While the artist Claude Monet’s paintings can be blurry and indistinguishable, a new foundational model of the same name may help bring clarity to other medical artificial intelligence systems.
In a recent paper published in the journal Nature Medicine, a team of researchers at the University of Washington and Stanford University co-led by Allen School professor Su-In Lee introduced a medical concept retriever, or MONET, that can connect images of skin diseases to semantically meaningful medical concept terms. Beyond annotating dermatology images, MONET has the potential to improve transparency and trustworthiness throughout the entire AI development pipeline, from data curation to model development.
Chanwoo Kim
“We took a very different approach from current medical AI research, which often focuses on training large medical foundation models with the goal of achieving high performance in diagnostic tasks,” said Allen School Ph.D. student and lead author of the paper Chanwoo Kim, who works with Lee in the AI for bioMedical Sciences (AIMS) Lab. “We leverage these large foundation models’ capabilities to enhance the transparency of existing medical AI models with a focus on explainability.”
Prior to MONET, annotating medical images was a manual process and difficult to do on a large scale. Instead, MONET automates this process by employing an AI technique called contrastive learning, which enables it to generate plain language descriptions of images. The researchers trained MONET on over 100,000 dermatology image-text pairs from PubMed articles and medical textbooks and then had the model score each image based on how well it represents the concept. These medical concepts are “terms that a physician can understand and would use to make a diagnosis such as dome-shaped, asymmetrical or ulcer,” Kim said.
The team found that MONET could accurately annotate concepts across dermatology images as verified by board-certified dermatologists, and it was comparable to other supervised models built on previously concept-annotated dermatology datasets of small size.
These annotations can help researchers detect potential biases in datasets and undesirable behavior within AI systems. The researchers used MONET to audit the International Skin Imaging Collaboration (ISIC) dataset, the largest collection of over 70,000 dermoscopic images commonly used in training dermatology AI models, and found differences in how concepts correlate with being benign or malignant. For example, MONET showed that images of skin lesions where dermatologists placed orange stickers on them were mostly benign, which was not always the case. One explanation is that the orange stickers were often used in pediatric patients who tended to have benign cases, Kim noted.
This insight is crucial for understanding which factors affect the transferability of medical AI models across different sites. Usually, such data auditing at scale is not feasible due to the lack of concept labels.
Su-In Lee
“In the AI pipeline, MONET works at the entry level, providing a ‘lens’ through which each image can be ‘featurized’ based on available information to map it with relevant language-based features,” Lee said. “This allows MONET to be combined with an existing medical AI development pipeline, including data curation and model development, in a plug-in-play manner.
“You don’t have to worry about it going through a model as it goes right to the data — that’s one way we can make dataset and model auditing more transparent and trustworthy,” Lee continued.
The framework of MONET can also help medical AI model developers create inherently interpretable models. Physicians, in particular, are interested in such models, like concept bottleneck models (CBMs), because it is easy to decipher and understand what factors are influencing the AI’s decisions. However, CBMs are limited because they require concept annotation in the training data which may not always be available; MONET’s automatic annotation has the potential to help build CBMs that were previously impossible.
“While we only focused on a foundation model based on OpenAI’s CLIP model, we expect that this whole idea can be applied to other more advanced large foundation models,” Kim noted. “Nowadays, AI is developing very rapidly but our framework of using large foundation models’ amazing capabilities to improve transparency of medical AI systems will still be applicable.”
This is part of a broader research effort in the AIMS Lab to ensure AI in dermatology and medical imaging is safe, transparent and explainable. Other projects include a new framework for auditing AI-powered medical-image classifiers that can help dermatologists understand how the model determines whether an image depicts melanoma or a benign skin condition. Another paper sheds light on the reasoning process medical image classifiers use to identify patients’ sex from images of skin lesions. Additionally, counterfactual AI prompts have the potential to show how patient data may change based on genetic mutations, treatments or other factors. These research initiatives have potential applications beyond dermatology to other medical specialties, Lee said.
According to the United Nations, more than half of the world’s population — 55% — lives in urban areas, with that figure projected to rise to 70% by the year 2050. While urban communities are vibrant centers of economic, cultural and civic activity, this vibrancy tends to be accompanied by concerns about housing affordability, aging or inadequate infrastructure, environmental impacts and more.
Such vibrancy also yields a lot of data that could provide a window onto how our urban environments function, with a view to creating more sustainable and equitable communities as well as planning for future growth. But it’s difficult to extract usable insights, let alone turn those insights into practical actions, when the data is stored in different formats, spread across different systems and maintained by different agencies.
A team of researchers that includes professor Jon Froehlich, director of the Allen School’s Makeability Lab, is pursuing a more unified approach that will democratize data analysis and exploration at scale and empower urban communities. Froehlich is co-principal investigator on a new, five-year project dubbed OSCUR — short for Open-Source Cyberinfrastructure for Urban Computing — that recently earned a $5 million grant from the National Science Foundation. The project is spearheaded by the NYU Tandon School of Engineering’s Visualization Imaging and Data Analytics Research Center (VIDA) in partnership with the University of Washington and the University of Illinois Chicago.
Jon Froehlich
“The true beauty and promise of OSCUR is in how it attempts to unify long-standing and deeply interconnected problems in urban science that often have disparate approaches spread across disciplines,” Froehlich said in the project announcement. “We are trying to develop standardized tools, datasets, and data standards to address problems related to climate change (e.g., urban heat island effects), walkability and bikeability, urban accessibility for people with disabilities, and more.”
To that end, Froehlich and his colleagues intend to cultivate a cohesive urban computing community spanning computer science, data science, urban planning, civil engineering, environmental sciences and other expertise. They will harness this combined wisdom to develop a set of scalable, open-source tools to enable interactive exploration and analysis of complex data, with an emphasis on findability, usability, interoperability, transparency and reproducibility. This would enable a variety of stakeholders, from researchers, to practitioners, to residents, to collaboratively address common challenges — without having to build something from scratch that may quickly become obsolete.
Communities stand to benefit from this more integrated approach in multiple ways. For example, the team envisions a set of tools that would enable agencies to make more robust use of citywide sensor data to monitor and mitigate noise pollution and improve quality of life. In addition, they could combine data from different sources to gain a more comprehensive understanding of how their infrastructure might withstand disaster — and where they may need to shore up their resilience. Such an approach would also enable communities to glean new insights into how the built environment affects pedestrian mobility, with a view to making their communities more accessible for all residents.
Froehlich is no stranger to urban accessibility issues — or collaborating with residents and decision makers to extend the real-world impact of his research. He previously co-founded Project Sidewalk, an effort that combines advances in artificial intelligence with the power of citizen science to identify and map pedestrian accessibility issues such as missing curb ramps and uneven surfaces. The initiative has spread to 21 cities in the U.S. and around the world, including Seattle and Chicago in the U.S., Mexico City, Mexico, and Amsterdam in the Netherlands. To date, contributors to Project Sidewalk have mapped more than 11,400 miles of infrastructure and contributed over one million labels — data that has been leveraged to improve city planning, build new interactive tools and train AI to automatically log accessibility issues.
“I have worked in urban computing for more than a decade,” said Froehlich. “OSCUR is one of those rare opportunities to push beyond the silos of academia and develop tools for and with communities that will take them far into the 21st century.”
When Miranda Wei attended her first Symposium on Usable Privacy and Security (SOUPS) conference in 2017, she had little experience in the field; she had only recently graduated with a degree in political science from the University of Chicago. But the community of researchers at the conference welcomed her in. That experience paved the way for her to continue doing research on privacy and security and, eventually, to pursue a Ph.D. at the Allen School.
Seven years after her first foray into the SOUPS community, Wei received the 2024 John Karat Usable Privacy and Security Student Research Award at the conference for her interdisciplinary contributions to the field and strong leadership. The award, named for the late researcher John Karat, recognizes graduate students for their research in usable privacy and security, efforts to mentor others and community service.
“As a researcher, we publish in many different venues, but SOUPS is the first conference that I went to and is the closest to my heart,” Wei said. “It’s a huge honor to be recognized for the work that I’ve done, especially as someone who came to this field from a non-traditional background.”
