Student Research

Stitched for Strength: The Physics of Stiff, Knitted Fabrics

admin — Wed, 30 Jul 2025 12:39:36 +0000

Stitched for Strength: The Physics of Stiff, Knitted Fabrics admin Wed, 07/30/2025 - 08:39

School of Physics Associate Professor Elisabetta Matsumoto is unearthing the secrets of the centuries-old practice of knitting through experiments, models, and simulations. Her goal? Leveraging knitting for breakthroughs in advanced manufacturing — including more sustainable textiles, wearable electronics, and soft robotics.

Matsumoto, who is also a principal investigator at the International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM2) at Hiroshima University, is the corresponding author on a new study exploring the physics of ‘jamming’ — a phenomenon when soft or stretchy materials become rigid under low stress but soften under higher tension.

The study, "Pulling Apart the Mechanisms That Lead to Jammed Knitted Fabrics," was published this week in Physical Review E, and also includes Georgia Tech Matsumoto Group graduate students Sarah Gonzalez and Alexander Cachine in addition to former postdoctoral fellow Michael Dimitriyev, who is now an assistant professor at Texas A&M University.

The work builds on the group’s previous research demonstrating that knitted materials can be mathematically ‘programmed’ to behave in predictable ways. “These properties are intuitively understood by people who knit by hand,” Matsumoto says, “but in order to manipulate and use these behaviors in an industrial setting, we need to understand the physics behind them. This new research is another step in that direction.”

An Unexpected Twist

Gonzalez, who led the research, first became interested in jamming while conducting adjacent research. “I was using model simulations to characterize how different yarn properties affect the behavior of knitted fabrics and noticed a strange stiff region,” she recalls. “In our previous research, we had also seen this behavior in lab experiments, which suggested that what we were seeing in the simulations was a genuine phenomenon. I wanted to investigate it further.”

After digging into the topic, she realized that what she was seeing was called ‘jamming.’ In knits, Gonzalez explains, jamming occurs when stitches are packed tightly together, and the fabric resists stretching. Although it’s a well-known phenomenon, the physics has mostly been investigated in granular systems, like snow or sand, rather than fabrics.

“In fabrics, when you pull softly, the response is surprisingly stiff, but when you start pulling harder and harder, the stitches rearrange, and the material softens,” Matsumoto says. “In granular systems, this is a little like how avalanches work. At low forces, the snow pack is solid, but when the slope is steep, the force of gravity liquidizes that snow pack into an avalanche.”

“In fabrics, it is a little like having a tangle in a piece of jewelry,” she adds. “If you pull on it, it gets quite stiff, but if you loosen the knot, the chain can reconfigure, and it's not so stiff.”

Unraveling the Physics of Jamming

Using a combination of experiments with industrially knitted fabrics and computer models, the team analyzed what causes jamming in fabrics and how to control it. “We wanted to determine how different yarn properties impacted jamming,” Gonzalez explains. “Our goal was to understand the mechanics of jamming through how yarn interacts at various touchpoints in stitches.”

The team found that both machine tension and yarn thickness played a key role in making a fabric more or less jammed, and that jamming behaves differently depending on which direction the fabric is stretched.

“When you stretch a knit along the rows, the stiffness of the yarn causes fabric jamming. Jamming in the other direction is due to yarn contacts,” says Gonzalez. “We also showed that the impacts of changing machine tension and yarn thickness differ depending on fabric direction.”

“Discovering that fabric jamming works differently in different directions was a key insight,” she adds. “To our knowledge, the physics of this has never been explored before.”

Modern Innovation — With a Centuries-Old Technique

The research dovetails with Matsumoto’s WPI-SKCM2 Center work, which involves investigating fundamental aspects of knots and chirality. The Center is interested in a class of materials called “knotted chiral meta matter” that could lead to more sustainable materials.

For example, knitting — which leverages chiral knots — could be used to create more elastic fabrics from natural materials. “In many cases, manufacturers use yarns that combine, for example, polyester, cotton, and elastane to create a desired elasticity,” Matsumoto says. “Our research suggests that manipulating the topology of the stitches could lead to a similar elasticity, reducing the need for petroleum-based fibers and creating a more sustainable textile.”

“Knitting has the potential to be extremely useful in manufacturing, but knowledge has typically been shared through intuition and word of mouth,” she adds. “By creating these mathematical models, we hope to formalize that knowledge in a way that’s accessible for large-scale manufacturing — so we can leverage this centuries-old intuition for modern innovation.”

Funding: This work was supported by the World Premier International Research Center Initiative (WPI), Ministry of Education, Culture, Sports, Science and Technology of Japan; National Science Foundation (NSF); and Research Corporation for Science Advancement (RCSA).

