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. 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.

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.”

[Related: Georgia Tech College of Computing Swarms to Yokohama, Japan, for CHI 2025]

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.”

Many communities rely on insights from computer-based models and simulations. This week, a nest of Georgia Tech experts are swarming an international conference to present their latest advancements in these tools, which offer solutions to pressing challenges in science and engineering.

Students and faculty from the School of Computational Science and Engineering (CSE) are leading the Georgia Tech contingent at the SIAM Conference on Computational Science and Engineering (CSE25). The Society of Industrial and Applied Mathematics (SIAM) organizes CSE25, occurring March 3-7 in Fort Worth, Texas.

At CSE25, the School of CSE researchers are presenting papers that apply computing approaches to varying fields, including:                   

• Experiment designs to accelerate the discovery of material properties
• Machine learning approaches to model and predict weather forecasting and coastal flooding
• Virtual models that replicate subsurface geological formations used to store captured carbon dioxide
• Optimizing systems for imaging and optical chemistry
• Plasma physics during nuclear fusion reactions

[Related: GT CSE at SIAM CSE25 Interactive Graphic] 

“In CSE, researchers from different disciplines work together to develop new computational methods that we could not have developed alone,” said School of CSE Professor Edmond Chow. 

“These methods enable new science and engineering to be performed using computation.” 

CSE is a discipline dedicated to advancing computational techniques to study and analyze scientific and engineering systems. CSE complements theory and experimentation as modes of scientific discovery. 

Held every other year, CSE25 is the primary conference for the SIAM Activity Group on Computational Science and Engineering (SIAG CSE). School of CSE faculty serve in key roles in leading the group and preparing for the conference.

In December, SIAG CSE members elected Chow to a two-year term as the group’s vice chair. This election comes after Chow completed a term as the SIAG CSE program director. 

School of CSE Associate Professor Elizabeth Cherry has co-chaired the CSE25 organizing committee since the last conference in 2023. Later that year, SIAM members reelected Cherry to a second, three-year term as a council member at large. 

At Georgia Tech, Chow serves as the associate chair of the School of CSE. Cherry, who recently became the associate dean for graduate education of the College of Computing, continues as the director of CSE programs. 

“With our strong emphasis on developing and applying computational tools and techniques to solve real-world problems, researchers in the School of CSE are well positioned to serve as leaders in computational science and engineering both within Georgia Tech and in the broader professional community,” Cherry said. 

Georgia Tech’s School of CSE was first organized as a division in 2005, becoming one of the world’s first academic departments devoted to the discipline. The division reorganized as a school in 2010 after establishing the flagship CSE Ph.D. and M.S. programs, hiring nine faculty members, and attaining substantial research funding.

Ten School of CSE faculty members are presenting research at CSE25, representing one-third of the School’s faculty body. Of the 23 accepted papers written by Georgia Tech researchers, 15 originate from School of CSE authors.

The list of School of CSE researchers, paper titles, and abstracts includes:
Bayesian Optimal Design Accelerates Discovery of Material Properties from Bubble Dynamics
Postdoctoral Fellow Tianyi Chu, Joseph Beckett, Bachir Abeid, and Jonathan Estrada (University of Michigan), Assistant Professor Spencer Bryngelson
[Abstract]

Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data
Ph.D. student Phillip Si, Assistant Professor Peng Chen
[Abstract]

A Goal-Oriented Quadratic Latent Dynamic Network Surrogate Model for Parameterized Systems
Yuhang Li, Stefan Henneking, Omar Ghattas (University of Texas at Austin), Assistant Professor Peng Chen
[Abstract]

Posterior Covariance Structures in Gaussian Processes
Yuanzhe Xi (Emory University), Difeng Cai (Southern Methodist University), Professor Edmond Chow
[Abstract]

Robust Digital Twin for Geological Carbon Storage
Professor Felix Herrmann, Ph.D. student Abhinav Gahlot, alumnus Rafael Orozco (Ph.D. CSE-CSE 2024), alumnus Ziyi (Francis) Yin (Ph.D. CSE-CSE 2024), and Ph.D. candidate Grant Bruer
[Abstract]

Industry-Scale Uncertainty-Aware Full Waveform Inference with Generative Models
Rafael Orozco, Ph.D. student Tuna Erdinc, alumnus Mathias Louboutin (Ph.D. CS-CSE 2020), and Professor Felix Herrmann
[Abstract]

