An AI-powered tool is changing how researchers study disasters and how students learn from them. 

In the International Disaster Reconnaissance (IDR) course, students now use Filio, a platform built by School of Computing Instruction Senior Lecturer Max Mahdi Roozbahani, to capture immersive 360° media, photos, and video that transform real disaster sites in India and Nepal into living digital classrooms. 

Offered by the School of Civil and Environmental Engineering and taught by IDR director and Regents’ Professor David Frost, the course pairs traditional fieldwork with Roozbahani’s expertise in immersive technology and data-driven learning, transforming on-the-ground observations into reusable, interactive educational resources. 

How Computing Can Capture Data 

Disasters are not only physical events; they are also information events, Roozbahani says. Effective response and long-term resilience depend on the ability to observe, record, and communicate critical data under pressure. Georgia Tech’s IDR course pairs structured on-campus preparation with international field experiences, enabling students to study the cascading effects of major disasters, including how local building practices, governance, and culture shape damage and recovery. 

“When students step into a disaster zone, they learn quickly that resilience is a systems problem: physical, social, and informational. Our job in computing is to help them capture and reason about that system responsibly,” Roozbahani said. 

Learning from the 2025 Himalayas Expedition 

During spring break last year, the cohort traveled along the Teesta River corridor in Sikkim, India. The region is shaped by steep terrain, fast-moving water, and critical infrastructure in narrow valleys. 

The visit followed the October 2023 glacial lake outburst flood from South Lhonak Lake, which destroyed the Teesta III hydropower dam and impacted downstream towns, including Dikchu and Rangpo. Field stops across India included Lachung, Chungthang, Dikchu, Rangpo, Gangtok, and New Delhi. 

Students explored both upstream and downstream consequences. 

Upstream, the team examined how steep terrain and river confinement amplify flood forces, creating cascading risks for infrastructure. Using Filio’s interactive 360° media, students captured conditions in Lachung and Chungthang, allowing viewers to explore the landscape through a 360° photo and 360° video that reveal how topography and river dynamics intensify disaster impacts. 

They studied community-scale effects downstream, including damaged buildings, disrupted access, and prolonged recovery timelines. 

Rangpo offered a glimpse of recovery in motion, with materials staged for rebuilding bridges and roads essential to commerce and emergency response.

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Just as it takes a village to raise a child, it takes a community of faculty, mentors, research collaborators, and staff to raise a Georgia Tech graduate.

The Yellow Jacket community swarmed campus for the final time of the fall semester to celebrate Commencement ceremonies held Dec. 11 to 13. Graduates from the School of Computational Science and Engineering (CSE) were among the 7,177 new alumni “getting out” of Tech.     

“We are immensely proud of School of CSE and CSE programs graduates in the Class of 2025,” said Haesun Park, Regents’ Professor and Chair of the School of CSE.

“Our collaborative approach to CSE education has prepared these graduates to attain roles in academia, national labs, industry, government, and beyond, where they will lead the next generation of interdisciplinary research.”

Along with administering its flagship CSE Ph.D. and M.S. CSE programs, the School of CSE offers doctoral degrees in computer science and machine learning. Ph.D. graduates who received their diplomas and doctoral hoods on Dec. 11 at McCamish Pavilion included:

• Grant Bruer (Ph.D. CSE-CSE 2025), advised by School of CSE Professor and Associate Chair Edmond Chow
• Dongjin Choi (Ph.D. CSE-CSE 2025), advised by School of CSE Regents’ Professor and Chair Haesun Park
• Hyungu Choi (Ph.D. CSE-AE 2025), advised by Daniel Guggenheim School of Aerospace Engineering Regents’ Professor Dimitri Mavris
• Maxfield Comstock (Ph.D. CSE-CSE 2025), advised by Elizabeth Cherry, College of Computing Associate Dean for Graduate Education and School of CSE Associate Professor
• Andrew Hornback (Ph.D. CS-CSE 2025), co-advised by School of CSE Assistant Professor Yunan Luo and Wallace H. Coulter Department of Biomedical Engineering Professor May Wang
• Ayush Jain (Ph.D. CSE-MSE 2025), advised by School of Materials Science and Engineering Regents’ Entrepreneur and Professor Rampi Ramprasad
• Anurendra Kumar (Ph.D. CS-CSE 2025), co-advised by School of CSE J.Z. Liang Early Career Associate Professor Xiuwei Zhang and Wallace H. Coulter Department of Biomedical Engineering Professor Saurabh Sinha
• Jiajia Xie (Ph.D. CSE-BME 2025), advised by Wallace H. Coulter Department of Biomedical Engineering Associate Professor Cassie Mitchell
• Yuchen Zhuang (Ph.D. ML-CSE 2025), advised by School of CSE Edenfield Early Career Associate Professor Chao Zhang
• Ziqi Zhang (Ph.D. CSE-CSE 2025), advised by School of CSE J.Z. Liang Early Career Associate Professor Xiuwei Zhang

Seven CSE Ph.D. students completed M.S. degrees this fall and will continue their studies at Georgia Tech. They are:

• Jesus Arias (M.S. CSE-CSE 2025), advised by School of CSE Assistant Professor Spencer Bryngelson
• Isabel Berry (M.S. CSE-CHEM 2025), advised by Regents’ Professor C. David Sherrill, who is jointly appointed with the School of Chemistry and Biochemistry and the School of CSE
• Max Hawkins (M.S. CSE-CSE 2025), co-advised by School of CSE Professor Rich Vuduc and Assistant Professor Spencer Bryngelson
• Xiao Jing (M.S. CSE-AE 2025), advised by Daniel Guggenheim School of Aerospace Engineering Regents’ Professor Dimitri Mavris
• Haoyun Li (M.S. CSE-CSE 2025), advised by Professor Felix Herrmann, who is jointly appointed with the Schools of Earth and Atmospheric Sciences, Electrical and Computer Engineering, and CSE
• Yuan Qiu (M.S. CSE-CSE 2025), advised by School of CSE Assistant Professor Peng Chen
• William Schertzer (M.S. CSE-MSE 2025), advised by School of Materials Science and Engineering Regents’ Entrepreneur and Professor Rampi Ramprasad

Georgia Tech’s CSE graduate program includes 12 schools and departments participating as home units. These home units represent the colleges of Computing, Engineering, and Sciences. This approach facilitates an immersive, interdisciplinary experience in which students study computational approaches within domain fields.

