The $1.1 billion Research enterprise at Georgia Tech is the embodiment of a commitment the advancement of technology and betterment of the human condition. Georgia Tech's Research enterprise through offerings such as the Enterprise Innovation Institute, the Georgia Tech Research Institute, Commercialization, and Interdisciplinary Research Institutes, to solve the most pressing challenges in a host of sectors, including computing, engineering, design, the sciences, liberal arts, and business.

This installment of the Faces of Research Q&A series is with Chaouki T. Abdallah, Executive Vice President for Research at Georgia Tech.

What is your field of expertise and why did you choose it?
My field of expertise is Systems Theory, and my degrees are all in Electrical Engineering. I chose it because it was heavily mathematical but can also be applied across multiple fields (aerospace, chemical, mechanical, electrical, biology, etc.).

What makes Georgia Tech research institutes unique?
Our IRIs (Interdisciplinary Research Institutes) connect research across colleges but what makes them even more impactful is their intra-connectivity. Problems that are even too big for one IRI, are being solved by researchers across multiple ones. 

What impact is your research having on the world?
My own research impact has been mostly through my students. However, I did use my research in systems and network science to study and improve the complexity of college curricula, leading to 150% improvement in the four-year graduation rate and tens of millions of dollars in savings for students.

What is the most profound advice you ever received?
Pick the hill you’re willing to die on.

What is something you wished you knew as a budding researcher that everyone considering research as a career should know?
The joy of knowing something is eclipsed by the joy of explaining it to others.

What song or album best describes you?
"With a Little Help From My Friends" by The Beatles.

Coral reef conservation is a steppingstone to protect marine biodiversity and life in the ocean as we know it. The health of coral also has huge societal implications: reef ecosystems provide sustenance and livelihoods for millions of people around the world. Conserving biodiversity in reef areas is both a social issue and a marine biodiversity priority.

In the face of climate change, Annalisa Bracco, professor in the School of Earth and Atmospheric Sciences at Georgia Institute of Technology, and Lyuba Novi, a postdoctoral researcher, offer a new methodology that could revolutionize how conservationists monitor coral. The researchers applied machine learning tools to study how climate impacts connectivity and biodiversity in the Pacific Ocean’s Coral Triangle — the most diverse and biologically complex marine ecosystem on the planet. Their research, recently published in Nature Communications Biology, overcomes time and resource barriers to contextualize the biodiversity of the Coral Triangle, while offering hope for better monitoring and protection in the future.

“We saw that the biodiversity of the Coral Triangle is incredibly dynamic,” Bracco said. “For a long time, it has been postulated that this is due to sea level change and distribution of land masses, but we are now starting to understand that there is more to the story.”

Connectivity refers to the conditions that allow different ecosystems to exchange genetic material such as eggs, larvae, or the young. Ocean currents spread genetic material and also create the dynamics that allow a body of water — and thus ecosystems — to maintain consistent chemical, biological, and physical properties. If coral larvae are spread to an ecoregion where the conditions are very similar to the original location, the larvae can start a new coral.

Bracco wanted to see how climate, and specifically the El Niño Southern Oscillation (ENSO) in its phases — El Niño, La Niña, and neutral conditions — impacts connectivity in the Coral Triangle. Climate events that move large masses of warm water in the Pacific Ocean bring enormous changes and have been known to exacerbate coral bleaching, in which corals turn white due to environmental stressors and become vulnerable to disease.

“Biologists collect data in situ, which is extremely important,” Bracco said. “But it’s not possible to monitor enormous regions in situ for many years — that would require a constant presence of scuba divers. So, figuring out how different ocean regions and large marine ecosystems are connected over time, especially in terms of foundational species, becomes important.”

Machine Learning for Discovering Connectivity

Years ago, Bracco and collaborators developed a tool, Delta Maps, that uses machine learning to identify “domains,” or regions within any kind of system that share the same dynamic. Bracco initially used it to analyze domains of climate variability in models but also suspected it could be used to study ecoregions in the ocean.

For this study, they used the tool to map out domains of connectivity in the Coral Triangle using 30 years of sea surface temperature data. Sea surface temperatures change in response to ocean currents over scales of weeks and months and across distances of tens of kilometers. These changes are relevant to coral connectivity, so the researchers built their machine learning tool based on this observation, using changes in surface ocean temperature to identify regions connected by currents. They also separated the time periods that they were considering into three categories: El Niño events, La Niña events, and neutral or “normal” times, painting a picture of how connectivity was impacted during major climate events in particular ecoregions.

Novi then applied a ranking system to the different ecoregions they identified. She used rank page centrality, a machine learning tool that was invented to rank webpages on the internet, on top of Delta Maps to identify which coral ecoregions were most strongly connected and able to receive the most coral larvae from other regions. Those regions would be the ones most likely sustain and survive through a bleaching event.

Climate Dynamics and Biodiversity

Bracco and Novi found that climate dynamics have contributed to biodiversity because of the way climate introduces variability to the currents in the equatorial Pacific Ocean. The researchers realized that alternation of El Niño and La Niña events has allowed for enormous genetic exchanges between the Indian and Pacific Oceans and enabled the ecosystems to survive through a variety of different climate situations.

