![]() ![]() ![]() Howerton shared, "How exciting it can be to work on meaningful, real-world problems in a collaborative environment. ![]() In my future career, I would like to apply my computational and analytical skills to contribute to 'a better working world.'" “This is the case for many of the challenges our world faces, like habitat destruction, climate change, and food insecurity. "It does not matter how complex the problem is if we bring people together, a solution is possible,” added Vanali. I'm super excited to see its successful application to frog detection and conservation." ![]() "They provide a set of powerful tools to analyze the large volume of publicly available data. "This experience reinforced my dedication to using machine learning and data science for environmental problems and conservation," Hu shared. "I would love to continue applying my skill set and contributing to meaningful interdisciplinary projects like this one and those in my Ph.D. "This experience emphasized the value of good-quality data, what we can learn from such data sets using quantitative methods, and the importance of storytelling and communicating our work," Tran commented. To the team’s surprise, after a panel of judges from the EY, NASA, Microsoft, and other scientific entities weighed in, Sweet Frogs came out on top. In the first round of the competition, wherein all participating teams’ models were reviewed and scored against existing models, Sweet Frogs scored well enough to move to the semi-finals.įor the next round, teams had to write a report and submit a brief video explaining their model and results. Additionally, the group shared that it took weeks to download the massive data sets consisting of field data, satellite imagery, and complementary geospatial data from NASA and other resources needed to start to try out their models. Team Sweet Frogs didn't expect to win in fact, they entered the Challenge just a month before the competition closed. "We thought sounded interesting - like a riddle - and we thought it might be fun to do over the summer," explained Yang. The Penn State team entered the competition under the whimsical moniker "Sweet Frogs," a name appropriate not only for the Challenge's goal of accurately predicting frog species' location, but also for this particular group of friends, who enjoyed frequenting a local Sweet Frog frozen yogurt establishment together. Hu graduated from Penn State in 2021 with a doctorate in geography. Penn State’s winning team consisted of Fuhan Yang, Thu Tran, Chiara Vanalli, and Emily Howerton, all doctoral candidates in the department of biology, along with Weiming Hu, machine learning postdoctoral researcher at the Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego. Improved methodologies could yield real improvements in global efforts to combat ongoing losses in biodiversity. Participants in the Challenge were tasked with developing the best possible methodology, given available public data for determining where specific frog species would be found. Species distribution models are among the most widely used ecological environmental regulation and conservation tools worldwide. Referred to officially as "the Challenge," the program is part of the Better Working World Data Challenge, sponsored by the Ernst and Young Global Limited organization (EY).įrogs were the focus of the competition because frogs are an indicator species - a go-to for scientists researching the environmental health of ecosystems. A group of Penn Staters placed first in the Global Frog Discovery Challenge, a competition that attracted over 9,000 participants across more than 100 countries. ![]()
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