According to the National Institutes of Health, nearly one-fourth of the U.S. population over age 45 suffers from foot and ankle issues, which reduce their quality of life, adversely affect walking and other daily functions, and increase the risk of falls.

For orthopedic patients recovering from surgery, walking properly can speed recovery, enabling them to more quickly regain mobility and quality of life. Walking issues or problems with one’s gait can also indicate larger medical problems, from vascular disease to brain, nerve, or spinal cord injuries.

Three alumni from Georgia Tech’s School of Electrical and Computer Engineering and School of Bioengineering hope to help doctors and patients analyze walking patterns through their wearable sensor startup,  StrideLink. 

“In the same way a cardiologist puts an EKG on you to monitor your heart, we essentially have designed that for walking ability,” says StrideLink founder and CEO Marzeah “Zea” Khorramabadi.

Initially targeting orthopedic practices for their platform, the HIPAA-compliant system wirelessly analyzes patients’ gaits to help doctors remotely monitor their walking ability before and after surgery to better address issues and provide more personalized treatment. 

The 26-year-old Georgia Tech graduate of computer engineering founded StrideLink in 2021 with two other Tech students: Cassandra McIltrot, a 2022 biomedical and medical engineering graduate, and Neel Narvekar, who completed his computer engineering studies in 2021.

Since starting StrideLink, the three have raised just under $1 million in pre-seed funding and are now starting their Series A funding push.

McIltrot, 24, serves as research director at StrideLink. She says talking to surgeons, physical therapists, and patients was invaluable in building the StrideLink platform, which includes a physical sensor that connects via Bluetooth to a mobile platform. Orthopedic physicians can then access a secure interface to view their patients’ gait data.

“Being able to learn from all those people helped us build something that will bring value,” she says.

Narvekar, the startup’s CTO, calls the technology “a game-changer,” noting, “For the first time, we can widely collect clinically relevant gait data. Starting in orthopedics, this means we can build datasets to predict recovery timelines, identify when patients are off track, and intervene before adverse events occur. Ultimately, this will pave the way for improved care across a range of health conditions."

The enterprising entrepreneurs didn’t do it alone. They leveraged CREATE-X, which supports students in launching successful startups through education, coaching, funding, and other resources. 

Below, Khorramabadi and McIltrot share more about their journey as members of the first cohort of CREATE-X’s Female Founders program in Fall 2020. In Summer 2021, the duo completed Startup Launch, a 12-week summer accelerator that helps students launch startups.  

Did you two always want to start your own business?

Khorramabadi: It was kind of inevitable for Cassie and me. My dad immigrated from Iran and met my mom here. He started his own business selling cars. So, I grew up with a family that was running a small business. I’ve always had that in me, and it was the expectation that I would go to college. I picked Georgia Tech specifically because they had showcased the CREATE-X program during the tour.

McIltrot: My dad had a construction consulting business, and my mom was a nurse. That’s where the medical influence came from for me. He’s also an engineer. The summer that we decided to pursue this, I was doing research on stroke rehab at Emory. 

How did you come up with  your big idea?    

Khorramabadi: In the middle of the pandemic, there was a lot of emphasis on technology — leaving the clinic and being in a patient's home. How are we going to deliver healthcare effectively when patients aren't directly in front of their doctor? 

At the same time, Cassie was doing stroke research, and there was a lot around how heavily walking ability, walking patterns, or your gait is affected. We talked to healthcare professionals, physical therapists, surgeons, everyone. And it was clear that there was a pretty big gap in the market in terms of the technology that would serve these patients who have any symptoms that show up in their walking ability. It wasn’t measured at all. So, we ended up landing on a gait monitor as a solution. 

We realized there was a very immediate, straightforward need for our product in orthopedics. If you're getting a knee replacement, ankle, or foot surgery, it's valuable to be able to put this product on a patient preoperatively to better prepare them for surgery. Surgeons can take real measurements of what their patients’ walking ability looks like before surgery and then track them throughout the entirety of their post-op recovery, which can be three months, six months, or even 12 months.

