Georgia Tech strives to cultivate thought leaders, advance knowledge, and solve societal challenges by embracing various aspects of the research ecosystem. Through the HBCU/MSI Research Initiative, Georgia Tech seeks to capture data surrounding its research impact in the Georgia Tech-HBCU Research Collaboration Data Dashboard. The dashboard allows users to see information regarding joint funding, publications, hubs, and awards won by the HBCU CHIPS Network, which is co-led by Georgia Tech.
“The data dashboard will represent a key resource for both Georgia Tech and HBCU researchers seeking to enhance research collaboration while substantiating Georgia Tech’s commitment as a valued partner,” said George White, senior director for strategic partnerships.
The Georgia Tech-HBCU Research Collaboration Data Dashboard will serve as a point of reference for faculty and staff in the various departments and colleges to identify opportunities of mutual benefits for collaboration and partnership.
To view the dashboard, visit https://hbcumsi.research.gatech.edu/data-dashboard
Taiesha Smith
Sr. Program Manager, HBCU-MSI Research Partnerships
AMPF facility
When Air Force veteran Michael Trigger began looking for a new career in 2022, he became fascinated by artificial intelligence (AI). Trigger, who left the military in 1989 and then worked in telecommunications, corrections, and professional trucking, learned about an AI-enhanced robotics manufacturing program at the VECTR Center. This training facility in Warner Robins, Georgia, helps veterans transition into new careers. In 2024, he enrolled and learned how to program and operate robots.
As part of the class, Trigger made several trips to the Georgia Tech Manufacturing Institute (GTMI). When the faculty asked if anyone wanted an internship, Trigger raised his hand.
“Coming to Georgia Tech allowed me to clarify what I wanted to do,” he said. “I’ve always been in service-based jobs, but I was interested in additive manufacturing,” or 3D printing.
For five months every weekday, Trigger drove from his home in Macon to Georgia Tech’s campus for his internship. The paid internship took place at Tech’s Advanced Manufacturing Pilot Facility (AMPF). This 20,000-square-foot, reconfigurable facility serves as the research and development arm of GTMI, functioning as a teaching laboratory, technology test bed, and workforce development space for manufacturing innovations.
During his time there, Trigger focused on computer-aided manufacturing and met with faculty and students to learn about their research. The internship wasn’t convenient, but it was worth it.
“From our campus visits, I understood the mission of AMPF, so the fact they offered me this opportunity was huge for me,” he said. “The internship had a big impact on my life in terms of the technical and soft skills I gained.”
Building the Workforce
Launching new careers is just one of AMPF’s goals in testing new manufacturing and growing the future U.S. workforce. Since 2022, AMPF has improved the manufacturing process at all parts of the talent pipeline — from giving corporate researchers space to test and adopt AI automation technologies to training and upskilling their employees. Collectively, GTMI and AMPF’s efforts have led to a stronger, bigger network of manufacturers that other companies and the U.S. government can rely on.
“We are going to need to manufacture more in the U.S. — from computer chips to cars — so we want to create jobs and fill them,” said Tom Kurfess, GTMI’s executive director. “We need more people working in the manufacturing sector, and we've got to make these jobs better and make people more efficient in them.”
AI is one way to boost efficiency, but artificial intelligence won’t cut humans out of the process entirely. Rather, people will be integral to monitoring the systems and advancing them. As AI becomes more widely adopted, a college degree won’t necessarily be required to work in the AI field.
“Our workforce is going to need the next generation of employees to be amenable to retraining as the technology updates,” said Aaron Stebner, a co-director of the Georgia Artificial Intelligence Manufacturing program (AIM). A statewide program, Georgia AIM helps fund AMPF and sponsored Trigger’s internship. “Education is going to be more of a lifelong learning process, and Georgia Tech can be at the forefront of that.”
While GTMI already integrates AI into many processes, it remains committed to staying ahead of the curve with the latest technologies that could boost manufacturing. The facility is in the process of an expansion that will nearly triple its size and make AMPF the leading facility for demonstrating what a hyperconnected and AI-driven manufacturing enterprise looks like. This will enable GTMI to build and sustain these educational pipelines, which is key to its work.
“We’re developing the workforce for the future, not of the future,” explained Donna Ennis, a co-director of Georgia AIM. “It’s AI today, but it could be something else five years from now. We are focused on creating a highly skilled, resilient workforce.”
