The Georgia Legislator AI Workshop took place at the Georgia State Capitol, drawing state lawmakers, academic experts, and industry leaders to explore the transformative role of artificial intelligence, Jan. 28, 2025.

The event was designed to provide legislators with a comprehensive understanding of how AI is reshaping key sectors, including energy, manufacturing, education and cybersecurity. Georgia Tech’s prominent role in AI research and application was highlighted through contributions from its leading faculty and research experts.

Tim Lieuwen, interim executive vice president for Research at Georgia Tech, opened the workshop, amplifying the strategic importance of AI for Georgia’s economic development and infrastructure resilience. Pascal Van Hentenryck, director of the Tech AI Hub and the NSF AI4OPT Institute, followed with a presentation on AI advancements and their implications for the state.

A significant portion of the workshop focused on AI’s impact on energy infrastructure. Lieuwen returned to discuss how AI is enhancing energy efficiency and supporting Georgia’s transition to smarter, more resilient energy systems. This session highlighted AI’s role in driving sustainable energy solutions.

The conversation then shifted to manufacturing, with Tom Kurfess, chief manufacturing officer at Georgia Tech, detailing how AI-driven innovations are optimizing production processes and revolutionizing industry practices. His insights described a future where AI maintains Georgia’s competitiveness in the manufacturing sector.

Cybersecurity and data privacy were other focal points. Michael Barker from Georgia Tech’s Manufacturing Extension Program addressed the challenges and opportunities surrounding AI-driven cybersecurity solutions. His presentation touched on data privacy and compliance with public information regulations.

The educational landscape also took center stage as Steve Harmon from Georgia Tech’s College of Lifelong Learning explored the ways AI is reshaping learning experiences. Harmon highlighted AI’s potential to deliver personalized education and better prepare students for a rapidly evolving workforce.

Donna Ennis, interim associate vice president for community-based engagement and co-director of Georgia AIM, wrapped up the program by presenting a comprehensive overview of state and national AI resources available to foster innovation and collaboration.

This event highlighted the importance of strategic investments and informed policymaking to harness the full potential of AI for Georgia’s future.

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

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.”

Deven Desai and Mark Riedl have seen the signs for a while. 

Two years since OpenAI introduced ChatGPT, dozens of lawsuits have been filed alleging technology companies have infringed copyright by using published works to train artificial intelligence (AI) models.

Academic AI research efforts could be significantly hindered if courts rule in the plaintiffs' favor. 

Desai and Riedl are Georgia Tech researchers raising awareness about how these court rulings could force academic researchers to construct new AI models with limited training data. The two collaborated on a benchmark academic paper that examines the landscape of the ethical issues surrounding AI and copyright in industry and academic spaces.

“There are scenarios where courts may overreact to having a book corpus on your computer, and you didn’t pay for it,” Riedl said. “If you trained a model for an academic paper, as my students often do, that’s not a problem right now. The courts could deem training is not fair use. That would have huge implications for academia.

“We want academics to be free to do their research without fear of repercussions in the marketplace because they’re not competing in the marketplace,” Riedl said. 

Desai is the Sue and John Stanton Professor of Business Law and Ethics at the Scheller College of Business. He researches how business interests and new technology shape privacy, intellectual property, and competition law. Riedl is a professor at the College of Computing’s School of Interactive Computing, researching human-centered AI, generative AI, explainable AI, and gaming AI. 

Their paper, Between Copyright and Computer Science: The Law and Ethics of Generative AI, was published in the Northwestern Journal of Technology and Intellectual Property on Monday.

Desai and Riedl say they want to offer solutions that balance the interests of various stakeholders. But that requires compromise from all sides.

Researchers should accept they may have to pay for the data they use to train AI models. Content creators, on the other hand, should receive compensation, but they may need to accept less money to ensure data remains affordable for academic researchers to acquire.

Who Benefits?

The doctrine of fair use is at the center of every copyright debate. According to the U.S. Copyright Office, fair use permits the unlicensed use of copyright-protected works in certain circumstances, such as distributing information for the public good, including teaching and research.

Fair use is often challenged when one or more parties profit from published works without compensating the authors.

Any original published content, including a personal website on the internet, is protected by copyright. However, copyrighted material is republished on websites or posted on social media innumerable times every day without the consent of the original authors. 

In most cases, it’s unlikely copyright violators gained financially from their infringement.

But Desai said business-to-business cases are different. The New York Times is one of many daily newspapers and media companies that have sued OpenAI for using its content as training data. Microsoft is also a defendant in The New York Times’ suit because it invested billions of dollars into OpenAI’s development of AI tools like ChatGPT.

“You can take a copyrighted photo and put it in your Twitter post or whatever you want,” Desai said. “That’s probably annoying to the owner. Economically, they probably wanted to be paid. But that’s not business to business. What’s happening with Open AI and The New York Times is business to business. That’s big money.”

