Georgia Institute of Technology is set to play a crucial role in a strategic effort funded by the Defense Advanced Research Project Agency (DARPA) to help bolster America’s national security and global military leadership.  

The project, led by the Texas Institute for Electronics (TIE) at The University of Texas at Austin, represents a total investment of $1.4 billion. The $840 million award from DARPA, announced by TIE in 2024, aims to develop the next generation of high-performing semiconductor microsystems for the Department of Defense (DoD). 

“We are honored to collaborate with TIE and its broader team on this far reaching and strategic program to enable best in class 3D heterogeneous integration (3DHI) processes and technologies in the United States,” said Muhannad S. Bakir, the Dan Fielder Professor in the School of Electrical and Computer Engineering and director of the 3D Systems Packaging Research Center, who is heading the project for Georgia Tech. 

3DHI is a semiconductor manufacturing process that incorporates different materials and components into microsystems with precision assembly. The use of 3DHI allows for the creation of high-performance, compact, and energy-efficient systems. 

The investment is part of DARPA’s Next Generation Microelectronics Manufacturing (NGMM) Program comprised of 32 defense electronics and leading commercial semiconductor companies and 18 nationally recognized academic institutions.

Under the agreement, TIE will establish a national open access R&D and prototyping fabrication facility. The facility will enable the DoD to create higher performance, lower power, lightweight, and compact defense systems. The advancements are expected to have wide-ranging applications, including radar, satellite imaging, and unmanned aerial vehicles.  

Georgia Tech will provide a wide range of expertise in 3DHI including design, fabrication and assembly processes, and characterization to support the NGMM national open-access R&D and prototyping facility at TIE.  

Regents' Professor and Morris M. Bryan, Jr. Professor Suresh K. Sitaraman in the George W. Woodruff School of Mechanical Engineering will be a key contributor to Georgia Tech’s efforts on the project.

“We are delighted to be partnering with UT/TIE on the establishment of a 3D Heterogeneous Integration Microsystem prototyping  facility,” said Sitaraman. “In addition to advancing fundamental science, this project is a great opportunity for Georgia Tech to demonstrate and integrate our ground-breaking and innovative 3DHI research approaches and technology solutions into TIE’s prototyping facility, and understand the challenges involved when translating lab-scale research work to a large industry-strength fabrication facility.” 

ECE Professors Saibal Mukhopadhyay, Arijit Raychowdhury, Visvesh Sathe, and Shimeng Yu will be working alongside Bakir and Sitaraman. 

A significant portion of the research will be conducted at the Institute for Matter and Systems (IMS), which operates Georgia Tech’s state-of-the-art electronics and nanotechnology core facilities. 

Read the press release from TIE and view the project’s team and partners.  

Effective January 1st, David Sherrill will serve as interim executive director of the Georgia Tech Institute for Data Engineering and Science (IDEaS). Sherrill is a Regents' Professor in the School of Chemistry and Biochemistry with a joint appointment in the College of Computing. Sherrill has served as associate director for IDEaS since its founding in 2016.

"David Sherrill's leadership role in IDEaS as associate director, together with his interdisciplinary background in chemistry and computer science, makes him the right person to support this transition as interim executive director," said Julia Kubanek, professor and vice president for interdisciplinary research at Georgia Tech. 

Sherrill succeeds Srinivas Aluru who will be taking a new position as Senior Associate Dean in the College of Computing. Aluru, a Regents' Professor in the School of Computational Science and Engineering, co-founded IDEaS and served as its co-executive director (2016-2019) and then as executive director (2019-date), spanning eight and a half years. Under his leadership IDEaS grew to more than 200 affiliate faculty spanning all colleges, encompassing multiple state, federal, and industry funded centers. Notable among these is the South Big Data Hub, catalyzing the Southern data science community to collectively accelerate scientific discovery and innovation, spur economic development in the region, broaden participation and diversity in data science, and the CloudHub, a Microsoft funded center that provides research funding and cloud resources for innovative applications in Generative Artificial Intelligence. More recently, Aluru established the Center for Artificial Intelligence in Science and Engineering (ARTISAN), and expanded the Institute’s research staff to provide needed cyberinfrastructure, software resources, and expertise to support faculty projects with large data sets and AI-driven discovery. "I've had the pleasure of serving as Associate Director of IDEaS since it was founded by Srinivas Aluru and Dana Randall, and I'm excited to step into this interim role.” said Sherrill. “IDEaS has an important mission to serve the many faculty doing interdisciplinary research involving data science and high performance computing."

