Dr. Ali Gooya

Dr. Ali Gooya

I joined the University of Leeds as a Lecturer in the September 2018. I obtained a 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 tumour image analysis till 2011. Subsequently, I served as an Assistant Professor in Tarbiat Modares University, Tehran. In 2014, I was awarded an IIF Marie-Curie Fellowship for statistical modelling of morphology and function of heart in University of Sheffield, where I was promoted to a Lecturer in the department of EEE prior to joining Leeds

Research Interests: My research interest includes probabilistic machine and deep learning, variational Bayesian inference, graphical models, and medical image computing. I have a particular expertise in population imaging studies, generative models for shape and motion atlases, and computational tumour image analysMy research interest includes probabilistic machine and deep learning, variational Bayesian inference, graphical models, and medical image computing. I have a particular expertise in population imaging studies, generative models for shape and motion atlases, and computational tumour image analysis

Funded Positions:I have funded positions for one PDRA and three international PhD students. If you have got the stamina to work within a world leading image computational group, and have the track record to innovate useful tools or contribute to the state-of-the-art, please contact me.

Qualifications
- BSc (University of Science and Technology, Iran)
- MSc (University of Tehran, Iran)
- PhD (University of Tokyo, Japan)
- PGCert (University of Sheffield, UK)

Professional memberships
- Member, IEEE
- Fellow, Higher Education Academy
- Member, MICCAI Society

Current Projects: BALMORAL: Variational Basis Learning for Statistical Motion Atlases: Application to Quantitative Dynamic Cardiac Imaging

Recent Publications:

S. Shamekhi, M. H. M. Baygi, B. Azarian, A. Gooya,"A Novel Multi-scale Hessian based Spot Enhancement Filter for Two Dimensional Gel Electrophoresis Images", Computers in Biology and Medicine, 2015, accepted.

Ali Gooya, Karim Lekadir, Xenia Alba, Alejandro Frangi, "Joint Clustering and Component Analysis of Correspondenceless Point Sets: Application to Cardiac Statistical Modelling", Information Processing in Medical Imaging (IPMI), 2015, (oral presentation, pdf).

Ali Gooya, Christos Davatzikos, Alejandro F Frangi, "A Bayesian Approach to Sparse Model Selection in Statistical Shape Models", SIAM Journal of Imaging Science, 8(2), 858-887, (pdf).

Ali Gooya, Kilian Pohl, Michel Billelo, Luigi Cirillo, George Biros, Elias R. Melhem, Christos Davatzikos, GLISTR: Glioma Image Segmentation and Registration, IEEE Transactions on Medical Imaging, 31(10), pp.1941-1954, Oct.2012.

Ali Gooya, H.Liao, I.Sakuma, Generalization of Geometrical Flux Maximizing Flow on Riemannian for Improved Volumetric Blood Vessel Segmentation, Computerized Medical Imaging and Graphics, 36(6), pp.474-83, Sep 2012.

Ali Gooya, George Biros, Christos Davatzikos,Deformable Registration of Glioma Images Using EM and Diffusion Reaction Modeling, IEEE Transactions on Medical Imaging, 30(2),pp.375-90,Feb.2011.

Ali Gooya, H.Liao, K.Matsumiya, K.Masamune, Y.Masutani, T.Dohi, variational method for geometric regularization of vascular segmentation in medical images, IEEE Transactions on Image Processing, 17(8),pp.1295-312,Aug 2008.

A Gooya, H Liao, K Matsumiya, K Masamune, T Dohi. "Effective statistical edge integration using a flux maximizing scheme for volumetric vascular segmentation in MRA", Information Processing in Medical Imaging (IPMI), 2007, (oral presentation).

Contact Details

  • E-mail:
  • Address
    Room 6.03, School of Computing, E C Stoner Building, Leeds
  • City
    Leeds
  • State or Province
    West Yorkshire
  • Zip Code
    LS2 9JT
  • Country
    United Kingdom
  • Telephone
    44 (0113) 34 31949