Find Academic Supervisors

Zahra NematzadehView profile

Assistant Professor

• Machine Learning in Medical Applications
• Digital Biomarkers
• AI-Driven Healthcare Solutions
• +4 more

Zahra Nematzadeh is an Assistant Professor at the Department of Electrical Engineering and Computer Science, Florida Institute of Technology. Her work bridges machine learning and healthcare, focusing on digital biomarkers and AI-driven medical solutions. She holds a PhD and Master's in Computer Science from Universiti Teknologi Malaysia. Education: PhD in Computer Science, Universiti Teknologi Malaysia Master's in Computer Science, Universiti Teknologi Malaysia Research interests include machine learning applications in medical diagnostics (e.g., laryngeal cancer detection via voice analysis), digital biomarkers for early disease detection, and interdisciplinary AI solutions. Her methods often involve hybrid models combining clustering algorithms (e.g., k-means, fuzzy C-means) with classification techniques. Publications span noise detection in datasets, medical classification systems (e.g., breast cancer diagnostics), and workflow reliability analysis using Petri nets. Recent work emphasizes high-dimensional medical data analysis through genetic algorithms and feature selection. Advising/Grants: No specific grants or advisees listed, but her research likely involves mentoring students in AI/healthcare projects. Lab involvement not detailed in provided text.

• Urban Sustainability
• Sports Injury Prevention
• System Dynamics Modeling
• +3 more

George Georgiou is an Assistant Professor in the Department of Anatomy and Surgery at the School of Medicine, European University Cyprus. His research focuses on interdisciplinary topics including urban sustainability, sports injury prevention, system dynamics modeling, and public health. He has contributed to studies on agritourism, workplace well-being for the elderly, and digital transformation in banking. His work integrates quantitative methods like structural equation modeling and factor analysis with real-world applications such as smart pedestrian networks and healthcare systems improvement. Dr. Georgiou’s publications highlight his expertise in biomechanics (e.g., ACL injury etiology), sustainable transportation frameworks, and technological innovations like the Smart Pedestrian Network (SPN) system. His research also addresses global health challenges through system dynamics models for knee osteoarthritis prevention and environmental studies on embodied carbon emissions in international trade. Key projects include developing frameworks for active mobility in smart cities, evaluating fatigue effects on athlete performance, and analyzing the strategic role of outsourcing in SMEs. Though no awards are explicitly listed, his prolific output reflects sustained academic engagement across multiple disciplines. His advising and grants focus on interdisciplinary projects bridging healthcare, urban planning, and technology. Collaborative efforts with organizations like the European University Cyprus emphasize practical solutions for sustainable development and healthcare optimization.

Shane GilroyView profile

Lecturer

• Autonomous Vehicles
• Image Sensors
• Automotive Engineering
• +3 more

Shane Gilroy is a Lecturer in Electronic Engineering at the Department of Mechatronic Engineering, ATU Sligo. His research focuses on automotive systems, particularly autonomous vehicles, image sensor technology, and object detection challenges in occluded environments. He actively contributes to improving road safety through advancements in pedestrian detection, sensor validation, and cybersecurity in connected vehicles. His work aligns with UN Sustainable Development Goals, emphasizing innovation in urban mobility and sustainable infrastructure. Gilroy’s expertise spans computer vision, robotics, and automotive engineering, with notable contributions to occlusion classification methods and lifecycle validation of automotive components. He is currently accepting PhD students to further explore these interdisciplinary areas. Key research themes include: Autonomous vehicle perception systems Occlusion challenges in road safety Image sensor performance optimization Cybersecurity for connected vehicles Lean manufacturing methodologies His publications demonstrate a strong emphasis on practical applications, from improving pedestrian detection algorithms to evaluating sensor reliability under real-world conditions. Recent work explores urban mobility solutions and the integration of machine learning models for occlusion analysis.

Houying ZhuView profile

Lecturer

• High-dimensional learning
• Statistical computing
• Modelling and visualisation
• +4 more

Dr. Houying Zhu is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. Her research focuses on high-dimensional learning, statistical computing, and modelling/visualization, with an emphasis on developing efficient methodologies for modern data analysis. She holds a PhD in Applied Mathematics from the University of New South Wales and has held research roles at City University of Hong Kong and the University of Melbourne as a Maurice Belz Research Fellow in Statistics. Dr. Zhu is Co-Director of the Statistical Modelling Research Group, providing mentorship to early-career researchers and HDR students through regular supervision and collaborative projects. She actively engages in academic leadership, serving on the Board of Directors for the IASC-ARS (2023-2027) and the SSA NSW branch council (2021-2023). Her teaching spans undergraduate and postgraduate units, including Bayesian Data Analysis, Generalized Linear Models, and Statistical Graphics. Research Highlights: Innovations in quasi-Monte Carlo methods, feature selection algorithms, and medical imaging applications. Awards: Includes the President’s Award for Leadership in Statistics (2022) and multiple funding grants (MATRIX Family Funding, SSA Fellowship). Community Engagement: Organized the MATRIX Computational Mathematics for High-Dimensional Data program (Feb 2023) and contributes to statistical education initiatives. Her interdisciplinary work bridges computational statistics and real-world applications, with recent projects addressing wildfire risk modelling, protein engineering, and 6G communication systems.

