Lab Members

Maliazurina B. Saad, Ph.D.
MBSaad@MDAnderson.org
Postdoctoral Fellow

Ph.D. in Mechatronics, Gwangju Institute of Science and Technology, South Korea
M.S. in Mechatronics, Gwangju Institute of Science and Technology, South Korea
B.S. in Computer and Communication Systems Engineering, University Putra Malaysia

Maliazurina Saad started as a postdoctoral fellow in Imaging Physics in December 2020. Her research interests lie in radiomics, radio-genomics, health informatics, machine learning and deep learning. She is currently working on developing imaging biomarkers for immunotherapy in lung cancer patients. She aims to correlate imaging biomarkers with clinical outcomes and integrate imaging with genomic biomarkers for better prediction.

Morteza Salehjahromi, Ph.D.
MSalehjahromi@MDAnderson.org
Research Scientist

Ph.D. in Electrical Engineering, University of Massachusetts
M.S. in Electrical Engineering, Shiraz University, Iran

Morteza Salehjahromi is a postdoctoral fellow in the department of Imaging Physics. His primary research interest is developing computational machine learning/deep learning frameworks in cancer diagnostics, prognostics, and therapeutics using multimodality medical imaging. His current focus is to identify high-risk lung cancer patients through longitudinal analysis.

Sheeba Jenifer Sujit, Ph.D.
SJSujit@MDAnderson.org
Research Scientist
Google Scholar Profile

Postdoctoral Research Fellowship, Department of Imaging Physics, MD Anderson Cancer Center
Postdoctoral Research Fellowship, Department of Diagnostic and Interventional Imaging/ Neurology/Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center 

Ph.D. in Biomedical Engineering, Monash University, Malaysia
M.Eng. in Electronics and Communication Engineering, Bharathiar University, India
B.Eng. in Electrical and Electronics Engineering, Bharathiar University, India

As a research scientist in Imaging Physics , Sheeba Sujit is dedicated to advancing the field of oncology using cutting-edge technologies. Her research focuses on leveraging artificial intelligence and machine learning to explore tumor heterogeneity and to identify high-risk cancer patients using image biomarkers. She aims to improve patient outcomes through meaningful contributions to the field of precision medicine.

Muhammad Aminu, Ph.D.
MAminu@MDAnderson.org
Postdoctoral Fellow

Ph.D. in Mathematics (Machine Learning), University Sains Malaysia (USM), Malaysia
B.S. in Mathematics, Kano University of Science and Technology, Wudil, Nigeria

Muhammad Aminu’s research in the Wu Laboratory mainly focuses on the application of statistics and mathematics for data analysis problems in bioinformatics. Associated topics include spatial transcriptomics, single-cell data analysis and multi-omics data integration.

Rukhmini Bandyopadhyay, Ph.D.
RBandyopadhyay2@MDAnderson.org
Postdoctoral Fellow

Ph.D. in Biomedical Engineering, Jadavpur University, India
M.Tech. in Mechatronics, Council of Scientific and Industrial Research, India
B.S. in Electronics and Communication Engineering, West Bengal University of Technology, India

Rukhmini Bandyopadhyay's research interests include developing deep learning/machine learning-based novel computational framework for cancer diagnosis and prognosis using histopathological images. Her aim is to determine patient outcome by meaningful contributions to the field of lung cancer diagnosis.

Lingzhi Hong, M.D., Ph.D
LHong@MDAnderson.org
Research Scientist

M.D., Bachelor of Medicine, Nanjing University, Nanjing, Jiangsu, China
Ph.D., Doctor of Clinical Medicine, Nanjing University, Nanjing, Jiangsu, China

Lingzhi Hong is a research scientist in Imaging Physics and Thoracic/Head and Neck Medical Oncology. Her research efforts focus on identifying pathologic, immunohistochemical and genetic markers that improve the treatment efficacy of lung cancer, provide information regarding immunotherapy and provide more precise prognostic and predictive information. Throughout her career, she has published over 30 papers in high-impact, peer-reviewed journals, including Nature Communications, Journal of Thoracic Oncology, Lancet Digital Health, and Clinical Cancer Research.

