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Fong, Ren, And Weir Win CRA Outstanding Undergraduate Researcher Honorable Mentions

Click the links that follow for more news about Grant Fong, Nathaniel Weir, last year's recipients of honorable mentions for this award, and recent awards and fellowships that Brown CS students have won.

The Computing Research Association (CRA) is a coalition of more than 200 organizations with the mission of enhancing innovation by joining with industry, government and academia to strengthen research and advanced education in computing. Every year, they recognize North American students who show phenomenal research potential with their Outstanding Undergraduate Researcher Award, and in 2019, Brown CS made one of the strongest showings in the Honorable Mentions category. Out of fifty-two students who received Honorable Mentions, three of them are Brown CS students: Grant Fong, Silei Ren, and Nathaniel Weir. Last year, Brown CS students received three Honorable Mentions as well. 

Grant explains that his work with Professor Jeff Huang is focused on the Sochiatrist project. "The overarching goal is to predict mental health issues from naturally-occurring social media data," he says. "I have been recently researching how messaging data can be used to predict mood in an ethical way. My work here is split mainly into two areas. The first is to develop robust ways to extract data from multiple platforms that can be difficult for an end user to access and provide these tools to clinical professionals so they can better understand how people communicate. The second aspect is the analysis of this data, which combines natural language processing, time series analysis, and social network analysis to deliver powerful insights."

"My primary research area," says Silei, "is security. Currently, I'm working with Professor Roberto Tamassia on designing a poisoning attack against learned index structures. Learned Index Structures are database indexing structures that uses machine learning models for faster indexing. In the attack, we showed that an adversary can slow down learned index structures by inserting a small amount of crafted data. Another project I worked on is a leakage attack against encrypted databases. Specifically, we answered the question of what an adversary can infer from an encrypted two-dimensional database based solely on the responses of k-nearest neighbors queries. Besides security, I also worked with Professor Theophilus Benson on software defined network (SDN) fuzz testing. We designed an algorithm that introduces systematic failures into networks for resiliency testing."

Nathaniel's work with Professor and Department Chair Ugur Çetintemel and Professor Ellie Pavlick is mostly focused on natural language processing and machine learning. "My main project," he says, "is on building an interface for users to explore databases using natural language text queries. I’ve specifically focused on how to get deep learning models to understand language in arbitrary contextual domains. Our aim is to have an interface that can be plugged into any database containing information from any context – for example, data about hospital patients or about airport flights. However, a model trained to understand language related to a hospital really struggles to understand language about the airport flights since it hasn't seen much of the language patterns in the latter domain. Our challenge is figuring out how to bootstrap this cross-domain generalization process without needing to spend the time collecting lots of training examples of queries in the new domain."

You can see the full list of Outstanding Undergraduate Researcher winners here. Congratulations, Grant, Nathaniel, and Silei!

For more information, click the link that follows to contact Brown CS Communication Outreach Specialist Jesse C. Polhemus.