How does the solution work?

Khorramabadi: We designed our platform from the ground up. Our physical sensor connects to a mobile application. That mobile application connects to an entire cloud architecture that has processing servers and database storage. On the physician side, we have an interface for them to view data that fits into their workflow, including receiving insurance reimbursement. The technology component was designed in-house by Neil and me, given our backgrounds in computer engineering.

Are you using AI or advanced analytics in your platform?

Khorramabadi: We have a lot of very advanced data processing methods that are entirely proprietary to our system. We’ve acquired enough data from all of the patients we've seen with Emory, and now we're tracking patients remotely, where we are starting to use real clinical data to train AI to deliver a performance score to these patients. It’s essentially one number that rates how you’re doing related to a healthy or normal gait. We're already using AI right now, and that's something that's going to be released with our product within the next six months.

Where are you in terms of product maturity?

Khorramabadi: We recently started with our first fully remote full-time customer. Before that, we were doing research with another physician at Emory, where they had used it for over a year. At this point, they've tracked over 250 patients, where they put the sensors on at their pre-op appointment and then track them during post-op follow-ups. 

They weren’t sent home with the sensors until our sensor was FDA-listed last year, and then we started our first pilot with a private practice in Amelia Island, Florida, last October. That has gone incredibly well, so we just expanded to an orthopedic practice in Alabama, and we should be getting two more practices started in 2025. We've solidified the product fit, and we’re now at the point of scaling it. We also have a research partnership with Children's Hospital Colorado to work on a pediatrics application. 

What was most helpful about the CREATE-X programs you participated in at Georgia Tech?

Khorramabadi: Georgia Tech makes exploring doing a startup easy and low-risk for any student. The fact that it was so accessible was monumental early on. In terms of programming, the most valuable part was the emphasis on customer discovery. They did a good job, saying, “You don't know what to build until you talk to enough customers.”

We needed a mentor as part of our first startup class, and we read how James Stubbs, a tenured professor in biomedical engineering, was a previous founder. He’d done a couple of medical device companies that had been acquired. At our first meeting, he told us we need to talk to people. From a business standpoint, it made more sense for us to go to orthopedics rather than physical therapy for a whole host of reasons. But the biggest takeaway of talking to customers was a very consistent experience with both the Startup Launch and the Female Founders program. 

McIltrot: The Female Founders program did a fantastic job of that, where we set goals as teams and were encouraged to talk to as many people we think are going to be our customers. We then met as a group and presented what we learned.  

So you have to get out of get out of your comfort zone, and not be shy about engaging with people.   Cassie, what was the big benefit for you?

McIltrot: We were the first cohort for Female Founders. We checked in every week with our team. Everyone would talk about what they learned that week while talking to people. We were the only medical-focused startup in the program, but being able to share the experience of how we approached people allowed us to learn from each other. We like keeping up with each other on LinkedIn. We learned one of the people in our cohort just closed a funding round.

Is having a community of other women entrepreneurs helpful?

Khorramabadi: Definitely. We’ve gotten a lot out of building a network, especially coming from starting this out of college, where you don't have any industry connections built up yet.  

What has been the biggest value from your experience participating in Startup Launch? 

Khorramabadi: Networking has been the biggest value for both Startup Launch and Female Founders. Both of those programs emphasized networking and customer discovery. Being involved in both programs at the same time kept us focused on that. 

Startup Launch was a good crash course in how you set up your company from a legal aspect, as well as the conversations you need to have with your co-founders, and this is how you pitch and how you raise investment. All these topics are very foreign, and there's not a lot of good information out there on them. So, it was important to have that in the program. It was also nice to connect with Georgia Tech founders who had started companies and seen some success. The program brought them in to talk to us and share what they'd learned. It was nice to have that extra guidance. 

What is the biggest benefit of your innovation?

