As Vladimir Putin retains his strong grip on political power in Russia, growing networks of conservative actors in the United States have drawn together the potential political futures of both countries (Stoeckl and Uzlaner, eds. 2020). Since the mid 2010s, conservative support for Russia has increased, a trend which continued during the commencement of the Ukrainian invasion in February 2022 (Gallup, 2022). Despite the bipartisan condemnation of the invasion and the humanitarian concerns expressed by many on the political left, several vocal leaders on the Christian right continue to express support for Putin and his authoritarian regime, and as data has demonstrated, this has significantly impacted the favorability that Americans show for Russia, especially the conservative far-right (Perry, Samuel L., 2021).

Climate change is the greatest existential threat to humanity. Throughout high school, I have connected myself to a massive global movement of youth fighting to change this. Over the past four years, I have dedicated my time to pursuing policy solutions to environmental issues and spearheading climate education initiatives in my local community, as well as at the national and global levels. Through these projects, I’ve engaged with other inspiring activists and learned the importance of collaboration and leadership - especially among young people whose futures are most impacted by global warming. When it came time for me to conduct my own research study in my senior year of high school, which I later submitted to Regeneron Science Talent Search, there was no question in my mind that I wanted to explore the social and political dimensions of climate change.

Recall that the problem statement of my project is to quickly and autonomously navigate and map a burning building, identify trapped inhabitants, and send recovery/escape routes to firefighters as fast as possible. During my background investigation of this subject, I discovered that navigating fires has traditionally posed a challenge for robotic systems. Traditional LiDAR (Light based) sensors are often employed for SLAM (Simultaneous Localization and Mapping: A class of perception and mapping algorithms), but these sensors lose accuracy significantly in smoke. Meanwhile, SONAR (Soundwave-based) or RADAR (Radio-based) mapping systems are often very bulky and technically complex, making them challenging to incorporate onto a dynamic robotic system. This posed a perception challenge: to develop a novel perception and navigation system that can function effectively in a fire environment.

I was never a STEM-oriented person. I like to think it was because I had talent in another subject that took my attention away from STEM, but it was more likely due to the humbling experience of going to an academically challenging high school that turned my love for science and math into the stress of finishing mountains of homework and fear of failing back-to-back assessments. I couldn’t picture myself pursuing a career in STEM when I obviously wasn’t good at it … I bounced from subject to subject, taking various non-STEM-related classes to find my interest. Music bore me, literature made no sense half the time, and Spanish required me to use more brain cells than I had. It seemed like I would end up majoring in a subject I disliked until I took AP Psychology during my junior year. While the kid next to me used the class to catch up on his sleep, I was engrossed in the material, taking notes on every little thing in each lecture. By the time the fall semester was over, I knew that I wanted to do something psychology related in the future … I contacted a research assistant professor at the Georgia Institute of Technology and introduced myself. I combined my interest in psychology, natural understanding of economics, and job in the food service industry to research tipping and how default tip options on an electronic payment system can affect the amount a customer tips.

The goal of my research is to find out the effects of showing different people various privacy suggestions in a Facebook-like system. In theory, this understanding could provide tools that allow users who want different amounts of privacy to achieve it conveniently. To adapt to users, however, one needs a model of the users. We picked Privacy Profiles, a previously established user model. Privacy Profiles basically categorize users of a social network based on the privacy features they use and are aware of. For example, people who often block people may be categorized as Privacy Maximizers. Based on this model, we designed three ways of creating adaptations, and three ways of showing them. When generating suggestions, we can use optimization, which helps the user with things they already do, solidification, which helps them with features inside of their profile (ones which they should be using), or self-actualization, which suggests things they might not do themselves. These are called Adaptation Methods. These adaptations can then be shown in various ways, called Introduction Methods: automation implements changes without asking the user first (with an undo button), highlighting makes features more visible or prominent, and suggestion shows a Privacy Dinosaur (Based on of a similar dinosaur on Facebook) to give personalized suggestions.