The Regeneron Science Talent Search is a prestigious math and science competition run by the Society for Science in which high school seniors can submit creative research to be judged by experts in each field.  Projects are focused on providing solutions for the challenges facing the world as a whole, along with uncovering information about these issues.  The annual competition began in 1942, with roughly 2,000 students competing each year.  Of the 2,000 students who take part in the competition, 300 are named Regeneron Scholars.  Schreiber’s research students did a phenomenal job this year, with three scholars chosen from the school: Noah Loewy, Hope Lane, and Zoya Unni.

This year, Regeneron Scholars were chosen from 1,760 applications sent from various schools, states, and countries.  Being named a scholar is a significant achievement that only a select few can boast, so it is a privilege that Schreiber has three students who excelled with their projects.  Senior Noah Loewy’s project focused on determining the most likely outcome of the 2020 presidential election.  He used multiple linear regressions and a machine learning algorithm called XGBoost to find the most probable result, while still accounting for other potential factors, such as the midterm election results, the advantage of currently holding the office, the state of the economy, approval ratings, and much more.  His model proved to be effective, predicting Biden’s victory by around 100 electoral votes (actual margin was 74). 

“Participating in the Regeneron competition was an extremely rewarding experience.  It’s truly an honor to be named a Regeneron Scholar alongside some of the most passionate and accomplished STEM students across the country,” said Loewy. 

Loewy also explained that he appreciated his research even more due to COVID-19.  The past few months have been challenging and unorthodox, but focusing on his project helped take his mind off of the current situation.  Finally, he expressed gratitude towards his mentors, Dr. Ashok Singh, Ms. Gallagher, and the organizers for helping him with the research process.

Another  scholar, senior Hope Lane, focused on a very different topic.  She worked to find the optimal sharing radius for donated livers to lower deaths and inequality in liver transplants.  She also became part of a transplant allocation system known as LivSim and wrote Python code to improve the network’s structure.

“It’s really exciting to be named a Regeneron Scholar.  Although the quantitative results of my study were not ideal, I became passionate about equitable organ allocation and learned that optimal is an ethical question in organ allocation,” said Lane.

Lane has done valuable research and her project is relevant in the organ transplant field.

Finally, senior Zoya Unni took advantage of social media and led a TikTok based project.  Since COVID-19 disrupted communication between people, many have turned to the Internet for social interaction and entertainment.

“After connecting with my mentor early last year, the coronavirus hit, and I decided that I should look at how the pandemic was affecting expression and communication on TikTok.  During the process, I was able to sharpen my research skills and connect with professionals in my area of study to create an incredible learning experience” said Unni. 

She conducted a study of COVID-19 related TikToks, including an analysis of common themes over time.  She found that “Pandemic Life” posts were most popular, alongside posts about the restrictions on regular activities.  Because COVID-19 isolated everyone, analyzing the current communication outlet is important.

These Regeneron finalists worked incredibly hard on their projects and presentations.  Their success in this prestigious and elite competition is praiseworthy.  Loewy’s accurate prediction of the outcome of the election, Unni’s study on the effects of the COVID-19 on communication, and Lane’s detailed work with liver transplants are all stellar projects, and being named a Regeneron Scholar is an amazing accomplishment for each of them.