• I am Minglang Yin. I am a Postdoc Fellow in the Department of Biomedical Engineering, Johns Hopkins University. I am working with Prof.Natalia Traynova on developing machine learning models for cardiovascular diseases. I finished my Ph.D. degree with Prof. George Em Karniadakis and the title of my thesis is Hybrid Computational-Machine Learning Models with Uncertainty Quantification for Aortic Dissection.

• My research interests are on developing mechanistic models and scientific machine learning for diagnosis and prognosis of diverse cardiovascular diseases. I am also interested in cardiovascular biomechanics, computational mechanics, and uncertainty quantification.

Recent News:

• (May. 2024) I am invited to give two oral presentations in HRS 2024 (Boston) on our latest AI research for advancing the quality of clinical care. See you in Boston.
• (Apr. 2024) Invited talk in Lu’s group at Yale. “Learning Solution Operators of Partial Differential Equations Across Geometries”
• (Mar. 2024) We proposed a computational-based shape categorization for left atrial appendage. This pipelines greatly improve the shortage in interoperatability in the current appendage classification system and will be tested on a very large cohort in the next few months!
• (Feb. 2024) Check out our latest paper. DIMON enables geometry-dependent operator learning with validation on over 1,000 personalized hearts digital twins derived from cardiac imaging of patients with heart disease.
• (Nov. 2023) I was selected by Hopkins as institutional candidates for the Moore Inventor Fellow.
• (Jul. 2023) I received the Kenneth M. Rosen Fellowship in Cardiac Pacing and Electrophysiology, Heart Rhythm Society 2023
• (Jun. 2023) Invited talk, Biophysics-informed Machine Learning, The Platform for Advanced Scientific Computing (PASC) Conference (Online) 2023
• (May. 2023) Poster session, Heart Rhythm 2023, New Orleans, LA
• (Apr. 2023) Invited talk at CIS/MINDS seminar, Johns Hopkins University
• (Apr. 2023) Check out our paper on DL for constitutive modeling. The framework learns the constitutive laws for a family of materials and infer the new samples without retraining! A generative modeling framework for inferring families of biomechanical constitutive laws in data-sparse regimes
• (Feb. 2023) Invited Lightening talk, School of Medicine/Whiting School of Engineering Research Retreat, Johns Hopkins University
• (Mar. 2023) Travel Award, 17th U. S. National Congress on Computational Mechanics
• (Oct. 2022) Invited talk, Finalist of Robert J. Melosh Competition, Civil & Environmental Engineering, Duke University
• (Sep. 2022) Invited talk at Complex Fluids and Soft Matters (CFSM) seminar series, Department of Mechanical Engineering, Clemson University (Online)
• (Aug. 2022) I start my new position as a postdoc in the department of biomedical engineering, Johns Hopkins University.
• (Jul. 2022) I have my thesis defense in late July, 2022.