Sherrill’s research group focuses on the development of ab initio electronic structure theory and its application to problems of broad chemical interest, including the influence of non-covalent interactions in drug binding, biomolecular structure, organic crystals, and organocatalytic transition states. The group seeks to apply the most accurate quantum models possible for a given problem and specializes in generating high-quality datasets for testing new methods or machine-learning purposes. 

Sherrill earned a B.S. in chemistry from MIT in 1992 and a Ph.D. in chemistry from the University of Georgia in 1996. From 1996-1999 Sherril was an NSF Postdoctoral Fellow, working under M. Head-Gordon, at the University of California, Berkeley.

Sherrill is a Fellow of the American Association for the Advancement of Science (AAAS), the American Chemical Society, and the American Physical Society, and he has been Associate Editor of the Journal of Chemical Physics since 2009. Sherrill has received a Camille and Henry Dreyfus New Faculty Award, the International Journal of Quantum Chemistry Young Investigator Award, an NSF CAREER Award, and Georgia Tech's W. Howard Ector Outstanding Teacher Award. In 2023, he received the Herty Medal from the Georgia Section of the American Chemical Society, and in 2024, he was elected to the International Academy of Quantum Molecular Science.

--Christa M. Ernst

Gold and white pompoms fluttered while Buzz, the official mascot of the Georgia Institute of Technology, danced to marching band music. But the celebration wasn’t before a football or basketball game — instead, the cheers marked the official launch of Georgia AIM Week, a series of events and a new mobile lab designed to bring technology to all parts of Georgia

Organized by Georgia Artificial Intelligence in Manufacturing (Georgia AIM), Georgia AIM Week kicked off September 30 with a celebration on the Georgia Institute of Technology campus and culminated with another celebration on Friday at the University of Georgia in Athens and aligned with National Manufacturing Day.

In between, the Georgia AIM Mobile Studio made stops at schools and community organizations to showcase a range of technology rooted in AI and smart technology.

“Georgia AIM Week was a statewide opportunity for us to celebrate Manufacturing Day and to launch our Georgia AIM Mobile Studio,” said Donna Ennis, associate vice president, community-based engagement, for Georgia Tech’s Enterprise Innovation Institute and Georgia AIM co-director. “Georgia AIM projects planned events in cities around the state, starting here in Atlanta. Then we headed to Warner Robins, Southwest Georgia, and Athens. We’re excited about the opportunity to bring this technology to our communities and increase access and ideas related to smart technology.”

Georgia AIM is a collaboration across the state to provide the tools and knowledge to empower all communities, particularly those that have been underserved and overlooked in manufacturing. This includes rural communities, women, people of color, and veterans. Georgia AIM projects are located across the state and work within communities to create a diverse AI manufacturing workforce. The federally funded program is a collaborative project administered through Georgia Tech’s Enterprise Innovation Institute and the Georgia Tech Manufacturing Institute.

A cornerstone of Georgia AIM Week was the debut of the Georgia AIM Mobile Studio, a 53-foot custom trailer outfitted with technology that can be used in manufacturing — but also by anyone with an interest in learning about AI and smart technology. Visitors to the mobile studio can experience virtual reality, 3-D printing, drones, robots, sensors, computer vision, and circuits essential to running this new tech.

There’s even a dog — albeit a robotic one — named Nova.

The studio was designed to introduce students to the possibilities of careers in manufacturing and show small businesses some of the cost-effective ways they can incorporate 21st century technology into their manufacturing operations.

“We were awarded about $7.5 million to build this wonderful studio here,” said Kenya Asbill, who works at the Russell Innovation Center for Entrepreneurs (RICE) as the Economic Development Administration project manager for Georgia AIM. “We will be traveling around the state of Georgia to introduce artificial intelligence in manufacturing to our targeted communities, including underserved rural and urban residents.”

Some technology on the Georgia AIM Mobile Studio was designed in consultation with project partners Kitt Labs and Technologists of Color. An additional suite of “technology vignettes” were developed by students at the University of Georgia College of Engineering. RICE and UGA served as project leads for the mobile studio development, and RICE will oversee its deployment across the state in the coming months.

