From new farming practices to paleontology, meet four Georgia Tech researchers who improve the climate and predict its future.

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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.”

Pursuing entrepreneurship is an exciting and rewarding experience. You have the power to solve real-world problems and make an impact. Here are six things you can do to begin your entrepreneurial journey.

1. Identify a Problem You’re Interested in Solving

We often gravitate toward familiar problems, but it's crucial to explore beyond our immediate surroundings. Take the time to venture off campus and learn about problems faced by small businesses, corporations, and communities. Engaging with diverse groups will help you uncover unique challenges that you might not have considered. We accept startups in a variety of industries, from fashion to healthcare.

2. Understand the Problem Before Creating a Solution

It's tempting to rush into building a solution once you've identified a problem. However, it's essential to thoroughly understand the problem first. Before you start building, conduct at least 10 – 20 customer discovery interviews. This will give you valuable insights into the problem you're solving and help you validate your business thesis.

3. Start With Your Business Thesis

Formulate a clear business thesis: "X will buy Y because of Z." Here, X represents your target customer, Y is your product, and Z is the reason they will purchase it. As you engage with potential customers, refine your hypothesis based on their feedback. This iterative process will help you develop a product that meets real needs.

4. Build a Version 1 of Your Product

This is easier than it sounds and can be very low-tech to start. Concentrate on developing the core functionality of your product that addresses the primary pain point for your users. This unlocks user insights that can help you know if and where to pivot your solution.

5. Think About Your Business Model

While you don't need to have a concrete business model from the start, it's beneficial to brainstorm potential models. Consider how your product could generate revenue and sustain itself. Your business model can evolve as you gain more insights and experience.

6. Put in the Time

Launching a startup requires a significant time commitment and focus. You can create real momentum when you can dedicate consistent time.

 

Looking for more support on your entrepreneurial journey? Be sure to check out Startup Launch, a 12-week accelerator that helps you move from idea/prototype to your first customer. You have access to expert mentors, exclusive founder-focused workshops, vendor discounts, and $5,000 in seed funding. 

The application deadline is Monday, March 17, for the summer cohort. 

A previous info session on Startup Launch and a Startup Launch sample application are available to help students prepare. Attend CREATE-X events to get insights into entrepreneurship, workshop business ideas, find teammates, and prepare your Startup Launch applications. For additional questions, email create-x@groups.gatech.edu.

Andrei Fedorov, Associate Chair for Graduate Studies, Rae S. and Frank H. Neely Chair, and professor in the George W. Woodruff School of Mechanical Engineering, will represent Georgia Tech in a new international research initiative. The program, Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE) for Top Scientists, is funded by the Japan Science and Technology Agency. It will receive approximately $3.2 million in funding over five years.

The award will support a broad spectrum of multidisciplinary research activities by the multinational teams and intermediate to long-term (three months to one year) collaborative visits to global research sites in Japan, Europe, and the U.S. A total of 46 proposals were submitted to ASPIRE for Top Scientists, out of which 14 were selected by expert evaluation. Each project is an international collaboration and the initiative's key focus is advancing science and technology on an international level.

Fedorov will lead a project titled "Construction of International Data and Analysis Platform for Inorganic Power-storage Materials Informatics with Nano/Micro-Structure" that will explore the intersection of Artificial Intelligence (AI) and Informatics, and Energy. He will represent Georgia Tech as a principal investigator. The planned research will also involve faculty members and graduate students from College of Engineering schools involved in the Strategic Energy Institute.

Read the full story on the George W. Woodruff School of Mechanical Engineering website.

CREATE-X is set to host its next Deep Startups panel event on Thursday, Jan. 30, at 7 p.m. in the Marcus Nanotechnology Building Rooms 1116– 1118. The event will feature S.K. Sharma — former chief analytics and AI officer at Universal Music Group — and an expert in AI, data science, and strategic analytics. During Deep Startups, Sharma will dive into startup development within the context of the music business industry. Seating is limited. Students can register for Deep Startups on Engage. Faculty, staff, and the general public can register for Deep Startups on Eventbrite. 