Tiago BresolinView profile

Assistant Professor

• Animal Genetics
• Livestock Genomics
• Precision Agriculture
• +3 more

Tiago Bresolin is an Assistant Professor in Animal Sciences at the University of Illinois at Urbana-Champaign, affiliated with the Center for Digital Agriculture and the National Center for Supercomputing Applications (NCSA). He holds a PhD in Animal Breeding and Genetics. His primary research focuses on integrating genomics, computer vision, and machine learning to advance livestock management and animal health. Research interests include genomic selection for cattle traits, precision phenotyping via computer vision systems, and predictive modeling for disease detection in dairy and beef cattle. Notable projects involve analyzing Nellore cattle genetics for growth and reproductive traits, developing ultrasound-based mammary gland monitoring systems, and deploying cloud computing frameworks for metabolic disorder prediction. His work bridges computational methods with agricultural challenges, emphasizing automated livestock monitoring, non-invasive biomarker detection, and data-driven breeding strategies. Collaborations span international institutions, reflecting his commitment to advancing global livestock science through interdisciplinary innovation.

• Machine Learning
• Healthcare Informatics
• Explainable AI
• +6 more

Kai-Chih Pai is an Associate Professor at China Medical University's College of Information Science and Technology, Department of Computer Science, with a distinguished research career spanning over 14 years. His work bridges computer science and healthcare, focusing on practical AI applications that address critical medical challenges. Dr. Pai's research interests center around Machine Learning applications in healthcare , Explainable AI systems , Natural Language Processing for Chinese language , and Educational Technology . His work demonstrates a strong commitment to developing AI solutions that are not only technically sophisticated but also interpretable and clinically useful. His research trajectory shows a clear evolution from educational technology applications to increasingly sophisticated healthcare AI systems. His publication record reveals significant contributions to medical decision support systems , particularly in acute kidney injury prediction , pneumonia diagnosis , and mortality prediction in critical care settings. More recently, he has been exploring cutting-edge applications of large language models for industrial knowledge management. His work consistently emphasizes the importance of model interpretability in medical contexts. Federated machine learning approaches for multi-institutional medical research Privacy-preserving healthcare technologies using homomorphic encryption Adaptive learning systems for Chinese language education Predictive analytics for critical care medicine Dr. Pai has established a productive research program with consistent publication output in high-impact venues, demonstrating expertise that spans both theoretical AI development and practical healthcare applications. His collaborative work with medical professionals across Taiwan highlights the interdisciplinary nature of his research and its real-world impact.

• Video Coding
• Energy Efficiency
• HEVC
• +4 more

Christian Herglotz is a researcher affiliated with the University of Erlangen-Nuremberg , Germany. His work focuses on energy efficiency in video coding and decoding systems, with a particular emphasis on HEVC and VVC standards. He has published extensively in IEEE journals and conferences like ICIP, ICASSP, and QoMEX, often collaborating with André Kaup and Matthias Kränzler. Key research themes: energy-aware video compression, decoding power optimization, rate-energy-distortion modeling. Co-edited special sections on deep learning-based video coding. Recent Publications (2022-2025): Explored power reduction in HDR video encoding, motion prediction for 360-degree video, and heterogeneous quantization for DNN accelerators. His studies integrate machine learning with traditional codec design to improve energy efficiency. Technical Contributions: Developed models for decoding energy estimation, analyzed carbon impact of streaming devices, and proposed methods for viewport-adaptive motion compensation. Collaborative work spans thermal imaging for power analysis and reliability-aware DNN hardware optimization.

