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.

The Energy, Policy, and Innovation Center (EPICenter) hosted the 2024 cohort of Energy Unplugged, a Science, Technology, Engineering, Art, and Math (STEAM) summer program for high school students. The weeklong camp was held at Georgia Tech’s Atlanta and Savannah campuses this summer and has earned a reputation as one of the most sought-after high-school-level summer camps hosted by Georgia Tech. 

Rich Simmons, director of Research and Studies at the Strategic Energy Institute, has been the driving force behind the camp since its inception in 2019. Simmons, a faculty instructor in the George W. Woodruff School of Mechanical Engineering at Georgia Tech, brings his award-winning teaching expertise to high school students, ensuring that each session of Energy Unplugged is both educational and engaging. The program covered a range of timely topics, from basic energy principles such as conservation laws, electric circuits, and battery storage to more complex subjects like environmental impacts, data analytics, and decision-making. In addition, students were immersed in hands-on activities, interactive demonstrations, and power plant site visits.

During the first two days, students formed teams to construct catapults and mousetrap cars, discussed the underlying physics involving energy conversion, and then launched projectiles and vehicles to test their predictions. In one of the camp’s most popular activities, students raced remote-controlled cars around an obstacle course to learn about the importance of balancing multiple objectives, such as energy use, elapsed time, safety, and cargo capacity. The week culminated in a small-group mini-project, where campers applied the skills they had acquired to solve a real-world challenge — to optimize a cooking process using solar energy. Given specific parameters on energy generation, storage, and meal demand, the students determined the best approach to convert solar energy for cooking and storage to meet a daily lunch and dinner schedule for a food truck business. The program concluded with the campers presenting their preferred designs to an audience of parents, faculty, and staff, adding public speaking and technical presentation skills to their summer experiences.

Every year, students highlight the energy field trips to power plants, data centers, robotics labs, and makerspaces as some of their favorite aspects of the camp. A student poll during the final presentations used words like fun, informative, interesting, magical, epic, exciting, educational, and fantastical to describe the camping experience. The camp introduced the students to STEM-related careers and the many undergraduate programs that could provide a pathway for them. 

Energy Unplugged provides a portal for Georgia Tech graduate student interns such as Jake Churchill and staff members such as Jordann Shields to engage students with energy concepts, activities, career paths, and information about attending Georgia Tech. 

Energy Unplugged is administered by Georgia Tech Summer P.E.A.K.S. (Program for Enrichment and Accelerated Knowledge in STEAM) at CEISMC (the Center for Education Integrating Science, Mathematics, and Computing), the primary connection point between Tech faculty and students and the K-12 STEAM education community. Annually, CEISMC programs are accessible to more than 39,000 students; 1,700 teachers; and 200 schools in over 75 school districts throughout Georgia.

As part of the Strategic Energy Institute, EPICenter taps into regional and national expertise within academia, businesses, non-governmental organizations, and research facilities to provide an unbiased and interdisciplinary framework for driving innovation in energy policy and technology in the Southeast.

For some students, an 8 a.m. class will take away the morning jog they enjoyed every day last semester. For others, a lab meeting time changed, and tennis doubles in the afternoon won’t be an option anymore.

Students returning to campus for a new semester often struggle to find time for physical activities because of their new routines and schedules. However, a new app developed at Georgia Tech helps busy students prioritize physical activity in their daily routines.

Ph.D. student Kefan Xu of the Ubicomp Health and Wellness Lab at Georgia Tech created Plannergy, a time management app that identifies open time blocks in users’ schedules. 

Xu introduced Plannergy at the Conference on Human Factors in Computing (CHI) in Honolulu, Hawaii in May. He says the app is ideal for college students because they tend to have busy and inconsistent schedules.

Plannergy allows users to track their schedules, reflect on what activities would be beneficial and timely, and strategize how to implement the activity into their schedule.

"Currently, the app is catered to people who’ve been physically inactive and have inconsistent schedules,” Xu said. “College students know their schedule will change when they begin a new semester. They need to get some physical activity and find opportunities in the day they can leverage. It could be as simple as walking to school instead of taking a scooter.”

Xu tested his app on 16 college students who planned their physical activities every seven days and followed a reflective iteration framework to track improvement. The results showed that Plannergy is an effective behavior change tool. The findings also indicate that it increases participants’ awareness of their schedules.

