Developing AI/ML solutions for oncology research, specializing in multimodal deep learning, digital pathology, and large language models for cancer diagnosis and prognosis.
About Me
I am an Applied Research Scientist at H. Lee Moffitt Cancer Center and Research Institute, working at the intersection of artificial intelligence and oncology. My research focuses on developing innovative machine learning frameworks for cancer diagnosis, prognosis, and treatment optimization.
I earned my Ph.D. (summa cum laude) in Electrical Engineering from the University of South Florida, where my dissertation explored graph theory for robust deep networks and graph learning for multimodal cancer analysis. My work spans multimodal data integration, digital pathology, federated learning for medical imaging, and the application of large language models in healthcare.
Prior to academia, I accumulated over 15 years of industry experience in IT infrastructure, data center architecture, and middleware administration across government and international organizations.
Research Focus
Developing frameworks like SeNMo and PARADIGM that integrate multi-omics data, pathology images, and clinical records for improved cancer outcome prediction across 32 cancer types.
Leveraging generative AI and foundation models to revolutionize diagnostic pathology, enhance tissue detection, and enable automated analysis of histopathological images.
Building personalized risk assessment models using low-dose CT screening data to improve early detection of lung and breast cancer.
Evaluating and deploying private, local large language models for extracting structured data from clinical records, pathology reports, and cancer registries.
Implementing privacy-preserving federated learning approaches for multi-institutional medical imaging research with uncertainty quantification.
Developing computational biomarkers to identify patient sub-populations with better survival outcomes for immunotherapy treatments.
Publications
A selection of recent peer-reviewed publications. For a complete list, visit my Google Scholar profile.
Tripathi A*, Waqas A*, Schabath MB, Yilmaz Y, Rasool G
8(1): 622
View PublicationTripathi A*, Waqas A*, Venkatesan K, Ullah E, Khan A, Khalil F, Chen WS, et al.
104272
View PublicationWaqas A*, Tripathi A*, Ahmed S, Mukund A, Farooq H, Schabath MB, Stewart P, Naeini M, Rasool G
26(15): 7358
View PublicationKoutsoubis N, Waqas A, Yilmaz Y, Ramachandran RP, Schabath MB, Rasool G
e240637
View PublicationAltinok O, Rasool G, Waqas A, Schabath MB, Guvenis A
14(24): e71481
View PublicationBackground
University of South Florida
Tampa, FL, USA
Summa Cum Laude (GPA 4.0/4.0)
Thesis: From Graph Theory for Robust Deep Networks to Graph Learning for Multimodal Cancer Analysis
Centre for Advanced Studies in Engineering (CASE)
Islamabad, Pakistan
Magna Cum Laude
National University of Sciences and Technology (NUST)
Islamabad, Pakistan
Moffitt Cancer Center
Moffitt Cancer Center
Moffitt Cancer Center / USF