For Allen School professor Franziska Roesner, one of Wei’s Ph.D. co-advisors and co-director of the Security and Privacy Research Lab alongside colleague Tadayoshi Kohno, Wei is already a “superstar in usable security and privacy.” Wei’s research focuses on how societal factors can impact individuals’ security and privacy. For example, her paper presented at the 2022 SOUPS conference analyzed how TikTok creators shared information on how to leverage technology to monitor or control others, especially within families or with romantic partners. The research was one of the first to consider the platform as a data source in the field.
“When I think of thought leadership within a field, I think of those who look into places that others are not,” Kohno said. “Miranda’s work with TikTok as a data source is a great example of such leadership.”
Wei’s other work has also made strides in the privacy and security field. Her 2023 paper at the IEEE Symposium on Security and Privacy was one of the first to explore gender stereotypes within computer security and privacy practices, Roesner noted. There is still more research to do: in work presented at the 2024 USENIX Security Symposium, Wei analyzed the field’s apparent lack of knowledge on how sociodemographic factors affect computer security behaviors. For her research advancing usable security and privacy, Wei was also one of 75 graduate students from around the world selected for the 2023 Google Ph.D. Fellowship program and one in four working in the privacy and security field.
“Miranda’s work is often deep and nuanced, drawing on methodology and theory from multiple fields (such as computer security, human-computer interaction and social science) to ask fundamental questions situated in complex social and societal dynamics,” said Roesner, the Brett Helsel Career Development Professor in the Allen School. “This includes exploring constructs of power and gender, and challenging the field’s norms around what we know and how we develop knowledge.”
Outside of her research contributions, Wei is heavily involved in mentorship and community building. As a senior Ph.D. student in the Security and Privacy Research Lab, Wei works as a sounding board for other students and has served in an advisory role on multiple research projects. She also has co-founded and volunteers with the Allen School’s Pre-Application Mentorship Service (PAMS) advising prospective graduate students. At the 2024 SOUPS conference, Wei co-organized the inaugural Gender, Online Safety and Sexuality (GOSS) workshop to help integrate feminist, LGBTQ+ and critical theories into research on online safety.
“Her research vision and agenda around advancing computer security, privacy and safety for all inherently embody a global ambition for social good,” Roesner said. “She cares deeply about expanding access to opportunities for and improving the experience of people in and around computer science.”
For Wei, she did not achieve this award on her own.
“All of my research papers and projects I’ve worked on have benefited from my friends in the Security and Privacy Research Lab and my mentors across the world,” Wei said. “I really think it takes a village.”
In the Marvel Universe, mutants known as the X-Men wield superhuman abilities ranging from shape-shifting to storm-summoning.
In the software universe, mutants may not bring the thunder, but they are no less marvelous. In 2014, Allen School professors René Just and Michael Ernst, along with their collaborators, demonstrated that mutants function as an effective substitute for real defects in software testing. Their work, which spawned a robust line of follow-on research over the ensuing decade, earned them the Most Influential Paper Award at the ACM International Conference on the Foundations of Software Engineering (FSE 2024) last month in Porto de Galinhas, Brazil.
Mutants are artificial defects (bugs) intentionally embedded throughout a program. If a test suite is good at detecting these artificial defects, it may be good at detecting real defects. Testing is an important element of the software development cycle; buggy code can be annoying, like when a video game glitches, or it can grind industries to a halt, like the world witnessed during the recent CrowdStrike incident. According to the Consortium for Information & Software Quality (CISQ), the costs associated with defective software surpassed $2 trillion in 2022 in the United States alone.
René Just
Among the solutions CISQ emphasized in its report were “tools for understanding, finding and fixing deficiencies.” Mutants play an integral role in the development and evaluation of such tools. While the software community historically had assumed such artificial defects were valid stand-ins for real ones, no one had empirically established that this was, indeed, the case.
“We can’t know what real errors might be in a program’s code, so researchers and practitioners relied on mutants as a proxy. But there was very little evidence to support that approach,” Ernst said. “So we decided to test the conventional wisdom and determine whether the practice held up under scrutiny.”
Ernst, Just and their colleagues applied this scrutiny through a series of experiments using 230,000 mutants and over 350 real defects contained in five open-source Java programs comprising 321,000 lines of code. To reassemble the real defects, which had already been identified and fixed by developers, the researchers examined the version history for bug-fixing commits. They then ran both developer-written and automatically generated test suites to ascertain how their ability to find known mutants in a program correlated with their ability to identify the real defects. During their testing, the researchers controlled for code coverage, or the proportion of each program’s code that was executed during the test, which otherwise could confound the results.
Those results revealed a statistically significant relationship between a test suite’s effectiveness at detecting mutants and its effectiveness at detecting real defects. But while the team’s findings confirmed the conventional wisdom in one respect, it upended it in another.
Michael Ernst
“Our findings validated the use of mutants in software test development,” said Just, who was first author of the paper and a postdoctoral researcher in the Allen School at the time of publication. “It also yielded a number of other new and practical insights — one being that a test suite’s ability to detect mutants is a better predictor of its performance on real defects than code coverage.”
Another of the paper’s insights was confirmation that a coupling effect exists between mutants and real defects. This effect is observed between a complex defect and a set of simple defects when a test that detects the latter also succeeds in detecting the former. While prior work had shown that the same effect exists between simple and complex mutants, it was unclear whether a similar coupling effect applied between real defects and simple mutants. The researchers found that this was, indeed, the case, identifying 73% of real defects that were coupled to mutants. Based on an analysis of the 27% that did not exhibit this coupling effect, the team recommended a set of concrete approaches for improving mutation analysis — and by extension, the effectiveness of test suites.
In addition to Just and Ernst, co-authors of the paper include Allen School alum Darioush Jalali (M.S., ‘14), now a software engineer at Ava Labs; then-Ph.D. student Laura Inozemtseva and professor Reid Holmes of the University of Waterloo, now a senior software engineer at Karius and a faculty member at the University of British Columbia, respectively; and University of Sheffield professor Gordon Fraser, now a faculty member at the University of Passau.
When you reach out to pet a dog, you expect it to feel soft. If it doesn’t feel like how you expect, your brain uses that feedback to inform your next action — maybe you pull your hand away. Previous models of how the brain works have typically separated perception and action. For Allen School professor Rajesh Rao, those two processes are closely intertwined, and their relationship can be mapped using a computational algorithm.
“This flips the traditional paradigm of perception occurring before action,” said Rao, the Cherng Jia and Elizabeth Yun Hwang Professor in the Allen School and University of Washington Department of Electrical & Computer Engineering and co-director of the Center for Neurotechnology.
In a recent paper titled “A sensory-motor theory of the neocortex” published in the journal Nature Neuroscience, Rao posited that the brain uses active predictive coding (APC) to understand the world and break down complicated problems into simpler tasks using a hierarchy. This architecture, which is inspired by previous work in artificial intelligence (AI), can in turn be potentially used to train AI algorithms on increasingly complex problems with less data and better predict different outcomes.
“Data from neuroscience suggests the brain uses a hierarchical generative model to constantly predict the consequences of actions,” Rao said. “The brain is creating its hypotheses saying, ‘Here’s what I predict will happen in the world. Now, let’s check this hypothesis with what’s really coming in through the sensors in my body.’ Errors in prediction can then be used to correct the hypothesis.”
For Rao, the anatomy of the neocortex indicates a “tight coupling” between sensory and motor processes, or perception and action, similar to the mathematical model used in reinforcement learning in AI. Reinforcement learning utilizes a generative model to capture the relationship between an agent’s motor output and the sensory input it receives from the environment. You reach out to pet a dog, for example, and you feel the texture of its fur.
Allen School professor Rajesh Rao
This generative model is also called a world model, represented mathematically as a state transition function specifying how the agent’s actions change their world. Alongside the world model is a policy function that selects a sequence of actions. These functions work together to help you learn, perform new tasks and predict the consequences of actions, such as what you expect this dog’s fur to feel like compared to other dogs you have touched.
Anatomically, each section of the neocortex is “like a six-layered layer cake,” Rao explained, “with the middle and top layers processing sensory information and the bottom layers sending information to action centers of the brain.” The model suggests that areas higher up in the hierarchy can modulate the dynamics of the lower level neural networks and change the function being computed there, similar to hypernetworks in AI.
