Experience

• JULY 2024 - FEBRUARY 2026

  MLOps Engineer - GCP @ Aignostics

  Developed ML pipelines for early-stage lung cancer discovery.

  • Redesigned ML training pipelines on GKE, cutting training time by 90%, which enabled the data science team to run significantly more experiments at lower cost while keeping critical research milestones on schedule.
  • Built an internal GPU provisioning AI platform on GCP that eliminated 18+ hours/day of wait time for the data science team, removing a major bottleneck and accelerating model development cycles.
  • Cut Docker image build time by 90%, shrinking CI/CD cycles so engineers could test and deploy changes in minutes instead of hours, which improved overall release velocity.
• DECEMBER 2023 - MAY 2024

  Software Engineer Intern - AWS @ NIO

  Develop car AI assistant in NOMI team.

  • Improved speech recognition accuracy by 35% over Microsoft's baseline by fine-tuning Whisper from HuggingFace, directly improving the reliability of NOMI in-car voice assistant, especially in multi-language use cases, e.g. "Navigate to Berlin Hauptbahnhof".
  • Built an automated benchmarking leaderboard for ASR models with Airflow, freeing up 10+ hours/week previously spent on manual QA, which let the team focus on model improvements instead of evaluation overhead.
• OCTOBER 2022 - MAY 2023

  Software Engineer Intern @ IBM

  Designed an ML-based anomaly detection algorithm for PostgreSQL on Kubernetes as part of my Master's thesis — laying groundwork for catching malicious or anomalous database activity early, before it causes downtime or security incidents.

  • Achieved 95% accuracy on spotting anomalous events with unsupervised learning models.
  • Reduced 85% of metric space by feature engineering to retain relevant metrics.
• May 2022 - SEPTEMBER 2022

  Student Research Assistant @ Universität Rostock

  Analyzed fuel consumption of large cargo vessels to identify key cost drivers, supporting route optimization for a shipping company.

  • Reduced fuel consumption prediction error by 60% by developing machine learning models to identify the primary factors driving fuel usage — giving the cargo company more reliable forecasts to optimize shipping routes and cut fuel costs.
  • Built an interactive data visualization dashboard for exploratory data analysis, enabling the research team to quickly identify and communicate the biggest factors behind fuel consumption across the fleet.
• APRIL 2021 - SEPTEMBER 2021

  Data Scientist Intern @ Teraki

  Developed radar object detection models for autonomous driving system.

  • Accelerated radar data preprocessing by 10x by rewriting the Python bottleneck in Cython, cutting training pipeline runtime from hours to minutes, which enabled the team to iterate on end-to-end radar object detection models for the first time.
  • Developed end-to-end deep learning models in PyTorch for radar-based vehicle and pedestrian detection, deployed on edge hardware, contributing working perception models to Teraki's safety-focused ADAS stack.
• OCTOBER 2018 - JUNE 2020

  Machine Learning Researcher @ ElsaLab

  Researched machine learning approaches in image processing and reinforcement learning; designed, implemented, and evaluated experimental frameworks. The results are published in top conferences such as CVPR and ICML.

  • Co-authored CVPR 2020 and ICML 2020 papers on visual odometry and reinforcement learning, contributing methods that advanced the lab's research on autonomous navigation and robot decision-making.
  • Built distributed training infrastructure across GCP and on-prem GPUs, cutting experiment turnaround from weeks to days — the speed-up that made the lab's publication output possible.
  • Implemented C++/ROS control systems to deploy trained models on physical robots, validating research results in real-world navigation conditions.
• 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.