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Articles by Keith Page
University of Buffalo to lead $2 million National Science Foundation project on wildfire resilience
University of Buffalo to lead $2 million National Science Foundation project on wildfire resilience By Keith Page The University of Buffalo September 16, 2026 Category: Forestry Region: US East University of Buffalo is leading a new $2 million research project funded by the National Science Foundation (NSF) to develop tools that help communities better understand and prepare for the cascading disasters that wildfires can unleash on people and critical infrastructure.
UB to lead $2 million National Science Foundation project on wildfire resilience
UB is leading a new $2 million research project funded by the National Science Foundation (NSF) to develop tools that help communities better understand and prepare for the cascading disasters that wildfires can unleash on people and critical infrastructure. The project brings together multidisciplinary researchers from three partner institutions: the University of California, Berkeley, Northeastern University and the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL).
AI helps turn citizen photos into water-level data
The images show waterlines on staff gauges. From left to right, 1) the initial blurred image, 2) filtering the image for uneven light, 3) binarizing the image to black and white, 4) scanning the image to detect the water line, and then transferring the water line to the original image.
UB researchers create early warning system to monitor housing evictions
The tool, from scholars in the Department of AI and Society, aims to help human service agencies respond faster during crises such as the COVID-19 pandemic Human service organizations play an important role in connecting people to housing, health care, food access and other essential services. Yet forecasting community needs can be difficult in times of rapid change or crisis due to limited resources and restricted access to data.
Researchers create early warning system to monitor housing evictions
Credit: Unsplash/CC0 Public Domain Human service organizations play an important role in connecting people to housing, health care, food access and other essential services. Yet forecasting community needs can be difficult in times of rapid change or crisis because of limited resources and restricted access to data. For instance, during the COVID-19 pandemic, concerns about a potential surge in evictions exposed gaps in the data used to assess housing instability.
AI and ‘Ramanomics’ could eliminate a major obstacle to studying living cells
Figure 1 shows how fluorescent labels help identify cell structures while collecting biochemical data, creating the examples needed to train the AI model. Figure 3 shows how AI distinguishes cell structures using their natural biochemical signatures alone. Credit: University at Buffalo. Fluorescent dyes have long been used in biological research to identify and visualize structures within living cells.
AI and 'Ramanomics' could eliminate a major obstacle to studying living cells
The left image shows how fluorescent labels help identify cell structures while collecting biochemical data, creating the examples needed to train the AI model. The image on the right shows how AI distinguishes cell structures using their natural biochemical signatures alone. Credit: University at Buffalo, from ACS Omega (2026). DOI: 10.1021/acsomega.5c12148. Fluorescent dyes have long been used in biological research to identify and visualize structures within living cells.
UB researchers unveil new method for rapidly creating advanced catalyst materials
Mark Swihart, far right, focuses on developing aerosol-based manufacturing techniques for high-performing materials. Photo: Meredith Forrest Kulwicki A UB-led team of researchers has developed a method for producing advanced nanoparticles that could accelerate the discovery of new materials for energy and electronic applications.
One?step process generates high entropy alloy nanoparticles in milliseconds
A rapid synthesis method could speed discovery of cheaper, multi-metal catalysts for fuel cells, batteries, and other clean energy uses. (Nanowerk News) A University at Buffalo-led team of researchers has developed a method for producing advanced nanoparticles that could accelerate the discovery of new materials for energy and electronic applications.
One‑step process generates high entropy alloy nanoparticles in milliseconds for catalyst creation
PhD student Kaiwen Chen. Credit: Meredith Forrest Kulwicki, University at Buffalo. A University at Buffalo-led team of researchers has developed a method for producing advanced nanoparticles that could accelerate the discovery of new materials for energy and electronic applications. The study, published in Nature Communications, introduces a one-step process that rapidly combines multiple metals into uniform nanoparticles in milliseconds.
AI spots hidden security flaws in hundreds of 5G smartphone models
New research led by UB has uncovered security flaws in more than 540 5G smartphone models worldwide that could allow attackers to disrupt service by exploiting a brief gap before devices confirm that a network connection is legitimate.
AI spots hidden security flaws in hundreds of 5G smartphone models
BUFFALO, N.Y. – New research led by the University at Buffalo has uncovered security flaws in more than 540 5G smartphone models worldwide that could allow attackers to disrupt service by exploiting a brief gap before devices confirm that a network connection is legitimate.
UB selected to compete in EcoCAR Innovation Challenge
UB has been selected as one of 20 universities across North America to compete in the EcoCAR Innovation Challenge, a program focused on advancing energy-efficient vehicle technologies and developing the next generation of auto-industry innovators.