DOI: https://doi.org/10.1103/g94g-c6tt

Summary sentence

Physicists unravel the secrets of the centuries-old practice of knitting in a new study that explores the physics of ‘jamming’ — a phenomenon when soft or stretchy materials become rigid under low stress but soften under higher tension.

Summary

Researchers in the School of Physics unravel the secrets of the centuries-old practice of knitting in a new study that explores the physics of ‘jamming’ — a phenomenon when soft or stretchy materials become rigid under low stress but soften under higher tension.

Dateline

Fri, 07/25/2025 - 12:00

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Written by Selena Langner

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683281

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Wed, 07/30/2025 - 08:38

Georgia Tech Celebrates 2025 Ph.D. Graduates in Cybersecurity and Privacy

dgivens8 — Mon, 28 Jul 2025 14:47:40 +0000

Georgia Tech Celebrates 2025 Ph.D. Graduates in Cybersecurity and Privacy dgivens8 Mon, 07/28/2025 - 10:47

The School of Cybersecurity and Privacy at Georgia Tech is proud to recognize the accomplishments of five doctoral students who finished their doctoral programs in Spring 2025. These scholars have advanced critical research in software security, cryptography, and privacy, collectively publishing 34 papers, most of which appear in top-tier venues.

Ammar Askar developed new tools for software security in multi-language systems, including a concolic execution engine powered by large language models. He highlighted DEFCON 2021, which he attended with the Systems Software and Security Lab (SSLab), as a favorite memory.

Zhengxian He persevered through the pandemic to lead a major project with an industry partner, achieving strong research outcomes. He will be joining Amazon and fondly remembers watching sunsets from the CODA building.

Stanislav Peceny focused on secure multiparty computation (MPC), designing high-performance cryptographic protocols that improve efficiency by up to 1000x. He’s known for his creativity in both research and life, naming avocado trees after famous mathematicians and enjoying research discussions on the CODA rooftop.

Qinge Xie impressed faculty with her adaptability across multiple domains. Her advisor praised her independence and technical range, noting her ability to pivot seamlessly between complex research challenges.

Yibin Yang contributed to the advancement of zero-knowledge proofs and MPC, building toolchains that are faster and more usable than existing systems. His work earned a Distinguished Paper Award at ACM CCS 2023, and he also served as an RSAC Security Scholar. Yang enjoyed teaching and engaging with younger students, especially through events like Math Kangaroo.

Faculty mentors included Regents’ Entrepreneur Mustaque Ahamad, Professors Taesoo Kim and Vladimir Kolesnikov, and Assistant Professor Frank Li, who played vital roles in guiding the graduates’ research journeys.

Learn more about the graduates and their mentors on the 2025 Ph.D. graduate microsite.

Subtitle

Summary sentence

Summary

Five Ph.D. students from Georgia Tech’s School of Cybersecurity and Privacy graduated in Spring 2025, contributing significant research in software security, cryptography, and privacy with 34 published papers. Highlights include Ammar Askar’s concolic execution tools, Zhengxian He’s industry collaboration, Stanislav Peceny’s advances in multiparty computation, Qinge Xie’s adaptability across domains, and Yibin Yang’s award-winning work on zero-knowledge proofs. Faculty mentors played key roles in supporting their success.

Dateline

Wed, 05/14/2025 - 12:00

jpopham3@gatech.edu

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JP Popham, Communications Officer II

College of Computing | School of Cybersecurity and Privacy

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Mon, 07/28/2025 - 10:46

Georgia Tech Students Help Illuminate Coffee County’s History

dgivens8 — Fri, 11 Jul 2025 21:07:46 +0000

Georgia Tech Students Help Illuminate Coffee County’s History dgivens8 Fri, 07/11/2025 - 17:07

Georgia Tech students played a pivotal role in the award-winning Coffee County Memory Project, an oral history initiative that preserves the stories of school desegregation in rural Georgia.

Launched in 2016, the project was supported by the Institute’s Sustainable Communities Summer Internship Program, run by the Center for Serve-Learn-Sustain (now the Center for Sustainable Communities Research and Education), in which students work full time with community partners across Atlanta and Georgia.

Beginning in 2017, trusted advisers contributed to the success of this work, including Vernon E. Jordan Jr., Christopher Lawton, Ann McCleary and G. Wayne Clough. Clough, who served as Georgia Tech’s president from 1994 to 2008, long advocated for public service, community-engaged research, and interdisciplinary teaching and learning.

In 2019, Georgia Tech students and participating interns Brice Minix and Nabil Patel combed through decades of local newspapers, digitized school board records, and conducted interviews with community members who lived in Coffee County during desegregation. In 2020, Kara Vaughan Adams and Bennett Bush transcribed countless interviews. Samina Patel’s contributions in 2020 and 2021 included graphic and web design.

