A lighted pathway through a sea of clouds.
The Cloud Hub, a key initiative of the Institute for Data Engineering and Science (IDEaS) at Georgia Tech, recently concluded a successful Call for Proposals focused on advancing the field of Generative Artificial Intelligence (GenAI). This initiative, made possible by a generous gift funding from Microsoft, aims to push the boundaries of GenAI research by supporting projects that explore both foundational aspects and innovative applications of this cutting-edge technology.
Call for Proposals: A Gateway to Innovation
Launched in early 2024, the Call for Proposals invited researchers from across Georgia Tech to submit their innovative ideas on GenAI. The scope was broad, encouraging proposals that spanned foundational research, system advancements, and novel applications in various disciplines, including arts, sciences, business, and engineering. A special emphasis was placed on projects that addressed responsible and ethical AI use.
Recognizing Microsoft’s Generous Contribution
This successful initiative was made possible through the generous support of Microsoft, whose contribution of research resources has empowered Georgia Tech researchers to explore new frontiers in GenAI. By providing access to Azure’s advanced tools and services, Microsoft has played a pivotal role in accelerating GenAI research at Georgia Tech, enabling researchers to tackle some of the most pressing challenges and opportunities in this rapidly evolving field.
Looking Ahead: Pioneering the Future of GenAI
The awarded projects, set to commence in Spring 2025, represent a diverse array of research directions, from improving the capabilities of large language models and AI-based systems to innovative applications in data use through interdisciplinary collaborations. These projects are expected to make significant contributions to the body of knowledge in GenAI and are poised to have a lasting impact on the industry and beyond.
IDEaS and the Cloud Hub are committed to supporting these teams as they embark on their research journeys. The outcomes of these projects will be shared through publications and highlighted on the Cloud Hub web portal, ensuring visibility for the groundbreaking work enabled by this initiative.
Congratulations to the Spring 2025 Winners
Christa M. Ernst - Research Communications Program Manager
christa.ernst@research.gatech.edu
At Georgia Tech, innovation flourishes where disciplines converge. By encouraging joint appointments, the Institute breaks down traditional academic silos and enables researchers to revolutionize the energy landscape.
The symposium is a chance for faculty to meet new robotics students on campus, as well as a chance to get a better idea of what IRIM colleagues are up to these days. The goal of the symposium is to spark new ideas, new collaborations, and even new friends!
New research from Georgia Tech is giving scientists more control options over generative artificial intelligence (AI) models in their studies. Greater customization from this research can lead to discovery of new drugs, materials, and other applications tailor-made for consumers.
The Tech group dubbed its method PRODIGY (PROjected DIffusion for controlled Graph Generation). PRODIGY enables diffusion models to generate 3D images of complex structures, such as molecules from chemical formulas.
Scientists in pharmacology, materials science, social network analysis, and other fields can use PRODIGY to simulate large-scale networks. By generating 3D molecules from multiple graph datasets, the group proved that PRODIGY could handle complex structures.
In keeping with its name, PRODIGY is the first plug-and-play machine learning (ML) approach to controllable graph generation in diffusion models. This method overcomes a known limitation inhibiting diffusion models from broad use in science and engineering.
“We hope PRODIGY enables drug designers and scientists to generate structures that meet their precise needs,” said Kartik Sharma, lead researcher on the project. “It should also inspire future innovations to precisely control modern generative models across domains.”
PRODIGY works on diffusion models, a generative AI model for computer vision tasks. While suitable for image creation and denoising, diffusion methods are limited because they cannot accurately generate graph representations of custom parameters a user provides.
PRODIGY empowers any pre-trained diffusion model for graph generation to produce graphs that meet specific, user-given constraints. This capability means, as an example, that a drug designer could use any diffusion model to design a molecule with a specific number of atoms and bonds.
The group tested PRODIGY on two molecular and five generic datasets to generate custom 2D and 3D structures. This approach ensured the method could create such complex structures, accounting for the atoms, bonds, structures, and other properties at play in molecules.