Part of Georgia AIM’s role is creating the pipelines that people like Trigger can follow. From bringing a mobile lab to technical colleges to hosting robotics competitions at schools, these efforts span the state of Georgia and touch populations from “K to gray.”
“Kids don’t say they want to be a manufacturer when they grow up, but that’s because they don’t know it’s a viable career path,” Ennis said. “We’re making manufacturing cool again.”
Creating Corporate Connection
To create these job opportunities, GTMI is also partnering with corporations. Companies can join a consortium to access the AMPF research facilities and collaborate with researchers. Any size or type of company can take advantage of AMPF facilities — from corporations including AT&T and Siemens to small startups like Alegna, which licenses and commercializes Navy research.
“The ability to manufacture domestically is critical, not only for national security purposes, but also to keep the U.S. economically competitive,” said Steven Ferguson, a principal research scientist and executive director for the GT Manufacturing 4.0 Consortium. “Having the AMPF puts Georgia Tech within the innovation epicenter for these areas and will help us reshore manufacturing.”
The benefit of such an arrangement is twofold. Companies can work with the newest manufacturing technologies and make their own advances, and Georgia Tech builds a network of manufacturers across the state and world that students can work with. For example, AT&T uses the AMPF to test sensors for expanding personal 5G networks, and George W. Woodruff School of Mechanical Engineering Professor Carolyn Seepersad has Ph.D. students funded by a Siemens partnership through AMPF.
Trigger was able to connect and collaborate with some of these corporations and researchers during his internship. “I told them about my interest in machine learning because I wanted to see how they were integrating machine learning into their research projects,” he said. “All of them invited me to come by to observe and be part of the research.”
Starting a New Path
Because of his research collaborations during his AMPF internship, Trigger now has a new focus. “The internship clarified for me that AI is where everybody is going,” he explained. He wants to be at the forefront of AI manufacturing and hopes to pursue a certificate in machine learning next.
While he knows he still has much to learn, AMPF gave Trigger a foot in the door and confidence about the future. He — and other veterans like him — will help build the workforce that propels America forward in manufacturing.
Tess Malone, Senior Research Writer/Editor
tess.malone@gatech.edu
Yongsheng Chen, Bonnie W. and Charles W. Moorman IV Professor in environmental engineering at Georgia Tech
Someday, your drinking water could be completely free of toxic “forever chemicals.”
These chemicals, called PFAS (per- and polyfluoroalkyl substances), are found in common household items like makeup, nonstick cookware, dental floss, batteries, and food packaging. PFAS permeate the soil, water, food, and air, and they can remain in the environment for millennia. Once inside the human body, PFAS can persist for years, suppressing the immune system and increasing cancer risk.
Georgia Tech researchers, armed with a cutting-edge machine learning (ML) model, are spearheading a multi-university initiative. Their goal? To design a better membrane that efficiently removes PFAS from drinking water, a significant source of human exposure.
“More than 200 million Americans in all 50 states are affected by PFAS in drinking water, with 1,400 communities having levels above health experts’ safety thresholds,” noted the study’s principal investigator Yongsheng Chen, Bonnie W. and Charles W. Moorman IV Professor in Georgia Tech’s School of Civil and Environmental Engineering. Chen also directs the Nutrients, Energy, and Water Center for Agriculture Technology, or NEW Center. “Our research aims to provide a scalable, efficient, and sustainable solution for mitigating these toxic chemicals’ impact on human health and the environment.”
The resulting work, funded with over $10 million in multiyear grants from the U.S. Department of Agriculture (USDA), the National Science Foundation, and the Environmental Protection Agency (EPA), was recently published in Nature Communications.
Sewage Treatment Limitations
Conventional water treatment processes are ineffective at removing PFAS. Too often, traditional cleansing methods, such as using chlorine to kill pathogens in water, create harmful byproducts.
“Solving one problem creates another problem,” said Chen.
He has already used ML and artificial intelligence in precision agriculture to monitor nutrient levels in plants and insists that tackling PFAS removal similarly requires new approaches. Rather than treating an entire body of water, Chen’s team first separated PFAS from the water stream. Success depended on finding the right membrane material to isolate the chemicals in the water.