Georgia Tech jointly celebrated master’s graduates at a ceremony on Dec. 13 at Bobby Dodd Stadium. After the Institute celebration, graduates were recognized during ceremonies held by their respective colleges.

Mawutor Kofi Amanfu (M.S. CSE 2025)

Sunyoung An (M.S. CSE 2025)

Nischal Bandi (M.S. CSE 2025)

Elijah Bellamy (M.S. CSE 2025)

Meiwen Bi (M.S. CSE 2025)

Hao-Cheng Chang (M.S. CSE 2025)

Tianyu Chen (M.S. CSE 2025)

Yilong Chen (M.S. CSE 2025)

Zhiyu Chen (M.S. CSE 2025)

Seung Eun Choi (M.S. CSE 2025)

Vinodhini Comandur (M.S. CSE 2025)

Zhiyi Dai (M.S. CSE 2025)

Alejandro Danies-Lopez (M.S. CSE 2025)

Zixing Fan (M.S. CSE 2025)

Stefan Faulkner (M.S. CSE 2025)

Mihiri Fernando (M.S. CSE 2025)

Alexandra Freeman (M.S. CSE 2025)

Yuhan Fu (M.S. CSE 2025)

Jack Ganem (M.S. CSE 2025)

Omar Atef Garib (M.S. CSE 2025)

Martin Graffigna (M.S. CSE 2025)

Bochun Guo (M.S. CSE 2025)

Moyi Guo (M.S. CSE 2025)

Xinyu Guo (M.S. CSE 2025)

Yuqi Han (M.S. CSE 2025)

Tianyang Hu (M.S. CSE 2025)

Mingzheng Huang (M.S. CSE 2025)

Po-Han Huang (M.S. CSE 2025)

Wentao Jiang (M.S. CSE 2025)

Boxiao Jin (M.S. CSE 2025)

William-Michael Johnson (M.S. CSE 2025)

Garyoung Lee (M.S. CSE 2025)

Tzu Jung Lee (M.S. CSE 2025)

Congyan Li (M.S. CSE 2025)

Peiru Li (M.S. CSE 2025)

Yuhan Li (M.S. CSE 2025)

Zhiyun Liang (M.S. CSE 2025)

Yuexi Liao (M.S. CSE 2025)

Chenyu Liu (M.S. CSE 2025)

Honglin Liu (M.S. CSE 2025)

Shuojiang Liu (M.S. CSE 2025)

Xuanzhang Liu (M.S. CSE 2025)

Yue Lu (M.S. CSE 2025)

Fang Lunt (M.S. CSE 2025)

Jinrui Ma (M.S. CSE 2025)

Yu Miao (M.S. CSE 2025)

Hui-Chun Mo (M.S. CSE 2025)

Prajwal Kumar (M.S. CSE 2025)

Kavya Krishnan (M.S. CSE 2025)

Felicity Nielson (M.S. CSE 2025)

Jonathan Perng (M.S. CSE 2025)

Yinzhu Quan (M.S. CSE 2025)

Devanshi Shah (M.S. CSE 2025)

Yuxuan Shen (M.S. CSE 2025)

Steven Stewart (M.S. CSE 2025)

Linjun Su (M.S. CSE 2025)

Jingyun Sun (M.S. CSE 2025)

Abdul Rehman Tariq (M.S. CSE 2025)

Yu Chu Tsai (M.S. CSE 2025)

Xunzhi Wen (M.S. CSE 2025)

Jinghua Weng (M.S. CSE 2025)

Andi Xia (M.S. CSE 2025)

Zihao Xiao (M.S. CSE 2025)

Yunxiang Yan (M.S. CSE 2025)

Ziyuan Ye (M.S. CSE 2025)

Linyuan Yu (M.S. CSE 2025)

Bingqing Zhang (M.S. CSE 2025)

Tiankuo Zhang (M.S. CSE 2025)

Yu Zheng (M.S. CSE 2025)

Boye Zhou (M.S. CSE 2025)

Xinjie Zhu (M.S. CSE 2025)

Zilu Zhu (M.S. CSE 2025)

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Ph.D. student Ziqi Zhang has built a career blending machine learning with single-cell biology. His work helps scientists study cellular mechanisms that advance disease research and drug development.

Though decorated with awards and appearances in leading journals, Zhang will achieve his greatest accomplishment tonight at McCamish Pavilion. He will join the Class of 2025 in walking across the stage, receiving diplomas, and graduating from Georgia Tech.

Before he “gets out” of Georgia Tech, we interviewed Zhang to learn more about his Ph.D. journey and where his degree will take him next. 

Graduate: Ziqi Zhang

Research Interests: Machine learning, foundational models, cellular mechanisms, single-cell gene sequencing, gene regulatory networks

Education: Ph.D. in Computational Science and Engineering

Faculty Advisor: School of CSE J.Z. Liang Early-Career Associate Professor Xiuwei Zhang

What persuaded you to study at Georgia Tech? 

