Nadia Petrova
Materials ML Engineer · Property prediction, active learning
Active-learning loop identified three experimentally-validated novel alloys. Maintains an open-source DFT active-learning toolkit.
Artifact Gallery
Open-source toolkit for active-learning loops over DFT calculations.
Aug 20, 2026ML pipeline for drug discovery with reproducible Snakemake workflows.
Jul 17, 2026Physics-informed model improving 72-hour wind forecasts by 18%.
Jun 14, 2026Jupyter notebook for ML property prediction with active learning.
May 11, 2026Containerized workflow with Nextflow and DOI assignment.
Apr 8, 2026Process Trace Summary
- Repository initializedJul 5
Created project scaffold with README and license.
- First commit pushedJul 7
Initial proof-of-concept with placeholder data.
- Evaluation harness addedAug 9
Wired up 50-question regression suite with LLM-as-judge.
- Peer review feedbackAug 11
Two reviewers flagged edge cases in retrieval fallback path.
- Iteration — fallback hardenedSep 13
Added retry + validation; eval score improved 12 points.
- Final submissionSep 15
Artifact submitted for oral defense scheduling.
Oral Defense Transcripts
Recorded Q&A from each verified oral defense session. Expand a session to read the transcript.
Walk us through the architecture of your artifact. Why did you choose this approach?
I chose a plan-and-execute topology because the task required multi-step retrieval with reflection. The plan node decomposes the query, sub-agents retrieve and draft in parallel, and a reflection node scores and routes for a second pass when below threshold.
What evaluation did you run, and what were the headline numbers?
I ran a 50-query regression suite scored by LLM-as-judge calibrated against a human panel (0.86 agreement). Baseline scored 71%; the reflection pass lifted it to 88% with a 14% latency cost, which stayed within budget.
Describe a failure mode you found and how you mitigated it.
Retrieval fallback returned stale context on schema changes. I added a freshness check + retry with a smaller context window, which reduced stale-grounded answers from 9% to under 2%.