Molecular generation experiments with PRODIGY directly impact chemistry, biology, pharmacology, materials science, and other fields. The researchers say PRODIGY has potential in other fields using large networks and datasets, such as social sciences and telecommunications.
These features led to PRODIGY’s acceptance for presentation at the upcoming International Conference on Machine Learning (ICML 2024). ICML 2024 is the leading international academic conference on ML. The conference is taking place July 21-27 in Vienna.
Assistant Professor Srijan Kumar is Sharma’s advisor and paper co-author. They worked with Tech alumnus Rakshit Trivedi (Ph.D. CS 2020), a Massachusetts Institute of Technology postdoctoral associate.
Twenty-four Georgia Tech faculty from the Colleges of Computing and Engineering will present 40 papers at ICML 2024. Kumar is one of six faculty representing the School of Computational Science and Engineering (CSE) at the conference.
Sharma is a fourth-year Ph.D. student studying computer science. He researches ML models for structured data that are reliable and easily controlled by users. While preparing for ICML, Sharma has been interning this summer at Microsoft Research in the Research for Industry lab.
“ICML is the pioneering conference for machine learning,” said Kumar. “A strong presence at ICML from Georgia Tech illustrates the ground-breaking research conducted by our students and faculty, including those in my research group.”
Visit https://sites.gatech.edu/research/icml-2024 for news and coverage of Georgia Tech research presented at ICML 2024.
Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu
A new machine learning (ML) model created at Georgia Tech is helping neuroscientists better understand communications between brain regions. Insights from the model could lead to personalized medicine, better brain-computer interfaces, and advances in neurotechnology.
The Georgia Tech group combined two current ML methods into their hybrid model called MRM-GP (Multi-Region Markovian Gaussian Process).
Neuroscientists who use MRM-GP learn more about communications and interactions within the brain. This in turn improves understanding of brain functions and disorders.
“Clinically, MRM-GP could enhance diagnostic tools and treatment monitoring by identifying and analyzing neural activity patterns linked to various brain disorders,” said Weihan Li, the study’s lead researcher.
“Neuroscientists can leverage MRM-GP for its robust modeling capabilities and efficiency in handling large-scale brain data.”
MRM-GP reveals where and how communication travels across brain regions.
The group tested MRM-GP using spike trains and local field potential recordings, two kinds of measurements of brain activity. These tests produced representations that illustrated directional flow of communication among brain regions.
Experiments also disentangled brainwaves, called oscillatory interactions, into organized frequency bands. MRM-GP’s hybrid configuration allows it to model frequencies and phase delays within the latent space of neural recordings.
MRM-GP combines the strengths of two existing methods: the Gaussian process (GP) and linear dynamical systems (LDS). The researchers say that MRM-GP is essentially an LDS that mirrors a GP.
LDS is a computationally efficient and cost-effective method, but it lacks the power to produce representations of the brain. GP-based approaches boost LDS's power, facilitating the discovery of variables in frequency bands and communication directions in the brain.
Converting GP outputs into an LDS is a difficult task in ML. The group overcame this challenge by instilling separability in the model’s multi-region kernel. Separability establishes a connection between the kernel and LDS while modeling communication between brain regions.
Through this approach, MRM-GP overcomes two challenges facing both neuroscience and ML fields. The model helps solve the mystery of intraregional brain communication. It does so by bridging a gap between GP and LDS, a feat not previously accomplished in ML.
“The introduction of MRM-GP provides a useful tool to model and understand complex brain region communications,” said Li, a Ph.D. student in the School of Computational Science and Engineering (CSE).
“This marks a significant advancement in both neuroscience and machine learning.”
Fellow doctoral students Chengrui Li and Yule Wang co-authored the paper with Li. School of CSE Assistant Professor Anqi Wu advises the group.
Each MRM-GP student pursues a different Ph.D. degree offered by the School of CSE. W. Li studies computer science, C. Li studies computational science and engineering, and Wang studies machine learning. The school also offers Ph.D. degrees in bioinformatics and bioengineering.
Wu is a 2023 recipient of the Sloan Research Fellowship for neuroscience research. Her work straddles two of the School’s five research areas: machine learning and computational bioscience.
