A new machine learning (ML) model created at Georgia Tech is helping neuroscientists better understand communications between brain regions. Insights from the model could lead to personalized medicine, better brain-computer interfaces, and advances in neurotechnology.

The Georgia Tech group combined two current ML methods into their hybrid model called MRM-GP (Multi-Region Markovian Gaussian Process). 

Neuroscientists who use MRM-GP learn more about communications and interactions within the brain. This in turn improves understanding of brain functions and disorders.

“Clinically, MRM-GP could enhance diagnostic tools and treatment monitoring by identifying and analyzing neural activity patterns linked to various brain disorders,” said Weihan Li, the study’s lead researcher. 

“Neuroscientists can leverage MRM-GP for its robust modeling capabilities and efficiency in handling large-scale brain data.” 

MRM-GP reveals where and how communication travels across brain regions. 

The group tested MRM-GP using spike trains and local field potential recordings, two kinds of measurements of brain activity. These tests produced representations that illustrated directional flow of communication among brain regions. 

Experiments also disentangled brainwaves, called oscillatory interactions, into organized frequency bands. MRM-GP’s hybrid configuration allows it to model frequencies and phase delays within the latent space of neural recordings.

MRM-GP combines the strengths of two existing methods: the Gaussian process (GP) and linear dynamical systems (LDS). The researchers say that MRM-GP is essentially an LDS that mirrors a GP.

LDS is a computationally efficient and cost-effective method, but it lacks the power to produce representations of the brain. GP-based approaches boost LDS's power, facilitating the discovery of variables in frequency bands and communication directions in the brain.

Converting GP outputs into an LDS is a difficult task in ML. The group overcame this challenge by instilling separability in the model’s multi-region kernel. Separability establishes a connection between the kernel and LDS while modeling communication between brain regions.

Through this approach, MRM-GP overcomes two challenges facing both neuroscience and ML fields. The model helps solve the mystery of intraregional brain communication. It does so by bridging a gap between GP and LDS, a feat not previously accomplished in ML.

“The introduction of MRM-GP provides a useful tool to model and understand complex brain region communications,” said Li, a Ph.D. student in the School of Computational Science and Engineering (CSE). 

“This marks a significant advancement in both neuroscience and machine learning.”

Fellow doctoral students Chengrui Li and Yule Wang co-authored the paper with Li. School of CSE Assistant Professor Anqi Wu advises the group. 

Each MRM-GP student pursues a different Ph.D. degree offered by the School of CSE. W. Li studies computer science, C. Li studies computational science and engineering, and Wang studies machine learning. The school also offers Ph.D. degrees in bioinformatics and bioengineering.

Wu is a 2023 recipient of the Sloan Research Fellowship for neuroscience research. Her work straddles two of the School’s five research areas: machine learning and computational bioscience. 

MRM-GP will be featured at the world’s top conference on ML and artificial intelligence. The group will share their work at the International Conference on Machine Learning (ICML 2024), which will be held July 21-27 in Vienna. 

ICML 2024 also accepted for presentation a second paper from Wu’s group intersecting neuroscience and ML. The same authors will present A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing.

Twenty-four Georgia Tech faculty from the Colleges of Computing and Engineering will present 40 papers at ICML 2024. Wu is one of six faculty representing the School of CSE who will present eight total papers.

The group’s ICML 2024 presentations exemplify Georgia Tech’s focus on neuroscience research as a strategic initiative.  

Wu is an affiliated faculty member with the Neuro Next Initiative, a new interdisciplinary program at Georgia Tech that will lead research in neuroscience, neurotechnology, and society. The University System of Georgia Board of Regents recently approved a new neuroscience and neurotechnology Ph.D. program at Georgia Tech. 

“Presenting papers at international conferences like ICML is crucial for our group to gain recognition and visibility, facilitates networking with other researchers and industry professionals, and offers valuable feedback for improving our work,” Wu said. 

“It allows us to share our findings, stay updated on the latest developments in the field, and enhance our professional development and public speaking skills.”

