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UW researchers ride a wave of innovation at the 2021 ACM CHI Conference

CHI 2021 logo

Researchers at the University of Washington contributed to four Best Papers and 11 Best Paper Honorable Mentions at the recent Conference on Human Factors in Computing Systems (CHI 2021) organized by the Association for Computing Machinery. The virtual conference offered UW researchers the opportunity to showcase the breadth and depth of their expertise in human-computer interaction (HCI) and design as well as the strength of interdisciplinary collaborations such as DUB. Members of the Allen School co-authored two of the Best Papers and eight of the papers recognized with Honorable Mentions. 

In addition to the papers, Allen School professor Jeffrey Heer, director of the Interactive Data Lab, was inducted into the CHI Academy for his substantial, cumulative contributions to the field of human-computer interaction, social computing and data visualization. He is one of only eight new members elected to the Academy this year. 

Wang, Bodik and Ko
Authors of the Best Paper: “Falx: Synthesis-Powered Visualization Authoring,” from left to right, Wang, Bodik and Ko.

Allen School Ph.D. student Chenglong Wang and professor Rastislav Bodik of the Programming Languages & Software Engineering (PLSE) group and iSchool professor and Allen School adjunct professor Amy J. Ko, co-authored the award-winning paper, “Falx: Synthesis-Powered Visualization Authoring,” with professors Yu Feng  at the University of California Santa Barbara and Isil Dilig at the University of Texas, Austin and former Allen School professor Alvin Cheung, now a faculty member at the University of California, Berkeley. In the publication, the team introduces Falx, a tool that enables data analysts to create data visualizations more easily through demonstrations. While modern visualization tools make data visualization design easier, when it comes to exploratory visualization, there can often be a mismatch of data layout and design that requires significant effort. Falx addresses the issue by automatically inferring the visualization specification and transforming the data to match the design from user demonstrations. 

Karusala and Anderson
Allen School authors of the Best Paper:”‘That Courage to Encourage’: Participation and Aspiration in Chat-Based Peer Support for Youth Living with HIV,” Karusala and Anderson.

Allen School Ph.D. student Naveena Karusala and professor Richard Anderson of the Information and Communication for Technology Development (ICTD) Lab  co-authored ‘‘’That courage to encourage’: Participation and Aspirations in Chat-based Peer Support for Youth Living with HIV” with UW Global Health professors Brandon Guthrie, Grace John-Stewart and Keshet Ronen; professor Megan A. Moreno at the University of Wisconsin, Madison; and Kenyatta National Hospital researchers David Odhiambo Seeh, who is also a research assistant at UW, Cyrus Mugo and Irene Inwani. For their award-winning paper, the team conducted a qualitative study of WhatsApp-based facilitated peer support groups for youth living with HIV in Nairobi, Kenya. While chat apps can make patient-provider communication and peer support more accessible outside of clinical settings, the experience of participants in such group chats in areas where phone sharing and intermittent data access are common is understudied. Using a combination of chat records and interviews with participants, the researchers found that the youth in the chat groups were motivated by newfound aspirations and a sense of community to manage their health despite the complexities of group dynamics, intermittent participation, and concerns about privacy. The paper offers takeaways for participation and privacy in chat-based health communities and how the role of aspirations can be factored into the design of health interventions. 

In addition to the two award-winning papers, Allen School researchers contributed to eight papers that earned Honorable Mentions for addressing topics such as medical making during a pandemic, remote collaboration and communication, the impact of prison surveillance, and more:

Do Cross-Cultural Differences in Visual Attention Patterns Affect Search Efficiency on Websites?” by Allen School Ph.D. students Amanda Baughan and Tal August, professor Katharina Reinecke, postdoctoral researcher Nigini Oliveira and researcher Naomi Yamashita of Nippon Telegraph and Telephone Corporation explored the differences between the way Westerners and people in East Asian societies — U.S. and Japanese citizens, specifically — absorb contextual information online and the implications of their findings for website design.  

Medical Maker Response to COVID-19: Distributed Manufacturing Infrastructure for Stop Gap Protective Equipment,” co-authored by professor Jennifer Mankoff and Ph.D. student Kelly Mack of the Allen School’s Make4All Lab, Georgia Institute of Technology professor Rosa Arriaga and Ph.D. student Udaya Lakshmi, and Carnegie Mellon University professor Scott Hudson and graduate student Megan Hofmann, assesses the efforts of medical makers to organize stopgap manufacturing capabilities to address acute and chronic shortages of personal protective equipment (PPE) during the pandemic to inform future infrastructure design that will enable community production of safe devices at scale.

