Lian Jiang

Bioinformaticist

Ph.D. in Seismic Imaging Data Analysis and Machine Learning

University of Houston

RESEARCH

My research interest is to investigate how the 3D structure of chromosomes influences functions of the genome using super-resolution imaging techniques, such as OligoSTORM and OligoDNA-PAINT developed by the Wu lab. These techniques have enabled us to address challenging problems in the biomedical field. For example, we have applied these techniques to distinguish the maternal and paternal homologous chromosomes in Drosophila and human systems. We have also been able to examine the structure of paired homologs. However, analyzing a large amount of 3D imaging data in a traditional way is a big challenge. It is not only time-consuming but could also cause uncertainties or biases due to weak signals and artifacts/noise existing in imaging data and the complex relationships between images. With a background in data/image processing and analysis and machine learning, I will leverage my knowledge and skills to overcome these challenges. First, I will develop a series of tools to manage and organize the 3D imaging data, so that it is easy for the team to access and perform preliminary image analysis. Second, I will develop basic image processing and analysis tools, such as towards the optimization of existing visualization tools and development of methods/tools to process 3D imaging data (e.g., denoising and automatic tracking). Third, I will attempt to optimize super-resolution fluorescence microscopy by, for example, developing novel computational methods to improve resolution and image quality. Fourth, I will investigate state-of-the-art methods for discovering the patterns and mechanisms of genome functions implicated in 3D imaging data, such as by developing methods to automatically analyze the pattern of 3D image data. This strategy could contribute to our understanding of the intrinsic mechanisms of chromosome behavior, positioning, and folding. Lastly, I will build various predictive models using machine learning with the aim of contributing to the diagnosis and treatment of disease and, thus, the improvement of human health.

PUBLICATIONS

Jiang L, B Russell, L Thomsen, and J P Castagna. Prediction of acoustic velocities using machine learning. Advances in subsurface data analytics: traditional and physics-based machine learning, edited by Shuvajit Bhattacharya and Haibin Di, Elsevier (2022).

Zhang Y J, X T Wen, L Jiang, J Liu, J X Yang, and S M Liu. Prediction of high-quality reservoirs using the reservoir fluid mobility attribute computed from seismic data. Journal of Petroleum Science and Engineering, https://doi.org/10.1016/j.petrol.2020.107007 (2020).

Jiang L, J P Castagna. On the rock physics basis for seismic hydrocarbon detection. Geophysics, 85(1), 25 - 35, https://doi.org/10.1190/geo2018-0801.1 (2020).

Feng Q X, L Jiang, M Q Liu, H Wan, L Chen, and W Xiao. Fluid substitution in carbonate rocks based on Gassmann equation and Eshelby-Walsh theory. Journal of Applied Geophysics, 106, 60-66, http://dx.doi.org/10.1016/j.jappgeo.2014.04.005 (2014).

Jiang L, X T Wen, D H Zhou, Z H He, and X L He. Constructing pore structure factors in carbonate rocks and the inversion of reservoir parameters. Applied Geophysics, http://dx.doi.org/10.1007/s11770-012-0333-5 (2012).

Jiang L, X T Wen, Z H He, and D J Huang. Pore structure model simulation and porosity prediction in reef-flat reservoirs. Chinese Journal of Geophysics, 54, 1624-1633, http://dx.doi.org/10.1002/cjg2.1623 (2011).

Jiang L, Y Zeng, X T Wen. Estimation of sand-body thickness based on seismic facies analysis. Fault-block Oil & Gas Field, 18, 273-276 (2011).

Jiang L, Z H He, D J Huang. Fluid identification in south sea reef-flat reservoir using seismic porosity inversion. Oil Geophysical Prospecting, 45 (2010).

Jiang L, Z H He, D J Huang. Research and application of M classification method in the identification of reservoir fluid with log data. Well Logging Technology, 34,356-359 (2010).

He X L, Z H He, R L Wang, X B Wang, and L Jiang. Calculations of rock matrix modulus based on a linear regression relation. Applied Geophysics, 8, 155-162, http://dx.doi.org/10.1007/s11770-011-0290-4 (2011).

He X L, Z H He, X B Wang, X J Xiong, and L Jiang. Rock skeleton models and seismic porosity inversion. Applied Geophysics, 9, 349-358, http://dx.doi.org/10.1007/s11770-012-0345-1 (2011).