OpenAI started as a nonprofit dedicated to the safe development of artificial general intelligence (AGI) — AI that, in theory, can rival human thinking and possess autonomy.

These AI models would require massive amounts of data and expensive supercomputers to process that data. OpenAI could not raise enough money to afford such resources, so it created a for-profit arm controlled by its parent nonprofit.

Desai, Riedl, and many others argue that OpenAI ceased its research mission for the public good and began developing consumer products. 

“If you’re doing basic research that you’re not releasing to the world, it doesn’t matter if every so often it plagiarizes The New York Times,” Riedl said. “No one is economically benefitting from that. When they became a for-profit and produced a product, now they were making money from plagiarized text.”

OpenAI’s for-profit arm is valued at $80 billion, but content creators have not received a dime since the company has scraped massive amounts of copyrighted material as training data.

The New York Times has posted warnings on its sites that its content cannot be used to train AI models. Many other websites offer a robot.txt file that contains instructions for bots about which pages can and cannot be accessed. 

Neither of these measures are legally binding and are often ignored.

Solutions

Desai and Riedl offer a few options for companies to show good faith in rectifying the situation.

• Spend the money. Desai says Open AI and Microsoft could have afforded its training data and avoided the hassle of legal consequences.
  
  “If you do the math on the costs to buy the books and copy them, they could have paid for them,” he said. “It would’ve been a multi-million dollar investment, but they’re a multi-billion dollar company.”
   
• Be selective. Models can be trained on randomly selected texts from published works, allowing the model to understand the writing style without plagiarizing. 
  
  “I don’t need the entire text of War and Peace,” Desai said. “To capture the way authors express themselves, I might only need a hundred pages. I’ve also reduced the chance that my model will cough up entire texts.”
   
• Leverage libraries. The authors agree libraries could serve as an ideal middle ground as a place to store published works and compensate authors for access to those works, though the amount may be less than desired.
  
  “Most of the objections you could raise are taken care of,” Desai said. “They are legitimate access copies that are secure. You get access to only as much as you need. Libraries at universities have already become schools of information.”

Desai and Riedl hope the legal action taken by publications like The New York Times will send a message to companies that develop AI tools to pump the breaks. If they don’t, researchers uninterested in profit could pay the steepest price.

The authors say it’s not a new problem but is reaching a boiling point.

“In the history of copyright, there are ways that society has dealt with the problem of compensating creators and technology that copies or reduces your ability to extract money from your creation,” Desai said. “We wanted to point out there’s a way to get there.”

The Automatic Speech Recognition (ASR) models that power voice assistants like Amazon Alexa may have difficulty transcribing English speakers with minority dialects.

A study by Georgia Tech and Stanford researchers compared the transcribing performance of leading ASR models for people using Standard American English (SAE) and three minority dialects — African American Vernacular English (AAVE), Spanglish, and Chicano English.

Interactive Computing Ph.D. student Camille Harris is the lead author of a paper accepted into the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) this week in Miami.

Harris recruited people who spoke each dialect and had them read from a Spotify podcast dataset, which includes podcast audio and metadata. Harris then used three ASR models — wav2vec 2.0, HUBERT, and Whisper — to transcribe the audio and compare their performances.

For each model, Harris found SAE transcription significantly outperformed each minority dialect. The models more accurately transcribed men who spoke SAE than women who spoke SAE. Members who spoke Spanglish and Chicano English had the least accurate transcriptions out of the test groups. 

While the models transcribed SAE-speaking women less accurately than their male counterparts, that did not hold true across minority dialects. Minority men had the most inaccurate transcriptions of all demographics in the study.

“I think people would expect if women generally perform worse and minority dialects perform worse, then the combination of the two must also perform worse,” Harris said. “That’s not what we observed. 

“Sometimes minority dialect women performed better than Standard American English. We found a consistent pattern that men of color, particularly Black and Latino men, could be at the highest risk for these performance errors.”

Addressing underrepresentation

Harris said the cause of that outcome starts with the training data used to build these models. Model performance reflected the underrepresentation of minority dialects in the data sets.

AAVE performed best under the Whisper model, which Harris said had the most inclusive training data of minority dialects.

Harris also looked at whether her findings mirrored existing systems of oppression. Black men have high incarceration rates and are one of the people groups most targeted by police. Harris said there could be a correlation between that and the low rate of Black men enrolled in universities, which leads to less representation in technology spaces.

“Minority men performing worse than minority women doesn’t necessarily mean minority men are more oppressed,” she said. “They may be less represented than minority women in computing and the professional sector that develops these AI systems.”

Harris also had to be cautious of a few variables among AAVE, including code-switching and various regional subdialects.

Harris noted in her study there were cases of code-switching to SAE. Speakers who code-switched performed better than speakers who did not. 