All their work laid the foundation for two virtual museum exhibits: emergingVOICES of Coffee County and Overcoming Segregation: A Journey Through Coffee County’s Forgotten Stories. The latter received the 2023 Award of Excellence from the American Association of State and Local History. Further recognition came this year when the project earned the 2025 Georgia Association of Museums’ Special Project Award for the PLAYBACK & FASTFORWARD seminar series.

T. Cat Ford, Project Director said, “The Serve-Learn-Sustain interns we partnered with from Georgia Tech were all graduates of Coffee High School. Their efforts turbo-charged our work—not only because they worked tirelessly but also because, as they preserved their own history, they offered valuable insights into their lived experience of this legacy.

Click here to learn more about SCoRE’s Sustainable Communities Internship Program.

Summary sentence

Georgia Tech students received the 2023 Award of Excellence from the American Association of State and Local History for the Coffee County Memory Project

Summary

Georgia Tech students played a pivotal role in the award-winning Coffee County Memory Project, an oral history initiative that preserves the stories of school desegregation in rural Georgia. Launched in 2016, the project was supported by the Institute’s Sustainable Communities Summer Internship Program, run by the Center for Serve-Learn-Sustain (now the Center for Sustainable Communities Research and Education), in which students work full time with community partners across Atlanta and Georgia.

Dateline

Fri, 07/11/2025 - 12:00

jennifer.martin@research.gatech.edu

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Jennifer Martin, Assistant Director of Research Communications Services

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683112

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Fri, 07/11/2025 - 17:07

Brain-Inspired AI Breakthrough Spotlighted at Global Conference

dgivens8 — Thu, 26 Jun 2025 16:46:41 +0000

Brain-Inspired AI Breakthrough Spotlighted at Global Conference dgivens8 Thu, 06/26/2025 - 12:46

Researchers at Georgia Tech have taken a critical step forward in creating efficient, useful and brain-like artificial intelligence (AI). The key? A new algorithm that results in neural networks with internal structure more like the human brain.

The study, “TopoNets: High-Performing Vision and Language Models With Brain-Like Topography,” was awarded a spotlight at this year’s International Conference on Learning Representations (ICLR), a distinction given to only 2 percent of papers. The research was led by graduate student Mayukh Deb alongside School of Psychology Assistant Professor Apurva Ratan Murty.

Thirty-two of Tech’s computing, engineering, and science faculty represented the Institute at ICLR 2025, which is globally renowned for sharing cutting-edge research.

“We started with this idea because we saw that AI models are unstructured, while brains are exquisitely organized,” says first-author Deb. “Our models with internal structure showed more than a 20 percent boost in efficiency with almost no performance losses. And this is out-of-the-box — it’s broadly applicable to other models with no extra fine-tuning needed.”

For Murty, the research also underscores the importance of a rapidly growing field of research at the intersection of neuroscience and AI. “There's a major explosion in understanding intelligence right now,” he says. “The neuro-AI approach is exciting because it helps emulate human intelligence in machines, making AI more interpretable.”

“In addition to advancing AI, this type of research also benefits neuroscience because it informs a fundamental question: Why is our brain organized the way it is?,” Deb adds. “Making AI more interpretable helps everyone.”

Brain-inspired blueprints

In the brain, neurons form topographic maps: neurons used for comparable tasks are closer together. The researchers applied this concept to AI by organizing how internal components (like artificial neurons) connect and process information.

This type of organization has been tried in the past but has been challenging, Murty says. “Historically, rules constraining how the AI could structure itself often resulted in lower-performing models. We realized that for this type of biophysical constraint, you simply can’t map everything — you need an algorithmic solution.”

“Our key insight was an algorithmic trick that gives the same structure as brains without enforcing things that models don't respond well to,” he adds. “That breakthrough was what Mayukh (Deb) worked on.”

The algorithm, called TopoLoss, uses a loss function to encourage brain-like organization in artificial neural networks, and it is compatible with many AI systems capable of understanding language and images.

“The resulting training method, TopoNets, is very flexible and broadly applicable,” Murty says. “You can apply it to contemporary models very easily, which is a critical advancement when compared to previous methods.”

Neuro-AI innovations

Murty and Deb plan to continue refining and designing brain-inspired AI systems. “All parts of the brain have some organization — we want to expand into other domains,” Deb says. “On the neuroscience side of things, we want to discover new kinds of organization in brains using these topographic systems.”

Deb also cites possibilities in robotics, especially in situations like space exploration where resources are limited. “Imagine running a model inside a robot with limited power,” he says. “Structured models can help us achieve 80 percent of performance with just 20 percent of energy consumption, saving valuable energy and space. This is still experimental, but it's the direction we are interested in exploring.”

