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Nullam in ornare quam. Quisque blandit nunc lacinia elit ornare fringilla. Praesent molestie justo nec neque venenatis, id dictum dolor convallis. Phasellus eu orci vitae mauris blandit lacinia sed sit amet arcu. Vestibulum porta vitae diam finibus porta. Nam vitae risus quis libero bibendum facilisis. Mauris auctor mi id lectus sodales, ut molestie massa convallis. Sed elit ipsum, ullamcorper at ornare sit amet, ultrices quis nisl. Nulla facilisi. Quisque dignissim tincidunt ligula, eu tristique magna sollicitudin eu.
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]]>There are three images, two I uploaded. A video and two files/pdfs.
]]>Needing to update this article to see the auto pull and notice ios the Infinite check box gets overwritten.
september 2, 2025
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Cupim pork loin flank shoulder burgdoggen. Hamburger burgdoggen t-bone tongue cupim. Pork loin frankfurter boudin tail, pancetta meatloaf pork chicken filet mignon cupim brisket alcatra. Fatback t-bone alcatra, cow filet mignon brisket hamburger picanha spare ribs. Tenderloin turducken sausage shoulder, brisket ground round tri-tip tongue chuck pork chop ball tip t-bone buffalo prosciutto. Boudin ball tip buffalo, pork loin bresaola leberkas pancetta salami doner. Pork chop tri-tip andouille beef tongue filet mignon strip steak.
Corned beef t-bone ball tip, sausage ham hock hamburger chuck strip steak shankle beef frankfurter chicken pork belly tenderloin. Frankfurter tenderloin ribeye pork chop, t-bone pork belly beef jerky shoulder. Cupim short ribs jerky drumstick. Tongue venison turducken landjaeger meatball pork loin burgdoggen ribeye spare ribs. Salami pork loin chicken capicola.
]]>tim.whelan@research.gatech.edu
Web Dev Lead
]]>But as carbon removal grows, so does a core problem: The carbon removal industry is largely unregulated, particularly for more novel technologies without long-standing norms around reporting and verification. In today’s “voluntary carbon market,” a private company can claim it removed a certain amount of carbon, list that amount for sale, and allow another company to buy it to offset its emissions — with little independent oversight or transparency.
A new Nature NPJ Climate Action article argues that this system isn’t enough to meet global climate goals, and could even end up causing harm. In the paper, Chris Reinhard, Georgia Power Chair and associate professor in Georgia Tech’s School of Earth and Atmospheric Sciences, and Noah Planavsky of the Yale Center for Natural Carbon Capture call for a fundamental shift: Carbon removal should be quantifiable, economically viable, and pursued in ways that create benefits for local communities — and greater transparency in carbon removal practice is necessary.
“We argue that it’s important to understand and quantify carbon removal practices that can benefit local communities, like better crop yields, and that this understanding is really only possible if these practices are pursued transparently,” Reinhard said. “The data used to quantify carbon removal and how much it costs need to be transparent — the surest route toward learning what works and building public trust in carbon removal as a solution.”
Reinhard and Planavsky bring a unique technical and policy perspective to the issue. As geochemists, they study how Earth’s chemical composition and geological processes control the carbon cycle. Reinhard also co-founded a carbon removal startup he has since divested from. That insider experience and academic background helped them see the disconnect between what’s technologically possible and what market logic culturally or commercially incentivizes.
Today’s carbon removal startups often guard their methods and data as proprietary intellectual property. Without regulatory requirements or pressure from corporate carbon buyers, these startups have little reason to disclose carbon accounting practices, cost structures, or actual long-term impacts. The researchers argue that policy guidance and advocacy are needed to shift the industry toward meaningful openness.
“Our expertise is most firmly grounded in the technical dimensions of these carbon removal processes,” Reinhard said, “but we saw an opportunity here to push for better policy and start this dialogue about what transparency really means, in part to foster more public debate about what carbon removal ought to be doing for society.”
