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]

It’s a fairly niche product now, but a new study from Georgia Tech engineers suggests insulation made from hemp fibers could be a viable industry in the U.S., creating jobs, a manufacturing base, and greener homes and buildings at the same time.

Making the switch could slash the impact of one of the biggest sources of greenhouse gas emissions: Buildings account for roughly 1/5 of emissions globally. By some estimates, using hemp-based products would reduce the environmental impact of insulation by 90% or more. 

The Georgia Tech researchers’ work, reported this month in the Journal of Cleaner Production, is one of the first studies to evaluate the potential for scaling up U.S. production and availability of hemp-based insulation products.

Read about their findings on the College of Engineering website.

Georgia Tech strives to cultivate thought leaders, advance knowledge, and solve societal challenges by embracing various aspects of the research ecosystem. Through the HBCU/MSI Research Initiative, Georgia Tech seeks to capture data surrounding its research impact in the Georgia Tech-HBCU Research Collaboration Data Dashboard. The dashboard allows users to see information regarding joint funding, publications, hubs, and awards won by the HBCU CHIPS Network, which is co-led by Georgia Tech. 

“The data dashboard will represent a key resource for both Georgia Tech and HBCU researchers seeking to enhance research collaboration while substantiating Georgia Tech’s commitment as a valued partner,” said George White, senior director for strategic partnerships.

The Georgia Tech-HBCU Research Collaboration Data Dashboard will serve as a point of reference for faculty and staff in the various departments and colleges to identify opportunities of mutual benefits for collaboration and partnership.

To view the dashboard, visit https://hbcumsi.research.gatech.edu/data-dashboard

When Air Force veteran Michael Trigger began looking for a new career in 2022, he became fascinated by artificial intelligence (AI). Trigger, who left the military in 1989 and then worked in telecommunications, corrections, and professional trucking, learned about an AI-enhanced robotics manufacturing program at the VECTR Center. This training facility in Warner Robins, Georgia, helps veterans transition into new careers. In 2024, he enrolled and learned how to program and operate robots.

As part of the class, Trigger made several trips to the Georgia Tech Manufacturing Institute (GTMI). When the faculty asked if anyone wanted an internship, Trigger raised his hand. 

“Coming to Georgia Tech allowed me to clarify what I wanted to do,” he said. “I’ve always been in service-based jobs, but I was interested in additive manufacturing,” or 3D printing.

For five months every weekday, Trigger drove from his home in Macon to Georgia Tech’s campus for his internship. The paid internship took place at Tech’s Advanced Manufacturing Pilot Facility (AMPF). This 20,000-square-foot, reconfigurable facility serves as the research and development arm of GTMI, functioning as a teaching laboratory, technology test bed, and workforce development space for manufacturing innovations.

During his time there, Trigger focused on computer-aided manufacturing and met with faculty and students to learn about their research. The internship wasn’t convenient, but it was worth it. 

“From our campus visits, I understood the mission of AMPF, so the fact they offered me this opportunity was huge for me,” he said. “The internship had a big impact on my life in terms of the technical and soft skills I gained.”

Building the Workforce

Launching new careers is just one of AMPF’s goals in testing new manufacturing and growing the future U.S. workforce. Since 2022, AMPF has improved the manufacturing process at all parts of the talent pipeline — from giving corporate researchers space to test and adopt AI automation technologies to training and upskilling their employees. Collectively, GTMI and AMPF’s efforts have led to a stronger, bigger network of manufacturers that other companies and the U.S. government can rely on. 

“We are going to need to manufacture more in the U.S. — from computer chips to cars — so we want to create jobs and fill them,” said Tom Kurfess, GTMI’s executive director. “We need more people working in the manufacturing sector, and we've got to make these jobs better and make people more efficient in them.” 

AI is one way to boost efficiency, but artificial intelligence won’t cut humans out of the process entirely. Rather, people will be integral to monitoring the systems and advancing them. As AI becomes more widely adopted, a college degree won’t necessarily be required to work in the AI field.

“Our workforce is going to need the next generation of employees to be amenable to retraining as the technology updates,” said Aaron Stebner, a co-director of the Georgia Artificial Intelligence Manufacturing program (AIM). A statewide program, Georgia AIM helps fund AMPF and sponsored Trigger’s internship. “Education is going to be more of a lifelong learning process, and Georgia Tech can be at the forefront of that.”

