Georgia Tech researcher Andrés García has been elected to the American Academy of Arts and Sciences, joining an honorary society that includes Benjamin Franklin, George Washington, Albert Einstein, and Martin Luther King Jr.

The Academy recognizes leaders across fields of study who have addressed humanity’s greatest challenges while also gathering knowledge to advance learning and the public good. This year’s class of 252 honorees was elected in academia, the arts, industry, journalism, philanthropy, policy, research, and science.  

García is one of nine honorees in the “Engineering and Technology” division. His research — both in the George W. Woodruff School of Mechanical Engineering where he serves as Regents’ Professor and in the Parker H. Petit Institute for Bioengineering and Bioscience where he is the executive director — aligns with the Academy’s service-minded mission.  

“I am inspired to find engineering solutions to serious health conditions to help people,” he said. “As a kid, I developed a musculoskeletal condition that required biomaterial devices to treat. Although imperfect, this treatment allowed me to lead a normal life.” 

Moved by his personal experience, García’s research centers on cellular and tissue engineering, which integrate biological and engineering principles to restore organ function lost to injury or disease. By studying how cells interact with the materials around them, he and his team have engineered biomaterials for the controlled delivery of therapeutic proteins and cells that enhance tissue regeneration, which could speed the healing process for patients.  

His future work will integrate biomaterials with lab‑grown replicas of human organs, known as organoids, that can be used to identify new therapies for a variety of human diseases. These organoids, though smaller and simpler than true organs, can mimic key functions that may help García and his team to find better ways to repair damaged tissues. 

García has spent the past 27 years at Georgia Tech and carries on the legacy of another Academy member — the Petit Institute’s founding executive director Robert Nerem, who was inducted in 1998. García credits his success to the support of his loved ones and the Yellow Jacket community.  

“I am deeply honored and humbled,” he said. “This award is only possible by the unending love and support of family, friends and mentors, my phenomenal past and present trainees, fantastic collaborators, and awesome ecosystem at Georgia Tech.” 

The Academy was chartered in 1780 during the American Revolution by a group that included John Adams and John Hancock. It was established to recognize accomplished individuals and engage them in addressing the greatest challenges facing the young republic. 

Membership has broadened over the years to celebrate excellence in a variety of fields. Honorees have included poet Robert Frost, musician John Legend, and chef José Andrés, who was given this year’s Ivan Allen Jr. Prize for Social Courage.  

García and the rest of this year’s class, which includes actor Jodie Foster, will be inducted in October.  

While not as highlight-reel worthy as the Winter Olympics and the World Cup, experts expect high-performance computing (HPC) to have an even bigger impact on daily life in 2026.

Georgia Tech researchers say HPC and artificial intelligence (AI) advances this year are poised to improve how people power their homes, design safer buildings, and travel through cities.

According to Qi Tang, scientists will take progressive steps toward cleaner, sustainable energy through nuclear fusion in 2026. 

“I am very hopeful about the role of advanced computing and AI in making fusion a clean energy source,” said Tang, an assistant professor in the School of Computational Science and Engineering (CSE). 

“Fusion systems involve many interconnected processes happening across different scales. Modern simulations, combined with data-driven methods, allow us to bring these pieces together into a unified picture.”

Tang’s research connects HPC and machine learning with fusion energy and plasma physics. This year, Tang is continuing work on large-scale nuclear fusion models.

Only a few experimental fusion reactors exist worldwide compared to more than 400 nuclear fission reactors. Tang’s work supports a broader effort to turn fusion from a promising idea into a practical energy source.

Nuclear fusion occurs in plasma, the fourth state of matter, where gas is heated to millions of degrees. In this extreme state, electrons are stripped from atoms, creating a hot soup of fast-moving ions and free electrons. In plasma, hydrogen atoms overcome their natural electrical repulsion, collide, and fuse together. This releases energy that can power cities and homes.

Computers interpret extreme temperatures, densities, pressures, and plasma particle motion as massive datasets. Tang works to assimilate these data types from computer models and real-world experiments.

To do this, he and other researchers rely on machine learning approaches to analyze data across models and experiments more quickly and to produce more accurate predictions. Over time, this will allow scientists to test and improve fusion reactor designs toward commercial use. 

Beyond energy and nuclear engineering, Umar Khayaz sees broader impacts for HPC in 2026.

“HPC is the need of the day in every field of engineering sciences, physics, biology, and economics,” said Khayaz, a CSE Ph.D. student in the School of Civil and Environmental Engineering. 