• Computer Architecture
• High Performance Computing
• GPU Programming
• +5 more

Josie Esteban Rodriguez Condia is a Fixed-term Assistant Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is a member of the CARS@PoliTO Interdepartmental Center - Center for Automotive Research and Sustainable Mobility and serves as an invited member of both the College of Electronic, Telecommunications, and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Her research focuses on computer architecture reliability, particularly in GPU and AI accelerator systems. Key areas include functional testing, general purpose graphics processing units (GPGPUs), hardware accelerators, hardware architecture, and parallel processing. Her work addresses critical challenges in reliability assessment of AI-based automotive systems, self-test libraries for tensor cores, and hardening techniques for neural networks on GPUs. Her recent publications demonstrate a strong trend toward reliability engineering for AI hardware, with particular emphasis on automotive applications and GPU-based neural network implementations. The research spans fault injection methodologies, error modeling, and architectural solutions to enhance system resilience against soft errors and permanent faults. Dr. Rodriguez Condia actively supervises PhD students including Gustavo Vilar De Farias, Giuseppe Esposito, and Robert Alexander Limas Sierra, all working on reliability evaluation and enhancement of neural networks. She is a member of the PNRR Research Group for the National Center for HPC, Big Data and Quantum Computing (2022-2025). She teaches multiple courses including GPU Programming and High Performance Computing for both Computer Engineering and Quantum Engineering programs. Her editorial work includes serving as Guest Editor for APPLIED SCIENCES in 2024.

Gjoreski HristijanView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Healthcare Technology
• +3 more

Gjoreski Hristijan is an Assistant Professor at the Faculty of Electrical Engineering and Information Technologies (Ss. Cyril and Methodius University in Skopje), with expertise in Artificial Intelligence and Machine Learning. He leads research in healthcare technology, focusing on developing intelligent systems using wearable sensors for activity recognition, fall detection, stress monitoring, and elderly care applications. His work bridges theoretical machine learning with practical healthcare solutions. PhD (2015): Jožef Stefan International Postgraduate School, Slovenia MSc (2011): Jožef Stefan International Postgraduate School, Slovenia BSc (2010): Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies, Skopje Research interests include: Machine learning for healthcare Wearable sensor systems Activity recognition algorithms Federated learning for privacy-aware applications Smart glasses integration for health monitoring His awards highlight impactful contributions, including first-place wins in international competitions like ChallengeUP (2019) and EvAAL (2013), and the prestigious Best Young Scientist Award (2017) from the President of Macedonia. Recent work focuses on OCOsense smart glasses for facial expression monitoring and real-time health diagnostics. He actively collaborates with institutions like the University of Sussex (UK) and Jožef Stefan Institute (Slovenia), advancing interdisciplinary research in AI and healthcare.

Ricardo da Silva TorresView profile

Professor

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

Prof. Ricardo da Silva Torres is a Professor in Data Science and Artificial Intelligence at Wageningen University & Research, Netherlands. Previously, he held professorships at NTNU (2019-2024) and the University of Campinas, Brazil (2005-2019). He earned his B.Sc. and Ph.D. in Computer Science from the University of Campinas (2000 and 2004, respectively). His research focuses on eScience projects involving multimedia analysis, machine learning, databases, and visualization. Education: B.Sc. in Computer Engineering, University of Campinas, Brazil (2000) Ph.D. in Computer Science, University of Campinas (2004) Research Interests: Multidisciplinary eScience projects Machine Learning applications in sports science and agriculture Knowledge graphs and digital twins for urban and agricultural planning Computer vision for environmental monitoring and aquaculture Notable Projects: KD-BIO: Large-scale AI infrastructure for green life sciences IUMENTA: Animal digital twin framework NorDark-DT: Urban lighting digital twin He serves as Associate Editor for Pattern Recognition Letters and is an IEEE member. His work bridges computational methods with real-world challenges in agriculture, sports, and urban systems.

Yvan PetillotView profile

Professor

• Marine Robotics
• Autonomous Systems
• Sensor Processing
• +4 more

Yvan Petillot is a Professor of Robotics and Autonomous Systems at Heriot-Watt University, co-academic lead of the National Robotarium, and chair of Robotics at the Scottish Research Partnership in Engineering. His expertise spans marine robotics, autonomous systems, sensor fusion, and subsea domain control. He leads a £3M grant portfolio focused on marine robotics innovations, emphasizing multi-vehicle collaboration and AI-driven autonomy. Petillot co-founded SeeByte Ltd to translate academic research into commercial solutions and pioneered EU robotics competitions fostering talent. His research emphasizes underwater robotics, including navigation, perception, and intervention in hazardous environments. Notable achievements include developing advanced underwater imaging techniques, reinforcement learning platforms (e.g., MarineGym), and frameworks for digital twins in teleoperation. He has received the Royal Society Industry Fellowship (2013) and was part of the 2022 Research Team of the Year award. Key technical contributions include synthetic aperture sonar systems, underwater SLAM (Simultaneous Localization and Mapping), and control strategies for vehicle-manipulator systems. His work addresses challenges in autonomous exploration, environmental monitoring, and safe human-robot collaboration in marine and industrial settings.

