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Personal profile

Biography

Dr. Emi Tanaka is an applied statistician whose primary interest is to develop impactful methods and tools that can be readily used by practitioners. Her research interests include mixed models and experimental designs, motivated primarily by the problems in bioinformatics and agricultural sciences. She obtained her PhD in statistical bioinformatics at The University of Sydney. She was a lecturer in statistics at School of Mathematics and Statistics, The University of Sydney before joining Department of Econometrics and Business Statistics, Monash University. 

Her personal website is at: https://emitanaka.org

Software

See here for the list of her software contributions and here for her GitHub profile. 

Research interests

  • Experimental design
  • Mixed models (also known as multi-level models, panel data models or hierarchical models)
  • Data visualisation and visual inference
  • Applications in bioinformatics and selective breeding (particularly plant breeding)
  • Statistical software development, particularly in R
  • Statistical workflow (encompassing reproducibility, design, infrastructure & communication)

Community service

  • President of Statistical Society of Australia Victoria Branch (since 2021)
  • Vice President of Statistical Society of Australia Victoria Branch (2020)
  • Councillor of International Biometrics Society Australiasian Region (since 2017)
  • Secretary of Statistical Society of Australia NSW Branch (2018-2019)
  • Assistant Secretary of Statistical Society of Australia NSW Branch (2017)

Monash teaching commitment

Past teaching commitment

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 2 - Zero Hunger
  • SDG 9 - Industry, Innovation, and Infrastructure

Education/Academic qualification

Statistics, PhD

Award Date: 11 Dec 2015

Research area keywords

  • statistics
  • data science
  • plant breeding
  • genetics
  • bioinformatics