• B.Sc. in Information Technology (AI) -- VNU-HCM University of Science (GPA 3.77/4.0).
• Focused on Multimodal LLMs (Vision + Language) & Swift & Android apps with on-device/connected AI.
• Learning: Python, Swift, Kotlin/Compose, PyTorch, Docker for reproducibility.

Languages: Python * Swift * Kotlin
ML/DL: PyTorch * scikit-learn * NumPy * Pandas * Matplotlib/Seaborn * Pytorch Practices: data preprocessing & feature engineering * training & evaluation * model implementation * prompt/LLM integration
Tools: Git * Docker * Jupyter * XCode * Pycharm * Android Studio * Gradle

• iOS On-device AI integration Application
  _{Swift * SwiftUI * FoundationModels Framework (Apple Intelligent)}
  > Implement iOS Application using Swift and integrate AI features with FoundationModels Framework (On-device AI).

• Vision-Zephyr -- Multimodal LLM for Visual Commonsense Reasoning
  _{Python * PyTorch * CLIP * Zephyr LLM}
  > Implement & evaluate MLLM pipeline; multi-stage training (projector align - LoRA fine-tune); custom gating/fusion over CLIP layers.
  Repo: https://github.com/baohuyvanba/Vision-Zephyr

• xWalletWise -- Personal finance Android app with AI assistance
  _{Kotlin * Jetpack Compose * Gemini API}
  > Expense tracking, budgeting, reports; integrates AI for insights & smart summaries.
  Repo: https://github.com/baohuyvanba/AndroidSE-xWalletWise

• Deep-Learning -- Implementations of classic DL models
  _{Python * PyTorch}
  Repo: https://github.com/baohuyvanba/Deep-Learning

• Machine-Learning -- From-scratch ML algorithms & experiments
  _{Python * scikit-learn * NumPy}
  Repo: https://github.com/baohuyvanba/Machine-Learning

• Internships/Fresher roles in AI/ML, Android, or Applied AI teams
• Research engineering (multimodal, VLM/MLLM, computer vision)

Reach me: baohuyvanba@gmail.com * LinkedIn