–2011). His research focuses on distributed systems, workflow management, cybersecurity, and IoT , with notable contributions to scientific workflow execution on cloud/manycore architectures and AR/VR applications in education. He has supervised over 18 bachelor/master theses addressing topics like IoT security, reinforcement learning, and edge computing. Teaching responsibilities include courses such as Distributed Systems , Virtual Reality , and Computer Architecture at MCI. He has also completed further training in online education tools (e.g., Sakai, Adobe Connect) and corporate communication. His publications span journals like Multimodal Technologies and Interaction and conference proceedings on workflow systems, cloud computing, and AR usability. Current work emphasizes large-scale IoT security assessments and edge computing optimizations.

Luigi PannocchiView profile

Researcher

• Computer Networking
• Real-Time Systems
• Autonomous Systems
• +3 more

Luigi Pannocchi is a Postdoctoral Researcher at Sant'Anna School of Advanced Studies since September 2021. His research focuses on optimizing network infrastructures, virtualized systems, and autonomous systems. He holds a PhD from 2015 to 2018, with a strong emphasis on real-time systems, embedded architectures, and control frameworks. Research interests include datacenter optimization, virtual machine (VM) placement in NFV environments, AI-driven network management, and the integration of real-time systems with autonomous robotics. His work bridges theoretical models with practical implementations in cloud computing and embedded systems. Recent publications highlight advancements in VM placement algorithms using logic programming, AI applications in network virtualization, and optimization techniques for NFV infrastructures. His 2023 IEEE TCCPS Early-Career Award recognizes contributions to softwarized network services and real-time systems. Key contributions include frameworks for autonomous systems prototyping, power consumption modeling in embedded systems, and HIL testing methodologies for multi-vehicle systems. His research spans interdisciplinary areas such as robotics, cloud engineering, and real-time computing.

• Thoracic Oncology
• Medical Imaging
• Diagnostic Imaging
• +5 more

Christopher Gange is an Assistant Professor of Radiology & Biomedical Imaging at Yale University, affiliated with the Yale School of Medicine. He serves as Fellowship Director for Thoracic Radiology and specializes in thoracic oncology imaging. His work focuses on improving diagnostic accuracy through advanced imaging technologies and machine learning applications. Education: BS in Biomedical Engineering, Boston University (2008) MMS in Medical Science, Drexel University (2008) MD in Medicine, Drexel University (2012) Radiology Residency, University of Rochester (2017) Thoracic Radiology Fellowship, Brigham and Women's Hospital (2018) Research Interests: Dr. Gange’s research integrates artificial intelligence and health informatics to optimize lung imaging techniques. He has pioneered studies on chest tomosynthesis advancements, pandemic-related imaging challenges (e.g., COVID-19), and incidental findings in lung cancer screening. Publications: His work spans over 20 peer-reviewed articles, emphasizing innovations in chest imaging, pandemic data analysis, and thoracic disease diagnostics. Key themes include machine learning for CT interpretation, pandemic-era imaging safety protocols, and standardized reporting frameworks. Advising & Grants: While no advisees are listed, his collaborative research teams include experts like Babina Gosangi and Ami Rubinowitz. He has contributed to multi-institutional studies funded by NIH and industry partnerships. Labs/Teams: Affiliated with Yale’s Cardiothoracic Imaging group and the Program for Innovation in Imaging Informatics, focusing on AI-driven radiology solutions and clinical data registries.