• 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:

• (Jul. 2023) I got 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.
• (Jun. 2022) Glad to present our works at USNC/TAM 2022 in Austin!
• (Mar. 2022) Check out our new CMAME paper of interfacing finite elements with neural operators! Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
• (Mar. 2022) Check out our CMAME paper, A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials
• (Feb. 2022) A new neural operator for simulating aortic dissection, Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator–regression neural network
• (Jan. 2022) Invited talk, Department of Biomedical Engineering, Johns Hopkins University, Multiscale Modeling and Machine Learning for Biomedicine
• (Jan. 2022) Finalist of the Distinguished Fellows Position, Department of Biomedical Engineering, Johns Hopkins University <!– - (Jan. 2022) Our new paper of PINNs for fluid mechanics, Physics-informed neural networks (PINNs) for fluid mechanics: A review
• (Oct. 2021) Conference Presentation: 2021 IACM Computational Fluids Conference, Imaging-Driven Inference of Biomaterial Properties with Physics-Informed Neural Networks
• (Aug. 2021) Invited talk: Northwestern Polytechnical University, Physics-Informed Machine Learning and its Application in Multiscale Modeling
• (Aug. 2021) Invited talk: Parallel-in-Time (PinT) Workshop Time parallel in PDEs using machine learning tools
• (Jan. 2021) New paper Multiscale Parareal Algorithm for Long-Time Mesoscopic Simulations of Microvascular Blood Flow in Zebrafish
• (Oct. 2020) New paper Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
• (May. 2020) New paper Non-invasive Inference of Thrombus Material Properties with Physics-informed Neural Networks –>