AI Research
FSU researchers receive $2.3 million National Science Foundation grant to strengthen wildfire management in hurricane-prone areas
Florida State University researchers have received a $2.3 million National Science Foundation (NSF) grant to develop artificial intelligence tools that will help manage wildfires fueled by hurricanes in the Florida Panhandle.
The four-year project will be led by Yushun Dong, an assistant professor of computer science, and is the largest research award ever for FSU’s Department of Computer Science. Dong and his interdisciplinary team will focus on wildfires in the wildland-urban interface, where forests such as the Apalachicola National Forest meet homes, roads and other infrastructure.
Dong’s project, “FIRE: An Integrated AI System Tackling the Full Lifecycle of Wildfires in Hurricane-Prone Regions,” will bring together computer scientists, fire researchers, engineers and educators to study how hurricanes change wildfire behavior and to build AI systems that can forecast ignition, predict roadway disruptions, and assess potential damage.
“The modern practice of prescribed burns began over 60 years ago, which was a huge leap in working with nature to help manage an ecosystem,” said Dong, who joined FSU’s faculty last year and established the Responsible AI Lab at FSU after earning his doctorate from the University of Virginia. “Now, we’re positioned to make another leap: we’re able to use powerful AI technology to transform wildfire risk management with tools such as ignition forecasting, roadway disruption prediction, condition estimations, damage assessments and more.”
The project is funded as part of an NSF program, Fire Science Innovations through Research and Education, or FIRE, which was established last year and funds research and education enabling large-scale, interdisciplinary breakthroughs that realign our relationship with wildland fire and its connected variables.
Two of the four projects funded so far by the competitive program are led by FSU researchers — Neda Yaghoobian, associate professor in the Department of Mechanical and Aerospace Engineering at the FAMU-FSU College of Engineering, was also funded for a project that will analyze unresolved canopy dynamics contributing to wildfires.
“This grant represents the department’s biggest research award to date and cements our leadership in applying cutting-edge AI to urgent, real-world problems in our region,” said Weikuan Yu, Department of Computer Science chair. “The funding enables the development of a holistic AI platform addressing Florida’s hurricane and wildfire challenges while advancing cutting-edge AI research. Additionally, the grant includes educational and workforce development initiatives in AI and disaster resilience, positioning the department as a leader in training the next generation of scientists working at the intersection of AI and wildfire research.”
WHY IT MATTERS
Fires, especially low-intensity natural wildfires and prescribed burns, can play a vital role in regulating certain forests, grasslands and other fire-adapted ecosystems. They decrease the risk and severity of large, destructive wildfires while supporting soil processes and, in many cases, limit pest and disease outbreaks.
In clearing fallen leaves that pose as hazardous fuel loads, fires lower forest density and recycle nutrients through the ecosystem. But when heaps of trees accumulate, as has happened following recurrent hurricanes in the Florida Panhandle, these fires can exhibit complex dynamics that threaten built infrastructure including homes and roadways in addition to natural landscapes. Understanding this hurricane-wildfire connection is critical for planning evacuations, protecting roads and safeguarding homes and lives.
“I became passionate about applying my research, which achieves responsible AI that directly contributes to critical AI infrastructures, to hurricane-related phenomena after experiencing my first hurricane living in Tallahassee,” Dong said. “I want to use AI techniques to help Florida Panhandle residents better understand and prepare for extreme events in this ecosystem with its unique hurricane-fire coupling dynamics.”
INTERDISCIPLINARY COLLABORATION
Eren Ozguven, associate professor in the FAMU-FSU College of Engineering Department of Civil and Environmental Engineering and Resilient Infrastructure and Disaster Response Center director, is the co-principal investigator on this project, and additional contributors include James Reynolds, co-director of STEM outreach for FSU’s Learning Systems Institute, and Jie Sun, a postdoctoral researcher in the Department of Earth, Ocean and Atmospheric Science.
“Yushun’s project stands out for its ambition, insight, and integrative approach,” Yu said. “It zeros in on the unique challenges of Florida’s landscape where hurricanes and wildfires intersect in the wildland-urban interface of the Panhandle. By focusing on hurricane-fire coupling dynamics and working closely with local stakeholders, his project ensures that scientific innovation translates into practical, community-centered solutions. His integrated approach brings the benefit of cutting-edge AI advances directly to major real-world applications, creating a wonderful research lifecycle that’s exceptionally rare in our field.”
To learn about research conducted in the Department of Computer Science, visit cs.fsu.edu.
This article first appeared on GuruFocus.
Meta Platforms Inc. (META, Financials) is advancing its artificial intelligence ambitions through a small research group called TBD Lab, which consists of a few dozen researchers and engineers, Chief Financial Officer Susan Li said Tuesday at the Goldman Sachs Communacopia + Technology conference.
The unit, whose placeholder name has stuck, is tasked with developing next-generation foundation models over the next one to two years. Li described the team as talent-dense and said its work would help push Meta’s AI portfolio closer to the frontier.
TBD Lab is one of four groups within Meta’s Superintelligence Labs, created earlier this year after the company reorganized its AI strategy. The other groups include a products team anchored by the Meta AI assistant, an infrastructure team, and the Fundamental AI Research (FAIR) lab.
