Lab Members

Pingjun Chen, Ph.D.
PChen6@mdanderson.org
Instructor, Department of Imaging Physics

Ph.D. in Biomedical Engineering, University of Florida, FL 
M.S.  in Software Engineering, Dalian University of Technology, China
B.S. in Software Engineering, Dalian University of Technology, China 

Pingjun Chen's research concentrates on developing novel computational tools and frameworks for biological imaging, particularly pathology, on advancing cancer diagnostics, prognostics, and therapeutics. He aims to harmonize the perspectives of pathologists, oncologists, biologists and computer scientists toward designing robust oncology solutions to address critical unmet needs for making cancer history.

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

Ph.D. in Mechatronics, Gwangju Institute of Science and Technology, South Korea (2018)
M.S. in Mechatronics, Gwangju Institute of Science and Technology, South Korea (2014)
B.S. in Computer and Communication Systems Engineering, University Putra Malaysia (2008)

Maliazurina Saad started as a postdoctoral fellow in the Department of 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
Postdoctoral Fellow

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

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 the Department of Imaging Physics at The University of Texas MD Anderson Cancer Center, 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
maminu@mdanderson.org
Postdoctoral Fellow

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

Muhammad Aminu’s research in Dr. Wu’s 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.

Qasem Al-Tashi, Ph.D.
qaal@mdanderson.org
Research Investigator

Ph.D. in Information Technology, University of Technology PETRONAS (UTP), Malaysia
M.S. in Computer Science (Software Technology), The National University of Malaysia (UKM), Malaysia
B.S. in Computer Science (Software Engineering), University of Technology Malaysia (UTM), Malaysia

Al-Tashi serves as a Research Investigator at The University of Texas MD Anderson Cancer Center, in the Department of Imaging Physics. Al-Tashi's research primarily focuses on developing computational pipelines to enhance cancer diagnosis, treatment, early detection, and prevention by leveraging evolutionary and swarm intelligence algorithms. Additionally, he investigates and develops statistical and machine learning models to identify cancer prognostic and predictive biomarkers, and implements novel machine learning algorithms to analyze imaging data for cancer research and applications. In essence, Al-Tashi seeks to harness the power of artificial and swarm intelligence to advance cancer research and enhance patient care.

Rizwan Qureshi, Ph.D.
frizwan@mdanderson.org
Postdoctoral Fellow

Rizwan Qureshi received his Ph.D. degree from the City University of Hong Kong in 2021 and his MS degree from the Institute of Space Technology, Islamabad, Pakistan in 2015. He began his academic career as a Lecturer in the electrical and computer engineering department at COMSATS University Islamabad, Wah Campus, from 2015 to 2018. After completing his Ph.D., he was appointed as Assistant Professor at the School of Computing, National University of Computer and Emerging Sciences, Karachi, Pakistan. He later joined Hamad Bin Khalifa University, Doha, Qatar, as a postdoctoral fellow, where he conducted research on drug-target binding affinity prediction. His research interests include applications of machine learning in life sciences, image processing, computer vision, and cancer data science. He has published several high-impact papers in these fields.

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

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

Rukhmini Bandyopadhyay's research interest includes 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 the Department of Imaging Physics and in the Department of Thoracic/Head and Neck Medical Oncology at The University of Texas MD Anderson Cancer Center. 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 the Department of Imaging Physics and Department of 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.
 

Mohamed Sayed Qayati Mohamed, M.D.
MSMohamed2@mdanderson.org
Research Assistant II

M.D. in Medicine & Surgery [MBBCh], Cairo University, Egypt
MSc. in Diagnostic and Interventional Radiology, Cairo University, Egypt

Mohamed S. Mohamed’s research primarily focuses on identifying, annotating and segmenting primary lung tumors and metastatic lesions to different body organs, which provides valuable information regarding screening, diagnosis, treatment and prognosis of lung cancer.
 

Amgad Muneer, M.S. 
amabdulraheem@mdanderson.org  
Research Assistant II

M.S in Information Technology, University of Technology PETRONAS (UTP), Malaysia
BEng (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@mdanderson.org
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. - Pattern Recognition and Intelligent Systems, University of Chinese Academy of Sciences, China
M.S. – Electronic and Communication Engineering, University of Chinese Academy of Sciences, China
B.S. - Communication Engineering, South China Normal University, China

Songhui Diao is a visiting student in the Department of Imaging Physics at The University of Texas MD Anderson Cancer Center. His research concentrates on developing novel computational pathology based on deep learning, on advancing cancer diagnostics, prognostics, and therapeutics. In essence, He seeks to harness the power of artificial to advance cancer research and enhance patient care.

John Boom
CPRIT-CURE

John Boom is a Duke undergraduate double majoring in biomedical engineering and chemistry. He is interested in rational drug design/discovery, global health and deep learning. John currently plans on pursuing an M.D./Ph.D., and his dream is to work at the intersection of medicine, research and entrepreneurship. John is an avid soccer fan, and in his free time he loves to hike, read sci-fi and play piano.

James George
CPRIT-CURE

James George graduated from the University of Michigan in 2020 with a B.S.E in biomedical engineering.  During his last summer as an undergraduate student, he participated in the CPRIT-CURE program at MD Anderson in the Wu Laboratory.  The experience solidified his interest in cancer research and computational methodologies. It also led him to his current work as a post-baccalaureate research associate which involves using in vivo models and high-throughput sequencing techniques to study cancer development at the Michigan Center for Translational Pathology in the University of Michigan Hospital.  

Thinh Huynh
CPRIT-CURE

Thinh Huynh is an undergraduate at the College of Wooster pursuing a major in Biochemistry and Molecular Biology with a minor in Statistical and Data Science. His research interest is incorporation of computational tools in wet-lab research. His goal is to pursue an M.D./Ph.D. Outside of lab, Thinh enjoys cooking, literature and writing.