“There is never an identical connection between ecoregions in all ENSO phases,” Bracco said. “In other parts of the world ocean, coral reefs are connected through a fixed, often small, number of ecoregions, and if you eliminate this fixed number of connections by bleaching all connected reefs, you will not be able to rebuild the corals in any of them. But in the Pacific the connections are changing all the time and are so dynamic that soon enough the bleached reef will receive larvae from completely different ecoregions in a different ENSO phase.”

They also concluded that, because of the Coral Triangle’s dynamic climate component, there is more possibility for rebuilding biodiversity there than anywhere else on the planet. And that the evolution of biodiversity in the Coral Triangle is not only linked to landmasses or sea levels but also to the evolution of ENSO through geological times. The researchers found that though ENSO causes coral bleaching, it has helped the Coral Triangle become so rich in biodiversity.

Better Monitoring Opportunities

Because coral reef survival has been designated a priority by the United Nations Sustainable Development Goals, Bracco and Novi’s research is poised to have broad applications. The researchers’ method identified which ecoregions conservationists should try hardest to protect and also the regions that conservationists could expect to have the most luck with protection measures. Their methodology can also help to identify which regions should be monitored more and the ones that could be considered lower priority for now due to the ways they are currently thriving.

“This research opens a lot of possibilities for better monitoring strategies, and especially how to monitor given a limited amount of resources and money,” Bracco said. “As of now, coral monitoring often happens when groups have a limited amount of funding to apply to a very specific localized region. We hope our method can be used to create a better monitoring over larger scales of time and space.”

 

CITATION: Novi, L., Bracco, A. “Machine learning prediction of connectivity, biodiversity and resilience in the Coral Triangle.” Commun Biol 5, 1359 (2022). 

DOI: https://doi.org/10.1038/s42003-022-04330-8

Plants, like animals and people, seek refuge from climate change. And when they move, they take entire ecosystems with them. To understand why and how plants have trekked across landscapes throughout time, researchers at the forefront of conservation are calling for a new framework. The key to protecting biodiversity in the future may be through understanding the past.

Jenny McGuire, assistant professor in the Schools of Biological Sciences and Earth and Atmospheric Sciences at Georgia Tech, spearheaded a special feature on the topic of biodiversity in The Proceedings of the National Academy of Sciences along with colleagues in Texas, Norway, and Argentina. In the special feature, “The Past as a Lens for Biodiversity Conservation on a Dynamically Changing Planet,” McGuire and her collaborators highlight the outstanding questions that must be addressed for successful future conservation efforts. The feature brings together conservation research that illuminates the complex and constantly evolving dynamics brought on by climate change and the ever-shifting ways humans use land. These factors, McGuire said, interact over time to create dynamic changes and illustrate the need to incorporate temporal perspectives into conservation strategies by looking deep into the past.

One example of this work highlighted in the journal is McGuire’s research about plants in North America, which investigates how and why they’ve moved across geography over time, where they’re heading, and why it’s important.

“Plants are shifting their geographic ranges, and this is happening whether we realize it or not,” McGuire said. “As seeds fall or are transported to distant places, the likelihood that the plant’s seed is going to be able to survive and grow is changing as climates are changing. Studying plants’ niche dynamics over thousands of years can help us understand how species adapt to climate change and can teach us how to protect and maintain biodiversity in the face of rapid climate change to come.”

Climate Fidelity: A New Metric for Understanding Vulnerability

The first step is to understand which type of plants exhibit what McGuire terms “climate fidelity,” and which do not. If a plant has climate fidelity, it means that the plant stays loyal to its preferred climatic niche, often migrating across geographies over thousands of years to keep up with its ideal habitat. Plants that don’t exhibit climate fidelity tend to adapt locally in the face of climate change. Being loyal to one’s climate, it turns out, doesn’t necessarily mean being loyal to a particular place.

To investigate the case of trees, McGuire and former Georgia Tech postdoctoral scholar Yue Wang (associate professor in the School of Ecology at Sun Yat-sen University in China) studied pollen data from the Neotoma Paleoecology Database, which contains pollen fossil data from sediment cores across North America. Each sediment core is sampled, layer by layer, producing a series of pollen data from different times throughout history. The data also contains breakdowns of the relative abundance of different types of plants represented by the pollen types – pine versus oak versus grass, for example – painting a picture of what types of plants were present in that location and when.

McGuire and Wang looked at data from 13,240 fossil pollen samples taken from 337 locations across the entirety of North America. For each of the 16 major plant taxa in North America, they divided the pollen data into six distinct chunks or “bins” of time of 4,000 years, starting from 18,000 years ago up to the present day. Wang used the data to identify all climate sites containing fossil pollen for any individual type of tree – such as oak, for example – for each period. Then, Wang looked at how each tree’s climate changed from one period to the next. Wang did this by comparing the locations of pollen types between adjacent time periods, which enabled the team to identify how and why each type of tree’s climate changed over time.

“This process allowed us to see the climate fidelity of these different plant taxa, showing that certain plants maintain very consistent climatic niches, even when climate is changing rapidly,” Wang said.

For example, their findings showed that when North American glaciers were retreating 18,000 years ago, spruce and alder trees moved northward to maintain the cool temperatures of their habitats.

Crucially, McGuire and Wang found that most plant species in North America have exhibited long-term climate fidelity over the past 18,000 years. They also found that plants that migrated farther did a better job of tracking climate during periods of change.

