🪧 Actively Seeking 2026 Full-Time Opportunities

👩‍💻 Data Scientist / Machine Learning Engineer
👩‍💻 Software Engineer (Python, SQL, Go)
🤖 Applied AI × Robotics × Multimodal Systems

🌱 Advocate for Diversity, Inclusion, Accessibility & Sustainability in Tech

🎮 Zelda | 🧘🏻‍♀️ Meditation | 🍻 Craft Beer | 🎢 Roller Coasters

I’m a Data Science Master’s student at the University of Colorado Boulder with strong foundations in machine learning, statistics, and software engineering, and hands-on experience building AI systems that actually run in the real world.

My work sits at the intersection of:

• 🤖 Machine Learning & AI Systems
• 🧠 Multimodal & LLM-based Applications
• 🦾 Robotics Perception & ROS Pipelines
• 🩺 Applied Computer Vision in Healthcare
• 🎶 Representation Learning for Bioacoustics

I enjoy taking ideas from research → prototype → deployment, and I care deeply about interpretability, reliability, and real-world constraints.

• Built a multimodal AI system combining LLMs, emotion recognition, and music recommendation
• Focused on empathetic human–AI interaction and practical deployment
• Designed end-to-end pipelines from data processing to model inference

Tech: Python, LLMs, Multimodal ML, ML Pipelines

• Developed a fine-grained image classification system for nail disease detection
• Trained ResNet18 & MobileNetV2 with Grad-CAM for interpretability
• Applied model calibration techniques to improve clinical reliability

Tech: PyTorch, Computer Vision, Model Interpretability

• Analyzed 100k+ bird audio recordings
• Used VQ-VAE to tokenize bird vocalizations into discrete representations
• Explored patterns for ecological monitoring and downstream modeling

Tech: PyTorch, VQ-VAE, Audio Processing, Unsupervised Learning

• Built vision-based perception pipelines for a quadruped robot using ROS
• Collected real-world data via teleoperation + rosbag recording
• Extracted image streams from rosbags and trained ResNet18-based perception models
• Deployed trained models back into the ROS runtime for real-time inference
• Explored the challenges of latency, data noise, and sim-to-real gaps

Tech: ROS, Python, OpenCV, PyTorch, Robotics Perception

• Designed automated Python testing pipelines for large-scale evaluation
• Improved testing efficiency by 30%+
• Built reusable validation workflows to support rapid experimentation

Tech: Python, Automated Testing, SQL, CI-style Pipelines

Languages

• Python · SQL · Go · R · JavaScript

ML / AI

• PyTorch · Computer Vision · Representation Learning · Model Calibration

Robotics

• ROS · OpenCV · rosbag · Robotics Perception

Engineering

• Automated Testing · Data Pipelines · Git · Linux

• 💼 LinkedIn: https://linkedin.com/in/wih
• 📷 Instagram: https://instagram.com/yuni.hsuan

I’m actively seeking 2026 full-time roles in:

• Machine Learning Engineering
• Data Science
• Software Engineering
• Applied AI / Robotics

If you’re building systems that combine ML + software + real-world impact, I’d love to chat.