Khorramabadi: The biggest value is knowing how you're doing right now, and also, if you're not doing well, your physician being able to make changes quickly to your plan of care. The platform also lets patients realize what may be contributing to their getting reinjured or having a slower recovery.

What has been the impact of your platform to date?

Khorramabadi: We've already seen the immediate ROI in terms of patients just feeling much better and much more comfortable in their recovery and being able to push themselves a little bit further than they would have otherwise, because they know they have this product that's tracking them, and they know their physician also is tracking them. 

On the physician side, there's a lot of incentive for them, because they see this as a tool to stay connected with their patients, which is incredibly valuable for them for delivering the best care or best experience for those patients. Also, this product is now covered by Medicare, CIGNA, and United Healthcare.

McIltrot: One of the things we have heard from patients is they’re using this to instill confidence in their walking ability and their recovery. Because these recovery timelines could be six months to a year to multiple years long, being able to have something that shows how much you've been able to improve is invaluable. 

Our future vision is being able to put this on a patient and have a projected recovery laid out. One day, this device could provide recommendations on what went wrong and how to fix it. Being proactive with the care that we deliver to patients is the end goal.

Any advice for Georgia Tech students thinking about taking an innovative idea to market? 

Khorramabadi: Go for it. Startups are always a risk, and Georgia Tech provides you with a safety net to take that risk. If you have an idea on how to solve a problem, why wait? Don't hesitate.

If you are looking for a supportive community to help you start your entrepreneurial journey, applications for the Female Founders Program are open until May 19 for Summer 2025. Apply for Female Founders today and over the summer learn entrepreneurship from an all-female coaching team, network with experts and successful entrepreneurs, build your network, and access funding to kick off a startup. Admissions are rolling.

 For those interested in seeing the latest startups coming out of CREATE-X, join us for Demo Day 2025! On Aug. 28 at 5 p.m., over 100 startups will fill Exhibition Hall, debuting technologies from clean tech to fashion. Register today for this free event that attracts over 1,500 attendees, from business leaders to enthusiasts, and see how our founders are tackling issues across industries.

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

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.

Unsurprisingly, Rahaman successfully defended his dissertation and is on his way to graduate in a few weeks.

“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 whole framework focusing on granular factoring. This computing technique stratifies brain data into smaller, localized subgroups. This makes it easier for computers and researchers to study data and find meaningful patterns.

Granular factoring overcomes the challenge of size and heterogeneity in neurological data science. Brain data is sourced from different modes, like neuroimaging, genomics, and behavioral datasets. Each of these sources are very large to study on their own, let alone analyzed at the same time 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 test 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 Pavillion, Rahaman’s next step in his career is to Amazon where he will work in the generative artificial intelligence (AI) field. 

Graduating from Georgia Tech is the peak of an educational summit spanning over a decade. Rahaman hails from Bangladesh where he graduated from Chittong 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 Dr. 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.”

Georgia Tech professors Michelle LaPlaca and W. Hong Yeo have been selected as recipients of Peterson Professorships with the Children’s Healthcare of Atlanta Pediatric Technology Center (PTC) at Georgia Tech. The professorships, supported by the G.P. “Bud” Peterson and Valerie H. Peterson Faculty Endowment Fund, are meant to further energize the Georgia Tech and Children’s partnership by engaging and empowering researchers involved in pediatrics.

In a joint statement, PTC co-directors Wilbur Lam and Stanislav Emelianov said, “The appointment of Dr. LaPlaca and Dr. Yeo as Peterson Professors exemplifies the vision of Bud and Valerie Peterson — advancing innovation and collaboration through the Pediatric Technology Center to bring breakthrough ideas from the lab to the bedside, improving the lives of children and transforming healthcare.”

LaPlaca is a professor and associate chair for Faculty Development in the Department of Biomedical Engineering, a joint department between Georgia Tech and Emory University. Her research is focused on traumatic brain injury and concussion, concentrating on sources of heterogeneity and clinical translation. Specifically, she is working on biomarker discovery, the role of the glymphatic system, and novel virtual reality neurological assessments.    

