
GenAI lead, Office of the CTO, Google Cloud
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UCLA
Doctor of Philosophy (PhD), Mechanical and Aerospace Engineering
January 1, 2010 – January 1, 2014
The Johns Hopkins University
MSE, Mechanical Engineering
January 1, 2008 – January 1, 2010
National Taiwan University
BSE, Mechanical Engineering
January 1, 2002 – January 1, 2006
Staff Software Engineer, Office of the CTO
January 1, 2025 – Present
Amazon
Applied Scientist, Amazon AGI
September 1, 2019 – November 1, 2024
Roche
Principal Deep Learning Engineer
July 1, 2017 – August 1, 2019
Genomic Health
Senior Data Scientist, Big Data Analytics
February 1, 2016 – July 1, 2017
Geneio Inc
Cofounder, Lead Data Scientist
November 1, 2014 – February 1, 2016
UCLA Jonsson Comprehensive Cancer Center
Research Scientist
July 1, 2014 – December 1, 2014
Los Angeles Metropolitan Area
Inference of Personalized Therapeutics by Deep Learning Model of Cancer Genomics
January 1, 2015 – Present
- Developed the mathematical model and implemented the Python code of a Hybrid Deep Learning Model which takes advantage of unsupervised pre-training to perform feature extraction from multiple sources, including gene mutations, gene expressions, methylation, chemical fingerprint, drug targets and lineage. The model was implemented with Theano and Pylearn2 to accelerate the code with GPU computing. - Deployed the deep learning code on AWS by Starcluster to facilitate parallel computing, and performed hyperparameter optimization. - Wrote object-relational API, deployed, and managed MongoDB, PostgreSQL databases to integrate multiple genomic and chemical data sources, and to control and record the numerical simulations.
Isoform Inference from Next-generation Sequencing Data
March 1, 2014 – Present
- Wrote and debug the Python code for maximum likelihood inference of noisy sequencing readout. Applied breadth first search to isoform variant graph traversal.
Homologous DNA Sequence Alignment and Inference of Genomic Sequence Variant
October 1, 2013 – November 1, 2013
- Wrote and tested the Python code to implement the dynamic programming algorithm to allow fast alignment of genetic sequences. - Applied Bayesian network inference to detect genetic mutations (SNP) from noisy DNA sequence readout.
Human Facial Image Reconstruction, Recognition, and Object detection
October 1, 2013 – December 1, 2013
- Programmed Eigen face representation of facial images using PCA and reconstructed testing images using them as basis. Applied Fishers discriminant analysis for gender classification. - Developed Matlab model for AdaBoost by constructing weak learners and applied for facial detection algorithm. The result was compared with RealBoost. - Implemented object classification using SVM library for objects such as car in different orientation and pedestrians by training different SVM model and combining them.
Searching of Optimal Combinatorial Cancer Therapy by Hybrid Network and Machine Learning Algorithms
June 1, 2012 – June 1, 2014
- Lead the team of scientists and engineers in this multidisciplinary project involving Machine Learning, High-throughput Robotic Cancer Drug Screening, Database Integration, and Design of Experiment. - Solving the combinatorial problem of multiple drug optimization by developing the mathematical model of a novel machine learning algorithm which combines Biological Network Feature Extraction and Gaussian Process Kernel Regression to generate drug efficacy projection. - Integrated large-scale messy biological dataset from multiple sources to achieve information gain. The dataset was assembled in PostgreSQL from sources including CCLE (cell genomic profile), STRING (protein network), STITCH (drug-chemical interaction), COSMIC(drug screening), all of which were used to train the machine learning model. - Verified the drug efficacy prediction with high-throughput cell-based screening, wrote the Matlab code to generate the input files to control and simulate a pipetting robot’s process.
Cultural Fit Analysis
The candidate's background is heavily research-oriented and focused on advanced scientific computing and machine learning in biotech/pharma and tech giants. While the technical skills are strong, the project diversity is concentrated within bioinformatics, genomics, and image processing. The target role of 'Data Analyst' might be a slight mismatch for someone with a 'Staff Software Engineer' and 'Applied Scientist' background, potentially indicating overqualification or a desire for a different type of challenge. The cultural fit would depend on the specific analytical challenges and innovation focus of the target team.
Soft Skills & Operational Fit
The candidate's project descriptions indicate strong problem-solving abilities, leadership in multidisciplinary teams, and a methodical approach to research and development. The experience in leading teams and integrating diverse datasets suggests good collaboration and operational fit for complex data initiatives.