Whether it’s developing new products, reducing costs, or increasing accessibility, innovations in manufacturing stand to improve the lives of companies and consumers alike. Georgia Tech recently took another step toward ensuring those innovations make it from lab to market with the launch of a Modular Pilot Scale Roll-to-Roll Manufacturing Facility. 

“As researchers develop new materials, one of the key aspects we’re missing is how to make them at scale. This is a major oversight because if we can’t make them at scale, we can’t transition from basic research to commercialization,” said Tequila Harris, a professor in the George W. Woodruff School of Mechanical Engineering. “With this new facility, we can prove our discoveries beyond lab-scale studies — and can go from materials innovation to product development at scale.”

Led by Harris, the new facility is the result of a partnership between the Georgia Tech Manufacturing Institute(GTMI), the Strategic Energy Institute, and the Woodruff School. As a pilot facility, it will serve as a testbed for scaling up manufacturing research open for Georgia Tech researchers as well as academic, government, and industry partners around the world.

“The larger vision I see at Georgia Tech involves innovation in manufacturing for large-scale industries,” said Georgia Tech’s Interim Executive Vice President for Research Tim Lieuwen at the facility’s unveiling event on Sept. 19. “It’s crucial that we’re innovating in basic science and technology, but we also need to be innovating in large-scale manufacturing.”

Roll-to-roll (R2R) manufacturing transforms flexible rolls of substrate materials, such as paper, metal foils, and plastics, into more complex, transportable rolls upon coating the surface with one or more fluids, such as inks, suspensions, and solutions, which are subsequently dried or cured on the base substrate. Its high yield and efficiency make R2R an ideal method for the sustainable, large-scale production of components for solar cells, batteries, flexible electronics, and separations — all industries that have expanded in Georgia in recent years.

“As a state institution, we’re ultimately here to serve our state,” said Lieuwen, who is also Regents’ Professor and David S. Lewis Jr. Chair in the Daniel Guggenheim School of Aerospace Engineering. “We’re seeing Georgia emerge as the national leader in terms of recruiting corporate investments in this space and in industries that will be served by this facility.”

Roll-to-Roll Innovations

The R2R process is similar to the production of newspapers, where a large roll of blank paper goes through a series of rollers printing text and photos. “The roll-to-roll aspect is the process of using a specialized tool to force fluid onto a moving surface,” says Harris. It’s one of the fastest-growing methods for producing thin film materials — photovoltaics used in solar cells, transistors in flexible electronics, and micro-batteries, for example — at a large scale. 

Harris’s group works to develop novel manufacturing tools, with a particular focus on understanding and improving the dynamics of thin film manufacturing to increase efficiency and minimize waste. Her group is particularly interested in slot die coating, an R2R technique where a liquid material is precisely deposited onto a substrate through a narrow slot. With the new pilot facility, researchers like Harris will be able to take their work to the next level.

“Slot die coating on a roll-to-roll can handle the broadest viscosity range of most coating methods. Therefore, you can process a lot of different materials very quickly and easily,” says Harris. “It’s one of the fastest-growing technologies in the U.S. — and currently, this is the most advanced modular pilot scale facility at an academic university in the United States.”

“Georgia Tech is way ahead of the curve in terms of our facilities,” says GTMI Executive Director and Regents’ Professor Thomas Kurfess. “This will grow our capability in the battery area, membranes, flexible electronics, and more to allow us to support the development of new technologies.”

“As technologies around cleantech continue to advance at an unprecedented pace, pilot manufacturing facilities provide a critical bridge between innovative benchtop research and commercial-scale production and manufacturing,” says Christine Conwell, interim executive director of the Strategic Energy Institute. “We are excited about the opportunities this R2R facility will provide to the Georgia Tech energy community and our industry partners.”

Is it a building or a street? How tall is the building? Are there powerlines nearby?

These are details autonomous flying vehicles would need to know to function safely. However, few aerial image datasets exist that can adequately train the computer vision algorithms that would pilot these vehicles.

That’s why Georgia Tech researchers created a new benchmark dataset of computer-generated aerial images.

Judy Hoffman, an assistant professor in Georgia Tech’s School of Interactive Computing, worked with students in her lab to create SKYSCENES. The dataset contains over 33,000 aerial images of cities curated from a computer simulation program.

Hoffman said sufficient training datasets could unlock the potential of autonomous flying vehicles. Constructing those datasets is a challenge the computer vision research community has been working for years to overcome.

“You can’t crowdsource it the same way you would standard internet images,” Hoffman said. “Trying to collect it manually would be very slow and expensive — akin to what the self-driving industry is doing driving around vehicles, but now you’re talking about drones flying around. 

“We must fix those problems to have models that work reliably and safely for flying vehicles.”

Many existing datasets aren’t annotated well enough for algorithms to distinguish objects in the image. For example, the algorithms may not recognize the surface of a building from the surface of a street.

Working with Hoffman, Ph.D. student Sahil Khose tried a new approach — constructing a synthetic image data set from a ground-view, open-source simulator known as CARLA.

CARLA was originally designed to provide ground-view simulation for self-driving vehicles. It creates an open-world virtual reality that allows users to drive around in computer-generated cities.

Khose and his collaborators adjusted CARLA’s interface to support aerial views that mimic views one might get from unmanned aerial vehicles (UAVs). 

What's the Forecast?

