Tanweer Rashid, Ph.D.

Tanweer Rashid received his BSc in Computer Engineering from North South University, Dhaka, Bangladesh. He completed his MSc and PhD in Modeling and Simulation from Old Dominion University in Norfolk, VA, USA. He has previously worked on the development of algorithms for 2-manifold surface mesh generation and multi-material deformable surface meshes. Tanweer has also done research work in functional MRI analysis for Parkinson’s disease subjects treated with deep brain stimulation. Tanweer’s primary research interests are in machine learning/deep learning and their applications in brain aging, small vessels disease, postmortem brains and neurological disorders such as Parkinson’s and Alzheimer’s disease. He is currently working on developing deep learning algorithms for the detection of cerebral microbleeds, enlarged perivascular spaces, infarcts and other types of small vessels diseases, and for the identification of potential biomarkers related to Alzheimer’s Disease.

Hangfan Liu, Ph.D.

Hangfan Liu received PhD with honors in computer science from Peking University, Beijing, China, in 2018, and has been a post doc with University of Pennsylvania since then. His research interests include image processing, computer vision, machine learning and medical image analysis. He was a recipient of the Best Student Paper Award at the 2017 IEEE Visual Communications and Image Processing, the 2019 Doctoral Dissertation Award of Beijing Society of Image and Graphics, and a co-recipient of the Best Paper Award at the 2019 MICCAI Workshop on Clinical Image-Based Procedures.


Haykel Snoussi, Ph.D.

Image and Data Analyst

Yuhan Cui, M.Sc.

Data Analyst

Elyas Fadaee, M.D.

Research Assistant

I got a double engineering degree in electrical engineering from National School of Engineers of Sfax and École Centrale de Nantes in 2014 and a master's degree in image processing from Pierre and Marie Curie University in co-habilitation with Télécom ParisTech in 2015. 

Since November 2015, I have joined Inria Rennes - Bretagne Atlantique research center as a PhD student, collaborating mainly with the Empenn research unit and partially with Rennes University Hospital and Neuropoly laboratory in Polytechnique Montréal. I obtained my PhD degree in September 2019. The main work was processing and analyzing diffusion MRI data from several hospitals in France of the spinal cord in the context of multiple sclerosis disease. Main contributions were geometric metrics to evaluate distortion correction by measuring the alignment of the reconstructed diffusion model with the spine and novel biomarkers to predict the presence of lesions in a given vertebral level.

I completed my Bachelor’s degree in Pharmacy in Shenyang Pharmaceutical University, China. Then I finished the Master of Biotechnology program at University of Pennsylvania. After that, I started working at The Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania as a Data Analyst. My work involved processing and analysis of multi-modal magnetic resonance images (MRI) using advanced algorithms, extraction of quantitative measurements from MRI scans, applying machine learning methods to calculate imaging biomarkers that quantify brain changes due to neurodegenerative diseases and aging, and visualization and statistical analysis of clinical associations of volumetric brain measurements.

Education: Graduated from Islamic Azad University of Najafabad, Iran (2012)

Ph.D. Students

Di Wang

I started my medical imaging research since 2016 at Center for Advanced Imaging Innovation and Research of the New York University. In the subsequent year, I obtained master degree in biomedical engineering from New York University. Since 2018, I started my PhD in biomedical engineering at Research Imaging Institute of  the University of Texas Health Science Center at San Antonio. I have acquired extensive knowledge regarding medical imaging processing, statistical analysis and machine learning/deep learning. Currently, my work and interest are focused on understanding and analyzing medical imaging using innovative machine learning and deep learning methods. 

Co-supervised by Prof. Peter Fox

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