David
PhD Candidate
Building the future of AI with robust & scalable vision systems.
I am a PhD candidate specializing in Computer Vision and Machine Learning. My research focuses on self-supervised learning, generative models, and their applications in medical imaging and autonomous systems.
Previously, I interned at Google DeepMind and Meta AI. I am passionate about open-source and making AI accessible to everyone.
campaignLatest Updates
Accepted to CVPR 2024
My paper on "Self-Supervised Learning for Small Datasets" was accepted for an oral presentation.
Guest Lecturer at Stanford
Invited to speak about the future of Generative AI in Healthcare diagnostics.
Publications
View Google Scholar ->Self-Supervised Vision Transformers for Medical Image Segmentation
David, Sarah Connor, John Doe
Generative Adversarial Networks for Climate Modeling
Jane Smith, David
Efficient Neural Architecture Search
David, Alan Turing
Selected Projects
Experience & Education
downloadDownload ResumePhD Student, Computer Science
2021 - PresentMassachusetts Institute of Technology (MIT)
Researching self-supervised learning methods for medical imaging. Advised by Prof. Brilliant Mind. TA for "Intro to Deep Learning".
Research Scientist Intern
Summer 2023Google DeepMind
Developed a novel transformer architecture for video understanding, improving SOTA on Kinetics-400 by 1.5%.
AI Research Intern
Summer 2022Meta AI (FAIR)
Contributed to PyTorch core library optimizations for distributed training.
B.S. in Computer Science
2017 - 2021Stanford University
Graduated with Honors. Focused on Artificial Intelligence and Human-Computer Interaction.