About Me

I am a Master’s student in Computer Science at the University of Massachusetts, Amherst, focusing on Machine Learning and Computer Vision.

Experience

• CrowdStrike, Boston, USA
  • Data Science Intern (Aug - Dec 2024)
• Data Core, Amherst, USA
  • Data Science Intern (Feb - May 2024)
    • Developed a tattoo recognition model, optimizing and compressing long-duration video analysis to streamline processing efficiency. Labelled images using advanced tools such as LabelImg and CVAT, ensuring high-quality data annotation.
    • Utilized YOLOv8 for high-accuracy tattoo detection from extensive video footage. Integrated the powerful CLIP model to effectively cluster images based on detected tattoos with detailed meanings and context for each tattoo.
• Binghamton University (SUNY), New York, USA
  • Summer Research Intern (Jan - May 2023)
    • Investigated backdoor attacks in deep learning, achieving a 89% success rate in trojan attacks, highlighting the need for secure algorithms in Domain Adaptation.
• Okayama University, Okayama, Japan
  • Research Intern (Aug - Dec 2022)
    • Authored papers on using Swin Transformer for maize leaf disease classification (95.9% accuracy) and Vision Transformer for bearing defect classification (98.8% accuracy) using STFT.
• University of Maryland, College Park, USA
  • Research Intern (May - Jul 2022)
    • Engineered TeliNet 2.0 model for ECCV, analyzing 140GB of medical images; improved accuracy by 10%.
    • Achieved a top-quartile performance ranking in the competition by reducing computational resource requirements by 30%, showcasing exceptional skills in optimizing deep learning models for large-scale health data.

Career Goals

I am seeking full-time opportunities in Machine Learning, Computer Vision, or Applied Scientist roles. My goal is to contribute my skills and knowledge to the field, while continuing to develop my expertise in these cutting-edge areas.