The team also created new virtual scenarios to mimic the real world by accounting for changes in weather, times of day, various altitudes, and population per city. The algorithms will struggle to recognize the objects in the frame consistently unless those details are incorporated into the training data.

“CARLA’s flexibility offers a wide range of environmental configurations, and we take several important considerations into account while curating SKYSCENES images from CARLA,” Khose said. “Those include strategies for obtaining diverse synthetic data, embedding real-world irregularities, avoiding correlated images, addressing skewed class representations, and reproducing precise viewpoints.”

SKYSCENES is not the largest dataset of aerial images to be released, but a paper co-authored by Khose shows that it performs better than existing models. 

Khose said models trained on this dataset exhibit strong generalization to real-world scenarios, and integration with real-world data enhances their performance. The dataset also controls variability, which is essential to perform various tasks.

“This dataset drives advancements in multi-view learning, domain adaptation, and multimodal approaches, with major implications for applications like urban planning, disaster response, and autonomous drone navigation,” Khose said. “We hope to bridge the gap for synthetic-to-real adaptation and generalization for aerial images.”

Seeing the Whole Picture

For algorithms, generalization is the ability to perform tasks based on new data that expands beyond the specific examples on which they were trained.

“If you have 200 images, and you train a model on those images, they’ll do well at recognizing what you want them to recognize in that closed-world initial setting,” Hoffman said. “But if we were to take aerial vehicles and fly them around cities at various times of the day or in other weather conditions, they would start to fail.”

That’s why Khose designed algorithms to enhance the quality of the curated images.

“These images are captured from 100 meters above ground, which means the objects appear small and are challenging to recognize,” he said. “We focused on developing algorithms specifically designed to address this.”

Those algorithms elevate the ability of ML models to recognize small objects, improving their performance in navigating new environments.

“Our annotations help the models capture a more comprehensive understanding of the entire scene — where the roads are, where the buildings are, and know they are buildings and not just an obstacle in the way,” Hoffman said. “It gives a richer set of information when planning a flight.

“To work safely, many autonomous flight plans might require a map given to them beforehand. If you have successful vision systems that understand exactly what the obstacles in the real world are, you could navigate in previously unseen environments.”

For more information about Georgia Tech Research at ECCV 2024, click here.

In a major step forward for deploying artificial intelligence (AI) in industry, Georgia Tech’s newly established AI hub, Tech AI, has partnered with the Center for Scientific Software Engineering (CSSE). This collaboration aims to bridge the gap between academia and industry by advancing scalable AI solutions in sectors such as energy, mobility, supply chains, healthcare, and services.

Building on the Foundation of Success

CSSE, founded in late 2021 and supported by Schmidt Sciences as part of their VISS initiative, was created to advance and support scientific research by applying modern software engineering practices, cutting-edge technologies, and modern tools to the development of scientific software within and outside Georgia Tech. CSSE is led by Alex Orso,  professor and associate dean in the College of Computing,  and Jeff Young, a principal scientist at Georgia Tech. The Center's team boasts over 60 years of combined experience, with engineers from companies such as Microsoft, Amazon, and various startups, working under the supervision of the Center’s Head of Engineering, Dave Brownell. Their focus is on turning cutting-edge research into real-world products.

“Software engineering is about much more than just writing code,” Orso explained. “It’s also about specifying, designing, testing, deploying, and maintaining these systems.”

A Partnership to Support AI Research and Innovation

Through this collaboration, CSSE’s expertise will be integrated into Tech AI to create a software engineering division that can support AI engineering and also create new career opportunities for students and researchers.

Pascal Van Hentenryck, the A. Russell Chandler III Chair and professor in the H. Milton Stewart School of Industrial Engineering (ISyE)  and director of both the NSF AI Research Institute for Advances in Optimization (AI4OPT) and Tech AI, highlighted the potential of this partnership.

“We are impressed with the technology and talent within CSSE,” Van Hentenryck said. “This partnership allows us to leverage an existing, highly skilled engineering team rather than building one from scratch. It’s a unique opportunity to build the engineering pillar of Tech AI and push our AI initiatives forward, moving from pilots to products.”

“Joining our forces and having a professional engineering resource within Tech AI will give Georgia Tech a great competitive advantage over other AI initiatives,” Orso added.

One of the first projects under this collaboration focuses on AI in energy, particularly in developing new-generation, AI-driven, market clearing optimization and real-time risk assessment. Plans are also in place to pursue several additional projects, including the creation of an AI-powered search engine assistant, demonstrating the center’s ability to tackle complex, real-world problems.

This partnership is positioned to make a significant impact on applied AI research and innovation at Georgia Tech. By integrating modern software engineering practices, the collaboration will address key challenges in AI deployment, scalability, and sustainability, and translate AI research innovations into products with real societal impact.

“This is a match made in heaven,” Orso noted, reflecting on the collaboration’s alignment with Georgia Tech’s strategic goals to advance technology and improve human lives. Van Hentenryck added that “the collaboration is as much about creating new technologies as it is about educating the next generation of engineers.”

Promoting Open Source at Tech AI

A crucial element supporting the new Tech AI and CSSE venture is Georgia Tech’s Open Source Program Office (OSPO), a joint effort with the College of Computing, PACE, and the Georgia Tech Library. As an important hub of open-source knowledge, OSPO will provide education, training, and guidance on best practices for using and contributing to open-source AI frameworks.