The same team also contributed to “The Right to Help and the Right Help: Fostering and Regulating Collective Action in a Medical Making Reaction to COVID-19,” which explores the tension between the medical field’s “do no harm” ethos and maker communities’ desire to rapidly innovate in response to shortages of PPE. To address this tension and strike a balance between action-oriented and regulated practices, the researchers recommend that regulatory bodies build coalitions with makers, online platforms give communities more control over the presentation of information, and repositories to balance the need to distribute information while limiting the spread of misinformation.

Scraps: Enabling Contextual Mobile Capture, Contextualization, and Use of Document Resources,” co-authored by Allen School alumna  Amanda Swearngin (Ph.D., ‘19), now an engineer at Apple, and Microsoft researchers Shamsi Iqbal, Victor Poznanski, Mark Encarnación, Paul Bennett and Jaime Teevan, presents an app that makes it easy for people to capture and add context to information from their phone and later link that information to a document on their desktop.  

Allen School Ph.D. student Chunjong Park is one of the researchers behind another app “Significant Otter: Understanding the Role of Biosignals in Communication,” co-authored by Carnegie Mellon University professors Laura Dabbish and Geoff Kaufman and Snap, Inc. researchers Fannie Liu, Yu Jiang Tham and Tsung-Yu Tsai. Significant Otter is an Apple Watch/iPhone app with animated otter avatars that enables romantic partners to share and respond to each other’s biosignals to create a more authentic communication that fosters social connection.

Allen School Ph.D. student Ruotong Wang co-authored “Tabletop Games in the Age of Remote Collaboration: Design Opportunities for a Socially Connected Game Experience” with University of Minnesota professor Svetlana Yarosh and Ph.D. student Irene Ye Yuan and Northwestern Ph.D. student Jan Cao. The paper examines how people adapted existing technologies and their offline practices in pursuit of a shared tabletop gaming experience in the context of the pandemic. The team also reflects on challenges and opportunities for designing a better communal gaming experience in the age of remote collaboration.  

What Do We Mean by ‘Accessibility Research’? A Systematic Review of Accessibility Papers in CHI and ASSETS from 1994 to 2019,” by Kelly Mack, professor Jon Froehlich and Ph.D. student Dhruv Jain in the Allen School’s Makeability Lab; UW Human Centered Design & Engineering professor Leah Findlater and graduate student Emma McDonnell and Allen Institute for Artificial Intelligence researcher Lucy Lu Wang, reflects on gaps in accessibility research presented at CHI and the International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) over a 25-year period and offers guidance for future work in the field.

You Gotta Watch What You Say”: Surveillance of Communication with Incarcerated People, by Allen School Ph.D. student Kentrell Owens, alumna Camille Cobb (Ph.D., ‘19), now a postdoc at Carnegie Mellon University, and CMU professor Lorrie Cranor examines the impact of third-party surveillance of communications between families and relatives who are in prison. Using semi-structured interviews, the team explored the implications of family members’ inaccurate understanding of surveillance and the misalignment of incentives between end-users and vendors to enhance ongoing conversations around carceral justice and the need for more privacy-sensitive communication tools.

All told, UW authors contributed a total of 50 papers presented at this year’s conference. See the complete list of UW CHI papers here.

June 10, 2021

Allen School’s Richard Anderson receives ACM Eugene L. Lawler Award for humanitarian contributions through computing

Portrait of Richard Anderson
Credit: Dana Brooks/University of Washington

Allen School professor Richard Anderson earned the ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics from the Association for Computing Machinery for his work bridging computer science, education and global health. Anderson, who co-directs the Information and Communication Technology for Development (ICTD) Lab, has devoted himself over the past two decades to advancing computing innovations that improve quality of life for people in rural and low-income communities around the globe.

After beginning his research and teaching career focused on theoretical computer science, Anderson embraced the opportunity to generate a tangible impact on underserved populations by helping to build up the emerging field of ICTD beginning in the early 2000s. One of his earliest contributions was to community-led education via the Digital StudyHall project, which brought high-quality teaching and interactive content to rural classrooms in India via facilitated video instruction. He subsequently teamed up with Seattle-based global health organization PATH on Projecting Health, an initiative aimed at using digital communications to help people in low-resource areas, including those with low literacy, to learn about and practice health-oriented behaviors to support maternal and child health. To date, Projecting Health has reached an estimated 190,000 people across 180 villages through local-produced videos addressing topics such as nutrition, immunization, and family planning.

“Through empowering community teams with this novel simplified filming and editing process to develop messaging for their own communities, Richard and our team achieved what rarely is achieved in global health — full ownership of a process by the very communities who would then use the output,” said Dr. Kiersten Israel-Ballard, the team lead for Maternal, Newborn, Child Health and Nutrition at PATH and an affiliate professor in the UW Department of Global Health. “As a global health specialist, it is rare to work with a visionary like Richard, who bridges fields and cultures to create innovative solutions. Our work is better having learned from him and his team.”