Visit https://sites.gatech.edu/research/icml-2024 for news and coverage of Georgia Tech research presented at ICML 2024.

Whether it’s typing an email or guiding travel from one destination to the next, artificial intelligence (AI) already plays a role in simplifying daily tasks.

But what if it could also help people live more efficiently — that is, more sustainably, with less waste?

It’s a concept that often runs through the mind of Iesha Baldwin, the inaugural Georgia AIM Fellow with the Partnership for Inclusive Innovation (PIN) at the Georgia Institute of Technology’s Enterprise Innovation Institute. Born out of the Georgia Tech Manufacturing Institute, the Georgia AIM (Artificial Intelligence in Manufacturing) project works with PIN fellows to advance the project's mission of equitably developing and deploying talent and innovation in AI for manufacturing throughout the state of Georgia.

When she accepted the PIN Fellowship for 2023, she saw an opportunity to learn more about the nexus of artificial intelligence, manufacturing, waste, and education. With a background in environmental studies and science, Baldwin studied methods for waste reduction, environmental protection, and science education.

“I took an interest in AI technology because I wanted to learn how it can be harnessed to solve the waste problem and create better science education opportunities for K-12 and higher education students,” said Baldwin.

This type of unique problem-solving is what defines the PIN Fellowship programs. Every year, a cohort of recent college graduates is selected, and each is paired with an industry that aligns with their expertise and career goals — specifically, cleantech, AI manufacturing, supply chain and logistics, and cybersecurity/information technology. Fellowships are one year, with fellows spending six months with a private company and then six months with a public organization.

Through the experience, fellows expand their professional network and drive connections between the public and private sectors. They also use the opportunity to work on special projects that involve using new technologies in their area of interest.

With a focus on artificial intelligence in manufacturing, Baldwin led an inventory management project at the Georgia manufacturer Freudenberg-NOK, where the objective was to create an inventory management system that reduced manufacturing downtime and, as a result, increased efficiency, and reduced waste.

She also worked in several capacities at Georgia Tech: supporting K-12 outreach programs at the Advanced Manufacturing Pilot Facility, assisting with energy research at the Marcus Nanotechnology Research Center, and auditing the infamous mechanical engineering course ME2110 to improve her design thinking and engineering skills.

“Learning about artificial intelligence is a process, and the knowledge gained was worth the academic adventure,” she said. “Because of the wonderful support at Georgia Tech, Freudenberg NOK, PIN, and Georgia AIM, I feel confident about connecting environmental sustainability and technology in a way that makes communities more resilient and sustainable.”

Since leaving the PIN Fellowship, Baldwin connected her love for education, science, and environmental sustainability through her new role as the inaugural sustainability coordinator for Spelman College, her alma mater.  In this role, she is responsible for supporting campus sustainability initiatives.

Ask a person to find a frying pan, and they will most likely go to the kitchen. Ask a robot to do the same, and you may get numerous responses, depending on how the robot is trained.

Since humans often associate objects in a home with the room they are in, Naoki Yokoyama thinks robots that navigate human environments to perform assistive tasks should mimic that reasoning.

Roboticists have employed natural language models to help robots mimic human reasoning over the past few years. However, Yokoyama, a Ph.D. student in robotics, said these models create a “bottleneck” that prevents agents from picking up on visual cues such as room type, size, décor, and lighting. 

Yokoyama presented a new framework for semantic reasoning at the Institute of Electrical and Electronic Engineers (IEEE) International Conference on Robotics and Automation (ICRA) last month in Yokohama, Japan. ICRA is the world’s largest robotics conference.

Yokoyama earned a best paper award in the Cognitive Robotics category with his Vision-Language Frontier Maps (VLFM) proposal.

Assistant Professor Sehoon Ha and Associate Professor Dhruv Batra from the School of Interactive Computing advised Yokoyama on the paper. Yokoyama authored the paper while interning at the Boston Dynamics’ AI Institute.