“I am thrilled to be chosen as one of the Peterson Professors and appreciate Bud and Valerie Peterson’s dedication to pediatric research,” she said. “The professorship will allow me to broaden research in pediatric concussion assessment and college student concussion awareness, as well as to identify biomarkers in experimental models of brain injury.”

In addition to the research lab, LaPlaca will work with an undergraduate research class called Concussion Connect, which is part of the Vertically Integrated Projects program at Georgia Tech.

“Through the PTC, Georgia Tech and Children’s will positively impact brain health in Georgia’s pediatric population,” said LaPlaca.

Yeo is the Harris Saunders, Jr. Professor in the George W. Woodruff School of Mechanical Engineering and the director of the Wearable Intelligent Systems and Healthcare Center at Georgia Tech. His research focuses on nanomanufacturing and membrane electronics to develop soft biomedical devices aimed at improving disease diagnostics, therapeutics, and rehabilitation.

“I am truly honored to be awarded the Peterson Professorship from the Children’s PTC at Georgia Tech,” he said. “This recognition will greatly enhance my research efforts in developing soft bioelectronics aimed at advancing pediatric healthcare, as well as expand education opportunities for the next generation of undergraduate and graduate students interested in creating innovative medical devices that align seamlessly with the recent NSF Research Traineeship grant I received. I am eager to contribute to the dynamic partnership between Georgia Tech and Children’s Healthcare of Atlanta and to empower innovative solutions that will improve the lives of children.”

The Peterson Professorships honor the former Georgia Tech President and First Lady, whose vision for the importance of research in improving pediatric healthcare has had an enormous positive impact on the care of pediatric patients in our state and region.

The Children’s PTC at Georgia Tech brings clinical experts from Children’s together with Georgia Tech scientists and engineers to develop technological solutions to problems in the health and care of children. Children’s PTC provides extraordinary opportunities for interdisciplinary collaboration in pediatrics, creating breakthrough discoveries that often can only be found at the intersection of multiple disciplines. These collaborations also allow us to bring discoveries to the clinic and the bedside, thereby enhancing the lives of children and young adults. The mission of the PTC is to establish the world’s leading program in the development of technological solutions for children’s health, focused on three strategic areas that will have a lasting impact on Georgia’s kids and beyond.

In a groundbreaking study published in Nature, researchers from Georgia Tech and Emory University have developed a new model that could enable precise, life-saving medication delivery for blood clot patients. The novel technique uses a 3D microchip

Wilbur Lam, professor at Georgia Tech and Emory University, and a clinician at Children’s Healthcare of Atlanta, led the study. He worked closely with Yongzhi Qiu, an assistant professor in the Department of Pediatrics at Emory University School of Medicine. 

The significance of the thromboinflammation-on-a-chip model, is that it mimics clots in a human-like way, allowing them to last for months and resolve naturally. This model helps track blood clots and more effectively test treatments for conditions including sickle cell anemia, strokes, and heart attacks. 

Read the full story from Emory University

Peter Yunker boils down his advice for researchers wanting to commercialize their lab advances. 

“You can’t go it alone,” said Yunker, an associate professor of physics at Georgia Tech. 

In January, Yunker co-founded the biotechnology startup TopoDx LLC, with David Weiss, an Emory University School of Medicine researcher and director of the Emory Antibiotic Resistance Center, and Yogi Patel, a Georgia Tech alumnus with a background in business development and bioengineering. 

“Researchers often think that they have a good commercialization idea to help people, but that alone does not guarantee success,” said Yunker. “Look for partners with complementary skills who understand aspects of the commercialization process that you don’t. Find mentors with business and scientific backgrounds in the specific industry you want to enter.”

TopoDx has developed a microbial test to identify antibiotic resistance and susceptibility rapidly and accurately. Current tests produce a result in three to five days. TopoDx’s approach can gain a result within four hours. Every hour counts in treating serious infections. Delays in accurate treatment can increase antibiotic resistance, which is a global challenge, causing up to 1 million deaths a year. 

