Find Academic Supervisors

• Biomedical Imaging
• Optical Coherence Tomography
• Ophthalmic Diagnostics
• +2 more

Myeong Jin Ju is Assistant Professor in the Department of Ophthalmology & Visual Sciences at the University of British Columbia, with joint appointment in the School of Biomedical Engineering. Research focuses on developing advanced optical imaging technologies for vision science applications. Laboratory specializes in optical coherence tomography (OCT) systems design, signal processing algorithms, and multi-modal imaging integration. Current work aims to improve clinical ophthalmic diagnostics through high-resolution retinal and choroidal visualization, automated pathology quantification, and functional assessment techniques. Applications target diabetic retinopathy, age-related macular degeneration, and inherited retinal diseases. Recent innovations include MHz-range OCT systems, polarization-diversity techniques, and computational approaches for motion correction and image enhancement. Research bridges engineering innovation with preclinical and clinical translation in vision science.

Egor MatveyevView profile

Senior Lecturer

• Corporate Finance
• Organizational Economics
• Corporate Governance
• +4 more

Egor Matveyev is a Senior Lecturer and Research Scientist in Finance at the MIT Sloan School of Management. He holds an MA in Economics from the New Economic School (Moscow) and a PhD in Finance from the University of Rochester. His research focuses on corporate finance, organizational economics, and corporate governance, particularly examining CEO value, executive compensation, and director labor markets. Education: PhD in Finance, University of Rochester MA in Economics, New Economic School Research Interests: Matveyev explores how top executives create firm value through studies on CEO performance measurement, executive labor markets, and governance systems. His work also addresses investment option valuation challenges faced by investors and nonprofits. Program Leadership: He co-leads the MITx MicroMasters Program in Finance, an online initiative offering pathways to a MIT Sloan Master's degree. This program features courses like Foundations of Modern Finance and Derivative Markets taught by Sloan faculty. Key Themes in Research: His publications analyze CEO effectiveness, asset allocation strategies for nonprofits, and capital structure optimization under imperfect competition. Recent work emphasizes measuring CEO contributions and understanding executive labor market dynamics.

Kasra KhosoussiView profile

Senior Lecturer

• Robot Perception
• Robotic Autonomy
• Mathematical Foundations
• +3 more

Dr. Kasra Khosoussi is a Senior Lecturer (equivalent to US Associate Professor) at the University of Queensland's School of Electrical Engineering and Computer Science. He leads research on mathematical and algorithmic foundations of robot perception and autonomy, with prior roles including Robotic Mapping Team Leader at CSIRO and Research Scientist at MIT. His research focuses on: Robotic perception systems including SLAM and multi-sensor fusion Autonomous navigation in extreme environments Distributed optimization for multi-robot collaboration Probabilistic modeling for robotic decision-making Recent publications demonstrate strong emphasis on resilient SLAM systems, energy-aware robotics, multi-agent coordination, and real-time perception algorithms for challenging environments like subterranean spaces and marine contexts. Awards include: Hilti SLAM Challenge Winner (ICRA 2022) IEEE Transactions on Robotics Best Paper Honorable Mention (2021) ICRA Best Paper Finalist (2018) Professional activities include Associate Editorship for IEEE Robotics and Automation Letters and committee roles at leading robotics conferences including ICRA and RSS.

Maria OrlowskaView profile

Academic

• Workflow Management
• Database Systems
• Data Mining
• +3 more

Honorary Professor Maria Orlowska is affiliated with the School of Electrical Engineering & Computer Science at the University of Queensland. Her research focuses on workflow systems, database integration, data mining, and wireless sensor networks. She has contributed extensively to collaborative business process technologies, flexible workflow modeling, and RFID data management. Her work spans theoretical foundations in process constraints, distributed systems, and practical applications in enterprise integration and real-time data analytics. Key research areas include workflow exception handling, multidatabase integration methodologies, and optimization of dynamic processes. Her studies on sensor networks address routing algorithms and telemetry systems. She has collaborated on projects involving smart shop floors, e-learning platforms, and spatial data management. Notable contributions include methodologies for business contract compliance and service-oriented architecture advancements. Publications emphasize interdisciplinary applications of computer science principles, with a focus on real-world system implementations. Her work bridges theoretical computer science with practical engineering challenges in distributed environments.