Deep Startups is a series that brings together knowledgeable entrepreneurs and Startup Launch alumni from various business sectors to discuss their experiences forming companies that address significant, contemporary challenges. Attendees spend an informative evening discovering the intersection of technology and entrepreneurship.

From 2016 until recently, S.K. Sharma led a global team of Ph.D. data scientists, engineers, and strategists at Universal Music Group (UMG) to develop innovative and scalable solutions that drive real-time market insights and audience engagement. His leadership has been instrumental in creating differentiated intellectual property and market-leading capabilities in AI, machine learning, and prescriptive analytics, earning him multiple patents in marketing analytics.

Sharma's academic background includes a Ph.D. in chemical physics and physical chemistry from Caltech. His research has been published in numerous peer-reviewed journals, and he has held concurrent roles in academia and industry, including senior research scientist at Caltech's Beckman Institute. His corporate career includes significant positions such as vice president at Lehman Brothers, executive director at UBS, and vice president and partner at Mitchell Madison Group, where he advised global private equity funds and venture capital managers.

In addition to his role at UMG, Sharma is an entrepreneur in residence at UC San Diego's Office of Innovation and Commercialization, where he supports pioneering advancements in science and engineering. He is also an investor at Provisio Medical, a company revolutionizing endovascular procedures with its Sonic Lumen Tomography technology.

Sharma's contributions to the field of AI and analytics have been widely recognized. He was awarded Billboard magazine's 40 Under 40 and has been a commencement speaker at UC San Diego's Jacobs School of Engineering. His work in developing AI-driven marketing technologies has set new standards in the industry, ensuring compliance with global privacy regulations while driving significant improvements in marketing efficiency.

Attendees of Deep Startups will hear practical knowledge and actionable advice on entrepreneurship from Sharma. Each CREATE-X event is an opportunity to network, build ideas, and prepare for the Startup Launch program, which provides $5,000 in optional seed funding, $150,000 in in-kind services, mentorship, entrepreneurial workshops, networking events, and resources to help build and scale startups. Students, faculty, researchers, and alumni interested in developing their own startups are encouraged to apply. The deadline to apply for Startup Launch is March 17, 2025. Spots are limited. Apply now for a higher chance of acceptance and early feedback. If you have any questions about getting started, email us at create-x@groups.gatech.edu.

The Space Research Institute (SRI) at Georgia Tech has initiated an internal search for its inaugural executive director. This new Interdisciplinary Research Institute (IRI) will build upon the foundation laid by the Space Research Initiative.

The SRI is dedicated to advancing cutting-edge research in space-related fields, fostering interdisciplinary collaborations, and establishing strong partnerships with industry, government, academic, and international organizations. As leader of the newly established IRI, the executive director will lead the Institute's strategic vision, nurture a culture of innovation, and champion initiatives that position Georgia Tech, via the SRI, as a global leader in space research and exploration.

The SRI is composed of faculty and staff across campus who have a common interest in space exploration and discovery. Collectively, SRI will research a wide range of topics on space and how it relates to human perspective and be an ultimate hub of all things space related at Georgia Tech. It will connect all the research institutes, labs, facilities, and colleges to pioneer the conversation about space in the state of Georgia. By working hand-in-hand with academics, business partners, and students we are committed to staying at the cutting edge of innovation. 

Click here to learn more about this position and how to apply.

Three Georgia Tech faculty members are being recognized as 2024 ACM Fellows for significant contributions to computing.

College of Computing Professors Michael Bailey, Dana Randall, and Thad Starner are among 55 Fellows named today by ACM, the Association for Computing Machinery, for their “transformative contributions to computing science and technology.”

"Computing technology has had a tremendous impact in shaping how we live and work today,” said ACM President Yannis Ioannidis. “The ACM Fellows program honors the creativity and hard work of ACM members whose specific accomplishments drive innovation and make broader advances possible.”

According to its news release, ACM is recognizing Bailey, founding chair of the School of Cybersecurity and Privacy, for his “contributions to cybersecurity and internet measurement.” Bailey has authored more than 90 papers on the performance and security of complex distributed systems.