The company’s testing method was inspired by a fundamental biophysics project in Yunker’s lab. His team was interested in understanding how bacterial colonies behave. They tested white-light interferometry, a technology that can measure bacterial colonies down to the nanometer level. 

“White-light interferometry allowed us to identify changes in the topography of a colony that indicated larger changes in the volume of cells in the entire colony,” said Yunker. “We thought this might have practical applications.” 

The next step was giving research talks at meetings and looking for collaborators. “I wanted to find someone with expertise on the bacteriology side, and I was very fortunate to meet David Weiss,” Yunker said, noting his proficiency in heteroresistance, a phenomenon where a small subset of a bacterial colony resists an antibiotic. 

“If you have just one antibiotic-resistant cell in a hundred cells, it can cause treatments to fail,” said Yunker. 

The two collaborators hoped to commercialize their technology, identifying heteroresistance in microbial samples. However, they needed guidance in creating a business model. They consulted Harold Solomon, an entrepreneur with Georgia Tech VentureLab and a principal in the Quadrant-i program, a specialized program helping Georgia Tech faculty and students commercialize research.  

Solomon became a key mentor. He guided them away from an ill-advised partnership and instead introduced them to Yogi Patel, who became a co-founder and the company CEO. 

This new collaboration provided the team with an important lesson — one that Yunker passes along to other researchers looking to commercialize their discoveries. “Seek expertise outside your field, be humble about your knowledge limitations, and view collaboration as a strategic partnership,” he says.

When Patel came on board, he conducted extensive interviews with more than 15 clinical professionals.

“You need to interview end users or purchasers of whatever solution you want to build,” said Patel. “Ask them if the problem you think you may have solved is a problem with scale, with a market need.” 

Clinicians, Patel learned, did not see heteroresistance as a significant issue. Instead, the slow pace of antibiotic testing was identified as a major problem. Faster testing could allow clinicians to prescribe targeted drugs more quickly and accurately, reducing unnecessary antibiotic use and the risk of multi-resistant infections. 

With this survey information, Patel asked Yunker and Weiss to rethink how their technology could be commercialized. 

“A company must solve a real-world problem,” said Patel. “I recommended that we switch from heteroresistance to solving slow antibiotic testing. We could keep heteroresistance as something we can still do as a second or third priority.” 

TopoDx’s new technology can measure, with single-nanometer accuracy, how bacterial colonies are responding to antibiotics in real time. This method could revolutionize how antibiotics are tested and prescribed. Testing would be conducted on a countertop device about the size of a large microwave. The co-founders envision the device as eventually being used by urgent care facilities and hospitals. 

“We want to make microbial testing susceptibility accessible anywhere and everywhere,” said Patel.   

Adam Krueger, once a Ph.D. student in Yunker's lab, has continued to refine the technology. Now a post-doctoral researcher, Krueger joined TopoDx in a technical leadership role to expand the technology’s capabilities for microbiological diagnostics.

“We will keep pushing the envelope forward scientifically while we try to commercialize the accomplishments that we have already made,” Yunker said. “We hope that some fundamental studies we are doing now out of scientific curiosity could lead to further commercial applications.”

Georgia Tech faculty members and graduate students, join the Quadrant-i Startup Launch Program to commercialize your research this summer: Over 12 weeks, you'll receive comprehensive support including guidance from experienced mentors, a $10,000 commercialization grant, and $150,000 worth of in-kind services. Showcase your innovation at Demo Day, where you'll have the opportunity to present to over 1,500 attendees, including industry leaders and investors. Apply today! Applications close April 11.

Georgia Tech researchers have developed a pacifier that can constantly monitor a baby’s electrolyte levels in real time, eliminating the need for repeated invasive blood draws.

Read more »

Georgia Tech researchers have developed biosensors with advanced sleuthing skills and the technology may revolutionize cancer detection and monitoring. 

