Dr Sharib Ali

Profile

I have over 10 years experience on working on various medical and biomedical data (mostly imaging) analysis. I have master’s degree by research in Computer Vision from University of Burgundy, France (2010-2012) with thesis on “Retinal image analysis from fundus imaging”, a collaborative project between Oak Ridge National Laboratory, USA and Le2I, Le Creusot at the University of Bourgogne. I obtained my PhD in image analysis (awarded in 01/2016) from the University of Lorraine from CNRS - CRAN laboratory at Nancy, France. I developed robust computer vision algorithms for monitoring bladder cancer progression by accurately creating mosaics from endoscopic videos. The key element in this work was to enable computer to match reliable features based on frame motion and perform mosaicking of the surface under observation (Thesis here).

Prior to joining University of Leeds as a lecturer (assistant professor), I worked as a post-doctoral researcher at the Department of Engineering Sciencee, University of Oxford from 2018 – 2022 (over 4 years). I currently hold a visiting fellow position here. I designed robust models for computer assisted endoscopy in gastroenterology, especially the oesophagus. The work resulted in several high-quality publication and patents. I led various endoscopy related project developments in close collaboration with consultant gastroenterologists from the Oxford NHS University Hospitals. I also co-supervised two DPhil students, one graduated and one working towards thesis submission. I was also appointed as a post-doctoral researcher at Biomedical and Computer vision group of Prof. Karl Rohr at the University of Heidelberg and DKFZ, Germany (2015-2018). During my work there I collaborated extensively with neuroscience researchers (led by Prof. Katrin Amunts) and physicists (led by Markus Axer) at Forschungszentrum, Jülich, Germany. I designed mathematically plausible phyics-based deformable multi-modal image registraiton techinque that allowed for precise 3D reconstruction of ultra-high resolution (1.3um) 2D histology brain images between CCD acquired image and high-throughput polarized light microscopy (more info here).

I have worked as researcher in different capacities in leading research teams in France, Spain, Germany, Switzerland and the UK. I am an initiator and lead organiser of series of endoscopic computer vision challenge that started in 2019 (follow-us here). Together with Prof. Adrien Bartoli and colleagues, I have intiatiated P2ILF challenge at the MICCAI 2022. I am also organising member of  a series of Workshop on Cancer Prevention, Detection and Intervention workshop at MICCAI. I am a founding member of NAAMII, Kathmandu, Nepal and holds a volunteering adjunct research scientist position to train selected students from LMIC listed countries (more info here). I have supervised several research interns and currently supervising/co-supervising PhD students on topics around artificial intelligence in medical and biomedical image analysis including 3D/2D vision-based automations and immersion technologies.

Current PhD students:

• Edward Ellis
• Raneem Toman
• Pedro-Chavarrias-Solano 
• Gerardo Loza Galindo (Co-supervisor @ Univ. of Leeds)
• Ziang Xu (Co-supervisor @ Univ. of Oxford)
• Mansoor Ali (Co-supervisor @ Tec de Monterrey)

Graduated PhDs/DPhis:

Soumya Gupta (Thesis at: University of Oxford, graduate in 2022)

I also supervise/support LMIC students as volunteer position at NAAMII, Nepal.

I am actively involved in reviewiewing for several international journals (e.g., nature communication, nature biomedical engineering, IEEE TMI, Medical Image Analysis, Pattern Recognition..), and conferences (MICCAI, IEEE ISBI, IPCAI, ICPR..). I also serve as chair and panel member for international conferences and workshops. I have published in major journals and conferences including medical image analysis, pattern recognition, IEEE TNLLS, CVIU, Scientific reports, MICCAI conference, IEEE ISBI, IEEE EMBC, ICPR, and more. 

Public activitiy (available on Spotify): My recent podcast interview highlighting the need for multi-modality, multi-center approach for tackling generalisability and bias in AI for medical image analysis. 

Responsibilities

• Teaching/Research

Research interests

My research interest lies in solving computer vision and image processing related problems which includes building new mathematical models and implementation of the methods for both research purposes and practical use. I mostly work around applications in medical and biomedical image analysis and multimodal data analysis. I am keen towards learning about new technologies and open to meeting new people, establishing interesting and innovative collaborations (both academia and industry), mentoring students and looking forward for new and exciting opportunities.

I have an extensive experience in building cutting edge computer vision technologies for object detection, semantic segmentation, image restoration, depth estimation, optical flow, image registration and 3D reconstruction. I have developed tools in these topics with both classical mathematical models and deep learning models. I am a passionate researcher with knowledge, energy and enthusiasm to contribute towards healthcare technologies that can benefit patients and assist clinicians around the world. I am a promotor of translational research and equality in access to digital healthcare for all. 

Qualifications

• PhD in medical image analysis from University of Lorraine, France
• MSc in Computer Vision (by research), University of Dijon, France

Professional memberships

• IEEE
• MICCAI Society
• IEEE EMBS

Student education

Deep Learning

Research groups and institutes

• Leeds Cancer Research Centre

Current postgraduate researchers

• Edward Ellis
• Raneem Toman
• Pedro Chavarrias
• Omar Choudhry

Projects

<li><a href="//phd.leeds.ac.uk/project/2145-federated-learning-for-tackling-multimodal-and-class-imbalance-problems-in-healthcare">Federated learning for tackling multimodal and class-imbalance problems in healthcare</a></li> <li><a href="//phd.leeds.ac.uk/project/2041-mixed-reality-in-surgery">Mixed reality in surgery</a></li>