A Georgia Tech alum’s dissertation introduced ways to make artificial intelligence (AI) more accessible, interpretable, and accountable. Although it’s been a year since his doctoral defense, Zijie (Jay) Wang’s (Ph.D. ML-CSE 2024) work continues to resonate with researchers.

Wang is a recipient of the 2025 Outstanding Dissertation Award from the Association for Computing Machinery Special Interest Group on Computer-Human Interaction (ACM SIGCHI). The award recognizes Wang for his lifelong work on democratizing human-centered AI.

“Throughout my Ph.D. and industry internships, I observed a gap in existing research: there is a strong need for practical tools for applying human-centered approaches when designing AI systems,” said Wang, now a safety researcher at OpenAI.

“My work not only helps people understand AI and guide its behavior but also provides user-friendly tools that fit into existing workflows.”

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

Wang’s dissertation presented techniques in visual explanation and interactive guidance to align AI models with user knowledge and values. The work culminated from years of research, fellowship support, and internships.

Wang’s most influential projects formed the core of his dissertation. These included:

• CNN Explainer: an open-source tool developed for deep-learning beginners. Since its release in July 2020, more than 436,000 global visitors have used the tool.
• DiffusionDB: a first-of-its-kind large-scale dataset that lays a foundation to help people better understand generative AI. This work could lead to new research in detecting deepfakes and designing human-AI interaction tools to help people more easily use these models.
• GAM Changer: an interface that empowers users in healthcare, finance, or other domains to edit ML models to include knowledge and values specific to their domain, which improves reliability.
• GAM Coach: an interactive ML tool that could help people who have been rejected for a loan by automatically letting an applicant know what is needed for them to receive loan approval.
• Farsight: a tool that alerts developers when they write prompts in large language models that could be harmful and misused.  

“I feel extremely honored and lucky to receive this award, and I am deeply grateful to many who have supported me along the way, including Polo, mentors, collaborators, and friends,” said Wang, who was advised by School of Computational Science and Engineering (CSE) Professor Polo Chau.

“This recognition also inspired me to continue striving to design and develop easy-to-use tools that help everyone to easily interact with AI systems.”

Like Wang, Chau advised Georgia Tech alumnus Fred Hohman (Ph.D. CSE 2020). Hohman won the ACM SIGCHI Outstanding Dissertation Award in 2022.

Chau’s group synthesizes machine learning (ML) and visualization techniques into scalable, interactive, and trustworthy tools. These tools increase understanding and interaction with large-scale data and ML models. 

Chau is the associate director of corporate relations for the Machine Learning Center at Georgia Tech. Wang called the School of CSE his home unit while a student in the ML program under Chau.

Wang is one of five recipients of this year’s award to be presented at the 2025 Conference on Human Factors in Computing Systems (CHI 2025). The conference occurs April 25-May 1 in Yokohama, Japan. 

SIGCHI is the world’s largest association of human-computer interaction professionals and practitioners. The group sponsors or co-sponsors 26 conferences, including CHI.

Wang’s outstanding dissertation award is the latest recognition of a career decorated with achievement.

Months after graduating from Georgia Tech, Forbes named Wang to its 30 Under 30 in Science for 2025 for his dissertation. Wang was one of 15 Yellow Jackets included in nine different 30 Under 30 lists and the only Georgia Tech-affiliated individual on the 30 Under 30 in Science list.

While a Georgia Tech student, Wang earned recognition from big names in business and technology. He received the Apple Scholars in AI/ML Ph.D. Fellowship in 2023 and was in the 2022 cohort of the J.P. Morgan AI Ph.D. Fellowships Program.

Along with the CHI award, Wang’s dissertation earned him awards this year at banquets across campus. The Georgia Tech chapter of Sigma Xi presented Wang with the Best Ph.D. Thesis Award. He also received the College of Computing’s Outstanding Dissertation Award.

“Georgia Tech attracts many great minds, and I’m glad that some, like Jay, chose to join our group,” Chau said. “It has been a joy to work alongside them and witness the many wonderful things they have accomplished, and with many more to come in their careers.”

For three days, a cybercriminal unleashed a crippling ransomware attack on the futuristic city of Northbridge. The attack shut down the city’s infrastructure and severely impacted public services, until Georgia Tech cybersecurity experts stepped in to stop it. 

This scenario played out this weekend at the DARPA AI Cyber Challenge (AIxCC) semi-final competition held at DEF CON 32 in Las Vegas. Team Atlanta, which included the Georgia Tech experts, were among the contest’s winners.  

Team Atlanta will now compete against six other teams in the final round that takes place at DEF CON 33 in August 2025. The finalists will keep their AI system and improve it over the next 12 months using the $2 million semi-final prize.

