I do research in both Statistical Signal Processing and Engineering Education.
Also, I am a Science Training and Research to Inform DEcisions (STRIDE) Fellow for the years 2018-2020 and currently enrolled in C-STRIDE Advanced Graduate Certificate to build interdisciplinary skills to assist, create, and eventually lead in the translation of complex data-enabled research into informed decisions and sound policies.
Statistical Signal Processing: My research lies in the field of statistical signal processing, where I research models and methods that aid in decision making. One of the most acclaimed methodologies used across fields is Bayesian analysis. It is a statistical framework in which unknowns are treated as random variables and are approximated using data along with prior knowledge about the system. Bayesian models and methods are exceptionally well suited to decision-making because they naturally accommodate uncertainties, they can be used for hypothesis testing as well as forecasting, and they can be updated continuously as data becomes available.
I personally work on Markov Chain Monte-Carlo(MCMC) methods- specifically Sequential MCMC methods like adaptive filtering(Particle Filtering) and their optimization. I have worked on different applications such as: Smart Grid synchronization and Gene regulatory network .
Engineering Education: I do research in Engineering outreach for middle and high school students and teachers. I study how engineering education in k-12 systems can be implemented effectively through university based model such as: outreach classes, mini camps, workshops, and summer camps I design, instruct , and direct at Stony Brook University under the guidance and management of Professor Monica Bugallo .
My interest in that field lies in the policy of education and the way to make effective decisions that can play a big role in both the United States k-12/Higher ed educational system and workforce.