[ 03 ] Research
Deep dives into cutting-edge topics at the intersection of computer science, AI, and distributed systems.
Exploring automated machine learning techniques that work on edge devices. How can we design neural networks that discover their own optimal architectures while respecting memory and computational constraints?
Investigating the boundaries between pattern recognition and genuine creativity in generative systems. What does it mean for a machine to be creative?
A practical look at CAP theorem trade-offs in modern distributed architectures. Examining real-world systems and their consistency models.
How do we design interfaces where humans and AI systems work together effectively? Lessons from observing collaborative workflows in the wild.
Investigating methods to reduce transformer computational overhead while maintaining performance. The path to democratizing large language models.