For the past six years, Anderson has led the development and deployment of open-source software tools for mobile data collection and analysis known as the Open Data Kit (ODK). Initially the brainchild of Anderson’s late friend and colleague Gaetano Borriello, ODK started out as a customizable survey tool designed to be “easy to try, easy to use, easy to modify and easy to scale.” Governments and non-profit organizations in more than 130 countries have relied on successive versions of ODK — including a second-generation version of the toolkit developed under Anderson’s leadership that enabled non-linear workflows and longitudinal surveys — to advance public health, wildlife conservation, election monitoring, essential infrastructure, and more. 

Shortly after taking the helm, Anderson and his collaborators managed the successful transition of ODK from a UW initiative to a stand-alone enterprise. Anderson and his team subsequently extended the original software’s capabilities with the release of ODK-X, which enables users such as PATH, the World Mosquito Program and the International Federation of Red Cross and Red Crescent Societies to build customized Javascript-based apps for managing and visualizing data in the field in addition to the traditional survey forms. Recent applications of ODK-X include vaccine cold-chain management, vector-borne disease monitoring, and humanitarian response.

Anderson has also led the charge to bring secure digital financial services to areas that lack access to traditional banking. In 2016, Anderson and other members of the ICTD Lab joined forces with the Allen School’s Security and Privacy Research Lab to launch the Digital Financial Services Research Group (DFSRG) with the goal of making financial products such as mobile payments and savings accounts more accessible to underserved communities. With funding from the Bill & Melinda Gates Foundation, the DFSRG addresses fundamental challenges to the development and large-scale adoption of digital financial products to benefit some of the lowest-income people in the world to enable them to participate in digital commerce and ensure that, in Anderson’s own words, “an event like an accident or a pregnancy doesn’t send them over the edge.” 

As one of the founding champions of the ICTD movement, Anderson has been instrumental in uniting various communities under the umbrella of ACM COMPASS — short for Computing and Sustainable Societies — and organizing conferences, workshops, and tutorials to engage more researchers, practitioners and students in this work. He has also been a vocal proponent of diversifying host countries to include conference sites such as Ecuador, Ghana and Pakistan. In conjunction with his research and community leadership, Anderson has been credited with demonstrating how to build effective collaborations between computer scientists and non-governmental organizations (NGOs). By combining the former’s technical expertise with the latter’s geographical and domain expertise, Anderson has forged partnerships that ensure solutions developed in the lab can be effectively deployed in the field by people without computing experience — and that they actually address the real-world problems of the people they aim to serve.

“Richard is a top-notch computer scientist and a more than capable teacher, but his real contribution is creating an environment in which CS innovation can be brought to bear on the real problems of real people in developing regions,” said Eric Brewer, a professor of computer science at the University of California, Berkeley who is also Fellow and VP Infrastructure at Google. “His work is inspiring to students across many disciplines, especially when they see the impact of his work on others.”

Anderson joined the Allen School faculty in 1986 after completing a postdoc at the Mathematical Sciences Research Institution in Berkeley, California. He earned his Ph.D. in computer science from Stanford University and his bachelor’s in mathematics from Reed College. Anderson is the first Allen School faculty member to receive the ACM Eugene L. Lawler Award, which is typically given once every two years in honor of an individual or group who has made a significant humanitarian contribution through the application of computing technology.

Read the ACM citation here, and learn more about the Eugene L. Lawler Award here.

Congratulations, Richard!

June 9, 2021

Allen School’s Tim Althoff, Ashish Sharma and Inna Lin win Best Paper Award at WWW for their work to improve online mental health support

Collage of Althoff, Sharma and Lin
From left to right: Althoff, Sharma, Lin

Allen School Ph.D. student Ashish Sharma, professor Tim Althoff and incoming Ph.D. student Inna Lin won the Best Paper Award at the The Web Conference 2021 (WWW) for “Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach.” In the paper, the group presented computational methods for improving empathy in online mental health support conversations. Their work was chosen from among 1,736 submissions to the conference, which focuses on advancements in the technologies supporting the World Wide Web and their impact on society and culture. 

“We felt the work is going to have a significant impact on the community based on the algorithmic approach and the very thorough experiments that were done in the paper itself,” said Kira Radinsky, the Best Paper Award chair. “This is the direction we felt is going to bring the most value to the community.” 

