Throughout his career, professor James Fogarty, who joined the Allen School faculty in 2006, has grown to become a central figure in Seattle’s human-computer interaction (HCI) community and beyond. His research has made key contributions in sensor-based interactions, interactive machine learning, personal health informatics and accessibility, publishing over 100 peer-reviewed papers. At the same time, he has played a pivotal role in founding and growing Design, Use, Build (DUB) — University of Washington’s cross-campus HCI alliance bringing together faculty, students, researchers and industry partners.
The ACM Special Interest Group on Computer-Human Interaction (SIGCHI) recognized Fogarty’s contributions and inducted him into the SIGCHI Academy Class of 2025. Each class represents the principal leaders of the field, whose research has helped shape how we think of HCI.
“I am honored to be among the SIGCHI Academy Class of 2025,” Fogarty said. “I’m grateful for the amazing students and collaborators that I’ve had the pleasure to work with over the years, advancing HCI, interactive machine learning, personal health informatics and accessibility research.”
Since the beginning of his career, Fogarty has made breakthroughs in HCI research. As a first-generation student, he was introduced to HCI research at Virginia Tech and was part of the first cohort of Ph.D. students in the Human-Computer Interaction Institute at Carnegie Mellon University. Key research from his dissertation, which focused on using sensor-based interactions to predict the best time to interrupt someone, received a 2005 CHI Best Paper Award.
Upon joining the UW, Fogarty launched a new research emphasis in interaction with artificial intelligence (AI) and machine learning. Fogarty’s research into new methods for engaging end-users in machine learning training and assessment and understanding difficulties that machine learning developers encounter was considered ahead of its time. The researchers contributed to what is now known as human-AI interaction before it became a trending topic, and this line of research went on to directly impact industry guidelines for the field.
In the same period, Fogarty and his collaborators developed Prefab, a system for real-time interpretation and enhancement of graphical interfaces through reverse engineering their pixel-level appearance. Prefab, which earned a 2010 CHI Best Paper Award, was a breakthrough in interface systems research, foreshadowing current work using AI to understand, interact with and enhance graphical interfaces.
Outside of health tracking, Fogarty has also made important strides in accessibility research. He and his team drew inspiration from epidemiology to conduct the first large-scale assessment of accessibility in 10,000 Android apps. The Department of Justice cited the work as part of its updates to the Americans with Disabilities Act. He also extended his work on interface understanding and enhancement to demonstrate real-time repair of mobile app accessibility failures. This research helped directly motivate and inform Apple’s launch of accessibility repair in its pixel-based Screen Recognition.
“James has made an exemplary impact across research disciplines and industry,” Allen School professor Jeffrey Heer said. “His research prowess, volunteer spirit, deep care, thoughtfulness and community-mindedness have helped guide DUB and advance the HCI community in Seattle and across the globe.”
Fogarty is one of five UW faculty being recognized with ACM SIGCHI Awards this year. Department of Human Centered Design & Engineering (HCDE) professor and Allen School adjunct faculty member Kate Starbird joins Fogarty as part of the CHI Academy Class of 2025, and her work sits at the intersection of HCI and computer-supported cooperative work.
Information School professor and Allen School adjunct faculty member Alexis Hiniker also won an ACM SIGCHI Societal Impact Award for her research into ways that consumer-facing technologies can hurt young people instead of helping them thrive. Nadya Peek and Cecilia Aragon, both HCDE professors and Allen School adjunct faculty members, were honored with ACM SIGCHI Special Recognitions. Peek was recognized for “democratizing automation through open-source hardware, building global maker communities and bridging academic research with grassroots fabrication practices,” and Aragon “for establishing human-centered data science as a new field bridging HCI and data science, demonstrating its impact through applications from astrophysics to energy systems.”
One of the foundations of database theory is efficient query execution. On the theory side, researchers have been tackling this issue by finding the upper bounds on the query size results to determine the fundamental hardness of query execution. Other researchers have been taking a practical approach by honing query optimizers that can automatically create query evaluation algorithms based on various data properties. These two approaches have converged in the form of worst-case optimal join algorithms, which ensure the optimal execution time for query evaluations by taking into account certain statistics about the queried dataset.
In a paper titled “Partition Constraints for Conjunctive Queries: Bounds and Worst-Case Optimal Joins,” Allen School Ph.D. student Kyle Deeds introduced an innovative statistic called partition constraints that can improve existing worst-case optimal join algorithms by capturing the latent structure in relations within the data. The rigid nature of traditional constraints used in worst-case optimal join algorithms, such as degree constraints or cardinality constraints, can end up hindering query execution speed. Instead, partition constraints extend the notion of degree constraints, enabling the relationships between database attributes to be divided into smaller and more manageable sub-relations that each have their own degree constraints.
“A core problem of database theory is query execution,” said Deeds, who is advised by professors Dan Suciu and Magdalena Balazinska in the University of Washington’s Database Group. “What this work did was present a new kind of statistic, called partition constraints, on that data that we can take into account when we are executing a query, essentially speeding up the whole process for data that has this nice structure.”
An example of a use of partition constraints is in an algorithm that records who has key card access to which rooms within a university. Faculty and students may only need to enter a few rooms such as lecture halls and offices, while security and cleaning staff may need to be able to reach many more, or maybe all, rooms. It makes sense to then partition keycard clearance by tracking the access restrictions of faculty and students, and of the various room caretakers, to efficiently determine who needs access to which rooms.
Deeds took inspiration for partition constraints from another field of computer science called graph theory, or the study of networks. Degeneracy in graph theory measures how sparse a graph is. In a network with low degeneracy, researchers can design algorithms that minimize the performance impact of highly connected vertices, which can make some graph algorithms more efficient. When it comes to partitioning and databases, this property can also help maintain database performance, even in cases where the tables have very high degrees. The researchers developed partition constraints as a declarative version of graph degeneracy for higher-arity data, or data with more attributes or fields.