But some plants fared better than others. For example, the small seeds of willow trees can fly over long distances – enabling them to track their preferred climates very effectively. But the large seeds of ash trees, for example, can only be dispersed short distances from parent trees, hindering their ability to track climate. Habitat disruptions from humans could make it even more difficult for ash trees to be able to take hold in new regions. If there are no adjacent habitats for ash trees, their seeds are under pressure to move even farther – a particular challenge for ash, which slows their migration movements even more.

Protecting the Fabric of Life

On the bright side, by identifying which plants have historically been most sensitive to changing climates, McGuire and Wang’s research can help conservation organizations like The Nature Conservancy prioritize land where biodiversity is most vulnerable to climate change.

As a final step, McGuire and Wang identified “climate fidelity hotspots,” regions that have historically exhibited strong climate fidelity whose plants will most urgently need to move as their climates change. They compared these hotspots to climate-resilient regions identified by The Nature Conservancy that could serve as refuge areas for those plants. While plants in these resilient regions can initially adapt to impending climate change by shifting their distributions locally, the plants will likely face major challenges when a region’s climate change capacity is exceeded due to lack of connectivity and habitat disruptions from humans. Refining these priorities helps stakeholders identify efficient strategies for allowing the fabric of life to thrive.

“I think that understanding climate fidelity, while a new and different idea, will be very important going forward, especially when thinking about how to prioritize protecting different plants in the face of climate change,” McGuire said. “It is important to be able to see that some plants and animals are more vulnerable to climate change, and this information can help build stronger strategies for protecting the biodiversity on the planet.”

 

Citation: Yue Wang, Silvia Pineda-Munoz, and Jenny L. McGuire, "Plants maintain climate fidelity in the face of dynamic climate change." PNAS (2023).

DOI: doi.org/10.1073/pnas.2201946119

 

Inclusivity and understanding past policies and their effects on underserved and marginalized communities must be part of urban planning, design, and public policy efforts for cities.

An international coalition of researchers — led by Georgia Tech — have determined that advancements and innovations in urban research and design must incorporate serious analysis and collaborations with scientists, public policy experts, local leaders, and citizens. To address environmental issues and infrastructure challenges cities face, the coalition identified three core focus areas with research priorities for long-term urban sustainability and viability. Those focus areas should be components of any urban planning, design, and sustainability initiative.

The researchers found that the core focus areas included social justice and equity, circularity, and a concept called “digital twins.” The team — which consists of 13 co-authors and scholars based in the U.S., Asia, and Europe — also provided guidance and future research directions for how to address these focus areas. They detailed their findings in the Journal of Industrial Ecology, published in January 2023.

“Climate change has certainly increased the amount and intensity of extreme weather events and because of that, it makes our decision making today critical to the manner in which our economy and our day to day lives can operate,” said Joe F. Bozeman III, the lead author and an assistant professor in Georgia Tech’s School of Civil and Environmental Engineering. He is also the director of Tech’s Social Equity & Environmental Engineering Lab and has a courtesy appointment in the School of Public Policy. “Our quality of life can be negatively affected if we don't make good decisions today.”

Three core areas of focus to achieve urban sustainability

The researchers’ first core focus area, justice and equity, addresses innovations and trends that disproportionately benefit middle and high-income communities. Trends like IoT, “smart cities,” and the urban “green movement” are part of a broader push by cities to become more sustainable and resilient. But communities of color and low-income neighborhoods — the same areas often home to environmental contaminations, infrastructure challenges, and other hazards — have often been overlooked.

The researchers’ findings showed a consistent trend with marginalized communities across several countries, including Canada, the Netherlands, India, and South Africa. They call for mandatory equity analyses which incorporate the experiences and perspectives of these marginalized communities, and, more importantly, ensure members of those communities are actively engaged in decision-making processes.

“Planning, professional, and community stakeholders,” the researchers write in the paper, “should recognize that working together gets cities closer to harmonizing the technological and social dimensions of sustainability.”

The second focus area, circularity, addresses resource consumption of staple commodities including food, water, and energy; the waste and emissions they generate; and the opportunities to increase conservation of those resources by boosting efficiencies.

“What we mean by circularity is basic reuse, remanufacturing, and recycling efforts across the entire urban system — which not only includes cities and under resourced areas within those cities — but also rural communities that supply and take resources from those city hubs,” Bozeman said. The idea is aligned with the circular economy concept which addresses the need to move away from the resource-wasteful and unsustainable cycle of taking, making, and throwing away.

Instead, the researchers argue, cities should look for ways to improve efficiency and maximize local resource use. That has potential benefits not only for urban areas, but rural communities, too. One example, Bozeman said, is the Lifecycle Building Center in Atlanta. It takes old building supplies and sells them locally for reuse.

“By doing that, they’re at the beginning stages of creating an economic system, a regional engine where we share resources between cities and rural areas,” he said. “We can start creating an economic framework, not only where both sides can make money and get what they need, but something that can actually turn into a sustainable economic engine without having to rely on another state or another country's import or export economic pressures.”

To strengthen circularity and make it more robust, the researchers call for more expansive metrics beyond measuring recycling rates and zero waste efforts, to include other parts of the supply chain that may yield new ideas and solutions.