“I think the cognitive robotic category represents a significant portion of submissions to ICRA nowadays,” said Yokoyama, whose family is from Japan. “I’m grateful that our work is being recognized among the best in this field.”

Instead of natural language models, Yokoyama used a renowned vision-language model called BLIP-2 and tested it on a Boston Dynamics “Spot” robot in home and office environments.

“We rely on models that have been trained on vast amounts of data collected from the web,” Yokoyama said. “That allows us to use models with common sense reasoning and world knowledge. It’s not limited to a typical robot learning environment.”

What is Blip-2?

BLIP-2 matches images to text by assigning a score that evaluates how well the user input text describes the content of an image. The model removes the need for the robot to use object detectors and language models. 

Instead, the robot uses BLIP-2 to extract semantic values from RGB images with a text prompt that includes the target object. 

BLIP-2 then teaches the robot to recognize the room type, distinguishing the living room from the bathroom and the kitchen. The robot learns to associate certain objects with specific rooms where it will likely find them.

From here, the robot creates a value map to determine the most likely locations for a target object, Yokoyama said.

Yokoyama said this is a step forward for intelligent home assistive robots, enabling users to find objects — like missing keys — in their homes without knowing an item’s location. 

“If you’re looking for a pair of scissors, the robot can automatically figure out it should head to the kitchen or the office,” he said. “Even if the scissors are in an unusual place, it uses semantic reasoning to work through each room from most probable location to least likely.”

He added that the benefit of using a VLM instead of an object detector is that the robot will include visual cues in its reasoning.

“You can look at a room in an apartment, and there are so many things an object detector wouldn’t tell you about that room that would be informative,” he said. “You don’t want to limit yourself to a textual description or a list of object classes because you’re missing many semantic visual cues.”

While other VLMs exist, Yokoyama chose BLIP-2 because the model:

• Accepts any text length and isn’t limited to a small set of objects or categories.
• Allows the robot to be pre-trained on vast amounts of data collected from the internet.
• Has proven results that enable accurate image-to-text matching.

Home, Office, and Beyond

Yokoyama also tested the Spot robot to navigate a more challenging office environment. Office spaces tend to be more homogenous and harder to distinguish from one another than rooms in a home. 

“We showed a few cases in which the robot will still work,” Yokoyama said. “We tell it to find a microwave, and it searches for the kitchen. We tell it to find a potted plant, and it moves toward an area with windows because, based on what it knows from BLIP-2, that’s the most likely place to find the plant.”

Yokoyama said as VLM models continue to improve, so will robot navigation. The increase in the number of VLM models has caused robot navigation to steer away from traditional physical simulations.

“It shows how important it is to keep an eye on the work being done in computer vision and natural language processing for getting robots to perform tasks more efficiently,” he said. “The current research direction in robot learning is moving toward more intelligent and higher-level reasoning. These foundation models are going to play a key role in that.”

Top photo by Kevin Beasley/College of Computing.

Georgia Tech researchers say non-English speakers shouldn’t rely on chatbots like ChatGPT to provide valuable healthcare advice. 

A team of researchers from the College of Computing at Georgia Tech has developed a framework for assessing the capabilities of large language models (LLMs).

Ph.D. students Mohit Chandra and Yiqiao (Ahren) Jin are the co-lead authors of the paper Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries. 

Their paper’s findings reveal a gap between LLMs and their ability to answer health-related questions. Chandra and Jin point out the limitations of LLMs for users and developers but also highlight their potential. 

Their XLingEval framework cautions non-English speakers from using chatbots as alternatives to doctors for advice. However, models can improve by deepening the data pool with multilingual source material such as their proposed XLingHealth benchmark.     

“For users, our research supports what ChatGPT’s website already states: chatbots make a lot of mistakes, so we should not rely on them for critical decision-making or for information that requires high accuracy,” Jin said.   

“Since we observed this language disparity in their performance, LLM developers should focus on improving accuracy, correctness, consistency, and reliability in other languages,” Jin said. 

