Career Profile

A biostatistician with broad experience in quantitative biomedical research and industry.

Experiences

Doctoral Research Assistant

09.2022 - Present
CHUV - Biomedical Data Science Center, Lausanne

• Focusing on spatial transcriptomics, single-cell RNA-seq, proteomics, and Bayesian methods.

Computational Scientist

01.2022 - 08.2022
Bayer - Department of Oncology Statistical Analysis, Mississauga

• Initiated and managed the overall project scope and timeline for two sub-teams:

  1. Linked SDTM to ADaM by adopting admiral R package to a Bayer study
  2. Linked ADaM to TLFs by a R & Shiny cross-validation pipeline

• Designed templates that streamlined the automation of AE reporting in CSR
• Drafted internal developer’s bookdown guide
• Assist other R initiatives in the department and in several industry-wide working groups

Open Source Volunteer Maintainer

08.2021 - 12.2021
R Consortium – RStudio, Remote

• Merged pull requests to harmonize data source for the R package COVID19 Data Hub

Research Professional

01.2021 - 12.2021
Analysis Group - Department of Healthcare, Montreal

Selected cases as a health economics outcome research consultant:
• Used random survival forest to predict the remission of rare disease
• Proposed and implemented R workflows to automate data quality check and outreaching
• Used IPTW to construct external control cohorts for Phase I/II trials

Biostatistics and Data Sciences Intern

05.2019 - 12.2020
Roche - Department of Biostatistics and US Medical Affairs, Mississauga

• Reviewed protocols and conducted SAP/BAP for late phase oncology biomarker studies
• Implemented exploratory RNA-seq functions and R shiny dashboard for HA submission
• Drafted proposal for integrating deep-learning-based imaging endpoints into clinical trial
• Developed a network analysis animation for real world drug use pattern recognition

Teaching Assistant

01.2020 - 12.2020
University of Toronto - Department of Statistics, Toronto

• Lead tutorial sessions for Multivariate Data, Data Analysis II, and Elementary Statistics

Student Analyst

09.2019 - 09.2020
Institute for Clinical Evaluative Sciences (ICES) - UofT, Toronto

• Proposed and conducted administrative pharmacoepidemiological data cleaning pipeline
• Master thesis: developed continuous-time marked point processes algorithms

Statistical Consulting Practicum

09.2018 - 05.2019
Princess Margaret Cancer Center - Department of Biostatistics, Toronto

• Performed multiple imputation and regularized regression for psychometric and radiomics data

Summer Research Fellowship

07.2018 - 08.2018
École polytechnique fédérale de Lausanne (EPFL) - Brain Computer Interface Lab, Geneva

• Research on transfer learning in decoding EEG signals with convolutional neural networks
• Gave talks and presented at the internal poster symposium

Machine Learning Algorithm Developer

05.2017 - 05.2017
BC Cancer Research Center - Department of Integrative Oncology, Imaging Unit, Vancouver

• Automated cancer diagnosis by linking machine learning algorithms with software engines
• Customized deep learning algorithms to classify breast/prostate H&E and Feulgen scan images

Research Assistant

01.2017 - 05.2017
Danish Research Center for Magnetic Resonance (DRCMR), Copenhagen

• Established the ground truth simulation for a Bayesian framework on fMRI BOLD signals
• Presented at the internal poster symposium

Teaching Assistant

09.2016 - 04.2018
University of British Columbia - Department of Statistics, Vancouver

• Lead tutorial sessions for Elementary Statistics

Data Analyst

04.2016 - 08.2016
UBC Hospital - Department of Neurology, Vancouver

• Managing Alzheimer’s, Multiple Sclerosis patient files
• Provided data entry and summary statistics

Publications

TBD
Dong, Y., TBD
TBD

Conferences

Yan, M., Chang, Y., Raghavan, V., Dong, Y., Klein, C., Nielsen T.G., Paulson, J.N., and Hatzi, K. (Oral Presentation)
American Society of Hematology, San Diego, United States / Virtual. (2020)
Marginal Structural Models for Cumulative Exposure Effects in Pharmacoepidemiology
Dong, Y., Saarela, O. and Cadarette, S. (Oral Presentation)
Canadian Statistics Student Conference, Ottawa, Canada / Virtual. (2020)
Computational Parametric Mapping for Neurometric Power Law Testing and Analysis for Experimental Design Efficiency
Dong, Y. and Hulme, O. (Poster)
UBC Neuroscience Conference, Vancouver, Canada. (2018)