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.
I received my BS in Biology from Texas State University, San Marcos, TX in 2000. Following graduation, I began working at the Research Imaging Institute at UT Health San Antonio. I play a major role in the processing and analysis of functional and structural magnetic resonance imaging, positron emission tomography and clinical data using highly sophisticated computer systems, statistical software tools, and algorithms for multiple funded imaging projects. In addition, I train faculty, post docs, students and incoming staff procedures and software used for analysis. I enjoy learning new techniques and software for processing and interpreting neuroimaging data.
Anoop Benet Nirmala, Ph.D.
Anoop Benet Nirmala received his M. Tech degree in Signal Processing from the National Institute of Technology Calicut, India. He has completed his Ph.D. in Computer Science and Engineering from the National Institute of Technology Karnataka, India, in the topic Development of automated methods for the Optical Coherence Tomography (OCT) Image analysis. Anoop’s primary research interests are in image processing, machine learning/deep learning and their applications in Biomedical Images such as MRI and OCT images. He is currently developing deep learning algorithms for the identification of cerebral small vessels diseases from the ex-vivo MRI, which are the potential biomarkers related to Alzheimer’s Disease.
Karl Li, MD/PhD
(co-supervised with Mitzi Gonzales)
Karl received his MD/PhD from the University of Texas Health at San Antonio. For his PhD training he worked in Dr. Peter Fox's lab, where he did his dissertation on assessing functional connectivity changes in the default mode network with normal aging as well as other common comorbidities better understand normal aging without neurodegenerative disease.
My work sought to establish a functional connectivity biomarker of the default mode network to search for potential early indicators of neurodegenerative conditions.
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.
Elyas Fadaee, M.D.
(co-supervised with Mitzi Gonzales)
He graduated from the Islamic Azad University of Najafabad, Iran (2012) with an MD degree. Currently, he is a postdoc on the clinical track.
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.
Yuhan Cui, M.Sc. Data Analyst. New position: bioinforatician at CBICA.
Haykel Snoussi, Ph.D. Image and Data Analyst. New position: postdoctoral fellow at Baylor College of Medicine.