About
Biography
I am a computational researcher aiming to develop technologies to translate and transform pediatric clinical care using machine learning. My foundational training is in economics and statistics. I strive to improve the efficiency and impact of health interventions by getting more out of the information already collected through standards of care.
My work primarily focuses on extracting more information from biomedical imaging data, with the goal of enabling faster and easier interpretation by a larger share of clinicians, such as novices and those who don’t specialize in pediatrics.
I have already developed models for prediction in hydronephrosis ultrasound, pediatric echocardiography, hepatocellular carcinoma recurrence, irritable bowel disease (IBD) diagnosis and drug response, and the diagnosis of motile ciliopathy. I have been a researcher for over 10 years and began working at Cincinnati Children’s in 2023.
Research Areas
Publications
Automated right ventricular assessment in pediatric echocardiography via deep learning improves measurement reliability and reduces variability. Intelligence-Based Medicine. 2026; 13:100344.
Implications of Model Loss and Configuration for Sparse Histological Segmentation. In: Machine Learning Methods in Biomedical Field. Springer Nature; 2026:189-217.
Deep Learning for Pediatric Right Ventricle Segmentation in Echocardiography: Challenges and Strategies. In: Machine Learning Methods in Biomedical Field. Springer Nature; 2026:137-159.
The 2025 update on artificial intelligence models in pediatric urology: Results from the AI-PEDURO collaborative. Journal of Pediatric Urology. 2025.
Predicting non-response to urotherapy in pediatric bowel and bladder dysfunction: A machine learning approach. Journal of Pediatric Urology. 2025.
Peripheral blood DNA methylation predicts the early onset of primary tumor in TP53 mutation carriers. Nature Communications. 2025; 16(1):7976.
Longitudinal image-based prediction of surgical intervention in infants with hydronephrosis using deep learning: Is a single ultrasound enough? PLOS Digital Health. 2025; 4(8):e0000939.
Validation of the Toronto recurrence inference using machine-learning for post-transplant hepatocellular carcinoma model. Communications Medicine. 2025; 5(1):284.
Machine Learning Analysis of Videourodynamics to Predict Incident Hydronephrosis in Patients With Spina Bifida. Journal of Urology. 2025; 214(1):80-89.
Reply by Authors. Journal of Urology. 2025; 214(1):88-89.
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