Scholar
Daniel Etaat
Google Scholar ID: S-o9TCwAAAAJ
PhD in Statistics, Columbia University
Citations & Impact
Citations
3
H-index
1
i10-index
0
Publications
1
Co-authors
3
list available
Publications
Resume (English only)
Research Experience
- At UC Berkeley, involved in research to reconstruct table tennis matches from monocular video and developed an uncertainty-aware controller that anticipates opponent actions, improving responses to high-speed hits in simulation.
Education
- Currently a first-year Ph.D. student in Statistics at Columbia University; previously studied Computer Science and Mathematics at UC Berkeley, where he worked with Professor Shankar Sastry on computer vision and robotics.
Background
- Research interests broadly span the theory and practice of modern machine learning, including uncertainty quantification, generalization in deep learning, and the statistical foundations of AI. Particularly interested in how ideas from high-dimensional statistics can inform the design and understanding of scalable learning systems.
Miscellany
- Enjoys traveling, running, and playing basketball.