Optimizing Coupled Systems: Insights from Co-Design Imaging and Optical Chemistry
Assistant Professor Raphaël Pestourie, Wenchao Ma and Steven Johnson (MIT), Lu Lu (Yale University), Zin Lin (Virginia Tech)
[Abstract]

Multifidelity Linear Regression for Scientific Machine Learning from Scarce Data
Assistant Professor Elizabeth Qian, Ph.D. student Dayoung Kang, Vignesh Sella, Anirban Chaudhuri and Anirban Chaudhuri (University of Texas at Austin)
[Abstract]

LyapInf: Data-Driven Estimation of Stability Guarantees for Nonlinear Dynamical Systems
Ph.D. candidate Tomoki Koike and Assistant Professor Elizabeth Qian
[Abstract]

The Information Geometric Regularization of the Euler Equation
Alumnus Ruijia Cao (B.S. CS 2024), Assistant Professor Florian Schäfer
[Abstract]

Maximum Likelihood Discretization of the Transport Equation
Ph.D. student Brook Eyob, Assistant Professor Florian Schäfer
[Abstract]

Intelligent Attractors for Singularly Perturbed Dynamical Systems
Daniel A. Serino (Los Alamos National Laboratory), Allen Alvarez Loya (University of Colorado Boulder), Joshua W. Burby, Ioannis G. Kevrekidis (Johns Hopkins University), Assistant Professor Qi Tang (Session Co-Organizer)
[Abstract]

Accurate Discretizations and Efficient AMG Solvers for Extremely Anisotropic Diffusion Via Hyperbolic Operators
Golo Wimmer, Ben Southworth, Xianzhu Tang (LANL), Assistant Professor Qi Tang 
[Abstract]

Randomized Linear Algebra for Problems in Graph Analytics
Professor Rich Vuduc
[Abstract]

Improving Spgemm Performance Through Reordering and Cluster-Wise Computation
Assistant Professor Helen Xu
[Abstract]

Florian Schäfer leads the “Matter and Information” research initiative for the Institute for Matter and Systems at Georgia Tech. In this role, his research focuses on numerical analysis, computational statistics, multi-agent optimization, and game-theoretic approaches in deep learning. Schäfer is an assistant professor in the School of Computational Science and Engineering.

In this brief Q&A, Schäfer discusses his research focus, how it relates to Matter and Systems’ core research focuses, and the national impact of this initiative.

What is your field of expertise and at what point in your life did you first become interested in this area?
I work on using statistical insights for designing algorithms to design physical systems. I can trace my interest in the interplay between physical systems and information processes all the way to high school times, when I was fascinated by the question of what it is that makes us think of some complex physical systems as "computers," but not of others.

What questions or challenges sparked your current research? 
Simulating physics is in many ways like statistical analysis with data produced by computation. My aim is to understand the implications of this perspective for algorithm design in scientific computing.

Matter and systems refer to the transformational technological and societal systems that arise from the convergence of innovative materials, devices, and processes. Why is your initiative important to the development of the IMS research strategy? 
An exciting current development is the two-fold convergence of physical and information sciences: The use of statistical /machine learning approaches for physical simulation and of new physical processes for computation. IMS is the perfect environment pursuing this goal.

What are the broader global and social benefits of the research you and your team conduct? 
The main societal contribution of my research is the efficient and reliable simulation of complex engineering system to aid the development of improved designs.

What are your plans for engaging a wider Georgia Tech faculty pool with the Institute for Matter and Systems research? 
I plan to engage researchers across GT through reading groups and seminars, with the goal of converging on a sufficiently concrete idea for an externally funded project. I hope that this will serve as a nucleus for exploring the use of novel physical processes for computation. 

Many communities rely on insights from computer-based models and simulations. This week, a nest of Georgia Tech experts are swarming an international conference to present their latest advancements in these tools, which offer solutions to pressing challenges in science and engineering.

Students and faculty from the School of Computational Science and Engineering (CSE) are leading the Georgia Tech contingent at the SIAM Conference on Computational Science and Engineering (CSE25). The Society of Industrial and Applied Mathematics (SIAM) organizes CSE25, occurring March 3-7 in Fort Worth, Texas.