The American Heart Association says adults can reduce the risk of heart disease by participating in at least 150 minutes of moderate-intensity physical activity weekly.

The Centers for Disease Control and Prevention released a report in 2023 that found 72% of Americans aren’t meeting that standard.

As Xu points out in his paper, studies have shown that incorporating physical activity into a person’s routine usually helps them maintain it. However, he’s identified two common problems:

• People lack understanding about their schedules and routines.
• People have schedules that fluctuate from one day to the next.

“Individuals face a lot of changes in their life,” Xu said. “Maybe they’re a student who has graduated, and they’re going into industry, which means their daily routine will be different from what it was while they were in school. This app allows them to experiment with different time slots and activity types to figure out another way and help them update their activity routine no matter what life changes they face.”

CUSTOM FIT

Some users who have been inactive for extended periods may be unsure how much exercise they need. Plannergy can also help them determine the intensity level of the activity to help avoid overexertion. 

“If someone has been inactive for months, it’s hard to ask them to run two miles daily,” Xu said. “There’s much for them to figure out. How much do they want to do, and at what intensity level? This app lets them gradually figure out the ideal activity. They can continue to track their progress and see if improvements are needed.”

Plannergy is not limited to physical activity. Xu says one of the students in his study who worked out daily used the app to identify times in her schedule to take breaks or focus on more spiritual disciplines.

“She added yoga and removed some high-intensity physical activities, and her sleeping routine also changed,” Xu said.

Xu is working to improve the app. Future versions will have sensing technology to leverage health informatics so users can make better decisions. He also wants the app to record user data and make customized suggestions for activities that fit the user’s schedule and preferred exercise intensity level.

“The app requires manual tracking, which can create user burden,” he said. “I think in the future, the process could be more automated. We want to keep it flexible but add more scaffolding to enhance user experience.”

 

Nylon, Teflon, Kevlar. These are just a few familiar polymers — large-molecule chemical compounds — that have changed the world. From Teflon-coated frying pans to 3D printing, polymers are vital to creating the systems that make the world function better. 

Finding the next groundbreaking polymer is always a challenge, but now Georgia Tech researchers are using artificial intelligence (AI) to shape and transform the future of the field. Rampi Ramprasad’s group develops and adapts AI algorithms to accelerate materials discovery. 

This summer, two papers published in the Nature family of journals highlight the significant advancements and success stories emerging from years of AI-driven polymer informatics research. The first, featured in Nature Reviews Materials, showcases recent breakthroughs in polymer design across critical and contemporary application domains: energy storage, filtration technologies, and recyclable plastics. The second, published in Nature Communications, focuses on the use of AI algorithms to discover a subclass of polymers for electrostatic energy storage, with the designed materials undergoing successful laboratory synthesis and testing. 

“In the early days of AI in materials science, propelled by the White House’s Materials Genome Initiative over a decade ago, research in this field was largely curiosity-driven,” said Ramprasad, a professor in the School of Materials Science and Engineering. “Only in recent years have we begun to see tangible, real-world success stories in AI-driven accelerated polymer discovery. These successes are now inspiring significant transformations in the industrial materials R&D landscape. That’s what makes this review so significant and timely.”

AI Opportunities

Ramprasad’s team has developed groundbreaking algorithms that can instantly predict polymer properties and formulations before they are physically created. The process begins by defining application-specific target property or performance criteria. Machine learning (ML) models train on existing material-property data to predict these desired outcomes. Additionally, the team can generate new polymers, whose properties are forecasted with ML models. The top candidates that meet the target property criteria are then selected for real-world validation through laboratory synthesis and testing. The results from these new experiments are integrated with the original data, further refining the predictive models in a continuous, iterative process. 

While AI can accelerate the discovery of new polymers, it also presents unique challenges. The accuracy of AI predictions depends on the availability of rich, diverse, extensive initial data sets, making quality data paramount. Additionally, designing algorithms capable of generating chemically realistic and synthesizable polymers is a complex task. 

The real challenge begins after the algorithms make their predictions: proving that the designed materials can be made in the lab and function as expected and then demonstrating their scalability beyond the lab for real-world use. Ramprasad’s group designs these materials, while their fabrication, processing, and testing are carried out by collaborators at various institutions, including Georgia Tech. Professor Ryan Lively from the School of Chemical and Biomolecular Engineering frequently collaborates with Ramprasad’s group and is a co-author of the paper published in Nature Reviews Materials.