“This paper is like a translation style work,” Deeds said. “The goal of this work was to translate ideas from graph theory over in a way that made sense to the database community. Once we found the right way to do the translation, things just clicked into place.”
These ideas started clicking into place when Deeds and Merkl met and began collaborating on their research.
“Kyle and Camillo first met at a previous instance of the same conference, at ICDT in 2023 in Greece. Since then they worked remotely, without any help or guidance from their respective advisors,” said Suciu, who holds the Microsoft Endowed Professorship in the Allen School. “They started from an existing, elegant concept in graph theory, called ‘degeneracy,’ and extended it to a practical method for processing relational databases. Their main idea is that, by carefully partitioning each relation into a small number of fragments, then computing a query separately on each fragment, one can significantly reduce the query’s execution time.”
For Deeds, partition constraints point to a new direction for database theory research to pursue. Partition constraints help uncover underlying and useful structures within data, and understanding the different correlations and properties that a database has can help find ways to further optimize query executions.
“The same partitioning method that Kyle and Camillo developed has many other applications beyond query evaluation. For example, in one of my research projects we are developing improved techniques for cardinality estimation, and asked Kyle to join us to adapt his method for this problem,” Suciu said.
Outside of partition constraints, Deeds has been researching ways to optimize other programs. He introduced Galley, a system for declarative sparse tensor computing that enables users to draft efficient tensor programs without needing to make complex algorithm decisions.
Allen School professor Su-In Lee, who directs the University of Washington’s AI for bioMedical Sciences (AIMS) Lab, is shaping the future of biology and medicine through artificial intelligence. Her research focuses on fundamentally advancing AI and machine learning (ML) techniques to provide insights into complex biological systems and drive healthcare breakthroughs, transforming fields from basic biology to clinical medicine, including cancer biology, dermatology and critical care.
“I am really grateful and honored to be named an ISCB Fellow,” said Lee, who holds the Paul G. Allen Professorship in Computer Science & Engineering. “This recognition fuels my desire to contribute further and push the field to the next stage.”
Lee’s research on using generative AI and physician expertise to audit medical-image classifiers was recently featured on the cover of Nature Biomedical Engineering.
One of Lee’s most pivotal contributions to the field is her work on the SHAP, or SHapley Additive exPlanations, values. The technique uses a game theory approach to help explain the output of an ML model. Her research using SHAP techniques tackles the accuracy versus interpretability problem. Simpler models can be more interpretable at the expense of being less accurate, but complex models are more accurate but difficult to interpret. Instead, Lee and her collaborators balance the two to develop high-performance, expressive models that are effective for biomedicine.
Using SHAP values as a foundation, Lee and her collaborators developed a novel framework called Prescience that could predict and also explain a patient’s risk of developing hypoxemia during surgery. Their follow-up research CoAI, also known as Cost-Aware Artificial Intelligence, used SHAP values to reduce the time, effort and resources needed to predict patient outcomes and inform the treatment plan. Lee has also used SHAP values to understand factors influencing aging to better treat age-related disorders using the ENABL Age, or ExplaiNAble BioLogical Age, framework.
Lee has also developed explainable AI methods that can analyze biological data and find new therapeutic targets for diseases. She and her colleagues introduced ConstrastiveVI, a deep learning framework that applies contrastive analysis to single-cell datasets that help separate variations in the target cells from the healthy control cells during experiments. For Lee, explainable AI also has the potential to identify novel treatments for Alzheimer’s disease, which is one of the 10 leading causes of death in the U.S., and other neurodegenerative conditions. Lee has also used explainable AI to audit and dissect the reasoning process that AI systems use to analyze medical images, such as chest X-rays for predicting COVID-19 status or dermatological images for detecting melanoma.
“Su-In’s body of work has completely transformed how people analyze and interpret AI/ML models. Her SHAP approach is used by essentially the entire field, not only in computational biology but all of computer science,” said Trey Ideker, professor of medicine, bioengineering and computer science at the University of California San Diego.
In addition to her research, Lee has helped promote interdisciplinary education and collaboration. Since 2020, she has served as director of UW’s Computational Molecular Biology program. Lee has also played a pivotal role in establishing the Medical Scientist Training Program and Allen School collaboration to train Ph.D. students in both computer science and clinical medicine. She has also taken her leadership skills beyond UW as co-founder and co-chair of the Machine Learning in Computational Biology conference.
Having transitioned from studying computer science to biology herself, Lee understands the challenges Allen School students may face in researching biomedical applications, but also the rewards.
“It would be great for the field, the world and science in general if more computer science students pursue computational biology,” Lee said.
Examples of computational illusion knit design including a gray slate that transitions to the Mona Lisa, and an image of Vincent Van Gogh’s sunflowers shifting into his self portrait.
When you look at a knitted panel head on, all you might see is gray noise, but taking a step to the side and looking at an angle reveals another image — a hidden Mona Lisa.
This technique is called illusion knitting, where stitches of varying heights can create different images depending on what angle you view the piece. Traditionally, the intricate process of designing these illusions, by selecting stitches, choosing their colors and deciding whether they are raised or not was done by hand. Now, a team of Allen School researchers have introduced computational illusion knitting. The design framework helps automate the process by computationally generating knitting patterns for input designs, making illusion knitting more accessible and allowing for more complex and multi-view patterns that were previously impossible. The researchers presented the paper at the SIGGRAPH 2024 conference.
Allen School Ph.D. student Amy Zhu
“If we write down the mathematical properties of the illusion knits, then we have a foundation with which to create algorithms that help the user iterate on and optimize their designs,” lead author and Allen School Ph.D. student Amy Zhu said. “With this foundation you can start to think, ‘where can I push the boundaries of what’s possible with illusion knitting?’”
