Academic Work
I am a researcher in Computer Science. My work focuses on distributed systems, machine learning efficiency, and the formal foundations of concurrent computation.
I am a researcher in Computer Science. My work sits at the intersection of distributed systems and machine learning, with a focus on making large-scale systems more reliable, efficient, and easier to reason about.
This example site is intentionally compact: it shows how the theme handles research outputs, working notes, downloadable documents, software projects, teaching material, and a dedicated Theme reference page in a single academic profile.
We present AdaptRaft, a variant of the Raft consensus algorithm that dynamically adjusts its timing parameters using a lightweight online learning model, reducing tail latency by up to 34% in geo-distributed deployments.
A structured survey of Byzantine fault-tolerant consensus protocols deployed since 2015, identifying the practical trade-offs between safety, liveness, and throughput that are often obscured in theoretical treatments.
We explore the applicability of learned index structures to distributed key-value stores, demonstrating that model-based indices can reduce lookup latency by 22% with a modest increase in memory footprint on skewed workloads.
Notes on the FABRIC strategy for verifying nonlinear neural feedback systems by combining forward reachability, backward reachability, polyhedral enclosures, MILP encodings, DRiPy refinement, and FITS inner-set construction.
Notes on Vincent & Schwager (2022) introducing the Reachable Polyhedral Marching (RPM) algorithm for exact reachability analysis of ReLU neural network controllers.
A showcase note demonstrating image embedding, a borderless striped table, lists, code, and LaTeX rendering in the Academic Work theme.
Full academic CV including education, publications, talks, awards, and service.
A curated reading list for anyone starting research in distributed systems — foundational papers, key textbooks, and a handful of essential blog posts.
My undergraduate thesis comparing PBFT, HotStuff, and Tendermint across latency, throughput, and message complexity in a simulated WAN environment.
A modified Raft implementation with an online bandit learner that adapts election and heartbeat timeouts to observed network conditions. Companion codebase for the SOSP 2024 paper.
A configurable benchmarking harness for distributed consensus protocols. Supports pluggable protocol implementations, synthetic and replay workloads, and structured JSON output for reproducible experiments.