Scholar
Connor Ding
Google Scholar ID: CYUwVC8AAAAJ
Stanford University
Citations & Impact
Citations
1
H-index
1
i10-index
0
Publications
3
Co-authors
0
Publications
Resume
Background
- Currently a second-year MS student in Electrical Engineering at Stanford University.
- Research interests include information theory, generative modeling, image compression, coding theory, and statistical estimation.
- Focuses on both theoretical foundations of information theory and its applications in generative modeling of discrete data, image compression using implicit neural representations, algebraic coding for efficient communication/storage, and statistical estimation for improved sampling and inference.
- Previously trained as a neuroscientist and remains interested in the mathematical underpinnings of neuroscience methods and better techniques for neural data acquisition, processing, and analysis.
- Hopes to eventually apply his work in information theory and machine learning to neuroscience—for example, deploying powerful ML models inside an MRI scanner.