To request a mobile studio visit, please visit the Georgia AIM website.

During Monday’s kickoff, the Georgia Tech cheerleaders and Buzz fired up the crowd before an event that featured remarks by Acting Assistant Secretary of the U.S. EDA Christina Killingsworth; Jay Bailey, president and CEO of RICE; Beshoy Morkos, associate professor of mechanical engineering at the University of Georgia; Aaron Stebner, co-director of Georgia AIM; David Bridges, vice president of Georgia Tech’s Enterprise Innovation Institute; and lightning presentations by Georgia AIM project leads from around the state.

Following the presentations, mobile studio tours were led by Jon Exume, president and executive director, and Mark Lawson, director of technology, for Technologists of Color. The organization works to create a cohesive and thriving community of African Americans in tech.

“I’m particularly excited to witness the launch of the Georgia AIM Mobile Studio. It really will help demystify AI and bring its promise to underserved rural areas across the state,” Killingsworth said. “AI is the defining technology of our generation. It’s transforming the global economy, and it will continue to have tremendous impact on the global workforce. And while AI has the potential to democratize access to information, enhance efficiency, and allow humans to focus on the more complex, creative, and meaningful aspects of work, it also has the power to exacerbate economic disparity. As such, we must work together to embrace the promise of AI while mitigating its risks.”

Other events during Georgia AIM week included the Middle Georgia Innovation Corridor Manufacturing Expo in Warner Robins, West Georgia Manufacturing Day – Student Career Expo in LaGrange, and a visit to Colquitt County High School in Moultrie. The week wrapped on Friday, Oct. 4, at the University of Georgia in Athens with a National Manufacturing Day celebration.

“We’re focused on growing our manufacturing economy,” Ennis said. “We’re also focused on the development and deployment of innovation and talent in the manufacturing industry as it relates to AI and other technologies. Manufacturing is cool. It is a changing industry. We want our students and younger people to understand that this is a career.”

Georgia Tech researchers have created a dataset that trains computer models to understand nuances in human speech during financial earnings calls. The dataset provides a new resource to study how public correspondence affects businesses and markets. 

SubjECTive-QA is the first human-curated dataset on question-answer pairs from earnings call transcripts (ECTs). The dataset teaches models to identify subjective features in ECTs, like clarity and cautiousness.   

The dataset lays the foundation for a new approach to identifying disinformation and misinformation caused by nuances in speech. While ECT responses can be technically true, unclear or irrelevant information can misinform stakeholders and affect their decision-making. 

Tests on White House press briefings showed that the dataset applies to other sectors with frequent question-and-answer encounters, notably politics, journalism, and sports. This increases the odds of effectively informing audiences and improving transparency across public spheres.   

The intersecting work between natural language processing and finance earned the paper acceptance to NeurIPS 2024, the 38th Annual Conference on Neural Information Processing Systems. NeurIPS is one of the world’s most prestigious conferences on artificial intelligence (AI) and machine learning (ML) research.

"SubjECTive-QA has the potential to revolutionize nowcasting predictions with enhanced clarity and relevance,” said Agam Shah, the project’s lead researcher. 

“Its nuanced analysis of qualities in executive responses, like optimism and cautiousness, deepens our understanding of economic forecasts and financial transparency."

[MICROSITE: Georgia Tech at NeurIPS 2024]

SubjECTive-QA offers a new means to evaluate financial discourse by characterizing language's subjective and multifaceted nature. This improves on traditional datasets that quantify sentiment or verify claims from financial statements.

The dataset consists of 2,747 Q&A pairs taken from 120 ECTs from companies listed on the New York Stock Exchange from 2007 to 2021. The Georgia Tech researchers annotated each response by hand based on six features for a total of 49,446 annotations.

The group evaluated answers on:

• Relevance: the speaker answered the question with appropriate details.
• Clarity: the speaker was transparent in the answer and the message conveyed.
• Optimism: the speaker answered with a positive outlook regarding future outcomes.
• Specificity: the speaker included sufficient and technical details in their answer.
• Cautiousness: the speaker answered using a conservative, risk-averse approach.
• Assertiveness: the speaker answered with certainty about the company’s events and outcomes.