Chen relied on a team of 10 Ph.D. students and nine research scientists to perform the ML modeling. In addition to Georgia Tech, two other schools contributed people and laboratory expertise. The University of Wisconsin-Madison (UWM) validated the model with molecular simulations, while Arizona State University (ASU) trained it using data from scientific literature and their lab.
“Applying machine learning to membrane separation represents an exciting frontier for environmental engineering,” said Tiezheng Tong, an associate professor of environmental engineering in ASU’s School of Sustainable Engineering and the Built Environment.
This is another step in tackling PFAS pollution, a widespread problem that has recently received significant public attention due to PFAS’ toxic nature and the recent EPA ruling on PFAS in drinking water, he said.
“By integrating with molecular simulation tools, we can better understand PFAS transport across nanofiltration and reverse osmosis membranes, pushing the boundary of fundamental science relating to membrane separation,” Tong said.
ML Accelerates Membrane-Material Discoveries
Using ML modeling significantly sped up the discovery process. For instance, one Ph.D. student in Chen’s lab used trial and error over two years to pinpoint one promising membrane. Machine learning modeling allowed the team to find eight membrane candidates 10 to 20 times faster, reducing discovery time from years to a few months.
“Our molecular dynamics simulations reveal that electrostatic interactions, size exclusion, and dehydration play critical roles in governing the transport of PFAS molecules across polyamide membranes,” Ying Li explained. Li is an associate professor of mechanical engineering at UWM. “These calculations indicate that electrostatic interactions dominate PFAS rejection, with charged functional groups significantly influencing transport behavior. The simulation results provide fundamental insights that align with ML predictions, highlighting the key molecular determinants of PFAS removal efficiency.”
Addressing PFAS Exposure in Agriculture
By addressing PFAS contamination, this research could also benefit the agriculture industry, which depends on fertilizer sourced from water treatment plants. Wastewater biosolids are processed into fertilizer, offering farmers and ranchers a cheaper alternative to chemical fertilizers. Unfortunately, PFAS-tainted fertilizers from sewage sludge have contaminated significant amounts of land and livestock. Industry groups estimate that almost 70 million acres of U.S. farmland could be contaminated by these forever chemicals.
By funding this research, the USDA hopes that an effective membrane will help the United States reclaim this crucial resource.
“Synthesizing a very smart membrane to get rid of PFAS also allows us to recover the fertilizer from municipal wastewater treatment plants,” Chen said. “Such a membrane could enable us to get rid of things we don’t want and keep the things we need, so we can keep the water for irrigation or other applications.”
Eliminating PFAS in fertilizers also could help address the mismatch of food and water demand in urban versus rural areas since 80% of the demand resides in cities. PFAS removal could directly support urban area resource recovery and food production.
“Our goal is achieving a circular economy where materials never become waste, and nature is regenerated,” Chen said.
What’s Next
The team will fine-tune the model and add more data to improve its training features. Chen will synthesize membranes in his lab to further test the model's PFAS removal predictions.
Today, scientists have found ways to remove long chains of PFAS, but the shorter chains of these chemicals persist, explained Chen.
“If we can better understand the mechanism, we’ll be able to design a good material membrane to get rid of all PFAS. That could be game-changing.”
— By Anne Wainscott-Sargent
Funding
This work is partially supported by the NSF (Award Nos. 2112533, 2427299, 2345543, Y.C.; 2448130, T.T.; and 2345542, Y.L.).
Y.C. acknowledges the financial support by the USDA (Award No.2018−68011-28371), NSF-USDA (Award No. 2020-67021-31526), and EPA (Award No. 840080010).
T.T. acknowledges the support of the USDA National Institute of Food and Agriculture (Hatch Project COL00799, accession 1022591).
Y.L. acknowledges the financial support by the National Alliance for Water Innovation (NAWI), funded by the US DOE, Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office, under Funding Opportunity announcement Number DE-FOA-0001905, through a subcontract to the University of Wisconsin-Madison.
Ying Li, associate professor of mechanical engineering at University of Wisconsin-Madison
Tiezheng Tong, associate professor of environmental engineering at Arizona State University
Shelley Wunder-Smith | Director of Research Communications
shelley.wunder-smith@research.gatech.edu
Georgia Tech Regents’ Professor Srinivas Aluru is the recipient of the Charles Babbage Award for 2025. Aluru was awarded for pioneering research contributions that intersect parallel computing and computational biology.