The tiny detectives can identify key biological markers using logical reasoning inspired by the “AND” function in computers — like, when you need your username and password to log in. And unlike traditional biosensors comprised of genetic materials — cells, bits of DNA — these are made of manufactured molecules.

These new biosensors are more precise and simpler to manufacture, reducing the number of false positives and making them more practical for clinical use. And because the sensors are cell-free, there’s a reduced risk for immunogenic side effects.

“We think the accuracy and simplicity of our biosensors will lead to accessible, personalized, and effective treatments, ultimately saving lives,” said Gabe Kwong, associate professor and Robert A. Milton Endowed Chair in the Wallace H. Coulter Department of Biomedical Engineering, who led the study, published this month in Nature Nanotechnology. 

Breaking With Tradition

The researchers set out to address the limitations in current biosensors for cancer, like the ones designed for CAR-T cells to allow them to recognize tumor cells. These advanced biosensors are made of genetic material, and there is growing interest to reduce the potential for off-target toxicity by using Boolean “AND-gate” computer logic. That means they’re designed to release a signal only when two specific conditions are met.

“Traditionally, these biosensors involve genetic engineering using cell-based systems, which is a complex, time-consuming, and expensive process,” said Kwong.

So, his team developed biosensors made of iron oxide nanoparticles and special molecules called cyclic peptides. Synthesizing nanomaterials and peptides is a simpler, less costly process than genetic engineering, according to Kwong, “which means we can likely achieve large-scale, economical production of high-precision biosensors.”

Unlocking the AND-gate

Biosensors detect cancer signals and track treatment progress by turning biological signals into readable outputs for doctors. With AND-gate logic, two distinct inputs are required for an output. 

Accordingly, the researchers engineered cyclic peptides — small amino acid chains — to respond only when they encounter two specific types of enzymes, proteases called granzyme B (secreted by the immune system) and matrix metalloproteinase (from cancer cells). The peptides generate a signal when both proteases are present and active.

Think of a high-security lock that needs two unique keys to open. In this scenario, the peptides are the lock, activating the sensor signal only when cancer is present and being confronted by the immune system. 

“Our peptides allow for greater accuracy in detecting cancer activity,” said the study’s lead author, Anirudh Sivakumar, a postdoctoral researcher in Kwong’s Laboratory for Synthetic Immunity. “It’s very specific, which is important for knowing when immune cells are targeting and killing tumor cells.”

Super Specific

In animal studies, the biosensors successfully distinguished between tumors that responded to a common cancer treatment called immune checkpoint blockade therapy — ICBT, which enhances the immune system — from tumors that resisted treatment. 

During these tests, the sensors also demonstrated their ability to avoid false signals from other, unrelated health issues, such as when the immune system confronted a flu infection in the lungs, away from the tumor.

“This level of specificity can be game changing,” Kwong said. “Imagine being able to identify which patients are responding to the therapy early in their treatment. That would save time and improve patient outcomes.”

The first step toward this simpler, precise form of cancer diagnostics began with an ambitious but humble ($50,000) seed grant from the Petit Institute for Bioengineering and Bioscience five years ago for a collaboration between Kwong’s lab and the lab of M.G. Finn, professor and chair in the School of Chemistry and Biochemistry.

It evolved into a multi-institutional project supported by grants from the National Science Foundation and National Institutes of Health that included researchers from the University of California-Riverside, as well as Georgia Tech faculty researchers Finn and Peng Qiu, associate professor in the Coulter Department.

“The progression of the research, from an initial seed grant all the way to animal studies, was very smooth,” Kwong said. “Ultimately, a collaborative, multidisciplinary effort turned our early vision into something that could have a great impact in healthcare.”

