Building the Trusted Intelligence Layer for Oncology
OncoFabric transforms fragmented healthcare data — clinical notes, imaging, pathology, molecular profiles — into structured, time-aware clinical intelligence that clinicians, researchers, and AI systems can act on. Our work in clinical data intelligence and multimodal AI for precision oncology unifies disparate data modalities into representations that capture the full complexity of each patient's journey.
Born at Moffitt Cancer Center, we ground every model in clinical evidence and published guidelines, ensuring outputs are trustworthy, validated, and suitable for real-world clinical and regulatory use. Through privacy-preserving and federated approaches, we enable cross-institutional collaboration without exposing raw patient data. Every answer traces back to source.
Asim Waqas, Ph.D.
Research Scientist at Moffitt Cancer Center. Develops graph neural networks and multimodal learning frameworks for cancer survival prediction and multi-omics integration. PhD in Electrical Engineering, University of South Florida.
Aakash Tripathi, Ph.D.
Machine Learning Engineer at Moffitt Cancer Center. Develops open-source multimodal AI frameworks including HONeYBEE (npj Digital Medicine) and MINDS, and multi-agent clinical NLP systems for oncology. PhD in Electrical Engineering, University of South Florida.
Sabeen Ahmed, Ph.D.
Develops AI-driven models for cancer cachexia detection and multi-omics neural networks for pan-cancer prognostication at Moffitt Cancer Center. Co-author of SeNMo and research on transformers for time-series analysis.
Iryna Hartsock, Ph.D.
Postdoctoral fellow at Moffitt Cancer Center. Applies topological data analysis and conversational AI to oncology, including tumor board decision support. PhD in Mathematics, University of Florida.
Nikolas Koutsoubis
Graduate research assistant at Moffitt Cancer Center. Researches privacy-preserving federated learning and uncertainty quantification in medical imaging. Published in Radiology: Artificial Intelligence. MS in Electrical & Computer Engineering, Rowan University.
Muhammad Maaz
Machine Learning Engineer at Moffitt Cancer Center. Builds and deploys clinical NLP pipelines, speech processing systems, and production MLOps infrastructure for oncology AI. MS in Electrical & Computer Engineering, Rowan University, where his thesis introduced a novel evaluation metric for safety-critical ASR systems.