Eleven high school students who worked with faculty mentors from Stony Brook University are among the top 300 scholars in the national 2022 Regeneron Science Talent Search (Regeneron STS) competition — the nation’s oldest and most prestigious science and math competition for high school seniors. Four of the mentors are from Stony Brook’s Department of Ecology and Evolution.
On January 20, 40 of the 300 scholars will be named Regeneron Science Talent Search finalists. The finalists will then compete for more than $1.8 million in awards during a week-long competition taking place March 10-16.
The following are the Stony Brook faculty mentors:
Nancy Hollingsworth, Distinguished Teaching Professor, Biochemistry and Cell Biology, worked with Sabrina Chen, Syosset High School, Syosset, NY, on “Negative Charges at T168, S169, and/or S170 of ECM11 Promote Wild-Type Meiotic Progression and Synapsis in Meiotic Saccharomyces cerevisiae Cells”
Professor William Holt and Associate Professor Troy Rasbury, Geosciences, worked with Yash Naryan, The Nuevo School, San Mateo, CA, on “DeepWaste: Applying Deep Learning on a Mobile Device for Accurate, Low Cost, and Ubiquitous Waste Classification”
Ji Liu, Assistant Professor, Electrical and Computer Engineering, worked with Ben Choi, Potomac School, McLean, VA, on “An Ultra-Low Cost, Mind-Controlled Transhumeral Prosthesis Operated via a Novel Artificial Intelligence-Driven Brainwave Interpretation Algorithm”
Dianna Padilla, Professor, Ecology and Evolution, (and SBU alumna Rebecca Grella) worked with Roberto Lopez, Brentwod High School, Brentwood, NY, on “Evaluating Phragmites australis Wrack Accumulation in a Long Island Salt Marsh Ecosystem and Assessing Its Effect on Carbon Sequestration, the Nitrogen Cycle, and Sediment Biota” and with Ricardo Lopez Brentwod High School, Brentwood, NY, on “Evaluating Salt Marsh Restoration at Sunken Meadow: Analysis of Sediment Loss and Accretion”
Memming Park, Associate Professor, Neurobiology and Behavior, worked with Amy Feng, Pittsford Sutherland High School, Pittsford, NY, on “Extending Choice Probability to High Dimensional Neural Data”
Miriam Rafailovich, Distinguished Professor, Materials Science and Chemical Engineering, worked with Emily Zhou, The Harker School, San Jose, CA, on “Computer-Assisted Detection of Intracranial Aneurysms Using a Transformer Deep Neural Network in 3D MR Angiography”
Nicolette Sipperly, PhD candidate in Ecology and Evolution, worked with Harshita Sehgal, Roslyn High School, Roslyn, NY, on “Understanding the Evolutionary Development of Radio-Resistance in the Brassicae Family”
Howard Sirotkin, Associate Professor, Neurobiology and Behavior, worked with Sarah Schubel, Smithtown High School East, Saint James, NY, on “Loss of NMDA Receptor Signaling Results in Excess Proliferation of CNS and Neural Crest-Derived Cells”
Robert Thacker, Professor and Chair, Ecology and Evolution, worked with Jonathan Chung, Smithtown High School East, St. James, NY, on “Microbial Associations Constrain Coral Adaptations to Heat Stress: An Integrative Multi-Dataset Analysis”
Wei Zhu, Professor, Applied Mathematics and Statistics, worked with Jessica Liang, Coppell High School, Coppell, TX, on “CS-VAE: Compressive Sensing-Based Variational Autoencoder: Theory and Design”
About the Regeneron STS
Started in 1942 as the Westinghouse Science Talent Search, the Regeneron STS recognizes and empowers the nation’s most promising young scientists. Each year, nearly 1,900 students enter the competition, submitting original research in critically important scientific fields of study. The Regeneron STS provides students with a national stage to present original research and celebrates the hard work and novel discoveries of young scientists who are bringing a fresh perspective to significant global challenges. This year, research projects cover topics from tracking countries’ progress on Sustainable Development Goals to the impact of states’ individual COVID-19 responses, and improving the tools used to diagnose Alzheimer’s to analyzing the effects of virtual learning on education.