“A large majority of the software driving our current accomplishments in AI research and development is built on a long history of open-source software and data sets, including frameworks like PyTorch and models like Meta’s LLaMA,” said Jeff Young, principal investigator at OSPO. “Understanding how we can best use and contribute to open-source AI is critical to our future success with Tech AI, and OSPO is well-suited to provide guidance, training, and expertise around these open-source tools, frameworks, and pipelines.”

Looking Ahead

As the partnership between Tech AI and CSSE evolves, both groups anticipate a future in which interdisciplinary research drives innovation. By integrating AI with real-world software engineering, the collaboration promises to create new opportunities for students, researchers, and Georgia Tech as a whole.

With a strong foundation, a talented team, and a clear vision, Tech AI and CSSE together are set to break new ground in AI and scientific research, propelling Georgia Tech to the forefront of technological advancement in the AI field.

About the Center for Scientific Software Engineering (CSSE)

The CSSE at Georgia Tech, supported by an $11 million grant from Schmidt Sciences, is one of four scientific software engineering centers within the Virtual Institute for Scientific Software (VISS). Its mission is to develop scalable, reliable, open-source software for scientific research, ensuring maintainability and effectiveness. Learn more at https://ssecenter.cc.gatech.edu.

About Georgia Tech’s Open Source Program Office (OSPO)

Georgia Tech’s OSPO supports the development of open-source research software across campus. Funded by a Sloan Foundation grant, OSPO provides community guidelines, training, and outreach to promote a thriving open-source ecosystem. Learn more at https://ospo.cc.gatech.edu.

About Schmidt Sciences

Schmidt Sciences is a nonprofit organization founded in 2024 by Eric and Wendy Schmidt that works to advance science and technology that deepens human understanding of the natural world and develops solutions to global issues. The organization makes grants in four areas—AI and advanced computing, astrophysics and space, biosciences and climate—as well as supporting researchers in a variety of disciplines through its science systems program. Learn more at https://www.schmidtsciences.org/. 

About Tech AI

Tech AI is Georgia Tech’s AI hub, advancing AI through research, education, and responsible deployment. The hub focuses on AI solutions for real-world applications, preparing the next generation of AI leaders. Learn more at https://ai.gatech.edu.

A multi-institutional research team led by Georgia Tech’s Hailong Chen has developed a new, low-cost cathode that could radically improve lithium-ion batteries (LIBs) — potentially transforming the electric vehicle (EV) market and large-scale energy storage systems. 

“For a long time, people have been looking for a lower-cost, more sustainable alternative to existing cathode materials. I think we’ve got one,” said Chen, an associate professor with appointments in the George W. Woodruff School of Mechanical Engineering and the School of Materials Science and Engineering.

The revolutionary material, iron chloride (FeCl3), costs a mere 1-2% of typical cathode materials and canstore the same amount of electricity. Cathode materials affect capacity, energy, and efficiency, playing a major role in a battery’s performance, lifespan, and affordability.

“Our cathode can be a game-changer,” said Chen, whose team describes its work in Nature Sustainability. “It would greatly improve the EV market — and the whole lithium-ion battery market.”

First commercialized by Sony in the early 1990s, LIBs sparked an explosion in personal electronics, like smartphones and tablets. The technology eventually advanced to fuel electric vehicles, providing a reliable, rechargeable, high-density energy source. But unlike personal electronics, large-scale energy users like EVs are especially sensitive to the cost of LIBs. 

Batteries are currently responsible for about 50% of an EV’s total cost, which makes these clean-energy cars more expensive than their internal combustion, greenhouse-gas-spewing cousins. The Chen team’s invention could change that.

Building a Better Battery

Compared to old-fashioned alkaline and lead-acid batteries, LIBs store more energy in a smaller package and power a device longer between charges. But LIBs contain expensive metals, including semiprecious elements like cobalt and nickel, and they have a high manufacturing cost. 

So far, only four types of cathodes have been successfully commercialized for LIBs. Chen’s would be the fifth, and it would represent a big step forward in battery technology: the development of an all-solid-state LIB.

Conventional LIBs use liquid electrolytes to transport lithium ions for storing and releasing energy. They have hard limits on how much energy can be stored, and they can leak and catch fire. But all-solid-state LIBs use solid electrolytes, dramatically boosting a battery’s efficiency and reliability and making it safer and capable of holding more energy. These batteries, still in the development and testing phase, would be a considerable improvement. 

As researchers and manufacturers across the planet race to make all-solid-state technology practical, Chen and his collaborators have developed an affordable and sustainable solution. With the FeCl3 cathode, a solid electrolyte, and a lithium metal anode, the cost of their whole battery system is 30-40% of current LIBs. 

“This could not only make EVs much cheaper than internal combustion cars, but it provides a new and promising form of large-scale energy storage, enhancing the resilience of the electrical grid,” Chen said. “In addition, our cathode would greatly improve the sustainability and supply chain stability of the EV market.”

Solid Start to New Discovery

Chen’s interest in FeCl3 as a cathode material originated with his lab’s research into solid electrolyte materials. Starting in 2019, his lab tried to make solid-state batteries using chloride-based solid electrolyteswith traditional commercial oxide-based cathodes. It didn’t go well — the cathode and electrolyte materials didn’t get along. 

The researchers thought a chloride-based cathode could provide a better pairing with the chloride electrolyte to offer better battery performance.

