Graduate-level systems research, distributed computing analysis, and practical algorithm experimentation.
Influential publications I reference when studying distributed systems, fault tolerance, and modern infrastructure design.
How Google processes massive datasets on commodity hardware with a fault-tolerant, distributed model; a cornerstone for batch processing systems.
Lamport's classic on ordering events in distributed systems and the rationale for logical clocks.
Architecture and design decisions behind a scalable file system used for large data processing workloads.
A foundational exploration of eventual consistency, gossip protocols, and partition-tolerant system design.
A consensus algorithm designed for clarity and practical implementation which is often used as an approachable alternative to Paxos.
Practical experience building distributed algorithms that improve system reliability, performance, and scalability.
// A fixed-size sliding window implementation designed to handle high-velocity caption ingestion while maintaining constant memory usage in the browser.
// Implementing thread barriers to study synchronization primitives and expose race conditions in concurrent programs.
// A distributed key-value store built with thread pools and replication primitives to explore consistency and performance tradeoffs.
// A fault-tolerant master-worker implementation that manages task distribution, handles worker failure detection, and aggregates intermediate data reduction.
// An ordering algorithm for the Django Scheduler that manages task execution based on priority weights and time-windows to prevent job starvation.
Open to discussing new projects and opportunities.
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