The third focus area, digital twins, addresses the development of automated technologies in smart buildings and infrastructure, such as traffic lights to respond to weather and other environmental factors.

“Let's say there's a heavy rain event and that the rainwater is being stored into retainment,” said Bozeman. “An automated system can open another valve where we can store that water into a secondary support system, so there's less flooding, and that can happen automatically, if we utilize the concept of digital twins.” 

Creating a new urban planning model

The research came about as part of the mission of the Sustainable Urban Systems Section of the International Society for Industrial Ecology, which aims to be a conduit for scientists, engineers, policymakers, and others who want to marry environmental concerns and economic activity. Bozeman is a board member of the Sustainable Urban Systems Section.

“In that role, part of we do is set a vision and foundation for how other researchers should operate within the city and urban system space,” he said. 

For urban sustainability, engineers and policy makers must come to the table and make collective decisions around social justice and equity, circularity, and the digital twins concepts. 

“I think we're at a really critical decision point when it comes to engineers and others being able to do work that is forward looking and human sensitive,” said Bozeman. “Good decision making involves addressing social justice and equity and understanding its root causes, which will enable cities to create solutions that integrate cultural dynamics.”

CITATION: Joe F. Bozeman III, Shauhrat S. Chopra, Philip James, Sajjad Muhammad, Hua Cai, Kangkang Tong, Maya Carrasquillo, Harold Rickenbacker, Destenie Nock, Weslynne Ashton, Oliver Heidrich, Sybil Derrible, Melissa Bilec. “Three research priorities for just and sustainable urban systems: Now is the time to refocus.” (Journal of Industrial Ecology, January 2023)

Whether they know it or not, most city dwellers have probably been inside a so-called “green” building. Plaques boasting various types of environmental or energy certifications — known as ecolabels — often hang prominently in their lobbies. They’re visible, but how can we know if ecolabels have a real impact or are mostly about showing off?

Daniel Matisoff, professor of public policy at Georgia Tech, illuminates the role and impact of green building ecolabels in his book, Ecolabels, Innovation, and Green Market Transformation: Learning to LEED, which traces the curve of ecolabel adoption in the building market, revealing how ecolabels have transformed the economy and construction industry to achieve green market transformation. Co-authored by Douglas Noonan, professor of public policy at Indiana University-Purdue University Indianapolis, it is the first book to comprehensively assess the green building movement. The book was published by Cambridge University Press in October 2022.

Green building ecolabels, simply stated, are marks or designations that indicate environmental performance and sustainability certifications. Matisoff and Noonan investigated prominent ecolabels, such as LEED, and examined how they work, exploring the theory and economics behind them. They also studied factors and initiatives that drive the adoption of green building ecolabels, breaking down the green building movement step-by-step.

“A central premise of the book is that early adopters, whether they are creating a demonstration project — such as Georgia Tech’s own Kendeda Building — or adopting an ecolabel early on produce positive information spillovers that help accelerate adoption of green technologies,” Matisoff said.

According to the authors, early adopters do this by moving both supply and demand curves for new energy and environmental technologies. When early adopters employ and experiment with new green building technologies, they help build supply chains, lowering costs for others interested in adopting the technologies. Undertaking green building projects also proves the market performance of new energy and environmental technologies, thereby reducing uncertainty and increasing demand by making them more visible and widely available.

“Early adopters often build pilot and demonstration projects largely for a marketing or reputational benefit, but then that provides positive information spillover to the market,” Matisoff said. “For example, once contractors become familiar with new energy and environmental technologies, they can recommend them to clients for new building projects.”

By looking at data, Matisoff found that there has been a rapid uptake of buildings using the LEED label. But the question that remained was, what does it ultimately accomplish? To answer that question, Matisoff and Noonan looked at several case studies. One such case study is The Kendeda Building for Innovative Sustainable Design, a certified “Living Building,” at Georgia Tech.

The Kendeda Building: Tossing a Pebble in a Pond

The goal of The Kendeda Building was to create a facility that would transform the building and construction industry in the Southeast. Matisoff considered that a testable hypothesis. The Kendeda building inspired Matisoff and his collaborators to dig into 30 years of LEED data to look at the effect of pilot and demonstration projects. They found that if you have a demonstration project in a particular geographic location, it doubles the probability that another green building is going to be built that has similar technologies.

For example, an electrical contracting company working on Kendeda noted that being forced to work with high density poly-ethylene (HDPE) piping — a sustainable alternative to using PVC piping for electrical conduit — led them to realize that HDPE was cheaper and easier to work with, in addition to being a more ecofriendly alternative. The contractor intends to switch to HDPE piping in future projects.

“We at Georgia Tech, by building the Living Building, are providing all this information to the marketplace,” Matisoff said. “And the hope is that other universities or institutions may see this building and say, ‘Hey, we want one of those.’”

Moving Forward

Lessons in Matisoff’s book include how to harness information spillover in addition to more traditional price tools such as subsidies, taxes, and cap-and-trade emissions policies. The authors highlight the importance of leveraging private actors to provide information to the market and suggest that policymakers think carefully about how to incentivize early adopters into the green building market, beyond just prices.