The AI systems in the finals must be open sourced and ready for immediate, real-world launch. The AIxCC final competition will award a $4 million grand prize to the ultimate champion.

Team Atlanta is made up of past and present Georgia Tech students and was put together with the help of SCP Professor Taesoo Kim. Not only did the team secure a spot in the final competition, they found a zero-day vulnerability in the contest. 

“I am incredibly proud to announce that Team Atlanta has qualified for the finals in the DARPA AIxCC competition,” said Taesoo Kim, professor in the School of Cybersecurity and Privacy and a vice president of Samsung Research. 

“This achievement is the result of exceptional collaboration across various organizations, including the Georgia Tech Research Institute (GTRI), industry partners like Samsung, and international academic institutions such as KAIST and POSTECH.” 

After noticing discrepancies in the competition score board, the team discovered and reported a bug in the competition itself. The type of vulnerability they discovered is known as a zero-day vulnerability, because vendors have zero days to fix the issue. 

While this didn’t earn Team Atlanta additional points, the competition organizer acknowledged the team and their finding during the closing ceremony. 

“Our team, deeply rooted in Atlanta and largely composed of Georgia Tech alumni, embodies the innovative spirit and community values that define our city,” said Kim. 

“With over 30 dedicated students and researchers, we have demonstrated the power of cross-disciplinary teamwork in the semi-final event. As we advance to the finals, we are committed to pushing the boundaries of cybersecurity and artificial intelligence, and I firmly believe the resulting systems from this competition will transform the security landscape in the coming year!”

The team tested their cyber reasoning system (CRS), dubbed Atlantis, on software used for data management, website support, healthcare systems, supply chains, electrical grids, transportation, and other critical infrastructures.

Atlantis is a next-generation, bug-finding and fixing system that can hunt bugs in multiple coding languages. The system immediately issues accurate software patches without any human intervention. 

AIxCC is a Pentagon-backed initiative that was announced in August 2023 and will award up to $20 million in prize money throughout the competition. Team Atlanta was among the 42 teams that qualified for the semi-final competition earlier this year. 

A Georgia Tech researcher will continue to mitigate harmful post-deployment effects created by artificial intelligence (AI) as he joins the 2024-2025 cohort of fellows selected by the Berkman-Klein Center (BKC) for Internet and Society at Harvard University.

Upol Ehsan is the first Georgia Tech graduate selected by BKC. As a fellow, he will contribute to its mission of exploring and understanding cyberspace, focusing on AI, social media, and university discourse.

Entering its 25th year, the BKC Harvard fellowship program addresses pressing issues and produces impactful research that influences academia and public policy. It offers a global perspective, a vibrant intellectual community, and significant funding and resources that attract top scholars and leaders.

The program is highly competitive and sought after by early career candidates and veteran academic and industry professionals. Cohorts hail from numerous backgrounds, including law, computer science, sociology, political science, neuroscience, philosophy, and media studies. 

“Having the opportunity to join such a talented group of people and working with them is a treat,” Ehsan said. “I’m looking forward to adding to the prismatic network of BKC Harvard and learning from the cohesively diverse community.”

While at Georgia Tech, Ehsan expanded the field of explainable AI (XAI) and pioneered a subcategory he labeled human-centered explainable AI (HCXAI). Several of his papers introduced novel and foundational concepts into that subcategory of XAI.

Ehsan works with Professor Mark Riedl in the School of Interactive Computing and the Human-centered AI and Entertainment Intelligence Lab.

Ehsan says he will continue to work on research he introduced in his 2022 paper The Algorithmic Imprint, which shows how the potential harm from algorithms can linger even after an algorithm is no longer used. His research has informed the United Nations’ algorithmic reparations policies and has been incorporated into the National Institute of Standards and Technology AI Risk Management Framework.

“It’s a massive honor to receive this recognition of my work,” Ehsan said. “The Algorithmic Imprint remains a globally applicable Responsible AI concept developed entirely from the Global South. This recognition is dedicated to the participants who made this work possible. I want to take their stories even further."

While at BKC Harvard, Ehsan will develop a taxonomy of potentially harmful AI effects after a model is no longer used. He will also design a process to anticipate these effects and create interventions. He said his work addresses an “accountability blindspot” in responsible AI, which tends to focus on potential harmful effects created during AI deployment.

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.

The College of Computing’s countdown to commencement began on April 11 when students, faculty, and staff converged at the 33rd Annual Awards Celebration.

The banquet celebrated the college community for an exemplary academic year and recognized the most distinguished individuals of 2023-2024. For Alex Orso, the reception was a high-water mark in his role as interim dean.

“I always say that the best part about my job is to brag about the achievements and accolades of my colleagues,” said Orso.