“We found a candidate (FeCl3) worth trying, as its crystal structure is potentially suitable for storing and transporting Li ions, and fortunately, it functioned as we expected,” said Chen.

Currently, the most popularly used cathodes in EVs are oxides and require a gigantic amount of costly nickel and cobalt, heavy elements that can be toxic and pose an environmental challenge. In contrast, the Chen team’s cathode contains only iron (Fe) and chlorine (Cl)—abundant, affordable, widely used elements found in steel and table salt.

In their initial tests, FeCl3 was found to perform as well as or better than the other, much more expensive cathodes. For example, it has a higher operational voltage than the popularly used cathode LiFePO4 (lithium iron phosphate, or LFP), which is the electrical force a battery provides when connected to a device, similar to water pressure from a garden hose. 

This technology may be less than five years from commercial viability in EVs. For now, the team will continue investigating FeCl3 and related materials, according to Chen. The work was led by Chen and postdoc Zhantao Liu (the lead author of the study). Collaborators included researchers from Georgia Tech’s Woodruff School (Ting Zhu) and the School of Earth and Atmospheric Sciences (Yuanzhi Tang), as well as the Oak Ridge National Laboratory (Jue Liu) and the University of Houston (Shuo Chen).

“We want to make the materials as perfect as possible in the lab and understand the underlying functioning mechanisms,” Chen said. “But we are open to opportunities to scale up the technology and push it toward commercial applications.”

CITATION: Zhantao Liu, Jue Liu, Simin Zhao, Sangni Xun, Paul Byaruhanga, Shuo Chen, Yuanzhi Tang, Ting Zhu, Hailong Chen. “Low-cost iron trichloride cathode for all-solid-state lithium-ion batteries.” Nature Sustainability.

FUNDING: National Science Foundation (Grant Nos. 1706723 and 2108688)

A new algorithm tested on NASA’s Perseverance Rover on Mars may lead to better forecasting of hurricanes, wildfires, and other extreme weather events that impact millions globally.

Georgia Tech Ph.D. student Austin P. Wright is first author of a paper that introduces Nested Fusion. The new algorithm improves scientists’ ability to search for past signs of life on the Martian surface. 

In addition to supporting NASA’s Mars 2020 mission, scientists from other fields working with large, overlapping datasets can use Nested Fusion’s methods toward their studies.

Wright presented Nested Fusion at the 2024 International Conference on Knowledge Discovery and Data Mining (KDD 2024) where it was a runner-up for the best paper award. KDD is widely considered the world's most prestigious conference for knowledge discovery and data mining research.

“Nested Fusion is really useful for researchers in many different domains, not just NASA scientists,” said Wright. “The method visualizes complex datasets that can be difficult to get an overall view of during the initial exploratory stages of analysis.”

Nested Fusion combines datasets with different resolutions to produce a single, high-resolution visual distribution. Using this method, NASA scientists can more easily analyze multiple datasets from various sources at the same time. This can lead to faster studies of Mars’ surface composition to find clues of previous life.

The algorithm demonstrates how data science impacts traditional scientific fields like chemistry, biology, and geology.

Even further, Wright is developing Nested Fusion applications to model shifting climate patterns, plant and animal life, and other concepts in the earth sciences. The same method can combine overlapping datasets from satellite imagery, biomarkers, and climate data.

“Users have extended Nested Fusion and similar algorithms toward earth science contexts, which we have received very positive feedback,” said Wright, who studies machine learning (ML) at Georgia Tech.

“Cross-correlational analysis takes a long time to do and is not done in the initial stages of research when patterns appear and form new hypotheses. Nested Fusion enables people to discover these patterns much earlier.”

Wright is the data science and ML lead for PIXLISE, the software that NASA JPL scientists use to study data from the Mars Perseverance Rover.

Perseverance uses its Planetary Instrument for X-ray Lithochemistry (PIXL) to collect data on mineral composition of Mars’ surface. PIXL’s two main tools that accomplish this are its X-ray Fluorescence (XRF) Spectrometer and Multi-Context Camera (MCC).

When PIXL scans a target area, it creates two co-aligned datasets from the components. XRF collects a sample's fine-scale elemental composition. MCC produces images of a sample to gather visual and physical details like size and shape. 

A single XRF spectrum corresponds to approximately 100 MCC imaging pixels for every scan point. Each tool’s unique resolution makes mapping between overlapping data layers challenging. However, Wright and his collaborators designed Nested Fusion to overcome this hurdle.

In addition to progressing data science, Nested Fusion improves NASA scientists' workflow. Using the method, a single scientist can form an initial estimate of a sample’s mineral composition in a matter of hours. Before Nested Fusion, the same task required days of collaboration between teams of experts on each different instrument.

“I think one of the biggest lessons I have taken from this work is that it is valuable to always ground my ML and data science problems in actual, concrete use cases of our collaborators,” Wright said. 

“I learn from collaborators what parts of data analysis are important to them and the challenges they face. By understanding these issues, we can discover new ways of formalizing and framing problems in data science.”

Wright presented Nested Fusion at KDD 2024, held Aug. 25-29 in Barcelona, Spain. KDD is an official special interest group of the Association for Computing Machinery. The conference is one of the world’s leading forums for knowledge discovery and data mining research.

Nested Fusion won runner-up for the best paper in the applied data science track, which comprised of over 150 papers. Hundreds of other papers were presented at the conference’s research track, workshops, and tutorials. 

