Find Academic Supervisors

• Biomedical Engineering
• Machine Learning
• Computer Vision
• +3 more

Aggelos K. Katsaggelos is a Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research focuses on biomedical imaging, machine learning, and computer vision applications in healthcare. He has collaborated extensively with interdisciplinary teams, including clinicians and engineers, to develop advanced algorithms for medical diagnosis and image analysis. Key research interests include medical image processing, deep learning for diagnostics, and computational methods in cardiology. His work spans applications such as MRI and ultrasound analysis, automated pathology detection, and multimodal sensing for health monitoring. Recent articles highlight contributions to myocardial scar quantification, lung ultrasound scoring, and AI-driven cough detection. His methodologies often combine domain-specific physics with modern machine learning techniques to solve real-world clinical challenges. Notable collaborations include projects with institutions like the University of Chicago and international teams in astrophysics and cognitive science. His work emphasizes translating algorithmic advancements into practical clinical tools.

• Robotics
• Human-Robot Interaction
• Autonomous Agent Systems
• +3 more

Mauro Dragone is an Associate Professor at Heriot-Watt University's School of Engineering & Physical Sciences, affiliated with the Institute of Sensors, Signals & Systems. He holds a BSc in Computer Science from Bologna University (1999) and a PhD from University College Dublin (2009). Before academia, he worked as a software developer and project manager. His research focuses on robotics, human-robot interaction, IoT, and Ambient Assisted Living (AAL), with leadership roles in EU projects like METRICS/HEART-MET and the OpenAAL initiative. He co-directs the MSc Robotics program and established the Robotic Assisted Living Tested lab within the UK National Robotarium. Key research interests include assistive robotics for frailty, healthcare robotics, and self-adaptive robotic systems. Notable contributions include the FP7 RUBICON project (2011-2014) and the Cognitive Assistive Robotic Environment (CARE) group. He has received awards such as the 2024 Principal's Research Impact Award and 2022 Research Team of the Year. Recent articles emphasize healthcare applications, modular robotics, and trustworthy AI. His work aligns with UN SDGs addressing healthy aging and sustainable cities.

• Machine Learning
• Computer Vision
• Human Behavior Analysis
• +5 more

Dr. Harshala Gammulle is a Research Fellow at Queensland University of Technology (QUT), School of Electrical Engineering & Robotics. She holds a PhD in Computer Vision from QUT (2019), receiving the QUT Executive Dean's Commendation for Outstanding Doctoral Thesis. Her expertise spans machine learning, computer vision, and spatio-temporal modeling for human behavior understanding. She leads interdisciplinary projects with funding from DST Group, SmartSat CRC, QLD DESI, and others. Research focuses include: human action recognition, medical anomaly detection, satellite image analysis, and AI for environmental monitoring. Key projects involve quantum-classical hybrid ML for biomedical signal analysis, disaster forecasting via hyperspectral data, and autonomous combat vision systems. She has supervised PhD/MPhil candidates in ML and quantum hybrid ML. Education: PhD (Computer Vision, QUT 2019), BSc (University of Peradeniya, Sri Lanka). Awards: WiT Emerging Achiever Technology Award finalist (2021), University Award for Academic Excellence (2015). Teaching includes units like Digital Signals and Image Processing (EGH444), and Computing & Data for Engineers (EGB103). Current grants involve QLD DESI, SmartSat CRC, and Rheinmetall Defence Australia collaborations. Active in labs like SAIVT and QUT's Early Career Research schemes.

Antoine MiechView profile

Researcher

• Computer Vision
• Machine Learning
• Natural Language Processing
• +7 more

Antoine Miech is a Researcher at DeepMind's Vision Group , with prior affiliations at Inria and Ecole Normale Supérieure where he completed his computer vision Ph.D. under Ivan Laptev and Josef Sivic . He has collaborated with researchers from Facebook AI and Google during his academic career. Research Interests span video understanding, weakly-supervised machine learning, and multimodal analysis. His work focuses on: Text-video embedding Self-supervised video representation Action localization Anticipatory video modeling Scalable multimodal learning Scientific Contributions include: HowTo100M - A massive dataset of narrated instructional videos MIL-NCE - A novel loss function for video-text alignment MEE - A model for handling heterogeneous data Context Gating - Learnable pooling architecture Awards & Recognition : Google Ph.D. Fellowship (2018) Technical Leadership : Created the LOUPE TensorFlow toolbox for feature pooling and maintained annotated video dataset catalogs. Organized the Data Science Game competition (2016-2017).