Using XLingEval, the researchers found chatbots are less accurate in Spanish, Chinese, and Hindi compared to English. By focusing on correctness, consistency, and verifiability, they discovered: 

• Correctness decreased by 18% when the same questions were asked in Spanish, Chinese, and Hindi. 
• Answers in non-English were 29% less consistent than their English counterparts. 
• Non-English responses were 13% overall less verifiable. 

XLingHealth contains question-answer pairs that chatbots can reference, which the group hopes will spark improvement within LLMs.  

The HealthQA dataset uses specialized healthcare articles from the popular healthcare website Patient. It includes 1,134 health-related question-answer pairs as excerpts from original articles.  

LiveQA is a second dataset containing 246 question-answer pairs constructed from frequently asked questions (FAQs) platforms associated with the U.S. National Institutes of Health (NIH).  

For drug-related questions, the group built a MedicationQA component. This dataset contains 690 questions extracted from anonymous consumer queries submitted to MedlinePlus. The answers are sourced from medical references, such as MedlinePlus and DailyMed.   

In their tests, the researchers asked over 2,000 medical-related questions to ChatGPT-3.5 and MedAlpaca. MedAlpaca is a healthcare question-answer chatbot trained in medical literature. Yet, more than 67% of its responses to non-English questions were irrelevant or contradictory.  

“We see far worse performance in the case of MedAlpaca than ChatGPT,” Chandra said. 

“The majority of the data for MedAlpaca is in English, so it struggled to answer queries in non-English languages. GPT also struggled, but it performed much better than MedAlpaca because it had some sort of training data in other languages.” 

Ph.D. student Gaurav Verma and postdoctoral researcher Yibo Hu co-authored the paper. 

Jin and Verma study under Srijan Kumar, an assistant professor in the School of Computational Science and Engineering, and Hu is a postdoc in Kumar’s lab. Chandra is advised by Munmun De Choudhury, an associate professor in the School of Interactive Computing. 
 
The team will present their paper at The Web Conference, occurring May 13-17 in Singapore. The annual conference focuses on the future direction of the internet. The group’s presentation is a complimentary match, considering the conference's location.  

English and Chinese are the most common languages in Singapore. The group tested Spanish, Chinese, and Hindi because they are the world’s most spoken languages after English. Personal curiosity and background played a part in inspiring the study. 

“ChatGPT was very popular when it launched in 2022, especially for us computer science students who are always exploring new technology,” said Jin. “Non-native English speakers, like Mohit and I, noticed early on that chatbots underperformed in our native languages.” 

School of Interactive Computing communications officer Nathan Deen and School of Computational Science and Engineering communications officer Bryant Wine contributed to this report.

Cardiologists and surgeons could soon have a new mobile augmented reality (AR) tool to improve collaboration in surgical planning.

ARCollab is an iOS AR application designed for doctors to interact with patient-specific 3D heart models in a shared environment. It is the first surgical planning tool that uses multi-user mobile AR in iOS.

The application’s collaborative feature overcomes limitations in traditional surgical modeling and planning methods. This offers patients better, personalized care from doctors who plan and collaborate with the tool.

Georgia Tech researchers partnered with Children’s Healthcare of Atlanta (CHOA) in ARCollab’s development. Pratham Mehta, a computer science major, led the group’s research.

“We have conducted two trips to CHOA for usability evaluations with cardiologists and surgeons. The overall feedback from ARCollab users has been positive,” Mehta said. 

“They all enjoyed experimenting with it and collaborating with other users. They also felt like it had the potential to be useful in surgical planning.”

ARCollab’s collaborative environment is the tool’s most novel feature. It allows surgical teams to study and plan together in a virtual workspace, regardless of location.

ARCollab supports a toolbox of features for doctors to inspect and interact with their patients' AR heart models. With a few finger gestures, users can scale and rotate, “slice” into the model, and modify a slicing plane to view omnidirectional cross-sections of the heart.

