Reports

Introduction Hey there, I’m Michael. This is my final report for the Reconfigurable and Placement-Aware Replication for Edge Systems project, under the mentorship of Fadhil Kurnia. In my introduction blog I described the goal as extending Distrobench with the ability to benchmark placement algorithms.

In my GSoC Project Proposal, I set out to expand the LINQS Lab’s LMS-Toolkit functionality to support Moodle. I was eager to begin my open-source journey and excited to use my programming skills to contribute to a production software system.

My final report for OSRE 2026 — TileTalk, a framework that answers natural-language, cell-aware questions about H&E histology images using spatial-omics supervision only at training time, together with an open benchmark, a full baseline suite, and an end-to-end reproducible pipeline.

This is the final update about the status of my project to improve OCEAN’s infrastructure and introducing an experimental analytical model for CXL tiering evaluation and studies

A final reflection on building and deploying NRPilot: an open-source AI assistant that combines read-only Kubernetes diagnostics with official NRP documentation.

This is a midterm update about the status of my project to improve OCEAN’s infrastructure to enable hierarchical memory tiering studies

Midterm update for my OSRE'26 project with UC Santa Cruz OSPO: CauST now runs end-to-end on real spatialLIBD DLPFC data, where 25 causally-selected genes beat the HVG baseline — and the full 2,000-gene pool — on a donor the selection never saw.

Midterm update for my Google Summer of Code 2026 project with UC OSPO, covering CarbonCast, EnergyAPI, regional forecasting models, and the initial implementation of JobAPI and TemporalAPI.

A mid-term update on StaR — the largest seed-sensitivity study of spatial-domain methods to date (6 methods x 1,000 seeds x 12 tissue sections), and SpaGVAE, the structured-spatial-prior model we built to fix the instability we found.

A mid-term update on building CellQuery-ST — a framework (which we call TileTalk) that answers natural-language, cell-aware questions about H&E histology images using spatial-omics supervision, with an open benchmark and reproducible baselines.