• Virtual Reality
• Augmented Reality
• Human-Computer Interaction
• +4 more

Francisco R. Ortega is a researcher at Colorado State University specializing in Virtual Reality (VR) , Augmented Reality (AR) , and Human-Computer Interaction (HCI) . His work focuses on improving user experience through multimodal interaction, cognitive load theory, and immersive analytics. Recent publications explore diverse applications including: Stress reduction via VR forest bathing Instruction methods for AR-based autism support systems Annotation design in extended reality AR notifications in cooking environments Gender-swapping VR for stereotype threat mitigation Comparative studies between AR and VR for data-driven storytelling His research integrates principles from: Computer Science Psychology Human Factors Education Technology Collaborations span institutions such as University of Florida, University of Seville, and technical teams across North America. While specific educational details remain undisclosed, his 20+ peer-reviewed publications since 2018 demonstrate deep contributions to VR/AR methodology, particularly in: Unconstrained gesture elicitation Spatial transformation challenges Immersive training systems Hybrid display environments Imperfect machine learning integration

Flora SalimView profile

Professor

• Machine Learning
• Time-series Analysis
• Spatio-temporal Data Mining
• +5 more

Flora Salim is a Professor in the School of Computing Technologies at RMIT University. She serves as co-Deputy Director of the RMIT Centre for Information Discovery and Data Analytics (CIDDA) and an Associate Investigator of the ARC Centre of Excellence in Automated Decision Making and Society. Her research focuses on human behavior modeling, machine learning with time-series and spatio-temporal data, and edge AI applications in IoT and wearables. Flora has secured over $10M in research funding from ARC, industry partners, and government bodies. Notable awards include the 2021 PACM IMWUT Distinguished Paper Award, 2019 Humboldt-Bayer Fellowship, and RMIT's 2018 Research Impact Award. She leads the CRUISE research group and has held visiting professorships at the University of Kassel and University of Cambridge. Editorial roles: Associate Editor of PACM on IMWUT, Area Editor of Pervasive and Mobile Computing Steering Committee member of ACM UbiComp Her work bridges ubiquitous computing and machine learning, with applications in urban analytics, mobility, and health monitoring. Recent projects include self-supervised learning for multimodal data and forecasting with heterogeneous time-series. Supervision areas: Deep learning for sensor data, explainable AI, and wearable-based emotion sensing Teaching programs: Master of Artificial Intelligence and Master of Data Science

Sonit SinghView profile

Lecturer

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +7 more

Dr. Sonit Singh is a Lecturer at the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney, Australia. Prior to this role, he served as a Postdoctoral Research Fellow at the same institution. He holds a PhD from Macquarie University, completed in collaboration with Macquarie. His research focuses on artificial intelligence, computer vision, natural language processing, and their applications in healthcare, particularly in medical imaging and diagnostic systems. Dr. Singh's academic background includes a strong emphasis on biomedical imaging, intelligent robotics, and computing education. His work bridges machine learning advancements with real-world clinical challenges, such as tumor segmentation, radiology report generation, and disease detection through imaging modalities like CT scans and ultrasound. His recent publications highlight innovations in 3D medical image localization, robust tumor segmentation networks, and clinical context-aware radiology report systems. These contributions underscore his expertise in integrating deep learning techniques with healthcare workflows. Dr. Singh collaborates with multidisciplinary teams, including medical professionals, to develop solutions for challenges like placenta segmentation and dietary intake monitoring using AI. While no specific awards or grants are explicitly listed, his extensive publication record reflects sustained academic engagement in high-impact areas. His research often addresses practical clinical needs, such as improving diagnostic accuracy and patient care through advanced computational methods.

Michael BronsteinView profile

Professor

• Geometric Deep Learning
• Graph Representation Learning
• Computer Vision
• +4 more

Affiliations and Roles Michael Bronstein is a Professor at the Università della Svizzera italiana (USI) in the Faculty of Informatics and the Institute of Computational Science . He holds the Chair in Machine Learning and Pattern Recognition at Imperial College London and serves as Head of Graph Learning Research at Twitter . Previously, he was affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) as an Adjunct Professor. Education Ph.D. in Computer Science, Technion–Israel Institute of Technology (2007) Visiting appointments at Stanford University, MIT, Harvard University, and Tel Aviv University Research Interests Bronstein's work focuses on geometric deep learning , graph representation learning , and non-rigid shape analysis . He pioneered methods for extending machine learning to non-Euclidean domains like graphs and manifolds. His research combines theoretical advancements in spectral geometry with practical applications in computer vision, robotics, and medical imaging. Publications Trends His articles emphasize geometric deep learning frameworks, functional maps for shape correspondence, and spectral methods for manifold analysis. Key themes include invariant representations, partial shape matching, and applications in 3D reconstruction and graph neural networks. Awards and Honors Five ERC Grants Royal Society Wolfson Research Merit Award IEEE and IAPR Fellowships World Economic Forum Young Scientist Advising and Entrepreneurship Bronstein is a serial entrepreneur, founding companies like Novafora , Invision (acquired by Intel), and Fabula AI (acquired by Twitter). His academic advising spans PhD and Master’s students in machine learning and geometry processing. Labs and Teams Active in the Institute of Computational Science at USI and leads Twitter’s Graph Learning Research team, focusing on real-world applications of geometric deep learning.

