👤 About Me
I am a mathematician and machine learning researcher passionate about principled, rigorous approaches to artificial intelligence. My work lies at the intersection of theoretical foundations and practical deployment — from clustering algorithms to distributed inference at the edge.
Driven by intellectual curiosity, I pursue genuinely difficult problems with a focus on precision, independence, and depth.
Previous web presence: Site v1 | Site v2 | Current
| 🧩 Clustering & Multi-View Learning | Developing robust algorithms for unsupervised structure discovery across heterogeneous data representations. |
| 🤝 Hybrid Learning Architectures | Bridging classical supervised methods with modern deep learning pipelines for improved generalization. |
| 🌐 Federated & Distributed Learning | Privacy-preserving model training across decentralized nodes with communication constraints. |
| ⚡ Edge AI & Cyber-Physical Systems | Deploying intelligent inference in resource-constrained, real-time environments. |
| 🔍 Pattern Recognition | Statistical and geometric approaches to identifying structure in high-dimensional data. |
| Domain | Competencies |
|---|---|
| Machine Learning | Supervised, unsupervised, and semi-supervised learning; model evaluation and selection |
| Deep Learning | Neural architecture design, optimization, regularization |
| Data Analysis | Statistical modeling, dimensionality reduction, visualization |
| Edge AI | Model compression, on-device inference, latency-aware deployment |
| Pattern Recognition | Feature engineering, classification, anomaly detection |
| Certification | Issued | Credential | Issuer |
|---|---|---|---|
| Machine Learning Specialization | Oct 2022 | View Certificate ↗ | Coursera · DeepLearning.AI |
| TensorFlow Developer Specialization | Nov 2022 | View Certificate ↗ | Coursera · DeepLearning.AI |
🌍 Availability
I am open to global opportunities in machine learning research, edge AI systems, and distributed computing. My work combines strong mathematical foundations with practical, hands-on implementation.
📬 Contact: kristinasinaga41@gmail.com
🌟 What Sets Me Apart
- Mathematical rigour: Analysing and designing models with precision beyond implementation.
- Independent thinking: Assessing problems on their merits rather than following trends.
- Resourcefulness: Treating constraints as design parameters, not obstacles.
- Depth of focus: Prioritising mastery and high standards in all output.
■ Advancing research in distributed and federated learning
■ Preparing for new professional engagements (actively seeking new opportunities)
"Rigour is not a constraint on creativity — it is its foundation."