“This is a very well-deserved recognition for Srinivas as he joins the illustrious list of past recipients of the Charles Babbage Award,” said Vivek Sarkar, the John P. Imlay Jr. Dean of the College of Computing.
“Srinivas’ accomplishments reflect positively on himself and all of us at Georgia Tech. This is indeed an occasion to celebrate.”
The IEEE Computer Society presents the Babbage Award annually. The award recognizes significant contributions to parallel computation.
[Related: IEEE-CS interview with Aluru on his award-winning career]
The award is named after Charles Babbage, widely considered to be a “father of the computer.” Babbage and Ada Lovelace are credited with inventing the first mechanical computers in the 19th century, eventually leading to more complex designs.
Aluru is a pioneer in computational genomics, an area of biology that studies the order, structure, function, and evolution of genetic material. Throughout his career, his lab has developed software and algorithms to analyze the genomes of several species of plants, animals, and microorganisms.
Genome base pair sizes can number into the billions, which can be interpreted as massive datasets. Ever since the early years of his career, Aluru championed parallel computing as a practical approach to studying these challenging datasets.
Parallelism divides a large problem into smaller ones, allowing different processors on a computer to solve the simpler tasks simultaneously. This approach breaks a genome into smaller segments, allowing computers to efficiently transcribe genetic code and identify insightful patterns.
“Srinivas Aluru’s groundbreaking contributions have profoundly shaped the intersection of parallel processing and bioinformatics. His work is nothing short of extraordinary,” said Yves Robert, awards chair of the IEEE Computer Society Babbage Committee.
“It is a privilege to recognize a researcher whose work will undoubtedly have a lasting impact for generations to come.”
IEEE selected Aluru as a fellow in 2010, and he recently served as the editor-in-chief of the journal IEEE/ACM Transactions on Computational Biology and Bioinformatics.
Aluru has fellowships with the American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Society of Industrial and Applied Mathematics. He is a past recipient of the NSF CAREER Award, IBM Faculty Award, and the Swarnajayanti Fellowship from the government of India.
Along with receiving the Babbage Award, Aluru’s leadership acumen earned him the recent appointment as senior associate dean of Georgia Tech’s College of Computing.
Aluru helped form the Institute for Data Engineering and Science (IDEaS) at Georgia Tech in 2016, serving as co-executive director. Later, he became the institute’s sole executive director from 2019 to 2025. Regents’ Professor C. David Sherrill became interim executive director of IDEaS when Aluru accepted his associate dean appointment.
Aluru started at Georgia Tech in 2013 to join the new School of Computational Science and Engineering, established in 2010. He served as the School’s interim chair from 2019 to 2020. In 2023, the University System of Georgia appointed Aluru as Regents’ Professor.
Aluru completed his Ph.D. at Iowa State University in 1994. He then worked at Ames National Laboratory, Syracuse University, and New Mexico State University before returning to his alma mater from 1999 to 2013.
“This award is a recognition of over two and a half decades of research efforts in my group, reflecting not only my work but that of numerous graduate students and collaborators,” said Aluru.
“I hope the award draws attention to the importance of parallel methods in computational biology and points key advancements to new entrants in the field.”
Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu
A new NASA-funded project will have Georgia Tech aerospace engineers developing new technology to one day study planets outside our solar system.
It's a $10 million joint mission led by the University of Michigan called STARI — STarlight Acquisition and Reflection toward Interferometry. Georgia Tech’s engineers will build the propulsion systems for a pair of briefcase-sized CubeSats that will fly in orbit a few hundred yards away from one another, bouncing starlight back and forth.
The technology could be used someday to better understand if any known exoplanets are capable of supporting life as we know it.
Interferometry is already used to study stars, gas clouds, and galaxies. Instead of using one large telescope, several smaller telescopes work as a team. The machines swap starlight to create higher resolution images than are possible from a single telescope.
Scientists and engineers have recently proposed using interferometry to locate exoplanets.
STARI will determine if the same type of coordination and light transmission can be done using less expensive CubeSats.
Read the entire story on the College of Engineering website.
Jason Maderer
College of Engineering
maderer@gatech.edu
Materials scientist Antonio Facchetti is one of the newest members of the National Academy of Engineering (NAE).
The Academy announced his election Feb. 11 as part of a 2025 class that included 127 other U.S. members and 22 international members. Election to the NAE is among the highest professional recognitions for engineers and an honor bestowed on just 2,800 professionals worldwide.