Ladan TahvildariView profile

Professor

• Software Engineering
• Cloud Computing
• Self-Adaptive Systems
• +5 more

Ladan Tahvildari is a Full-time Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. Her research focuses on software engineering, self-adaptive systems, cloud computing, and test case prioritization. She leads the Software Technologies Applied Research Laboratory (STAR Lab), emphasizing hands-on industry collaboration and practical software solutions. Her work spans over two decades, with notable contributions to adaptive software systems, runtime adaptation frameworks (GRAF), and cloud modeling (StratusML/Adoop). She has pioneered techniques for flaky test detection (FlaKat), spatiotemporal auto-scaling (STaleX), and POMDP-based uncertainty management for security systems. Key research areas include: Self-protecting software systems Component-based software evolution Defect detection and prioritization Autonomic computing decision models Cloud infrastructure optimization Her recent work bridges academia and industry through hands-on learning approaches and frameworks like Semeru Cloud Compiler for performance enhancement. She has served as workshop chair for ACSOS 2023 and contributed to IBM tool integration in educational curricula. Her lab focuses on transforming theoretical concepts into practical tools like StarMX for self-managing systems and ReLACK for VoIP steganography. Current efforts emphasize adaptive machine learning frameworks and scalable microservice architectures.

Nawa Raj PokhrelView profile

Assistant Professor

• Data Science
• Machine Learning
• Cybersecurity
• +3 more

Nawa Raj Pokhrel is an Assistant Professor in the Department of Data Science at Xavier University of Louisiana. His research focuses on applying machine learning and deep learning techniques to solve real-world problems in environmental science, healthcare, finance, and cybersecurity. He specializes in predictive modeling for sequential data, vulnerability analysis in software systems, and developing frameworks for data-driven decision-making. His work spans interdisciplinary applications, including air quality prediction in Gulf Coast communities, stock market volatility modeling using ESG indices, and coronary artery disease diagnosis through machine learning. He has also explored the impact of news sentiment on stock prices and developed frameworks like LSTM-SDM and Deep-sdm for sequential data analysis. In cybersecurity, he has contributed predictive models for software vulnerabilities and network security risk assessment. His recent publications emphasize leveraging deep learning for environmental and financial forecasting while maintaining a strong focus on algorithmic innovation and practical implementation. No scientific awards or grants are explicitly listed in the provided information. His advising activities and lab affiliations are not detailed in the available texts.

Andrzej KordeckiView profile

Assistant Professor

• Object classification using CNNs
• Autonomous robot navigation
• Image correction methods
• +3 more

Andrzej Kordecki, Ph.D., is an Assistant Professor at the Warsaw University of Technology's Faculty of Power and Aeronautical Engineering, Department of Theory of Machines and Robots. His research focuses on computer vision applications for robotics, including object classification with convolutional neural networks, autonomous navigation systems using LIDAR and camera data, and advanced image correction techniques. He also specializes in color management systems and calibration of imaging devices. Teaching responsibilities include courses on neural networks, digital image processing, and sensors in robotics. His work has been published in journals like Journal of Intelligent & Robotic Systems and Signal, Image and Video Processing , with conference contributions at venues such as the International Conference on Machine Vision (ICMV). Research highlights include development of the OffRoadSynth dataset for autonomous ground vehicle navigation and pioneering work on vignetting correction in camera systems. His technical expertise spans both theoretical and applied aspects of computer vision and robotics engineering.

• Medical Imaging
• Nuclear Medicine
• Biomedical Engineering
• +4 more

Tianyi Zeng is an Associate Research Scientist at the Yale PET Center and junior faculty in the Department of Radiology and Biomedical Imaging at Yale School of Medicine. He holds a Ph.D. in Biomedical Engineering from the University of Chinese Academy of Sciences (2021) and a B.E. from Xi'an Jiaotong University (2016). His research focuses on advancing PET imaging technologies, particularly motion correction and reconstruction methods for the NeuroEXPLORER brain PET scanner. Key areas include deep learning applications, MRI compatibility, and improving image quality. Research Interests PET image reconstruction and motion correction Development of high-resolution PET systems (e.g., NeuroEXPLORER, SIAT bPET) Deep learning for medical imaging Multi-modal imaging (PET/MRI integration) Recent Articles Recent work emphasizes motion correction algorithms, NeuroEXPLORER performance evaluation, and semi-supervised learning in medical image segmentation. His studies address challenges in clinical PET accuracy and hardware innovation. Awards 2024 IEEE NSS/MIC/RTSD Trainee Grant 2024 SNMMI Poster Award (Third Place) 2023 Best Paper Award (2nd Place) at Machine Learning in Clinical Neuroimaging workshop Grants & Collaborations Recipient of multiple IEEE and SNMMI grants. Collaborations include United Imaging Healthcare America for multi-tracer PET studies. Labs & Teams Primary affiliation: Yale PET Center. Active in the Department of Radiology and Biomedical Imaging, contributing to translational imaging research.