“HPC is important enough to say that we need to employ resources to also solve social problems.”

Khayaz studies dynamic fracture and phase-field modeling. These areas explore how materials break under sudden, rapid loads. 

Like nuclear fusion, Khayaz says dynamic fracture problems are complex and data-intensive. In 2026, he expects to see more computing resources and computational capabilities devoted to understanding these problems and other emerging civil engineering challenges.

CSE Ph.D. student Yiqiao (Ahren) Jin sees a similar relationship between infrastructure and self-driving vehicles. He believes AI will innovate this area in 2026.

At Georgia Tech, Jin develops efficient multimodal AI systems. An autonomous vehicle is a multimodal system that uses camera video, laser sensors, language instructions, and other inputs to navigate city streets under changing scenarios like traffic and weather patterns.

Jin says multimodal research will move beyond performance benchmarks this year. This shift will lead to computer systems that can reason despite uncertainty and explain their decisions. In result, engineers will redefine how they evaluate and deploy autonomous systems in safety-critical settings.

“Many foundational problems in perception, multimodal reasoning, and agent coordination are being actively addressed in 2026. These advances enable a transition from isolated autonomous systems to safer, coordinated autonomous vehicle fleets,” Jin said. 

“As these systems scale, they have the potential to fundamentally improve transportation safety and efficiency.”

A first-of-its-kind algorithm developed at Georgia Tech is helping scientists study interactions between electrons. This innovation in modeling technology can lead to discoveries in physics, chemistry, materials science, and other fields.

The new algorithm is faster than existing methods while remaining highly accurate. The solver surpasses the limits of current models by demonstrating scalability across chemical system sizes ranging from large to small. 

Computer scientists and engineers benefit from the algorithm’s ability to balance processor loads. This work allows researchers to tackle larger, more complex problems without the prohibitive costs associated with previous methods.

Its ability to solve block linear systems drives the algorithm’s ingenuity. According to the researchers, their approach is the first known use of a block linear system solver to calculate electronic correlation energy.

The Georgia Tech team won’t need to travel far to share their findings with the broader high-performance computing community. They will present their work in Atlanta at the 2024 International Conference for High Performance Computing, Networking, Storage and Analysis (SC24).

[MICROSITE: Georgia Tech at SC24] 

“The combination of solving large problems with high accuracy can enable density functional theory simulation to tackle new problems in science and engineering,” said Edmond Chow, professor and associate chair of Georgia Tech’s School of Computational Science and Engineering (CSE).

Density functional theory (DFT) is a modeling method for studying electronic structure in many-body systems, such as atoms and molecules. 

An important concept DFT models is electronic correlation, the interaction between electrons in a quantum system. Electron correlation energy is the measure of how much the movement of one electron is influenced by presence of all other electrons.

Random phase approximation (RPA) is used to calculate electron correlation energy. While RPA is very accurate, it becomes computationally more expensive as the size of the system being calculated increases.

Georgia Tech’s algorithm enhances electronic correlation energy computations within the RPA framework. The approach circumvents inefficiencies and achieves faster solution times, even for small-scale chemical systems.

The group integrated the algorithm into existing work on SPARC, a real-space electronic structure software package for accurate, efficient, and scalable solutions of DFT equations. School of Civil and Environmental Engineering Professor Phanish Suryanarayana is SPARC’s lead researcher.

The group tested the algorithm on small chemical systems of silicon crystals numbering as few as eight atoms. The method achieved faster calculation times and scaled to larger system sizes than direct approaches.

“This algorithm will enable SPARC to perform electronic structure calculations for realistic systems with a level of accuracy that is the gold standard in chemical and materials science research,” said Suryanarayana.

RPA is expensive because it relies on quartic scaling. When the size of a chemical system is doubled, the computational cost increases by a factor of 16. 

Instead, Georgia Tech’s algorithm scales cubically by solving block linear systems. This capability makes it feasible to solve larger problems at less expense. 

Solving block linear systems presents a challenging trade-off in solving different block sizes. While larger blocks help reduce the number of steps of the solver, using them demands higher computational cost per step on computer processors. 

Tech’s solution is a dynamic block size selection solver. The solver allows each processor to independently select block sizes to calculate. This solution further assists in scaling, and improves processor load balancing and parallel efficiency.

“The new algorithm has many forms of parallelism, making it suitable for immense numbers of processors,” Chow said. “The algorithm works in a real-space, finite-difference DFT code. Such a code can scale efficiently on the largest supercomputers.”

