0125 min
Architecture Review
Pressure-test your system design, data flow, model choice, and deployment plan before you build too far in the wrong direction.
A cleaner architecture map and next technical decision.
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Mei Chen
RESEARCH SCIENTIST
Computer Vision
Computer VisionRESEARCH SCIENTIST
+ Session Playbook
How to use this mentor.
0220 min
Data Pipeline Clinic
Bring schema, ingestion, validation, or evaluation issues and leave with a concrete next implementation step.
One pipeline bottleneck isolated with a fix path.
0320 min
Model Evaluation Review
Get feedback on metrics, test sets, baselines, failure modes, and whether your AI system is proving the right thing.
Sharper metrics, baselines, and failure-mode checks.
0415 min
Demo Readiness
Tighten the story, technical proof, and product walkthrough so judges can understand what you built fast.
A tighter demo arc and judge-facing explanation.
+ Challenge Fit
Best track match.
Track 01
AI Systems
For engineers who care about what happens after the model is trained. Build the infrastructure that makes AI reliable at scale.
Track 02
Data Infrastructure
For engineers who believe clean data is the hardest problem in ML. Build the pipelines, extraction systems, and data quality layers that ML teams depend on.
+ Before Session
Prep checklist.
01
Bring the system map
A rough diagram, data flow, or repo link helps the mentor find the real bottleneck fast.
02
Pick one decision
Use the session for one hard tradeoff, not ten vague questions.
03
Leave with an action
Every session should end with one implementation step your team can ship next.