My Essay:
My practicum site was the Center for Environmental Energy Engineering (CEEE) at UMD, and my site supervisor is Dr. Ohadi, who’s a research professor at that department. I found the site and him as my supervisor by emailing him if he was interested in supervising me for a research project. I didn’t just email him though- I emailed some other professors in similar STEM departments to maximize my chances, because most of them are busy and won’t have time. What also helped a lot was that my dad worked with him and his lab decades ago when he was pursuing his doctorate at UMD, so I had an indirect connection with Dr. Ohadi from my dad. My advice to anyone trying to seek the supervision from a research professor is to ask (by emailing) as many as possible. Most probably won’t respond, or might respond saying they’re too busy, but if you get lucky some might be willing. Make sure to sound truly interested and have a general idea of what you want to do in your email (don’t just say any research activity, you should be motivated enough to have a sense of what topics you want to do). Again, make sure you ask a lot of professors, because most will probably not have time.
For my work, I had virtual lab calls with my professor and my lab group on a weekly basis. I didn’t actually go to any physical lab to do my work, due to the nature of my research being most related to computer science and that my professor wasn’t on campus most of last year. This turned out to be completely fine, as I could ask any questions I had during our lab meetings, email him or my graduate co-supervisor, or just google the questions. During my lab meetings, either he or his graduate student would give a presentation to teach something related to building energy efficiency or machine learning, or I would present about what I did during the week regarding my project, and they would give me feedback and advice. More specifically, I did research in regards to the impact of climate change, and then we moved onto more specifics about building’s energy systems and the importance of making them more efficient, and finally I did my final project on optimizing it using machine learning.
There were definitely many aspects of science which I learned about my site. The CEEE works with all sorts of environmental engineering, not just buildings and thermal systems, ect. My professor, however, works more with thermal systems and other systems in buildings. One seemingly very easy yet super important fact I learned during my research was that there was a lot of potential for making buildings more energy efficient (around twice as much!) according to the US Dept of Energy. This fact is very significant, because it’s one thing to say that buildings use a lot of energy (which they do, 70% of US electricity and 40% of fossil fuels), but it’s much more impactful to also show that buildings actually can be made more efficient by a large margin. This makes our research in this topic a lot more meaningful, as it’s something that can definitely be done. As for the more specifics regarding which factors influence buildings’ energy optimization the most, we found that the type of wall insulation was a big contributor in many cases. Though for any particular building, lots of other factors can play into what would make it more optimized, such as the location, how many people use it, what the building is used for, ect.
This project was influential to me in many ways. On the climate change side, I never even realized how much energy/fossil fuels buildings use in the first place, because most of the high fossil fuel users I hear in the media are cars, the meat industry, ect. On the machine learning side, I didn’t realize how important machine learning was for optimizing building’s energy efficiency, and how environmental energy and machine learning can be so intertwined. This project also taught me a lot about data science and machine learning, which is important for my CS major, and has definitely made data science or ML a much more enticing route to study in CS. For career work, a lot of ML is still very research oriented, but working with ML is definitely an option. At the moment however, data science is more of a viable goal since it encompasses more statistics, which is (or will be) my second major. This research project undoubtedly made both of these options more viable, and they’re both something I wish to learn more about with my free time and college courses.