Chief Scientist for the Decision Systems Group in the Asymmetric Operations Sector

Graph modeling in Java for data mining and pattern discovery.

I continued my work from my previous position. This includes studies of gravitational wave parameter estimation for massive binary black hole (BBH) systems and spinning BBHs. I am also continuing studies in applying machine learning to astrophysics. Ongoing work includes applying various machine learning methods to Swift data analysis and to LIGO detection pipeline improvement.

My research involved the detection and characterization of gravitational wave signals. I use Bayesian inference techniques to detect and measure the source parameters of gravitational waves in noisy data for the LIGO Scientific Collaboration. This utilizes many waveform models which can be added to real or simulated noise.My research also involves the application of machine learning, specifically artificial neural networks, to astrophysics. This is through the improvement of Bayesian inference as well as using the predictive power of neural networks for direct data analysis and data mining.

Conduct weekly supervisions for students in first-year mathematicsAssign examples, mark work, and review materialFour groups of 2 or 3 students each, meeting one hour per week per group during term

Research student working towards completion of PhD in Astrophysics Group of Department of Physics.

Helped students perform weekly experiments in a first-year physics laboratory classMarked lab reports for 6-10 students per week

Led discussion sections in an introductory mechanics class

Earned my Bachelor of Science degrees (summa cum laude) in Physics and Mathematics while a member of the Honors College.

Developed an improved method for initial lock acquisition of the Michelson interferometers used in the LIGO observatories using pseudo-random noise.MATLAB simulations were used to test concepts; models were planned to be used to test methods on the 40-meter interferometer at Caltech.Work performed with Dr