The Georgia Tech group validated their dataset by training eight computer models to detect and score these six features. Test models comprised of three BERT-based pre-trained language models (PLMs), and five popular large language models (LLMs) including Llama and ChatGPT. 

All eight models scored the highest on the relevance and clarity features. This is attributed to domain-specific pretraining that enables the models to identify pertinent and understandable material.

The PLMs achieved higher scores on the clear, optimistic, specific, and cautious categories. The LLMs scored higher in assertiveness and relevance. 

In another experiment to test transferability, a PLM trained with SubjECTive-QA evaluated 65 Q&A pairs from White House press briefings and gaggles. Scores across all six features indicated models trained on the dataset could succeed in other fields outside of finance. 

"Building on these promising results, the next step for SubjECTive-QA is to enhance customer service technologies, like chatbots,” said Shah, a Ph.D. candidate studying machine learning. 

“We want to make these platforms more responsive and accurate by integrating our analysis techniques from SubjECTive-QA."

SubjECTive-QA culminated from two semesters of work through Georgia Tech’s Vertically Integrated Projects (VIP) Program. The VIP Program is an approach to higher education where undergraduate and graduate students work together on long-term project teams led by faculty. 

Undergraduate students earn academic credit and receive hands-on experience through VIP projects. The extra help advances ongoing research and gives graduate students mentorship experience.

Computer science major Huzaifa Pardawala and mathematics major Siddhant Sukhani co-led the SubjECTive-QA project with Shah. 

Fellow collaborators included Veer Kejriwal, Abhishek Pillai, Rohan Bhasin, Andrew DiBiasio, Tarun Mandapati, and Dhruv Adha. All six researchers are undergraduate students studying computer science. 

Sudheer Chava co-advises Shah and is the faculty lead of SubjECTive-QA. Chava is a professor in the Scheller College of Business and director of the M.S. in Quantitative and Computational Finance (QCF) program.

Chava is also an adjunct faculty member in the College of Computing’s School of Computational Science and Engineering (CSE).

"Leading undergraduate students through the VIP Program taught me the powerful impact of balancing freedom with guidance,” Shah said. 

“Allowing students to take the helm not only fosters their leadership skills but also enhances my own approach to mentoring, thus creating a mutually enriching educational experience.”

Presenting SubjECTive-QA at NeurIPS 2024 exposes the dataset for further use and refinement. NeurIPS is one of three primary international conferences on high-impact research in AI and ML. The conference occurs Dec. 10-15.

The SubjECTive-QA team is among the 162 Georgia Tech researchers presenting over 80 papers at NeurIPS 2024. The Georgia Tech contingent includes 46 faculty members, like Chava. These faculty represent Georgia Tech’s Colleges of Business, Computing, Engineering, and Sciences, underscoring the pertinence of AI research across domains. 

"Presenting SubjECTive-QA at prestigious venues like NeurIPS propels our research into the spotlight, drawing the attention of key players in finance and tech,” Shah said.

“The feedback we receive from this community of experts validates our approach and opens new avenues for future innovation, setting the stage for transformative applications in industry and academia.”

A new machine learning (ML) model from Georgia Tech could protect communities from diseases, better manage electricity consumption in cities, and promote business growth, all at the same time.

Researchers from the School of Computational Science and Engineering (CSE) created the Large Pre-Trained Time-Series Model (LPTM) framework. LPTM is a single foundational model that completes forecasting tasks across a broad range of domains. 

Along with performing as well or better than models purpose-built for their applications, LPTM requires 40% less data and 50% less training time than current baselines. In some cases, LPTM can be deployed without any training data.

The key to LPTM is that it is pre-trained on datasets from different industries like healthcare, transportation, and energy. The Georgia Tech group created an adaptive segmentation module to make effective use of these vastly different datasets.

The Georgia Tech researchers will present LPTM in Vancouver, British Columbia, Canada, at the 2024 Conference on Neural Information Processing Systems (NeurIPS 2024). NeurIPS is one of the world’s most prestigious conferences on artificial intelligence (AI) and ML research.

“The foundational model paradigm started with text and image, but people haven’t explored time-series tasks yet because those were considered too diverse across domains,” said B. Aditya Prakash, one of LPTM’s developers. 

“Our work is a pioneer in this new area of exploration where only few attempts have been made so far.”

