| CARVIEW |
David McAllister
I'm a PhD student at UC Berkeley advised by Prof. Angjoo Kanazawa. I'm deeply interested in
Education
UC Berkeley
PhD Student in EECS
August 2024-Present
5th Year Research M.S. in EECS
Graduate Student Researcher at Berkeley AI Research
Graduated May 2024
B.S. in EECS + French Minor
Graduated May 2023, GPA: 3.92
Academic 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
Publications
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
Experience
DoorDash - Machine Learning Engineering Intern
- Engineered distributed LLM training and inference infrastructure to optimize and deploy across dozens of GPUs (Ray)
- Planned, developed and trained new transformerized restaurant recommendation algorithm integrating discrete order history and human language food descriptions, outperforming previous algorithm in accuracy and diversity
- Prototyped personalized notification generation with QLoRA fine-tuned Llama 2 large language model
DoorDash - Software Engineering Intern
- Built full-stack Mailchimp app integration for DoorDash merchants to sync customer contacts for marketing campaigns
- Engineered fault tolerant secret encryption and storage (HashiCorp Vault), data retrieval (Snowflake), contact uploads (REST), monitoring for failed jobs, and recurring incremental syncs through multiple microservices (AWS, gRPC)
- Developed and deployed production web interface to perform OAuth2-compliant handshake, onboard merchants, and reflect sync state (ReactJS, Redux)
Apple - Machine Learning Intern
- Implemented unsupervised ML models for manufacturing technicians to identify and diagnose failures (XGBoost)
- Improved interpretability for non-technical audiences using dimensionality reduction (T-SNE) and feature extraction
Gilead Sciences - Software Image Analysis and Machine Learning Intern
- Developed deep learning models to measure the development of fibrosis in cross-sectional scans of diabetic mice
For more information, please refer to my resume above.
I take a lot of photos
I have experience in portraiture, photojournalism and nature photography. Feel free to check out some of my work.