I chose Georgia Tech because it is one of the top engineering institutions in the United States, known for its strength in machine learning and data science. The university offers exceptional research resources and the opportunity to work with leading scholars in my field. Georgia Tech also has very good research infrastructure. The Coda Building is one of the most well-designed and productive research environments I have experienced. Having access to such a space has been a genuine privilege.

How has working on your CSE degree helped you so far in your career?

Working toward my CSE degree has been instrumental in my career development. As an interdisciplinary program, CSE has equipped me with strong computational skills while also deepening my understanding of key application domains. This breadth of training has opened more opportunities during my job and internship searches. In addition, CSE community events, such as HotCSE, the weekly coffee hour, and faculty recruiting activities, have helped me strengthen my scientific communication skills, which are essential for my long-term career growth.

What research project from Georgia Tech are you most proud of?

My favorite research project was scMoMaT, a matrix tri-factorization algorithm for single-cell data integration. I invested a significant amount of time and effort into this work, iterating on the model many times. I’m very proud that it ultimately evolved into a clean, robust, and elegant algorithm.

What advice would you give someone interested in graduate school?

It is important to find an advisor who is supportive and genuinely invested in your career development. A Ph.D. is not an easy journey, and you will inevitably encounter challenges along the way. Having an advisor who can provide thoughtful guidance and dedicated mentorship is one of the most crucial factors in helping you navigate those difficulties.

What is your most favorite memory from Georgia Tech?

CSE’s new student campus visit day every year was one of my favorite times of the year. It was always fun to meet new people, have good food, and enjoy the beautiful view from the Coda rooftop.

What are your plans after graduation?

I plan to keep working in academia after graduation. I’m on the job hunt, currently applying for positions and preparing for interviews.

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Proteins, including antibodies, hemoglobin, and insulin, power nearly every vital aspect of life. Breakthroughs in protein research are producing vaccines, resilient crops, bioenergy sources, and other innovative technologies.

Despite their importance, most of what scientists know about proteins only comes from a small sample size. This stands in the way of fully understanding how most proteins work and unlocking their full potential.

Georgia Tech’s Yunan Luo believes artificial intelligence (AI) could fill this knowledge gap. The National Science Foundation agrees. Luo is the recipient of an NSF Faculty Early Career Development (CAREER) award. 

“So much of biology depends on knowing what proteins do, but decades of research have concentrated on a relatively small set of well-studied proteins. This imbalance in scientific attention leads to a distorted view of the biological landscape that quietly shapes our data and our algorithms,” Luo said.

“My group’s goal is to build machine learning (ML) models that actively close this gap by generating trustworthy function predictions for the many proteins that remain understudied.”

[Related: Yunan Luo to use AI for Protein Design and Discovery with Support of $1.8 Million NIH Grant]

In his proposal to NSF, Luo coined this rich-get-richer effect “annotation inequality.” 

One problem of annotation inequality is that it slows progress in disease prognosis, drug discovery, and other critical biomedical areas. It is challenging to innovate the few proteins that scientists already know so much about. 

A cascading effect of annotation inequality is that it diminishes the effectiveness of studying proteins with AI.  

AI methods learn from existing experimental data. Datasets skewed toward well-known proteins propagate and become entrenched in models. Over time, this makes it harder for computers to research understudied proteins. 

“Protein annotation inequality creates an effect analogous to a vast library where 95% of patrons only read the top 5% popular books, leaving the rest of the collection to gather dust,” Luo said.

“This has resulted in knowledge disparities across proteins in current literature and databases, biasing our understanding of protein functions.”

The NSF CAREER award will fund Luo with over $770,000 for the next five years to tackle head-on the problem of protein annotation inequality.

Luo will use the grant to build an accurate, unbiased protein function prediction framework at scale. His project aims to:

• Reveal how annotation inequality affects protein function prediction systems
• Create ML techniques suited for biological data, which is often noisy, incomplete, and imbalanced  
• Integrate data and ML models into a scalable framework to accelerate discoveries involving understudied proteins

More enduring than the ML framework, Luo will leverage the NSF award to support educational and outreach programs. His goal is to groom the next generation of researchers to study other challenges in computational biology, not just the annotation inequality problem.

Luo teaches graduate and undergraduate courses focused on computational biology and ML. Problems and methods developed through the CAREER project can be used as course material in his classes.

Luo also championed collaboration with Georgia Tech’s Center for Education Integrating Science, Mathematics, and Computing (CEISMC) in his proposal. 

Through this partnership, local high school teachers and students would gain access to his data and models. This promotes deeper learning of biology and data science through hands-on experience with real-world tools.  

Luo sees reaching students and the community as a way of paying forward the support he received from Georgia Tech colleagues. 

“I am incredibly grateful for this recognition from the NSF,” said Luo, an assistant professor in the School of Computational Science and Engineering (CSE). 

“This would not have been possible without my students and collaborators, whose hard work laid the groundwork for this proposal.”

Luo praised CSE faculty members B. Aditya Prakash, Xiuwei Zhang, and Chao Zhang for their guidance. All three study machine learning and computational bioscience, two of CSE’s five core research areas. 

Luo also thanked Haesun Park for her support and recommendation for the CAREER award. Park is a Regents’ Professor and the chair of the School of CSE.

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Spaceflight is becoming safer, more frequent, and more sustainable thanks to the largest computational fluid flow simulation ever ran on Earth.

Inspired by SpaceX’s Super Heavy booster, a team led by Georgia Tech’s Spencer Bryngelson and New York University’s Florian Schäfer modeled the turbulent interactions of a 33-engine rocket. Their experiment set new records, running the largest ever fluid dynamics simulation by a factor of 20 and the fastest by over a factor of four.

