Dr Ali Gooya
- Position: Lecturer in Computer Science
- Areas of expertise: Deep Learning for Medical Vision, Cancer and Cardiac Image Analysis, Probabilistic Graphical Models, Population Imaging, Shape and Motion Analysis
- Email: A.Gooya@leeds.ac.uk
- Phone: +44(0)113 343 1949
- Location: 2.23B Bragg Building
- Website: Allen Turing Page | Googlescholar | Researchgate
I joined the University of Leeds as a Lecturer in the September 2018. I obtained an MSc in Bioelectric Engineering from Tehran University and a PhD in Information Science from the University of Tokyo, supported by a Japanese Monbusho scholarship. In 2008, I was awarded a post-doctoral fellowship by Japan Society of Promotion of Science and shortly after, I moved to the University of Pennsylvania, USA, and worked on tumor image analysis till 2011. Subsequently, I served as an Assistant Professor at Tarbiat Modares University, Tehran. In 2014, I was awarded an IIF Marie-Curie Fellowship for statistical modeling of morphology and function of the heart in the University of Sheffield, where I was promoted to a Lecturer in the department of EEE prior to joining Leeds. I am an Allen Turing Fellow.
Ali Gooya’s research interest broadly lies in the intersection of machine learning, computer vision, and medical imaging and consists of probabilistic deep learning, reinforcement learning, with the target applications in cancer image analysis, computer-aided decision support systems, prediction and marker discovery, statistical inference on populations, and computational anatomy. His research vision is to aspire to unsupervised machine learning for AI in healthcare, as expert annotations in this particular field are sparse. He is particularly experienced in creating methodologically innovative deep Bayesian frameworks, often involving rigorous mathematical modeling, as evidenced by his publications in IEEE TPAMI as the first author.
Software and Tools - I have developed multiple tools that are available for free academic use. If you use them, please cite the corresponding references. I would be very thankful!
- A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment (MedIA, 2022)
- GLISTR: Glioma Image Segmentation and Registration, Model Personalization (IEEE TMI, 2012)
- Variational Bayesian Mixture of Probabilistic PCA for Shapes from Point Sets (IEEE TPAMI, 2018)
- Group Wise Point Set Registration and Statistical Shape Modeling (SIAM, 2015)
- BSc (University of Science and Technology, Iran)
- MSc (University of Tehran, Iran)
- PhD (University of Tokyo, Japan)
- PGCert (University of Sheffield, UK)
- Member, IEEE
- Allen Turing Fellow
- Fellow, Higher Education Academy
- Member, MICCAI Society
I will be focusing on Machine Learning and Artifical Intelligence.
Research groups and institutes
- Computational Medicine