While recent legislation has created a lot of price incentives, subsidies, and tax breaks designed to encourage people to make greener choices, Matisoff’s work emphasizes that, especially at early stages, prices probably aren't enough.

“It's unlikely that there's enough momentum in the policy space to get to where we need to be to address climate change,” Matisoff said. “We hope the book will help us think more carefully about how we leverage information and learning to accelerate the uptake of advanced energy and environmental technologies to facilitate green market transformation.”

Matisoff also hopes the comprehensive study will show the roughly 100,000 certified green building professionals around the world that their efforts have been worth it. 

“We wanted to tell a story, especially to green building professionals, about what they’ve accomplished over the past few decades, and the impact their work will have for years to come.”

Artificial intelligence is already making headlines in the new year with the box office success of the movie M3GAN. Along with a TikTok dance craze and lots of laughs, the over-the-top horror movie/dark comedy about an AI-powered robot that runs amok is also inspiring discussion about the growing presence and impact of artificial intelligence in everyday life.

From the movie house to the warehouse to your house, AI seems like it's everywhere. That's because with a steady stream of new research and innovative applications reaching into nearly every industry and business sector, it is everywhere. Nevertheless, AI still holds enormous potential as the field continues to evolve.

To get a sense of what this evolution could look like in 2023, we turned to a small group of Ph.D. students from the College of Computing community that are currently pushing foundational and applied AI research forward in a broad spectrum of disciplines and fields.

The students shared their opinions on where AI might be headed in the new year, what some of the big tech stories could be, and why ethics in AI are so critically important.

Where will artificial intelligence and machine learning have the most impact in 2023?

"Artificial intelligence and machine learning will continue to have a huge impact on manufacturing and warehouses with labor shortages and worker turnover continuing to be a concern as more manufacturing and operations jobs are brought back to the United States from overseas. Additionally, AI/ML will continue to help ensure that manufacturing and warehouse facilities are operating as efficiently as possible from energy and material savings to worker safety and parts quality." – Zoe Klesmith Alexander, computational science and engineering Ph.D. student

"Right now, deep learning is on a trajectory to transform the creation space. Artwork and images, videos, data representation and storytelling, co-authoring, and summarizing documents... It's gotten really good." – Ben Hoover, machine learning Ph.D. student

"I think machine learning and AI will keep playing a huge role in how the world and society will be shaped over the next decade in many ways. It will make many other fields more efficient through ML and AI tools we are developing. In 2023, I think ML and AI will have the most impact on social media platforms, helping reduce hate speech, rumor spread, etc." – Agam A. Shah, machine learning Ph.D. student

"One of the big impacts this year may be driverless cars being in your neighborhood. Otherwise, it will be a slow steady drip of GPT3 and other OpenAI models suffusing all applications, making programmers much faster, making journalists faster, making academic articles and lit reviews much faster. We're at a 4th grader level, and I hope by the end of this year we'll be at the 6th grader level. Also, indoor turn-by-turn navigation will be everywhere in 2023 as well." – Brandon Biggs, human-centered computing Ph.D. student

What will be some of the big tech stories in 2023?

"ChatGPT and the GitHub Copilot lawsuit will keep making it into the news and cause more controversies. In general, AI ethics will become more important and get more focus as the technology keeps advancing." – Fabian Fleischer, cybersecurity, and privacy Ph.D. student

"Driverless car fleets will be coming to a city near you. A new battery technology will allow phones to keep their charge for a week. Meta realizes virtual reality (VR) head-mounted displays are for a limited market and uses headphones and phones to provide VR experiences." – Brandon Biggs

What’s an issue or industry that you think could benefit from a computing solution?

"Our reinterpretation of modern deep learning as energy-based associative memories has the potential to transform any industry that relies on foundation models -- giant architectures that require models that are "self-supervised" (learn on their own from data)." – Ben Hoover

"Inclusion in everything. Over 90 percent of websites on the internet have elements that are inaccessible to 25 percent of the world's population who have disabilities. Inclusive design will be the most important area where technology can be redesigned and created to have multiple sensory modalities and be properly programmed." – Brandon Biggs

"Currently, financial markets are far from efficient because they do not fully incorporate information available in large unstructured text data. With the latest development in natural language processing techniques, we can better understand the economy and therefore price financial markets better." – Agam A. Shah

There’s been increasing recognition of the vital role ethics should play in artificial intelligence. How do you see this issue evolving in the next year?

"Specifically in my research, I think explainable AI (XAI) is very important, especially if non-experts in ML will be using black-box ML solutions in a factory. It will be important for humans to trust and to understand the models especially if the models are being using to monitor quality on a safety-critical part.

"Additionally, using XAI for human interaction with robots that utilize deep learning to make decisions will be increasingly important as technologies like collaborative robots (cobots) are integrated into factories. I think in my area of research that it is always important to use automation to aid humans in jobs that are safe for humans to do and not to replace them." – Zoe Klesmith Alexander

"Big data is pretty much at its peak. Deep data, where your Alexa knows everything about you, or your phone knows everything about you, and rather than saying 'other people who watched this show liked this show,' it's going to say, 'I know you liked these shows, I think you'll like this show because of these reasons, one of which is because other people who liked all these other shows liked this show.' The ethical element will be how much of this data should these models use, and are people going to build a personal dataset that they can share with other apps, or is each app going to need to build their own dataset? The ethical question is who owns this data." – Brandon Biggs

"I think ethics will become more and more important going forward. We are making huge breakthroughs in machine learning and artificial intelligence, but the systems we are creating are producing racist, sexist, and stereotypical results. For example, a recent system, Galactica, developed by Facebook (Meta) is powerful. It can produce research articles by just simply providing it with the title. It comes with some serious ethical concerns, in some cases, it produces racist, sexist text. So, as we will keep developing better models and making success in parallel, we need to always keep in mind the ethical implications of these models." – Agam A. Shah

What research are you working on that you think people should know about or will have impact in 2023?

