Dedicated to advancing the field of Industrial Engineering through the integration of cutting-edge methodologies in the following areas:
Stochastic and Data-Driven Optimization: Focus on creating stochastic models and algorithms that incorporate uncertainty and real-world data to make optimal decisions in complex environments. This area is pivotal in industries like transportation, supply chain, and energy management, where data uncertainty plays a significant role.
Statistical Machine Learning: Focus on creating predictive models and algorithms that enhance decision-making accuracy in complex systems, particularly those influenced by dynamic and uncertain environments.
Computational Statistics: Focus on developing efficient algorithms to implement statistical methods, enabling the analysis of large-scale industrial datasets.
Granger Causality Tests: Explore causal relationships in time-series data to improve industrial processes and forecasting.
These research areas drive innovation in industrial engineering by providing robust, data-driven solutions to contemporary challenges. Aim to bridge the gap between theoretical advancements and practical applications, ensuring that industry practices remain at the forefront of technological development.