The team ran its custom software on the world’s two fastest supercomputers, as well as the eighth fastest, to construct such a massive model.

Applications from the simulation reach beyond rocket science. The same computing methods can model fluid mechanics in aerospace, medicine, energy, and other fields. At the same time, the work advances understanding of the current limits and future potential of computing. 

The team finished as runners-up for the 2025 Gordon Bell Prize for its impactful, multi-domain research. Referred to as the Nobel Prize of supercomputing, the award was presented at the world’s top conference for high-performance computing (HPC) research.

“Fluid dynamics problems of this style, with shocks, turbulence, different interacting fluids, and so on, are a scientific mainstay that marshals our largest supercomputers,” said Bryngelson, an assistant professor with the School of Computational Science and Engineering (CSE).

“Larger and faster simulations that enable solutions to long-standing scientific problems, like the rocket propulsion problem, are always needed. With our work, perhaps we took a big dent out of that issue.”

The Super Heavy booster reflects the space industry’s move toward reusable multi-engine first-stage rockets that are easier to transport and more economical overall. 

However, this shift creates research and testing challenges for new designs.

Each of Super Heavy’s 33 thrusters expels propellant at ten times the speed of sound. As individual engines reach extreme temperatures, pressures, and densities, their combined interactions with the airframe make such violent physics even more unpredictable.

Frequent physical experiments would be expensive and risky, so scientists rely on computer models to supplement the engineering process. 

Bryngelson’s flagship Multicomponent Flow Code (MFC) software anchored the experiment. MFC is an open-source computer program that simulates fluid dynamic models. Bryngelson’s lab has been modifying MFC since 2022 to run on more powerful computers and solve larger problems. 

In computing terms, this MFC-enhanced model simulated fluid flow resolution at 200 trillion grid points and one quadrillion degrees of freedom. These metrics exceeded previous record-setting benchmarks that tallied 10 trillion and 30 trillion grid points.

This means MFC simulations provide greater detail and capture smaller-scale features than previous approaches. The rocket simulation also ran four times faster and achieved 5.7 times the energy efficiency of comparable methods.   

Integrating information geometric regularization (IGR) into MFC played a key role in attaining these results. This new approach improved the simulation’s computational efficiency and overcame the challenge of shock dynamics.

In fluid mechanics, shock waves occur when objects move faster than the speed of sound. Along with hampering the performance of airframes and propulsion systems, shocks have historically been difficult to simulate.

Computational scientists have used empirical models based on artificial viscosity to account for shocks. Although these approaches mimic the physical effects of shock waves at the microscopic scale, they struggle to effectively capture the large-scale features of the flow. 

Information geometry uses curved spaces to study concepts of statistics and information. IGR uses these tools to modify the underlying geometry in fluid dynamics equations. When traveling in the modified geometry, fluid in the model preserves the shocks in a more natural way. 

“When regularizing shocks to much larger scales relevant in these numerical simulations, conventional methods smear out important fine-scale details,” said Schäfer, an assistant professor at NYU’s Courant Institute of Mathematical Sciences.

“IGR introduces ideas from abstract math to CFD that allow creating modified paths that approach the singularity without ever reaching it. In the resulting fluid flow, shocks never become too spiky in simulations, but the fine-scale details do not smear out either.” 

Simulating a model this large required the Georgia Tech researchers to run MFC on El Capitan and Frontier, the world's two fastest supercomputers. 

The systems are two of four exascale machines in existence. This means they can solve at least one quintillion (“1” followed by 18 zeros) calculations per second. If a person completed a simple math calculation every second, it would take that person about 30 billion years to reach one quintillion operations.

Frontier is housed at Oak Ridge National Laboratory and debuted as the world’s first exascale supercomputer in 2022. El Capitan surpassed Frontier when Lawrence Livermore National Laboratory launched it in 2024.

To prepare MFC for performance on these machines, Bryngelson’s lab followed a methodical approach spanning years of hardware acquisition and software engineering. 

In 2022, Bryngelson attained an AMD MI210 GPU accelerator. Optimizing MFC on the component played a critical step toward preparing the software for exascale machines.

AMD hardware underpins both El Capitan and Frontier. The MI300A GPU powers El Capitan while Frontier uses the MI250X GPU. 

After configuring MFC on the MI210 GPU, Bryngelson’s lab ran the software on Frontier for the first time during a 2023 hackathon. This confirmed the code was ready for full-scale deployment on exascale supercomputers based on AMD hardware. 

In addition to El Capitan and Frontier, the simulation ran on Alps, the world’s eight-fastest supercomputer based at the Swiss National Supercomputing Centre. It is the largest available system that features the NVIDIA GH200 Grace Hopper Superchip.

Like with AMD GPUs, Bryngelson acquired four GH200s in 2024 and began configuring MFC to the latest hardware innovation powering New Age supercomputers. Later that year, the Jülich Research Centre accepted Bryngelson’s group into an early access program to test JUPITER, a developing supercomputer based on the NVIDIA superchip.

The group earned a certificate for scaling efficiency and node performance on the way toward validating that their code worked on the GH200. The early access project proved successful for JUPITER, which launched in 2025 as Europe’s fastest supercomputer and fourth fastest in the world.

“Getting the level of hands-on experience with world-leading supercomputers and computing resources at Georgia Tech through this project has been a fantastic opportunity for a grad student,” said CSE Ph.D. student Ben Wilfong.

“To leverage these machines, I learned more advanced programming techniques that I’m glad to have in my tool belt for future projects. I also enjoyed the opportunity to work closely with and learn from industry experts from NVIDIA, AMD, and HPE/Cray.”