"In our day-to-day research, we extensively use the machine learning models Rampi’s team has developed,” Lively said. “These tools accelerate our work and allow us to rapidly explore new ideas. This embodies the promise of ML and AI because we can make model-guided decisions before we commit time and resources to explore the concepts in the laboratory."

Using AI, Ramprasad’s team and their collaborators have made significant advancements in diverse fields, including energy storage, filtration technologies, additive manufacturing, and recyclable materials.

Polymer Progress

One notable success, described in the Nature Communications paper, involves the design of new polymers for capacitors, which store electrostatic energy. These devices are vital components in electric and hybrid vehicles, among other applications. Ramprasad’s group worked with researchers from the University of Connecticut.

Current capacitor polymers offer either high energy density or thermal stability, but not both. By leveraging AI tools, the researchers determined that insulating materials made from polynorbornene and polyimide polymers can simultaneously achieve high energy density and high thermal stability. The polymers can be further enhanced to function in demanding environments, such as aerospace applications, while maintaining environmental sustainability. 

“The new class of polymers with high energy density and high thermal stability is one of the most concrete examples of how AI can guide materials discovery,” said Ramprasad. “It is also the result of years of multidisciplinary collaborative work with Greg Sotzing and Yang Cao at the University of Connecticut and sustained sponsorship by the Office of Naval Research.”

Industry Potential

The potential for real-world translation of AI-assisted materials development is underscored by industry participation in the Nature Reviews Materials article. Co-authors of this paper also include scientists from Toyota Research Institute and General Electric. To further accelerate the adoption of AI-driven materials development in industry, Ramprasad co-founded Matmerize Inc., a software startup company recently spun out of Georgia Tech. Their cloud-based polymer informatics software is already being used by companies across various sectors, including energy, electronics, consumer products, chemical processing, and sustainable materials. 

“Matmerize has transformed our research into a robust, versatile, and industry-ready solution, enabling users to design materials virtually with enhanced efficiency and reduced cost,” Ramprasad said. “What began as a curiosity has gained significant momentum, and we are entering an exciting new era of materials by design.”

 

 

While the U.S. federal government has clean energy targets, they are not binding. Most economically developed countries have mandatory policies designed to bolster renewable electricity production. Because the U.S. lacks an enforceable federal mandate for renewable electricity, individual states are left to develop their own regulations. 

Marilyn Brown, Regents’ and Brook Byers Professor of Sustainable Systems in Georgia Tech’s School of Public Policy; Shan Zhou, an assistant professor at Purdue University and Georgia Tech Ph.D. alumna; and Barry Solomon, a professor emeritus of environmental policy at Michigan Technological University, investigated how clean electricity policies affect not only the states that adopt them, but neighboring states as well. Using data-driven comparisons, the researchers found that the impact of these subnational clean energy policies is far greater — and more nuanced — than previously known. 

Their research was recently published in the journal Proceedings of the National Academy of Sciences. 

“Analysts are asking if the U.S. should have a federal renewable mandate to put the whole country on the same page, or if individual state policies are sufficient,” Brown said. “To answer that question, it is useful to know if states with renewable energy policies are influencing those without them.”

Brown, Solomon, and Zhou examined a common clean energy policy tool: the Renewable Portfolio Standard (RPS). Adopted by more than half of U.S. states, RPSs are regulations requiring a state’s utility providers to generate a certain percentage of their electricity from renewable resources, such as wind or solar. Many of these standards are mandatory, with utility companies facing fines if they fail to reach targets within a given time.

To investigate the influence of these policies across state lines, the researchers first created a dataset that included 31 years (1991-2021) of annual renewable electricity generation data for 48 U.S. states and the District of Columbia. They then used the dataset to generate pairs of states linking each state to its geographic neighbors or electricity trading partners, allowing them to examine the influence of the RPS policy adopted by one of the pair on the renewable energy generation of the other — a total of 1,519 paired comparisons. 

“By only looking at the pairs, we can see if an RPS in one state directly affects renewable electricity generation in another state, and, if that’s the case, whether it is because they are geographic neighbors or if it’s because they are participating in the same wholesale electricity market,” Zhou said. 