The restructuring followed senior staff departures and what was seen as a muted reception for Meta’s latest open-source Llama 4 model. CEO Mark Zuckerberg has since taken a direct role in recruiting AI talent, making offers to startups and contacting candidates himself through WhatsApp with multimillion-dollar packages.
Investors will look to Meta’s next earnings update for signs of progress in AI development and how new models could fit into its products and services.
AI Research
Commanders vs. Packers NFL props, SportsLine Machine Learning Model AI picks: Jordan Love Over 223.5 passing
The NFL Week 2 schedule gets underway with a Thursday Night Football matchup between NFC playoff teams from a year ago. The Washington Commanders battle the Green Bay Packers beginning at 8:15 p.m. ET from Lambeau Field in Green Bay. Second-year quarterback Jayden Daniels led the Commanders to a 21-6 opening-day win over the New York Giants, completing 19 of 30 passes for 233 yards and one touchdown. Jordan Love, meanwhile, helped propel the Packers to a dominating 27-13 win over the Detroit Lions in Week 1. He completed 16 of 22 passes for 188 yards and two touchdowns.
NFL prop bettors will likely target the two young quarterbacks with NFL prop picks, in addition to proven playmakers like Terry McLaurin, Tucker Kraft and Josh Jacobs. Green Bay’s Jayden Reed has been dealing with a foot injury, but still managed to haul in a touchdown pass in the opener. The Packers enter as a 3.5-point favorite with Green Bay at -187 on the money line. Before betting any Commanders vs. Packers props for Thursday Night Football, you need to see the Commanders vs. Packers prop predictions powered by SportsLine’s Machine Learning Model AI.
Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop.
For Packers vs. Commanders NFL betting on Monday Night Football, the Machine Learning Model has evaluated the NFL player prop odds and provided Bears vs. Vikings prop picks. You can only see the Machine Learning Model player prop predictions for Washington vs. Green Bay here.
Top NFL player prop bets for Commanders vs. Packers
After analyzing the Commanders vs. Packers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model says Packers quarterback Love goes Over 223.5 passing yards (-112 at FanDuel). Love passed for 224 or more yards in eight games a year ago, despite an injury-filled season. In 15 regular-season games in 2024, he completed 63.1% of his passes for 3,389 yards and 25 touchdowns with 11 interceptions.
In a 30-13 win over the Seattle Seahawks on Dec. 15, he completed 20 of 27 passes for 229 yards and two touchdowns. Love completed 21 of 28 passes for 274 yards and two scores in a 30-17 victory over the Miami Dolphins on Nov. 28. The model projects Love to pass for 259.5 yards, giving this prop bet a 4.5 rating out of 5. See more NFL props here, and new users can also target the FanDuel promo code, which offers new users $300 in bonus bets if their first $5 bet wins:
How to make NFL player prop bets for Washington vs. Green Bay
In addition, the SportsLine Machine Learning Model says another star sails past his total and has four additional NFL props that are rated four stars or better. You need to see the Machine Learning Model analysis before making any Commanders vs. Packers prop bets for Thursday Night Football.
Which Commanders vs. Packers prop bets should you target for Thursday Night Football? Visit SportsLine now to see the top Commanders vs. Packers props, all from the SportsLine Machine Learning Model.
FactSet Research Systems (FDS) has recently integrated MarketAxess’ AI-powered CP+ data into its workstation, making it the sole provider of such capabilities in a terminal desktop environment. This integration is designed to provide users with real-time bond pricing and insights on a vast array of securities, centralizing financial data and enhancing trade execution. Over the past week, FDS saw a 1.08% price increase, aligning with broader market trends as the S&P 500 and Nasdaq reached record highs. FactSet’s addition of cutting-edge features may have added positive weight to its recent price movement amid a generally robust technology sector.
You should learn about the 1 warning sign we’ve spotted with FactSet Research Systems.
The integration of MarketAxess’ AI-powered CP+ data into FactSet’s workstation could significantly enhance the company’s appeal by providing advanced data analytics to its users. This move aligns with FactSet’s strategy of expanding its service offerings through acquisitions and new product launches, potentially boosting revenue and earnings in the future. As FactSet continues to integrate acquisitions and enhance its product lineup, these innovations could strengthen the firm’s position in the competitive financial services market. The recent 1.08% share price increase may partly reflect investor optimism about these enhancements and their potential to drive growth.
Over the last five years, FactSet’s total return, including share price and dividends, was 16.06%. However, the company’s recent performance over the past year fell short of both the broader US market, which achieved a 20.5% return, and the Capital Markets industry, which returned 34.2%. This underperformance could highlight investor concerns regarding rising technology costs and potential challenges in the asset management sector.
The current share price of US$372.86 remains at a discount compared to the consensus analyst price target of US$428.38, suggesting additional upside potential if the company can successfully execute its growth strategies. With revenue forecasted to grow at 5.4% annually, analysts project earnings to rise to US$725.4 million by 2028. To align with these projections, FactSet’s strategic moves in technology and acquisitions will be crucial in achieving the expected revenue and earnings growth.
This article by Simply Wall St is general in nature. We provide commentary based on historical data
and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your
financial situation. We aim to bring you long-term focused analysis driven by fundamental data.
Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material.
Simply Wall St has no position in any stocks mentioned.
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