To create an illusion knit object, the researchers first characterize the design’s unique microgeometry, or the small-scale geometry on the surface of a knitted piece. The goal is to then add a layer of abstraction over the complex microgeometry to streamline the design process, Zhu explained. This layer uses logical units called bumps, or raised sections made by a knit with a purl in the next row below it, that can block previous stitches, as well as flats, where the knit surface is level and is made by a knit stitch with another in the next row.
These observations led the researchers to develop various constraints that capture both the viewing behavior, or what image is seen at each angle, and the physical behavior, also known as the result from the knitted design or fabrication choices. Single image illusion knits, such as the hidden Mona Lisa, are easier to design as they only change the color between rows.
Using computational techniques, the team became the first to design and execute illusion knitting patterns that require mixed colorwork and texture between rows. The researchers first used automated methods such as gradient descent and the maximum satisfiability problem (MaxSAT) to find a compromise that satisfies the most constraints and relaxes others, Zhu explained. However, these systems are still limited by how well they can express constraints about readability, knittability as well as how accurate the theoretical model is to reality.
A knit image of Edvard Munch’s “The Scream” quantized using a diffusion model on the left, and one quantized by hand on the right.
Instead of relying solely on the algorithm for solutions, the researchers created a user-in-the-loop framework that allows the user to easily edit the design. The user can then further simplify the design by breaking it down into tiles made of bumps and flats to fill the image.
“There’s many tricky things to consider when you’re in fabrication land that are often difficult to anticipate or capture when working theoretically — maybe the machine settings are wrong and the white stitches are smaller, making the contrast between colors lower,” Zhu said. “One reason I like our approach is that we put the fabricated reality first, so our priority is providing ways for the user to edit things that come out of the machine, so they can observe if something didn’t look right and know how to fix it.”
For example, using a knitting machine and this design framework, Zhu created a knit piece that shows artist Vincent Van Gogh’s self portrait from one angle and his sunflowers from another. The quantized input image of the self portrait had too much noise around his head which made the design unreadable when knit. For a cleaner result, all she had to do was edit the input image to remove the noise or modify the tiles.
Zhu said she hopes this framework gives users the foundation to create more innovative illusion knit designs such as clothes that display images with certain poses or knit animations. Next, Zhu is researching ways to capture and explore different fabrication plans of knit objects.
“Approaching crafts from a computer science angle allows us to expand the boundaries of what can be achieved — whether that’s developing new algorithms for machine knitting, creating novel design tools or even inventing new forms of visual art,” said senior author and Allen School professor Zach Tatlock, who co-advises Zhu alongside colleague Adriana Schulz. “It’s fascinating to see how the rigor of computer science can enhance the creativity of traditional crafts.”
Allen School Ph.D. student Shangbin Feng envisions the work of large language models (LLMs) as a collaborative endeavor, while fellow student Rock Yuren Pang is interested in advancing the conversation around unintended consequences of these and other emerging technologies. Both were recently honored among the 2024 class of IBM Ph.D. Fellows, which recognizes and supports students from around the world who pursue pioneering research in the company’s focus areas. For Feng and Pang, receiving a fellowship is a welcome validation of their efforts to challenge the status quo and change the narrative around AI.
Shangbin Feng: Championing AI development through multi-LLM collaboration
Shangbin Feng
For Shangbin Feng, the idea of singular general purpose LLM can be difficult, the same way there are no “general purpose” individuals. Instead, people have varied and specialized skills and experiences, and they collaborate with each other to achieve more than they could on their own. Feng adapts this idea into his research into multi-LLM collaboration, helping to develop a range of protocols enabling information exchange across LLMs with diverse expertise.
“I’m super grateful for IBM’s support to advance multi-LLM collaboration, challenging the status quo with a collaborative and participatory vision,” Feng said. “As academic researchers with limited resources, we are not powerless: we can put forward bold proposals to enable the collaboration of many in AI development, not just the resourceful few, such that multiple AI stakeholders can participate and have a say in the development of LLMs.”
Feng’s research focuses on developing different methods for LLM collaboration. He introduced Knowledge Card, a modular framework that helps fill in information gaps in general purpose LLMs. The researchers augmented these LLMs using a pool of domain-specialized small language models that provide relevant and up-to-date knowledge and information. Feng and his collaborators then proposed a text-based approach, where multiple LLMs evaluate and provide feedback on each others’ responses to identify knowledge gaps and improve reliability. Their research received an Outstanding Paper Award at the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024).
He also helped develop the framework Modular Pluralism that enables aggregation of community language models representing the preferences of diverse populations at the token probabilities level. Most recently, Feng proposed the collaborative search algorithm called Model Swarms. In this weight-level approach, diverse LLM experts collectively move in the parameter search space using swarm intelligence.
In future research, Feng plans to focus on ways to reprocess and recycle the over one million publicly available LLMs.
“It is often costly to retrain or substantially modify LLMs, thus reusing and composing existing LLMs would go a long way to reduce the carbon footprint and environmental impact of language technologies,” Feng said.
Rock Yuren Pang: Uncovering unintended consequences from emerging technologies
Rock Yuren Pang
As AI becomes more commonplace and integrated into different sectors of society, Rock Yuren Pang wants to help researchers and practitioners grasp the potential adverse effects of these emerging technologies.
“I work in the intersection of human-computer interaction (HCI) and responsible AI,” Pang said. “Through the IBM fellowship, I’ll continue to design systems and sociotechnical approaches for researchers to anticipate and understand the unintended consequences of our own research products, especially with fast-growing AI advancements. I’d like to change the narrative that doing so is a burden, but rather a fun and rewarding experience to communicate potential risks as well as the benefits for many diverse user populations.”
