Hello!

I am a computational scientist with interest in process modeling, materials discovery and optimization using molecular modeling, mathematical modeling, and machine learning.

Motivations

Computers are very effective to study various physical systems and then discover materials at certain conditions for properties/quantities of interest. Simulations and mathematical modeling (besides machine learning and other surrogating modeling methods) are amazing tools that helps us to conduct these experiments. Besides I like the elegance of mathematical models and the scalability and universality they provide.
Computation-based predictions are a great support pillar to real-life experiments. Besides in many cases, experiments may have limitations such as challenges in creating a specific condition, synthesis of some rare material, scaling a method to large feature-space, or perhaps cost. For such cases, simulations or mathematical models can lead the way and fill the gap to guide investigations.

My current research at University of Notre Dame is focused on applying molecular simulations (monte carlo and molecular dynamics), machine learning, and statistics to understand adsorption, selectivity, and other properties in nanoporous systems. I am interested in discovering and optimizating materials, engineering systems for energy, and healthcare applications.
I have communicated my research work to a wide audience through speaking at conferences, publishing in journals, and of course many academic meetings.
My expertise is in this order: mechanistic modeling, machine learning models, mathematical modeling, and optimization, molecular simulations, first-principle calculations of small molecules. For more details, I encourge to visit my research and publications page.
Below I have tried to summarize some major research topics I have worked and related skills I gained: