Syosset students Michael Wren and Winston Zhao named Regeneron STS semifinalists

Heather Hall, lead research facilitator for Syosset High School, introduced two Regeneron Science Talent Search semifinalists and thanked the board and community for supporting the district’s research program.

Michael Wren, a senior, presented computational-biology work that layered machine learning on mass spectrometry to predict antibiotic resistance. “I added a machine learning layer on top of MS that allowed the MS analyses to be used to predict antibiotic resistance,” Wren said, adding he achieved “high accuracy metrics up to 0.97.” He emphasized the potential for rapid, low-cost antibiotic screening if the approach is adapted for clinical use.

Winston Zhao described a machine-learning approach to improve short-term forecasting of the El Niño–Southern Oscillation (ENSO). Zhao said his preprocessing techniques reduced computational load and improved forecasting metrics, reporting “a 170% increase in hit rate and a 145% increase in success index” compared with a baseline machine-learning model and noting his model achieved “up to an improved 93 correlation to 94% hit rate” in short-term forecasts.

Board members congratulated the students and noted the district’s investment in research facilities. Hall said the program submitted more than 41 projects to Regeneron this year and credited district investments and community support for enabling expanded student research opportunities.

Ending: The board recognized the semifinalists and invited the public to the district research symposium; staff said planned facility expansions will create capacity for more student research.