While tracking the downstream impacts of AI and other technologies can be overwhelming, Pang has worked with his Ph.D. advisor, Allen School professor Katharina Reinecke, to introduce tools to help make it more manageable. In a white paper dubbed PEACE, or “Proactively Exploring and Addressing Consequences and Ethics,” Pang and his collaborators propose a holistic approach that both makes it easier for researchers to access resources and support to predict and mitigate unintended consequences of their work, and also intertwine these concerns into the school’s teaching and research. Their work is supported through a grant from the National Science Foundation’s Ethical and Responsible Research (ER2) program.
He also helped develop Blip, a system that consolidates real-world examples of undesirable impacts of technology from across online articles. The system then summarizes and presents the information in a web-based interface, assisting researchers in identifying consequences that they “had never considered before,” Pang explained. Most recently, Pang has been investigating the growing influence of LLMs on HCI research.
Outside of his work in responsible AI, Pang is also interested in accessibility research. As part of the Center for Research and Education on Accessible Technology and Experiences (CREATE), he and his team developed an accessibility guide for data science and other STEM classes. Their work was honored last year as part of the UW IT Accessibility Task Force’s Digital Accessibility Awards. Pang also contributed to AltGeoViz, a system that allows screen-reader users to explore geovisualizations by automatically generating alt-text descriptions based on their current map view. The team received the People’s Choice Award at the Allen School’s 2024 Research Showcase.
In the future, Pang aims to design more detailed guidelines for addressing potential unintended consequences from AI, and novel interactions to collaborate with AI.
Allen School professor Amy X. Zhang (Photo by University of Washington)
As the head of the Allen School’s Social Futures Lab, professor Amy X. Zhang wants to reimagine how social platforms can empower end users and communities to take control of their own online experiences for social good. Her research draws on the design of offline public institutions and communities to then develop new social computing systems that can help online platforms become more democratic instead of top-down, and more customizable as opposed to one-size-fits-all.
These efforts were commended by the Alfred P. Sloan Foundation, which recognized Zhang among its 2025 class of Sloan Research Fellows. The fellowship highlights early-career researchers whose innovative work represents the next generation of scientific leaders.
“It’s an honor to be recognized for my work, so that we can further the impact our lab’s research has had across social computing and human-computer interaction (HCI) research communities,” Zhang said. “This fellowship will support my research into redesigning social and collaborative computing systems to give greater agency to users and further societally good aims.”
One of Zhang’s main lines of research focuses on participatory governance in online communities. Taking inspiration from the idea of “laboratories for democracy,” she has developed tools to help users have a greater say in the policies governing their online actions. For example, Zhang and her collaborators introduced PolicyKit, an open-source computational framework that enables communities to design, carry out and implement procedures such as elections, juries and direct democracy on their platform of choice such as Slack or Reddit. Their research has prompted follow-up projects including Pika, which enables non-programmers to author a wide range of executable governance policies, and MlsGov, a distributed version of PolicyKit for governing end-to-end encrypted groups.
At the same time, she is also interested in how governance can manifest at the platform level. Because platforms are much larger than communities and have millions of users, it becomes a challenge to maintain democratic systems, Zhang explained. To address this, Zhang focuses on workflows supporting procedural justice in platform-level content moderation design. She found that using digital juries for content moderation were perceived as more procedurally just compared to existing algorithms and contract workers, but overall, expert panels had the highest perception. Because of her expertise, Zhang was invited by Facebook to give input on their Community Review program and X (formerly known as Twitter) guidance on their Community Notes program for everyday users to weigh in on potential content violations.
Another line of Zhang’s research addresses the issue of content moderation, but from a different angle. Instead of platforms moderating content, Zhang builds tools to help users “decide for themselves how they would like to customize what content they do or do not want to see.” For example, when it comes to online harassment, Zhang found that platform-level solutions do not account for the different ways that people can define and want to address harassment. Zhang helped develop the system called FilterBuddy that helps content creators who face harassment create their own filters to moderate their comment sections. She has also introduced other personalized moderation controls, customized social media feed curation algorithms as well as content labels for misinformation.
Zhang’s other research interests lie in the space of public interest technology. This includes supporting the many people, often acting in a volunteer capacity, who take on civic roles online such as spreading important information or responding to misinformation. In their research, Zhang and her team found that these people may spend significant amounts of time, effort and emotional labor often without knowing if they are making a difference. To help support their work, the team developed an augmented large language model that can address misinformation by identifying and explaining inaccuracies in a piece of content. Zhang also helped interview fact-checkers on how they prioritize which claims to verify and what tools may assist them in their work. From this research, Zhang and her collaborators introduced a framework to help fact-checkers prioritize their efforts based on the harm the misinformation could cause.
In her ongoing and future research, Zhang plans to explore how offline institutional structures can also be useful for rethinking the governance of artificial intelligence technologies.
“My long-term goal is to build social computing systems that make our online spaces as rich and varied as our offline ones, while also striving for a more pro-social, resilient and inclusive society,” Zhang said.
Zhang, in addition to being named a Sloan Research Fellow, has received Best Paper Awards at the Association of Computing Machinery CHI conference on Human Factors in Computing Systems (ACM CHI) and ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (ACM CSCW), a National Science Foundation CAREER Award and a Google Research Scholar Award.
Joining Zhang in the 2025 class of Sloan Research Fellows is Allen School alum Lydia Chilton (Ph.D. ‘16), now faculty at Columbia University. Her research focuses on HCI and how AI can help people with design, innovation and creative problem-solving.
Zhang is one of three University of Washington faculty to earn Sloan Research Fellowships this year. The other honorees are Amy L. Orsborn, a professor of electrical & computer engineering and bioengineering who earned a fellowship in the neuroscience category, and chemistry professor Dianne J. Xiao.