MRM-GP will be featured at the world’s top conference on ML and artificial intelligence. The group will share their work at the International Conference on Machine Learning (ICML 2024), which will be held July 21-27 in Vienna.
ICML 2024 also accepted for presentation a second paper from Wu’s group intersecting neuroscience and ML. The same authors will present A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing.
Twenty-four Georgia Tech faculty from the Colleges of Computing and Engineering will present 40 papers at ICML 2024. Wu is one of six faculty representing the School of CSE who will present eight total papers.
The group’s ICML 2024 presentations exemplify Georgia Tech’s focus on neuroscience research as a strategic initiative.
Wu is an affiliated faculty member with the Neuro Next Initiative, a new interdisciplinary program at Georgia Tech that will lead research in neuroscience, neurotechnology, and society. The University System of Georgia Board of Regents recently approved a new neuroscience and neurotechnology Ph.D. program at Georgia Tech.
“Presenting papers at international conferences like ICML is crucial for our group to gain recognition and visibility, facilitates networking with other researchers and industry professionals, and offers valuable feedback for improving our work,” Wu said.
“It allows us to share our findings, stay updated on the latest developments in the field, and enhance our professional development and public speaking skills.”
Visit https://sites.gatech.edu/research/icml-2024 for news and coverage of Georgia Tech research presented at ICML 2024.
Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu
Some Georgia Tech researchers solve cosmic mysteries such as how supermassive black holes were born — and others now are getting a better, sharper look at those black holes.
The International Conference for High Performance Computing, Networking, Storage, and Analytics, or “Supercomputing” (SC) for short, was held in Denver from November 12-17 and hosted nearly 14,000 attendees. SC is the premier event for advances in algorithms, applications, architectures and networks, clouds and distributed computing, data analytics and visualization, machine learning and HPC, programming systems, system software, and state of the practice in large-scale data storage, deployment and integration. Each year, SC provides a unique opportunity to meet leaders in the field of high-performance computing, including researchers at universities and government labs, and hardware vendors like Intel, AMD, NVIDIA, and Penguin Computing.
The theme for this year’s event was “I am HPC; Impact and Future Direction,” focusing on the ways in which HPC impacts society as well as the community of researchers in HPC. An interdisciplinary cohort comprised of Georgia Tech researchers from the Partnership for an Advanced Computing Environment, the Center for High-Performance Computing, the School of Computational Science and Engineering, the Center for Research into Novel Computing Hierarchies, the Institute for Data Engineering and Science, and the School of Computer Science at various levels of their career were in attendance to present technical talks, participate in workshops and promote HPC research at Georgia Tech with a booth in the exhibit hall.
Georgia Tech teams were well represented across the research themes, including presentations on; large graph analytics, new methods for high-performance data-intensive workloads, challenges presented by the exascale computing, and Large Language Models (LLMs) applications in codesign. Jeffrey Young and Richard Vuduc were feature speakers in the tutorial “Leveraging SmartNICs for HPC Applications” that offered attendees an in-depth exploration of the state-of-the-art for SmartNICs and the emerging software ecosystems supporting them. Georgia Tech was also represented in the Birds of a Feather Co-Hort Panel Discussions on “Software Testing for Scientific Computing in HPC” and “Scientific Software and the People Who Make It Happen: Building Communities of Practice”. Of special mention is the Special Topic Workshop on “Machine Learning with Graphs in High Performance Computing Environments” organized by Richard Vuduc of Georgia Tech as well as Seung-Hwan Lim, Catherine Schuman, and Jose Moreira.
Georgia Tech researchers had numerous discussions with potential collaborators and new partners for initiatives in high performance computing, including conference attendees from universities, government labs, and industry. The team also had a great opportunity to reconnect with numerous alumni, who stopped by the GT booth to tell us about their careers since graduation. Georgia Tech graduates are doing some amazing things in computing hardware, algorithms, and software, with applications across a wide range of engineering and science problems.
Christa M. Ernst - Research Communications Program Manager
christa.ernst@research.gatech.edu