Randall is a professor with joint appointments in the School of Computer Science and the School of Mathematics. The ACM fellowship acknowledges her “contributions to the theory of Markov chains and programmable active matter.” Her research in randomized algorithms and stochastic processes connects computer science, discrete mathematics, and statistical physics.

A professor in the School of Interactive Computing, Starner is being honored as an ACM Fellow for “contributions to and leadership in the wearable computing research community.” His research combines wearable and ubiquitous computing technologies with AI, pattern recognition, and human-computer interaction. 

Starner, Randall, and Bailey are longstanding ACM members. The 2024 ACM Fellows were selected by the ACM membership “for making possible the computing technologies we use every day.”

"We congratulate Michael Bailey, Dana Randall, and Thad Starner on this significant achievement. Their contributions to the field of computing have not only advanced knowledge but have also benefited society,” said College of Computing Dean Vivek Sarkar.

“We are fortunate to have such distinguished colleagues from different schools in our College. Their achievements are a source of pride for us at Georgia Tech and will continue to inspire future generations of computer scientists."

ACM will formally recognize the 2024 Fellows at its annual awards banquet on June 14 in San Francisco. Additional information about the 2024 ACM Fellows is available through the ACM Fellows website.

Exponential growth in big data and computing power is transforming climate science, where machine learning is playing a critical role in mapping the physics of our changing climate.

 “What is happening within the field is revolutionary,” says School of Earth and Atmospheric Sciences Associate Chair and Professor Annalisa Bracco, adding that because many climate-related processes — from ocean currents to melting glaciers and weather patterns — can be described with physical equations, these advancements have the potential to help us understand and predict climate in critically important ways. 

Bracco is the lead author of a new review paper providing a comprehensive look at the intersection of AI and climate physics.

The result of an international collaboration between Georgia Tech’s Bracco, Julien Brajard (Nansen Environmental and Remote Sensing Center), Henk A. Dijkstra (Utrecht University), Pedram Hassanzadeh (University of Chicago), Christian Lessig (European Centre for Medium-Range Weather Forecasts), and Claire Monteleoni (University of Colorado Boulder), the paper, ‘Machine learning for the physics of climate,’ was recently published in Nature Reviews Physics. 

“One of our team’s goals was to help people think deeply on how climate science and AI intersect,” Bracco shares. “Machine learning is allowing us to study the physics of climate in a way that was previously impossible. Coupled with increasing amounts of data and observations, we can now investigate climate at scales and resolutions we’ve never been able to before.”

Connecting hidden dots

The team showed that ML is driving change in three key areas: accounting for missing observational data, creating more robust climate models, and enhancing predictions, especially in weather forecasting. However, the research also underscores the limits of AI — and how researchers can work to fill those gaps.

“Machine learning has been fantastic in allowing us to expand the time and the spatial scales for which we have measurements,” says Bracco, explaining that ML could help fill in missing data points — creating a more robust record for researchers to reference. However, like patching a hole in a shirt, this works best when the rest of the material is intact.

“Machine learning can extrapolate from past conditions when observations are abundant, but it can’t yet predict future trends or collect the data we need,” Bracco adds. “To keep advancing, we need scientists who can determine what data we need, collect that data, and solve problems.”

Modeling climate, predicting weather

Machine learning is often used when improving climate models that can simulate changing systems like our atmosphere, oceans, land, biochemistry, and ice. “These models are limited because of our computing power, and are run on a three-dimensional grid,” Bracco explains: below the grid resolution, researchers need to approximate complex physics with simpler equations that computers can solve quickly, a process called ‘parameterization’.

Machine learning is changing that, offering new ways to improve parameterizations, she says. “We can run a model at extremely high resolutions for a short time, so that we don’t need to parameterize as many physical processes — using machine learning to derive the equations that best approximate what is happening at small scales,” she explains. “Then we can use those equations in a coarser model that we can run for hundreds of years.”

