In this episode of the University of Pittsburgh’s Health and Explainable AI (HexAI) Podcast, host Jordan Gass-Pooré speaks with Daniel Heller, PhD, Head of the Cancer Nanomedicine Laboratory at Memorial Sloan Kettering Cancer Center and Professor at Weill Cornell Medicine. Anchoring the discussion around the convergence of computational medicine, nanotechnologies, and explainable machine learning, Daniel shares his journey from early interest in carbon nanotubes at Rice University to leading cutting-edge research in cancer diagnostics, and then goes on to provide listeners with an overview on the scope of biosensor technology and variety of internal and external biosensors being studied and applied today, ranging from micro-scale pill cameras and implantable devices down to optical nanosensors operating at one hundred-thousandth the width of a human hair.
Bridging the gap between material chemistry and artificial intelligence, Daniel details how his lab utilizes arrays of surface-modified carbon nanotubes to capture biological data. Drawing an analogy to the human olfactory system, he explains how these sensors forgo single-biomarker recognition in favor of creating multi-signal optical "fingerprints" from blood plasma, which are then evaluated by supervised machine learning models to detect early-stage ovarian cancer and differentiate brain tumor subtypes for example. Crucially, the conversation highlights how explainable AI techniques, such as feature importance analyses, SHAP, and LIME, are inverted to drive scientific discovery. By identifying which nanosensors contribute most to the model’s diagnostic decisions, researchers can wash off bound proteins to isolate rare, disease-specific biomarkers and map out the broader systemic "tumor ecosystem".
Aligning with the mission of the University of Pittsburgh's HexAI Research Laboratory, Daniel offers insights for students and computational researchers on navigating sparse clinical datasets, developing robust machine learning architectures for rare diseases, and advancing interinstitutional sample-sharing networks. Reflecting core priorities of Pitt's Computational Pathology and AI Center of Excellence (CPACE), the discussion illustrates how explainable machine learning and sensor-driven molecular profiling advance the frontiers of computational pathology and precision diagnostics. He closes the episode with a call to action for students with interdisciplinary backgrounds across programming, chemistry, and clinical biology to explore careers at the intersection of nanotechnology and explainable AI.
Host: Jordan Gass-Pooré | Producer: Brent Phillips | Guest: Daniel Heller, PhD (Head of Cancer Nanomedicine, Memorial Sloan Kettering Cancer Center & Weill Cornell Medicine)
Topics: Cancer Nanomedicine | Explainable AI (XAI) | Nanosensors | Feature Importance Analysis | Biomarker Discovery | Micro-Robotics
Institutions: University of Pittsburgh | Memorial Sloan Kettering Cancer Center | Weill Cornell Medicine
Links:
[YouTube] https://www.youtube.com/watch?v=idE_5qxqvb4&t=5s
[Guest Profile] https://www.mskcc.org/research/ski/labs/daniel-heller
[Pitt HexAI Lab] https://pitthexai.github.io/
