David Yang ad3ar1@r.postjobfree.com 587-***-**** Calgary, Canada

github.com/davidhy8 linkedin.com/in/david-h-yang/ davidhyang.com SKILLS

Languages & Technologies: Python, R, Java, SQL, Tableau, MS Excel, Git, Minitab, UNIX, HTML, CSS, LaTeX Frameworks & Libraries: Data cleaning and analysis (Pandas, NumPy, dplyr), Data Visualization (Matplotlib, ggplot2), Machine Learning (scikit-learn, Tensorflow), Data mining (BeautifulSoup), Automation Testing (Selenium), Web development (Flask, R Shiny) EDUCATION

M.Sc. Mathematics and Statistics – Specialization: Statistics Sep 2021 – Mar 2024 (Expected) University of Calgary GPA 3.7/4.0 Thesis project: Parallelization of MCMC Phylogenetic Analyses TA: Calculus I Coursework: Deep Learning, Generalized Linear Models, Statistical Inference, Bayesian Statistics, Theory of Probability B.Sc. First Class Honours, Cellular, Molecular, and Microbial Biology Sep 2017 - May 2021 University of Calgary GPA 3.96/4.00 Honours project: Eliminating Sampling Bias in SARS-CoV-2 Analysis Coursework: Computer science I & II, Calculus I & II & III (AU), Linear Methods I & II (AU), Special Topics in Computer Science EXPERIENCE

Graduate Researcher Sep 2021 – Present

University of Calgary Calgary, Canada

• Pinpointed ~50 out of >30,000 significant genomic factors related to Glaucoma disease with R by employing dimensionality reduction (regularization, PCA), data wrangling (normalization, data imputation), and statistical testing techniques (Wald/LRT test, Bootstrapping, Regression methods) on noisy biological datasets with high dimensionality and multi-collinearity.

• Generated scientific figures using data visualization libraries in R which elucidated key research findings from exploratory data analysis to external institutions leading to the receival of monetary grants valuing greater than $50,000.

• Created an asynchronous parallelization method for the Markov chain Monte Carlo (MCMC) Algorithm involved in Bayesian inference (evolutionary) which reduced computational run-times by more than 2900%.

• Identified ~10 key components related to cancer metastasis with time-series analysis in R on human blood biomarker data. Web Automation Developer – Part-time Apr 2023 – Present ADM Lucid Solutions Inc. Calgary, Canada

• Developed automation test scripts with Selenium and Java to validate the integrity of web applications (Cucumber, POM, JMeter).

• Produced video tutorials discussing automation testing frameworks (i.e. Lighthouse, Netbeans, Docker) reaching >35,000 people. Undergraduate Researcher May 2018 – Sep 2021

University of Calgary Calgary, Canada

• Identified sampling bias in SARS-CoV-2 sequence collection by analyzing and visualizing COVID-19 data via Python & R Shiny.

• Devised a novel representative sampling strategy based on scientific deductions of COVID-19 and implemented a data pipeline involving Python and Perl which reduced sampling bias during SARS-CoV-2 sequence selection by around 100%. Chief Information Officer, Co-Founder Jun 2018 – Aug 2021 Canadian Organization for Undergraduate Health Research Calgary, Canada

• Designed the framework for an Android mobile health tracking application (palz) with Android SDK in Android Studio (Java).

• Leveraged data analytics from social media platforms and website traffic to guide internal recruitment of five regional teams and various national committees which resulted in the employment of almost 100 individuals. PROJECTS

NBA prediction web application: Python Flask web application that webscrapes >8000 games of NBA data using BeautifulSoup and trains a neural network with hyperparameter tuning (Tensorflow) to predict NBA win-loss with ~60% accuracy. Image Classification with deep learning: Developed and deployed a convolutional neural network with Tensorflow that performs repurposed image classification by building upon a model pretrained on the ImageNet dataset via transfer learning with 98% accuracy. Predictive modelling for heart disease: Engineered logistic and lasso regression (i.e. feature selection & model evaluation) predictive models in R for a clinical dataset which performed best amongst peers during the implementation of cross-validation. Bayesian Inference of Zero-Inflated Dataset: Programmed custom Bayesian statistical models in R using OpenBUGS to statistically model zero-inflated datasets with Gibbs sampling to obtain Bayesian credible intervals.