"Part of my research focuses on data-driven modeling of additive manufacturing processes to better control dimensional quality of the final part. Another part of my research focuses on detecting anomalies in real-time using computer vision and machine learning for both warehouses and manufacturing processes." – Zoe Klesmith Alexander

"Right now, deep learning is built on feed-forward mathematical operations that have little resemblance to the brain. I am working on a physics inspired approach to deep learning built around recurrent networks and energy functions. These architectures have the same mathematical foundation as the famous, biologically plausible Hopfield Network." – Ben Hoover

"I am currently working on two projects which, in my opinion, will have an impact in 2023. In one project, we are measuring the exposure of public firms to ongoing inflation. We are also understanding how inflation affects different firms differently based on the pricing power of the firm. As inflation is the highest in the last 40 years, our study is highly relevant now and in the coming years till we get inflation back in control.

"The second work is related to the first work in some ways. As inflation is rising, to control the inflation Federal Reserve Bank is tightening its monetary policy. In our second work, we are measuring the stance of monetary policy (measuring hawkish vs dovish stance) of the Fed using state-of-the-art NLP models to see its impact in various financial markets (Treasury market, Stock market, Crypto market, etc.)" – Agam A. Shah

Major technology advances such as the development of hypersonic vehicles – and less dramatic enhancements to existing systems – require overcoming a multitude of complex and costly challenges, many of them interconnected. That requires making strategic decisions on where limited research and development resources should be invested to provide maximum progress.

Researchers at the Georgia Tech Research Institute (GTRI) are developing a set of tools and methodologies that could help companies, federal agencies, and other organizations make those decisions by creating a roadmap of the science and technology (S&T) investments needed to realize a particular capability. Known as Science and Technology Research and Investment for Digital Engineering (STRIDE), the technique helps its users consider the costs and benefits across an entire system lifecycle. 

“STRIDE is really a portfolio management tool,” said Clement Smartt, a GTRI principal research scientist who leads the team developing it. “It helps an organization understand what projects in a given research portfolio they should focus on to meet operational needs given limitations of funding, time, and other considerations.”

Though STRIDE was originally developed to support decision making in the hypersonics community, its core methods and tools can be applied to any S&T portfolio targeted at enhancing performance of existing systems – or building entirely new ones. The output of STRIDE includes information on preferred S&T investment options and allows leadership to ask “what if” questions about potential alternatives.

“The goal is to make better decisions by doing trade space studies to get the answers before any metal is bent,” said Brent Peavy, a GTRI principal research engineer who is also part of the research team. “We are developing STRIDE to support the goal of making decisions based on modeling done with real data.”

The system’s output can include a prioritized set of investment opportunities along with data on the cost of each, the projected benefits, the timeline required to mature the program, and the tradeoffs that should be considered. For inputs, the tool leverages digital models, including those done for engineering, cost, sustainment, and operational analysis. It also can leverage test data for model creation or validation, and consider a project’s effects on an organization’s other investment opportunities.

STRIDE also considers issues that aren’t purely technical. For instance, research program managers must often determine what would happen if additional funding were added to a project, or if budgets were reduced. They also must often know the impacts of extending project deadlines – or shortening them to meet urgent goals. STRIDE also can help assess the impact of changing performance goals such as an air vehicle’s range, top speed, or payload.

“It makes recommendations based on multiple criteria, and a number of technical, cost, and schedule requirements,” said Smartt. “Slider bars associated with the relative importance of those parameters can be moved back and forth, and the recommendations will reflect those changes in priority.”

For decisions such as making improvements to established systems or platforms, STRIDE can consider how implementing those enhancements may affect existing capabilities. Examples might include making a lighter-weight part to improve range, or altering a design to reduce manufacturing costs. Any undesirable consequences of the new capability or enhancement would be factored into the recommendations.

“We can provide a more structured way to select S&T projects by considering their impact on systems of interest that will have to be integrated with the new capability, and then the long-term consequences and tradeoffs in terms of issues such as performance and schedule,” Smartt said.

The novel contribution of STRIDE, however, may be as a systems engineering model that holistically integrates data from all other models, he added. The digital engineering model uses advanced multi-attribute design and portfolio selection methodologies to arrive at recommendations for S&T options. 

For hypersonic vehicles, for instance, decisions on how to get the most return on investment could start with decomposing the technology development goals into subsystems and then trying to understand what may be holding back progress on each subsystem. For example, there could be roadblocks affecting such areas as guidance and navigation, propulsion, sensing, thermal protection, or other technologies. STRIDE can help make decisions about where to invest to make the most progress toward overcoming those roadblocks.

