Our ongoing research spans intelligent systems, applied machine learning, and data-driven solutions for real-world problems.
Developing machine learning models for real-time anomaly detection and predictive analysis in large-scale network infrastructures. Our approach combines unsupervised learning with domain-specific heuristics to achieve low false-positive rates in production environments.
Applying deep learning techniques to automate visual inspection and structural assessment of civil engineering assets.
Building scalable data pipelines and intelligent analytics tools for extracting actionable insights from heterogeneous data sources.
Researching advanced NLP methods for information extraction, text understanding, and multilingual document processing.