“This success highlights the potential of a new approach, designing systems that benefit both neuroscience and AI — and beyond,” Murty adds. “We can learn so much from the human brain, and this project shows that brain-inspired systems can help current AI be better. We hope our work stimulates this conversation.”

Subtitle

Summary sentence

Researchers at Georgia Tech have developed an algorithm that helps AI models develop internal organization just like the human brain — boosting efficiency by 20 percent.

Summary

Researchers at Georgia Tech have developed an algorithm that helps AI models develop internal organization just like the human brain — boosting efficiency by 20 percent.

Dateline

Thu, 06/26/2025 - 12:00

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Written by Selena Langner

Contact: Jess Hunt-Ralston

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682905

Source updated

Thu, 06/26/2025 - 12:46

Computing Framework Could Reveal Signs of Neuro Disorders Hidden within Brain Data

admin — Mon, 05 May 2025 13:58:41 +0000

Computing Framework Could Reveal Signs of Neuro Disorders Hidden within Brain Data admin Mon, 05/05/2025 - 09:58

A Georgia Tech doctoral student’s dissertation could help physicians diagnose neuropsychiatric disorders, including schizophrenia, autism, and Alzheimer’s disease. The new approach leverages data science and algorithms instead of relying on traditional methods like cognitive tests and image scans.

Ph.D. candidate Md Abdur Rahaman’s dissertation studies brain data to understand how changes in brain activity shape behavior.

Computational tools Rahaman developed for his dissertation look for informative patterns between the brain and behavior. Successful tests of his algorithms show promise to help doctors diagnose mental health disorders and design individualized treatment plans for patients.

“I've always been fascinated by the human brain and how it defines who we are,” Rahaman said.

“The fact that so many people silently suffer from neuropsychiatric disorders, while our understanding of the brain remains limited, inspired me to develop tools that bring greater clarity to this complexity and offer hope through more compassionate, data-driven care.”

Rahaman’s dissertation introduces a framework focusing on granular factoring. This computing technique stratifies brain data into smaller, localized subgroups, making it easier for computers and researchers to study data and find meaningful patterns.

Granular factoring overcomes the challenges of size and heterogeneity in neurological data science. Brain data is obtained from neuroimaging, genomics, behavioral datasets, and other sources. The large size of each source makes it a challenge to study them individually, let alone analyze them simultaneously, to find hidden inferences.

Rahaman’s research allows researchers and physicians to move past one-size-fits-all approaches. Instead of manually reviewing tests and scans, algorithms look for patterns and biomarkers in the subgroups that otherwise go undetected, especially ones that indicate neuropsychiatric disorders.

“My dissertation advances the frontiers of computational neuroscience by introducing scalable and interpretable models that navigate brain heterogeneity to reveal how neural dynamics shape behavior,” Rahaman said.

“By uncovering subgroup-specific patterns, this work opens new directions for understanding brain function and enables more precise, personalized approaches to mental health care.”

Rahaman defended his dissertation on April 14, the final step in completing his Ph.D. in computational science and engineering. He will graduate on May 1 at Georgia Tech’s Ph.D. Commencement.

After walking across the stage at McCamish Pavilion, Rahaman’s next step in his career is to go to Amazon, where he will work in the generative artificial intelligence (AI) field.

Graduating from Georgia Tech is the summit of an educational trek spanning over a decade. Rahaman hails from Bangladesh where he graduated from Chittagong University of Engineering and Technology in 2013. He attained his master’s from the University of New Mexico in 2019 before starting at Georgia Tech.

“Munna is an amazingly creative researcher,” said Vince Calhoun, Rahman’s advisor. Calhoun is the founding director of the Translational Research in Neuroimaging and Data Science Center (TReNDS).

TReNDS is a tri-institutional center spanning Georgia Tech, Georgia State University, and Emory University that develops analytic approaches and neuroinformatic tools. The center aims to translate the approaches into biomarkers that address areas of brain health and disease.

“His work is moving the needle in our ability to leverage multiple sources of complex biological data to improve understanding of neuropsychiatric disorders that have a huge impact on an individual’s livelihood,” said Calhoun.

Summary sentence

A Georgia Tech doctoral student’s dissertation could help physicians diagnose neuropsychiatric disorders, including schizophrenia, autism, and Alzheimer’s disease.

Summary

Ph.D. candidate Md Abdur Rahaman’s dissertation studies brain data to understand how changes in brain activity shape behavior.