Hui Li, M.D., Ph.D.
HLi27@MDAnderson.org
Research Scientist

M.D. in Clinical Medicine, Shanxi Medical University, China
Ph.D. in Oncology, Peking University, China

Hui Li is a research scientist in Imaging Physics and Thoracic/Head and Neck Medical Oncology. Her research is primarily focused on the early detection of lung cancer and exploring biomarkers associated with treatment effectiveness and prognosis in lung cancer patients. The goal of her research is to improve the prognosis of patients by identifying malignant lung nodules early and recognizing those who are likely to benefit most from a variety of anti-cancer treatments, including chemotherapy, targeted therapy, immunotherapy and local consolidation therapy.

Amgad Muneer, M.S. 
AMAbdulraheem@MDAnderson.org
Research Assistant II

M.S in Information Technology, University of Technology PETRONAS (UTP), Malaysia
B.Eng. (Hons) in Mechatronics Engineering, Asia Pacific University of Technology and Innovation, Malaysia

Amgad Muneer has a strong research focus on developing advanced machine learning and deep learning algorithms, intending to improve the prediction and effectiveness of immunotherapy responses for cancer patients. He is dedicated to making a significant contribution to the fields of bioinformatics and medical imaging through his work, which is focused on investigating imaging-based biomarkers for immunotherapy treatment planning, thereby enhancing the quality of life of cancer patients.

Eman Showkatian, M.S. 
EShowkatian@MDAmdanderson.org
Senior Research Assistant

M.S. in Medical Physics, Iran University of Medical Science, Iran
B.S. in Physics, Shahid Beheshti University, Iran

With a strong focus on developing state-of-the-art image segmentation and registration algorithms, Eman Showkatian is dedicated to improving the accuracy and efficiency of radiotherapy treatment planning for cancer patients. Through his work, he aims to make a meaningful impact in the field of medical imaging and radiation oncology, with the goal of personalized treatment planning and improving patient outcomes. Eman's passion for innovation and collaboration drives him to work closely with medical professionals and skilled researchers to develop cutting-edge solutions that have the potential to revolutionize cancer treatment.

Songhui Diao, Ph.D.
SDiao1@MDAnderson.org
Visiting Graduate Student

Ph.D. in Pattern Recognition and Intelligent Systems, University of Chinese Academy of Sciences, China
M.S. in Electronic and Communication Engineering, University of Chinese Academy of Sciences, China
B.S. in Communication Engineering, South China Normal University, China

Songhui Diao's research concentrates on developing novel computational pathology based on deep learning, advancing cancer diagnostics, prognostics and therapeutics. He seeks to harness the power of artificial intelligence to advance cancer research and enhance patient care.

Wentao Li, M.S.
WLi12@MDAnderson.org
Graduate Student Research Assistant

Ph.D. student in Biomedical Informatics, The University of Texas Health Science Center at Houston
M.S. in Statistics, University of California, San Diego
B.S. in Mathematics, Shanghai Maritime University

A passionate researcher in privacy-preserving AI and medical imaging, Wentao Li specializes in developing cutting-edge algorithms for medical imaging within a privacy-preserving framework. He strikes to connect isolated medical data islands securely, unlocking the potential of multimodal data, including genomics, imaging and clinical notes. Ongoing projects and latest publications can be found on his personal website.

Muhammad Waqas
MWaqas@MDAnderson.org
Postdoctoral Fellow

Ph.D. in Computer Science, National University of Computer and Emerging Sciences (FAST-NCUES), Pakistan
M.S. in Computer Science, Air University, Pakistan

Muhammad Waqas is a postdoctoral fellow in the department of Imaging Physics. His research is focused on enhancing multiple-instance learning (MIL) methodologies. His current work involves developing bag encoding strategies that employ deep learning architectures and ensemble methods to overcome the learning challenges that arise from sets of instances. His primary objective is to improve the theoretical foundations of MIL and provide practical solutions for complex real-world problems.