Jia Wu is an NIH-funded principal investigator and a trained computational scientist. He obtained a Ph.D. in bioengineering and civil engineering from the University of Pittsburgh. He was a postdoctoral research fellow at the University of Pennsylvania and an instructor at Stanford University.
Pingjun Chen, Ph.D.
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.
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 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.
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.
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.
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 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.
Postdoctoral Fellow, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, USA
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 scientist 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.
Rukhmini Bandyopadhyay, Ph.D.
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 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
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.
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.
Mohamed Sayed Qayati Mohamed, M.D.
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.
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.
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.
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.
Former Lab Members
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 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 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.
Aparajith Kannapiran is an undergraduate student at the University of Texas at Austin majoring in biomedical engineering with a focus on cellular and biomolecular engineering. His research interests surround biomaterial and polymer sciences, machine learning, and healthcare. After his undergraduate degree, Aparajith plans on pursuing an M.D. Outside of academia, Aparajith spends his free time eating spicy foods, watching Indian movies and playing soccer.
Richard Lee is an undergraduate at Emory University majoring in biology. His research interests include deep learning in clinical settings, radiology and cancer biology. Richard is considering pursuing an M.D./Ph.D. in hopes of one day incorporating his oncology research into daily patient care. Outside of academics and research, Richard loves to swim, play the guitar and try out new restaurants.