[MICROSITE: Georgia Tech at NeurIPS 2024]

Foundational models are trained with data from different fields, making them powerful tools when assigned tasks. Foundational models drive GPT, DALL-E, and other popular generative AI platforms used today. LPTM is different though because it is geared toward time-series, not text and image generation.  

The Georgia Tech researchers trained LPTM on data ranging from epidemics, macroeconomics, power consumption, traffic and transportation, stock markets, and human motion and behavioral datasets.

After training, the group pitted LPTM against 17 other models to make forecasts as close to nine real-case benchmarks. LPTM performed the best on five datasets and placed second on the other four.

The nine benchmarks contained data from real-world collections. These included the spread of influenza in the U.S. and Japan, electricity, traffic, and taxi demand in New York, and financial markets.   

The competitor models were purpose-built for their fields. While each model performed well on one or two benchmarks closest to its designed purpose, the models ranked in the middle or bottom on others.

In another experiment, the Georgia Tech group tested LPTM against seven baseline models on the same nine benchmarks in zero-shot forecasting tasks. Zero-shot means the model is used out of the box and not given any specific guidance during training. LPTM outperformed every model across all benchmarks in this trial.

LPTM performed consistently as a top-runner on all nine benchmarks, demonstrating the model’s potential to achieve superior forecasting results across multiple applications with less and resources.

“Our model also goes beyond forecasting and helps accomplish other tasks,” said Prakash, an associate professor in the School of CSE. 

“Classification is a useful time-series task that allows us to understand the nature of the time-series and label whether that time-series is something we understand or is new.”

One reason traditional models are custom-built to their purpose is that fields differ in reporting frequency and trends. 

For example, epidemic data is often reported weekly and goes through seasonal peaks with occasional outbreaks. Economic data is captured quarterly and typically remains consistent and monotone over time. 

LPTM’s adaptive segmentation module allows it to overcome these timing differences across datasets. When LPTM receives a dataset, the module breaks data into segments of different sizes. Then, it scores all possible ways to segment data and chooses the easiest segment from which to learn useful patterns.

LPTM’s performance, enhanced through the innovation of adaptive segmentation, earned the model acceptance to NeurIPS 2024 for presentation. NeurIPS is one of three primary international conferences on high-impact research in AI and ML. NeurIPS 2024 occurs Dec. 10-15.

Ph.D. student Harshavardhan Kamarthi partnered with Prakash, his advisor, on LPTM. The duo are among the 162 Georgia Tech researchers presenting over 80 papers at the conference. 

Prakash is one of 46 Georgia Tech faculty with research accepted at NeurIPS 2024. Nine School of CSE faculty members, nearly one-third of the body, are authors or co-authors of 17 papers accepted at the conference. 

Along with sharing their research at NeurIPS 2024, Prakash and Kamarthi released an open-source library of foundational time-series modules that data scientists can use in their applications.

“Given the interest in AI from all walks of life, including business, social, and research and development sectors, a lot of work has been done and thousands of strong papers are submitted to the main AI conferences,” Prakash said. 

“Acceptance of our paper speaks to the quality of the work and its potential to advance foundational methodology, and we hope to share that with a larger audience.”

Tequila A.L. Harris, a professor in the George W. Woodruff School of Mechanical Engineering at Georgia Tech, leads energy and manufacturing initiatives at the Strategic Energy Institute. Her research explores the connectivity between the functionality of nano- to macro-level films, components, and systems based on their manufacture or design and their life expectancy, elucidating mechanisms by which performance or durability can be predicted. She uses both simulations and experimentation to better understand this connectivity.

By addressing complex, fundamental problems, Harris aims to make an impact on many industries, in particular energy (e.g., polymer electrolyte membrane fuel cells), flexible electronics (e.g., organic electronics), and clean energy (e.g., water), among others. 

Harris has experience in developing systematic design and manufacturing methodologies for complex systems that directly involve material characterization, tooling design and analysis, computational and analytical modeling, experimentation, and system design and optimization. Currently, her research projects focus on investigating the fundamental science associated with fluid transport, materials processing, and design issues for energy/electronic/environmental systems. Below is a brief Q&A with Harris, where she discusses her research and how it influences the energy and manufacturing initiatives at Georgia Tech.

• What is your field of expertise and at what point in your life did you first become interested in this area?

