Renato Figueiredo
Oregon State University, OR·Computer Science
Algae blooms in lakes and reservoirs, made of tiny drifting organisms called phytoplankton, threaten drinking water. They can release toxins, form scums and change taste and odor.
- Fields
Computer Science
Ecology & Evolution
Earth & Environmental Science
- Topics
ecological forecasting
phytoplankton
harmful algal blooms
- Grants
- 2 known grants, through Oct 2027
What they work on
- Build an automated, real-time system that forecasts lake phytoplankton blooms 1 to 35 days ahead with quantified uncertainty, combining ecosystem models, individual-based models and statistical methods.
- Develop cyberinfrastructure that moves lake sensor data to the cloud, using cloud and edge computing to run daily forecasts for several U.S. lakes.
- Develop FaaSr, an open-source R package that lets scientists write event-driven workflows in R and run them on Function-as-a-Service platforms such as AWS Lambda and GitHub Actions.
- Create unified interfaces in FaaSr for triggering functions, accessing cloud storage and mapping directed acyclic graph workflows to different platforms, tested on lake and reservoir forecasting.
More about the research
If water managers had forecasts of these blooms, they could adjust treatment ahead of time. These two collaborative projects connect computer science and ecology. One builds a real-time bloom forecasting system. The other builds cloud computing software that makes such forecasts practical for scientists who write code in R, the main programming language in environmental science.
Why it matters
Forecasts of algae blooms in lakes and reservoirs could let drinking water utilities adapt treatment before water quality declines, lowering costs and improving water safety. Making cloud computing easy and cheap for R users could also help environmental scientists run ecological forecasts at scale. This could support better decisions about managing water and ecosystems.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Collaborative Research: Elements: FaaSr: Enabling Cloud-native Event-driven Function-as-a-Service Computing Workflows in R
NSF · Oct 2024 – Oct 2027
Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management
NSF · Oct 2024 – Sep 2027
Closest labs
Cayelan Carey
Virginia Polytechnic Institute and State University, VA·Ecology & Evolution
Test how far ahead physical, chemical and biological variables in two drinking-water reservoirs can be predicted, by iteratively evaluating real-time forecasts made by competing models against new monitoring data.
Nasrin Alamdari
Florida Agricultural and Mechanical University, FL·Earth & Environmental Science
Identify the hydrological, meteorological, biological, physical and geochemical processes that control nutrient export from urban landscapes into freshwater lakes.
Tianyin Xu
University of Illinois Urbana-Champaign, IL·Computer Science·Early-career
Establish the foundations of configuration testing for cloud and datacenter software, a missing piece of how production configuration changes are checked today.
The school
Oregon State University: 29 labs on PI Pages, $372M a year on research, 238 research doctorates a year.
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