Wright’s mentors, Scott Davidoff and Polo Chau, co-authored the Nested Fusion paper. Davidoff is a principal research scientist at the NASA Jet Propulsion Laboratory. Chau is a professor at the Georgia Tech School of Computational Science and Engineering (CSE).

“I was extremely happy that this work was recognized with the best paper runner-up award,” Wright said. “This kind of applied work can sometimes be hard to find the right academic home, so finding communities that appreciate this work is very encouraging.”

The Cloud Hub, a key initiative of the Institute for Data Engineering and Science (IDEaS) at Georgia Tech, recently concluded a successful Call for Proposals focused on advancing the field of Generative Artificial Intelligence (GenAI). This initiative, made possible by a generous gift funding from Microsoft, aims to push the boundaries of GenAI research by supporting projects that explore both foundational aspects and innovative applications of this cutting-edge technology.

Call for Proposals: A Gateway to Innovation

Launched in early 2024, the Call for Proposals invited researchers from across Georgia Tech to submit their innovative ideas on GenAI. The scope was broad, encouraging proposals that spanned foundational research, system advancements, and novel applications in various disciplines, including arts, sciences, business, and engineering. A special emphasis was placed on projects that addressed responsible and ethical AI use.

The response from the Georgia Tech research community was overwhelming, with 76 proposals submitted by teams eager to explore this transformative technology. After a rigorous selection process, eight projects were selected for support. Each awarded team will also benefit from access to Microsoft’s Azure cloud resources..

Recognizing Microsoft’s Generous Contribution

This successful initiative was made possible through the generous support of Microsoft, whose contribution of research resources has empowered Georgia Tech researchers to explore new frontiers in GenAI. By providing access to Azure’s advanced tools and services, Microsoft has played a pivotal role in accelerating GenAI research at Georgia Tech, enabling researchers to tackle some of the most pressing challenges and opportunities in this rapidly evolving field.

Looking Ahead: Pioneering the Future of GenAI

The awarded projects, set to commence in Fall 2024, represent a diverse array of research directions, from improving the capabilities of large language models to innovative applications in data management and interdisciplinary collaborations. These projects are expected to make significant contributions to the body of knowledge in GenAI and are poised to have a lasting impact on the industry and beyond.

IDEaS and the Cloud Hub are committed to supporting these teams as they embark on their research journeys. The outcomes of these projects will be shared through publications and highlighted on the Cloud Hub web portal, ensuring visibility for the groundbreaking work enabled by this initiative.

Congratulations to the Fall 2024 Winners

• Annalisa Bracco | EAS "Modeling the Dispersal and Connectivity of Marine Larvae with GenAI Agents" [proposal co-funded with support from the Brook Byers Institute for Sustainable Systems]
• Yunan Luo | CSE “Designing New and Diverse Proteins with Generative AI”
• Kartik Goyal | IC “Generative AI for Greco-Roman Architectural Reconstruction: From Partial Unstructured Archaeological Descriptions to Structured Architectural Plans”
• Victor Fung | CSE “Intelligent LLM Agents for Materials Design and Automated Experimentation”
• Noura Howell | LMC “Applying Generative AI for STEM Education: Supporting AI literacy and community engagement with marginalized youth”
• Neha Kumar | IC “Towards Responsible Integration of Generative AI in Creative Game Development”
• Maureen Linden | Design “Best Practices in Generative AI Used in the Creation of Accessible Alternative Formats for People with Disabilities”
• Surya Kalidindi | ME & MSE “Accelerating Materials Development Through Generative AI Based Dimensionality Expansion Techniques”
• Tuo Zhao | ISyE “Adaptive and Robust Alignment of LLMs with Complex Rewards”

- Written by Benjamin Wright -

As Georgia Tech establishes itself as a national leader in AI research and education, some researchers on campus are putting AI to work to help meet sustainability goals in a range of areas including climate change adaptation and mitigation, urban farming, food distribution, and life cycle assessments while also focusing on ways to make sure AI is used ethically.

Josiah Hester, interim associate director for Community-Engaged Research in the Brook Byers Institute for Sustainable Systems (BBISS) and associate professor in the School of Interactive Computing, sees these projects as wins from both a research standpoint and for the local, national, and global communities they could affect.

“These faculty exemplify Georgia Tech's commitment to serving and partnering with communities in our research,” he says. “Sustainability is one of the most pressing issues of our time. AI gives us new tools to build more resilient communities, but the complexities and nuances in applying this emerging suite of technologies can only be solved by community members and researchers working closely together to bridge the gap. This approach to AI for sustainability strengthens the bonds between our university and our communities and makes lasting impacts due to community buy-in.”

Flood Monitoring and Carbon Storage

Peng Chen, assistant professor in the School of Computational Science and Engineering in the College of Computing, focuses on computational mathematics, data science, scientific machine learning, and parallel computing. Chen is combining these areas of expertise to develop algorithms to assist in practical applications such as flood monitoring and carbon dioxide capture and storage.

He is currently working on a National Science Foundation (NSF) project with colleagues in Georgia Tech’s School of City and Regional Planning and from the University of South Florida to develop flood models in the St. Petersburg, Florida area. As a low-lying state with more than 8,400 miles of coastline, Florida is one of the states most at risk from sea level rise and flooding caused by extreme weather events sparked by climate change.