Looking into the electricity market is important, because states often purchase electricity from other states through wholesale markets rather than exclusively producing their own power, and the purchased power can be generated from renewables. Utilities in some states may be allowed to meet their own RPS requirements by purchasing renewable energy credits based on the renewable electricity generated in other states. 

In their analyses, the team also considered the concept of “policy stringency.” A stringency measure evaluates a state’s renewable electricity targets relative to the amount currently produced in the state. For example, if a state requires electric utilities to generate 30% of their electricity from renewable sources by 2030 and the state already has 25%, it isn’t a very stringent policy. On the other hand, if a state has a 30% target and only uses 10% renewables currently, it has a more ambitious and stringent RPS.

Though policy experts have used the metric in related work for over a decade, the research team improved the design. 

“Our stringency variable includes interim targets as well as the existing share of renewable energy generation,” Solomon said.

The team found that the amount of renewable electricity generation in a state is not only influenced by whether that state has its own RPS, but also by the RPS policies of neighboring states. 

“We also learned that the stronger a neighboring state’s RPS policy is, the more likely a given state is to generate more renewable electricity,” Brown said. “It’s all a very interactive web with many co-benefits.”

The authors were surprised to find that a given state’s electricity trading partners did not hold the most influence over renewable generation, but rather the geographical proximity to RPS states. They suggest that past RPS policy research focusing on within-state impacts likely underestimated an RPS’s full impact. While the researchers have not yet identified all factors that can cause spillover effects, they plan to investigate this further. 

“The spillover effect is very significant and should not be overlooked by future research, especially for states without RPSs,” Zhou said. “For states without policies, their renewable electricity generation is very heavily influenced by their neighbors.”

Citation: Shan Zhou, Barry D. Solomon, and Marilyn A. Brown, “The spillover effect of mandatory renewable portfolio standards.” PNAS (June 2024). 
DOI: https://doi.org/10.1073/pnas.2313193121
 

 

Anu Iyer, a Georgia Tech Dean’s Scholar, published her first research article as a first-year student — based on research conducted while she was in high school. She is the lead co-author of the paper published in Scientific Reports, a Nature Portfolio journal.

Iyer, now a second-year undergraduate majoring in biology with a pre-med focus, worked with researchers at the University of Arkansas for Medical Sciences (UAMS) to develop a novel voice-based diagnostic tool for Parkinson’s disease (PD).

“Essentially, we proved the feasibility of a telemedicine approach towards detecting PD,” says Iyer. “Through a three-second phone call, our machine-learning model recognizes patterns in data to detect Parkinson’s with a 97 percent accuracy rate.”

Iyer states that additional strengths of the project include the potential for detecting PD at an early stage, leading to improved treatment outcomes, and the practical benefits of a virtual diagnostic tool.

“Parkinson’s disease is a nervous system disorder that primarily affects the elderly population, and one of the many issues with detection is that symptoms must be analyzed in person,” explains Iyer. “In Arkansas, 75 percent of our population resides in medically underserved areas — it can be hard for them to access health facilities. Our research addresses the need for convenient detection via telemedicine.”

From science fairs to academic researcher

Iyer’s teachers at her STEM middle school encouraged her passion for science and discovery. A science fair enthusiast, Iyer led a sixth-grade team to win the state title for the Verizon Innovative Learning app, creating a smartphone app that turns off text notifications when a car reaches more than five miles per hour.

Iyer credits her middle school teachers for inspiring her to seek answers beyond what she found in her textbooks. During the summer between eighth and ninth grade, Iyer watched YouTube videos to teach herself machine learning, appreciating the opportunity to use artificial intelligence to analyze data and make predictions.

“Machine learning fascinates me because it holds so much potential,” says Iyer. “I've always been interested in computer science, but machine learning opened my eyes to new possibilities and taught me that I can pay it forward through applied bioinformatics.”

In ninth grade, she emailed UAMS professors with a research idea incorporating medicine and computer science. Her outreach led to a post as an undergraduate researcher, helping create a computer algorithm to detect eye disease. While working on a diagnostic AI model for malignancy, she began collaborating with Fred Prior, the chair of Bioinformatics at UAMS, who became a valued mentor.