While GTMI already integrates AI into many processes, it remains committed to staying ahead of the curve with the latest technologies that could boost manufacturing. The facility is in the process of an expansion that will nearly triple its size and make AMPF the leading facility for demonstrating what a hyperconnected and AI-driven manufacturing enterprise looks like. This will enable GTMI to build and sustain these educational pipelines, which is key to its work.

“We’re developing the workforce for the future, not of the future,” explained Donna Ennis, a co-director of Georgia AIM. “It’s AI today, but it could be something else five years from now. We are focused on creating a highly skilled, resilient workforce.”

Part of Georgia AIM’s role is creating the pipelines that people like Trigger can follow. From bringing a mobile lab to technical colleges to hosting robotics competitions at schools, these efforts span the state of Georgia and touch populations from “K to gray.” 

“Kids don’t say they want to be a manufacturer when they grow up, but that’s because they don’t know it’s a viable career path,” Ennis said. “We’re making manufacturing cool again.”

Creating Corporate Connection

To create these job opportunities, GTMI is also partnering with corporations. Companies can join a consortium to access the AMPF research facilities and collaborate with researchers. Any size or type of company can take advantage of AMPF facilities — from corporations including AT&T and Siemens to small startups like Alegna, which licenses and commercializes Navy research.

“The ability to manufacture domestically is critical, not only for national security purposes, but also to keep the U.S. economically competitive,” said Steven Ferguson, a principal research scientist and executive director for the GT Manufacturing 4.0 Consortium. “Having the AMPF puts Georgia Tech within the innovation epicenter for these areas and will help us reshore manufacturing.”

The benefit of such an arrangement is twofold. Companies can work with the newest manufacturing technologies and make their own advances, and Georgia Tech builds a network of manufacturers across the state and world that students can work with. For example, AT&T uses the AMPF to test sensors for expanding personal 5G networks, and George W. Woodruff School of Mechanical Engineering Professor Carolyn Seepersad has Ph.D. students funded by a Siemens partnership through AMPF.

Trigger was able to connect and collaborate with some of these corporations and researchers during his internship. “I told them about my interest in machine learning because I wanted to see how they were integrating machine learning into their research projects,” he said. “All of them invited me to come by to observe and be part of the research.”

Starting a New Path

Because of his research collaborations during his AMPF internship, Trigger now has a new focus. “The internship clarified for me that AI is where everybody is going,” he explained. He wants to be at the forefront of AI manufacturing and hopes to pursue a certificate in machine learning next.

While he knows he still has much to learn, AMPF gave Trigger a foot in the door and confidence about the future. He — and other veterans like him — will help build the workforce that propels America forward in manufacturing.

Calculating and visualizing a realistic trajectory of ink spreading through water has been a longstanding and enormous challenge for computer graphics and physics researchers.

When a drop of ink hits the water, it typically sinks forward, creating a tail before various ink streams branch off in different directions. The motion of the ink’s molecules upon mixing with water is seemingly random. This is because the motion is determined by the interaction of the water’s viscosity (thickness) and vorticity (how much it rotates at a given point).

“If the water is more viscous, there will be fewer branches. If the water is less viscous, it will have more branches,” said Zhiqi Li, a graduate computer science student.

Li is the lead author of Particle-Laden Fluid on Flow Maps, a best paper winner at the December 2024 ACM SIGGRAPH Asia conference. Assistant Professor Bo Zhu advises Li and is the co-author of six papers accepted to the conference.

Zhu said they must correctly calculate and simulate the interaction between viscosity and vorticity before they can accurately predict the ink trajectory.

“The ink branches generate based on the intricate interaction between the vorticities and the viscosity over time, which we simulated,” Zhu said. “Using a standard method to simulate the physics will cause most of the structures to fade quickly without being able to see any detailed hierarchies.”

Zhu added that researchers had yet to develop a method for this until he and his co-authors proposed a new way to solve the equation. Their breakthrough has unlocked the most accurate simulations of ink diffusion to date.

“Ink diffusion is one of the most visually striking examples of particle-laden flow,” Zhu said.