In graduate school, I aimed to become a roboticist but shifted my focus after realizing I was not passionate about coding. This led me to explore manufacturing, particularly scaled manufacturing processes that transform fluids into thin films for applications in energy systems. Subsequently, my expertise is in coating science and technology and manufacturing system development. 

• What questions or challenges sparked your current energy research? What are the big issues facing your research area right now?

We often ask how we can process materials more cost-effectively and create complex architectures that surpass current capabilities. In energy systems, particularly with fuel cells, reducing the number of manufacturing steps is crucial, as each additional step increases costs and complexity. As researchers, we focus on understanding the implications of minimizing these steps and how they affect the properties and performance of the final devices. My group studies these relationships to find innovative manufacturing solutions. A major challenge in the manufacture of materials lies in scaling efficiently while maintaining performance and keeping costs low enough for commercial adoption. This is a pressing issue, especially for enabling technologies such as batteries, fuel cells, and flexible electronics needed for electric vehicles, where the production volumes are on the order of billions per year. 

• What interests you the most in leading the research initiative on energy and manufacturing? Why is your initiative important to the development of Georgia Tech’s energy research strategy?

What interests me most is the inherent possibility of advancing energy technologies holistically, from materials sourcing and materials production to public policy. More specifically, my interests are in understanding how we can scale the manufacture of burgeoning technologies for a variety of areas (energy, food, pharmaceuticals, packaging, and flexible electronics, among others) while reducing cost and increasing production yield. In this regard, we aim to incorporate artificial intelligence and machine learning in addition to considering limitations surrounding the production lifecycle. The challenges that exist to meet these goals cannot be done in a silo but rather as part of interdisciplinary teams who converge on specific problems. Georgia Tech is uniquely positioned to make significant impacts in the energy and manufacturing ecosystem, thanks to our robust infrastructure and expertise. With many manufacturers relocating to Georgia, particularly in the "energy belt" for EVs, batteries, and recycling facilities, Georgia Tech can serve as a crucial partner in advancing these industries and their technologies.

• What are the broader global and social benefits of the research you and your team conduct on energy and manufacturing?

The global impact of advancing manufacturing technologies is significant for processing at relevant economy of scales. To meet such demands, we cannot always rely on existing manufacturing know-how.  The Harris group holds the intellectual property on innovative processes that allow for the faster fabrication of individual or multiple materials, and that exhibit higher yields and improved performance than existing methods. Improvements in manufacturing systems often result in reduced waste, which is beneficial to the overall materials development ecosystem. Another global and societal benefit is workforce development. The students on my team are well-trained in the manufacture of materials using tools that are amenable to the most advanced and scalable manufacturing platform, roll-to-roll manufacturing, with integrated coating and printing tools. This unique skill set equips our students to thrive and become leaders in their careers.

• What are your plans for engaging a wider Georgia Tech faculty pool with the broader energy community?

By leveraging the new modular pilot-scale roll-to-roll manufacturing facility that integrates slot die coating, gravure/flexography printing, and inkjet printing, I plan to continue reaching out to faculty and industrial partners to find avenues for us to collaborate on a variety of interdisciplinary projects. The goal is to create groups that can help us advance materials development more rapidly by working as a collective from the beginning, versus considering scalable manufacturing pathways as an afterthought. By bringing interdisciplinary groups (chemists, materials scientists, engineers, etc.) together early, we can more efficiently and effectively overcome traditional delays in getting materials to market or, worse, the inability to push materials to market (which is commonly known as the valley of death). This can only be achieved by dismantling barriers that hinder early collaboration. This new facility aims to foster collaborative work among stakeholders, promoting the integrated development and characterization of various materials systems and technologies, and ultimately leading to more efficient manufacturing practices.

• What are your hobbies? 

I enjoy cooking and exploring my creativity in this space by combining national and international ingredients to make interesting and often delicious fusion cuisines. I also enjoy roller skating, cycling, and watching movies with my family and friends. 

• Who has influenced you the most?

From a professional standpoint, my research team influences me the most. After I present them with a problem, they are encouraged and expected to think beyond our initial starting point.  This ability to freely think and conceive of novel solutions sparks many new ideas on which to build future ideas. The best cases have kept me up at night, inspiring me to think about how to approach new problems and funding opportunities. I carry their experiences and challenges with me. Their influence on me is profound and is fundamentally why I am a professor.