El Capitan, Frontier, JUPITER, and Alps maintained their rankings at the 2025 International Conference for High Performance Computing Networking, Storage and Analysis (SC25). Of note, the TOP500 announced at SC25 that JUPITER surpassed the exaflop threshold. 

The SC Conference Series is one of two venues where the TOP500 announces updated supercomputer rankings every June and November. The TOP500 ranks and details the 500 most powerful supercomputers in the world. 

The SC Conference Series serves as the venue where the Association for Computing Machinery (ACM) presents the Gordon Bell Prize. The annual award recognizes achievement in HPC research and application. The Tech-led team was among eight finalists for this year’s award.

Along with Bryngelson, Georgia Tech members included Ph.D. students Anand Radhakrishnan and Wilfong, postdoctoral researcher Daniel Vickers, alumnus Henry Le Berre (CS 2025), and undergraduate student Tanush Prathi.

Schäfer’s partnership with the group stems from his previous role as an assistant professor at Georgia Tech from 2021 to 2025. 

Collaborators on the project included Nikolaos Tselepidis and Benedikt Dorschner from NVIDIA, Reuben Budiardja from ORNL, Brian Cornille from AMD, and Stephen Abbot from HPE. All were co-authors of the paper and named finalists for the Gordon Bell Prize. 

“I’m elated that we have been nominated for such a prestigious award. It wouldn't have been possible without the combined and diligent efforts of our team,” Radhakrishnan said. 

“I’m looking forward to presenting our work at SC25 and connecting with other researchers and fellow finalists while showcasing seminal work in the field of computing.”

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Viral videos abound with humanoid robots performing amazing feats of acrobatics and dance but finding videos of a humanoid robot performing a common household task or traversing a new multi-terrain environment easily, and without human control, are much rarer. This is because training humanoid robots to perform these seemingly simple functions involves the need for simulation training data that lack the complex dynamics and degrees of freedom of motion that are inherent in humanoid robots. 

To achieve better training outcomes with faster deployment results, Fukang Liu and Feiyang Wu, graduate students under Professor Ye Zhao from the Woodruff School of Mechanical Engineering and faculty member of the Institute for Robotics and Intelligent Machines, have published a duo of papers in IEEE Robotics and Automation Letters. This is a collaborative work with three other IRIM affiliated faculties, Profs. Danfei Xu, Yue Chen, and Sehoon Ha, as well as Prof. Anqi Wu from School of Computational Science and Engineering.

To develop more reliable motion learning for humanoid robots and enable humanoid robots to perform complex whole-body movements in the real world, Fukang led a team and developed Opt2Skill, a hybrid robot learning framework that combines model-based trajectory optimization with reinforcement learning.  Their framework integrates dynamics and contacts into the trajectory planning process and generates high-quality, dynamically feasible datasets, which result in more reliable motion learning for humanoid robots and improved position tracking and task success rates. This approach shows a promising way to augment the performance and generalization of humanoid RL policies using dynamically feasible motion datasets. Incorporating torque data also improved motion stability and force tracking in contact-rich scenarios, demonstrating that torque information plays a key role in learning physically consistent and contact-rich humanoid behaviors.

While other datasets, such as inverse kinematics or human demonstrations, are valuable, they don’t always capture the dynamics needed for reliable whole-body humanoid control.” said by Fukang Liu. “With our Opt2Skill framework, we combine trajectory optimization with reinforcement learning to generate and leverage high-quality, dynamically feasible motion data. This integrated approach gives robots a richer and more physically grounded training process, enabling them to learn these complex tasks more reliably and safely for real-world deployment. - Fukang Liu

In another line of humanoid research, Feiyang established a one-stage training framework that allows humanoid robots to learn locomotion more efficiently and with greater environmental adaptability. Their framework, Learn-to-Teach (L2T), unlike traditional two-stage “teacher-student” approaches, which first train an expert in simulation and then retrain a limited-perception student, teaches both simultaneously, sharing knowledge and experiences in real time. The result of this two-way training is a 50% reduction in training data and time, while maintaining or surpassing state-of-the-art performance in humanoid locomotion. The lightweight policy learned through this process enables the lab’s humanoid robot to traverse more than a dozen real-world terrains—grass, gravel, sand, stairs, and slopes—without retraining or depth sensors.

By training an expert and a deployable controller together, we can turn rich simulation feedback into a lightweight policy that runs on real hardware, letting our humanoid adapt to uneven, unstructured terrain with far less data and hand-tuning than traditional methods. - Feiyang Wu

By the application of these training processes, the team hopes to speed the development of deployable humanoid robots for home use, manufacturing, defense, and search and rescue assistance in dangerous environments. These methods also support advances in embodied intelligence, enabling robots to learn richer, more context-aware behaviors.Additionally, the training data process can be applied to research to improve the functionality and adaptability of human assistive devices for medical and therapeutic uses.

As humanoid robots move from controlled labs into messy, unpredictable real-world environments, the key is developing embodied intelligence—the ability for robots to sense, adapt, and act through their physical bodies,” said Professor Ye Zhao. “The innovations from our students push us closer to robots that can learn robust skills, navigate diverse terrains, and ultimately operate safely and reliably alongside people. - Prof. Ye Zhao

Author - Christa M. Ernst

Citations

Liu F, Gu Z, Cai Y, Zhou Z, Jung H, Jang J, Zhao S, Ha S, Chen Y, Xu D, Zhao Y. Opt2skill: Imitating dynamically-feasible whole-body trajectories for versatile humanoid loco-manipulation. IEEE Robotics and Automation Letters. 2025 Oct 13.