Each year, the Association for Computing Machinery (ACM) recognizes the top 1 percent of its members who have made notable contributions to the field of computing science and technology as ACM Fellows.
Allen School professors Brian Curless and Jeffrey Heer were among the 55 ACM fellows who will be recognized at a ceremony in June in San Francisco, California. The 2024 inductees’ contributions range from 3D scanning to interactive machine learning and much more. Their work has helped transform how we use computing technologies today.
Brian Curless: “For contributions to 3D shape and appearance capture and reconstruction and to computational photography.”
The ACM commended Curless for his “contributions to 3D shape and appearance capture and reconstruction and to computational photography.”
“It is an incredible honor to be named an ACM Fellow,” Curless said. “This recognition is as much mine as it is the amazing collaborators, both at the Allen School and beyond, that I’ve worked with over the years researching computational photography, 3D scanning and more.”
Curless’ work has helped lay the groundwork for major milestones in 3D scanning research. He invented what is now the standard for merging range images together to reconstruct 3D surfaces such as the ridges and scales in a detailed dragon model. In addition, previous methods for extracting range data from optical triangulation scanners, which use lasers to shine light on an object and then measure the reflection to estimate its shape, were often not accurate for surfaces that are curved, discontinuous or had varying degrees of reflectance. Curless introduced a new and more accurate method using spacetime analysis. His volumetric 3D reconstruction research has been used for feature film development, and has since then been incorporated into Google’s Tango augmented reality (AR) computing platform and Microsoft’s Hololens.
The volumetric reconstruction method was also key to the success of the Digital Michelangelo Project, the first, detailed 3D computer archive of the Renaissance artist’s sculptures including the statue “David.” The project led to more research into using 3D scanning for archaeological preservation.
His work in 3D scanning technology extends beyond digitizing artwork. Curless has pioneered methods for using large 3D datasets to create accurate human body models to fit the general population, ushering in new research in data-driven human body shape modeling. Further, he and collaborators combined 3D scanning with traditional photography to introduce a new paradigm — surface light fields — to concisely represent the view-dependent appearance of objects.
Outside of 3D shape scanning, Curless has made strides in computational photography. He helped develop the interactive digital photomontage method, a technique for combining multiple images together without visible seams. The technique was featured in Adobe Photoshop CS4 as well as in Google Maps to create satellite imagery. Its interface for stitching together the best parts of photos has led to Google Pixel phone’s “Best Take” feature and “Photomerge Group Shots” in Adobe Photoshop Elements 10.
In addition to being named an ACM Fellow, Curless has also received the National Science Foundation CAREER Award, Alfred P. Sloan Foundation Faculty Fellowship and the UW ACM Teaching Award.
“Brian is a pioneer in 3D shape scanning and computational photography,” said Seitz. “He invented techniques that became widely used both in academia and industry, and it’s amazing to see him recognized by this major honor.”
Jeffrey Heer: “For contributions to information visualization, human-centered data science and interactive machine learning.”
Jeffrey Heer
Allen School professor Jeffrey Heer, who co-directs the UW Interactive Data Lab alongside colleague Leilani Battle, has published over 120 peer-reviewed papers and has developed some of the most popular open-source visualization tools used by developers around the world. Heer’s interest in visualization began during his undergraduate studies at University of California, Berkeley, and since then, his work has already helped change the way that people interact with data, charts and graphs. But for Heer, “there’s still so much to do.”
“I’m honored to be named an ACM Fellow, and I am particularly grateful to my amazing students and collaborators over the years,” said Heer, who holds the Jerre D. Noe Endowed Professorship in the Allen School. “Whether visualizing complex information, wrangling data into shape, authoring analyses or making sense of statistical and machine learning results, together we’ve helped make it easier for people to work with, make sense of and communicate data more effectively.”
The ACM recognized Heer for “contributions to information visualization, human-centered data science and interactive machine learning.”
Heer is best known for his work on interactive information visualization. Building interactive visualizations requires knowledge in fields such as user interface development, graphic design as well as algorithmic implementation. Over the years, Heer and his collaborators introduced widely-adopted visualization languages such as Prefuse, Protovis, D3, Vega, Vega-Lite and Mosaic that have become the standard in fields like industry, data science and journalism.
“I am incredibly excited that Jeffrey Heer is being recognized by the ACM for the tremendous impact he has had in the areas of Visualization and Human-Computer Interaction,” said Maneesh Agrawala, computer science professor at Stanford University. “His work has opened new directions for research on things like narrative in visualization, data preparation for analysis and grammars for visualization. It has also had immense impact beyond research with his open source tools like D3 and Vega regularly used by developers worldwide.”
Since joining the Allen School faculty in 2013, Heer has received the ACM’s Grace Murray Hopper Award, the 2017 IEEE Visualization Technical Achievement Award and Best Paper Awards at the ACM Conference on Human Factors in Computing (CHI), EuroVis and IEEE InfoVis conferences. He was also inducted into the IEEE Visualization Academy in 2019 — one of the most prestigious honors in the field of visualization.
Shayan Oveis Gharan (Photo by Dennis Wise/University of Washington)
Allen School professor Shayan Oveis Gharan and Ph.D. alumnus Kuikui Liu, now a professor at MIT, are part of a team of researchers that received this year’s Michael and Sheila Held Prize from the National Academy of Sciences for introducing a new method for counting the bases of matroids. The annual prize honors “outstanding, innovative, creative and influential research” in the field of combinatorial and discrete optimization published in the past eight years.