The authors also stress that carbon removal should deliver benefits beyond atmospheric cleanup that communities can see and advocate for. For example, liming, or adding limestone to soil, can remove carbon while also improving crop yields and reducing erosion. Coastal ecosystem restoration can sequester carbon while strengthening shorelines and supporting fisheries. Georgia Tech’s own direct air capture work builds community engagement into the process to ensure that carbon removal is equitable.
Reinhard and Planavsky say the next best step for the carbon removal industry is to identify which removal pathways offer the clearest benefits, what they cost, and where transparency gaps are most damaging. This foundation will help create policies that make carbon removal reliable, verifiable, and community-centered.
Without oversight, they argue, carbon removal risks remaining a niche, market-defined practice — when the climate challenge demands a trusted, scalable, and democratically governed solution.
CITATION: Reinhard, C.T., Planavsky, N.J. The importance of radical transparency for responsible carbon dioxide removal. npj Clim. Action 5, 7 (2026). https://doi.org/10.1038/s44168-025-00324-4
]]>Brian Magerko has been appointed as the James and Mary Wesley Chair for New Media Studies in the School of Literature, Media, and Communication. Magerko is a Regents’ Professor of Digital Media and head of the Expressive Machinery Lab. He has served as director of graduate studies for the Digital Media Program for eight years and will step down from this leadership role on July 31.
The endowed chair appointment is a renewable, three-year term, starting on Aug. 1. The endowed professorship is supported by a gift from James and Mary Wesley, long-time Atlanta philanthropists. The chair conducts research and develops and teaches courses within the School of Literature, Media, and Communication (LMC) and has recognized expertise in digital environments across media platforms.
"Dr. Magerko's innovative work in cognition, coding education, and creativity makes him an ideal fit to serve as our Wesley Chair,” said Amanda Murdie, dean of the Ivan Allen College of Liberal Arts, Regents’ Professor, and Ivan Allen Jr. Chair in Liberal Arts and Technology. “This chair will help advance his groundbreaking research while also supporting his transformative leadership of LMC’s Digital Media Program.”
Magerko’s expertise lies at the intersections of computation, cognition, and the social/creative interactions between humans and artificial intelligence. His research explores how studying human and machine cognition can inform the creation of new human/computer creative experiences. He has been research lead on more than $15 million of federally funded research and has authored over 100 peer-reviewed articles related to computational media, cognition, and learning.
His work has been shown at galleries and museums internationally, and he co-founded a music-based learning environment for computer science, called EarSketch, that has been used by about 2 million learners worldwide. Magerko and his work have appeared in the New Yorker, USA Today, CNN, Yahoo! Finance, NPR, and other global and regional outlets.
Magerko joined Georgia Tech in 2008 as an assistant professor of digital media. He received his B.S. in Cognitive Science from Carnegie Mellon University and his M.S. and Ph.D. in Computer Science and Engineering from the University of Michigan.
“Dr. Magerko exemplifies our teaching and educational mission in his work on behalf of our Digital Media program,” said Kelly Ritter, chair of LMC and professor of Writing and Communication. “He has built an already strong and forward-looking program into an even better one that provides pathways for students and fellow faculty to accomplish their own professional and intellectual goals.”
Editor's note. This story was first published by Georgia Tech Research.
“Organizations need to shift the way they treat bias in AI,” said Reeham Mohammed, a postdoctoral fellow in Georgia Tech’s Jimmy and Rosalynn Carter School of Public Policy. “Bias isn’t a problem that can be solved once and for all, and it affects everyone. We are all stakeholders in how this technology is implemented.”
Most efforts to address AI bias focus on fixing systems after they are built. Reeham proposes starting earlier. In a recent book chapter, she outlines an approach called participatory modeling, where stakeholders help map how AI systems function before they are deployed. The process reveals where bias can enter along the way.