Georgia Tech alumni Shikhar Shah (Ph.D. CSE 2024), Hua Huang (Ph.D. CSE 2024), and Ph.D. student Boqin Zhang led the algorithm’s development. The project was the culmination of work for Shah and Huang, who completed their degrees this summer. John E. Pask, a physicist at Lawrence Livermore National Laboratory, joined the Tech researchers on the work.

Shah, Huang, Zhang, Suryanarayana, and Chow are among more than 50 students, faculty, research scientists, and alumni affiliated with Georgia Tech who are scheduled to give more than 30 presentations at SC24. The experts will present their research through papers, posters, panels, and workshops. 

SC24 takes place Nov. 17-22 at the Georgia World Congress Center in Atlanta. 

“The project’s success came from combining expertise from people with diverse backgrounds ranging from numerical methods to chemistry and materials science to high-performance computing,” Chow said.

“We could not have achieved this as individual teams working alone.”

Hepatic, or liver, disease affects more than 100 million people in the U.S. About 4.5 million adults (1.8%) have been diagnosed with liver disease, but it is estimated that between 80 and 100 million adults in the U.S. have undiagnosed fatty liver disease in varying stages. Over time, undiagnosed and untreated hepatic diseases can lead to cirrhosis, a severe scarring of the liver that cannot be reversed. 

Most hepatic diseases are chronic conditions that will be present over the life of the patient, but early detection improves overall health and the ability to manage specific conditions over time. Additionally, assessing patients over time allows for effective treatments to be adjusted as necessary. The standard protocol for diagnosis, as well as follow-up tissue assessment, is a biopsy after the return of an abnormal blood test, but biopsies are time-consuming and pose risks for the patient. Several non-invasive imaging techniques have been developed to assess the stiffness of liver tissue, an indication of scarring, including magnetic resonance elastography (MRE).

MRE combines elements of ultrasound and MRI imaging to create a visual map showing gradients of stiffness throughout the liver and is increasingly used to diagnose hepatic issues. MRE exams, however, can fail for many reasons, including patient motion, patient physiology, imaging issues, and mechanical issues such as improper wave generation or propagation in the liver. Determining the success of MRE exams depends on visual inspection of technologists and radiologists. With increasing work demands and workforce shortages, providing an accurate, automated way to classify image quality will create a streamlined approach and reduce the need for repeat scans. 

Professor Jun Ueda in the George W. Woodruff School of Mechanical Engineering and robotics Ph.D. student Heriberto Nieves, working with a team from the Icahn School of Medicine at Mount Sinai, have successfully applied deep learning techniques for accurate, automated quality control image assessment. The research, “Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results,” was published in the Journal of Magnetic Resonance Imaging.

Using five deep learning training models, an accuracy of 92% was achieved by the best-performing ensemble on retrospective MRE images of patients with varied liver stiffnesses. The team also achieved a return of the analyzed data within seconds. The rapidity of image quality return allows the technician to focus on adjusting hardware or patient orientation for re-scan in a single session, rather than requiring patients to return for costly and timely re-scans due to low-quality initial images.

This new research is a step toward streamlining the review pipeline for MRE using deep learning techniques, which have remained unexplored compared to other medical imaging modalities.  The research also provides a helpful baseline for future avenues of inquiry, such as assessing the health of the spleen or kidneys. It may also be applied to automation for image quality control for monitoring non-hepatic conditions, such as breast cancer or muscular dystrophy, in which tissue stiffness is an indicator of initial health and disease progression. Ueda, Nieves, and their team hope to test these models on Siemens Healthineers magnetic resonance scanners within the next year.

            

Publication
Nieves-Vazquez, H.A., Ozkaya, E., Meinhold, W., Geahchan, A., Bane, O., Ueda, J. and Taouli, B. (2024), Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results. J Magn Reson Imaging. https://doi.org/10.1002/jmri.29490

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Two College of Engineering professors are among the newest members of the National Academy of Engineering (NAE), the organization announced Feb. 6.

Patricia Mokhtarian and David Sholl are part of a 2024 class that includes 114 new members and 21 international members. Election to the NAE is among the highest professional recognitions for engineers and an honor bestowed on just 2,600 professionals worldwide.

New members are nominated and voted on by the Academy’s existing membership. With Mokhtarian and Sholl, Georgia Tech now has 48 NAE members.

Get the full story on the College of Engineering website.