Citation: Anirudh Sivakumar, Hathaichanok Phuengkham, Hitha Rajesh, Quoc D. Mac, Leonard C. Rogers, Aaron D. Silva Trenkle, Swapnil Subhash Bawage, Robert Hincapie, Zhonghan Li, Sofia Vainikos, Inho Lee, Min Xue, Peng Qiu, M. G. Finn, Gabriel A. Kwong. “AND-gated protease-activated nanosensors for programmable detection of anti-tumour immunity.” Nature Nanotechnology (January 2025).  https://doi.org/10.1038/s41565-024-01834-8

Funding: This research was supported in part by National Institutes of Health (NIH) grants 5U01CA265711, 5R01CA237210, 1DP2HD091793, and 5DP1CA280832.

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

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

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

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

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

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

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

[MICROSITE: Georgia Tech at NeurIPS 2024]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Amino acids are essential for nearly every process in the human body. Often referred to as ‘the building blocks of life,’ they are also critical for commercial use in products ranging from pharmaceuticals and dietary supplements, to cosmetics, animal feed, and industrial chemicals. 

And while our bodies naturally make amino acids, manufacturing them for commercial use can be costly — and that process often emits greenhouse gasses like carbon dioxide (CO2).

In a landmark study, a team of researchers has created a first-of-its kind methodology for synthesizing amino acids that uses more carbon than it emits. The research also makes strides toward making the system cost-effective and scalable for commercial use. 

“To our knowledge, it’s the first time anyone has synthesized amino acids in a carbon-negative way using this type of biocatalyst,” says lead corresponding author Pamela Peralta-Yahya, who emphasizes that the system provides a win-win for industry and environment. “Carbon dioxide is readily available, so it is a low-cost feedstock — and the system has the added bonus of removing a powerful greenhouse gas from the atmosphere, making the synthesis of amino acids environmentally friendly, too.”

The study, “Carbon Negative Synthesis of Amino Acids Using a Cell-Free-Based Biocatalyst,” published today in ACS Synthetic Biology, is publicly available. The research was led by Georgia Tech in collaboration with the University of Washington, Pacific Northwest National Laboratory, and the University of Minnesota.

The Georgia Tech research contingent includes Peralta-Yahya, a professor with joint appointments in the School of Chemistry and Biochemistry and School of Chemical and Biomolecular Engineering (ChBE); first author Shaafique Chowdhury, a Ph.D. student in ChBE; Ray Westenberg, a Ph.D student in Bioengineering; and Georgia Tech alum Kimberly Wennerholm (B.S. ChBE ’23).

Costly chemicals

There are two key challenges to synthesizing amino acids on a large scale: the cost of materials, and the speed at which the system can generate amino acids.

While many living systems like cyanobacteria can synthesize amino acids from CO2, the rate at which they do it is too slow to be harnessed for industrial applications, and these systems can only synthesize a limited number of chemicals.

Currently, most commercial amino acids are made using bioengineered microbes. “These specially designed organisms convert sugar or plant biomass into fuel and chemicals,” explains first author Chowdhury, “but valuable food resources are consumed if sugar is used as the feedstock — and pre-processing plant biomass is costly.” These processes also release CO2 as a byproduct.

Chowdhury says the team was curious “if we could develop a commercially viable system that could use carbon dioxide as a feedstock. We wanted to build a system that could quickly and efficiently convert CO2 into critical amino acids, like glycine and serine.”

The team was particularly interested in what could be accomplished by a ‘cell-free’ system that leveraged some process of a cellular system — but didn’t actually involve living cells, Peralta-Yahya says, adding that systems using living cells need to use part of their CO2 to fuel their own metabolic processes, including cell growth, and have not yet produced sufficient quantities of amino acids.

“Part of what makes a cell-free system so efficient,” Westenberg explains, “is that it can use cellular enzymes without needing the cells themselves. By generating the enzymes and combining them in the lab, the system can directly convert carbon dioxide into the desired chemicals. Because there are no cells involved, it doesn’t need to use the carbon to support cell growth — which vastly increases the amount of amino acids the system can produce.”

A novel solution

While scientists have used cell-free systems before, one of the necessary chemicals, the cell lysate biocatalyst, is extremely costly. For a cell-free system to be economically viable at scale, the team needed to limit the amount of cell lysate the system needed.