Her framework draws on an unexpected comparison: the human immune system. When the body detects a virus, multiple systems respond and remain active until the threat is contained. Addressing AI bias, she argues, requires a similar approach — one that is continuous, adaptive, and systemwide.
“It takes the whole body to react to a virus, and the immune system stays active until it contains it,” Reeham said. “Stopping AI bias also requires ongoing intervention. AI is rapidly evolving, and our response should evolve with it.”
To test this idea, Reeham and her co-author, Erik Johnston of Arizona State University, wrote the recent chapter reporting on three empirical studies examining how bias can emerge across complex systems.
In Peoria, Illinois, the research mapped how health services, social services, and other support systems used AI and how it affected their operations. Three main issues stood out: Access barriers to resources kept some stakeholders out, AI systems reflected their designers more than the intended users, and the complexity of public institutions made public trust challenging.
Johnston analyzed 311 service request data, showing how citizen input, usage patterns, and response systems can reflect — and sometimes reinforce — existing disparities between neighborhoods. For example, low-income, ethnically diverse communities often don’t use 311 due to language barriers or low government trust. In a third study, Reeham conducted focus groups with 57 stakeholders at a higher education institution, including students, instructors, and administrators. These interviews explored perceptions of AI use and oversight within the higher education environment. Many of the conversations addressed faculty policies on student AI usage, with students reflecting frustration that they were not consulted in the making of these policies.
Together, the findings show AI systems cannot be designed effectively from the top down without excluding important groups that actually use the AI services. Instead, participatory modeling calls for stakeholders to be involved early to help identify where bias begins. Whether through town halls or focus groups, stakeholders should be part of discussions when designing a new system. Stakeholders also need to remain engaged after deployment to ensure that systems stay fair; this could include everything from community advisory boards to third-party consultants.
“People should know that if they see bias in AI, they need to speak up,” Reeham said. “We often assume machines are more objective than humans, but that’s not always true.”
CITATION: Johnston, E.W., Mohammed, R.R. (2026). How Participatory Modeling Can Enable Collective Bias Mitigation when AI Is Used across Systems and Institutions. In: Ahrweiler, P., Gilbert, N. (eds) Participatory Modelling and Simulation to Improve AI-based Public Social Services. Artificial Intelligence, Simulation and Society. Springer, Cham. https://doi.org/10.1007/978-3-032-15283-1_13
]]>“Organizations need to shift the way they treat bias in AI,” said Reeham Mohammed, a postdoctoral fellow in Georgia Tech’s Jimmy and Rosalynn Carter School of Public Policy. “Bias isn’t a problem that can be solved once and for all, and it affects everyone. We are all stakeholders in how this technology is implemented.”
Most efforts to address AI bias focus on fixing systems after they are built. Reeham proposes starting earlier. In a recent book chapter, she outlines an approach called participatory modeling, where stakeholders help map how AI systems function before they are deployed. The process reveals where bias can enter along the way.
Her framework draws on an unexpected comparison: the human immune system. When the body detects a virus, multiple systems respond and remain active until the threat is contained. Addressing AI bias, she argues, requires a similar approach — one that is continuous, adaptive, and systemwide.
“It takes the whole body to react to a virus, and the immune system stays active until it contains it,” Reeham said. “Stopping AI bias also requires ongoing intervention. AI is rapidly evolving, and our response should evolve with it.”
To test this idea, Reeham and her co-author, Erik Johnston of Arizona State University, wrote the recent chapter reporting on three empirical studies examining how bias can emerge across complex systems.
In Peoria, Illinois, the research mapped how health services, social services, and other support systems used AI and how it affected their operations. Three main issues stood out: Access barriers to resources kept some stakeholders out, AI systems reflected their designers more than the intended users, and the complexity of public institutions made public trust challenging.