Dateline

Thu, 04/24/2025 - 12:00

Contact

Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu

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Biotechnology, Health, Bioengineering, Genetics

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Mercury ID

682026

Source updated

Mon, 05/05/2025 - 09:58

Gianna Fiduccia Follows in Her Family's Footsteps

admin — Thu, 01 May 2025 12:31:41 +0000

Gianna Fiduccia Follows in Her Family's Footsteps admin Thu, 05/01/2025 - 08:31

Gianna Fiduccia didn’t know much about Georgia Tech when deciding where to go to college, but with an affinity for STEM and two generations of chemical engineers in her family, she instantly felt a connection to the Institute during her first visit in 2021.

Fiduccia’s grandmother, Edita Asuncion, immigrated to the United States 50 years ago from the Philippines before becoming one of the few female chemical engineering students at her university and entering the field as a large-scale manufacturer of chemical products. Fiduccia’s mother, Deanna, also became an engineer, specializing in the production of commercial cosmetic products.

She watched as her role models made their mark in a male-dominated field and wanted to follow in their footsteps.

“They paved the way for me and other women. A lot of young adults choose their career paths based on influence, and so, having two amazing female chemical engineers in my life showed me that the doors were wide open for any opportunity I wanted to pursue. I recognized how special and unique that privilege was and decided to pursue a challenging, meaningful career path because of it,” Fiduccia said.

In high school, she became interested in pharmaceutical research before choosing to study chemical and biomolecular engineering at Georgia Tech. As a student and while completing internships with Moderna and Merck, Fiduccia always felt she “had a seat at the table” because of women like her mother and grandmother.

Eager to explore new biotechnology research on campus, she joined Mark Prausnitz’s lab to assist with developing an mRNA-lipid nanoparticle microneedle patch for vaccine delivery. The project, which could assist in the ease of drug delivery and accessibility, is one that Fiduccia says led her to pursue a Ph.D.

“My time at Georgia Tech has shown me how creative science can be and how to take something you’re curious about, like a new drug-delivery system, and explore it without limitation. I decided to do a Ph.D. because of this freedom of exploration and a deep understanding that early-stage research could one day influence the future of healthcare,” she said.

Fiduccia will attend the University of Pennsylvania for her doctorate, but having attended a small, all-girls high school in her hometown of Yardley, Pennsylvania, she said that meeting people from all over the world has been among the most rewarding aspects of her Georgia Tech experience.

Outside of the classroom, she was a member of Miracle, a student-run nonprofit raising funds for Children’s Healthcare of Atlanta.

With her family as her guide, Fiduccia identified a goal to become an engineer. As she completes her undergraduate degree here, she’ll mark a major milestone on her path toward using her dream “for the betterment of society.”

Subtitle

After watching her mother and grandmother pave the way, Gianna Fiduccia is carrying on the tradition of chemical engineers in her family.

Summary sentence

After watching her mother and grandmother pave the way, Gianna Fiduccia is carrying on the tradition of chemical engineers in her family.

Summary

After watching her mother and grandmother pave the way, Gianna Fiduccia is carrying on the tradition of chemical engineers in her family.

Dateline

Thu, 05/01/2025 - 12:00

Contact

Steven Gagliano – Institute Communications

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Spring Commencement

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Student Research

Mercury ID

682136

Source updated

Thu, 05/01/2025 - 08:30

Thesis on Human-Centered AI Earns Honors from International Computing Organization

dgivens8 — Tue, 22 Apr 2025 14:30:41 +0000

Thesis on Human-Centered AI Earns Honors from International Computing Organization dgivens8 Tue, 04/22/2025 - 10:30

A Georgia Tech alum’s dissertation introduced ways to make artificial intelligence (AI) more accessible, interpretable, and accountable. Although it’s been a year since his doctoral defense, Zijie (Jay) Wang’s (Ph.D. ML-CSE 2024) work continues to resonate with researchers.

Wang is a recipient of the 2025 Outstanding Dissertation Award from the Association for Computing Machinery Special Interest Group on Computer-Human Interaction (ACM SIGCHI). The award recognizes Wang for his lifelong work on democratizing human-centered AI.

“Throughout my Ph.D. and industry internships, I observed a gap in existing research: there is a strong need for practical tools for applying human-centered approaches when designing AI systems,” said Wang, now a safety researcher at OpenAI.

“My work not only helps people understand AI and guide its behavior but also provides user-friendly tools that fit into existing workflows.”

Wang’s dissertation presented techniques in visual explanation and interactive guidance to align AI models with user knowledge and values. The work culminated from years of research, fellowship support, and internships.

Wang’s most influential projects formed the core of his dissertation. These included:

CNN Explainer: an open-source tool developed for deep-learning beginners. Since its release in July 2020, more than 436,000 global visitors have used the tool.
DiffusionDB: a first-of-its-kind large-scale dataset that lays a foundation to help people better understand generative AI. This work could lead to new research in detecting deepfakes and designing human-AI interaction tools to help people more easily use these models.
GAM Changer: an interface that empowers users in healthcare, finance, or other domains to edit ML models to include knowledge and values specific to their domain, which improves reliability.
GAM Coach: an interactive ML tool that could help people who have been rejected for a loan by automatically letting an applicant know what is needed for them to receive loan approval.
Farsight: a tool that alerts developers when they write prompts in large language models that could be harmful and misused.