“The ability to change the functions being computed at different levels of a computational hierarchy endows the APC network with remarkable versatility,” Rao said.
Break it down: How AI can learn from the brain
The APC model’s hierarchical approach can break down a complicated, abstract problem into smaller parts in a process known as compositionality. If the higher level goal, for example, is to go to the grocery store, that task can be decomposed into a sequence of simpler steps, Rao explained. First, you might walk to the garage where your car is parked, open the door and then unlock the car door. Eventually, the tasks can be broken down to specific muscles in the hand and to the lowest level in the spinal cord controlling your hand.
Compositionality may help address one of the problems holding back traditional AI models. For every new problem that the AI model faces, it needs to be trained, potentially using reinforcement learning, on lots of new data, whereas the human brain is very good at quick generalization with little data, Rao noted.
Instead of using trial-and-error learning or planning each discrete step, if the agent has already learned to solve simpler tasks such as navigating smaller rooms or moving from corner to corner, it can use that knowledge to break down the task into a sequence of simpler tasks it already knows how to solve.
“The compositionality inherent in the APC model allows it to compose solutions to new problems in the world really quickly,” Rao said. “Suppose I already learned how to get into the car. I can keep using that policy function for all kinds of other tasks like driving to school or going to meet a friend.”
The same APC model architecture can also be used for visual perception and learning. An APC model learns to read and write the number eight, for example, by breaking it down into different strokes. It can then use those parts to compose other new characters.
“The APC model builds on past ideas of hierarchical reinforcement learning but goes beyond the usual hierarchy of policy functions,” Rao said. “The architecture of the neocortex suggests that there is a great benefit to modeling the world itself as a hierarchy. Coupling such a hierarchical world model with a hierarchy of policy functions may be how our brain tackles the complexity of the world we live in.”
The next step for Rao and his students in the Allen School’s Neural Systems Laboratory is to look at how to apply this architecture to large-scale AI problems such as language and robotics and test the model’s predictions in collaboration with neuroscientists.
Allen School professors Vikram Iyer, left, and Adriana Schulz received 2024 NSF CAREER Awards to advance their research in autonomous microrobotics and computer-aided design for manufacturing, respectively. Background image: National Science Foundation
When it comes to miniature robots, Allen School professor Vikram Iyer has big ideas.
“Imagine a fleet of tiny robotic sensors that automatically self-disperse to monitor crops on a farm,” Iyer said. “Others might routinely track inventory in a warehouse or inspect essential infrastructure.”
Before these so-called microrobots can autonomously cross that bridge, however, Iyer seeks to ditch the batteries — and add onboard perception and computation so they can navigate on their own — with the help of artificial intelligence. Meanwhile, his faculty colleague Adriana Schulz has designs on a different kind of power problem: how to use AI to supercharge a new era of creativity and eco-consciousness in computer-aided manufacturing.
“AI has been transformative in so many domains, from health care and e-commerce, to music and the visual arts,” Schulz said. “We haven’t yet seen the same progress in manufacturing, despite the potential to dramatically improve the process for turning ideas into tangible products that shape our daily lives.”
Schulz and Iyer earned National Science Foundation (NSF) CAREER Awards through the agency’s Faculty Early Career Development Program for research that promises to fundamentally alter the way we create and interact with objects and our environment.
Beyond batteries: A powerful new approach to building truly autonomous robotic sensors
The problem of how to untether robots from the limitations of battery power weighs heavily on Iyer’s mind — not least because batteries would add too much payload to a device roughly the size of a bug. In addition, a short battery life can limit the distance they move and the applications they can enable. And then there is the environmental impact, which also can’t be taken lightly.
“Aside from the costs of repeated recharging and replacement, which would be prohibitive and impractical, battery manufacturing and disposal have significant environmental impacts and require critical minerals,” he noted.
Iyer and his students have developed battery-less sensors that can run on solar energy or wireless power from radio signals; however, in real world conditions, the power available changes over time — making it challenging to fuel a moving robot. Using this NSF CAREER Award, his research group is exploring strategies like buffering small amounts of harvested energy in a capacitor allowing a robot to move in discrete steps.
“This motion is similar to how insects and birds perch between jumps or flights, which require bursts of energy,” Iyer explained. “This allows us to use power sources that provide small or variable amounts of energy.”
Iyer intends to design robots that incorporate this capability to travel on wheels, jump or do both. Whether via sun, signal or stop-and-go, battery-free power will only get microrobots so far; tasks such as measuring soil quality, detecting hazards or even taking readings in space will also require new kinds of onboard sensing, data processing and control that are optimized for intermittent power.
“We’re also exploring ways to use AI to help design and optimize both the software and hardware,” Iyer explained. “Beyond making single robots that can move on their own, we’re also interested in ways they can communicate with larger robots and AI systems that could coordinate a fleet of tiny robots to work together.”
Taking shape: A new era in AI-driven design and manufacturing grounded in geometry
Tiny robots are not the only objects that could see big gains with the help of AI. As Schulz sees it, we are on the cusp of an exciting new era of computer-aided design and manufacturing just about anything, from couches to cars. To unlock this potential, she aims to leverage advances in program synthesis, large language models and other innovations to assist makers in squaring the need for precision with the urge to experiment.
“Engineering design is a paradoxical process. On the one hand, it demands exactness and the ability to incorporate fine-grained information — which also makes it complex and time-consuming,” Schulz explained. “On the other hand, design tends to thrive on an iterative and exploratory spirit.”
Schulz will apply her NSF CAREER Award to solving that paradox through the development of a new, geometry-based system dubbed SmartCAD that combines formal reasoning techniques with neural abstractions. The new system will be based on ShapeScript, a new domain-specific language capable of interpreting the geometry of new designs — meaning users will no longer need to have their intended shapes in mind at the start of the design process, as they do with conventional CAD systems. She also plans to integrate foundational code generation models, such as GPT, that have been trained on geometric knowledge to support multi-modal user input through text, sketches or images, in addition to enabling users to make structural changes as they edit and optimize their designs.
“Formal methods offer verifiability and synthesis that adheres to constraints, which is crucial for the precision and analytical reasoning inherent in engineering,” Schulz noted. “By combining these methods with recent developments in AI, we can better facilitate and automate design generation and iteration.”
This iterative capability could save not only manufacturing costs but potentially the planet, by allowing users to optimize their designs to make maximum use of materials while minimizing waste. Such tools could also democratize the design and fabrication process by empowering both novice and experienced fabricators alike.
“Our approach has the potential to completely upend the traditional design process to empower a new generation of fabricators,” said Schulz. “And since virtually every object in the world originated as a CAD model, even small advancements can have a profound impact.”
Earlier this year, the Computing Research Association honored a select group of undergraduate students from around the country who have made notable contributions to the field through research. The CRA Outstanding Undergraduate Researcher Awards competition historically has been good to Allen School students. To the four most recent honorees — award winner Kianna Bolante, finalists Claris Winston and Andre Ye, and honorable mention recipient Nuria Alina Chandra — even more rewarding than national recognition is realizing the impact their contributions can have on individuals and communities in Washington and beyond.
Kianna Bolante: Empowering students to think critically about technology
As a member of multiple groups underrepresented in computing, CRA award winner Kianna Bolante is always on the lookout for ways to blend her curiosity about technology with a desire to create positive social change.
“This mindset has guided me towards opportunities aligning with my values,” she said.
In addition to multiple service and leadership roles on campus, those opportunities have included research projects spanning accessibility, robotics and social computing. Bolante’s interest in the latter led her last year to pursue what is arguably her most high-profile work to date: a comprehensive social computing curriculum aimed at guiding middle and high school students to consider the different ways they and others interact with technology. Her interest in the project was inspired in part by her realization that, although students spend increasing amounts of their time online, most secondary education curricula do not prepare them to engage meaningfully on the topic of how people interact with digital technologies.
“A community’s core comes from how its individuals interact with each other, and social computing systems are a vital network affecting how humans build community daily — even more so due to the COVID-19 pandemic,” observed Bolante. “Yet, despite its pervasive influence, social computing topics are not explicitly taught in secondary-level courses in the U.S.”
