Lab members and friends enjoyed the 117th Annual SEAS Campfire on Sep 22 in Saginaw Forest. Yi and Jiali won a cool "prize" in the traditional log sawing competition, and Tim wore waders, winning the championship!
Michigan Institute for Data Science (MIDAS) announces 2023 Propelling Original Data Science (PODS) Grant awardees Kai Zhu (School for Environment and Sustainability) and Kerby Shedden (Statistics) Bayesian modeling of multi-source phenology to forecast airborne allergen concentration We aim to improve the short-term and long-term predictions of airborne allergens under climate change, an emerging public health concern. To achieve this, we propose to develop novel data science tools to effectively assimilate multiple data sources and integrate various data-driven and [...]
From June 18 to 23, members of the Zhu Lab, together with Dr. Inés Ibáñez, embarked on a research trip to the University of Minnesota to explore various field experiments there. We first visited the Cedar Creek Long Term Ecological Research at Cedar Creek Ecosystem Science Reserve. Dr. Peter Reich introduced the BioCON (Biodiversity, CO2, and Nitrogen) experiment, the "Big" Biodiversity Experiment, the BAC (biodiversity and climate) experiment, a forest biodiversity experiment as part of [...]
On May 18, members of the Zhu Lab and Dr. Yang Chen's group in the Department of Statistics gathered to celebrate Yiluan completing her PhD program exit talk as well as the new collaboration between the two research groups.
Yiluan Song was admitted into the 2023 Cohort of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship Program. This program is hosted at the Michigan Institute for Data Science (MIDAS) at the University of Michigan. She will be working on a Bayesian process-guided machine learning framework to project nature’s calendar under climate change, advised by Dr. Kai Zhu and Dr. Yang Chen.
Yiluan Song visited the National University of Singapore (NUS), her home university, where she received her Bachelor of Science degree in Life Sciences. In the week of Feb 27, 2023, Yiluan gave special seminars entitled "Phenology in the Anthropocene: ecological synchrony, land use, and public health" at the Department of Biological Sciences and the Department of Geography.
Yiluan Song received the EFI Futures Outstanding Presentation Award for her presentation “Ecological forecasting of leafing and flowering phenology during climate change to inform public health” at Ecological Forecasting Initiative 2022 Virtual Conference during May 23 – 25, 2022. She also participated in the Near-term Ecological Forecasting Initiative short course in Boston University June 26 – July 1, 2022.
From NEON blog. Building a Forecasting Community: The EFI-RCN NEON Forecasting Challenge June 9, 2022 ... Phenology Yiluan Song, a grad student at the University of California – Santa Cruz, submitted her forecast in the phenology challenge. Phenology is the science of the timing of recurring biological events, such as leaf-out for plants or migration for birds. The EFI-RCN NEON Forecasting Challenge focused on plant phenology, using color (red and green) data from the NEON [...]
Lab alumnus Chris Zajic was awarded an NSF graduate research fellowship in STEM Education and Learning Research on 4 April 2022. Chris will use the funding provided through the fellowship to investigate the role of social connections in undergraduate life science students’ access to research experiences. This work will be carried out under the guidance of Dr. Erin Dolan at the University of Georgia, where Chris is pursuing a PhD in biochemistry and molecular biology. [...]
From NEON blog Want a Faster Way to Process Microbial DNA data? Try the neonMicrobe R Package February 17, 2022 Microbial DNA sequencing data from the NEON terrestrial field sites are freely available on the NEON Data Portal—but extracting useful information from all that data can be daunting for ecologists without a bioinformatics background. Thanks to the neonMicrobeR package, researchers can now automate much of the work of downloading, processing, and assembling microbial data from NEON terrestrial [...]