Welcome to My Blog #

🌟 About Me #

I am Zeren Shen, a Machine Learning Engineer with a focus on Generative AI, 3D Motion Analysis, and Computer Vision. My journey began with a solid academic foundation from the University of Toronto, where I pursued a Master’s in Mechanical and Industrial Engineering with an emphasis on Analytics, and the University of Waterloo, where I earned a Bachelor’s degree in Statistics and Actuarial Science with a minor in Computer Science. Over the years, I have had the privilege of applying these skills to various projects, where I’ve collaborated with talented teams to develop AI-driven solutions aimed at solving complex real-world problems.

• 📍 Location: Toronto, ON, Canada
• 🎓 Education:
  • Master’s: Mechanical & Industrial Engineering (University of Toronto)
  • Bachelor’s: Statistics and Actuarial Science (University of Waterloo)
• 📬 Contact:
  • Email: zeren71415@gmail.com
  • GitHub: Followb1ind1y
  • LinkedIn: Zeren Shen
  • Resume: Download my Resume

🚀 Professional Experience #

Machine Learning Engineer #

ThinkGenAI Lab Inc. (May 2023 - Nov 2024)

• Spearheaded the development of an exoskeleton glove to correct piano hand gestures using a conditional Diffusion Model and the MANO hand model, enhancing gesture accuracy by 25%.
• Designed and implemented algorithms for extracting and analyzing 3D body joint data from single RGB videos for golf posture evaluation, employing real-time classification with diffusion models to provide actionable feedback and improve user training experiences.
• Engineered a voice-activated dialogue toy integrating GPT APIs and TTS tools, which enhanced system architecture and cloud service integration, significantly boosting user engagement with real-time, interactive educational content.

Research Assistant #

University of Toronto (Sep 2021 - May 2022)

• Implemented and deployed FCN, U-Net and Deeplabv3 models with TensorFlow and Keras for precisely detecting and tracking pores in the metal 3D printing process, attaining a MeanIoU score of 0.93.
• Conducted extensive experiments to optimize model hyperparameters, reducing the error rate from 10% to 6%.
• Designed a solution for tracking bubbles during the 3D printing process in industrial production, resulting in a 15% improvement in bubble detection and analysis.

Autonomous Driving Research Intern #

Zhejiang University (May 2018 - Aug 2018)

• Led critical data collection initiatives from driving environments to support further training of computer vision models, contributing to the research in self-driving vehicle technology.
• Collected and analyzed data from over 50 driving hours, capturing 100,000+ frames of video data and 500,000+ data points for various sensor modalities, including LiDAR and GPS.

🛠️ Projects #

• State Farm Distracted Driver Detection: Developed advanced image classification models using ensemble methods to identify distracted driving behaviors from dashboard camera images, improving safety insights and insurance applications.

• Semantic Segmentation of Aerial Imagery: Implemented semantic segmentation models to classify every pixel in aerial images into six categories, enabling precise analysis of urban landscapes and infrastructure.

• Face Mask Object Detection: Built real-time object detection models to identify and classify masked and unmasked faces, addressing public health needs during the COVID-19 pandemic with live-stream and video applications.

• Generated Pokémon:Designed and trained diffusion models (DDPM and DDIM) to generate novel Pokémon designs, showcasing creativity through AI-powered synthesis of original and diverse characters.

Explore more on my GitHub!

📚 Key Skills #