Wu F, Nal X, Jang J, Zhu W, Gu Z, Wu A, Zhao Y. Learn to teach: Sample-efficient privileged learning for humanoid locomotion over real-world uneven terrain. IEEE Robotics and Automation Letters. 2025 Jul 23.
 

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Scientists across Georgia Tech rely on powerful software tools to propel breakthroughs in fields ranging from physics to biology. Now, software experts who make that research possible are gaining a new leader. 

The College of Computing named Professor Rich Vuduc as director of the Center for Scientific Software Engineering (CSSE). The Georgia Tech hub is dedicated to building reliable, high-performance software for scientists.  

Under Vuduc’s leadership, CSSE strives to accelerate the pace and increase the quality of scientific discovery by developing custom software tools and best practices tailored to researchers’ needs.

“There is a reproducibility and reliability problem right now with scientific software,” Vuduc said. “The promise of CSSE is to leverage capabilities shared between Georgia Tech, Schmidt Sciences, and industry experts to address this problem.” 

Issues arise because scientists often need to develop their own software for experiments or data analysis. However, troubleshooting coding issues and other bugs can slow down research.

To assist these scientists, CSSE receives their input to create custom software tools and best practices. The center employs professional software engineers who build and deliver products tailor-made to the needs of researchers at Georgia Tech and broader scientific communities.

Beyond its research focus, CSSE helps Georgia Tech fulfill its educational mission. The center provides students with direct access and exposure to real-world software engineering.

As the center enters its third year, Vuduc wants to better prepare students for employment by enhancing their hands-on experience while learning from CSSE engineers.

To achieve this goal, Vuduc is working to establish a Ph.D. fellowship program in which CSSE engineers mentor students. This program would connect academic inquiry with industry expertise, creating the next generation of dynamic leaders in computational science.  

Vuduc also envisions pairing CSSE with Georgia Tech’s Vertically Integrated Projects (VIP) program. This approach would allow undergraduate students to earn class credit while working with CSSE engineers on large software engineering projects spanning multiple semesters.

“The center gives our students access to something that is very unique to find in a university environment,” Vuduc said. 

“The software engineers in CSSE mostly come from industry. They have over 65 years of combined experience doing real-world software engineering that students can learn from.”

Vuduc is a 2010 recipient of the Gordon Bell Prize and a leading expert in high-performance computing (HPC). He was a finalist for the award in 2020 and 2022.

The Gordon Bell Prize, often referred to as the Nobel Prize in supercomputing due to the scope and magnitude of research it recognizes, celebrates achievement in HPC research and application. 

Vuduc joined Georgia Tech in 2007 as one of the first faculty hired for the new Division of Computational Science and Engineering (CSE). Not a stranger of leading new units, he saw CSE begin offering M.S. and Ph.D. degrees in 2008 and attain school status in 2010.  

Since 2021, Vuduc has served as co-director of the Center for Research into Novel Computing Hierarchies (CRNCH). 

CRNCH is an interdisciplinary research center at Georgia Tech that explores technologies and approaches that will usher the next generation of computing. Areas CRNCH studies include quantum computing, brain-inspired computing, and approximate computing. 

Vuduc will step down as CRNCH co-director to fulfill his role as CSSE director. The College of Computing will lead a search for CRNCH’s next co-director.

“In a sense, the CRNCH to CSSE transition was partly a natural one because one thing that contributes to software challenges is that hardware platforms are also changing and evolving very rapidly,” said Vuduc. 

“People are exploring radically new hardware systems and we will have to write software configured for those too. Centers, like CRNCH and CSSE, strongly position Georgia Tech to lead these endeavors.” 

Alessandro (Alex) Orso, the previous CSSE director, departed Georgia Tech earlier this year to become dean of the University of Georgia’s College of Engineering. Orso and Distinguished Professor Irfan Essa wrote the proposal to bring CSSE to Georgia Tech.

Georgia Tech formed CSSE in 2022 after securing an $11 million grant from Schmidt Sciences. Former Google CEO Eric Schmidt and his spouse, Wendy Schmidt, founded the philanthropic venture that funds science and technology research and talent networking programs. 

Georgia Tech’s CSSE is part of Schmidt Sciences’ Virtual Institute for Scientific Software (VISS) program. This network helps scientists obtain more robust, flexible, scalable open-source software. 

Schmidt Sciences is investing $40 million in VISS over five years at four universities: Georgia Tech, University of Washington, Johns Hopkins University, and University of Cambridge.

CSSE uses the funding to employ a software engineering lead, three senior and two junior software engineers. The Schmidt Sciences grant equips these engineers with computing resources to build scientific software. Along with the director, an advisory board guides the group’s work to meet the point of need for scientists in the field. 

“I am grateful to Schmidt Sciences for their support of CSSE. It aligns with our college’s strategic goals and expertise in scientific software, and I am delighted that Rich has agreed to take on this important role,” said Vivek Sarkar, Dean and John P. Imlay Jr. Chair of Computing.

“I know that Rich is committed to growing CSSE's internal and external visibility and long-term sustainability. I am confident that he will also help further socialize CSSE among internal stakeholders across Georgia Tech.”

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Students in machine learning and linear algebra courses this semester are learning from one of Georgia Tech’s most celebrated instructors.

Raphaël Pestourie has earned back-to-back selections to the Institute’s Course Instructor Opinion Survey (CIOS) honor roll, placing him among the top-ranked teachers for Fall 2024 and Spring 2025.

By returning to the classroom this semester to teach two more courses, Pestourie continues to leverage proven experience to mentor the next generation of researchers in his field.

“Students played a very important part in the survey process, and I thank them for making the classes great,” said Pestourie, an assistant professor in the School of Computational Science and Engineering (CSE).