At CSE25, the School of CSE researchers are presenting papers that apply computing approaches to varying fields, including:                   

• Experiment designs to accelerate the discovery of material properties
• Machine learning approaches to model and predict weather forecasting and coastal flooding
• Virtual models that replicate subsurface geological formations used to store captured carbon dioxide
• Optimizing systems for imaging and optical chemistry
• Plasma physics during nuclear fusion reactions

[Related: GT CSE at SIAM CSE25 Interactive Graphic] 

“In CSE, researchers from different disciplines work together to develop new computational methods that we could not have developed alone,” said School of CSE Professor Edmond Chow. 

“These methods enable new science and engineering to be performed using computation.” 

CSE is a discipline dedicated to advancing computational techniques to study and analyze scientific and engineering systems. CSE complements theory and experimentation as modes of scientific discovery. 

Held every other year, CSE25 is the primary conference for the SIAM Activity Group on Computational Science and Engineering (SIAG CSE). School of CSE faculty serve in key roles in leading the group and preparing for the conference.

In December, SIAG CSE members elected Chow to a two-year term as the group’s vice chair. This election comes after Chow completed a term as the SIAG CSE program director. 

School of CSE Associate Professor Elizabeth Cherry has co-chaired the CSE25 organizing committee since the last conference in 2023. Later that year, SIAM members reelected Cherry to a second, three-year term as a council member at large. 

At Georgia Tech, Chow serves as the associate chair of the School of CSE. Cherry, who recently became the associate dean for graduate education of the College of Computing, continues as the director of CSE programs. 

“With our strong emphasis on developing and applying computational tools and techniques to solve real-world problems, researchers in the School of CSE are well positioned to serve as leaders in computational science and engineering both within Georgia Tech and in the broader professional community,” Cherry said. 

Georgia Tech’s School of CSE was first organized as a division in 2005, becoming one of the world’s first academic departments devoted to the discipline. The division reorganized as a school in 2010 after establishing the flagship CSE Ph.D. and M.S. programs, hiring nine faculty members, and attaining substantial research funding.

Ten School of CSE faculty members are presenting research at CSE25, representing one-third of the School’s faculty body. Of the 23 accepted papers written by Georgia Tech researchers, 15 originate from School of CSE authors.

The list of School of CSE researchers, paper titles, and abstracts includes:
Bayesian Optimal Design Accelerates Discovery of Material Properties from Bubble Dynamics
Postdoctoral Fellow Tianyi Chu, Joseph Beckett, Bachir Abeid, and Jonathan Estrada (University of Michigan), Assistant Professor Spencer Bryngelson
[Abstract]

Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data
Ph.D. student Phillip Si, Assistant Professor Peng Chen
[Abstract]

A Goal-Oriented Quadratic Latent Dynamic Network Surrogate Model for Parameterized Systems
Yuhang Li, Stefan Henneking, Omar Ghattas (University of Texas at Austin), Assistant Professor Peng Chen
[Abstract]

Posterior Covariance Structures in Gaussian Processes
Yuanzhe Xi (Emory University), Difeng Cai (Southern Methodist University), Professor Edmond Chow
[Abstract]

Robust Digital Twin for Geological Carbon Storage
Professor Felix Herrmann, Ph.D. student Abhinav Gahlot, alumnus Rafael Orozco (Ph.D. CSE-CSE 2024), alumnus Ziyi (Francis) Yin (Ph.D. CSE-CSE 2024), and Ph.D. candidate Grant Bruer
[Abstract]

Industry-Scale Uncertainty-Aware Full Waveform Inference with Generative Models
Rafael Orozco, Ph.D. student Tuna Erdinc, alumnus Mathias Louboutin (Ph.D. CS-CSE 2020), and Professor Felix Herrmann
[Abstract]

Optimizing Coupled Systems: Insights from Co-Design Imaging and Optical Chemistry
Assistant Professor Raphaël Pestourie, Wenchao Ma and Steven Johnson (MIT), Lu Lu (Yale University), Zin Lin (Virginia Tech)
[Abstract]

Multifidelity Linear Regression for Scientific Machine Learning from Scarce Data
Assistant Professor Elizabeth Qian, Ph.D. student Dayoung Kang, Vignesh Sella, Anirban Chaudhuri and Anirban Chaudhuri (University of Texas at Austin)
[Abstract]

