My job with the data lab is to analyze LEED-certified and non-LEED buildings. The LEED certification process is aimed at evaluating and measuring the environmental performance of a building, and to encourage more sustainable and efficient design. In my work, I am comparing component failures in LEED versus non-LEED buildings. I am attempting to answer the question: Is obtaining a LEED certification worth it and why?
Our preliminary observations suggest that LEED-certified and non-LEED buildings have similar maintenance issues with HVAV and plumbing among the most frequent failures. Findings suggest that there are generally fewer maintenance issues post-LEED certification, except for electrical and elevator repairs. For now, more data needs to be collected on the specifics to what ASU has done to achieve the LEED certifications for certain buildings. Then, that will allow room to explore whether achieving the certifications is beneficial according to the topics we accumulate.
I have had the opportunity to work with the data lab as a part of the NEPTUNE project team. My objective has been to extract meaningful and actionable insights regarding the primary sources of imperfections, with a focus on operational dynamics, pertaining to nuclear power plants spread across the United States for the benefit of the Nuclear Regulatory Commission.
By virtue of this project, I have ventured into a wide variety of emerging and overlapping domains, including Natural Language Processing, Statistical Machine Learning with a peek into Deep Learning, and Data Visualization, to name a few. As a graduate student in software engineering with preliminary exposure to data science and statistics, I found this project to be complementary to my aspirations and helpful in guiding me toward possibilities for a career.
With a quintillion bytes of data being generated every day, there is so much valuable information that can be extracted. This information, when channelized in the right way, can shape our intelligence and can change the very foundation of life. During my master’s program, it was my desire to learn and apply algorithms that can leverage this data for vital information. I worked on projects which had applications in several domains, including the U.S. Navy, Energy, Facilities, etc. I was not only able to apply my classroom learning to find solutions to crucial problems but also research new methods.
In one such project, we tried to find meaningful insights from Tweets of a disaster event. This enabled us to understand the sequence of events that unfold after a disaster. Such information could be vital in designing preventative measures for the future. The research that we put into this project really impressed the folks at Tesla (the makers of the car that orbits in space now!) and eventually helped me land an internship there – thus, helping me find the career path that I wanted to pursue.