Developing ARCollab on iOS works twofold. This streamlines deployment and accessibility by making it available on the iOS App Store and Apple devices. Building ARCollab on Apple’s peer-to-peer network framework ensures the functionality of the AR components. It also lessens the learning curve, especially for experienced AR users.

ARCollab overcomes traditional surgical planning practices of using physical heart models. Producing physical models is time-consuming, resource-intensive, and irreversible compared to digital models. It is also difficult for surgical teams to plan together since they are limited to studying a single physical model.

Digital and AR modeling is growing as an alternative to physical models. CardiacAR is one such tool the group has already created. 

However, digital platforms lack multi-user features essential for surgical teams to collaborate during planning. ARCollab’s multi-user workspace progresses the technology’s potential as a mass replacement for physical modeling.

“Over the past year and a half, we have been working on incorporating collaboration into our prior work with CardiacAR,” Mehta said. 

“This involved completely changing the codebase, rebuilding the entire app and its features from the ground up in a newer AR framework that was better suited for collaboration and future development.”

Its interactive and visualization features, along with its novelty and innovation, led the Conference on Human Factors in Computing Systems (CHI 2024) to accept ARCollab for presentation. The conference occurs May 11-16 in Honolulu.

CHI is considered the most prestigious conference for human-computer interaction and one of the top-ranked conferences in computer science.

M.S. student Harsha Karanth and alumnus Alex Yang (CS 2022, M.S. CS 2023) co-authored the paper with Mehta. They study under Polo Chau, an associate professor in the School of Computational Science and Engineering.

The Georgia Tech group partnered with Timothy Slesnick and Fawwaz Shaw from CHOA on ARCollab’s development.

“Working with the doctors and having them test out versions of our application and give us feedback has been the most important part of the collaboration with CHOA,” Mehta said. 

“These medical professionals are experts in their field. We want to make sure to have features that they want and need, and that would make their job easier.”

Thanks to a Georgia Tech researcher's new tool, application developers can now see potential harmful attributes in their prototypes.

Farsight is a tool designed for developers who use large language models (LLMs) to create applications powered by artificial intelligence (AI). Farsight alerts prototypers when they write LLM prompts that could be harmful and misused.

Downstream users can expect to benefit from better quality and safer products made with Farsight’s assistance. The tool’s lasting impact, though, is that it fosters responsible AI awareness by coaching developers on the proper use of LLMs.

Machine Learning Ph.D. candidate Zijie (Jay) Wang is Farsight’s lead architect. He will present the paper at the upcoming Conference on Human Factors in Computing Systems (CHI 2024). Farsight ranked in the top 5% of papers accepted to CHI 2024, earning it an honorable mention for the conference’s best paper award.

“LLMs have empowered millions of people with diverse backgrounds, including writers, doctors, and educators, to build and prototype powerful AI apps through prompting. However, many of these AI prototypers don’t have training in computer science, let alone responsible AI practices,” said Wang.

“With a growing number of AI incidents related to LLMs, it is critical to make developers aware of the potential harms associated with their AI applications.”

Wang referenced an example when two lawyers used ChatGPT to write a legal brief. A U.S. judge sanctioned the lawyers because their submitted brief contained six fictitious case citations that the LLM fabricated.

With Farsight, the group aims to improve developers’ awareness of responsible AI use. It achieves this by highlighting potential use cases, affected stakeholders, and possible harm associated with an application in the early prototyping stage. 

A user study involving 42 prototypers showed that developers could better identify potential harms associated with their prompts after using Farsight. The users also found the tool more helpful and usable than existing resources. 

Feedback from the study showed Farsight encouraged developers to focus on end-users and think beyond immediate harmful outcomes.

“While resources, like workshops and online videos, exist to help AI prototypers, they are often seen as tedious, and most people lack the incentive and time to use them,” said Wang.

“Our approach was to consolidate and display responsible AI resources in the same space where AI prototypers write prompts. In addition, we leverage AI to highlight relevant real-life incidents and guide users to potential harms based on their prompts.”

