About

Pallavi Kumari is a highly skilled Ph.D. candidate with expertise in process modeling, control, optimization, Bayesian statistics, and machine learning. She has strong analytical and leadership skills and is seeking a full-time position starting in January 2022. Pallavi has a specialization in Bayesian network, statistical analysis, reduced-order models, machine learning tools, CFD models, first-principle models, abnormality diagnosis, and consequence analysis. Currently, Pallavi is working as a Graduate Teaching Assistant at Texas A&M University, where she is teaching CHEN 460 Quantitative risk analysis. In the past, she has worked as a Graduate Research Assistant at Texas A&M University, where she developed computationally efficient models for abnormality diagnosis and consequence estimation. She has also developed statistical tools such as Bayesian network and support vector machine for the diagnosis of process abnormality. Pallavi has also developed an artificial neural network model to diagnose pipeline failure cause. Pallavi has a Doctor of Philosophy - Ph.D. degree in Chemical Engineering from Texas A&M University, which she completed in December 2021. She also holds a Master's degree and Bachelor of Technology (B.Tech.) degree in Chemical Engineering from the Indian Institute of Technology, Kanpur, which she completed in 2014 and 2009, respectively. Pallavi has a strong tech stack, including ML, SVM, Research Scientist, and Neural Networks. She has relevant experience of 6.61 years, making her a highly skilled and experienced candidate. Pallavi is a motivated and dedicated individual who is passionate about her work and strives to achieve excellence in everything she does.