Researcher focused on Operations Research and Machine Learning, exploring the synergy between optimization and intelligent systems.
Optimization × ML
Branch-and-Price • Neural Networks • Time Series
I'm a researcher and academic with a strong focus on Operations Research and Machine Learning. My work explores principled optimization methods and modern learning techniques to improve decision-making in engineering and real-world systems.
My research interests span the intersection of classical optimization theory with contemporary machine learning, particularly in developing efficient algorithms for large-scale problems and intelligent decision support systems.
Branch-and-Price, Decomposition, Large-Scale Optimization
Optimization-aware ML, Neural OR, Learning to Decide
Deep Learning, Sequence Modeling, Multi-step Prediction
Built from titles, abstracts, and keywords across publications.
Bucket Graph Labeling
Modern C++ implementation of Branch-Cut-and-Price bucket graph labeling algorithm for Vehicle Routing Problems with state-of-the-art optimization techniques.
Time Series Library
Flexible and modular PyTorch library for time series forecasting with multiple neural architectures and strategies.
Interior Point Method
Fast C++-based Interior Point Method library with Python bindings tailored for Linear Programming.
NLP Solver
Modular Nonlinear Programming solver using SQP, Interior Point, and DFO-L1 strategies.