In their winning paper “Log-Concave Polynomials II: High-Dimensional Walks and an FPRAS for Counting Bases of a Matroid,” the researchers bridge three different and unexpected subfields — Hodge theory for matroids and combinatorial geometries, Markov chains analysis and high dimensional expanders — to resolve a 30-year-old conjecture by Mihail and Vazirani, one of the most important open questions in the field of approximate counting. Their work has a number of real-world applications including network reliability, data transmission and machine learning and has led to other notable developments in theoretical computer science.
“We came up with a solution that not only answered an open problem many people had tried for years, but the answer was so simple, elegant, universal and easy to use that it changed the field,” Oveis Gharan said. “Because these different research communities have evolved independently, they have a lot of tools that the other one doesn’t have. Often, you can use tools from one community to answer questions in the other community — which is what we did.”
Oveis Gharan, Liu and their collaborators University of Washington Mathematics professor Cynthia Vinzant and Nima Anari from Stanford University introduced the first fully polynomial randomized approximation scheme (FPRAS) to count the number of bases of any matroid given by an independent set oracle. Their algorithm utilizes the simple two-step Monte Carlo Markov Chain (MCMC) method to address the Mihail-Vazirani conjecture which says that the bases exchange graph of any matroid has edge expansion of at least one.
This algorithm is especially useful in situations that require the assignment of resources or transmission of information such as network reliability problems. For example, if there was a network of routers connected by wires, this algorithm can help estimate the probability that the network gets disconnected based on which wires become independently damaged. These types of problems are usually solved using Markov chains to check if random subnetworks are connected.
However, in most cases it can be very difficult to figure out how long you should run the Markov chain to get the correct answer. The researchers developed a new tool which uses polynomials to analyze the Markov chains, answering the long-standing open problem.
In another example, a company has a certain number of jobs it needs completed along with a set of machines that can do a subset of those jobs. If you assume each machine can only be assigned one job and account for the probability of each machine independently failing, then this method can help generate an efficient algorithm that can approximate the probability that all the jobs will be finished with up to a 1 percent error margin.
“Since our research came out, there has been a sudden birth of so many papers using our techniques and finding new applications and answering other problems,” Oveis Gharan said. “It’s rare for a theoretician to feel an impact of their work.”
The Held Prize is not the first award Oveis Gharan, Liu and their collaborators have received for their research. The team received the Best Paper Award from the Association for Computing Machinery’s 51st annual Symposium on the Theory of Computing (STOC 2019).
“As far as I am concerned, Shayan’s combination of brilliance, creativity, relentless enthusiasm and generosity are unsurpassed. My collaborations with him have been one of the absolute highlights of my career,” Allen School professor Anna Karlin said.
Many students first develop their research skills in their Allen School courses. Professors Leilani Battle and Maya Cakmak, who co-chair the undergraduate research committee, designed a sequence of seminars that introduce students to research and give them the opportunity to work on a hands-on research project with a faculty or graduate student mentor.
“Participating in research allows students to exercise their creativity and apply their computing skills to advance science and technology, in a way that is not possible in their classes,” Cakmak said. “Undergraduate research is often the first step toward a research career and the Allen School is committed to enabling as many of our students to experience research and have those career opportunities.”
Research opportunities can help students realize new and unexpected applications for computer science.
“Research reveals a side of computer science that students might not see in a class or a typical internship, such as how computer science can lead to community-driven initiatives, exciting opportunities with nonprofit organizations and even founding your own startup company,” Battle said.
From helping farmers improve their networks to sell their products to developing innovative ways for children to interact with technology, these CRA-recognized students have shown that computer science research does not have to look like how you expect it to be.
Chun-Cheng Chang: Pushing the boundaries of wireless communication, robotics and AR
Chun-Cheng Chang
Chun-Cheng Chang’s passion for combining hands-on creativity with technical innovation began when he founded his middle school’s carpentry club. Since then, Chang has focused on developing cutting-edge “solutions that push the boundaries of wireless communication, robotics and augmented reality (AR) interfaces.”
“My research interest lies within the paradigm of low-power sensing and human-computer interaction (HCI). My low-power sensing work focuses on the practical solutions for battery-free systems that may be used in environmental monitoring and health sensing,” Chang said. “For HCI, I designed systems that benefit different applications, including improving robotics manipulation data collection, user experience and designing large language model (LLM) assistance systems for mobile health data analysis.”
Analog backscatter communication is promising for use in low-power wireless devices as it can send messages with less energy compared to popular methods such as Bluetooth. However, existing systems are limited in their transmission range and security. Instead, Chang, as part of the Allen School’s UbiComp Lab led by professor Shwetak Patel, helped develop an analog backscatter tag that reaches a transmitting range of up to 600 meters in outdoor line-of-sight scenarios, compared to state-of-the-art systems that can only transmit a maximum of 20 meters.
Chang has also turned his research skills toward building artificial intelligence (AI) assistants for mental health. The UbiComp Lab’s project looked at how LLMs could be used to interpret ubiquitous mobile health data from smartphones and wearables to generate mental health insights. Chang helped finetune and train three LLMs for mental health evaluation, and he conducted user study interviews with clinicians. The team’s study revealed an opportunity for clinicians and patients to use collaborative human-AI tools to explore self-tracking data. Their research was published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT 2024).
In addition to wireless sensing systems and AI, Chang’s research spans the intersection between sensing and robotics. He helped design millimeter scale battery-free microrobots that can be used in environmental monitoring or exploration. When Chang joined the project, he noticed issues with the MilliMobile microrobot prototype’s precise motor and wheel alignment. To address this, Chang redesigned and improved the prototype’s schematics, printed circuit board and software. Chang’s changes helped the team, alongside collaborators at Columbia University, develop Phaser, a system framework that enables laser-based wireless power delivery and communication for microrobots.