A new machine learning method could help engineers detect leaks in underground reservoirs earlier, mitigating risks associated with geological carbon storage (GCS). Further study could advance machine learning capabilities while improving safety and efficiency of GCS.

The feasibility study by Georgia Tech researchers explores using conditional normalizing flows (CNFs) to convert seismic data points into usable information and observable images. This potential ability could make monitoring underground storage sites more practical and studying the behavior of carbon dioxide plumes easier.

The 2023 Conference on Neural Information Processing Systems (NeurIPS 2023) accepted the group’s paper for presentation. They presented their study on Dec. 16 at the conference’s workshop on Tackling Climate Change with Machine Learning.

“One area where our group excels is that we care about realism in our simulations,” said Professor Felix Herrmann. “We worked on a real-sized setting with the complexities one would experience when working in real-life scenarios to understand the dynamics of carbon dioxide plumes.”

CNFs are generative models that use data to produce images. They can also fill in the blanks by making predictions to complete an image despite missing or noisy data. This functionality is ideal for this application because data streaming from GCS reservoirs are often noisy, meaning it’s incomplete, outdated, or unstructured data.

The group found in 36 test samples that CNFs could infer scenarios with and without leakage using seismic data. In simulations with leakage, the models generated images that were 96% similar to ground truths. CNFs further supported this by producing images 97% comparable to ground truths in cases with no leakage.

This CNF-based method also improves current techniques that struggle to provide accurate information on the spatial extent of leakage. Conditioning CNFs to samples that change over time allows it to describe and predict the behavior of carbon dioxide plumes.

This study is part of the group’s broader effort to produce digital twins for seismic monitoring of underground storage. A digital twin is a virtual model of a physical object. Digital twins are commonplace in manufacturing, healthcare, environmental monitoring, and other industries.   

“There are very few digital twins in earth sciences, especially based on machine learning,” Herrmann explained. “This paper is just a prelude to building an uncertainty aware digital twin for geological carbon storage.”

Herrmann holds joint appointments in the Schools of Earth and Atmospheric Sciences (EAS), Electrical and Computer Engineering, and Computational Science and Engineering (CSE).

School of EAS Ph.D. student Abhinov Prakash Gahlot is the paper’s first author. Ting-Ying (Rosen) Yu (B.S. ECE 2023) started the research as an undergraduate group member. School of CSE Ph.D. students Huseyin Tuna Erdinc, Rafael Orozco, and Ziyi (Francis) Yin co-authored with Gahlot and Herrmann.

NeurIPS 2023 took place Dec. 10-16 in New Orleans. Occurring annually, it is one of the largest conferences in the world dedicated to machine learning.

Over 130 Georgia Tech researchers presented more than 60 papers and posters at NeurIPS 2023. One-third of CSE’s faculty represented the School at the conference. Along with Herrmann, these faculty included Ümit Çatalyürek, Polo Chau, Bo Dai, Srijan Kumar, Yunan Luo, Anqi Wu, and Chao Zhang.

“In the field of geophysics, inverse problems and statistical solutions of these problems are known, but no one has been able to characterize these statistics in a realistic way,” Herrmann said.

“That’s where these machine learning techniques come into play, and we can do things now that you could never do before.”

In keeping with a strong strategic focus on AI for the 2023-2024 Academic Year, the Institute for Data Engineering and Science (IDEaS) has announced the winners of its 2023 Seed Grants for Thematic Events in AI and Cyberinfrastructure Resource Grants to support research in AI requiring secure, high-performance computing capabilities. Thematic event awards recipients will receive $8K to support their proposed workshop or series and Cyberinfrastructure winners will receive research support consisting of 600,000 CPU hours on the AMD Genoa Server as well as 36,000 hours of NVIDIA DGX H-100 GPU server usage and 172 TB of secure storage.

Congratulations to the award winners listed below!