Farsight employs an in-situ user interface to show developers the potential negative consequences of their applications during prototyping. 

Alert symbols for “neutral,” “caution,” and “warning” notify users when prompts require more attention. When a user clicks the alert symbol, an awareness sidebar expands from one side of the screen. 

The sidebar shows an incident panel with actual news headlines from incidents relevant to the harmful prompt. The sidebar also has a use-case panel that helps developers imagine how different groups of people can use their applications in varying contexts.

Another key feature is the harm envisioner. This functionality takes a user’s prompt as input and assists them in envisioning potential harmful outcomes. The prompt branches into an interactive node tree that lists use cases, stakeholders, and harms, like “societal harm,” “allocative harm,” “interpersonal harm,” and more.

The novel design and insightful findings from the user study resulted in Farsight’s acceptance for presentation at CHI 2024.

CHI is considered the most prestigious conference for human-computer interaction and one of the top-ranked conferences in computer science.

CHI is affiliated with the Association for Computing Machinery. The conference takes place May 11-16 in Honolulu.

Wang worked on Farsight in Summer 2023 while interning at Google + AI Research group (PAIR).

Farsight’s co-authors from Google PAIR include Chinmay Kulkarni, Lauren Wilcox, Michael Terry, and Michael Madaio. The group possesses closer ties to Georgia Tech than just through Wang.

Terry, the current co-leader of Google PAIR, earned his Ph.D. in human-computer interaction from Georgia Tech in 2005. Madaio graduated from Tech in 2015 with a M.S. in digital media. Wilcox was a full-time faculty member in the School of Interactive Computing from 2013 to 2021 and serves in an adjunct capacity today.

Though not an author, one of Wang’s influences is his advisor, Polo Chau. Chau is an associate professor in the School of Computational Science and Engineering. His group specializes in data science, human-centered AI, and visualization research for social good.  

“I think what makes Farsight interesting is its unique in-workflow and human-AI collaborative approach,” said Wang. 

“Furthermore, Farsight leverages LLMs to expand prototypers’ creativity and brainstorm a wide range of use cases, stakeholders, and potential harms.”

On April 12, the Energy, Policy, and Innovation Center (EPICenter) hosted its second round of the “Friday Lightning Talk Series” at the Scholars Event Network space in the Price Gilbert Library.

Eight multidisciplinary participants from Georgia Tech, including postdoctoral students, graduate students, research faculty, and research associates from public policy, economics, electrical and computer engineering, industrial and systems engineering, and EPICenter, presented an overview of an energy-related research project during the session.

Laura Taylor, chair of the School of Economics and interim director of EPICenter, introduced the organization’s new faculty affiliate program through which affiliates, their students, and postdocs present and share research ideas and receive feedback from the audience.

Topics covered during the session included understanding the social costs of natural gas deregulation, managing EV charging during emergencies, exploring whether daylight saving time saves energy, the green energy workforce, the effects of community solar on household energy use, the Atlanta Energyshed project, clean hydrogen production in Georgia, and household responses to grid emergencies.

The interactive session was well attended with over 25 attendees asking thought-provoking questions and providing suggestions on future areas to explore.

The first round was held on March 1 and was such a success that this second round had a full slate of presenters and a full house of audience members. The agendas for both lightning round talks are available below, along with links to presentation slides.

A unit of the Strategic Energy Institute of Georgia Tech, EPICenter’s mission is to conduct rigorous research and deliver high-impact insights that address the energy needs of the southeastern U.S., while keeping a national and global perspective. EPICenter calls upon broad, multidisciplinary expertise to engage the public and create solutions for critical emerging issues as our nation’s energy transformation unfolds.

Georgia Institute of Technology School of Electrical and Computer Engineering Ph.D. candidate Fabia Farlin Athena received the prestigious Stanford Energy Postdoctoral Fellowship, becoming Georgia Tech’s first recipient of the distinction.

