Masha Bondarenko

I graduated from UC Berkeley with a degree in Electrical Engineering & Computer Science in 2024. I currently work as a deep learning researcher in the Sohn Lab at UCSF, where I collaborate with doctors, engineers, and industry partners to improve diagnostics through imaging and big data. My current technical interests revolve around AI post-training reliability, uncertainty, and safety within mostly LLMs. I've also worked on foundation models, longitudinal modeling, and risk analyses across multimodal inputs (EHR & CT/XR/MRI).

News

October 3, 2026 Four of our works ranging on topics of homomorphic encryption, LLM uncertainty quantification, robustness, and reliability (hallucination, contradictions, + other bizarre errors) are accepted to two NeurIPS workshops in Sydney, Australia!

July 23, 2026 Excited to present our work at COLM in the LLM/VLM Deployment Opportunities and Risks in Healthcare workshop!

July 22, 2026 Our work was accepted to RSNA as oral and poster presentations on time-conditioned growth and morphological predictions of subsolid nodules!

Research

Prediction of Subsolid Pulmonary Nodule Evolution from Baseline CT Using Temporal Imaging Models

Manuscript In revision

Conference RSNA 2026 | Oral & poster

Automated Detection of Interstitial Lung Abnormalities on Chest CT: Multi-Center Validation and Clinical Impact Evaluation

Manuscript In submission

Conference RSNA 2025 | Poster

Imaging Biomarkers Predicting Progression of Interstitial Lung Abnormalities to Idiopathic Pulmonary Fibrosis: Multicontinental Validation Study

Manuscript In revision

Investigating AI's potential in risk-stratifying lung cancer screenings for future follow-ups: A retrospective analysis of a tertiary hospital cohort

Manuscript In revision

Clinical and Imaging Factors Associated with Growth of Subsolid Pulmonary Nodule on CT

Conference Radiological Society of North America Annual Meeting, 2023