“I'm incredibly grateful that students shared their feedback so that I could go the extra mile to not only apply my expertise to teach in ways that I think work, but transform my instruction to reach students in the most impactful way I can.”

CIOS honor rolls recognize instructors for outstanding teaching and educational impact, based on student feedback provided through end-of-course surveys. 

Student praise of Pestourie’s CSE 8803: Scientific Machine Learning class placed him on the Fall 2024 CIOS honor roll. He earned selection to the Spring 2025 honor roll for his instruction of CX 4230: Computer Simulation. 

CSE 8803 is a graduate-level, special topics class that Pestourie created around his field of expertise. Scientific machine learning involves merging two traditionally distinct fields: scientific computing and machine learning.

In scientific computing, researchers build and use models based on established physical laws. Machine learning differs in that it employs data-driven models to find patterns without prior assumptions. Combining the two fields opens new ways to analyze data and solve challenging problems in science and engineering.

Pestourie organized student-focused scientific machine learning symposiums in Fall 2023 and 2024. CSE 8803 students work on projects throughout the course and present their work at these symposiums. Pestourie will use the same approach this semester. 

Compared to CSE 8803, CX 4230 is an undergraduate course that teaches students how to create computer models of complex systems. A complex system has many interacting entities that influence each other’s behaviors and patterns. Disease spread in a human network is one example of a complex system. 

CX 4230 is a required course for computer science students studying the Modeling & Simulation thread. It is also an elective course in the Scientific and Engineering Computing minor.  

“I see 8803 as my educational baby. Being acknowledged for it with a CIOS honor roll felt great,” Pestourie said. 

“In a way, I'm prouder of CX 4230 because it was a large, undergraduate regular offering that I was teaching for the first time. The honor roll selection came almost as a surprise.”

To be eligible for the honor roll recognition, instructors must have a minimum CIOS response rate of 70%. Composite scores for three CIOS items are then used to rank instructors. Those items are:

• Instructor’s respect and concern for students
• Instructor’s level of enthusiasm about the course
• Instructor’s ability to stimulate interest in the subject matter

Georgia Tech’s Center for Teaching and Learning (CTL) and the Office of Academic Effectiveness present the CIOS Honor Rolls. CTL recognizes honor roll recipients at its Celebrating Teaching Day events, held annually in March.

CTL offers the Class of 1969 Teaching Fellowship, in which Pestourie participated in the 2024-2025 cohort. The program aims to broaden perspectives with insight into evidence-based best practices and exposure to new and innovative teaching methods.

The fellowship offers one-on-one consultations with a teaching and learning specialist. Cohorts meet weekly in the fall semester and monthly in the spring semester for instruction seminars. 

The fellowship facilitates peer observations where instructors visit other classrooms, exchange feedback, and learn effective techniques to try in their own classes.

“I'm very grateful for the Class of 1969 fellowship program and to Karen Franklin, who coordinates it,” Pestourie said. “The honor roll is not just a one-person award. Support from the Institute and other people in the program made it happen.”

Like in Fall 2023 and 2024, Pestourie is teaching CSE 8803: Scientific Machine Learning again this semester. Additionally, he teaches CSE 8801: Linear Algebra, Probability, and Statistics.

Linear algebra and applied probability are among the fundamental subjects in modern data science. Like his scientific machine learning class, Pestourie created CSE 8801. This semester marks the second time Pestourie is teaching the course since Fall 2024.

Pestourie designed CSE 8801 as a refresher course for newer graduate students. This addresses a point of need to help students get off to a good start at Georgia Tech. By offering guidance early in their graduate careers, Pestourie’s work in the classroom also aims to cultivate future collaborators and serve his academic community.

“I see teaching as our one shot at making a good first impression as a research field and a community,” he said. 

“I see my work as a teacher as training my future colleagues, and I see it as my duty to our community to do my best in attracting the best talent toward our research field.”

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A recently awarded $20 million NSF Nexus Supercomputer grant to Georgia Tech and partner institutes promises to bring incredible computing power to the CODA building. But what makes this supercomputer different and how will it impact research in labs on campus, across disciplinary units, and across institutions? 

Purpose Built for AI Discovery

Nexus is Georgia Tech’s next-generation supercomputer, replacing the HIVE. Most operational high-performance computing systems utilized for research were designed before the explosion in Machine Learning and AI. This revolution has already shown successes for scientific research and data analysis in many domains, but the compute power, complex connectivity, and data storage needs for these systems have limited their access to the academic research community. The Nexus supercomputer design process retained a robust HPC system as a base while integrating artificial intelligence, machine learning and large-scale data science analysis from the ground up.

Expert Support for Faculty and Researchers 

The Institute for Data Engineering and Science (IDEaS) and the College of Computing house the Center for Artificial Intelligence in Science and Engineering (ARTISAN) group. This team has collective experience in working with national computational, cloud, commercial and institutional resources for computational activities, and decades of experience in scientific tools that aid in assisting both teaching and research faculty. Nexus is the next logical step, bringing together everything they’ve learned to build a national resource optimized for the future of AI-driven science.

Principal Research Scientist for the ARTISAN team, Suresh Marru, highlighted the need for this new resource, “AI is a core part of the Nexus vision. Today, researchers often spend more time setting up experiments, managing data, or figuring out how to run jobs on remote clusters than doing science. With Nexus, we’re flipping that script. By embedding AI into the platform, we help automate routine tasks, suggest optimal ways to run simulations, and even assist in generating input or analyzing results. This means researchers can move faster from question to insight. Instead of wrestling with infrastructure, they can focus on discovery.”