With climate change becoming one of the a defining issue of the 21st century, the transition to a low-carbon energy system will solve about three-fourths of the problem, according to the fellowship’s website. At the same time, the new energy system needs to be affordable, reliable, and available to the average person.

The three-year fellowship sponsored in Stanford’s Precourt Institute of Energy and Doerr School of Sustainability aims to identify, develop, and connect the next generation of energy leaders — from science and engineering to policy and economics — to translate theoretical climate change solutions into tangible realities.

At Stanford, Athena, who is advised by Eric M. Vogel in the School of Materials Science and Engineering, will work on emerging materials and devices for energy-efficient sustainable computing. She will be working with H.-S. Philip Wong, professor of electrical engineering, and Alberto Salleo, professor of materials science and engineering.

After being selected as a finalist, she presented her current Ph.D. research on adaptive oxide devices for energy-efficient computing, as well as her proposed research to the fellowship’s advisory board.

“It was an amazing experience to go through the selection process of writing the proposal and finally getting interviewed by the honorable advisory board,” Athena said. “It was humbling to get the opportunity to discuss my research with a person I have always looked up to in Professor Steven Chu, a Nobel Laureate in Physics and former U.S. Secretary of Energy!”

Athena is just one of 10 fellows selected globally this year. The fellowship provides her the opportunity to explore new and profound postdoctoral research that is distinct from her Ph.D. work.

“I am deeply grateful to my advisor Prof. Eric M. Vogel for his constant kind support throughout my Ph.D. and for believing in me,” Athena said. “He has been a pillar of constant support throughout my journey. I am also grateful to Prof. Samuel Graham for his kind constant support, including for this fellowship. I am thankful to my respected P.I.s at Stanford, Professor H.-S. Philip Wong, and Professor Alberto Salleo for their support of my proposal. I am also grateful to my respected mentors Prof. Suman Datta, Prof. William Alan Doolittle, Dr. Takashi Ando, and Dr. Vijay Narayanan for their kind support, advice, and opportunities. Finally, I would like to thank Georgia Tech ECE for providing the platform for learning, exploration, and collaboration.”

Before her time at Georgia Tech, Athena received her undergraduate degree in materials science and engineering from the Bangladesh University of Engineering and Technology. She then spent two semesters at Purdue University as a graduate researcher, where she collaborated with the Idaho National Lab on nuclear materials for next-generation energy.

Athena’s research has been recognized with the Georgia Tech ECE Ph.D. Fellowship, 2022 Cadence Diversity in Technology Scholarship, 2023 EECS Rising Stars, 2023 Colonel Oscar P. Cleaver Award for the most outstanding Ph.D. dissertation proposal in Georgia Tech ECE, 2023 MRS Graduate Student Award, and IBM Ph.D. Fellowship from 2022-2024.

The Strategic Energy Institute (SEI) of Georgia Tech is excited to announce that Bettina Arkhurst is the 2023 recipient of the James G. Campbell Fellowship Award. Arkhurst’s commitment to academics, research, and community service has been recognized by the award committee. She is a Ph.D. candidate advised by Katherine Fu, professor in the George W. Woodruff School of Mechanical Engineering.

Arkhurst holds a bachelor’s degree in mechanical engineering from Massachusetts Institute of Technology and a master’s degree in mechanical engineering from Georgia Tech. Her research seeks to understand how concepts of energy justice can be applied to renewable energy technology design to better consider marginalized and vulnerable populations. She strives to create frameworks and tools for mechanical engineers to apply as they design energy technologies for all communities.

As an energy equity intern at the National Renewable Energy Laboratory, Arkhurst has worked with colleagues to better understand the role of researchers and engineers in the pursuit of a more just clean energy transition. She is also a leader in the Woodruff School’s graduate student mental health committee, which seeks to improve the culture around graduate student mental health and well-being. Additionally, Arkhurst is working with the Georgia Tech Center for Sustainable Communities Research and Education (SCoRE) to develop a course on community engagement and engineering that will launch in Spring 2024.