“I feel extremely honored and lucky to receive this award, and I am deeply grateful to many who have supported me along the way, including Polo, mentors, collaborators, and friends,” said Wang, who was advised by School of Computational Science and Engineering (CSE) Professor Polo Chau.

“This recognition also inspired me to continue striving to design and develop easy-to-use tools that help everyone to easily interact with AI systems.”

Like Wang, Chau advised Georgia Tech alumnus Fred Hohman (Ph.D. CSE 2020). Hohman won the ACM SIGCHI Outstanding Dissertation Award in 2022.

Chau’s group synthesizes machine learning (ML) and visualization techniques into scalable, interactive, and trustworthy tools. These tools increase understanding and interaction with large-scale data and ML models.

Chau is the associate director of corporate relations for the Machine Learning Center at Georgia Tech. Wang called the School of CSE his home unit while a student in the ML program under Chau.

Wang is one of five recipients of this year’s award to be presented at the 2025 Conference on Human Factors in Computing Systems (CHI 2025). The conference occurs April 25-May 1 in Yokohama, Japan.

SIGCHI is the world’s largest association of human-computer interaction professionals and practitioners. The group sponsors or co-sponsors 26 conferences, including CHI.

Wang’s outstanding dissertation award is the latest recognition of a career decorated with achievement.

Months after graduating from Georgia Tech, Forbes named Wang to its 30 Under 30 in Science for 2025 for his dissertation. Wang was one of 15 Yellow Jackets included in nine different 30 Under 30 lists and the only Georgia Tech-affiliated individual on the 30 Under 30 in Science list.

While a Georgia Tech student, Wang earned recognition from big names in business and technology. He received the Apple Scholars in AI/ML Ph.D. Fellowship in 2023 and was in the 2022 cohort of the J.P. Morgan AI Ph.D. Fellowships Program.

Along with the CHI award, Wang’s dissertation earned him awards this year at banquets across campus. The Georgia Tech chapter of Sigma Xi presented Wang with the Best Ph.D. Thesis Award. He also received the College of Computing’s Outstanding Dissertation Award.

“Georgia Tech attracts many great minds, and I’m glad that some, like Jay, chose to join our group,” Chau said. “It has been a joy to work alongside them and witness the many wonderful things they have accomplished, and with many more to come in their careers.”

Summary sentence

Zijie (Jay) Wang (Ph.D. ML-CSE 2024) is a recipient of the 2025 Outstanding Dissertation Award from the Association for Computing Machinery Special Interest Group on Computer-Human Interaction (ACM SIGCHI).

Summary

Dateline

Thu, 04/17/2025 - 12:00

Contact

Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu

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artificial intelligence (AI)

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Source updated

Tue, 04/22/2025 - 10:29

NSF Awards Fellowships to Georgia Tech Graduate Students

admin — Wed, 16 Apr 2025 02:10:40 +0000

NSF Awards Fellowships to Georgia Tech Graduate Students admin Tue, 04/15/2025 - 22:10

Twenty Georgia Tech students have been awarded Graduate Research Fellowships (GRF) from the National Science Foundation. The fellowships — valued at $159,000 — include funding for three years of graduate study and tuition. Graduate students pursuing full-time, research-based master’s and doctoral degrees in science, technology, engineering, and mathematics (STEM) or STEM education are eligible for the fellowship.

This year’s Georgia Tech recipients represent various areas of study, fulfilling the GRF initiative’s commitment to ensuring the “quality, vitality, and diversity of the scientific and engineering workforce of the United States.”

Nicole Allen – biomedical engineering

Christopher E. Bain – bioengineering

Anna R. Burson – chemical engineering

Huyun Chen – biomedical engineering

Jenna Corbin – bioengineering

Connor M. Davel – photonic materials

Stephanie Gonzalez – aeronautical and aerospace engineering

Zachary Steven Grieser – aeronautical and aerospace engineering

Charles Hong – robotics, control, automation

Vibha Ramanathan Iyer – biomedical engineering

Alexey Denisovich Khotimsky – robotics, control, automation

Samuel Kirschner – mechanical engineering

Jeffrey Li – chemical engineering

Jason Marfey – atomic, molecular, and optical physics

Nora Phoebe O'Kelly – materials science and engineering

Nadia Qutob – astronomy and astrophysics

Ethan Daniel Ray – photonic materials

Atticus Rex – computational and data-enabled science

Claire Su – biomedical engineering

Alessandro Zerbini-Flores – electrical and electronic engineering

Eight additional fellowship recipients completed their undergraduate degrees at Georgia Tech before attending graduate school at other universities.