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +5 more

Dr. Miaomiao Liu is a Research Fellow at the School of Computing, The Australian National University. Her research focuses on computer vision, 3D reconstruction, and neural rendering, with applications in robotics, autonomous systems, and renewable energy forecasting. She leads multiple projects including Next-Generation Aviation Safety Net Air Traffic Management, Machine Vision Techniques for Solar Power Forecasting, and 3D Vision Geometric Optimization in Deep Learning. Her work integrates cutting-edge techniques such as neural radiance fields, depth estimation, and self-supervised learning to address challenges in dynamic scene reconstruction, motion forecasting, and image deblurring. She has pioneered methods for mining supervision signals in dynamic regions and developing language-driven deblurring networks. Key Projects: Aviation safety systems, solar irradiance prediction, and geometric optimization in deep learning Research Themes: 3D scene understanding, human motion prediction, and neural rendering Technical Expertise: Neural networks, multi-view stereo, and physics-based modeling Dr. Liu's research has been published in top-tier venues like CVPR and IEEE conferences, with over 2,450 citations. While not explicitly listed as part of a lab, her work demonstrates strong collaboration with industry partners such as CSIRO and aerospace stakeholders. She actively supervises research students in areas like 3D vision and energy systems.

Ian BrittonView profile

Lecturer

• Performance Analysis
• Sports Coaching
• Elite Sport Development
• +2 more

Dr Ian Britton serves as Lecturer in Sports Performance Analysis at Manchester Metropolitan University's Department of Sport and Exercise Sciences since January 2024, providing performance analysis support to elite sports teams and individuals while teaching across academic levels. His educational background features a PhD from Manchester Metropolitan University investigating video-based feedback in elite sport through ethnographic methodology, with ongoing publication of related research findings. Research focuses on integrating Performance Analysis and Sports Coaching to optimize athlete and team development, emphasizing practical applications of analytical insights in high-performance environments through interdisciplinary approaches. He actively supervises undergraduate and postgraduate projects examining bowling/batting analysis, set-piece impacts, technical match demands, and scoring patterns across team and individual sports. Based at the Institute of Sport (room 1.02), Dr Britton contributes to MMU's high-performance sport ecosystem through teaching, supervision, and applied research translation.

Xia CuiView profile

Senior Lecturer

• Natural Language Processing
• Speech Processing
• Multi-modal Learning
• +4 more

Xia Cui is a Senior Lecturer in Software Engineering at the Manchester Metropolitan University, affiliated with the Manchester Metropolitan Joint Institute and the Department of Computing and Mathematics. She leads research in the Human-Centred Computing Lab and Natural Language Processing (NLP) Lab. Her expertise spans Natural Language Processing, Speech Processing, Multi-modal Learning, and Machine Learning Applications. Xia holds a PhD (Computer Science, University of Liverpool 2020), MSc (Web Science and Big Data Analytics, UCL 2015), and BSc (Internet Computing, University of Liverpool 2013). Notable projects include a KTP collaboration with University of Manchester and VoiceIQ Ltd to develop AI systems for detecting speaker vulnerability indicators. She also worked with BBC R&D on speech/music discrimination tools during her MSc. Her research trends focus on advancing NLP techniques for mental health detection, multimodal data fusion, and domain adaptation challenges. Recent work includes developing annotator ranking models for mixed-annotator datasets and multimodal approaches for vulnerability identification. She actively participates in conferences like Society of Psychotherapy Research and Cyberpsychology Annual Conference. Xia advises prospective students on MSc/PhD projects requiring Python and ML/NLP proficiency. She is a member of interdisciplinary labs exploring human-computer interaction and AI ethics.

Abhijit MahalanobisView profile

Associate Professor

• Computational Sensing and Imaging
• Video/Image Processing
• Automatic Target Recognition (ATR)
• +3 more

Dr. Abhijit Mahalanobis is an Associate Professor at the University of Central Florida (UCF), affiliated with the Center for Research in Computer Vision (CRCV). Previously, he served as a Senior Fellow at Lockheed Martin and held academic positions at the University of Arizona and the University of Maryland. He earned his B.S. from UC Santa Barbara (1984) and M.S./Ph.D. from Carnegie Mellon University (1985/1987). His research focuses on computational sensing, imaging systems, and automatic target recognition (ATR). Notable contributions include work on correlation filters, compressive sensing, and 3D imaging. He has published over 170 papers, holds four patents, and co-authored a book on pattern recognition. Dr. Mahalanobis has received prestigious awards such as the IEEE Fellow (2015), OSA Fellow (2004), and SPIE Fellow (1997). He has been honored with the Lockheed Martin NOVA Award (2005), Scientist of the Year (2006), and Innovator of the Year (1999). He has served on editorial boards for journals like Applied Optics and Pattern Recognition , and chairs for OSA and SPIE conferences. His research spans topics including infrared target detection, compressive sensing architectures, and deep learning for target recognition. The CRCV lab, under his leadership, develops advanced algorithms for defense and surveillance applications.

