David McAllister
I'm a PhD student at UC Berkeley advised by Prof. Angjoo Kanazawa. I'm deeply interested in
EECS
August 2024-Present
EECS
Graduated May 2024
EECS
+ French MinorAcademic Honors
Tech Coursework: Data Structures and Algorithms, Structure and Interpretation of Computer Programs, Multivariable Calculus, Discrete Math and Probability Theory, Computer Architecture, Probability and Stochastic Processes, Machine Learning, Database Systems
Student Organizations: Venture Strategy Solutions, The Daily Californian
D. McAllister*, M. Tancik, J. Song, A. Kanazawa, "Decentralized Diffusion Models" In Submission. | arXiv
D. McAllister*, S. Ge*, J. Huang, D. Jacobs, A. Efros, A. Holynski, A. Kanazawa, "Rethinking Score Distillation as a Bridge Between Image Distributions" in NeurIPS 2024 | arXiv
E. Whang*, D. McAllister*, A. Reddy, A. Kohli, and L. Waller, "SeidelNet: an aberration-informed deep learning model for spatially varying deblurring" in AI and Optical Data Sciences IV. Vol. 12438. SPIE, 2023 | Publication
A. Kohli*, A. Angelopoulos*, D. McAllister, E. Whang, S. You, K. Yanny, and L. Waller, "Ring Deconvolution Microscopy" | arXiv
M. Tancik*, E. Weber*, E. Ng*, R. Li, B. Yi, J. Kerr, T. Wang, A. Kristoffersen, J. Austin, K. Salahi, A. Ahuja, D. McAllister, and A. Kanazawa, "Nerfstudio: A Modular Framework for Neural Radiance Field Development", in ACM SIGGRAPH 2023 Conference Proceedings | arXiv | GitHub
For more information, please refer to my resume above.
I have experience in portraiture, photojournalism and nature photography. Feel free to check out some of my work.