Chen’s novel approach to flood monitoring takes existing high-resolution hydrological and hydrographical mapping and uses machine learning to incorporate real-time updates from social media users and existing traffic cameras to run rapid, low-cost simulations using deep neural networks. Current flood monitoring software is resource and time-intensive. Chen’s goal is to produce live modeling that can be used to warn residents and allocate emergency response resources as conditions change. That information would be available to the general public through a portal his team is working on.

“This project focuses on one particular community in Florida,” Chen says, “but we hope this methodology will be transferable to other locations and situations affected by climate change.”

In addition to the flood-monitoring project in Florida, Chen and his colleagues are developing new methods to improve the reliability and cost-effectiveness of storing carbon dioxide in underground rock formations. The process is plagued with uncertainty about the porosity of the bedrock, the optimal distribution of monitoring wells, and the rate at which carbon dioxide can be injected without over-pressurizing the bedrock, leading to collapse. The new simulations are fast, inexpensive, and minimize the risk of failure, which also decreases the cost of construction.

“Traditional high-fidelity simulation using supercomputers takes hours and lots of resources,” says Chen. “Now we can run these simulations in under one minute using AI models without sacrificing accuracy. Even when you factor in AI training costs, this is a huge savings in time and financial resources.”

Flood monitoring and carbon capture are passion projects for Chen, who sees an opportunity to use artificial intelligence to increase the pace and decrease the cost of problem-solving.

“I’m very excited about the possibility of solving grand challenges in the sustainability area with AI and machine learning models,” he says. “Engineering problems are full of uncertainty, but by using this technology, we can characterize the uncertainty in new ways and propagate it throughout our predictions to optimize designs and maximize performance.”

Urban Farming and Optimization

Yongsheng Chen works at the intersection of food, energy, and water. As the Bonnie W. and Charles W. Moorman Professor in the School of Civil and Environmental Engineering and director of the Nutrients, Energy, and Water Center for Agriculture Technology, Chen is focused on making urban agriculture technologically feasible, financially viable, and, most importantly, sustainable. To do that he’s leveraging AI to speed up the design process and optimize farming and harvesting operations.

Chen’s closed-loop hydroponic system uses anaerobically treated wastewater for fertilization and irrigation by extracting and repurposing nutrients as fertilizer before filtering the water through polymeric membranes with nano-scale pores. Advancing filtration and purification processes depends on finding the right membrane materials to selectively separate contaminants, including antibiotics and per- and polyfluoroalkyl substances (PFAS). Chen and his team are using AI and machine learning to guide membrane material selection and fabrication to make contaminant separation as efficient as possible. Similarly, AI and machine learning are assisting in developing carbon capture materials such as ionic liquids that can retain carbon dioxide generated during wastewater treatment and redirect it to hydroponics systems, boosting food productivity.

“A fundamental angle of our research is that we do not see municipal wastewater as waste,” explains Chen. “It is a resource we can treat and recover components from to supply irrigation, fertilizer, and biogas, all while reducing the amount of energy used in conventional wastewater treatment methods.”

In addition to aiding in materials development, which reduces design time and production costs, Chen is using machine learning to optimize the growing cycle of produce, maximizing nutritional value. His USDA-funded vertical farm uses autonomous robots to measure critical cultivation parameters and take pictures without destroying plants. This data helps determine optimum environmental conditions, fertilizer supply, and harvest timing, resulting in a faster-growing, optimally nutritious plant with less fertilizer waste and lower emissions.

Chen’s work has received considerable federal funding. As the Urban Resilience and Sustainability Thrust Leader within the NSF-funded AI Institute for Advances in Optimization (AI4OPT), he has received additional funding to foster international collaboration in digital agriculture with colleagues across the United States and in Japan, Australia, and India.

Optimizing Food Distribution

At the other end of the agricultural spectrum is postdoc Rosemarie Santa González in the H. Milton Stewart School of Industrial and Systems Engineering, who is conducting her research under the supervision of Professor Chelsea White and Professor Pascal Van Hentenryck, the director of Georgia Tech’s AI Hub as well as the director of AI4OPT.

Santa González is working with the Wisconsin Food Hub Cooperative to help traditional farmers get their products into the hands of consumers as efficiently as possible to reduce hunger and food waste. Preventing food waste is a priority for both the EPA and USDA. Current estimates are that 30 to 40% of the food produced in the United States ends up in landfills, which is a waste of resources on both the production end in the form of land, water, and chemical use, as well as a waste of resources when it comes to disposing of it, not to mention the impact of the greenhouses gases when wasted food decays.

To tackle this problem, Santa González and the Wisconsin Food Hub are helping small-scale farmers access refrigeration facilities and distribution chains. As part of her research, she is helping to develop AI tools that can optimize the logistics of the small-scale farmer supply chain while also making local consumers in underserved areas aware of what’s available so food doesn’t end up in landfills.

“This solution has to be accessible,” she says. “Not just in the sense that the food is accessible, but that the tools we are providing to them are accessible. The end users have to understand the tools and be able to use them. It has to be sustainable as a resource.”

Making AI accessible to people in the community is a core goal of the NSF’s AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE), one of the partners involved with the project.

“A large segment of the population we are working with, which includes historically marginalized communities, has a negative reaction to AI. They think of machines taking over, or data being stolen. Our goal is to democratize AI in these decision-support tools as we work toward the UN Sustainable Development Goal of Zero Hunger. There is so much power in these tools to solve complex problems that have very real results. More people will be fed and less food will spoil before it gets to people’s homes.”

