Experience

• JULY 2024 - CURRENT

  Machine Learning Engineer - MLOps @ Aignostics

  I work in a cross-functional team to build AI solutions for drug discovery. My main focus is on optimizing machine learning (ML) algorithms and maintaining scalable ML infrastructure on cloud platforms.

  • Deployed scalable ML training pipelines using Google Kubernetes Engine (GKE), reducing model training time by 10x through improvements in infrastructure and TensorFlow/PyTorch algorithms.
  • Optimized Docker images and CI/CD build processes, reducing build time by 90% by refining image layers and caching Python dependencies.
  • Developed an internal tool for cost-efficient GPU access on Google Cloud Platform (GCP), saving the team over 18 hours per day in development time.
• DECEMBER 2023 - MAY 2024

  Software Engineer Intern - AWS @ NIO

  I contributed to the NOMI team by building an Automatic Speech Recognition (ASR) system. My responsibilities included data collection, preprocessing, model training, and evaluation. I also created a benchmark leaderboard for tracking ASR model performance.

  • Improved German ASR accuracy by 35% on internal benchmarks by fine-tuning OpenAI Whisper models and applying custom audio preprocessing in Python.
  • Built a benchmarking pipeline using Apache Airflow to automate weekly ASR model evaluations and developed a front-end for data visualization, saving over 10 hours of manual work per week.
• OCTOBER 2022 - MAY 2023

  Software Engineer Intern @ IBM

  Worked with the cloud database team to research anomaly detection for distributed SQL databases. Completed my Master's thesis with IBM, focusing on AI-driven event detection for PostgreSQL systems.

  • Designed and implemented a data-driven error detection algorithm for PostgreSQL running on Kubernetes clusters using Python and machine learning techniques.
• APRIL 2021 - SEPTEMBER 2021

  Data Scientist Intern @ Teraki

  Teraki provides edge AI software for vehicles and IoT devices. I developed object detection models for time-series radar data to identify vehicles and pedestrians.

  • Accelerated radar data preprocessing pipelines by 10x by integrating Cython into the Python codebase.
  • Developed end-to-end deep learning solutions for radar object detection using PyTorch and deployed models on edge devices.
• OCTOBER 2018 - JUNE 2020

  Research Assistant @ ElsaLab

  Led machine learning projects in image processing and reinforcement learning, from research to deployment. My work was published in top conferences such as CVPR and ICML.

  • Developed state-of-the-art visual odometry solutions on the KITTI benchmark using TensorFlow.
  • Designed distributed training strategies for ML models using on-premises servers and Google Cloud Platform (GCP).
  • Implemented robot control systems for self-driving cars using C++ and Robot Operating System (ROS).
• MAY 2019 - AUGUST 2019

  Student Research Assistant @ National Tsing Hua University

  Led an industrial-academic collaboration project to develop an intelligent seeding machine for Malabar Chestnut.

  • Designed image classification algorithms using OpenCV and Python to improve seed orientation detection and increase production rate by 90%.
  • Implemented automation control systems for motors and pneumatic devices using C language and embedded systems.