 

Thematic Events in AI Awards

Proposed Workshop: “Foundation of scientific AI (Artificial Intelligence) for Optimization of Complex Systems”
Primary PI: Peng Chen, Assistant Professor, School of Computational Science and Engineering

Proposed Series: “Guest Lecture Seminar Series on Generative Art and Music”
Primary PI: Gil Weinberg, Professor, School of Music

 

Cyber-Infrastructure Resource Awards

Title: Human-in-the-Loop Musical Audio Source Separation
Topics: Music Informatics, Machine Learning
Primary PI: Alexander Lerch, Associate Professor, School of Music
Co-PIs: Karn Watcharasupat, Music Informatics Group | Yiwei Ding, Music Informatics Group | Pavan Seshadri, Music Informatics Group

Title: Towards A Multi-Species, Multi-Region Foundation Model for Neuroscience
Topics: Data-Centric AI, Neuroscience
Primary PI: Eva Dyer, Assistant Professor, Biomedical Engineering

Title: Multi-point Optimization for Building Sustainable Deep Learning Infrastructure
Topics: Energy Efficient Computing, Deep Learning, AI Systems OPtimization
Primary PI: Divya Mahajan, Assistant Professor, School of Electrical and Computer Engineering, School of Computer Science

Title: Neutrons for Precision Tests of the Standard Model
Topics: Nuclear/Particle Physics, Computational Physics
Primary PI: Aaron Jezghani - OIT-PACE

Title: Continual Pretraining for Egocentric Video
Primary PI: : Zsolt Kira, Assistant Professor, School of Interactive Computing
Co-PI: Shaunak Halbe, Ph.D. Student, Machine Learning

Title: Training More Trustworthy LLMs for Scientific Discovery via Debating and Tool Use
Topics: Trustworthy AI, Large-Language Models, Multi-Agent Systems, AI Optimization
Primary PIs: Chao Zhang, School of Computational Science and Engineering & Bo Dai, College of Computing

Title: Scaling up Foundation AI-based Protein Function Prediction with IDEaS Cyberinfrastructure
Topics: AI, Biology
Primary PI: Yunan Luo, Assistant Professor, School of Computational Science and Engineering        

• Christa M. Ernst

The Institute for Data Engineering and Science, in conjunction with several Interdisciplinary Research Institutes (IRIs) at Georgia Tech, have awarded seven teams of researchers from across the Institute a total of $105,000 in seed funding geared to better position Georgia Tech to perform world-class interdisciplinary research in data science and artificial intelligence development and deployment. 

The goals of the funded proposals include identifying prominent emerging research directions on the topic of AI, shaping IDEaS future strategy in the initiative area, building an inclusive and active community of Georgia Tech researchers in the field that potentially include external collaborators, and identifying and preparing groundwork for competing in large-scale grant opportunities in AI and its use in other research fields.

Below are the 2023 recipients and the co-sponsoring IRIs:

 

Proposal Title: "AI for Chemical and Materials Discovery" + “AI in Microscopy Thrust”
PI: Victor Fung, CSE | Vida Jamali, ChBE| Pan Li, ECE | Amirali Aghazadeh Mohandesi, ECE
Award: $20k (co-sponsored by IMat)

Overview: The goal of this initiative is to bring together expertise in machine learning/AI, high-throughput computing, computational chemistry, and experimental materials synthesis and characterization to accelerate material discovery. Computational chemistry and materials simulations are critical for developing new materials and understanding their behavior and performance, as well as aiding in experimental synthesis and characterization. Machine learning and AI play a pivotal role in accelerating material discovery through data-driven surrogate models, as well as high-throughput and automated synthesis and characterization.

Proposal Title: " AI + Quantum Materials”
PI: Zhigang JIang, Physics | Martin Mourigal, Physics
Award: $20k (Co-Sponsored by IMat)

Overview: Zhigang Jiang is currently leading an initiative within IMAT entitled “Quantum responses of topological and magnetic matter” to nurture multi-PI projects. By crosscutting the IMAT initiative with this IDEAS call, we propose to support and feature the applications of AI on predictive and inverse problems in quantum materials. Understanding the limit and capabilities of AI methodologies is a huge barrier of entry for Physics students, because researchers in that field already need heavy training in quantum mechanics, low-temperature physics and chemical synthesis. Our most pressing need is for our AI inclined quantum materials students to find a broader community to engage with and learn. This is the primary problem we aim to solve with this initiative.

PI: Jeffrey Skolnick, Bio Sci | Chao Zhang, CSE
Proposal Title: Harnessing Large Language Models for Targeted and Effective Small Molecule 4 Library Design in Challenging Disease Treatment
Award: $15k (co-sponsored by IBB)

Overview: Our objective is to use large language models (LLMs) in conjunction with AI algorithms to identify effective driver proteins, develop screening algorithms that target appropriate binding sites while avoiding deleterious ones, and consider bioavailability and drug resistance factors. LLMs can rapidly analyze vast amounts of information from literature and bioinformatics tools, generating hypotheses and suggesting molecular modifications. By bridging multiple disciplines such as biology, chemistry, and pharmacology, LLMs can provide valuable insights from diverse sources, assisting researchers in making informed decisions. Our aim is to establish a first-in-class, LLM driven research initiative at Georgia Tech that focuses on designing highly effective small molecule libraries to treat challenging diseases. This initiative will go beyond existing AI approaches to molecule generation, which often only consider simple properties like hydrogen bonding or rely on a limited set of proteins to train the LLM and therefore lack generalizability. As a result, this initiative is expected to consistently produce safe and effective disease-specific molecules.