Grace Fanson

Haaris Jilani

Mollie Johnson

Matthew Liu

Madeleine M. Pollack

Aeva Georganne Silverman

Dima Tretiak

Ian Yang

Subtitle

The fellowships are awarded to outstanding graduate students pursuing STEM research and education.

Summary sentence

The fellowships are awarded to outstanding graduate students pursuing STEM research and education.

Summary

The fellowships are awarded to outstanding graduate students pursuing STEM research and education.

Dateline

Tue, 04/15/2025 - 12:00

Contact

Steven Gagliano - Institute Communications

Associated importer

Keywords

NSF Fellowships

News room topics

Campus and Community

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Student Honors and Achievements

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Mercury ID

681816

Source updated

Tue, 04/15/2025 - 22:10

Machine Learning Encoder Improves Weather Forecasting and Tsunami Prediction

admin — Wed, 26 Mar 2025 01:20:41 +0000

Machine Learning Encoder Improves Weather Forecasting and Tsunami Prediction admin Tue, 03/25/2025 - 21:20

Successful test results of a new machine learning (ML) technique developed at Georgia Tech could help communities prepare for extreme weather and coastal flooding. The approach could also be applied to other models that predict how natural systems impact society.

Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.

In experiments predicting medium-range weather forecasting and shallow water wave propagation, Latent-EnSF demonstrated higher accuracy, faster convergence, and greater efficiency than existing methods for sparse data assimilation.

“We are currently involved in an NSF-funded project aimed at providing real-time information on extreme flooding events in Pinellas County, Florida,” said Si, who studies computational science and engineering (CSE).

“We're actively working on integrating Latent-EnSF into the system, which will facilitate accurate and synchronized modeling of natural disasters. This initiative aims to enhance community preparedness and safety measures in response to flooding risks.”

Latent-EnSF outperformed three comparable models in assimilation speed, accuracy, and efficiency in shallow water wave propagation experiments. These tests show models can make better and faster predictions of coastal flood waves, tides, and tsunamis.

In experiments on medium-range weather forecasting, Latent-EnSF surpassed the same three control models in accuracy, convergence, and time. Additionally, this test demonstrated Latent-EnSF's scalability compared to other methods.

These promising results support using ML models to simulate climate, weather, and other complex systems.

Traditionally, such studies require employment of large, energy-intensive supercomputers. However, advances like Latent-EnSF are making smaller, more efficient ML models feasible for these purposes.

The Georgia Tech team mentioned this comparison in its paper. It takes hours for the European Center for Medium-Range Weather Forecasts computer to run its simulations. Conversely, the ML model FourCastNet calculated the same forecast in seconds.

“Resolution, complexity, and data-diversity will continue to increase into the future,” said Chen, an assistant professor in the School of CSE.

“To keep pace with this trend, we believe that ML models and ML-based data assimilation methods will become indispensable for studying large-scale complex systems.”

Data assimilation is the process by which models continuously ingest new, real-world data to update predictions. This data is often sparse, meaning it is limited, incomplete, or unevenly distributed over time.

Latent-EnSF builds on the Ensemble Filter Scores (EnSF) model developed by Florida State University and Oak Ridge National Laboratory researchers.

EnSF’s strength is that it assimilates data with many features and unpredictable relationships between data points. However, integrating sparse data leads to lost information and knowledge gaps in the model. Also, such large models may stop learning entirely from small amounts of sparse data.

The Georgia Tech researchers employ two variational autoencoders (VAEs) in Latent-EnSF to help ML models integrate and use real-world data. The VAEs encode sparse data and predictive models together in the same space to assimilate data more accurately and efficiently.

Integrating models with new methods, like Latent-EnSF, accelerates data assimilation. Producing accurate predictions more quickly during real-world crises could save lives and property for communities.

To share Latent-EnSF to the broader research community, Chen and Si presented their paper at the SIAM Conference on Computational Science and Engineering (CSE25). The Society of Industrial and Applied Mathematics (SIAM) organized CSE25, held March 3-7 in Fort Worth, Texas.

Chen was one of ten School of CSE faculty members who presented research at CSE25, representing one-third of the School’s faculty body. Latent-EnSF was one of 15 papers by School of CSE authors and one of 23 Georgia Tech papers presented at the conference.

The pair will also present Latent-EnSF at the upcoming International Conference on Learning Representations (ICLR 2025). Occurring April 24-28 in Singapore, ICLR is one of the world’s most prestigious conferences dedicated to artificial intelligence research.

