I’m Emmanuel Sekyi, a machine learning researcher and engineer. My work focuses on developing novel machine learning systems, with particular emphasis on anomaly detection and active learning.
Research Interests
My research interests lie at the intersection of:
- Machine Learning Systems
- Active Learning
- Anomaly Detection
- Deep Learning
- Scientific Machine Learning
Publications
2022
- Learning to Detect Interesting Anomalies
Alireza Vafaei Sadr, Bruce A. Bassett, Emmanuel Sekyi
arXiv preprint
arXiv:2210.16334
Abstract: Anomaly detection algorithms are typically applied to static, unchanging, data features hand-crafted by the user. But how does a user systematically craft good features for anomalies that have never been seen? Here we couple deep learning with active learning – in which an Oracle iteratively labels small amounts of data selected algorithmically over a series of rounds – to automatically and dynamically improve the data features for efficient outlier detection. This approach, AHUNT, shows excellent performance on MNIST, CIFAR10, and Galaxy-DESI data, significantly outperforming both standard anomaly detection and active learning algorithms with static feature spaces.
Projects
AHUNT
An active learning framework that combines deep learning with dynamic feature space adaptation for improved anomaly detection. The system demonstrates superior performance on various datasets including MNIST, CIFAR10, and astronomical data.
Paper · GitHub