An Accessible AI Resource for GT & US Scientific Research

90% of Nexus capacity will be made available to the national research community through the NSF Advanced Computing Systems & Services (ACSS) program. Researchers from across the country, at universities, labs, and institutions of all sizes, will have access to this next-generation AI-ready supercomputer. For Georgia Tech research faculty and staff, the new system has multiple benefits:

• 10% of the time on the machine will be available for use by Georgia Tech researchers
• Nexus will allow GT researchers a chance to try out the latest hardware for AI computing
• Thanks to cyberinfrastructure tools from the ARTISAN group, Nexus will be easier to access than previous NSF supercomputers

Interim Executive Director of IDEaS and Regents' Professor David Sherrill notes, "Nexus brings Georgia Tech's leadership in research computing to a whole new level. It will be the first NSF Category I Supercomputer hosted on Georgia Tech's campus. The Nexus hardware and software will boost research in the foundations of AI, and applications of AI in science and engineering."

JUPITER became the world’s fourth fastest supercomputer when it debuted last month. Though housed in Germany at the Jülich Supercomputing Centre (JSC), Georgia Tech played a supporting role in helping the system land on the latest TOP500 list.

In November 2024, JSC granted Assistant Professor Spencer Bryngelson exclusive access to the system through the JUPITER Research and Early Access Program (JUREAP).

By preparing Europe’s fastest supercomputer for launch, the joint project yielded valuable simulation data on the effects of shock waves in medicine and transportation.

“The shock-droplet problem has been a hallmark test problem in fluid dynamics for some decades now. It is sufficiently challenging to study that it keeps me scientifically interested, though the results are manifestly important,” Bryngelson said. 

“Understanding the droplet behavior in some extreme regimes remains an open scientific problem of high engineering value.”

Through JUREAP, JSC engineers tested Bryngelson’s Multi-Component Flow Code (MFC) on their computers. The project simulated how liquid droplets behave when struck by a large, high-velocity shock wave moving much faster than the speed of sound.

Tests produced visualizations of droplets deforming into pancake shapes before ejecting vortex rings as they broke apart from the shock wave. The experiments measured the swirls of air flow formed behind the droplets, known as vorticity.

Vorticity is one variable aerospace engineers consider when building aircraft designed to fly at supersonic and hypersonic speeds. Small droplets and vortices pose significant hazards for high-Mach vessels.

These computer models reduce the risk and cost associated with physical test runs. By simulating extreme scenarios, the JUREAP project demonstrated a safer and more efficient way to evaluate aerospace systems.

The human body is another fluid space where fast, high-energy flows can occur.

Simulations help medical researchers create less invasive shock wave treatments. This technology can be further applied for uses ranging from breaking up kidney stones to treating inflammation. 

MFC’s versatility for large- and small-scale applications made it suitable for testing JUPITER in its early stages. The project’s success even earned it a JUREAP certificate for scaling efficiency and node performance.

“The use of application codes to test supercomputers is common. We’ve participated in similar programs for OLCF Frontier and LLNL El Capitan,” said Bryngelson, a faculty member with Georgia Tech’s School of Computational Science and Engineering.

“Engineers at supercomputer sites usually find and sort most problems on their own. But deploying workloads characteristic of what the JUPITER will run in practice stresses it in new ways. In these instances, we usually end up identifying some failure modes.”

The JSC and Georgia Tech researchers named their joint project Exascale Multiphysics Flows (ExaMFlow).

ExaMFlow helps keep JUPITER on pace to become Europe’s first exascale supercomputer. This designation refers to any machine capable of computing one exaflop, or one quintillion (“1” followed by 18 zeros) calculations per second. 

All three systems that rank ahead of JUPITER are exascale supercomputers. They are El Capitan at Lawrence Livermore National Laboratory, Frontier at Oak Ridge National Laboratory, and Aurora at Argonne National Laboratory. 

JUPITER calculates more than 60 billion operations per watt. This makes the supercomputer the most energy-efficient system among the top five. 

ExaMFlow ran Bryngelson’s software on JSC’s JUWELS Booster and JUPITER Exascale Transition Instrument (JETI). The two modules form the backbone of JUPITER’s full design.

ExaMFlow’s report showed that MFC performed with near-ideal scaling behavior on JUWELS and JETI compared to similar systems based on NVIDIA A100 GPUs.

Access to NVIDIA hardware at Georgia Tech played a key role in ExaMFlow’s success.

The Institute hosts the Phoenix Research Computing Cluster, which includes A100 GPUs among its arsenal of components. Bryngelson’s lab owns NVIDIA A100 GPUs and four GH200 Grace Hopper Superchips. 

Since JUPITER is equipped with around 24,000 Grace Hopper Superchips, Bryngelson’s work with the hardware proved especially insightful for the ExaMFlow project.   

“The Grace Hopper chip is interesting. It’s not challenging to use like a regular GPU device when one is familiar with running NVIDIA hardware. The more fun part is using its tightly coupled CPU to GPU interconnect to make use of the CPU as well,” Bryngelson said. 

“It’s not immediately obvious how to best do this, though we used a few tricks to tune its use to our application. They appear to work nicely.”

JSC researchers Luis Cifuentes, Rakesh Sarma, Seong Koh, and Sohel Herff played important roles in running Bryngelson’s MFC software on early JUPITER modules. 

The ExaMFlow team included NVIDIA scientists Nikolaos Tselepidis and Benedikt Dorschner. 

The pair observed their company’s hardware used in the field. They return to NVIDIA with notes that help the corporation build the next devices tailored to the need of scientific computing researchers. 

“We try to be prepared for the latest, biggest computers. Being able to take immediate advantage of the largest systems is a valuable capability,” Bryngelson said. 

“When the early access systems arrive, it’s a great opportunity for the teams involved to test the machines, demonstrate and tune scientific software, and meet very capable new collaborators.”

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