IBSG Webinar Tháng 7-2022: Sequencing Vietnamese Genomes

Kính mời quý độc giả tham dự buổi Webinar tháng 7-2022 do Nhóm Học Thuật Y Sinh IBSG tổ chức trực tuyến qua Zoom.

Trong buổi webinar chuyên đề này, IBSG hân hạnh được sự tham gia của:

T.s. Võ Sỹ Nam: Viện Dữ Liệu Lớn Vingroup, GeneStory, và VinUniversity, Vietnam.

Chủ đề: Sequencing Vietnamese Genomes at Scale: Opportunities and Challenges

Host: Nguyễn Thu Thuỷ, Ph.D.

Thời gian: 9:00-10:00 sáng thứ Bảy giờ Việt Nam, ngày 02.07.2022.

Zoom Meeting:

https://uwmadison.zoom.us/j/95412018464?pwd=VFljSzMwMUovcmVveXNRQ3pTTzhNZz09
Meeting ID: 954 1201 8464
Passcode: IBSGW2022

Về Diễn Giả:

Dr. Nam Sy Vo is Director of Center for Biomedical Informatics at Vingroup Big Data Institute, Co-Founder, CSO of GeneStory JSC, a spinoff company from Vingroup/VinBigdata, and an Affiliate Faculty at College of Engineering and Computer Science at VinUniversity, Vietnam. Previously, Dr. Vo worked as a Senior Bioinformatics Scientist at Center for Translational Data Science at The University of Chicago. He was trained as a postdoc at The University of Texas MD Anderson Cancer Center after obtaining a PhD in Computer Science from The University of Memphis, USA.

Dr. Vo’s current research interests focus on analysis and interpretation of large-scale multi-omics data towards understanding disease risk and adverse drug reaction. He is leading various projects focusing on sequencing and analyzing Vietnamese genomes at population scale for studying complex diseases in Viet Nam. His group is also building various platforms for managing, analyzing, and sharing large-scale biomedical datasets where reproducibility, portability, and scalability are maximized. He is also interested in applications of Machine Learning and Data Science in bioinformatics.

Previously, Nam has developed various computational methods for sequence alignment and variant calling using next-generation sequencing data. Some of these methods focus on analyzing complex regions of the genomes such as human leukocyte antigens and T-cell receptors. He has also developed various methods for gene expression analysis which focus on predicting patterns of gene response. His work has been applied to several world’s largest datasets including The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Therapies (TARGET).