莱斯大学计算机系主任Luay Nakhleh教授学术报告通知

2019-09-10 20:40

  应计算机科学与技术学院院长王亚东教授邀请,在国际项目管理中心的大力支持下,美国莱斯大学计算机系主任Luay Nakhleh教授将于2019915日至922日对我校计算机学院进行学术访问。访问期间将开展三次学术报告。欢迎感兴趣的师生参加。

报告一

  报告主题:Issue with CS curriculum in the era of data science and machine learning

  报告时间:2019917 12:00-13:30

  报告地点:珍妮咖啡(哈工大一校区16公寓侧面)

  报告摘要In the era of big data and AI, how to improve and expand the traditional CS curriculum system? What new courses should be set up and what new knowledge should be updated in order to adapt to the era which IT technology develops rapidly? In this talk, I will introduce the curriculum reform of Rice University in big data and machine learning, and showing my thoughts about it.

 

报告二

  报告主题:Inference of Phylogenetic Networks in the Post-genomic Era

  报告时间:2019918 14:00-15:30

  报告地点:一校区新技术楼618

  报告摘要:Using genome-wide data for phylogenetic inference and analysis has become commonplace in the post-genomic era, giving rise to the field of phylogenomics. The multispecies coalescent (MSC) model has emerged as the main stochastic process that helps capture the intricate relationship between species trees and gene trees.  Combined with models of sequence evolution, the MSC can be viewed as a generative model of genomic sequence data in the context of a (species) phylogenetic tree.

     A significant outcome of the use of genome-wide data has been the increasing evidence, or hypotheses, of reticulation (e.g., hybridization) during the evolution of various groups of eukaryotic species. Reticulate evolutionary histories are best represented as phylogenetic networks, which extend the tree model to allow for admixtures of genetic material. In this talk, I will describe the multispecies network coalescent (MSNC) model, which extends the MSC model so that it operates within the branches of a phylogenetic network. This extended model naturally allows for modeling vertical and horizontal evolutionary processes acting within and across species boundaries. In particular, it simultaneously accounts for gene tree incongruence across loci due to both hybridization and incomplete lineage sorting. I will then describe a likelihood function for this model, as well as a method for Bayesian sampling of phylogenetic networks and their parameters using reversible-jump Markov chain Monte Carlo (RJMCMC).

 

报告三

  报告主题:Elucidating Intratumor Heterogeneity from Single-cell DNA Sequencing Data

  报告时间:2019919 14:00-15:30

  报告地点:一校区新技术楼618

  报告摘要:Intra-tumor heterogeneity, as caused by a combination of mutation and selection, poses significant challenges to the diagnosis and clinical therapy of cancer. Resolving this heterogeneity to identify the tumor cell populations (clones) and delineate their evolutionary history is of critical importance in improving cancer diagnosis and therapy. This heterogeneity can be readily elucidated and understood through the reconstruction of the clonal genotypes and evolutionary history of the tumor cells. These tasks are challenging since genomic data is most often collected from one snapshot during the evolution of the tumor's constituent cells. Consequently, using computational methods that infer the tumor phylogeny and tumor subpopulations from sequence data is the approach of choice. Recently emerged single-cell DNA sequencing (SCS) technologies promise to resolve intra-tumor heterogeneity to a single-cell level. However, inherent technical errors in SCS datasets, including false-positive (FP) errors, false-negatives (FN) due to allelic dropout, cell doublets and coverage non-uniformity significantly complicate these tasks.

    In this talk, I will first describe a maximum likelihood method for inferring tumor trees from imperfect SCS genotype data with potentially missing entries, under a finite-sites model of evolution. I will then describe a non-parametric Bayesian method that simultaneously reconstructs the clonal populations as clusters of single cells, mutations associated with each clone, and the genealogical relationships between the clonal populations. I will demonstrate the performance of the methods on both synthetic and real data sets.

 

  主讲人:Luay Nakhleh教授,博士毕业于德克萨斯大学奥斯汀分校计算机科学专业。研究领域为生物信息学和计算生物学。尤其是针对生物进化和基因学领域的研究。曾出版100余篇学术文章,发表100余场学术报告。获得过众多奖项,如德克萨斯杰出教学奖(德克萨斯州大学中最有声望的奖项之一);DOE事业奖等奖项。

  莱斯大学(Rice University),简称Rice,位于美国得克萨斯州休斯敦市郊。为美国南方最高学府,美国大学协会AAU)成员,是一所世界著名的顶尖私立研究型大学,“新常春藤”名校之一。

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