New members are nominated and voted on by NAE’s existing membership. Facchetti is Georgia Tech’s 49th member.
“I was quite shocked and honored when I received the news. NAE includes some of the greatest minds in the engineering field, and to be named among them is truly humbling,” said Facchetti, professor and Hightower Chair in the School of Materials Science and Engineering. “I’m inspired to continue contributing to the future of unconventional electronic materials.”
Read more about Facchetti's work on the College of Engineering website.
Joshua Stewart
College of Engineering
When Ashley Cotsman arrived as a freshman at Georgia Tech, she didn’t know how to code. Now, the fourth-year Public Policy student is leading a research project on AI and decarbonization technologies.
When Cotsman joined the Data Science and Policy Lab as a first-year student, “I had zero skills or knowledge in big data, coding, anything like that,” she said.
But she was enthusiastic about the work. And the lab, led by Associate Professor Omar Asensio in the School of Public Policy, included Ph.D., master’s, and undergraduate students from a variety of degree programs who taught Cotsman how to code on the fly.
She learned how to run simple scripts and web scrapes and assisted with statistical analyses, policy research, writing, and editing. At 19, Cotsman was published for the first time. Now, she’s gone from mentee to mentor and is leading one of the research projects in the lab.
“I feel like I was just this little freshman who had no clue what I was doing, and I blinked, and now I’m conceptualizing a project and coming up with the research design and writing — it’s a very surreal moment,” she said.
Cotsman, right, presenting a research poster on electric vehicle charging infrastructure, another project she worked on with Asensio and the Data Science and Policy Lab.
Cotsman’s project. “Scaling Sustainability Evaluations Through Generative Artificial Intelligence.” uses the large language model GPT-4 to analyze the sea of sustainability reports organizations in every sector publish each year.
The authors, including Celina Scott-Buechler at Stanford University, Lucrezia Nava at University of Exeter, David Reiner at University of Cambridge Judge Business School and Asensio, aim to understand how favorability toward decarbonization technologies vary by industry and over time.
“There are thousands of reports, and they are often long and filled with technical jargon,” Cotsman said. “From a policymaker’s standpoint, it’s difficult to get through. So, we are trying to create a scalable, efficient, and accurate way to quickly read all these reports and get the information.”
The team trained a GPT-4 model to search, analyze, and see trends across 95,000 mentions of specific technologies over 25 years of sustainability reports. What would take someone 80 working days to read and evaluate took the model about eight hours, Cotsman said. And notably, GPT-4 did not require extensive task-specific training data and uniformly applied the same rules to all the data it analyzed, she added.
So, rather than fine-tuning with thousands of human-labeled examples, “it’s more like prompt engineering,” Cotsman said. “Our research demonstrates what logic and safeguards to include in a prompt and the best way to create prompts to get these results.”
The team used chain-of-thought prompting, which guides generative AI systems through each step of its reasoning process with context reasoning, counterexamples, and exceptions, rather than just asking for the answer. They combined this with few-shot learning for misidentified cases, which provides increasingly refined examples for additional guidance, a process the AI community calls “alignment.”
The final prompt included definitions of favorable, neutral, and opposing communications, an example of how each might appear in the text, and an example of how to classify nuanced wording, values, or human principles as well.
It achieved a .86 F1 score, which essentially measures how well the model gets things right on a scale from zero to one. The score is “very high” for a project with essentially zero training data and a specialized dataset, Cotsman said. In contrast, her first project with the group used a large language model called BERT and required 9,000 lines of expert-labeled training data to achieve a similar F1 score.
“It’s wild to me that just two years ago, we spent months and months training these models,” Cotsman said. “We had to annotate all this data and secure dedicated compute nodes or GPUs. It was painstaking. It was expensive. It took so long. And now, two years later, here I am. Just one person with zero training data, able to use these tools in such a scalable, efficient, and accurate way.”
Through the Federal Jackets Fellowship program, Cotsman was able to spend the Fall 2024 semester as a legislative intern in Washington, D.C.
While Cotsman’s colleagues focus on the results of the project, she is more interested in the methodology. The prompts can be used for preference learning on any type of “unstructured data,” such as video or social media posts, especially those examining technology adoption for environmental issues. Asensio and the Data Science and Policy team use the technique in many of their recent projects.
