 
                                                Computational Study of Transonic Buffet
                                                
                                                
                                                    Employ computational fluid dynamics in Fluent to 
                                                    study transonic buffet for a NACA 0012 airfoil.
                                                    Analyze the impact of operating conditions and 
                                                    modeling choices on buffet onset and limit cycle oscillations.
                                                    Process and interpret simulation results using Python 
                                                    and MATLAB to gain insight into physical trends. This 
                                                    research is conducted at Duke Univeristy as part of 
                                                    the Aeroelasticity group.
                                                
                                                
                                            
                                            
                                                 
                                                Interpretable Machine Learning for Causal Inference
                                                
                                                
                                                    Apply and contribute to machine learning algorithms 
                                                    in Python and R for causal inference.
                                                    Created and maintain a website to document 
                                                    the algorithms and their usage and 
                                                    contibuted an interactive online demo. This research 
                                                    is conducted at Duke University as part of the 
                                                    Almost Matching Exactly Lab (AME Lab).
                                                
                                                
                                            
                                            
                                                 
                                                Nanocomposite Fabrication for FDM Printing
                                                
                                                
                                                    Studied thermal conductivity in FDM printing and 
                                                    fabricated polymer nanocomposites for improved mechanical 
                                                    strength and thermal properties in 3D printed parts.
                                                    Conducted research under the direction of Dr. Miriam Rafailovich. 
                                                    This research took place at Stony Brook University 
                                                    as part of the Garcia Center for Polymers at Engineered Interfaces.