Publications

I designed a novel stochastic optimization algorithm with stochastic constraints, aimed at optimizing control chart tuning parameters with greater efficiency compared to traditional numerical methods.

I developed a modified bisection algorithm for computing control limits in complex settings where standard methods are inefficient. The approach removes the need for a predefined search range and scales efficiently to multi-chart scenarios.

I formalized the theoretical behavior of parameter learning schemes in relation to outlier detection performance in control charts. This work enabled the generalization of alternative parameter learning methods, leading to a more efficient and accurate detection scheme compared to traditional approaches.

We developed a general methodology to monitor processes involving mixed-type data (continuous, ordinal, categorical), common in real-world applications. The method enables effective sequential monitoring under serial correlation.

I developed an innovative statistical quality control method for detecting shape defects in complex geometries obtained via additive manufacturing. I introduced a novel nonparametric control chart based on kurtosis analysis which provides superior defect detection for 3D-printed objects compared to existing approaches.

We developed a likelihood-ratio methodology to monitor processes involving continuous and categorical data. The approach makes use of adaptive kernel density estimation in order to approximate the likelihood ratio, thus providing efficient detection power.