Many of the decision-making principles on which STRIDE is based grew from research in the Aerospace Systems Design Laboratory (ASDL) in Georgia Tech’s School of Aerospace Engineering. ASDL is a leader in the area of systems design, architecting, and optimization, and is the largest lab of its kind in the world. Two GTRI researchers who are graduates of ASDL, Senior Research Engineers Annie Jones-Wyatt and William Engler, identified the potential of STRIDE for making technology decisions for advanced DoD systems, such as hypersonics, and have prototyped the methodology to prove its applicability.

Smartt and Peavy believe that investment priorities will increasingly be driven by structured approaches such as STRIDE and the data-driven principles behind them. They caution that the tool is itself a research project under development that will need refinement before it can be provided as a service or software product.

“We are figuring out how to do this as we go,” Peavy said. “We are trying to answer fundamental questions about how to use digital information to help make decisions.”

STRIDE could support digital engineering goals that are becoming increasingly important to organizations that make large investments in new technology, including the U.S. Department of Defense (DoD). But one of the challenges of using it can be providing the quantity of data on which the system depends to make its recommendations.

“Right now, STRIDE is ahead of where most organizations are in digital engineering, but we believe this decision analytics approach will ultimately be the way that key program choices are made, including in the DoD space,” Smartt said. “GTRI is the right organization to help mature this methodology and help organizations adopt it to meet their needs for guiding S&T investments.”

 

Writer: John Toon (john.toon@gtri.gatech.edu).

 

GTRI Communications

Georgia Tech Research Institute

Atlanta, Georgia USA

The Georgia Tech Research Institute (GTRI) is the nonprofit, applied research division of the Georgia Institute of Technology (Georgia Tech). Founded in 1934 as the Engineering Experiment Station, GTRI has grown to more than 2,800 employees, supporting eight laboratories in over 20 locations around the country and performing more than $700 million of problem-solving research annually for government and industry. GTRI's renowned researchers combine science, engineering, economics, policy, and technical expertise to solve complex problems for the U.S. federal government, the state, and industry. For more information, please visit www.gtri.gatech.edu.

Though it is a cornerstone of virtually every process that occurs in living organisms, the proper folding and transport of biological proteins is a notoriously difficult and time-consuming process to experimentally study.

In a new paper published in eLife, researchers in the School of Biological Sciences and the School of Computer Science have shown that AF2Complex may be able to lend a hand.

Building on the models of DeepMind’s AlphaFold 2, a machine learning tool able to predict the detailed three-dimensional structures of individual proteins, AF2Complex — short for AlphaFold 2 Complex — is a deep learning tool designed to predict the physical interactions of multiple proteins. With these predictions, AF2Complex is able to calculate which proteins are likely to interact with each other to form functional complexes in unprecedented detail.

“We essentially conduct computational experiments that try to figure out the atomic details of supercomplexes (large interacting groups of proteins) important to biological functions,” explained Jeffrey Skolnick, Regents’ Professor and Mary and Maisie Gibson Chair in the School of Biological Sciences, and one of the corresponding authors of the study. With AF2Complex, which was developed last year by the same research team, it’s “like using a computational microscope powered by deep learning and supercomputing.”

In their latest study, the researchers used this ‘computational microscope’ to examine a complicated protein synthesis and transport pathway, hoping to clarify how proteins in the pathway interact to ultimately transport a newly synthesized protein from the interior to the outer membrane of the bacteria — and identify players that experiments might have missed. Insights into this pathway may identify new targets for antibiotic and therapeutic design while providing a foundation for using AF2Complex to computationally expedite this type of biology research as a whole.

Computing complexes

Created by London-based artificial intelligence lab DeepMind, AlphaFold 2 is a deep learning tool able to generate accurate predictions about the three-dimensional structure of single proteins using just their building blocks, amino acids. Taking things a step further, AF2Complex uses these structures to predict the likelihood that proteins are able to interact to form a functional complex, what aspects of each structure are the likely interaction sites, and even what protein complexes are likely to pair up to create even larger functional groups called supercomplexes.

“The successful development of AF2Complex earlier this year makes us believe that this approach has tremendous potential in identifying and characterizing the set of protein-protein interactions important to life,” shared Mu Gao, a senior research scientist at Georgia Tech. “To further convince the broad molecular biology community, we [had to] demonstrate it with a more convincing, high impact application.”

The researchers chose to apply AF2Complex to a pathway in Escherichia coli (E. coli), a model organism in life sciences research commonly used for experimental DNA manipulation and protein production due to its relative simplicity and fast growth. 

To demonstrate the tool’s power, the team examined the synthesis and transport of proteins that are essential for exchanging nutrients and responding to environmental stressors: outer membrane proteins, or OMPs for short. These proteins reside on the outermost membrane of gram-negative bacteria, a large family of bacteria characterized by the presence of inner and outer membranes, like E. coli. However, the proteins are created inside the cell and must be transported to their final destinations. 

“After more than two decades of experimental studies, researchers have identified some of the protein complexes of key players, but certainly not all of them,” Gao explained. AF2Complex “could enable us to discover some novel and interesting features of the OMP biogenesis pathway that were missed in previous experimental studies.”