“It is my distinct honor and privilege to recognize these award winners and the collective success of the College of Computing.”

Orso’s colleagues from the School of Computational Science and Engineering (CSE) were among the celebration’s honorees. School of CSE students, faculty, and alumni earning awards this year include:

• Grace Driskill, M.S. CSE student - The Donald V. Jackson Fellowship
• Harshvardhan Baldwa, M.S. CSE student - The Marshal D. Williamson Fellowship
• Mansi Phute, M.S. CS student- The Marshal D. Williamson Fellowship
• Assistant Professor Chao Zhang- Outstanding Junior Faculty Research Award
• Nazanin Tabatbaei, teaching assistant in Associate Professor Polo Chau’s CSE 6242 Data & Visual Analytics course- Outstanding Instructional Associate Teaching Award
• Rodrigo Borela (Ph.D. CSE-CEE 2021), School of Computing Instruction Lecturer and CSE program alumnus - William D. "Bill" Leahy Jr. Outstanding Instructor Award
• Pratham Metha, undergraduate student in Chau’s research group- Outstanding Legacy Leadership Award
• Alexander Rodriguez (Ph.D. CS 2023), School of CSE alumnus - Outstanding Doctoral Dissertation Award

At the Institute level, Georgia Tech recognized Driskill, Baldwa, and Phute for their awards on April 10 at the annual Student Honors Celebration.

Driskill’s classroom achievement earned her a spot on the 2024 All-ACC Indoor Track and Field Academic Team. This follows her selection for the 2023 All-ACC Academic Team for cross country.

Georgia Tech’s Center for Teaching and Learning released in summer 2023 the Class of 1934 Honor Roll for spring semester courses. School of CSE awardees included Assistant Professor Srijan Kumar (CSE 6240: Web Search & Text Mining), Lecturer Max Mahdi Roozbahani (CS 4641: Machine Learning), and alumnus Mengmeng Liu (CSE 6242: Data & Visual Analytics).

Accolades and recognition of School of CSE researchers for 2023-2024 expounded off campus as well.

School of CSE researchers received awards off campus throughout the year, a testament to the reach and impact of their work.

School of CSE Ph.D. student Gaurav Verma kicked off the year by receiving the J.P. Morgan Chase AI Research Ph.D. Fellowship. Verma was one of only 13 awardees from around the world selected for the 2023 class.

Along with seeing many of his students receive awards this year, Polo Chau attained a 2023 Google Award for Inclusion Research. Later in the year, the Institute promoted Chau to professor, which takes effect in the 2024-2025 academic year.

Schmidt Sciences selected School of CSE Assistant Professor Kai Wang as an AI2050 Early Career Fellow to advance artificial intelligence research for social good. By being part of the fellowship’s second cohort, Wang is the first ever Georgia Tech faculty to receive the award.

School of CSE Assistant Professor Yunan Luo received two significant awards to advance his work in computational biology. First, Luo received the Maximizing Investigator’s Research Award (MIRA) from the National Institutes of Health, which provides $1.8 million in funding for five years. Next, he received the 2023 Molecule Make Lab Institute (MMLI) seed grant.

Regents’ Professor Surya Kalidindi, jointly appointed with the George W. Woodruff School of Mechanical Engineering and School of CSE, was named a fellow to the 2023 class of the Department of Defense’s Laboratory-University Collaboration Initiative (LUCI).

2023-2024 was a monumental year for Assistant Professor Elizabeth Qian, jointly appointed with the Daniel Guggenheim School of Aerospace Engineering and the School of CSE.

The Air Force Office of Scientific Research selected Qian for the 2024 class of their Young Investigator Program. Earlier in the year, she received a grant under the Department of Energy’s Energy Earthshots Initiative.

Qian began the year by joining 81 other early-career engineers at the National Academy of Engineering’s Grainger Foundation Frontiers of Engineering 2023 Symposium. She also received the Hans Fischer Fellowship from the Institute for Advance Study at the Technical University of Munich.

It was a big academic year for Associate Professor Elizabeth Cherry. Cherry was reelected to a three-year term as a council member-at-large of the Society of Industrial and Applied Mathematics (SIAM). Cherry is also co-chair of the SIAM organizing committee for next year’s Conference on Computational Science and Engineering (CSE25).

Cherry continues to serve as the School of CSE’s associate chair for academic affairs. These leadership contributions led to her being named to the 2024 ACC Academic Leaders Network (ACC ALN) Fellows program.

School of CSE Professor and Associate Chair Edmond Chow was co-author of a paper that received the Test of Time Award at Supercomputing 2023 (SC23). Right before SC23, Chow’s Ph.D. student Hua Huang was selected as an honorable mention for the 2023 ACM-IEEE CS George Michael Memorial HPC Fellowship.