BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260820T150848
DTSTART;TZID=America/Detroit:20260930T160000
DTEND;TZID=America/Detroit:20260930T170000
SUMMARY:Workshop / Seminar:CCMB/DCMB Weekly Bioinformatics Seminar Series featuring Jianzhi Zhang\, PhD (Professor\, Ecology and Evolutionary Biology\, U-M)
DESCRIPTION:Jianzhi (George) Zhang is a Professor of Ecology and Evolutionary Biology interested in the relative roles of chance and necessity in evolution. He got his B. S. from Fudan University in Shanghai\, China\, and his Ph. D. in Genetics from Pennsylvania State University. He was a  Fogarty postdoctoral fellow at the National Institute of Allergy and Infectious Diseases before moving to the University of Michigan.\n\nProfessor Zhang’s research focuses on two main research areas:  (1) yeast as an experimental system for studying evolution\, where his research group uses the budding yeast Saccharomyces cerevisiae and its relatives as model organisms to understand a variety of evolutionary processes such as the genetic basis of phenotypic variations among strains and species\, or molecular and genomic bases of heterosis\; and (2) computational evolutionary genomics where they use evolutionary\, genomic\, and/or systemic approaches to analyze publicly available data to characterize and understand pleiotropy\, robustness\, epistasis\, gene-environment interaction\, gene expression noise\, translational regulation\, RNA editing\, convergent evolution\, adaptation\, origin of new genes\, among-protein evolutionary rate variation\, and other important genetic and evolutionary phenomena. Projects may also involve modeling and simulation\, including the MICDE catalyst grant project where the team is using deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences\, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.
UID:150650-21909759@events.umich.edu
URL:https://events.umich.edu/event/150650
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Basic Science,Bioinformatics,Biology,Biosciences,Ecology,Genome
LOCATION:Medical Science Unit I - 4B700
CONTACT:
END:VEVENT
END:VCALENDAR