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Maliazurina B. Saad, Ph.D.
Instructor
MBSaad@MDAnderson.org

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

In December 2020, Maliazurina Saad, a machine learning scientist, joined the Wu Lab in the department of Imaging Physics at MD Anderson Cancer Center. Her research focuses on developing imaging biomarkers for immunotherapy in lung cancer by integrating radiomics, genomics and machine learning.

Muhammad?Aminu, Ph.D.
Instructor
MAminu@MDAnderson.org

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

Muhammad's research focuses?on applying statistical and mathematical methods to bioinformatics, with an?emphasis on spatial transcriptomics, single-cell analysis and multi-omics data?integration.

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

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

Morteza's research focuses on developing machine learning frameworks for cancer diagnostics and prognostics using multimodal medical imaging. His current work aims to identify high-risk lung cancer patients through longitudinal analysis.

Kavitha Muthu Subash, Ph.D.
Research Scientist
KMuthu@MDAnderson.org

Ph.D. in Information Engineering, Hiroshima University, Japan
M.S. in Computer Science, Madurai Kamaraj University

Kavitha's research focuses on developing robust and adaptive AI systems for lung cancer detection, with a particular interest in continual learning, foundation models and self-supervised representation learning.

Amgad Muneer Abdulraheem, M.S.
Senior Research Assistant
AMabdulraheem@MDAnderson.org

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

Amgad¡¯s research focus is on developing machine and deep learning algorithms to predict immunotherapy response and treatment-related toxicities in lung cancer, including radiation-induced pneumonitis and immune-related adverse events.

Anas Zafar, B.S
Research Assistant II
AZafar2@MDAnderson.org

B.S. in Computer Science, National University of Computer and Emerging Sciences (FAST-NUCES), Pakistan.

As a research scientist in the Wu Lab, Anas's work focuses on quantitative analysis of PET/CT imaging using radiomics and machine learning to identify imaging biomarkers for tumor heterogeneity and treatment response. He is particularly interested in image segmentation, feature extraction and computational modeling to advance personalized medicine.

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

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

Rukhmini's research focuses on computational pathology and the application of machine learning in lung cancer oncology. She is currently developing deep learning models for whole slide image analysis and integrating histopathology, radiomics and clinical data to improve cancer diagnosis, prognosis and treatment stratification.

Muhammad Waqas, Ph.D.
Postdoctoral Fellow
MWaqas@MDAnderson.org

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

Muhammad's research focus is on deep learning and multiple-instance learning (MIL) for whole-slide image analysis, particularly in early lung cancer detection. He is developing novel bag-encoding strategies and ensemble methods to  improve diagnostic accuracy and address key challenges in multi-modal biomedical data integration.

Daniel Kwabena Marri, Ph.D. 
Postdoctoral Fellow
DKMarri@MDAnderson.org

Ph.D. in Biomedical Engineering, Michigan State University
M.S. in Mathematical Sciences, African Institute for Mathematical Sciences (AIMS), Rwanda
B.S. in Mathematics, University of Energy and Natural Resources, Ghana 

Daniel¡¯s research focuses on systems biology and computational modeling, applying statistical and mathematical methods to bioinformatics, with an emphasis on spatial transcriptomics, single-cell analysis and multi-omics data integration in cancer research.

Ivan Coronado, Ph.D.
T32 Postdoctoral Fellow
ICoronado1@MDAnderson.org

Ph.D. in Biomedical Informatics, Â鶹ӳ»­ Health Science Center at Houston, Houston, TX
M.Eng. and B.S. in Electrical Engineering, Texas A&M University, College Station, TX

Ivan¡¯s research focuses on artificial intelligence and deep learning for medical image processing, segmentation and clinical outcome prediction in lung cancer. He is currently working on survival analysis and biomarker identification to improve risk stratification in small cell lung cancer, with additional interests in AI-driven analysis of X-ray imaging.

Hui Xu, Ph.D.
Postdoctoral Fellow
HXu12@MDAnderson.org

Ph.D. and B.S. in Biomedical Engineering, Southern Medical University, China

Hui's research focuses on radiomics, quantitative imaging analysis and data-driven predictive modeling. She is currently working on identifying imaging biomarkers to assess response to cancer therapies in lung cancer patients ¡ª including targeted therapy, cell therapy and radiotherapy. Her goal is to integrate advanced imaging with multi-source data to improve outcome prediction and enable personalized treatment strategies.