Anomaly Detection Using Gaussian Model

Gaussian anomaly detection with density modeling, threshold tuning, and F1 evaluation.

Track

University / Personal Experiments

Problem, execution, and outcomes

Problem

Anomaly detection needed a simple probabilistic baseline with measurable threshold selection.

Execution

Implemented univariate and multivariate Gaussian modeling, validation-set threshold tuning, and F1-based evaluation.

Outcomes

Built a statistical anomaly workflow.

Compared false-positive and detection behavior.

Documented threshold selection logic.

Proof and Metrics

Gaussian modelThreshold tuningF1 evaluation

Capability Proven: Applied ML workflow design with evaluation-first optimization under operational constraints.

Clinical ML Pipeline

Stack

MATLABGaussian ModelThresholding

Electrical engineering AI assistant with retrieval, cited answers, and review workflows.

Proof

RAG workflow

Stack

Next.js / TypeScript / RAG / +2

SEER survival analysis with Kaplan-Meier, Cox PH, and Random Survival Forest models.

Proof

ICECET 2023

Stack

Python / Scikit-learn / Survival Analysis / +1

PMSM FOC assessment with PI controller redesign and response-quality validation.

Proof

PMSM FOC

Stack

MATLAB / Simulink / FOC / +2