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

Anna Fabijańska is a Professor at the Institute of Applied Computer Science within the Faculty of Electrical, Electronic, Computer and Control Engineering at Lodz University of Technology, Poland. Her research focuses on computer vision, medical image analysis, and machine learning applications in biomedical engineering and environmental science. Her primary research interests include developing advanced algorithms for image segmentation, 3D reconstruction, and neural network applications in medical diagnostics (especially ophthalmology and pulmonology) and forestry research. She specializes in techniques like graph convolutional networks, hyperspectral imaging, and generative adversarial networks for data augmentation. Through analysis of her recent publications, Fabijańska demonstrates consistent focus on improving computational efficiency and accuracy in image-based diagnostics and monitoring systems. Her work spans industrial tomography, corneal endothelium analysis, timber identification, and glacial sediment studies. Her laboratory develops practical computer vision pipelines for healthcare and environmental applications, demonstrating interdisciplinary collaboration between computer science, medicine, and ecology.

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

Mehmet Aktas is an Associate Professor in the School of Data Science and Analytics at Kennesaw State University, part of the College of Computing and Software Engineering. His research focuses on machine learning and data science, particularly applying topological data analysis (TDA) to network science challenges such as influence maximization and network matching in social, biological, and business networks. He holds a Ph.D. in Mathematics from Florida State University and an M.S. in Mathematics from Bilkent University. His educational background includes studies under Eriko Hironaka (Florida State) and Alex Degtyarev (Bilkent). Research interests emphasize TDA tools for addressing network problems, with applications across domains. Key work includes exploring deception effects in social networks, hypergraph classification, and graph compression techniques. His GitHub repositories (e.g., hypergraphPH, deception) reflect active engagement in algorithm development for network analysis. Publications span topics like influence maximization dynamics, bot detection via persistent homology, and topological approaches to graph classification. Despite no listed awards, his work demonstrates significant contributions to TDA applications in network science. Advising/grants details are not provided, but his research portfolio indicates active involvement in computational and topological method development. Research foci include sheaf Laplacian methods, diffusion processes on graphs, and higher-order interaction analysis in complex systems. He collaborates across disciplines, blending mathematical rigor with data-driven approaches to solve real-world network challenges.

Mohammad DadashzadehView profile

Professor

• Management Information Systems
• Database Management
• IT Strategy
• +5 more

Mohammad Dadashzadeh is a Professor of Management Information Systems at Oakland University’s School of Business Administration, within the Decision and Information Sciences Department. He has held roles including Director of Applied Technology in Business (ATiB) from 2003 to 2009. His education includes a B.Sc. in Electrical Engineering (MIT), M.Sc. in Computer Science (MIT), MBA, and Ph.D. in Computer & Information Science (University of Massachusetts). Previously, he served at Wichita State University as W. Frank Barton Endowed Chair in MIS, and the University of Detroit. Research Interests : Enterprise Data Integration, MIS Audit, Strategic IT Planning, Database Management, IT Governance, and Data-Driven Decision Making. His work often focuses on practical case studies to bridge theory and application in business systems. Awards & Recognition : Recipient of multiple teaching awards, including the Board of Trustees’ Excellence in Teaching (2000), Co-op Education Adviser of the Year (2003), and MIT Teaching Excellence (1977). Recognized for editorial contributions to journals like Journal of Database Management and leadership roles in academic and professional organizations. Publications & Contributions : Authored 4 books and over 50 articles, with recent works addressing online education challenges, database design, and IT strategy. His 2023 article explores data-driven decision-making in ecotourism, while earlier works covered cybersecurity, lean manufacturing, and green IT. He has also contributed to standards like the Iranian Standard Code for Information Interchange (ISCII). Professional Engagement : Served on editorial boards, conference committees, and advisory panels globally. Co-founder of Sinasoft and active in academic consulting, having trained over 6,000 professionals worldwide through seminars.