• Artificial Intelligence
• Pattern Recognition
• Biometrics
• +6 more

Venu Govindaraju is a SUNY Distinguished Professor of Computer Science and Engineering at the University at Buffalo, where he also serves as Vice President for Research and Economic Development. He founded the Center for Unified Biometrics and Sensors (CUBS) in 2003 and leads the National AI Institute for Exceptional Education, funded by a $20M NSF grant. His research focuses on artificial intelligence, pattern recognition, biometrics, and handwriting analysis, with notable contributions to postal automation, cybersecurity, and early screening for dyslexia/dysgraphia in children. Govindaraju holds IEEE and ACM Fellowships, and was named Person of the Year by the Council of Heritage and Arts of India in 2024. He has advised 47 doctoral students and pioneered foundational AI technologies impacting global industries. Research interests include: AI-driven education tools, handwriting recognition systems, document analysis, and cross-domain biometric technologies. His work bridges AI with humanities through projects like the Marianne Moore Digital Archive. Recent initiatives focus on leveraging AI to address speech-language pathologies and enhance K-12 education accessibility. Leadership roles include co-chair of SUNY’s AI Task Group and advisor to the SUNY-IBM AI Research Alliance. He is a vocal advocate for responsible AI policy, advising lawmakers at White House events and contributing to legislative briefings. His innovations are highlighted in Empire AI, New York’s $400M computing consortium.

Fenglong MaView profile

Associate Professor

• Data Mining
• Health Care Informatics
• Machine Learning
• +5 more

Fenglong Ma is an Associate Professor at Pennsylvania State University, affiliated with the Institute for Computational and Data Sciences and the Center for Socially Responsible Artificial Intelligence. His research focuses on data mining, healthcare informatics, machine learning, natural language processing, and multimodal learning. He holds a Ph.D. from the University at Buffalo (2019) and degrees from Dalian University of Technology. His work addresses challenges in federated learning, medical AI, adversarial robustness, and multimodal systems. Key contributions include innovations in quantization for large language models, federated knowledge injection, and medical vision-language benchmarking. Recent publications explore topics like collaborative fairness in federated learning, robust medical vision-language models, and adversarial attack mitigation. His research bridges theory and practical applications in healthcare, cybersecurity, and personalized recommendation systems. He leads the PSU Data Science Lab and collaborates on projects involving AI ethics, multimodal data integration, and scalable medical foundation models.

• Transportation Safety
• Automated Vehicles
• Human Factors in Transportation
• +3 more

Rakotonirainy Andry is a Professor at Queensland University of Technology (QUT), affiliated with the Centre for Accident Research & Road Safety - Queensland (CARRS-Q). His research focuses on transportation safety, automated vehicles, human factors, and intelligent transportation systems (ITS). He leads interdisciplinary projects exploring driver behavior, connected vehicle technologies, and the societal impacts of automation. Key areas include accident prevention, human-vehicle interaction, and equity in transport systems. His work integrates machine learning, simulation studies, and behavioral analysis to address challenges in road safety. Notable contributions include studies on driver stress detection, automated vehicle acceptance, and the Australian Naturalistic Driving Study (ANDS). He collaborates with institutions globally, advancing innovations like connected vehicle pilots and multimodal AI for traffic safety. Research interests span automated driving systems, vulnerable road user protection, and policy implications of emerging technologies. His findings contribute to safer transportation policies and technologies, emphasizing both technical and human-centric perspectives.