“Dr. Prior introduced me to the joys of research and how small changes can make a big difference in our world,” says Iyer.

Prior assigned her to the team focusing on Parkinson’s in her 11th grade year — and she soon began taking on more of an active leadership role in the research. She spent the rest of high school juggling coursework with constructing code and drafting proposals to create the computer algorithm capable of detecting PD.

Progress and service

Iyer’s desire to improve the world through research led her to Georgia Tech.

“One thing that spoke to me is the Progress and Service motto,” says Iyer. “My career goals include becoming an empathetic researcher focused on reducing healthcare disparities. Specifically, I hope to specialize in developing diagnostic tools that are affordable and available for underserved areas.”

As lead co-author of the PD research study, Iyer spent much of her first year working with Prior and UAMS, participating in Zoom calls every Saturday. As a second-year, Iyer intends to continue working with UAMS on PD and machine-learning research. She has also taken on a new role as multiple principal investigator for a study related to chronic back pain management.

Lainie Pomerleau, who taught Iyer’s first-year English course, and is now an assistant professor of English at the College of Coastal Georgia, helped Iyer prepare the PD paper for publication. “Anu embodies Georgia Tech's mission to develop leaders who advance technology to improve the human condition,” says Pomerleau.

Despite her busy schedule, Iyer has immersed herself in the Georgia Tech community. She loves the climbing wall at the Campus Recreation Center and points to Cognitive Psychology as her favorite class. Iyer considers Explore, the science-centered living and learning community, to be one of the highlights of her first year.

“I really enjoyed being a part of Explore, living with other students who prioritize science,” says Iyer. “It was easy to make friends because we all had similar classes.”

In the spring of her first year, she was selected as a College of Sciences Ambassador, accompanying prospective students and their parents to science-related courses and answering their questions about campus life.

She plans to get more involved with researchers at Georgia Tech.

“I am a biology major, but one amazing thing about Georgia Tech is that there is a lot of encouragement to join labs outside of your major and pursue your interests,” says Iyer. “I’d like to work in a Georgia Tech lab, particularly in neurology.”

Looking forward to her next few years at the Institute, she’s excited about the possibilities ahead:

“Georgia Tech is well known for groundbreaking research,” she says. “I want to take advantage of Tech’s many opportunities — and fulfill my ultimate goal of making a positive impact in the world.”

From data breaches to widespread systemic shutdowns, cyberattacks like the  2024 Fulton County (Georgia) government attack now occur as regularly as natural disasters — and cause just as much destruction. And, like severe weather, they can be predicted thanks to a new artificial intelligence (AI) tool that analyzes social media to determine who could cause the next big cyberattack. 

Researchers in Georgia Tech’s Scheller College of Business, together with colleagues at the University of the District of Columbia, Washington, D.C. (UDC),  developed a chatbot that analyzed sentiment on popular social media sites like X (formerly known as Twitter) to determine cyberthreats. The chatbot tweeted information to engage Twitter users who either tweeted about news events or holidays, or retweeted cyberattack news. It interacted with 100,000 users over a three-month period. Sentiment analysis — gauging users’ feelings, attitudes, and moods— was performed on human responses to the bot’s tweets.  

Applying sentiment analysis to human-chatbot interactions is not new. Globally, companies use chatbots to determine customers’ reactions to brands and products.  During the Covid-19 pandemic, governments and health organizations employed chatbots to determine public attitudes toward vaccinations, preventive measures, and mask wearing. However, identifying potential cyberthreats via sentiment analysis represents a unique — and complicated — application.

“When you examine sentiment analysis on a chatbot through a cybersecurity lens, you are looking for potential hackers,” said Scheller Professor John McIntyre, who is also the executive director of the Center for International Business Education and Research.  “Catching hackers using sentiment analysis is challenging, but  predictive models can be built to find them. 

“AI can target a particular population to understand its expressions of approval, disapproval, or even intent to harm, attack, or misuse the technology.”

A team led by McIntyre and UDC Associate Professors Amit Arora and Anshu Arora conducted the research. They set out to see if cybersecurity threats could be discovered through social media, but the study is just the beginning of a potentially fertile cyberthreat prevention method. McIntyre believes the study could expand to analyzing sentiment in other languages and even on other platforms. 

