Developing deep learning, machine learning models and other data analytic or statistic solutions for internal and outside customers.Work with business, technology, and data science teams to develop a strategy that will clearly outline how the enterprise platforms can enable business growth & productivity by seamlessly combining structured data into a single, self-service analytical environment.

1. Provide integrated consulting services in statistical modeling, numerical analysis and computational engineering sciences to the internal Business, Technology and engineering community. 2. Perform applied research on new engineering modeling capabilities for process, product and business improvement and coordinate the introduction of pertinent technologies. 3. Develop mathematical models, solve numerical problems, write specific code as needed, and effectively perform applications testing to ensure robust performance. 4. Work on large-scale data analysis and develop predictive models based on regression, classification, machine learning and time series, etc. approaches in both simulation and optimization frameworks. 5. Derive actionable insights and Communicate results to a wide audience in a manner that the solution strategy and business impact are readily understood. Drive business decision-making based on modeling and analysis. 6. Teach in-house courses on topics including, DOE, Regression Analysis, Multivariate Quality Control.

I have focused on import issues in the field of catalysis research, namely the development of generic tools for the "in silico" search of new catalysts. I studied three industrially important reactions: methane steam/dry reforming, methanation and synthetic natural gas conversion. My research has led to a significantly improved understanding of the detailed trends for these reactions, and an universal method has been developed to screen/design active, selective and stable alloy catalysts.