“We can very quickly use GPT-4 to read through these things and pull out insights that are difficult to do with traditional coding,” Cotsman said. “Obviously, the results will be interesting on the electrification and carbon side. But what I’ve found so interesting is how we can use these emerging technologies as tools for better policymaking.”
While concerns over the speed of development of AI is justifiable, she said, Cotsman’s research experience at Georgia Tech has given her an optimistic view of the new technology.
“I’ve seen very quickly how, when used for good, these things will transform our world for the better. From the policy standpoint, we’re going to need a lot of regulation. But from the standpoint of academia and research, if we embrace these things and use them for good, I think the opportunities are endless for what we can do.”
Ivan Allen College of Liberal Arts
Calculating and visualizing a realistic trajectory of ink spreading through water has been a longstanding and enormous challenge for computer graphics and physics researchers.
When a drop of ink hits the water, it typically sinks forward, creating a tail before various ink streams branch off in different directions. The motion of the ink’s molecules upon mixing with water is seemingly random. This is because the motion is determined by the interaction of the water’s viscosity (thickness) and vorticity (how much it rotates at a given point).
“If the water is more viscous, there will be fewer branches. If the water is less viscous, it will have more branches,” said Zhiqi Li, a graduate computer science student.
Li is the lead author of Particle-Laden Fluid on Flow Maps, a best paper winner at the December 2024 ACM SIGGRAPH Asia conference. Assistant Professor Bo Zhu advises Li and is the co-author of six papers accepted to the conference.
Zhu said they must correctly calculate and simulate the interaction between viscosity and vorticity before they can accurately predict the ink trajectory.
“The ink branches generate based on the intricate interaction between the vorticities and the viscosity over time, which we simulated,” Zhu said. “Using a standard method to simulate the physics will cause most of the structures to fade quickly without being able to see any detailed hierarchies.”
Zhu added that researchers had yet to develop a method for this until he and his co-authors proposed a new way to solve the equation. Their breakthrough has unlocked the most accurate simulations of ink diffusion to date.
“Ink diffusion is one of the most visually striking examples of particle-laden flow,” Zhu said.
“We introduce a new viscosity model that solves for the interaction between vorticity and viscosity from a particle flow map perspective. This new simulation lets you map physical quantities from a certain time frame, allowing us to see particle trajectory.”
In computer simulations, flow is the digital visualization of a gas or liquid through a system. Users can simulate these liquids and gases through different scenarios and study pressure, velocity, and temperature.
A particle-laden flow depicts solid particles mixing within a continuous fluid phase, such as dust or water sediment. A flow map traces particle motion from the start point to the endpoint.
Duowen Chen, a computer science Ph.D. student also advised by Zhu and co-author of the paper, said previous efforts by researchers to simulate ink diffusion depended on guesswork. They either used limited traditional methods of calculations or artificial designs.
“They add in a noise model or an artificial model to create vortical motions, but our method does not require adding any artificial vortical components,” Chen said. “We have a better viscosity force calculation and vortical preservation, and the two give a better ink simulation.”
Zhu also won a best paper award at the 2023 SIGGRAPH Asia conference for his work explaining how neural network maps created through artificial intelligence (AI) could close the gaps of difficult-to-solve equations. In his new paper, he said it was essential to find a way to simulate ink diffusion accurately independent of AI.
“If we don’t have to train a large-scale neural network, then the computation time will be much faster, and we can reduce the computation and memory costs,” Zhu said. “The particle flow map representation can preserve those particle structures better than the neural network version, and they are a widely used data structure in traditional physics-based simulation.”
Ben Snedeker, Communications Manager
Georgia Tech College of Computing
albert.snedeker@cc.gtaech.edu
Pascal Van Hentenryck, professor and chair of the School of Industrial and Systems Engineering at Georgia Tech, as well as director of Tech AI and the NSF AI4OPT Institute, presented at the Georgia Department of Human Services’ Annual HR Conference, held Jan. 28-30, 2025, at the Savannah Convention Center.
Themed “Customer-Centric Culture,” the event explored how leaders and employees can harness AI for customer engagement. Key topics included: defining AI, guiding workforce adaptation to AI-driven changes, and debunking myths, emphasizing AI's role as a vital tool rather than a threat.
To learn more about the Georgia Department of Human Services, click here.