New insights

Using the Summit supercomputer at the Oak Ridge National Laboratory, the team, which included computer science undergraduate Davi Nakajima An, put AF2Complex to the test. They compared a few proteins known to be important in the synthesis and transport of OMPs to roughly 1,500 other proteins — all of the known proteins in E. coli’s cell envelope — to see which pairs the tool computed as most likely to interact, and which of those pairs were likely to form supercomplexes. 

To determine if AF2Complex’s predictions were correct, the researchers compared the tool’s predictions to known experimental data. “Encouragingly,” said Skolnick, “among the top hits from computational screening, we found previously known interacting partners.” Even within those protein pairs known to interact, AF2Complex was able to highlight structural details of those interactions that explain data from previous experiments, lending additional confidence to the tool’s accuracy.

In addition to known interactions, AF2Complex predicted several unknown pairs. Digging further into these unexpected partners revealed details on what aspects of the pairs might interact to form larger groups of functional proteins, likely active configurations of complexes that have previously eluded experimentalists, and new potential mechanisms for how OMPs are synthesized and transported. 

“Since the outer membrane pathway is both vital and unique to gram-negative bacteria, the key proteins involved in this pathway could be novel targets for new antibiotics,” said Skolnick. “As such, our work that provides molecular insights about these new drug targets might be valuable to new therapeutic design.”

Beyond this pathway, the researchers are hopeful that AF2Complex could mean big things for biology research. 

“Unlike predicting structures of a single protein sequence, predicting the structural model of a supercomplex can be very complicated, especially when the components or stoichiometry of the complex is unknown,” Gao noted. “In this regard, AF2Complex could be a new computational tool for biologists to conduct trial experiments of different combinations of proteins,” potentially expediting and increasing the efficiency of this type of biology research as a whole.

AF2Complex is an open-source tool available to the public and can be downloaded here.

This work was supported in part by the DOE Office of Science, Office of Biological and Environmental Research (DOE DE-SC0021303) and the Division of General Medical Sciences of the National Institute Health (NIH R35GM118039). DOI: https://doi.org/10.7554

Gasoline, diesel, and jet fuel — the most commonly used transportation fuels — are among the largest sources of greenhouse gas emissions, and their use is affecting the climate in significant and long-term ways. A new national report, however, provides a powerful toolkit to help researchers and policymakers better evaluate low-carbon technologies and work toward reducing emissions.

Valerie Thomas, Anderson-Interface Chair of Natural Systems and professor in the H. Milton Stewart School of Industrial and Systems Engineering and the School of Public Policy at Georgia Tech, served as chair for the report titled “Current Methods for Life Cycle Analyses of Low-Carbon Transportation Fuels in the United States.” Issued by the National Academies of Sciences, Engineering, and Medicine, the report presents life-cycle assessment as an essential tool in helping researchers and policymakers evaluate low-carbon fuel standards to reduce emissions. Thomas, whose research focuses on energy, environmental impacts, and technology development and policy, is affiliated with Georgia Tech’s Strategic Energy Institute, Brook Byers Institute for Sustainable Systems, and Renewable Bioproducts Institute.

Alternative fuel sources such as electricity for electric vehicles, biofuels for aircraft, and hydrogen for fuel-cell trucks do emit carbon dioxide and other greenhouse gases, whether by resource extraction, production processes, or other supply-chain and market contributions. When considering low-carbon fuel standards to reduce emissions, policymakers are often met with a range of questions from stakeholders, from potential impacts of a specific policy to total emissions released from the production of a particular fuel.

“If a new transportation fuel is meant to reduce greenhouse gas emissions, we need to be confident that emissions are indeed likely to be reduced,” Thomas said. “Determining the total net emissions of alternative fuels requires an understanding of how they are made and how they affect markets.”

Life-cycle assessments are a method used to evaluate environmental impacts of fuels and technologies throughout their production and use, but according to Thomas, more research is needed to strengthen their reliability. The 16-member committee led by Thomas evaluated current methods for life-cycle analyses of low-carbon transportation fuels in the U.S., with the goal of establishing a comprehensive and reliable approach for applying life-cycle assessment to developing low-carbon fuel standards.

In preparing the report, the committee gathered input from life-cycle assessment experts, including researchers specializing in aviation fuels, biofuels, hydrogen fuels, fossil fuels, and soil carbon implications of biofuel production. The report, which includes 70 total recommendations, includes suggestions for improving models, increasing transparency, and verifying emissions. The report provides an understanding of the state-of-the-science in quantifying the climate impact of a transition to new transportation fuels.

“We suggest that the approach to life-cycle assessment needs to be guided by the question the analysis is trying to answer,” Thomas said. “Different types of assessment are better suited for answering different questions. While some methods work well for fine tuning a well-defined supply chain, other methods are needed to understand the global, economy-scale effect of a major technology or policy change.”

Thomas hopes that research programs will be created to advance key theoretical, computational, and modeling needs to better evaluate the transition to low carbon fuels.

The National Academy of Sciences was founded in 1863 by an act of Congress and it includes the National Academies of Science, Engineering, and Medicine. Its charge is to “provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions.”

 

CITATION: National Academies of Sciences, Engineering, and Medicine. 2022. “Current Methods for Life Cycle Analyses of Low-Carbon Transportation Fuels in the United States.” Washington, D.C.: The National Academies Press.

DOI: https://doi.org/10.17226/26402