Researchers develop AI tool to assess lung cancer surgery complication risk
Researchers from the University at Buffalo and Roswell Park Comprehensive Cancer Center are developing an artificial intelligence tool to help surgeons better identify lung cancer patients at risk for postoperative complications. The work builds on a longstanding collaboration between the institutions, bringing together UB's strengths in AI and Roswell Park's expertise in thoracic oncology. Subscribe to our newsletter for the latest sci-tech news updates.
Researchers develop AI tool to assess lung cancer surgery complication risk
Illustration depicting MIRACLE, our intervenable Deep Learning architecture that combines clinical and radiological features, along with providing surgeons with a point of intervention and explainability. Credit: arXiv (2026). DOI: 10.48550/arxiv.2601.14154 Researchers from the University at Buffalo and Roswell Park Comprehensive Cancer Center are developing an artificial intelligence tool to help surgeons better identify lung cancer patients at risk for postoperative complications.
New AI method curbs hallucinations in automated chest X-ray reports
A UB research team is developing an artificial intelligence framework to improve the accuracy and clinical reliability of automated chest X-ray analysis used by radiologists. The method tackles a long‑standing problem in medical imaging: AI vision‑language models tend to drift away from the image as they write. The new framework, Category‑Wise Contrastive Decoding (CWCD), keeps them anchored to what’s actually visible on the X‑ray.
AI tool can help spot forged radiology reports
From left: UB researchers Tanvi Ranga, Nalini Ratha and Arjun Ramesh Kaushik have developed what they believe is the first AI system designed to distinguish between radiology reports written by humans and those generated by AI. Photo: Meredith Forrest Kulwicki Impersonating a doctor to create a fake radiology report. Inserting nonexistent fractures into real X-ray images.
As wildfires intensify, UB researchers test if AI can improve fire spread prediction
With wildfires growing more destructive both in the United States and around the world, UB researchers have conducted one of the most extensive evaluations to date of artificial intelligence-based deep learning models for predicting wildfire spread. Their findings show how AI can complement but not yet fully replace established physics‑based fire modeling tools.
As wildfires intensify, researchers test if AI can improve fire spread prediction
Credit: Unsplash/CC0 Public Domain With wildfires growing more destructive both in the United States and around the world, University at Buffalo researchers have conducted one of the most extensive evaluations to date of artificial intelligence-based deep learning models for predicting wildfire spread. Their findings show how AI can complement but not yet fully replace established physics-based fire modeling tools.
AI helps turn citizen photos into water-level data for UB researchers
The images show waterlines on staff gauges. From left to right, 1) the initial blurred image, 2) filtering the image for uneven light, 3) binarizing the image to black and white, 4) scanning the image to detect the water line, and then transferring the water line to the original image.
UB selected to compete in EcoCAR Innovation Challenge
Energy Department contest pairs students with automaker Stellantis to develop new tech for Jeep Cherokee hybrid BUFFALO, N.Y. – The University at Buffalo has been selected as one of 20 universities across North America to compete in the EcoCAR Innovation Challenge, a program focused on advancing energy-efficient vehicle technologies and developing the next generation of auto-industry innovators.
New AI method curbs hallucinations in automated chest X-ray reports
BUFFALO, N.Y. – A research team at the University at Buffalo is developing an artificial intelligence framework to improve the accuracy and clinical reliability of automated chest X-ray analysis used by radiologists. The method tackles a long‑standing problem in medical imaging: AI vision‑language models tend to drift away from the image as they write. The new framework, Category‑Wise Contrastive Decoding (CWCD), keeps them anchored to what’s actually visible on the X‑ray.
UB to lead $2 million National Science Foundation project on wildfire resilience Original
Research will develop AI-powered tools to examine cascading wildfire impacts, including flash floods, debris flows and infrastructure disruptions BUFFALO, N.Y. – The University at Buffalo is leading a new $2 million research project funded by the National Science Foundation (NSF) to develop tools that help communities better understand and prepare for the cascading disasters that wildfires can unleash on people and critical infrastructure.
UB researchers unveil new method for rapidly creating advanced catalyst materials
Oneāstep process generates high entropy alloy nanoparticles in milliseconds BUFFALO, N.Y. – A University at Buffalo-led team of researchers has developed a method for producing advanced nanoparticles that could accelerate the discovery of new materials for energy and electronic applications. The study, published in Nature Communications under the journal’s early access policy, introduces a one-step process that rapidly combines multiple metals into uniform nanoparticles in just milliseconds.
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