While a full climate model based solely on machine learning may remain out of reach, the team found that ML is advancing our ability to accurately predict weather systems and some climate phenomena like El Niño. 

Previously, weather prediction was based on knowing the starting conditions — like temperature, humidity, and barometric pressure — and running a model based on physics equations to predict what might happen next. Now, machine learning is giving researchers the opportunity to learn from the past. “We can use information on what has happened when there were similar starting conditions in previous situations to predict the future without solving the underlying governing equations,” Bracco says. “And all while using orders-of-magnitude less computing resources.”

The human connection

Bracco emphasizes that while AI and ML play a critical role in accelerating research, humans are at the core of progress. “I think the in-person collaboration that led to this paper is, in itself, a testament to the importance of human interaction,” she says, recalling that the research was the result of a workshop organized at the Kavli Institute for Theoretical Physics — one of the team’s first in-person discussions after the Covid-19 pandemic.

“Machine learning is a fantastic tool — but it's not the solution to everything,” she adds. “There is also a real need for human researchers collecting high-quality data, and for interdisciplinary collaboration across fields. I see this as a big challenge, but a great opportunity for computer scientists and physicists, mathematicians, biologists, and chemists to work together.”

 

Funding: National Science Foundation, European Research Council, Office of Naval Research, US Department of Energy, European Space Agency, Choose France Chair in AI.

DOI: https://doi.org/10.1038/s42254-024-00776-3

 

Georgia Institute of Technology is set to play a crucial role in a strategic effort funded by the Defense Advanced Research Project Agency (DARPA) to help bolster America’s national security and global military leadership.  

The project, led by the Texas Institute for Electronics (TIE) at The University of Texas at Austin, represents a total investment of $1.4 billion. The $840 million award from DARPA, announced by TIE in 2024, aims to develop the next generation of high-performing semiconductor microsystems for the Department of Defense (DoD). 

“We are honored to collaborate with TIE and its broader team on this far reaching and strategic program to enable best in class 3D heterogeneous integration (3DHI) processes and technologies in the United States,” said Muhannad S. Bakir, the Dan Fielder Professor in the School of Electrical and Computer Engineering and director of the 3D Systems Packaging Research Center, who is heading the project for Georgia Tech. 

3DHI is a semiconductor manufacturing process that incorporates different materials and components into microsystems with precision assembly. The use of 3DHI allows for the creation of high-performance, compact, and energy-efficient systems. 

The investment is part of DARPA’s Next Generation Microelectronics Manufacturing (NGMM) Program comprised of 32 defense electronics and leading commercial semiconductor companies and 18 nationally recognized academic institutions.

Under the agreement, TIE will establish a national open access R&D and prototyping fabrication facility. The facility will enable the DoD to create higher performance, lower power, lightweight, and compact defense systems. The advancements are expected to have wide-ranging applications, including radar, satellite imaging, and unmanned aerial vehicles.  

Georgia Tech will provide a wide range of expertise in 3DHI including design, fabrication and assembly processes, and characterization to support the NGMM national open-access R&D and prototyping facility at TIE.  

Regents' Professor and Morris M. Bryan, Jr. Professor Suresh K. Sitaraman in the George W. Woodruff School of Mechanical Engineering will be a key contributor to Georgia Tech’s efforts on the project.

“We are delighted to be partnering with UT/TIE on the establishment of a 3D Heterogeneous Integration Microsystem prototyping  facility,” said Sitaraman. “In addition to advancing fundamental science, this project is a great opportunity for Georgia Tech to demonstrate and integrate our ground-breaking and innovative 3DHI research approaches and technology solutions into TIE’s prototyping facility, and understand the challenges involved when translating lab-scale research work to a large industry-strength fabrication facility.” 

ECE Professors Saibal Mukhopadhyay, Arijit Raychowdhury, Visvesh Sathe, and Shimeng Yu will be working alongside Bakir and Sitaraman. 

A significant portion of the research will be conducted at the Institute for Matter and Systems (IMS), which operates Georgia Tech’s state-of-the-art electronics and nanotechnology core facilities. 

Read the press release from TIE and view the project’s team and partners.