Kianna Bolante
Keen to bridge that gap, Bolante teamed up with professor Amy Zhang and members of the Allen School’s Social Futures Lab to develop six educational modules that teachers could incorporate, in whole or in part, into their classroom lesson plans. The modules, which covered topics spanning online behavior, machine learning and bias, misinformation and more, offered a combination of lecture content, hands-on activities and resources to support in-class discussion as well as personal reflection. After creating the curriculum, Bolante proceeded to travel around to Seattle-area schools, where she shared the lesson content with more than 1,400 students and their teachers.
“That she has impacted so many young people is not only a show of her strong ability to lead and communicate but also the dedication she brings to educating young people,” Zhang said. “Kianna took modest expectations for the project and blew them out of the water!”
“This area of study affects us all through our constant use of collaborative systems and social technologies, such as social networking platforms, but we don’t all experience them all the same way,” Bolante noted. “We incorporated culturally responsive pedagogy in our design to help students connect their learning to their identities. A constant theme is to encourage students to think critically on the positives and negatives of social technology designs.”
Zhang and her Allen School colleague Maya Cakmak were delighted, but not surprised, that the CRA chose to recognize Bolante for her contributions with an Outstanding Undergraduate Researcher Award. Bolante had enrolled in Cakmak’s introductory research seminar as a sophomore, following an initial foray into research via a project analyzing language preferences in relation to disability and her work with Zhang through the DUB REU program during her first year at UW. Although the seminar is designed for those with no previous research experience — and Bolante already had a published paper to her name, after the aforementioned analysis appeared at the International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS ‘22) — she hoped to hone her independent research skills and connect with other like-minded students.
Bolante subsequently returned the favor by serving as a teaching assistant in the next offering of the course, sharing her knowledge and experience. In addition, she has participated in extensive outreach activities as a member of various student groups on campus in addition to her continued engagement with middle and high school students. She is also blending her interests in education pedagogy, social computing and accessibility in her work with another Allen School professor, Kevin Lin, to incorporate content that reflects a functional understanding of different physical, sensory and cognitive abilities into the school’s own Data Structures & Algorithms course. Her many contributions inspired her inclusion in the 2024 class of the Husky 100, a designation that recognizes students who are making the most of their time at UW.
“Kianna has contributed to so many research projects. She is a quick learner and resourceful in solving problems and adapting to the needs of each project. She is also a clear communicator who truly cares about her audience,” said Cakmak. “It is no wonder she has given so many talks and is asked to be on so many panels — and she never passes on an opportunity to use her voice to empower others.”
Nuria Alina Chandra: Decoding the genetic basis of disease
Ever since she first set foot on campus, Nuria Alina Chandra wanted to try her hand at research into the mechanisms of disease. But the difficulty she initially encountered trying to break into a lab — figuratively speaking, of course — galvanized her interest in giving other like-minded students a leg up.
“I originally struggled to find research opportunities in college, so once I found a position, I wanted to help others to do the same,” said Chandra. “As an Undergraduate Research Leader, I connect first-year students from underrepresented backgrounds with research opportunities and advise them on how and why to launch a research career.”
Chandra’s own research career has followed some twists and turns on the way to earning a CRA honorable mention for her contributions. As it turned out, her willingness to take an indirect route appealed to professor Sara Mostafavi in the Allen School’s Computational Biology group.
Nuria Alina Chandra
“Nuria approached me to ask about possible research internships,” recalled Mostafavi. “I was immediately impressed by her strong academic foundation as well as her diverse research experience.”
That diverse experience predated her arrival at the Allen School, when Chandra spent nearly two years working in the Bacteriophage Lab at The Evergreen State College. She then spent a summer as a research assistant in the Subramanian Lab at the Institute for Systems Biology before joining Seattle Children’s Pediatric Sleep & Pain Innovations Lab, where she investigated the development of posttraumatic and postsurgical pain.
In Mostafavi’s lab here at the UW, Chandra uses machine learning to analyze the role of regulatory DNA in cell-type specific gene expression to understand the downstream effects of DNA mutations associated with hereditary diseases such as type 1 diabetes and multiple sclerosis.
“Regulatory regions of the genome are difficult to study experimentally due to their cell-type-specific effects,” Chandra explained. “The ability to accurately predict the transcriptional consequences of regulatory variants is critical to decoding the genetic basis of disease and facilitating the development of personalized treatment systems.”
Previous research suggested that information about the distribution of chromatin accessibility, which regulates gene expression, at the level of DNA base pairs could offer useful insights into regulatory protein behaviors. Such insights would ostensibly improve deep learning models’ predictions about chromatin accessibility across cell types. When Chandra set out to test this hypothesis, however, she found that existing deep learning models like BPNet could only accurately predict the accessibility profiles of a subset of regulatory DNA; they weren’t up to the task of predicting differential accessibility for roughly 90 closely related immune cell types. So Chandra developed a new, more robust convolutional model, bpAI-TAC, that is capable of harvesting data from base-pair resolution accessibility profiles to improve regional accessibility predictions.
After demonstrating that bpAI-TAC outperforms the previous state of the art on regional chromatin accessibility prediction, Chandra has begun to explore techniques for extracting additional biological insights into the mechanisms of disease. In the future, researchers will be able to use Chandra’s model to catalog the effects of genomic variants in order to better understand how they contribute to disease and to assist with targeted drug development.
“I typically don’t assign such a challenging project to an undergraduate, but given Nuria’s enthusiasm and performance on her course work, I took a chance,” Mostafavi said. “And she exceeded my expectations! I’ve been very impressed with Nuria’s dedication, analytical abilities and approach to solving complex problems.”
Claris Winston: Creating technology that removes societal barriers
CRA finalist Claris Winston’s interest in research is not just academic; it’s personal. Based on her own experience, Winston began to appreciate firsthand the extent to which societal barriers and inaccessible environments challenge people with disabilities. The realization motivated her to develop a digital assistant called MyScoliCare that has helped patients and therapists around the world manage their treatment of the condition.
“This experience emboldened me to develop impactful, accessible technology during my time as an undergrad,” Winston said. “As my awareness has grown, so has my passion to create technology that improves accessibility for all and removes societal barriers for people with a variety of needs.”
Claris Winston
Before pursuing that passion, she got her feet wet doing research in a wet lab — specifically the Molecular Information Systems Lab, where she was lead author of a paper describing combinatorial polymerase chain reaction, a novel technique for selective retrieval of DNA oligo pools to enable DNA-based data storage at scale. With a published paper to her credit, Winston then teamed up with siblings Cailin, Caleb, Chloe and Cleah on a project that applied software engineering techniques to detect and repair faults in brain-computer interfaces. BCIs are implantable devices that augment or restore sensorimotor function in people with neurological disease or spinal cord injury.
“Claris knows how to seek out interesting research opportunities and is driven to engage in projects that are meaningful,” Allen School professor Jennifer Mankoff said.
One might even say Winston has a magic touch when it comes to computing research. In the fall of 2022, she joined Mankoff’s Make4All Group to build upon a pilot project that incorporated embroidery textures into scalable vector graphics. The goal was to develop a pipeline for producing embroidered tactile graphics that make visual media accessible to people who are blind or visually impaired (BVI).
“Common formats for tactile graphics degrade over time,” Winston explained. “Since they are made of fabric, embroidered graphics are more durable, can be washed, can be sent to low-resource regions, and may be used by different people over the years.”
Winston spearheaded multiple aspects of the pipeline, from ensuring the optimization algorithm would produce sufficient contrast between textures, to extending its capabilities to incorporate lines and points, to the actual printing and post-processing of the embroidered graphics. The latter required her to troubleshoot the process for printing on satin fabric, which is prone to wrinkles and slippage, in addition to developing a reliable approach for stitching on both sides to make it braille readable. Winston also took the lead in recruiting, running and analyzing data from user studies with BVI individuals.
