Michael Son Email & Phone Number

Santa Clara, Ca, Us

Männedorf, Zh, Ch

• Conducted preliminary feasibility experiments and V&V tests for the development of MagicPrep NGS kits on the Singular Genomics G4 sequencing platform.• Designed and conducted various high-profile R&D experiments, enhancing institutional knowledge and insight.• Developed proofs of concept for NGS library prep workflow automation, including instrument scripts.• Conducted experiments to support the product development and marketing of MagicPrep NGS.• Operate and maintain the Illumina MiniSeq and perform preliminary analysis of NGS sequencing data for R&D and QC samples.• Execute QC on oligo probes and enzymes while troubleshooting experiments and NGS sequencing operations.

Männedorf, Zh, Ch

• Designed and implemented experimental studies for the development of an in-house reverse transcriptase for NGS library prep kits.• Developed gel-based nucleic acid contamination assays and a robust enzyme kinetic assay for performance QC.• Executed the stability and shelf-life studies for the in-house reverse transcriptase.

Waltham, Ma, Us

• Conducted verification and validation tests for the Applied Biosystems SeqStudio Flex Series Genetic Analyzer, contributing to QA.• Documented the test results and revised the SOPs, improving operational efficiency and compliance.

San Francisco, California, Us

• Completed a hands-on curriculum with 1:1 mentorship and two in-depth capstone projects.• Gained expertise in Python, SQL, data analysis, visualization, hypothesis testing, and machine learning.Capstone Project 1: Lending Club Loan Approval Optimization• Constructed a binary classifier to differentiate unprofitable loans from the rest.• Tools Used: NumPy | pandas | Matplotlib | Seaborn | Scikit-Learn | Imbalanced-Learn | XGBoost• Best Model: eXtreme Gradient Boosting• Best Model Performance: Minority F1 = 0.56 | Majority F1 = 0.89 | ROC AUC = 0.80 | Accuracy = 83%• GitHub: github.com/Michael-J-Son/Lending_Club_CapstoneCapstone Project 2: Melanoma Tumor Size Prediction• Developed a regressor to predict melanoma tumor sizes.• Tools Used: NumPy | pandas | Matplotlib | Seaborn | Scikit-Learn | Keras• Best Model: Random Forest• Best Model Performance: MSE = 8.25 | R2 = 0.23• GitHub: github.com/Michael-J-Son/Melanoma_CapstoneTopics covered in mini-projects• Data science workflow: data wrangling, EDA, preprocessing, modeling, presentation• Data visualization and data storytelling• Inferential statistics• Hyperparameter tuning: grid search, bayesian optimization• Machine learning: random forest, xgboost, knn, k-means clustering• SQL and databases• Linear and logistic regression• Time series analysis

• Evaluated potential algorithms for the signal processing of 3D imaging sensors based on white light scanning interferometry (WSI).• Proposed and implemented an alternative SEST algorithm, achieving significant improvements in scanning speed while maintaining accuracy.• Inspected and revised technical translation of imaging device configurations for QA.

Seoul, Kr

• Performed cell culture, western blot, immunocytochemistry, DNA/RNA isolation, and molecular cloning for the in vitro loss-of-function studies on PAR 3/6 proteins.• Verified the extent of PAR 3/6 proteins' influence on axon regeneration of adult mice dorsal root ganglion.• Planned and conducted a chronic stress model study investigating axon regeneration in adult mice neurons.

Master Of Science, Chemical And Bioengineering

Honours Bachelor Of Science, Neuroscience And Cell Molecular Biology Double Major