LyapInf: Data-Driven Estimation of Stability Guarantees for Nonlinear Dynamical Systems
Ph.D. candidate Tomoki Koike and Assistant Professor Elizabeth Qian
[Abstract]

The Information Geometric Regularization of the Euler Equation
Alumnus Ruijia Cao (B.S. CS 2024), Assistant Professor Florian Schäfer
[Abstract]

Maximum Likelihood Discretization of the Transport Equation
Ph.D. student Brook Eyob, Assistant Professor Florian Schäfer
[Abstract]

Intelligent Attractors for Singularly Perturbed Dynamical Systems
Daniel A. Serino (Los Alamos National Laboratory), Allen Alvarez Loya (University of Colorado Boulder), Joshua W. Burby, Ioannis G. Kevrekidis (Johns Hopkins University), Assistant Professor Qi Tang (Session Co-Organizer)
[Abstract]

Accurate Discretizations and Efficient AMG Solvers for Extremely Anisotropic Diffusion Via Hyperbolic Operators
Golo Wimmer, Ben Southworth, Xianzhu Tang (LANL), Assistant Professor Qi Tang 
[Abstract]

Randomized Linear Algebra for Problems in Graph Analytics
Professor Rich Vuduc
[Abstract]

Improving Spgemm Performance Through Reordering and Cluster-Wise Computation
Assistant Professor Helen Xu
[Abstract]

Bradford “Brad” Greer (bottom) and Kevin Ge (top), both 2023 graduates from the George W. Woodruff School of Mechanical Engineering, have taken their startup, CADMUS Health Analytics, from a classroom project to a promising health tech company. In 2023, CADMUS was accepted into the CREATE-X Startup Launch program. Over the 12-week accelerator, CADMUS made significant strides, and program mentors provided expert guidance, helping the team focus their direction based on real-world needs. Their partnership with Northeast Georgia Health System (NGHS) was a direct result of connections made at Startup Launch’s Demo Day.

How did you first hear about CREATE-X?

We did the CREATE-X Capstone with an initial team of seven people, later transitioning to Startup Launch in the summer. Capstone required a hardware product, but for several reasons, we pivoted to software. By that point, we already had a grasp on the problem that we were working on but didn't have the resources to start working on a large hardware product.

Why did you decide to pursue your startup?

One of our close buddies was an emergency medical technician (EMT), and we also had family connections to EMTs. When we were doing our customer interviews, we found out that Emergency Medical Services (EMS) had multiple problems that we thought we'd like to work on and that were more accessible than the broader medical technology industry. 

What was Startup Launch like for you?

Startup Launch seemed to transition pretty seamlessly from the Capstone course. We came to understand our customer base and technical development better, and the program also led us through the process of starting and running a company. I found it very interesting and learned a whole lot.

What was the most difficult challenge in Startup Launch?

Definitely customer interviews. We spent a lot of time on that in the Startup Launch classes. It's a difficult thing to have a good takeaway from a customer interview without getting the conversation confused and being misled. We didn't mention the product, or we tried to wait as long as possible before mentioning the product, so as to not bias or elicit general, positive messaging from interviewees. 

We're working in EMS, and the products we are building affect healthcare. EMS is a little informal and a little rough around the edges. Many times, people don't want to admit how bad their practices are, which can easily lead to us collecting bad data. 

What affected you the most from Startup Launch?

The resources at our fingertips. When we were running around, it was nice to be able to consult with our mentor. It's great having someone around with the know-how and who's been through it themselves. I revisit concepts a lot.

How did the partnership with NGHS come about?

During Demo Day, we met a Georgia state representative. He put us in touch with NGHS. They were looking for companies to work with through their venture arm, Northeast Georgia Health Ventures(NGHV), so we pitched our product to them. They liked it, and then we spent a long time banging out the details. We worked with John Lanza, who's a friend of CREATE-X. He helped us find a corporate lawyer to read over the stuff we were signing. It took a little back and forth to get everything in place, but in September of last year, we finally kicked it off.

What’s the partnership like?

We provide them a license to our product, have weekly meetings where experts give feedback on the performance of the system, and then we make incremental changes to align the product with customer needs. 

While we're in this developmental phase, we're kind of keeping it under wraps until we make sure it’s fully ready. Our focus is primarily on emergent capabilities that NGHS and other EMS agencies are really looking for. Right now, the pilot is set to be a year long, so we're aiming to be ready for a full rollout by the end of the year. 