“We hope to bring attention to experts and domain scientists the exciting area of ML-based data assimilation by presenting our paper,” Chen said. “Our work offers a new solution to address some of the key shortcomings in the area for broader applications.”

Summary sentence

Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.

Summary

Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.

Dateline

Fri, 03/14/2025 - 12:00

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Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu

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Source updated

Tue, 03/25/2025 - 21:19

New Lab Expanding Healthcare Access Through Novel Sensing Prototypes

admin — Wed, 26 Mar 2025 01:18:41 +0000

New Lab Expanding Healthcare Access Through Novel Sensing Prototypes admin Tue, 03/25/2025 - 21:18

A new lab is working to expand access to practical sensing systems. These systems could benefit people struggling with addiction and alert people with limited healthcare access to potentially life-threatening medical issues.

Device prototypes like these usually require massive amounts of time and external resources to build, but thanks to the Uncommon Sense Lab, they can now be conveniently developed on Georgia Tech’s campus.

The lab is housed in Georgia Tech’s School of Interactive Computing and is managed by Assistant Professor Alexander Adams.

“Our overall goal is to give better access to healthcare,” Adams said. “We’re always looking at who we’re doing this for, how we’re getting it to them, how it addresses specific needs, and how to make it as financially accessible as possible.

“There’s always a space for high-end, high-precision equipment, but not everyone has access, and people are often afraid to get checked out because of the cost. If we can build something that doesn’t necessarily give someone a perfect measurement of a condition, but it can tell them they should go to the doctor, that might be enough to save a life.”

The lab provides resources to interdisciplinary researchers with backgrounds in computing, robotics, mechanical engineering, electrical engineering, and biomedical engineering to develop novel sensing and feedback system prototypes.

“We render physical prototypes that would be difficult to build without a centralized location for these resources,” said Adams, who is affiliated with the Institute for Robotics and Intelligent Machines and the Parker H. Petit Institute for Bioengineering and Bioscience. “We give students access to the tools and knowledge to build things that would typically seem unreachable.

“There’s nowhere else on campus with this collective that can go end-to-end from mechanical engineering to biomedical engineering to electrical engineering to usability.”

Examples of current prototypes being developed in the lab include a device that trains people with post-traumatic stress disorder to breathe in more regular patterns, and another that measures a person’s heart rate when they vape.

“We want to learn more about that behavior through these sensing devices, and then we’ll look at figuring out how we can help people correct their breathing patterns or quit their addiction,” Adams said.

The Uncommon Sense Lab offers high-tech, state-of-the-art machinery, including:

3D printers, including fused deposition modeling (FDM) printers for multi-material, high-precision prints
A laser cutter for producing printed circuit boards (PCBs)
Surface mount PCB manufacturing station with soldering tools, paste dispensers, and rework stations
Optical work benches for optical system design, including microscopes and fluidics workstations
Resin materials for casting and molding prosthetics
Vacuum chambers and pressure chambers
Saws, mills, lathes, and other mechanical tools for processing wood and soft metals
Saws, grinders, polishers, and other wet tools for glass, stone, and ceramics

Since he started at the School of Interactive Computing in 2022, Adams has envisioned the lab. The lab space in the Technology Square Research Building in Midtown was thoroughly renovated, including access control, a new ceiling grid, environmental controls, pressurized air, plumbing, and vacuum and air filtration systems.

“This is the result of having built two labs at previous institutions, what I’ve learned about my type of work and my field, and what the most useful things are to handle our diverse projects,” he said.

“One of the reasons I came to Georgia Tech was because they saw the value of being interdisciplinary in a computing world and having a full lab space instead of just an office.”

Adams said the lab will accelerate the timelines of current projects for the researchers who use it and create more bandwidth for them to take on more projects.

“I want my students to have everything at hand instead of waiting every time we need to do something,” he said. “This space is for someone who might have an idea for a remote diagnostic tool, but they’re wondering how to build it, add computation, and test it. This is the solution for those wondering how they can do that without spending a year finding and organizing access to facilities or ordering various parts.”

Adams said the lab is not a public space, but anyone interested in using it can make a written request for access. The work must be part of a collaboration, and faculty must provide funds to use resources. Access is contingent upon passing several safety courses and in-person training.

Summary sentence

School of Interactive Computing's Alexander Adams created the Uncommon Sense Lab to design, fabricate, and implement new ubiquitous and wearable sensing systems.

Summary

School of Interactive Computing's Alexander Adams created the Uncommon Sense Lab and works with students to design, fabricate, and implement new ubiquitous and wearable sensing systems.

Dateline

Thu, 02/27/2025 - 12:00

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Georgia Tech College of Computing

albert.snedeker@cc.gatech.edu

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Tue, 03/25/2025 - 21:18

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