As robots become more popular, Chang has also introduced software to help everyday users control the devices. He worked with professor Ranjay Krishna, who co-directs the Allen School’s RAIVN Lab, to design EVE, an iOS application that allows users to train robots using intuitive AR visualizations. With the app, users can preview the robot’s planned motion and see the robot arm’s reach-range boundary for the action. Chang and his collaborators presented their research at the 37th Annual ACM Symposium on User Interface Software and Technology (UIST 2024).
Ritesh Kanchi: Designing AI tools for accessibility and education
Ritesh Kanchi
Ritesh Kanchi was drawn to the HCI research community because it gave him “the opportunity to design technologies for those who benefit most; everyone stands to gain from HCI — it’s all about the humans.” His work as an undergraduate researcher tackles issues across HCI, computer science education and accessibility.
“Across my research experiences, I’ve defined my focus on developing Intelligent User Interfaces (IUIs) that empower niche communities in accessibility and education,” Kanchi said. “My work is rooted in creating context-aware, personalized and adaptive interfaces that address diverse user needs, making traditionally inaccessible experiences more inclusive.”
Since his first quarter at the University of Washington, Kanchi has been interested in designing new technology for children. He joined the Information School’s KidsTeam research group exploring how children use emerging generative AI tools to support, rather than take over, creative activities such as stories, songs or artwork. Their research received an honorable mention at the ACM CHI conference on Human Factors in Computing Systems (CHI 2024). In collaboration with the University of Michigan, Kanchi and his KidsTeam co-authors investigated how children understand computational data concepts such as “the cloud” and their privacy implications. They presented their paper at the 23rd annual ACM Interaction Design and Children Conference (IDC 2024) — the top conference in design of technology for children.
“How children interact with technology today influences the way that adults interact with technology tomorrow,” Kanchi said.
His HCI research continued at the Allen School’s Makeability Lab led by professor Jon Froehlich. Kanchi worked with Allen School Ph.D. student Arnavi Chheda-Kothary to investigate using AI for accessibility, especially among mixed visual-ability families. Many children express themselves through artwork that is primarily visual and non-tactile, such as using crayons, markers or paints, which can make it difficult for blind or low-vision (BLV) family members to experience and engage with the art.
To tackle this issue, Kanchi helped develop ArtInsight, a novel app that allows mixed visual-ability families to use AI to engage with children’s artwork. Previous research explored how AI can help BLV family members read with children, but none focused on art engagement. The app uses large language models to generate creative and respectful initial descriptions of the child’s artwork as well as questions to facilitate conversations between BLV family members and children about the art. For Kanchi, this research project was “a culmination of who I am as a researcher, combining my passions and prior research, including children’s design research, user interface development and accessibility.”
Kanchi’s passion for accessibility has even benefited his Allen School community. As a lead teaching assistant for CSE 121, the first course in the Allen School’s introduction to computer programming series, he converted course content to make it compatible with screen readers. He is working with Allen School professors Matt Wang and Miya Natsuhara on designing a system for creating accessible course diagrams in different content modalities including data structures such as binary trees and linked lists.
Alexander Metzger: Using mobile technologies for environmental sustainability and agricultural economic development
Alexander Metzger
From helping farmers in developing nations expand their networks to making it easier for users to understand the environmental impact of their electronics, Alexander Metzger has set his sights on making a global impact with his work.
“My research aims to bridge gaps in information access for underserved languages and communities through a combination of algorithm design, machine learning and edge device technology,” Metzger said.
During his first year at the Allen School, he joined the Information and Communication Technology for Development (ICTD) Lab, co-led by professors Richard Anderson and Kurtis Heimerl, to work on the eKichabi v2 study. Smallholder farmers across Tanzania often lack accessible networking platforms making them reliant on middlemen to sell their produce. The research team, alongside collaborators at Cornell University, collected the largest agricultural phone directory to date; however, they still faced the challenge of ensuring that the digital directory was accessible to users with limited internet and smartphone access.
To tackle this issue, Metzger helped develop and maintain an Unstructured Supplementary Service Data (USSD) application that allowed users to access the directory using simple menus on basic mobile phones. As the project advanced, he analyzed the USSD application’s technical performance to answer the question of how well an information system could address the divide between basic phones and smartphones. Metzger was the co-lead author on the paper examining the application’s HCI and economic challenges published at the ACM CHI Conference on Human Factors in Computing Systems (CHI 2024).
Continuing his research into mobile technologies, Metzger turned his attention toward addressing information access challenges that other smallholder farmers face. Smallholder farmers across countries including Kenya, India, Ethiopia and Rwanda often lack access to trustworthy sources to learn about sustainable farming practices and how to tackle issues such as crop disease. Metzger worked with Gooey.AI to help develop and deploy Farmer.CHAT, a GPT4-based, multilingual platform that helps extension workers with personalized advice and guidance. Farmer.CHAT presented their platform in front of the United Nation General Assembly.
Environmental decision making is not just a problem for farmers. As a member of the Allen School’s UbiComp Lab, Metzger is working on developing multi-modal hybrid machine learning and computer vision algorithms that can make environmental impact estimates more accessible. He is also designing a battery-free balloon and glider-based sensor systems that provide developing countries with the data to help predict and prevent environmental disasters, including wildfires.
Outside of his Allen School work, Metzger recently founded Koel Labs, a research-focused startup that trains open-source audio models to help language learners improve their pronunciation.
Kenneth Yang: Removing inefficiencies in software engineering and neuroscience
Kenneth Yang
When Kenneth Yang solves a problem — whether it is a tricky programming puzzle or a neuroscience research headache — his first thought is, “there has to be a better way to do this.”
“Because our lives are technology-infused, I turned to computer science as a tool to help me solve problems efficiently, predictably and accurately,” Yang said. “My research focuses on identifying and solving inefficiencies in existing processes and aims to improve algorithms and methods used in computer graphics, software engineering and neuroscience.”