Ruihui LiView profile

Professor

• Deep Geometry Learning
• Generative modeling
• 3D Vision
• +3 more

Ruihui Li - Academic Summary Professional Overview Prof. Ruihui Li is a faculty member at the College of Computer Science and Electronic Engineering, Hunan University , specializing in Deep Geometry Learning , Generative Modeling , and 3D Vision . His work focuses on advancing 3D reconstruction and generation with high controllability, particularly in applications like point cloud upsampling and neural rendering. Education & Career Dr. Li earned his Ph.D. in 2021 (oral defense passed in June 2021). His career includes significant contributions to research in geometric deep learning and 3D computer graphics. He actively collaborates with institutions like the Chinese University of Hong Kong and Tel Aviv University on projects such as PU-GAN and SP-GAN . Research Interests His research spans cutting-edge topics in: Neural Wavelet-Domain Diffusion for 3D Shape Generation Topology-Aware 3D Mesh Reconstruction Camera-Based 3D Scene Completion Point Cloud Analysis and Upsampling Professional Activities Dr. Li has delivered invited talks at prestigious events such as the Asia Graphics Webinar and Peking University . He serves as a reviewer for top conferences (CVPR, ICCV, ECCV) and journals (TPAMI, TVCG). His research is supported by grants from the National Natural Science Foundation of China and the Chinese Academy of Sciences. Technical Contributions Key projects include: PU-GAN : A point cloud upsampling adversarial network (ICCV 2019). SP-GAN : Sphere-guided 3D shape generation (SIGGRAPH 2021). Neural Wavelet-domain Diffusion : 3D shape generation and manipulation (TOG 2024). Labs & Teams He leads a research group at Hunan University, mentoring Ph.D./MPhil students, RAs, and postdocs. His team focuses on interdisciplinary projects combining AI, computer graphics, and geometric deep learning.

Ding Ze YangView profile

Lecturer

• Industrial AI
• Deep Learning
• Data-Driven Modeling
• +4 more

Dr. Ding Ze Yang is a Lecturer in the Department of Electrical and Robotics Engineering at Monash University Malaysia. He holds a PhD (2023) and Bachelor's degree (2019) in Engineering from the same institution. His research focuses on Industrial AI, emphasizing data-driven soft sensors for industrial process monitoring, with applications in manufacturing, energy, and logistics. He has published in journals like IEEE Transactions on Industrial Informatics and Soft Robotics. Education: PhD in Engineering, Monash University Malaysia (2019–2023) Bachelor of Engineering (Honours) in Electrical and Computer Systems Engineering, Monash University Malaysia (2015–2019) Research Interests: Industrial AI, deep learning, data-driven modeling, process monitoring, autonomous systems, and soft sensor development. His work addresses challenges in predictive maintenance, process optimization, and sustainable manufacturing through AI-driven solutions. Publications: Recent work includes contributions to soft sensor modeling, transfer learning for multi-agent systems, and Kalman filter optimization. These publications highlight advancements in industrial AI applications. Collaborations: Active collaborations include projects on soft robotics, energy storage systems, and autonomous transportation. He is open to supervising PhD students in these areas.

Carmine GuidaView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Medical Image Analysis
• +2 more

Carmine Guida is an Assistant Professor in the Department of Computer Science at Pace University's Seidenberg School of CSIS. He has taught at Pace for over 5 years and directs the Game Development program. His office is located at 15 Beekman, NYC. Education: BS in Computer Science from S.U.N.Y. Stony Brook MS in Computer Science from Georgia Tech PhD in Computer Science from Pace University Research Interests: Dr. Guida's work focuses on applying computer vision and machine learning to medical challenges. Key areas include osteoarthritis diagnosis via deep learning models, medical image segmentation, and multimodal data fusion. His research emphasizes practical healthcare solutions using radiographs, MRI, and clinical data integration. Publication Focus: Recent works demonstrate consistent specialization in medical AI, particularly osteoarthritis analysis using deep learning for joint measurement, classification, and segmentation across hand/knee imaging modalities. Articles show progressive refinement of computer vision techniques for clinical applications. Academic Service: He chairs the Computer Science Curriculum Committee and contributes to university initiatives including the Metaverse Committee, eSports Task Force, student orientation programs, and undergraduate research events. Professional membership includes IEEE.

Fangneng ZhanView profile

Research Fellow

• Computer Science
• Artificial Intelligence
• Neural Rendering
• +7 more

Currently a Research Fellow at Harvard University & MIT , Fangneng Zhan specializes in Neural Rendering and Generative AI . His research focuses on developing evolutive rendering frameworks, 3D-aware generative models, and multimodal synthesis techniques. Previously, he was a postdoctoral researcher at the Max Planck Institute for Informatics under Prof. Christian Theobalt. He earned his Ph.D. in Computer Science & Engineering from Nanyang Technological University, Singapore and a Bachelor's in Communication Engineering from the University of Electronic Science and Technology of China . His work spans 3D reconstruction, robotics applications , and lighting estimation , with significant contributions to SIGGRAPH , NeurIPS , and CVPR conferences. Recent research highlights include evolutive gauge transformations for neural fields, generalizable 3D style transfer via Gaussian splatting, and multimodal synthesis frameworks leveraging pre-trained models like CLIP and Stable Diffusion. He has co-authored Top50 Popular Paper in TPAMI 2023 and organized workshops at CVPR 2024 on generative models. Scientific Awards: Top50 Popular Paper, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023 Collaborative Network: Mentored students at institutions like Harvard, NTU, and ETH Zurich. His projects include datasets for lighting estimation and real-time scene text detection systems.