Harris also tried to include different regional speakers.

“It’s interesting from a linguistic and history perspective if you look at migration patterns of Black folks — perhaps people moving from a southern state to a northern state over time creates different linguistic variations,” she said. “There are also generational variations in that older Black Americans may speak differently from younger folks. I think the variation was well represented in our data. We wanted to be sure to include that for robustness.”

TikTok barriers

Harris said she built her study on a paper she authored that examined user-design barriers and biases faced by Black content creators on TikTok. She presented that paper at the Association of Computing Machinery’s (ACM) 2023 Conference on Computer Supported Cooperative Works. 

Those content creators depended on TikTok for a significant portion of their income. When providing captions for videos grew in popularity, those creators noticed the ASR tool built into the app inaccurately transcribed them. That forced the creators to manually input their captions, while SAE speakers could use the ASR feature to their benefit.

“Minority users of these technologies will have to be more aware and keep in mind that they’ll probably have to do a lot more customization because things won’t be tailored to them,” Harris said.

Harris said there are ways that designers of ASR tools could work toward being more inclusive of minority dialects, but cultural challenges could arise.

“It could be difficult to collect more minority speech data, and you have to consider consent with that,” she said. “Developers need to be more community-engaged to think about the implications of their models and whether it’s something the community would find helpful.”

School of Mathematics Associate Professor Molei Tao has been honored with a Sony Faculty Innovation Award for his work on the foundations of machine learning, particularly diffusion generative models. The award, which includes a $100,000 grant, is part of an international program sponsored by SONY that provides funding for cutting-edge academic research across a wide range of disciplines.

Tao is an applied and computational mathematician who designs and synergizes mathematical tools to solve practical problems. Recently, he has focused on the applications of these tools to machine learning. Tao works on multiple subareas of machine learning, including deep learning theory, probabilistic methods, generative modeling, and artificial intelligence for science (“AI4Science”). 

"Molei is doing breakthrough work on machine learning and artificial intelligence,” says Mike Wolf, chair of the School of Mathematics. “It is wonderful to see him recognized by Sony, both for his accomplishments so far and also his promise for the future. His unique perspectives, informed by an astonishing deep breadth of understanding of mathematics, have already made him one of the more prominent researchers in this extremely competitive and important field. I know that this award will fuel even more impactful works. We are just thrilled to have Molei on our faculty in the School of Mathematics."

Revolutionizing Generative AI

The award recognizes Tao’s research on the mathematical and algorithmic aspects of diffusion generative modeling, which is considered one of the foundations of modern Generative AI. Using advanced machine learning algorithms, these models have revolutionized the generation of image, video, and 3D content. 

“Exciting products such as ChatGPT, Stable Diffusion, and Sora are generative AI tools, and a good number of them are powered by diffusion models,” explains Tao. “The way the magic works is you basically give a machine learning model a collection of training data, and then the algorithm can generate more content that is similar to the training data. The ability of generating new content is called generative modeling. Diffusion model is one of the latest technologies for generative modeling.”

Tao’s work aims to make diffusion models more versatile and scalable. He hopes to broaden their application and possibly create the next generation of generative modeling tools. 

“The large-scale impact of this research is to make generative AI more accessible, more creative, safer, and more trustworthy,” he adds. 

To learn more about Tao's research, visit his blog or follow him on Twitter at @MoleiTaoMath.

Tech AI at Georgia Tech has appointed Tim Brown as interim director of professional education. He is also the new academic program director for AI, a joint appointment by Tech AI and Georgia Tech Professional Education (GTPE). Previously, Brown served as managing director of Georgia Tech’s Supply Chain and Logistics Institute for nearly 10 years, where he focused on program expansion and partnership development.

In his new role, Brown will work closely with the College of Lifetime Learning and Tech AI to develop innovative AI programs. He will identify industry needs and create interdisciplinary academic offerings serving a diverse range of learners, from K-12 students to executives. His initial emphasis will be on mid-career professionals seeking to upskill or reskill, equipping them with the technical skills essential for success in the AI field. He will also enhance existing programs and provide educational opportunities to companies and organizations, addressing current market demands.

Brown has more than 35 years of experience in professional education and supply chain optimization, including roles at IBM, Accenture, Chainalytics, Frito-Lay, and Tropicana. He has worked with executives in various industries, advising on supply chain management and securing $81 million in funding for AI in manufacturing through the Georgia AIM coalition.

In a statement, Brown said, “I look forward to contributing to innovative AI programs at Georgia Tech. Our goal is to create educational opportunities that meet the diverse needs of learners and equip them with the skills necessary to thrive in this evolving field.”

Brown’s leadership underscores Georgia Tech’s commitment to innovation and education in the rapidly changing landscape of AI. He is dedicated to establishing Georgia as a leader in AI and highlighting the resources and capabilities that Georgia Tech offers.