“As we move toward a world in which we'll rely more and more on communication technologies and social media, there will be an increasing number of threats,” he said. “We must know how to counter such threats.”

Funded by the Department of Defense’s Applied Research Lab for Intelligence and Security

Arora A, Arora A, McIntyre J. Developing Chatbots for Cyber Security: Assessing Threats through Sentiment Analysis on Social Media. Sustainability. 2023; 15(17):13178. https://doi.org/10.3390/su151713178

Studying the complex motility patterns of cells and microorganisms is key to understanding their behaviors and biomechanics. However, many conventional microscopes are constrained by fixed lenses and the lack of ability to track organisms over extended periods without manual intervention.

But researchers at the Georgia Institute of Technology have overcome these limitations through the development of an inexpensive, easy-to-assemble, modular, autonomous tracking microscope.

Costing $400 in parts with DIY assembly instructions available, Trackoscope is a frugal-science innovation accessible to a wide range of users, from high school laboratories to resource-constrained research environments.

Read the full story on the School of Chemical and Biomolecular Engineering website.

The federally funded IAC program provides small to mid-sized industrial facilities in the region with free assessments for energy, productivity, and waste, while also supporting workforce development, recruitment, and training.

“This IAC is a great example of the ways in which Georgia Tech is serving all of Georgia and the Southeast,” said Tim Lieuwen, executive director of Georgia Tech’s Strategic Energy Institute (SEI) and Regents’ Professor and holder of the David S. Lewis, Jr. Chair in the Daniel Guggenheim School of Aerospace Engineering.

“We support numerous small and medium-sized enterprises in rural, suburban, and urban areas, bringing the technical expertise of Georgia Tech to bear in solving real-world problems faced by our small businesses.”

Georgia Tech’s IAC, which serves Georgia, South Carolina, and North Florida, is administered jointly by the George W. Woodruff School of Mechanical Engineering and the Georgia Manufacturing Extension Partnership (GaMEP), part of the Enterprise Innovation Institute (EI2). The organization has performed thousands of assessments since its inception in the 1980s – usually at rate of 15 to 20 per year – and typically identifies upwards of 10% in energy savings for clients.

The assessment team, overseen by IAC associate director Kelly Grissom, comprises faculty and student engineers from Georgia Tech and the Florida A&M University/Florida State University College of Engineering.

In addition, Georgia Tech leads the Southeastern IACs Center of Excellence, which partners the institution with fellow University System of Georgia (USG) entity Kennesaw State University, local HBCU Clark Atlanta University, and neighboring state capital HBCU Florida A&M University.

Although mechanical engineering has historically been the chief area of concentration for IAC’s interns, the program currently accepts students across a range of disciplines. “Increased diversity from that standpoint enriches the potential of the recommendations we can make,” said Grissom.

Students are integral to the program, as is Grissom’s role in facilitating their experiences with client engagement and technical recommendations.

“Kelly is the reason our program has been recognized,” said Randy Green, energy and sustainability services group manager at GaMEP. “He works tirelessly to ensure that assessments are accomplished with success for our manufacturers and students.”

“We also recognize our partnership with the Woodruff School of Mechanical Engineering and with IAC program lead Comas Haynes, Ph.D., who works diligently to keep us on track and connected with our sponsors at the U.S. Department of Energy,” Green added.

The DoE accolade represents “a ‘one Georgia Tech’ win,” symbolic of the synergistic relationships forged across the Institute, said Haynes, who also serves as the Hydrogen Initiative Lead at Georgia Tech’s Strategic Energy Institute (SEI) and Energy branch head in the Intelligent Sustainable Technologies Division at the Georgia Tech Research Institute. Haynes specifically cited Green’s “technical prowess and managerial oversight” as another key to the IAC program’s success.

Said Devesh Ranjan, Eugene C. Gwaltney, Jr. School Chair and professor in the George W. Woodruff School of Mechanical Engineering, “It is truly an honor for Georgia Tech to be named the Department of Energy Industrial (Training and) Assessment Center of the Year. Clean energy and manufacturing have been a focus for the Institute and the Woodruff School for a long time, and GTRI, EI2, and SEI have collaboratively done phenomenal work in helping manufacturers save energy, improve productivity, and reduce waste.”

To check eligibility and apply for assistance from Georgia Tech’s IAC, click here.