As a research assistant to Allen School professor Michael Ernst in the Programming Languages and Software Engineering (PLSE) group, Yang is turning his problem-solving skills toward the challenge of merging code. Version control systems are merge tools that allow multiple developers to work on a code at the same time and then combine their edits into a single version. While there are many algorithms and merge tools available to automatically handle merging code, they often fail — leading to unexpected bugs and developers having to turn their attention to manually resolving and integrating changes.
However, these different merge tools, including new approaches, have not been evaluated against each other. The limited experiments that have been performed have excluded systems such as Git, the dominant version control system, or have only measured the benefits of correct merges, not the costs of incorrect ones. Yang and his collaborators addressed these problems and showed that many earlier merge tools are less effective than previously thought. They presented their results in their paper “Evaluation of Version Control Merge Tools” that appeared at the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024).
Yang is also using his software skills to help neuroscientists accelerate their research. Neuroscientists gather brain data using electrophysiology, where they insert probes into the brain to detect neurons’ electrical activity. The small scale of different brain regions alongside other factors can make the process difficult and hard to use in larger studies.
Instead, Yang’s research with the Virtual Brain Lab group within the UW Department of Neurobiology & Biophysics’ Steinmetz Lab develops software and tools that can make electrophysiology experiments more efficient, repeatable and automated. He co-developed the software platform Pinpoint that allows neuroscientists to interactively explore 3D brain models, plan experiments and control and maneuver the robotic electrode manipulators into the correct location.
To work alongside Pinpoint, he designed and wrote the Electrophysiology Manipulator Link, or Ephys Link, a unifying communication platform for popular electrophysiology manipulators that translates planning data into specific robotic movements. Yang’s tools have reduced the insertion process from 15 minutes per probe to 15 minutes total and with minimal researcher intervention. In future research, he aims to enable researchers to fully automate electrophysiology and make complex multi-probe studies possible.
In addition to honoring these four undergraduates at UW, the CRA recognized another student with an Allen School connection. Awardee Gene Kim, an undergraduate student at Stanford University, has collaborated with Allen School professor Jen Mankoff on the development of assistive technology to help make data visualization, personal fabrication and design tools more accessible for blind and visually impaired people.
Despite their growing potential and increasing popularity, large language models (LLMs) often produce responses that are factually inaccurate or nonsensical, also known as hallucinations.
Allen School Ph.D. student Akari Asai has dedicated her research to tackling these problems with the use of retrieval-augmented language models, a new class of LLMs that pull relevant information from an external datastore with a query that the LLM generates. For being a pioneer in Artificial Intelligence & Robotics, Asai was recognized as one of MIT Technology Review’s 2024 Innovators Under 35 Japan. The award honors young innovators from Japan who are “working to solve global problems.”
“Being named to MIT Technology Review 35 Under 35 is an incredible honor,” said Asai, who is a member of the Allen School’s H2 Lab led by professor Hannaneh Hajishirzi. “It highlights the power of collaboration and the potential of AI to address real-world challenges. With the rapid adoption of LLMs, the need to investigate their limitations, develop more powerful models and apply them in safety-critical domains has never been more urgent. This recognition motivates me to keep working on projects that can make a meaningful impact.”
Asai’s paper on adaptive retrieval-augmented LMs was one of the first to show that retrieval-augmented generation (RAG) is effective at reducing hallucinations. Without using RAG, traditional LLMs generate responses to user input based on the information it was trained on. In contrast, RAG adds an information retrieval component to the LLM that uses the user input to first pull information from a new, external data source so the LLM can generate responses that incorporate the latest information without needing additional training data.
As part of the study, Asai and her team compared the response accuracy from various conventional and retrieval-augmented language models across a dataset of 14,000 wide-ranging questions. They found that augmentation methods such as RAG significantly and more efficiently improved the performance of LMs compared to increasing their training data and scaling up models. Asai and her collaborators presented their work at the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), where they received the Best Video Award.
Building off of that research, Asai has helped develop the foundational components of retrieval-augmented language models along with improved architectures, training strategies and inference techniques. In 2024, she introduced a new framework called Self-reflective RAG, or Self-RAG, that enhances a model’s quality and factual accuracy using retrieval and self-reflection. While RAG only retrieves relevant information once or a fixed number of time steps, Self-RAG can retrieve multiple times, making it useful for diverse downstream queries such as instruction following. The research received the best paper honorable mention at the 37th Annual Conference on Neural Information Processing Systems (NeurIPS) Instruction Workshop and was evaluated at the top 1% of papers at the 12th International Conference on Learning Representations (ICLR 2024).
Asai is also interested in advancing the ways that retrieval-augmented language models can tackle real-world challenges. Recently, she launched Ai2 OpenScholar, a new model designed to help scientists more effectively and efficiently navigate and synthesize scientific literature. She has also explored how retrieval augmentation can help with code generation and helped develop frameworks that can improve information access especially among linguistic minorities. The latter includes Cross-lingual Open-Retrieval Answer Generation (CORA), Cross-lingual Open Retrieval Question Answering (XOR QA) and AfriQA, the first cross-lingual question answering dataset focused on African languages. In 2022, she received an IBM Ph.D. Fellowship to advance her work to ensure anyone can find what they need efficiently online including across multiple languages and domains.
In future research, Asai aims to tackle other limitations that come with LLMs. Although LLMs are becoming increasingly useful in fields that require high precision, they still face challenges such as lack of attribution, Asai explained. She aims to collaborate with domain specialists “to create systems that experts can truly rely on.”
“I’m excited about the future of my research, where I aim to develop more efficient and reliable AI systems. My focus is on creating models that are not only scalable but also transparent, making AI more accessible and impactful across diverse fields,” Asai said.