How did you pivot into this other avenue for your product?

EMS does not have many resources. That makes it not a popular space as far as applying emerging technologies. There's only competition in this very one specific vein, which is this central type of software that we plug into, so we're not competing directly with anyone.

EMS agencies, EMTs, and paramedics - the care that they give has to be enabled by a medical doctor. There has to be a doctor linked to the practices that they engage in and the procedures that they do. With the product that we're making now, we want to provide a low-cost, plug-and-play product that'll do everything they need it to do to enable the improvement of patient care. 

How are you supporting yourself during this period? 

I was paying myself last year, but we're out of money for that, so we're not currently paying for any labor. It's all equity now, but our burn rate outside of that is very low. The revenue we have now easily covers the cost of operating our system. I'm also working part-time as an EMT now. This helps cover my own costs while also deepening my understanding of the problems we are working on.

How are you balancing your work?

It's hard to balance. There's always stuff to do. I just do what I can, and the pace of development is good enough for the pilot. Every week, and then every month, Kevin and I sit down and analyze the rate at which we're working and developing. Then we project out. We're confident that we're developing at a rate that'll have us in a good spot by September when the pilot ends.

What’s a short-term goal for your startup?

Kevin and I are trying to reach back out and see if there's anyone interested in joining and playing a major role. The timing would be such that they start working a little bit after the spring semester ends. I think most Georgia Tech students would meet the role requirements, but generally, JavaScript and Node experience as well as a diverse background would be good.

Where do you want your startup to be in the next five years?

I want to have a very well-designed system. Despite all the vectors I’m talking about for our products, everything should be part of the same system in place at EMS agencies anywhere. I just want it to be a resource that EMS can use broadly.

Another issue in EMS is standards. Even the standards that are in place now aren’t broadly accessible. I think that these new AI tools can do a lot to bridge the lack of understanding of documentation, measures, and standards and make all of that more accessible for the layperson.

What advice would you give students interested in entrepreneurship?

Make sure the idea that you're working on, and the business model, is something you enjoy outside of its immediate viability. I think that's really what's helped me persevere. It's my enjoyment of the project that's allowed me to continue and be motivated. So, start there and then work your way forward.

Are there any books, podcasts, or resources you would recommend to budding entrepreneurs?

 I’d recommend Influence to prepare for marketing. I have no background in marketing at all. Influence is a nice science-based primer for marketing.

 I reread How to Win Friends and Influence People. I am not sure how well I'm implementing the concepts day-to-day, but I think most of the main points of that book are solid.

I also read The Mom Test. It's a good reference, a short text on customer interviews.

Want to build your own startup?

Georgia Tech students, faculty, researchers, and alumni interested in developing their own startups are encouraged to apply to CREATE-X's Startup Launch, which provides $5,000 in optional seed funding and $150,000 in in-kind services, mentorship, entrepreneurial workshops, networking events, and resources to help build and scale startups. The program culminates in Demo Day, where teams present their startups to potential investors. The deadline to apply for Startup Launch is Monday, March 17. Spots are limited. Apply now.

Men and women in California put their lives on the line when battling wildfires every year, but there is a future where machines powered by artificial intelligence are on the front lines, not firefighters.

However, this new generation of self-thinking robots would need security protocols to ensure they aren’t susceptible to hackers. To integrate such robots into society, they must come with assurances that they will behave safely around humans.

It begs the question: can you guarantee the safety of something that doesn’t exist yet? It’s something Assistant Professor Glen Chou hopes to accomplish by developing algorithms that will enable autonomous systems to learn and adapt while acting with safety and security assurances. 

He plans to launch research initiatives, in collaboration with the School of Cybersecurity and Privacy and the Daniel Guggenheim School of Aerospace Engineering, to secure this new technological frontier as it develops. 

“To operate in uncertain real-world environments, robots and other autonomous systems need to leverage and adapt a complex network of perception and control algorithms to turn sensor data into actions,” he said. “To obtain realistic assurances, we must do a joint safety and security analysis on these sensors and algorithms simultaneously, rather than one at a time.”

This end-to-end method would proactively look for flaws in the robot’s systems rather than wait for them to be exploited. This would lead to intrinsically robust robotic systems that can recover from failures.

