Founding Engineer at Ethical AI. Previously ML Research Intern at Axiom and at Scale AI.
I work on applied machine learning, with a recent focus on security and power-systems problems: anomaly and fraud detection, text and image classification, and representation learning on small tabular datasets.
| Project | What it does | Result |
|---|---|---|
| dga-domain-detection | Detects malware-generated domain names from the string alone (lexical + char n-gram features, XGBoost / LR / RF) on 300K domains from a 2.9M-row dataset. Includes recall-at-fixed-FPR operating points and a hold-out-a-malware-family generalization test. | 0.9957 ROC-AUC; 90.5% recall at 0.5% FPR |
| captcha-recognition-cnn | End-to-end PyTorch CNN that reads all four CAPTCHA characters without segmentation, using zone-wise convolutional classifiers and on-the-fly affine augmentation. | 82.3% whole-CAPTCHA accuracy on augmented test images |
| power-quality-fault-detection | Kernel PCA and a multi-head autoencoder as 3-D embeddings for an RBF SVM; K-Means vs. GMM clustering evaluated against the true fault labels. | 99% test accuracy; K-Means recovers 4 of 5 classes at ≥ 98% purity |
| power-system-fault-classification | Compares regression, multi-label, and multi-class framings of the same fault-classification task with a small MLP; binary fault detector as an extension. | 98.8% fault / no-fault accuracy |
| twitter-spam-detection | Character-trigram count and TF-IDF features with Naive Bayes, logistic regression, and linear SVM on 50K tweets from CRESCI-2017. | 95.7% test accuracy |
| credit-card-fraud-detection | Feature analysis and decision-tree baselines on a 0.17%-positive dataset; measures what SMOTE and Borderline-SMOTE actually buy. | 0.75 F1 on the fraud class |
Python · PyTorch · scikit-learn · XGBoost · pandas · NumPy · Hugging Face datasets · Jupyter