Computational & Applied Mathematics (CAM) Seminar
Student No.:40
Time:Wed 15:20-16:55
Instructor:Shi Zuoqiang  [Tsinghua University]
Place:Conference Room 1, Jin Chun Yuan West Bldg.
Starting Date:2018-3-8
Ending Date:2018-6-13




Speaker: 李沛尧  [解放军总医院]


Title: 临床大数据的分析与思考


Abstract: 随着医疗技术的进步,临床环境与实践产生了大量的数据,这些数据来源众多且形式各异,如电子健康档案(EHR)、医学影像、基因测序以及可穿戴设备等,基于“大数据”的新型临床决策辅助系统为改进临床诊疗实践提供了新的机会。但是目前的临床数据领域还面临着诸如数据结构不统一、“数据孤岛”、“数据荒漠”等挑战。如何在这种“机会与挑战”并存的“临床大数据”时代,利用数据,发现新的知识是我们需要深入思考的问题。在本次讨论中,我们将以重症监护领域风险模型的最新进展为例深入探讨,诸如深度学习在临床环境下的机会。同时我们还将分享我们团队在临床数据建设方面的进展和心得。





Speaker: Bao Chenglong [YMSC, Tsinghua University]


Title: Spare representations in image processing: algorithms, models and beyond


Abstract: In recent years, the concept of sparse representation has been widely used in many applications. Among extensive works in this direction, K-SVD is a typical method in the dictionary learning. However, the convergence of K-SVD is not clear. In this talk, I will introduce an efficient numerical algorithm for solving L0-norm related problems with convergence guarantee. Additionaly, some variants of dictionary learning models are proposed for dynamic texture classification and cerebellar functional parcellation. At last, the approximation analysis for the dictionary learning models is discussed.





Speaker: Songting Li [Courant Institute, NYU]


Title: Mathematical Modeling and Analysis of Single-Neuron Computation


Abstract: A neuron with dendrites is believed to be the fundamental computational unit in the brain. To understand information processing in the brain, mathematical modeling of single-neuron dynamics has proven to be an effective approach. Among all the neuron models, multi-compartment (PDEs) models and single-compartment (ODE) models are two popular frameworks that describe a neuron at different levels. In general, multi-compartment models incorporating dendritic features are biologically detailed but mathematically intractable and computationally inefficient, while single-compartment models only characterizing the cell body are mathematically tractable and computationally efficient but biologically oversimplified. A neuron model with both simple mathematical structure and rich biological detail is thus still lacking. In this talk, by using asymptotic analysis, I will derive a class of single-compartment neuron models, consisting of one ordinary differential equation, from the corresponding multi-compartment models consisting of hundreds of partial differential equations, and further verify the derived model in realistic neuron simulations and biological experiments. In contrast to the existing single-compartment models, our derived model is capable of performing detailed dendritic computations such as feature selectivity and sound localization, and can greatly reduce the computational cost in large-scale neuronal network simulations without the loss of dendritic functions.