- Minimizes architecture conflicts in model deployment by adapting current model exporting tools (Pytorch-ONNX-OpenVinoIR) to contrastive learning schema, achieved better CI/CD- Elevated model interpretation with a simplified inference pipeline for inspecting model checkpoints, capable of computing model performance metrics and plotting tSNE on inference embeddings Helped recognize problematic data augmentations causing negative pairs under-sampling in contrastive learning- Achieved 96% faster data processing in an inference pipeline by converting bottlenecks of Python ETL modules into C language and implementing cross-language parallel computing on HPC systems- Productionized our sequence alignment framework by extending inference pipeline with HNSW search and necessary data infrastructure with self-written internal APIs for data streaming- Reduce manual intervention in model development by utilizing GridSearch for hyper-parameters tuning and DWGSIM for biological data synthesizing

- Developed a fully local data preparation pipeline for benchmarking company’s ML models, including a JSON-based warehouse with I/O format handler, and an ETL modules transforming raw HTML files to clean, usable datasets- Carried out insights and 36 patterns in clients’ inquiries relating to the main product by conducting analysis on feedback responses from over 1,000 beta users; worked with heavily unstructured data from free-response fields - Assisted higher-ups in designing procedures to monitor anomalies in performance of a reinforcement-learning model

- Prepared class materials, graded homework and capstone projects for an intensive coding course teaching about basic Python programming concepts, and popular libraries for Data Science such as Sklearn, NumPy, Pandas, Matplotlib- Provided totally 20 hours of lecture in concepts introducing, detailed code explaining, questions answering to 28 students, resulted in them obtaining a solid foundation in using Python to work with data in only 2 week of the course