Johnston analyzed 311 service request data, showing how citizen input, usage patterns, and response systems can reflect — and sometimes reinforce — existing disparities between neighborhoods. For example, low-income, ethnically diverse communities often don’t use 311 due to language barriers or low government trust. In a third study, Reeham conducted focus groups with 57 stakeholders at a higher education institution, including students, instructors, and administrators. These interviews explored perceptions of AI use and oversight within the higher education environment. Many of the conversations addressed faculty policies on student AI usage, with students reflecting frustration that they were not consulted in the making of these policies.
Together, the findings show AI systems cannot be designed effectively from the top down without excluding important groups that actually use the AI services. Instead, participatory modeling calls for stakeholders to be involved early to help identify where bias begins. Whether through town halls or focus groups, stakeholders should be part of discussions when designing a new system. Stakeholders also need to remain engaged after deployment to ensure that systems stay fair; this could include everything from community advisory boards to third-party consultants.
“People should know that if they see bias in AI, they need to speak up,” Reeham said. “We often assume machines are more objective than humans, but that’s not always true.”
CITATION: Johnston, E.W., Mohammed, R.R. (2026). How Participatory Modeling Can Enable Collective Bias Mitigation when AI Is Used across Systems and Institutions. In: Ahrweiler, P., Gilbert, N. (eds) Participatory Modelling and Simulation to Improve AI-based Public Social Services. Artificial Intelligence, Simulation and Society. Springer, Cham. https://doi.org/10.1007/978-3-032-15283-1_13
tess.malone@gatech.edu
]]>"The most meaningful breakthroughs happen when intelligent hardware and thoughtful software come together to genuinely help people,” said Tapas Roy, vice president of Device Software and Services at Amazon. “Georgia Tech's world-class expertise in robotics and engineering, paired with Amazon's work in on-device intelligence and artificial intelligence, will accelerate our understanding of systems that can both respond to prompts and anticipate customer needs. I'm excited to see how this collaboration advances the science of how humans and intelligent machines work together.”
The Hub’s initial focus areas will explore challenges in autonomous systems, next-generation logistics infrastructure, and low-Earth-orbit communications, drawing on Georgia Tech’s well-known interdisciplinary research approach.
Georgia Tech is also joining the Fauna Robotics' ecosystem, using the Sprout research platform to study how humans and robots can live, work, and play side by side. This collaboration opens new ways to study mobility, perception, manipulation, and human-robot interaction. Leadership and alumni recently celebrated the launch at Lab 126, with guest appearances from Sprout and Buzz, the Georgia Tech mascot.
The Hub will support:
• Joint research projects between Georgia Tech faculty and Amazon scientists across scientific disciplines
• Regular symposia and workshops to foster collaboration between researchers from both institutions and the broader Atlanta tech community
• Community outreach programs designed to broaden participation from interdisciplinary scholars and innovators
“Georgia Tech and Amazon have worked together for years across research, technology, and student development," said Tim Lieuwen, Georgia Tech executive vice president for Research. "This new hub puts our partnership to work for Georgia. It connects research, education, and industry in ways that will strengthen our state’s innovation economy and create new opportunities for our students.”
About Georgia Tech
Georgia Institute of Technology is one of the nation’s top public research universities, preparing leaders who advance technology and improve the human condition. The Institute serves more than 56,000 students from all 50 states and over 146 countries and drives innovation and economic growth through research that benefits Georgia, the nation, and the world.
About Amazon in Georgia
Since 2010, Amazon has invested more than $30 billion in Georgia, supporting more than 30,000 full- and part-time jobs. Amazon’s research and technology teams in the Atlanta area work on challenges spanning supply chain optimization, logistics technology, and cloud computing. The establishment of the Georgia Tech–Amazon Science Hub further deepens Amazon’s engagement with one of the nation’s leading research institutions.
About Fauna Robotics
Fauna Robotics builds Sprout, a development platform designed to put advanced robotics capabilities into the hands of researchers and developers. Sprout gives builders hands-on access to study mobility, perception, motor control, and manipulation with the ambition to deploy robots into human-centric spaces.