Santa González hopes the tools they are building can be packaged and customized for food co-ops everywhere.

AI and Ethics

Like Santa González, Joe Bozeman III is also focused on the ethical and sustainable deployment of AI and machine learning, especially among marginalized communities. The assistant professor in the School of Civil and Environmental Engineering is an industrial ecologist committed to fostering ethical climate change adaptation and mitigation strategies. His SEEEL Lab works to make sure researchers understand the consequences of decisions before they move from academic concepts to policy decisions, particularly those that rely on data sets involving people and communities.

“With the administration of big data, there is a human tendency to assume that more data means everything is being captured, but that's not necessarily true,” he cautions. “More data could mean we're just capturing more of the data that already exists, while new research shows that we’re not including information from marginalized communities that have historically not been brought into the decision-making process. That includes underrepresented minorities, rural populations, people with disabilities, and neurodivergent people who may not interface with data collection tools.”

Bozeman is concerned that overlooking marginalized communities in data sets will result in decisions that at best ignore them and at worst cause them direct harm.

“Our lab doesn't wait for the negative harms to occur before we start talking about them,” explains Bozeman, who holds a courtesy appointment in the School of Public Policy. “Our lab forecasts what those harms will be so decision-makers and engineers can develop technologies that consider these things.”

He focuses on urbanization, the food-energy-water nexus, and the circular economy. He has found that much of the research in those areas is conducted in a vacuum without consideration for human engagement and the impact it could have when implemented.

Bozeman is lobbying for built-in tools and safeguards to mitigate the potential for harm from researchers using AI without appropriate consideration. He already sees a disconnect between the academic world and the public. Bridging that trust gap will require ethical uses of AI.

“We have to start rigorously including their voices in our decision-making to begin gaining trust with the public again. And with that trust, we can all start moving toward sustainable development. If we don't do that, I don't care how good our engineering solutions are, we're going to miss the boat entirely on bringing along the majority of the population.”

BBISS Support

Moving forward, Hester is excited about the impact the Brooks Byers Institute for Sustainable Systems can have on AI and sustainability research through a variety of support mechanisms.

“BBISS continues to invest in faculty development and training in community-driven research strategies, including the Community Engagement Faculty Fellows Program (with the Center for Sustainable Communities Research and Education), while empowering multidisciplinary teams to work together to solve grand engineering challenges with AI by supporting the AI+Climate Faculty Interest Group, as well as partnering with and providing administrative support for community-driven research projects.”

A new agreement between Los Alamos National Laboratory (LANL) and the National Science Foundation’s Artificial Intelligence Institute for Advances in Optimization (AI4OPT) at Georgia Tech is set to propel research in applied artificial intelligence (AI) and engage students and professionals in this rapidly growing field.

“This collaboration will help develop new AI technologies for the next generation of scientific discovery and the design of complex systems and the control of engineered systems,” said Russell Bent, scientist at Los Alamos. “At Los Alamos, we have a lot of interest in optimizing complex systems. We see an opportunity with AI to enhance system resilience and efficiency in the face of climate change, extreme events, and other challenges.”

The agreement establishes a research and educational partnership focused on advancing AI tools for a next-generation power grid. Maintaining and optimizing the energy grid involves extensive computation, and AI-informed approaches, including modeling, could address power-grid issues more effectively.

AI Approaches to Optimization and Problem-Solving

Optimization involves finding solutions that utilize resources effectively and efficiently. This research partnership will leverage Georgia Tech's expertise to develop “trustworthy foundation models” that, by incorporating AI, reduce the vast computing resources needed for solving complex problems.

In energy grid systems, optimization involves quickly sorting through possibilities and resources to deliver immediate solutions during a power-distribution crisis. The research will develop “optimization proxies” that extend current methods by incorporating broader parameters such as generator limits, line ratings, and grid topologies. Training these proxies with AI for energy applications presents a significant research challenge.

The collaboration will also address problems related to LANL’s diverse missions and applications. The team’s research will advance pioneering efforts in graph-based, physics-informed machine learning to solve Laboratory mission problems.

Outreach and Training Opportunities

In January 2025, the Laboratory will host a Grid Science Winter School and Conference, featuring lectures from LANL scientists and academic partners on electrical grid methods and techniques. With Georgia Tech as a co-organizer, AI optimization for the energy grid will be a focal point of the event.

Since 2020, the Laboratory has been working with Georgia Tech on energy grid projects. AI4OPT, which includes several industrial and academic partners, aims to achieve breakthroughs by combining AI and mathematical optimization.

“The use-inspired research in AI4OPT addresses fundamental societal and technological challenges,” said Pascal Van Hentenryck, AI4OPT director. “The energy grid is crucial to our daily lives. Our collaboration with Los Alamos advances a research mission and educational vision with significant impact for science and society.”

The three-year agreement, funded through the Laboratory Directed Research and Development program’s ArtIMis initiative, runs through 2027. It supports the Laboratory’s commitment to advancing AI. Earl Lawrence is the project’s principal investigator, with Diane Oyen and Emily Castleton joining Bent as co-principal investigators.

Bent, Castleton, Lawrence, and Oyen are also members of the AI Council at the Laboratory. The AI Council helps the Lab navigate the evolving AI landscape, build investment capacities, and forge industry and academic partnerships.