The Energy, Policy, and Innovation Center (EPICenter) and the Strategic Energy Institute are proud to announce the 2023 Spark Award recipients: Jake Churchill, Jordan R. Hale, Andrew G. Hill, Henry J. Kantrow, Emily Marshall, and Jacob W Tjards. The award honors outstanding leadership in advancing student engagement in energy research.

Churchill is a master’s student in mechanical engineering advised by Akanksha Menon, assistant professor in the Woodruff School. Working with Menon in the Water-Energy Research Lab, his research focuses on coupling reverse osmosis desalination with renewable energy and storage technologies to provide clean, sustainable, and affordable water in the face of growing global water stress. Churchill has led the Georgia Tech Energy Club’s Solar District Cup team for three years, guiding students interested in solar energy careers. He has also been involved with several SEI initiatives, including EPICenter’s high school summer camp, Energy Unplugged. He is currently facilitating a student-led study to quantify the benefits of cleaning photovoltaic panels using the rooftop array at the Carbon Neutral Energy Solutions Lab.

Hale is pursuing a Ph.D. in chemistry, specializing in theoretical and computational chemistry under Joshua Kretchmer, assistant professor in the School of Chemistry and Biochemistry. His current research focus is utilizing various quantum dynamics formalisms and unique computational techniques to identify the microscopic mechanisms of electron transport in perovskite solar cells. Hale has mentored high school students, teaching them the fundamentals of computational chemistry and various programming skills. Additionally, he has been actively engaged with undergraduate students from other universities both in and out of Georgia through the Summer Theoretical and Computational Chemistry workshop.

Hill is a Ph.D. candidate in the Soper Lab in the School of Chemistry and Biochemistry. His research is focused on the activation of strong chemical bonds using Earth-abundant metals for energy conversion and storage. He has taken an active leadership role on campus, in part through service as the president of the Georgia Tech Chemistry Graduate Student Forum.

Marshall is a second-year graduate student working for Alan Doolittle, professor in the School of Electrical and Computer Engineering. She uses specialized molecular beam epitaxy techniques to grow high-quality III-nitride materials for next-generation power, radio frequency, and optoelectronic devices. Her current research focuses on improving the fundamental understanding of the scandium catalytic effect to optimize the growth of scandium aluminum nitride, a material that shows great promise for applications in future power grids. In addition to her research, Marshall is committed to teaching, having volunteered for five semesters serving her fellow students as a peer instructor at the Hive Makerspace and currently training junior members of her lab to grow semiconductors via molecular beam epitaxy. After earning her master’s and Ph.D., she hopes to continue teaching, mentoring, and connecting others across the world in an effort to bring about a brighter future.

Kantrow is a Ph.D. candidate in the School of Chemical and Biomolecular Engineering, co-advised by Natalie Stingelin and Carlos Silva. His research seeks to understand the photo physics of semiconducting polymers operating in dynamic dielectric environments and to provide material design guidelines for solar fuel technologies. He is an active student leader in the Center for Soft Photo-Electrochemical Systems, where he also serves on the energy justice committee. He served as the secretary of the Association for Chemical Engineering Graduate Students (AChEGS) in 2022 and continues to mentor first-year graduate students in AChEGS and through the Pride Peers Program at Georgia Tech.

Tjards is a graduate research assistant at Georgia Tech’s Sustainable Thermal Systems Laboratory. He graduated with a bachelor’s degree in mechanical engineering from Georgia Tech in 2021 before beginning his Ph.D. program, where he is studying energy systems. Tjards’ research is focused on modeling new manufacturing processes of drywall and aluminum to reduce water consumption during production. Additionally, he is working on a new technique for water purification. While in school, he has been a teaching assistant and instructor for the undergraduate mechanical engineering course on energy systems analysis and design (ME 4315). In his free time, Tjards enjoys Formula 1 racing, Georgia Tech baseball games, and woodworking.