Her ability to drive the project toward a successful outcome, including a paper submitted to the journal Transactions on Accessible Computing (TACCESS), was even more remarkable considering the circumstances. Shortly after Winston joined the lab, Mankoff was compelled to take time off for a family obligation. Her mentor’s unexpected absence didn’t faze Winston, who excelled despite minimal supervision. She also happily extended her involvement beyond the expected end date she had originally been given to see the project through, culminating in the TACCESS paper submission, and is currently working towards a submission that, if accepted, would be her fourth published paper in as many years. Winston is also collaborating with other researchers to build a value based healthcare system that uses large language models. Her work on that project was accepted to a workshop at the IEEE International Conference on Healthcare Informatics (ICHI 2024).
In the past, Winston has worked on research at the intersection of computing and biology. As a research assistant in the Matsen Group at the Fred Hutchinson Cancer Research Center, she contributed to the development of methods for efficiently indexing COVID-19 phylogenetic trees using annotated directed acyclic graphs for the purposes of tracking variants of the virus. The project is yet another example of Winston’s maturity and commitment to real-world impact — an impact that earned her an Outstanding Senior Award at the Allen School’s recent graduation celebration.
“Claris already functions more like a graduate student than an undergraduate and is destined to grow into a strong independent researcher,” Mankoff said. “And with her commitment to service, she is also someone who will contribute to the ongoing work of making the field of computer science more inclusive.”
Andre Ye: Getting philosophical to build better models
CRA finalist Andre Ye is philosophical in his approach to machine learning research. This can be taken literally as well as figuratively; by pursuing degrees in both computer science and philosophy, he aims to explore the intersection of machine learning and topics such as human subjectivity and critical thinking.
“I believe building stronger connections between these two areas is essential to building more robust and usable models,” Ye said.
His first project uniting the two sought to account for human uncertainty in the annotation of medical images for training computer vision models intended to assist with clinical decision-making — a high-stakes task that affects the quality of a model’s downstream predictions and, potentially, patient care.
Andre Ye
But first, he had to convince Allen School professor Amy X. Zhang to allow him to pursue the project in her Social Futures Lab. It helped that he had read a recent paper from the lab suggesting a new approach for capturing calibrated uncertainty, which dovetailed nicely with his proposal inspired by a previous stint segmenting kidney tissue images at UW Medicine.
“Normally, I might not give such a young student so much free rein to start with, but Andre quickly showed his ability to independently make progress on research,” said Zhang. “And since neither I nor my Ph.D. student who helped mentor him had any prior experience with medical data, we really leaned on and learned from Andre, given his past work in medical imaging.”
Ye took the reins and ran with them. While conventional approaches attempt to compensate for human subjectivity by incorporating an uncertainty distribution drawn from existing samples, the results are difficult to interpret and don’t necessarily indicate clinical significance. As an alternative, Ye spearheaded the development of Confidence Contours, a novel framework for developing annotations that explicitly account for human-provided uncertainty across a range of possible segmentations that can be used to train any general-purpose segmentation model.
“Rather than providing a singular segmentation, annotators annotate both a region of high confidence and additional areas of lower confidence,” Ye explained. “By leveraging human subjectivity instead of working around it, our approach produces models that are more useful to clinicians than those that rely on standard annotations.”
His efforts were rewarded with an honorable mention at last year’s Conference on Human Computation and Crowdsourcing (HCOMP ‘23). Ye subsequently embarked on another project advised by Zhang, along with Allen School professor Ranjay Krishna, examining how variations in visual perception as a product of different linguistic and cultural backgrounds influence the output of image captioning models. In a paper submitted to the International Conference on Learning Representations (ICLR 2024), the team described how multilingual annotations convey more varied and comprehensive information about the objects, their relations and attributes depicted in an image compared to monolingual ones. The analysis showed how even supposedly objective “ground truth” is, in reality, shaped by human subjectivity.
Ye’s latest work explores whether language models, which can assist people with accelerating or even automating rote cognitive tasks, can also be effective tools for deeper thinking. Based on interviews with philosophers at academic institutions across the United States, Ye conceived of the selfhood-initiative model, a framework for defining critical thinking tools that can serve as a basis for designing language models that can be used to help human ideas take shape.
He intends to continue his investigation into the intersection between machine learning and philosophy as a graduate student; in the opinion of his mentors, Zhang and Krishna, he is already operating at that level.
“Andre is an astonishing young scholar in every sense,” said Krishna. “It would be hard to find another student who matches his level of curiosity, creativity and ambition.”
In addition to Ye and his peers, another undergraduate researcher with an Allen School connection, Thanh Dang, earned accolades from the CRA this year. Danh, who is studying computer science and mathematics at Colgate University, received an honorable mention in part for her work with Allen School professor Michael Ernst on a summer project in which she developed an evaluation infrastructure for comparing the performance of commit untangling tools.
For Allen School majors who are interested in pursuing research as part of their Husky experience, CSE 390R Introduction to Research in Computer Science & Engineering offers the opportunity to gain hands-on experience with typical research responsibilities before seeking positions with labs or external research organizations. The next offering of the course, in autumn 2024, will be led by professor Leilani Battle.
Visit the CRA website to learn more about the Outstanding Undergraduate Researcher Awards program.
Hakim Weatherspoon (B.S., ‘99) was a rare breed of computer engineering student. As a Husky football player — and one who was named to the Pac-10 All-Academic team, at that — Weatherspoon’s grit and determination on the gridiron was matched by his grit and determination in the classroom. A quarter century after he last took the field for the University of Washington, Weatherspoon scored a College of Engineering Diamond Award from his alma mater for “embracing the power of diversity, equity and inclusion” in his role as Associate Dean for Diversity, Equity and Inclusion in the Cornell Bowers College of Computing and Information Science.
Football and computing may appear to be an unlikely combination, but to Weatherspoon, his journey from student-athlete to computer science professor and college leader is proof that there is a place for everyone when it comes to the field that ultimately defined his career — even if it wasn’t the one he originally imagined for himself.
“Funny thing is, I actually wanted to be an electrical engineer, like my brother and uncle,” Weatherspoon said with a laugh.
As fate would have it, he had enrolled in the Allen School’s introductory programming course — his first exposure to programming — and was doing “pretty well,” he said. So well, in fact, that a counselor in the UW’s Minority Science & Engineering Program (MSEP), which served as a bridge program for aspiring engineering students from underrepresented groups, urged him to apply to computer engineering. At the time, Weatherspoon was skeptical, but he had not yet completed the prerequisites for electrical engineering when he was accepted into the Allen School.
He decided to run with it.
“It really clicked with me, and I enjoyed it a lot. And it made a lot of sense,” Weatherspoon recalled of his first foray into computing. “It was one of those things where you didn’t grow up with programming experience, but you had exposure in college and really liked it and excelled at it.”
His experience in the MSEP not only helped him find his way to computing, but it also helped shape his thinking about DEI as a faculty member.
“The bridge program really changed my life in a lot of ways,” Weatherspoon said. “The gap between high school and college is significant. The MSEP taught us how to behave — where to sit in class, how to actually study. It got you ahead in classes like calculus and chemistry by slowing things down and allowing you to see things for a second time.”
It also, crucially, created a sense of community.
“You have a marginalized or underrepresented population that can feel isolated. If you help offset that, they can do well,” Weatherspoon said.
Dean Nancy Allbritton (left) with Hakim Weatherspoon at the College of Engineering Diamond Awards in Seattle last month.
Professor Ed Lazowska, the Bill & Melinda Gates Chair Emeritus in Computer Science & Engineering at the Allen School, noted that Weatherspoon’s record of leadership on DEI makes him an extraordinary role model — and his influence extends far beyond Cornell.
“Hakim is one-of-a-kind,” said Lazowska, who chaired what was then the Department of Computer Science & Engineering when Weatherspoon was a student. “A true student-athlete. A top-tier computer engineer on the faculty of one of the nation’s top programs. An entrepreneur. A proud Husky. And a person who has changed the landscape for traditionally marginalized individuals in the field through his leadership, mentorship and service — and through the example that he sets.”