As highlighted in the Department of Energy’s Frontiers in Artificial Intelligence for Science, Security, and Technology (FASST) initiative, AI technologies will significantly enhance the contributions of laboratories to national missions. This partnership with Georgia Tech through AI4OPT is a key step towards that future.

A research group is calling for internet and social media moderators to strengthen their detection and intervention protocols for violent speech. 

Their study of language detection software found that algorithms struggle to differentiate anti-Asian violence-provoking speech from general hate speech. Left unchecked, threats of violence online can go unnoticed and turn into real-world attacks. 

Researchers from Georgia Tech and the Anti-Defamation League (ADL) teamed together in the study. They made their discovery while testing natural language processing (NLP) models trained on data they crowdsourced from Asian communities. 

“The Covid-19 pandemic brought attention to how dangerous violence-provoking speech can be. There was a clear increase in reports of anti-Asian violence and hate crimes,” said Gaurav Verma, a Georgia Tech Ph.D. candidate who led the study. 

“Such speech is often amplified on social platforms, which in turn fuels anti-Asian sentiments and attacks.”

Violence-provoking speech differs from more commonly studied forms of harmful speech, like hate speech. While hate speech denigrates or insults a group, violence-provoking speech implicitly or explicitly encourages violence against targeted communities.

Humans can define and characterize violent speech as a subset of hateful speech. However, computer models struggle to tell the difference due to subtle cues and implications in language.

The researchers tested five different NLP classifiers and analyzed their F1 score, which measures a model's performance. The classifiers reported a 0.89 score for detecting hate speech, while detecting violence-provoking speech was only 0.69. This contrast highlights the notable gap between these tools and their accuracy and reliability. 

The study stresses the importance of developing more refined methods for detecting violence-provoking speech. Internet misinformation and inflammatory rhetoric escalate tensions that lead to real-world violence. 

The Covid-19 pandemic exemplified how public health crises intensify this behavior, helping inspire the study. The group cited that anti-Asian crime across the U.S. increased by 339% in 2021 due to malicious content blaming Asians for the virus. 

The researchers believe their findings show the effectiveness of community-centric approaches to problems dealing with harmful speech. These approaches would enable informed decision-making between policymakers, targeted communities, and developers of online platforms.

Along with stronger models for detecting violence-provoking speech, the group discusses a direct solution: a tiered penalty system on online platforms. Tiered systems align penalties with severity of offenses, acting as both deterrent and intervention to different levels of harmful speech. 

“We believe that we cannot tackle a problem that affects a community without involving people who are directly impacted,” said Jiawei Zhou, a Ph.D. student who studies human-centered computing at Georgia Tech. 

“By collaborating with experts and community members, we ensure our research builds on front-line efforts to combat violence-provoking speech while remaining rooted in real experiences and needs of the targeted community.”

The researchers trained their tested NLP classifiers on a dataset crowdsourced from a survey of 120 participants who self-identified as Asian community members. In the survey, the participants labeled 1,000 posts from X (formerly Twitter) as containing either violence-provoking speech, hateful speech, or neither.

Since characterizing violence-provoking speech is not universal, the researchers created a specialized codebook for survey participants. The participants studied the codebook before their survey and used an abridged version while labeling. 

To create the codebook, the group used an initial set of anti-Asian keywords to scan posts on X from January 2020 to February 2023. This tactic yielded 420,000 posts containing harmful, anti-Asian language. 

The researchers then filtered the batch through new keywords and phrases. This refined the sample to 4,000 posts that potentially contained violence-provoking content. Keywords and phrases were added to the codebook while the filtered posts were used in the labeling survey.

The team used discussion and pilot testing to validate its codebook. During trial testing, pilots labeled 100 Twitter posts to ensure the sound design of the Asian community survey. The group also sent the codebook to the ADL for review and incorporated the organization’s feedback. 

“One of the major challenges in studying violence-provoking content online is effective data collection and funneling down because most platforms actively moderate and remove overtly hateful and violent material,” said Tech alumnus Rynaa Grover (M.S. CS 2024).

“To address the complexities of this data, we developed an innovative pipeline that deals with the scale of this data in a community-aware manner.”

Emphasis on community input extended into collaboration within Georgia Tech’s College of Computing. Faculty members Srijan Kumar and Munmun De Choudhury oversaw the research that their students spearheaded.

Kumar, an assistant professor in the School of Computational Science and Engineering, advises Verma and Grover. His expertise is in artificial intelligence, data mining, and online safety.

De Choudhury is an associate professor in the School of Interactive Computing and advises Zhou. Their research connects societal mental health and social media interactions.

The Georgia Tech researchers partnered with the ADL, a leading non-governmental organization that combats real-world hate and extremism. ADL researchers Binny Mathew and Jordan Kraemer co-authored the paper.

The group will present its paper at the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), which takes place in Bangkok, Thailand, Aug. 11-16 

ACL 2024 accepted 40 papers written by Georgia Tech researchers. Of the 12 Georgia Tech faculty who authored papers accepted at the conference, nine are from the College of Computing, including Kumar and De Choudhury.

“It is great to see that the peers and research community recognize the importance of community-centric work that provides grounded insights about the capabilities of leading language models,” Verma said. 

“We hope the platform encourages more work that presents community-centered perspectives on important societal problems.” 

Visit https://sites.gatech.edu/research/acl-2024/ for news and coverage of Georgia Tech research presented at ACL 2024.