I am an undergrad studying CS with a focus on ML & distributed systems at Stanford.

I'm interested in solving problems at scale involving search, compute, data, systems, and ML. This past summer, I worked on lowering the cost of pre-training foundation models at AWS AI on the Deep Engine Science team. At Stanford, I work with Jared Quincy Davis on Foundry as an intern member of the Technical Staff (also affiliated with the Future Data Systems Group). I also work on AI/data at Lux Capital.

I grew up in the San Francisco Bay Area.

If you want to get in touch, email me at sidshr@stanford.edu.

Projects

• Monosemanticity: a rapid indexing and visualization layer to understand how groups of neurons (features) activate based on the latest mechanistic interpretability research.
• Augment: open-source data infrastructure for labeling, RLHF, and augmenting ML training data.
• Cambrian: the co-pilot for AI research: ML-powered search, bookmarking, and QA.
• Cardinal Compass: Stanford's first natural language course search. Went viral and was featured in the school paper.
• CardOptimize: Schedule optimizer for requirements.
• Plato: AI pair-programming platform powered by your voice.

Writings

• The Path Forward: a primer on RL
• Less can be more: Sparsity as a paradigm for LLM development
• On Oppenheimer: Why the Truth must Prevail
• The Life-like Frontier: Simulation Theory and AGI
• Micro-Speed, Macro-Patience: knowing what to optimize for as a college student
• Universal Gradient Descent: a smorgasbord on the future of innovation, uniformity of thinking, globalization, surveillance, and what it means to be human.
• The AI Arms Race: technical nation-state creation in the age of AI
• Approaching Human: Reflections on Large Language Models
• An Intro to TensorFlow
• A Primer on Quantum Computing
• The Ultimate Beginners Guide to Reinforcement Learning
• Understanding Pose Estimation in Computer Vision
• Using ML to fight the coronavirus