After creating the ten enzymes necessary for the reaction, the team attempted to dilute the biocatalyst using a technique called ‘volumetric expansion.’ “We found that the biocatalyst we used was active even after being diluted 200-fold,” Peralta-Yahya explains. “This allows us to use significantly less of this high-cost material — while simultaneously increasing feedstock loading and amino acid output.”

It’s a novel application of a cell-free system, and one with the potential to transform both how amino acids are produced, and the industry’s impact on our changing climate. 

“This research provides a pathway for making this method cost-effective and scalable,” Peralta-Yahya says. “This system might one day be used to make chemicals ranging from aromatics and terpenes, to alcohols and polymers, and all in a way that not only reduces our carbon footprint, but improves it.”

Funding: Advanced Research Project Agency-Energy (ARPA-E), U.S. Department of Energy and the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program.

DOI: 10.1021/acssynbio.4c00359

Two new assistant professors joined the School of Computational Science and Engineering (CSE) faculty this fall. Lu Mi comes to Georgia Tech from the Allen Institute for Brain Science in Seattle, where she was a Shanahan Foundation Fellow. 

We sat down with Mi to learn more about her background and to introduce her to the Georgia Tech and College of Computing communities. 

Faculty: Lu Mi, assistant professor, School of CSE

Research Interests: Computational Neuroscience, Machine Learning

Education: Ph.D. in Computer Science from the Massachusetts Institute of Technology; B.S. in Measurement, Control, and Instruments from Tsinghua University

Hometown: Sichuan, China (home of the giant pandas) 

How have your first few months at Georgia Tech gone so far?

I’ve really enjoyed my time at Georgia Tech. Developing a new course has been both challenging and rewarding. I’ve learned a lot from the process and conversations with students. My colleagues have been incredibly welcoming, and I’ve had the opportunity to work with some very smart and motivated students here at Georgia Tech.

You hit the ground running this year by teaching your CSE 8803 course on brain-inspired machine intelligence. What important concepts do you teach in this class?

This course focuses on comparing biological neural networks with artificial neural networks. We explore questions like: How does the brain encode information, perform computations, and learn? What can neuroscience and artificial intelligence (AI) learn from each other? Key topics include spiking neural networks, neural coding, and biologically plausible learning rules. By the end of the course, I expect students to have a solid understanding of neural algorithms and the emerging NeuroAI field.

When and how did you become interested in computational neuroscience in the first place?

I’ve been fascinated by how the brain works since I was young. My formal engagement with the field began during my Ph.D. research, where we developed algorithms to help neuroscientists map large-scale synaptic wiring diagrams in the brain. Since then, I’ve had the opportunity to collaborate with researchers at institutions like Harvard, the Janelia Research Campus, the Allen Institute for Brain Science, and the University of Washington on various exciting projects in this field.

What about your experience and research are you currently most proud of?

I’m particularly proud of the framework we developed to integrate black-box machine learning models with biologically realistic mechanistic models. We use advanced deep-learning techniques to infer unobserved information and combine this with prior knowledge from mechanistic models. This allows us to test hypotheses by applying different model variants. I believe this framework holds great potential to address a wide range of scientific questions, leveraging the power of AI.

What about Georgia Tech convinced you to accept a faculty position?

Georgia Tech CSE felt like a perfect fit for my background and research interests, particularly within the AI4Science initiative and the development of computational tools for biology and neuroscience. My work overlaps with several colleagues here, and I’m excited to collaborate with them. Georgia Tech also has a vibrant and impactful Neuro Next Initiative community, which is another great attraction.

What are your hobbies and interests when not researching and teaching?

I enjoy photography and love spending time with my two corgi dogs, especially taking them for walks.

What have you enjoyed most so far about living in Atlanta? 

I’ve really appreciated the peaceful, green environment with so many trees. I’m also looking forward to exploring more outdoor activities, like fishing and golfing.