PI: Yiyi He, School of City & Regional Plan | Jun Rentschler, World Bank
Proposal Title: “AI for Climate Resilient Energy Systems”
Award: $15k (co-sponsored by SEI)

Overview: We are committed to building a team of interdisciplinary & transdisciplinary researchers and practitioners with a shared goal: developing a new framework which model future climatic variations and the interconnected and interdependent energy infrastructure network as complex systems. To achieve this, we will harness the power of cutting-edge climate model outputs, sourced from the Coupled Model Intercomparison Project (CMIP), and integrate approaches from Machine Learning and Deep Learning models. This strategic amalgamation of data and techniques will enable us to gain profound insights into the intricate web of future climate-change-induced extreme weather conditions and their immediate and long-term ramifications on energy infrastructure networks. The seed grant from IDEaS stands as the crucial catalyst for kick-starting this ambitious endeavor. It will empower us to form a collaborative and inclusive community of GT researchers hailing from various domains, including City and Regional Planning, Earth and Atmospheric Science, Computer Science and Electrical Engineering, Civil and Environmental Engineering etc. By drawing upon the wealth of expertise and perspectives from these diverse fields, we aim to foster an environment where innovative ideas and solutions can flourish. In addition to our internal team, we also have plans to collaborate with external partners, including the World Bank, the Stanford Doerr School of Sustainability, and the Berkeley AI Research Initiative, who share our vision of addressing the complex challenges at the intersection of climate and energy infrastructure.

PI: Jian Luo, Civil & Environmental Eng | Yi Deng, EAS
Proposal Title: “Physics-informed Deep Learning for Real-time Forecasting of Urban Flooding”
Award: $15k (co-sponsored by BBISS)

Overview: Our research team envisions a significant trend in the exploration of AI applications for urban flooding hazard forecasting. Georgia Tech possesses a wealth of interdisciplinary expertise, positioning us to make a pioneering contribution to this burgeoning field. We aim to harness the combined strengths of Georgia Tech's experts in civil and environmental engineering, atmospheric and climate science, and data science to chart new territory in this emerging trend. Furthermore, we envision the potential extension of our research efforts towards the development of a real-time hazard forecasting application. This application would incorporate adaptation and mitigation strategies in collaboration with local government agencies, emergency management departments, and researchers in computer engineering and social science studies. Such a holistic approach would address the multifaceted challenges posed by urban flooding. To the best of our knowledge, Georgia Tech currently lacks a dedicated team focused on the fusion of AI and climate/flood research, making this initiative even more pioneering and impactful.

Proposal Title: “AI for Recycling and Circular Economy”
PI: Valerie Thomas, ISyE and PubPoly | Steven Balakirsky, GTRI
Award: $15k (co-sponsored by BBISS)

Overview: Most asset management and recycling use technology that has not changed for decades. The use of bar codes and RFID has provided some benefits, such as for retail returns management. Automated sorting of recyclables using magnets, eddy currents, and laser plastics identification has improved municipal recycling. Yet the overall field has been challenged by not-quite-easy-enough identification of products in use or at end of life. AI approaches, including computer vision, data fusion, and machine learning provide the additional capability to make asset management and product recycling easy enough to be nearly autonomous. Georgia Tech is well suited to lead in the development of this application. With its strength in machine learning, robotics, sustainable business, supply chains and logistics, and technology commercialization, Georgia Tech has the multi-disciplinary capability to make this concept a reality, in research and in commercial application.

