Digital Heart

Trulli
Haibo Ni, Ph.D.
Associate Professor
Medical School
Nanjing University
China

Welcome to my webpage. I am currently an Associate Professor with the Medical School of Nanjing University, China. My research focuses on integration of multiscale and multiphysics computational models and deep learning approaches to uncover disease mechanisms and improve therapeutics for cardiovascular diseases.

长期招聘助理研究员、博士后、科研助理,招收博士或硕士研究生、访问学生、本科生等。诚挚欢迎对信息与医学交叉、数字医学、心血管疾病、人工智能等相关领域感兴趣的青年才俊加入,共创数字孪生驱动的精准医学新模式。

Trulli

重点招聘招生领域(博士后、博士、硕士、科研助理),也可推荐至附属鼓楼医院:

一、专业方向:计算机、电子信息、生物医学工程等医工交叉学科专业方向。在心脏数字孪生、复杂器官系统与疾病数字建模、深度学习驱动复杂系统模型代理、基于深度学习的因果推理、或虚拟现实技术等相关领域有相关经验者优先。
二、AI分析数字孪生构建技术人员岗位:
1.深度学习代理模型(Surrogate Model)开发;
2.基于深度学习的因果推理方法解析复杂病理机制;
3.跨学科协作;
三、多器官数字孪生建模技术人员岗位:
1.心脏数字孪生模型开发;
2.高性能计算与算法实现;
3.多器官模型集成与验证,跨学科协作;
Contact: hni@nju.edu.cn

The function of the heart as a robust blood pump is critically dependent on the rhythmic and coordinated electrical activation of the myocardium and the subsequent contraction through a process termed excitation-contraction (EC) coupling. Disruption of the normal activation rhythm or sequence, i.e. cardiac arrhythmia, is associated with numerous cardiovascular diseases, increases morbidity and mortality, and can lead to sudden cardiac death by causing ventricular dysfunction.

My research aims to develop computational systems frameworks and personalized simulation pipelines for constructing ‘digital twins’ of humans to transform clinical therapeutic strategies for diseases. My studies developed multi-scale and multi-physics computational modeling platforms of the heart incorporating biophysically accurate and anatomically realistic features to dissect cardiac arrhythmia mechanisms and perform high-throughput screening of therapeutics. The computational models are built on experimental data describing cardiac electrophysiological properties to construct biophysically detailed cellular models; CT/MRI imaging data are integrated with the cellular models to construct anatomically accurate heart models to study electrical and mechanical functions of the heart. Our quantitative frameworks are applied to decipher cardiac arrhythmia mechanisms, and explore new frontiers of clinical treatment, including high-throughput screening of anti-arrhythmic drugs and determining drugs cardiotoxicities. Our ‘digital twins’ of human heart also facilitate virtual clinical trials and patient-specific therapeutic (e.g., catheter ablation) planning.