Weatherspoon has set that example by developing a range of programs to support students at all stages of their academic journey — and offered a solid game plan for others to follow. After noticing how many students struggled in their second year, Weatherspoon co-founded an immersive summer program at Cornell, inspired by his experience in the MSEP, to prepare rising sophomores for upper-level computer science coursework. The program, known as CSMore, offers participants an opportunity to develop research skills and explore career pathways in academia and in industry. It also gives students an opportunity to build relationships with their professors as well as with each other. An extension program, CSMore Works, provides program alumni with even more opportunities to engage as a cohort with faculty, industry experts and other CSMore alumni.
“Sophomore-level courses at Cornell are really gateway courses — they are required to get into the major, and you have to get a certain GPA,” Weatherspoon explained. “I teach one of those courses and noticed that we were losing a lot of underrepresented minority students. My colleagues noticed the same, so we said, let’s do what we do for pre-freshmen, but as a bridge into sophomore year.”
For students who are interested in exploring research as a career, either in industry or academia, there’s the SoNIC program. This week-long summer research workshop is designed for students enrolled in an undergraduate or master’s degree program in science, technology, engineering or mathematics (STEM) in the United States and Puerto Rico. For those students who have already decided to jump into academic research, there’s Engage LEAP, which supports Cornell Ph.D. students’ academic, professional and personal growth through a variety of resources and activities, from dissertation workshops to networking opportunities. Weatherspoon is an advocate in the LEAP Alliance, which is a nationwide network of 30 Ph.D. programs — including the Allen School — working to diversify leadership in the professoriate.
According to Weatherspoon, the initiative has succeeded in changing the face of Cornell’s Ph.D. program. “We went from essentially zero to nearly 10% underrepresented minority students,” he said.
But Weatherspoon knows that diversifying leadership in the field requires reaching students before they even set foot on a college campus — perhaps even before they dare to dream of a career in computer science. Recognizing that such opportunities are not evenly distributed, particularly when it comes to low-resource communities, he co-organized Code Afrique with a mission “to give African students a window into the world of computer science and its vast potential for development in this era of technology.”
Code Afrique engages students from partner high schools in learning about the latest developments in computer science. The program includes interactive coding workshops culminating in a competitive hackathon along with mentorship sessions led by volunteers from Cornell, MIT, Google and more. Code Afrique has reached as many as 500 students in countries including Ghana, Eswatini and Nigeria. According to Weatherspoon, they’re not the only ones whose lives are changed by the experience.
“It has a deep impact on the volunteers,” he said. “The people who go there — the Cornell students who participate — are often not from those countries. So it affects their thinking and sometimes their career path, as well.”
Whether in a high school classroom half a world away or a college computer science lab close to home, Weatherspoon’s efforts to transform students’ lives — and the entire field of computing — continues to be an inspiration to those who know and work with him.
“He’s a very genuine person, and he cares deeply,” said Kavita Bala, Dean of the Cornell Bowers College of Computing and Information Science. ”He’s an optimist, and he brings this positive energy to effect change in a way that not everybody has.”
For students looking to follow in Weatherspoon’s footsteps in computer science, he’s clearly mastered the art of the assist.
“In a lot of places, he’s been the first,” said Ayanna Howard, Dean of Engineering at The Ohio State University and a frequent collaborator of Weatherpoon’s. “He ensures that he provides and builds up an environment where he is not the last.”
5,000 graduates, families, and friends attended the Allen School’s 2024 graduation celebration on the University of Washington campus. Kerry Dahlen
“Tonight, we celebrate the hard work and accomplishments of our graduates. Tomorrow, new adventures await.”
With that acknowledgment of the Class of 2024’s trials and triumphs, professor Magdalena Balazinska, director of the Allen School, welcomed a crowd of roughly 5,000 to the school’s graduation celebration. The Allen School expects to award more than 770 degrees in total during the 2023-2024 academic cycle; around 650 of the recipients assembled in the Hec Edmundson Pavilion at the Alaska Airlines Arena on June 7 to mark this life-changing milestone in the presence of their families and friends.
‘You write your own story’: Life advice from Andy Jassy
Andy Jassy: “There are more interesting opportunities to make a difference than you probably realize.” Matt Hagen
Among those celebrating the graduates’ achievements was Andy Jassy, President and CEO of Amazon and — for this evening, at least — honorary member of the Dawg Pack. Jassy took the stage as the Allen School’s 2024 graduation speaker to share with graduates the most important lessons he had learned over the course of his career, from his early aspirations of being the next Howard Cosell, to his leadership of Amazon Web Services to its position as the undisputed king of the cloud, in the hopes of inspiring them to each be the author of their own story.
Those early aspirations of sportscasting glory were on Jassy’s mind as he entered the arena. Describing the Allen School as “one of the very best engineering schools in the world,” he noted that the graduates had already achieved a remarkable feat. As they begin their next chapter, they could take both inspiration and some hard-earned lessons from Jassy’s own story — or, as he put it, “what I wish I knew when I was 22.”
First lesson to his younger self, and by extension, the newly-minted graduates? “I am not going to be a famous sportscaster,” he acknowledged, accepting that what he thought he wanted to do upon first graduating from college was not, in fact, destined to be his life’s work.
Should the graduates come to a similar realization, they needn’t get discouraged. As Jassy shared in his next lesson, “I’m going to pursue a lot of different jobs” — be it jobs for which he interviewed but didn’t receive an offer, or ones that he tried for some time but decided he didn’t want to spend his life doing. Over the course of his career, Jassy attempted a range of professions, from investment banking and consulting, to selling golf clubs and coaching a high school soccer team, on his way to finding his true calling at Amazon.
For the graduates, then, figuring out what they don’t want to do for a career will be as important as figuring out what they do want to do. While they’re at it, Jassy suggested, they should keep an open mind.
“Life is an adventure. It takes a lot of unpredictable twists and turns,” he said. “You meet people who influence you along the way; you’ll find yourself surprised by what inspires you that you would not have guessed. There are more interesting opportunities to make a difference than you probably realize. Be open to what’s out there.”
His third lesson? “Don’t let others tell you who you are,” Jassy advised, recalling his pre-kindergarten teacher’s assessment that he would never be an athlete because he struggled to hop on one leg. As it turned out, his performance at the tender age of five did not prevent him from going on to play multiple sports in high school and college. What is easy to ignore when we are young becomes harder to ignore as we get older, he acknowledged; and yet, the graduates should not allow the judgments of “uninformed people” to define them.
“Nobody writes your book for you,” Jassy insisted. “You write it.”
And as they do, they should remember that not every presentation or meeting equates to a pass/fail referendum on their competence — despite his own early preoccupation to the contrary. Jassy noted that his biggest regrets were not the occasions that he failed, which he referred to as his “proudest scars,” but rather the times he didn’t take a risk in the first place.
“There is no person in the world who performs perfectly, or has it right 100% of the time, or whose ideas are coherent or sensible every time. That’s not reality,” he said. “It is, however, a sure bet that you will never do something needle-moving if you don’t put yourself out there and take a shot.”
Andy Jassy advised graduates not to lose the learning mindset. “Life is much more fun and rewarding when you’re learning.” Matt Hagen
While there are many things the graduates won’t be able to control, Jassy pointed out that one thing they will always be in control of is their attitude. And although members of the Class of 2024 may be marking the official end to their time at the university, they should not regard their student days as being completely behind them.
“Be a willing and ravenous learner,” he urged. “Believe me, life is much more fun and rewarding when you’re learning.”
In closing, Jassy noted that the Class of 2024 graduates will have many options, and this next chapter represents just one of them.
“Remember that you write your own story,” he said.
Alumni Impact Awards: Leading by example
Continuing an annual tradition, Ed Lazowska, Professor, and Bill & Melinda Gates Chair Emeritus, in the Allen School, ascended the stage to announce the recipients of the Alumni Impact Awards. The awards reinforce for the next generation of alumni how an Allen School education can lead to real-world impact.
From left: Magdalena Balazinska, John Colleran, Karen Liu and Ed Lazowska. Matt Hagen
John Colleran (B.S., ‘87)
John Colleran barely had time to catch his breath following his graduation from UW in 1987; two days later, he was starting his new job at Microsoft. There, he has spent 37 years driving engineering investment and innovation in successive versions of the Windows operating system before stepping into his current role leading the company’s Developer Productivity team for the Windows and Azure Engineering Systems group. There, he has led the creation of the WAVE engineering productivity tools in addition to the development of industry-leading methods for measuring the impact of various tools and practices on developer productivity.