Proposal Title: “Data-Driven Platform for Transforming Subjective Assessment into Objective Processes for Artistic Human Performance and Wellness”
PI: Milka Trajkova, Research Scientist/School of Literature, Media, Communication | Brian Magerko, School of Literature, Media, Communication
Award: $15k (co-sponsored by IPaT)

Overview: Artistic human movement at large, stands at the precipice of a data-driven renaissance. By leveraging novel tools, we can usher in a transparent, data-driven, and accessible training environment. The potential ramifications extend beyond dance. As sports analytics have reshaped our understanding of athletic prowess, a similar approach to dance could redefine our comprehension of human movement, with implications spanning healthcare, construction, rehabilitation, and active aging. Georgia Tech, with its prowess in AI, HCI, and biomechanics is primed to lead this exploration. To actualize this vision, we propose the following research questions with ballet as a prime example of one of the most complex types of artistic movements: 1) What kinds of data - real-time kinematic, kinetic, biomechanical, etc. captured through accessible off-the-shelf technologies, are essential for effective AI assessment in ballet education for young adults?; 2) How can we design and develop an end-to-end ML architecture that assesses artistic and technical performance?; 3) What feedback elements (combination of timing, communication mode, feedback nature, polarity, visualization) are most effective for AI- based dance assessment?; and 4) How does AI-assisted feedback enhance physical wellness, artistic performance, and the learning process in young athletes compared to traditional methods?

-         Christa M. Ernst

This fall, the Institute will launch a foundational, interdisciplinary program to lead in research related to neuroscience, neurotechnology, and society. The Neuro Next Initiative is the result of the growth of GTNeuro, a grassroots effort over many years that has led in the hiring of faculty studying the brain and the creation of the B.S. in neuroscience in the College of Sciences, and contributed to exciting neuro-related research and education at Georgia Tech.

Neurosciences research holds enormous potential for wide-ranging health and societal impact, and Georgia Tech's culture of applied research and integrated interdisciplinary liberal arts scholarship is uniquely positioned to create the environment in which Neuro Next can become an international leader in the discovery, innovation, and translation in neuroscience and neurotechnology.

Guided by faculty members Christopher Rozell, professor and Julian T. Hightower Chair in the School of Electrical and Computer Engineering; Simon Sponberg, Dunn Family Associate Professor of Physics and Biological Sciences; and Jennifer S. Singh, associate professor in the School of History and Sociology, the Neuro Next Initiative at Georgia Tech will lead the development of a community that supports collaborative research, unique educational initiatives, and public engagement in this critical field.

“Georgia Tech has a very strong, but decentralized, neuroscience community,” said Sponberg. “The Neuro Next Initiative really sprung from a lot of thoughtful input from dozens of people across many schools, colleges, and roles, which reflects how neuro interfaces so broadly. Our goal with this initiative is really to open a new front door to the neuro community here, to highlight the leadership that Georgia Tech is already taking in many areas of neuro-related research, and to create new ways to support our interdisciplinary work.” Aiming to foster a broad community that is passionate about shaping the frontiers of neuroscience and neurotechnology to better serve humanity, the initiative will launch in October.

“Neuroscience and neurotechnology have advanced dramatically in the last few years, making it clear that there are few endeavors that have as much potential societal impact as our study of the brain,” Rozell said. “Georgia Tech is uniquely positioned to build on its existing strengths to create an effort tailored to meet the scientific, technical, and social needs of these promising research trajectories. I'm excited that the Neuro Next Initiative represents the next step in creating that collaborative community.” By bringing together a cohort of faculty experts from varied disciplines, members aim to create a holistic and integrative approach to neuroscience and neurotechnology that centers real human impact and broad accessibility.

Singh noted, “Neuro Next is an important and exciting initiative that is prioritizing the inclusion of a range disciplinary expertise, including social science, humanities, business, and the arts, to critically investigate how we can research and develop neurotechnologies that are accessible, responsible, and socially just. Building a collaborative neurocommunity that centers societal impacts from the start shares the commitment of Georgia Tech to developing leaders who advance technology and improve the human condition.”

Attend the Neuro Next Launch Event | Oct. 25 – 26 | Georgia Institute of Technology

Register for the Upcoming Neuro Next Launch Event Here

 

For faculty interested in participating in Neuro Next, click here to join our affiliates list.

New research findings show that embryonic stem cells unable to fully compact the DNA inside them cannot complete their primary task: differentiation into specific cell types that give rise to the various types of tissues and structures in the body.

Researchers from the Georgia Institute of Technology and Emory University found that chromatin compaction is required for proper embryonic stem cell differentiation to occur. Chromatin, which is composed of histone proteins and DNA, packages DNA into a smaller volume so that it fits inside a cell. 

A study published on May 10, 2012 in the journal PLoS Genetics found that embryonic stem cells lacking several histone H1 subtypes and exhibiting reduced chromatin compaction suffered from impaired differentiation under multiple scenarios and demonstrated inefficiency in silencing genes that must be suppressed to induce differentiation.

