(147d) Accelerating Digitalization Via Advanced Optimization, Machine Learning, and Simulation Techniques

Digitalization is a key driver of innovation and efficiency in the chemical industry, and this abstract outlines the contributions of an innovative researcher dedicated to accelerating this transformation. With a robust background in advanced optimization, machine learning, and simulation techniques, the researcher has an extensive portfolio of projects including:

• Development of optimization models for novel carbon-negative power plants and copper smelting operations,
• Customized algorithm development for logistics optimization problems,
• Application of machine learning techniques for surrogate modeling and product pricing,
• Utilization of simulation techniques to analyze and improve product transportation efficiency.

Each of these projects not only demonstrates a practical application of advanced computational techniques but also resulted in significant improvements in efficiency and cost-effectiveness. Collectively, they underscore the researcher’s ability to bridge the gap between theoretical research and practical industry challenges.
As a versatile problem solver with a knack for turning complex data into actionable insights, this researcher eagerly anticipates the opportunity to explore full-time roles at the "Meet the Industry Candidates" session.