As the spouse of another UW undergraduate alum and proud father of a student set to walk across the stage that very same evening to collect her own Allen School degree, Colleran is a Husky through and through.
“John has a long list of engineering accomplishments in the systems arena that have directly contributed to Windows’ dominance in both the business and consumer spaces,” said Lazowska. “John is also a good friend and a good person.”
Karen Liu (Ph.D., ‘05)
Since completing her degree working with professor Zoran Popović in the Allen School’s Graphics & Imaging Laboratory (GRAIL), Ph.D. recipient Karen Liu has made a series of fundamental contributions in computer graphics and robotics spanning physics-based animation, reinforcement learning, optimal control and more.
Liu launched her faculty career at the University of Southern California before she was recruited away by Georgia Tech and, later, Stanford University, where she currently directs The Movement Lab. Liu focuses on the development of algorithms and software that enable digital agents and physical robots to interact with the world through intelligent and natural movements, drawing upon principles from computer science, mechanical engineering, biomechanics, neuroscience and biology.
“It’s exciting, high-impact, interdisciplinary work for which she has been widely recognized,” said Lazowska, alluding to a litany of honors that includes a TR-35 Award, Sloan Research Fellowship and ACM SIGGRAPH Significant New Researcher Award. “Karen, like John, is building on her Allen School education to change the world.”
Student Awards: Recognizing scholarship, leadership and service
Before they go out and use their education to make a difference in the world, many Allen School students find ways to make a difference to the campus community through their scholarship, leadership and service. Each year, the school recognizes a subset of these students for going above and beyond in supporting their fellow students, advancing the field through research and contributing to a vibrant and inclusive school community.
From left: Undergraduate Service Award winners Lee Janzen-Morel, Kristy Nhan, Olivia Wang and Vidisha Gupta. Matt Hagen
Undergraduate Service Awards
In her role as chair of the student group Computing Community (COM2), honoree Vidisha Gupta stood out “as a phenomenal leader who has worked tirelessly to improve the Allen School experience for students.” Gupta’s contributions included organizing large school-wide events aimed at building community and fostering a sense of connection, whether virtual or in-person. Gupta also represented undergraduate students on the school’s Diversity Committee and during the hiring process for teaching faculty.
Meanwhile, award recipient Lee Janzen-Morel was a driving force behind the creation of the Diversity & Access Lounge, a space for students from underrepresented groups in computing to find community and share experiences. “In the two years since it opened, this space has positively impacted many students and will continue to have an impact in the years to come.” Janzen-Morel also provided extensive support to Ability, the student group focused on promoting accessibility at the Allen School.
During her time at the Allen School, Kristy Nhan “has done immense work to help improve the Allen School experience for students of color, first-generation students, and women in computing.” Nhan’s impact was also felt through her service as a lead CSE Ambassador performing outreach to local K-12 students. In addition, she was a volunteer leader with student groups GEN1 and Women in Computing, for which she developed a new internship program and coordinated high school visits for young women of color, respectively.
Olivia Wang approached their role as a peer adviser ”with passion, attention to detail, and kindness.” Wang was a peer adviser for two years, during which time they assisted current and prospective students in navigating the undergraduate experience and connecting with academic resources. Wang also was the first-ever peer adviser focused on undergraduate research, organizing events such as the “Getting into Research” workshop and spring research showcase to make opportunities in the Allen School’s labs more accessible to students.
Outstanding Senior Award winners, from left: Claris Winston, Matthew Shang, Heer Patel and Grace Brigham. Kerry Dahlen
Outstanding Senior Awards
Recipient Grace Brigham, who earned both her bachelor’s and master’s degrees in the Allen School’s B.S./M.S. program, was recognized for her research examining the use of artificial intelligence to generate non-consensual intimate imagery and the impact of AI bias on humans — the same project for which she earned a Best Master’s Thesis Award. In addition, Brigham earned accolades for her service as a teaching assistant for the direct admit seminar for new freshmen and her contributions as a mentor for Changemakers in Computing.
Fellow award winner Heer Patel was likewise recognized for research excellence — in this case, her work in data visualization that explored the application of AI to generate educational materials for data science students. In fact, Patel’s leadership, hard work, and dedication led to the submission of a paper on the subject, for which she is first co-author. Patel will remain in the Allen School to pursue her master’s degree as part of the B.S./M.S. program.
Matthew Shang was singled out for his “mathematical prowess” and his contributions to research in chaotic systems and probabilistic programming techniques for analyzing errors in laboratory procedures — the latter in collaboration with members of the Department of Electrical & Computer Engineering. Another undergraduate who is enrolling directly in the B.S./M.S. program, Shang has already completed an impressive amount of graduate-level coursework.
Claris Winston was honored for her research into embroidered tactile graphics to support individuals who are blind or visually impaired, a project for which she was lead author of a journal submission to Transactions on Accessible Computing. She also was a finalist in the Computing Research Association’s Outstanding Undergraduate Researcher Awards competition. She, too, will pursue her master’s degree at the Allen School starting this fall.
Thesis Awards
Kavel Rao (left) and Maya Cakmak. Kerry Dahlen
Professor Maya Cakmak, who chairs the Allen School’s Undergraduate Research Committee, presented the research thesis awards. Noting the school’s dual mission to both educate students and push the boundaries of computing via research, Cakmak reminded the audience that it’s not just faculty and Ph.D. students doing the latter.
“There are also opportunities for undergraduate and masters students to get involved in research labs, learn about the research process, and make their own contributions to the field,” she said.
Kavel Rao earned the Best Senior Thesis Award for “What Makes it Ok to Set a Fire? Iterative Self-Distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations,” completed under the supervision of professor Yejin Choi in the Allen School’s Natural Language Processing group. In the paper — which was published at last year’s Conference on Empirical Methods in Natural Language Processing (EMNLP 2023) — Rao put forward new methods for emphasizing contextual information and commonsense reasoning in models tasked with making moral decisions.
Grace Brigham, who also took home an Outstanding Senior Award, was honored with the Outstanding Master’s Thesis Award for “Violation of my body: Perceptions of non-consensual (intimate) imagery.” That work, which Brigham completed under the supervision of professor Tadayoshi Kohno in the Security and Privacy Research Lab, provided new insights into people’s perceptions of AI-generated non-consensual imagery that are already being used to inform national and international conversations about how to mitigate harms associated with such use of AI. The paper was accepted to the 20th Symposium on Usable Privacy and Security (SOUPS 2024).
Teaching Awards: Honoring those who inspire
Bob Bandes Memorial Awards for Excellence in Teaching
Each year, the Allen School honors the students who take on the role of teaching assistant, or TAs, who wear many hats: course administrator, supplemental instructor, grader, and cheerleader. In all, the school received more than 800 nominations for roughly 260 individual TAs for this year’s Bandes Awards; of those, the school selected four winners and three honorable mentions presented by teaching professors
Bandes Award winners, from left: Joe Spaniac, Jaela Field and Anjali Agarwal. Not pictured: Jasmine Chi. Kerry Dahlen
Award winner Anjali Agarwal served as a TA nine times, including for the Allen School’s introductory programming courses as well as Data Structures and Parallelism and Foundations of Computing. She also was an instructor for the latter as well as the school’s Mathematics for Computation Workshop. One student summed up the nominators’ assessment by saying, “What stands out most about Anjali is her kindness, how she approaches each student with understanding and without judgment to meet them where they are at and bring them along.” Agarwal will start this fall as teaching faculty at Northwestern University.
Jaela Field earned an award for her nine quarters of service as a TA for Software Design & Implementation. Saying that Field knows the content inside-out, one nominator enthused “Saying that Jaela goes above and beyond is an understatement. She goes above, beyond, out of this world, around it, and then a couple more times around it…She makes the impossible possible through her passion to help others learn.” The instructors she has worked with were similarly effusive about her dedication and willingness to jump in and help with all aspects of a course, citing her combination of technical and people skills.