Chou said this research will be useful in other domains, including advanced space exploration. If a space rover is sent to one of Saturn’s moons, for example, it needs to be able to act and think independently of scientists on Earth. 

Aside from fighting fires and exploring space, this technology could perform maintenance in nuclear reactors, automatically maintain the power grid, and make autonomous surgery safer. It could also bring assistive robots into the home, enabling higher standards of care. 

This is a challenging domain where safety, security, and privacy concerns are paramount due to frequent, close contact with humans.

This will start in the newly established Trustworthy Robotics Lab at Georgia Tech, which Chou directs. He and his Ph.D. students will design principled algorithms that enable general-purpose robots and autonomous systems to operate capably, safely, and securely with humans while remaining resilient to real-world failures and uncertainty.

Chou earned dual bachelor’s degrees in electrical engineering and computer sciences as well as mechanical engineering from University of California Berkeley in 2017, a master’s and Ph.D. in electrical and computer engineering from the University of Michigan in 2019 and 2022, respectively. He was a postdoc at MIT Computer Science & Artificial Intelligence Laboratory prior to joining Georgia Tech in November 2024. He is a recipient of the National Defense Science and Engineering Graduate fellowship program, NSF Graduate Research fellowships, and was named a Robotics: Science and Systems Pioneer in 2022.

Effective January 1st, David Sherrill will serve as interim executive director of the Georgia Tech Institute for Data Engineering and Science (IDEaS). Sherrill is a Regents' Professor in the School of Chemistry and Biochemistry with a joint appointment in the College of Computing. Sherrill has served as associate director for IDEaS since its founding in 2016.

"David Sherrill's leadership role in IDEaS as associate director, together with his interdisciplinary background in chemistry and computer science, makes him the right person to support this transition as interim executive director," said Julia Kubanek, professor and vice president for interdisciplinary research at Georgia Tech. 

Sherrill succeeds Srinivas Aluru who will be taking a new position as Senior Associate Dean in the College of Computing. Aluru, a Regents' Professor in the School of Computational Science and Engineering, co-founded IDEaS and served as its co-executive director (2016-2019) and then as executive director (2019-date), spanning eight and a half years. Under his leadership IDEaS grew to more than 200 affiliate faculty spanning all colleges, encompassing multiple state, federal, and industry funded centers. Notable among these is the South Big Data Hub, catalyzing the Southern data science community to collectively accelerate scientific discovery and innovation, spur economic development in the region, broaden participation and diversity in data science, and the CloudHub, a Microsoft funded center that provides research funding and cloud resources for innovative applications in Generative Artificial Intelligence. More recently, Aluru established the Center for Artificial Intelligence in Science and Engineering (ARTISAN), and expanded the Institute’s research staff to provide needed cyberinfrastructure, software resources, and expertise to support faculty projects with large data sets and AI-driven discovery. "I've had the pleasure of serving as Associate Director of IDEaS since it was founded by Srinivas Aluru and Dana Randall, and I'm excited to step into this interim role.” said Sherrill. “IDEaS has an important mission to serve the many faculty doing interdisciplinary research involving data science and high performance computing."

Sherrill’s research group focuses on the development of ab initio electronic structure theory and its application to problems of broad chemical interest, including the influence of non-covalent interactions in drug binding, biomolecular structure, organic crystals, and organocatalytic transition states. The group seeks to apply the most accurate quantum models possible for a given problem and specializes in generating high-quality datasets for testing new methods or machine-learning purposes. 

Sherrill earned a B.S. in chemistry from MIT in 1992 and a Ph.D. in chemistry from the University of Georgia in 1996. From 1996-1999 Sherril was an NSF Postdoctoral Fellow, working under M. Head-Gordon, at the University of California, Berkeley.

Sherrill is a Fellow of the American Association for the Advancement of Science (AAAS), the American Chemical Society, and the American Physical Society, and he has been Associate Editor of the Journal of Chemical Physics since 2009. Sherrill has received a Camille and Henry Dreyfus New Faculty Award, the International Journal of Quantum Chemistry Young Investigator Award, an NSF CAREER Award, and Georgia Tech's W. Howard Ector Outstanding Teacher Award. In 2023, he received the Herty Medal from the Georgia Section of the American Chemical Society, and in 2024, he was elected to the International Academy of Quantum Molecular Science.

--Christa M. Ernst

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.”