Risklens

Data ambiguity: Inputs were inconsistent and poorly labeled
Model reliability: Balancing false positives vs missed risks
Latency vs performance: Ensuring fast inference without degrading accuracy
Interpretability: Making outputs understandable for end users

RiskLens

Inspiration

Risk is often hidden inside noisy, unstructured data—whether in medical scans, operational logs, or visual inputs. The goal was to build a system that not only analyzes data but surfaces critical risks early, reducing reaction time and improving decision-making.

What I Learned

Integrating computer vision and deep learning into a practical pipeline
Working with noisy, real-world data instead of curated datasets
Designing for interpretability, not just accuracy
Managing trade-offs between model complexity and inference speed

How I Built It

Developed a pipeline: data ingestion → preprocessing → model inference → risk scoring
Applied deep learning models for feature extraction and anomaly detection
Implemented a scoring mechanism:

$$ Risk = f(x) = \sum_{i=1}^{n} w_i \cdot s_i $$

Where ( s_i ) are signals/features and ( w_i ) are learned weights