Winner Jasmine Chi is a seven-time TA who has assisted with the Allen School’s revamped introductory series as well as the freshman direct admit seminar. In addition to her enthusiasm, patience and reliability, students appreciated how Chi “ensures that every student that attends her lectures feels seen” while prioritizing their understanding of the concepts covered in the course. An instructor applauded her leadership on course infrastructure, including her management of assignment development and grading, while keeping up with course communication and supporting her fellow TAs — qualities that made her “mission critical” to the course.
Bandes Honorable Mention recipients, from left: Zhi Yang Lim, Yuxuan Mei and Hannah Lee. Matt Hagen
Award recipient Joe Spaniac served as a TA for an impressive 15 quarters, including multiple offerings of the introductory series, and as an instructor for one course — with a second to come this summer. One of the instructors with whom he worked raved that Spaniac was the most effective TA they had ever worked with, noting that “most issues that cropped up during the quarter were never seen by the instructors because Joe got there first.” Spaniac’s students particularly appreciated how he made them feel sufficiently comfortable, even encouraged, to ask the so-called dumb questions and get the support they needed.
Honorable mentions went to Hannah Lee, who has TAed a total of six times for multiple offerings of the undergraduate Machine Learning and the graduate-level Machine Learning for Neuroscience courses; Yuxuan Mei, a TA for three different courses — including Computational Design & Fabrication and Intermediate Data Programming, for which Mei will be an instructor this summer; and Zhi Yang Lim, who served as a TA for four quarters of Foundations of Computing II, including one as lead TA.
Matt Wang (left) and Kianna Bolante. Matt Hagen
COM2 Teaching Awards
While graduation is a time for celebrating students, each year the student group Computing Community, or COM2, turns the spotlight back on the faculty who, in chair Kianna Bolante’s words, “inspired us, challenged us, and shaped our paths” through its Undergraduate Teaching Awards.
The first honoree, teaching professor Matt Wang, earned accolades for being an “incredible educator who has made a remarkable impact in just his first year in the Allen School” as an instructor in the introductory programming series and System and Software Tools. Bolante highlighted in particular how Wang comes up with creative ways to engage students through interactive demonstrations and relatable examples, as well as his ability to create a welcoming classroom environment where everyone feels included and valued.
“His talent for simplifying complex concepts, and eagerness to offer additional assistance and encouragement, demonstrates his unwavering commitment to his students’ growth and achievement,” she said.
The second recipient, teaching professor Miya Natsuhara, was singled out by students for her “contagious energy,” “engaging teaching style” and “efforts to know her students” — even while running large introductory programming courses. It is an approach that has left a lasting impression on those who take her classes, and inspired many to pursue careers in computing.
“Her approachable character and genuine concern for her students’ well-being always creates a supportive environment,” Bolante said, noting that Natsuhara “not only excels in teaching, but also goes the extra mile” for her TAs by empowering them to contribute to course development and supporting their professional growth.
Miya Natsuhara. Matt Hagen
‘We will be cheering you on’
Speaking of professional growth, Balazinska had encouraging words for the graduates about to flip their tassels and depart the arena as Allen School alumni.
“You are starting your careers at an especially challenging time for society. But along with those challenges come many opportunities,” Balazinska reminded the graduates in the arena. “Opportunities to use your Allen School education, your passion, your kindness, and your creativity to make a positive impact on the people and the world around you.
“And just as we did tonight, we will be cheering you on.”
Congratulations to all of our graduates! We can’t wait to see what your next chapter brings!
Researchers at the University of Washington, Microsoft Research and Providence have developed an open-access foundation model for digital pathology, Prov-GigaPath, that combines real-world, whole-slide data with individual image tiles at an unprecedented scale. iStock/anemeja18
Digital pathology promises to revolutionize medicine by transforming tissue samples into high-resolution images that can be shared and analyzed using powerful new computational tools. Instead of looking at single slides through a microscope, scientists and clinicians can analyze vast quantities of tissue samples at once, identifying anomalies, searching for patterns, and — thanks to advances in artificial intelligence — making predictions about various disease characteristics to assist with clinical decision-making and personalize patient care.
But there are multiple challenges that have to be overcome to achieve this bold new vision for medicine, from the scarcity of real-world data to the amount of compute required for model pretraining. Now, thanks to researchers at the University of Washington’s Allen School, Microsoft Research and Providence, the prospects for digital pathology are looking even brighter — around a billion times brighter. In a paper recently published in the journal Nature, the team unveiled Prov-GigaPath, a groundbreaking new open-access foundation model for digital pathology that combines real-world, whole-slide data with individual image tiles at an unprecedented scale.
Hanwen Xu
Prov-GigaPath was developed with what the team believes to be the largest whole-slide pretraining effort to date, using samples collected from over 30,000 cancer center patients in the Providence health network. Prov-Path — a portmanteau of “Providence” and “pathology” — includes more than 1.3 billion image tiles derived from roughly 170,000 whole pathology slides. That’s five times larger than another whole-slide dataset, The Cancer Genome Atlas (TCGA), and is drawn from twice the number of patients. And whereas TCGA contains only tissue resections, the data underpinning Prov-GigaPath includes both resections and biopsies covering 31 major tissue types.
According to co-first author and Allen School Ph.D. student Hanwen Xu, the scale of the inputs, coupled with finer details about the samples, gives Prov-GigaPath the edge when it comes to potential integration with clinical workflows.
“The Prov-Path dataset is both really diverse and really robust, including meta information such as genomic mutation profiles, longitudinal data and real-world pathology reports,” explained Xu, “Those details provide us with a great opportunity to develop stronger models that can handle a wide range of real-world tasks, like tumor detection, cancer staging, and mutation prediction.”
The researchers developed Prov-GigaPath using an efficient vision transformer architecture, GigaPath, that was built on a version of LongNet’s dilated attention mechanism. This approach allowed for efficient aggregation of the tens of thousands of image tiles contained in a single slide, thus significantly lowering the computational cost for pretraining compared to the standard transformer model. It also enables Prov-GigaPath to pick out patterns other models cannot by embedding image tiles as visual tokens; not only is it trained on the characteristics of the individual tokens, but it can also detect patterns across a sequence of tokens that corresponds to an entire slide.
“The key technical challenge is that whole-slide pathology images are extremely large compared to other images studied in the computer vision domain. A pathology image could be as large as 120,000 by 120,000 pixels,” noted Allen School professor and co-corresponding author Sheng Wang. “On one hand, it is challenging for a pathologist to review the entire slide, necessitating the development of automated AI approaches. On the other hand, it is challenging for existing generative AI models, which are often transformer-based, to be scaled to sequences from such large images due to the computational burden.
Sheng Wang
“We address this problem by using a new neural network architecture that can scale to very long sequences,” Wang continued. “As a result, Prov-GigaPath makes predictions based on global patterns as well as localized ones, which yields state-of-the-art performance on multiple prediction tasks.”
Those tasks range from standard histopathology tasks like cancer subtyping to more challenging tasks like mutation prediction. In all, Wang and his colleagues evaluated Prov-GigaPath on a set of 26 prediction tasks, comparing their new model’s performance against that of other digital pathology foundation models such as HIPT and REMEDIS trained on more limited datasets. Prov-GigaPath outperformed them all on 25 out of the 26 tasks — even when the competing model was pretrained on the same dataset used for the task and Prov-GigaPath was not. The new model also set a new benchmark for predicting cancer subtypes from images, outperforming other models on each of nine cancer types; for six of the subtypes, the performance improvement over the next-best model was significant.
According to Xu, these results point to the potential utility of the team’s approach for a variety of downstream tasks — for cancer, and beyond.
“With Prov-GigaPath, we showed how to build a model that can do representation learning of high-resolution image data efficiently at a really large scale,” said Xu. “I’m excited to see how researchers will take this base model and apply it to other biomedical problems to advance AI-assisted diagnostics and decision support.”
Researchers and clinicians can freely access Prov-GigaPath on Hugging Face. Going forward, Xu and Wang hope to extend this approach to other imaging data, such as that derived very large microscopy images for proteins and single cells. They are actively seeking pathologists and physicians within UW Medicine who would like to collaborate on this effort, particularly those who are interested in using the existing model to analyze their data or those with large amounts of imaging data that the team could leverage to train a new model.