“We introduce a new viscosity model that solves for the interaction between vorticity and viscosity from a particle flow map perspective. This new simulation lets you map physical quantities from a certain time frame, allowing us to see particle trajectory.”

In computer simulations, flow is the digital visualization of a gas or liquid through a system. Users can simulate these liquids and gases through different scenarios and study pressure, velocity, and temperature.

A particle-laden flow depicts solid particles mixing within a continuous fluid phase, such as dust or water sediment. A flow map traces particle motion from the start point to the endpoint.

Duowen Chen, a computer science Ph.D. student also advised by Zhu and co-author of the paper, said previous efforts by researchers to simulate ink diffusion depended on guesswork. They either used limited traditional methods of calculations or artificial designs. 

“They add in a noise model or an artificial model to create vortical motions, but our method does not require adding any artificial vortical components,” Chen said. “We have a better viscosity force calculation and vortical preservation, and the two give a better ink simulation.”

Zhu also won a best paper award at the 2023 SIGGRAPH Asia conference for his work explaining how neural network maps created through artificial intelligence (AI) could close the gaps of difficult-to-solve equations. In his new paper, he said it was essential to find a way to simulate ink diffusion accurately independent of AI.

“If we don’t have to train a large-scale neural network, then the computation time will be much faster, and we can reduce the computation and memory costs,” Zhu said. “The particle flow map representation can preserve those particle structures better than the neural network version, and they are a widely used data structure in traditional physics-based simulation.”

Pascal Van Hentenryck, professor and chair of the School of Industrial and Systems Engineering at Georgia Tech, as well as director of Tech AI and the NSF AI4OPT Institute, presented at the Georgia Department of Human Services’ Annual HR Conference, held Jan. 28-30, 2025, at the Savannah Convention Center.

Themed “Customer-Centric Culture,” the event explored how leaders and employees can harness AI for customer engagement. Key topics included: defining AI, guiding workforce adaptation to AI-driven changes, and debunking myths, emphasizing AI's role as a vital tool rather than a threat.

To learn more about the Georgia Department of Human Services, click here.

The Georgia Legislator AI Workshop took place at the Georgia State Capitol, drawing state lawmakers, academic experts, and industry leaders to explore the transformative role of artificial intelligence, Jan. 28, 2025.

The event was designed to provide legislators with a comprehensive understanding of how AI is reshaping key sectors, including energy, manufacturing, education and cybersecurity. Georgia Tech’s prominent role in AI research and application was highlighted through contributions from its leading faculty and research experts.

Tim Lieuwen, interim executive vice president for Research at Georgia Tech, opened the workshop, amplifying the strategic importance of AI for Georgia’s economic development and infrastructure resilience. Pascal Van Hentenryck, director of the Tech AI Hub and the NSF AI4OPT Institute, followed with a presentation on AI advancements and their implications for the state.

A significant portion of the workshop focused on AI’s impact on energy infrastructure. Lieuwen returned to discuss how AI is enhancing energy efficiency and supporting Georgia’s transition to smarter, more resilient energy systems. This session highlighted AI’s role in driving sustainable energy solutions.

The conversation then shifted to manufacturing, with Tom Kurfess, chief manufacturing officer at Georgia Tech, detailing how AI-driven innovations are optimizing production processes and revolutionizing industry practices. His insights described a future where AI maintains Georgia’s competitiveness in the manufacturing sector.

Cybersecurity and data privacy were other focal points. Michael Barker from Georgia Tech’s Manufacturing Extension Program addressed the challenges and opportunities surrounding AI-driven cybersecurity solutions. His presentation touched on data privacy and compliance with public information regulations.

The educational landscape also took center stage as Steve Harmon from Georgia Tech’s College of Lifelong Learning explored the ways AI is reshaping learning experiences. Harmon highlighted AI’s potential to deliver personalized education and better prepare students for a rapidly evolving workforce.

Donna Ennis, interim associate vice president for community-based engagement and co-director of Georgia AIM, wrapped up the program by presenting a comprehensive overview of state and national AI resources available to foster innovation and collaboration.

This event highlighted the importance of strategic investments and informed policymaking to harness the full potential of AI for Georgia’s future.