"Science is an error-correcting process." — Charles S. Peirce

I'm a doctoral researcher at Tampere University, specializing in machine learning for audio understanding. I'm passionate about teaching machines to hear, interpret, and respond to sound like humans do.

Before entering research, I spent over 7 years as a software engineer, giving me a strong foundation in building scalable systems and solving real-world problems. I now work at the intersection of ML research, software engineering, and AI-driven audio applications, combining scientific depth with hands-on development skills.

• 🎧 Machine Learning for Audio Understanding (classification, detection, retrieval, generation)
• 🔍 Self-Supervised Representation Learning
• 🔄 Multimodal Learning (audio + text/vision)
• 🧩 Low-Resource Learning (zero-shot, few-shot)

• 💻 Programming: Python, Java, Scala, JavaScript, SQL, C/C++, R, Matlab, LaTeX, GDScript
• ⚛️ Machine Learning: PyTorch, TensorFlow, scikit-learn, Ray Tune, MLflow, Spark
• 🗣️ Audio & NLP: librosa, torchaudio, NLTK
• 📊 Data Analysis: NumPy, SciPy, Pandas, Jupyter, Matplotlib
• 🌐 Web, Backend & Architectures: Java EE, Spring, Hibernate, Django, Flask, RESTful APIs, Microservices, Event-Driven Architectures
• 📱 GUI & Game Development: PySide6, Godot Engine
• 🛢️ Databases & DevOps: MySQL, PostgreSQL, Linux, Docker, Git
• ⚙️ Concurrency & Systems: Multi-threaded Programming, HPC

• 🔎 Multimodal Audio-Text Retrieval System – Developing models that match audio clips with text queries using multimodal learning.
• 🤖 X-GoBot – 🔧[WIP] Developing a voice-enabled desktop AI assistant with local processing and contextual awareness.

I'm always open to conversations about audio ML, applied AI, or building smart, sound-aware systems. Whether you're in research, industry, or tinkering with side projects — feel free to reach out!

📫 Email: huang.xie@outlook.com 🔗 LinkedIn: linkedin.com/in/huang-xie-28b7872bb