CHANGING EXPECTATIONS Raed Mirza
Seniors run into unexpected roadblocks while conducting research for their final senior lab projects
E
xpectations are high, and seniors will present their final results at tjStar, some at even larger science fairs such as the Intel ISEF. But almost all seniors can agree their projects are far from what they expected junior year. Take senior Tony Liang, who is doing his project in the Computer Systems Lab. “Our original plan was to make a self-driving go-cart,” Liang said. “We originally wanted to train a neural network to follow some traffic patterns we create, such as stop signs, driving on the right, and changing lanes.” However, with the first semester of the year already over, his project has changed drastically from its original intentions. “We probably will not get to make a full-size go-cart. Also, instead of teaching the car to drive on a closed course following traffic rules that we make, we will train it to drive around the hallways without crashing into walls,” Liang said.
THE CAUSE
Liang is just one of many, and with so many seniors across all labs experiencing the same issues, it begs the question: why does this happen? According to lab directors, it’s a natural part of the research process and science as a whole. “It isn’t seen as a ‘problem’ but the natural course of things. As we learn more, we see that which we didn’t know…and evolve our approach and/or our expectations,” Laura Lock-
lear, director of the Neuroscience Lab said. Locklear also adds that in real research labs, many professional researchers go through the same struggles and limitations that Jefferson seniors face. “Let’s say you finish your doctoral degree and you go to a lab. You’re hired by a lab - you can’t just do anything you want to because of the limitations that are within the lab. Anybody who’s done a lot of reading into published scientific literature will know there’s no experiment that’s ever been published that is a perfect experiment; there are always limitations. So you get to a lab and you learn what those are. And then you do what you can do within those constraints,” Locklear said. Senior Avyuk Dixit’s experience confirms this sentiment. “After the New Zealand shooting last year, we thought of creating a method for detecting violence in real-time from video surveillance feeds; one of the things that happened in the new Zealand shooting was that there was a Facebook Live Stream that recorded it as it happened, but nobody saw it in time to react,” Dixit said. Originally, his group had planned to spend most of the year building the model for the data and writing the research report. However, building the model only took them a month. Instead, they have spent most of time researching methods to parse the vast amount of data to train their neural network.