“While researchers have observed that embryonic stem cells exhibit a relaxed, open chromatin structure and differentiated cells exhibit a compact chromatin structure, our study is the first to show that this compaction is not a mere consequence of the differentiation process but is instead a necessity for differentiation to proceed normally,” said Yuhong Fan, an assistant professor in the Georgia Tech School of Biology.

Fan and Todd McDevitt, an associate professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, led the study with assistance from Georgia Tech graduate students Yunzhe Zhang and Kaixiang Cao, research technician Marissa Cooke, and postdoctoral fellow Shiraj Panjwani.

The work was supported by the National Institutes of Health’s National Institute of General Medical Sciences (NIGMS), the National Science Foundation, a Georgia Cancer Coalition Distinguished Scholar Award, and a Johnson & Johnson/Georgia Tech Healthcare Innovation Award.

To investigate the impact of linker histones and chromatin folding on stem cell differentiation, the researchers used embryonic stem cells that lacked three subtypes of linker histone H1 -- H1c, H1d and H1e -- which is the structural protein that facilitates the folding of chromatin into a higher-order structure. They found that the expression levels of these H1 subtypes increased during embryonic stem cell differentiation, and embryonic stem cells lacking these H1s resisted spontaneous differentiation for a prolonged time, showed impairment during embryoid body differentiation and were unsuccessful in forming a high-quality network of neural cells.

“This study has uncovered a new, regulatory function for histone H1, a protein known mostly for its role as a structural component of chromosomes,” said Anthony Carter, who oversees epigenetics grants at NIGMS.  “By showing that H1 plays a part in controlling genes that direct embryonic stem cell differentiation, the study expands our understanding of H1’s function and offers valuable new insights into the cellular processes that induce stem cells to change into specific cell types.”

During spontaneous differentiation, the majority of the H1 triple-knockout embryonic stem cells studied by the researchers retained a tightly packed colony structure typical of undifferentiated cells and expressed high levels of Oct4 for a prolonged time. Oct4 is a pluripotency gene that maintains an embryonic stem cell’s ability to self-renew and must be suppressed to induce differentiation.

“H1 depletion impaired the suppression of the Oct4 and Nanog pluripotency genes, suggesting a novel mechanistic link by which H1 and chromatin compaction may mediate pluripotent stem cell differentiation by contributing to the epigenetic silencing of pluripotency genes,” explained Fan. “While a significant reduction in H1 levels does not interfere with embryonic stem cell self-renewal, it appears to impair differentiation.”

The researchers also used a rotary suspension culture method developed by McDevitt to produce with high efficiency homogonous 3D clumps of embryonic stem cells called embryoid bodies. Embryoid bodies typically contain cell types from all three germ layers -- the ectoderm, mesoderm and endoderm -- that give rise to the various types of tissues and structures in the body. However, the majority of the H1 triple-knockout embryoid bodies formed in rotary suspension culture lacked differentiated structures and displayed gene expression signatures characteristic of undifferentiated stem cells.

“H1 triple-knockout embryoid bodies displayed a reduced level of activation of many developmental genes and markers in rotary culture, suggesting that differentiation to all three germ layers was affected.” noted McDevitt.  

The embryoid bodies also lacked the epigentic changes at the pluripotency genes necessary for differentiation, according to Fan.

“When we added one of the deleted H1 subtypes to the embryoid bodies, Oct4 was suppressed normally and embryoid body differentiation continued,” explained Fan. “The epigenetic regulation of Oct4 expression by H1 was also evident in mouse embryos.”

In another experiment, the researchers provided an environment that would encourage embryonic stem cells to differentiate into neural cells. However, the H1 triple-knockout cells were defective in forming neuronal and glial cells and a neural network, which is essential for nervous system development. Only 10 percent of the H1 triple-knockout embryoid bodies formed neurites and they produced on average eight neurites each. In contrast, half of the normal embryoid bodies produced, on average, 18 neurites.

In future work, the researchers plan to investigate whether controlling H1 histone levels can be used to influence the reprogramming of adult cells to obtain induced pluripotent stem cells, which are capable of differentiating into tissues in a way similar to embryonic stem cells.

Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) under award number GM085261 and the National Science Foundation under award number CBET-0939511. The content is solely the responsibility of the principal investigators and does not necessarily represent the official views of the NIH or NSF.

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