Dengsheng ZhangView profile

Senior Lecturer

• Artificial Intelligence
• Big Data
• Image Classification
• +5 more

Dr. Dengsheng Zhang is a Senior Lecturer at Federation University Australia (formerly Monash University) and a Guest Professor at Xi'an University of Posts & Telecommunications, China. He holds a PhD in Computing (2002) and a GCHE (2006). His expertise spans over 25 years in artificial intelligence, big data, image processing, and music classification. He has published over 100 refereed papers (10,000+ citations) and authored the award-winning book Fundamentals of Image Data Mining (Springer, 2019). His research focuses on image classification, object detection, deep learning, and feature extraction. Dr. Zhang leads the Computational Science and Mathematics Group, contributing to projects like SIRBOT (Semantic Image Retrieval) and VFR (Visiting Friends and Relatives Travel Research). He serves as an Associate Editor for World Scientific's Transactions on Signal Processing. His teaching spans calculus, web design, computer networks, and project management. He has supervised 8 completed PhD students and actively engages in grants, including two ARC Discovery projects. His lab explores innovations in image retrieval, music classification, and machine learning applications.

• Nephrology
• Kidney Transplantation
• Transplant Medicine
• +3 more

Wai Lim is a Clinical Professor at the UWA Medical School (Internal Medicine) within The University of Western Australia. His primary affiliation includes the Perth campus at 35 Stirling Highway. He specializes in nephrology and transplantation medicine, contributing to UN Sustainable Development Goals through research impacting health equity and non-communicable diseases. Research interests focus on kidney transplantation outcomes, immunological challenges in transplants, vascular health in transplant recipients, and clinical practices in dialysis and organ preservation. His work spans HLA mismatch analysis, cancer screening protocols, and novel approaches to organ perfusion techniques. Key grants include leadership in the AN-INSPIRE study exploring alloantibody risks in heart disease among transplant recipients, and the Best-Parent study assessing immunological profiles for optimal kidney donors. Active projects also involve polygenic risk scores for graft loss prediction. Collaborations span international teams in Australia, Europe, and Asia, addressing global transplant medicine challenges. His research has been featured in high-impact journals like Transplant International and Clinical Kidney Journal.

Luca CosmoView profile

Associate Professor

• Shape Analysis
• Geometric Deep Learning
• Spectral Geometry
• +3 more

Luca Cosmo is an Associate Professor at Ca’ Foscari University of Venice, specializing in Shape Analysis, Geometric Deep Learning, and Spectral Geometry. His research focuses on 3D shape analysis, spectral methods, and generative models for computer vision tasks. He has contributed to projects like the Average Mixing Kernel Signature (AMKS) and isospectralization techniques, advancing robust shape representation and correspondence. Previously, he held postdoctoral positions at the Geometric Deep Learning group under Prof. Michael Bronstein and collaborated with the spin-off DigitalViews Srl on industrial projects for quality control. He actively serves on program committees for top conferences (CVPR, ICCV, ECCV) and journals (IJCV, PR). His work bridges spectral geometry with machine learning, addressing challenges in partial shape matching, adversarial attacks on deformable shapes, and latent space preservation in generative models. Key trends in his publications include advancements in 3D shape generation (e.g., UniMoGen, 3D-WAG), spectral methods for subgraph learning, and quantum-inspired approaches to shape analysis. He has organized tutorials on shape analysis and contributed benchmark datasets like SHREC’16/17. His research emphasizes practical applications in computer graphics, robotics, and industrial automation. Luca’s advising and grants involve collaborations with institutions like Oxford University (Antonio Norelli) and industry partners. His lab, GLADIA, focuses on geometric deep learning and generative models. Current work includes universal motion generation, high-fidelity 3D reconstruction, and scalable generative models for large datasets.

• Autonomous Vehicles
• Computer Vision
• Intelligent Transportation Systems
• +5 more

Jose Maria Armingol Moreno is a Full Professor at the University Carlos III of Madrid, affiliated with the Department of Systems Engineering and Automation. He serves as Director of the Master's Degree in Internet of Things: Applied Technologies and leads the Intelligent Systems Laboratory research group within the Duque de Santomauro Institute of Motor Vehicle Safety. His expertise spans autonomous systems, computer vision, robotics, and intelligent transportation infrastructure. Key research interests include vehicle safety, sensor fusion, and deep learning applications in traffic monitoring and autonomous navigation. He has authored over 100 peer-reviewed articles, with recent work focusing on 3D vehicle detection, UAV battery systems, and intelligent infrastructure for smart cities. Dr. Armingol has also led numerous projects on autonomous vehicle development, cooperative driving systems, and traffic safety technologies. His contributions include patented innovations in vehicle inspection and collision avoidance systems.