The paper, which was co-authored with UW Department of Psychiatry and Behavioral Sciences professor David Atkins and Stanford University Department of Psychiatry and Behavioral Sciences instructor Adam Miner, addresses improving web-based mental health conversations in online peer-to-peer support platforms, which could help improve access to treatment and reduce the global disease burden.

“We describe a system that can give feedback to help someone express empathy more effectively when supporting others based on natural language processing and machine learning innovations — reinforcement learning for dialogue,” said Althoff, who directs the Allen School’s Behavioral Data Science Group. “This is hugely exciting as this work has been a big step towards one of the biggest and most meaningful goals in my research for a long time.”

The team, which was based on a collaboration with the UW and Stanford Medical Schools and the largest online peer-to-peer support platform worldwide, Talklife, found that online mental health conversations could have significantly more empathy than what is currently expressed. To facilitate conversations with higher empathy levels, which is key for providing successful support, they introduced a new task of empathic rewriting. Their approach employs artificial intelligence (AI) tools for identifying and improving empathy to effectively increase the level of empathy in the chat posts. 

As part of this work, the researchers introduced PARTNER, a new deep reinforcement learning agent that learns to make edits to text to increase empathy in a conversation. Through a combination of automatic and human evaluation, the team demonstrated that PARTNER is capable of generating more empathetic, specific and diverse responses than what is currently being shared on Talklife or what current machine learning models can provide. 

“Rarely does mental health research have the practical application and potential for impact that we believe this research will have,” said Jamie Druitt, CEO of Talklife. “This proposed research directly addresses real challenges and can be implemented to create measurable change on mental health platforms.”

This research has been supported in part by a Microsoft AI for Accessibility grant, the Allen Institute for Artificial Intelligence, NSF grant IIS-1901386, NIH grant R01MH125179, and Bill & Melinda Gates Foundation (INV-004841).

Congratulations Ashish, Tim, Inna and the rest of the team! 

June 8, 2021

Allen School’s Della Welch, Linda Shapiro and Jon Froehlich win College of Engineering Awards

Each year, the University of Washington College of Engineering recognizes the dedication and drive of its students, teaching and research assistants, staff and faculty by honoring a select few with a College of Engineering Award. This year, the Allen School has three recipients: web computing specialist Della Welch won the Professional Staff Award, professor Linda Shapiro earned the College of Engineering Faculty Research Award and professor and alumnus Jon Froelich (Ph.D., 11) received the College of Engineering Outstanding Faculty Member Award.  

Della Welch

Della Welch

The College recognized Welch for her excellent customer service, resourcefulness, innovation and creativity as a member of the Computer Science Laboratory group, which oversees all information technology assets and services across the Allen School. Welch first joined the Allen School in 2016 as an undergraduate working as a student lab assistant while studying information systems in the Foster School of Business. Impressed by her reliability and dedication to her job, the team hired her full-time after graduation as an education tools specialist. According to Dan Boren, the Allen School’s applications systems engineer, Welch surprised them with her work ethic while still a student.

“By her last year before graduation, Della had formed a detailed and comprehensive image of our business processes and challenges. She took the initiative to root out wasted effort and energy, and began to design processes and tools to boost the efficiency of our employees and the convenience of our customers,” he said. “It began with a simple, self-service touch screen application to let people check out loaner equipment. This clever tool that she built in her spare time proved that she had a wise and insightful perspective, and the decision was made to try to attract her as a full-time developer when she graduated.”


Shortly thereafter, construction on the Allen School’s second building, the Bill & Melinda Gates Center, finished up and Welch joined a team of her peers to undertake a monumental project: orchestrating the move of people, labs and equipment into the new space. According to Boren, Welch was everywhere they needed her, pulling cables from behind desks, using her systems administration skills, and educating herself in key areas such as user-interface design, modern software engineering methodologies and the latest application frameworks — all of which proved to be quite useful in early 2020 when the pandemic hit.

With the rapid move to remote work, Welch and her team had to rework admissions processes. She dived into what Boren said was an incredibly complex piece of software to modernize and streamline it, taking good user design and testability to new levels to get through the admissions season. What she created in an emergency ended up being more useful and maintainable than what was already in place. Welch also volunteered to write a software registry to be shared with and used by IT teams across the entire university. 

“Through all of this, her systems administration skills have been invaluable, and somehow she finds the time to reprise her role as the cheerful help-desk person,” Boren said. “When you post an opening for a new hire, Della is the person you’re hoping will apply.”

Linda Shapiro

Linda Shapiro

The College honored Shapiro, who holds a joint appointment in the Allen School and the Department of Electrical & Computer Engineering and is also an adjunct professor of biomedical informatics and medical education, for her extraordinary and innovative contributions to research and support of diverse students in research. Since she first arrived at the UW in 1986, Shapiro has cultivated a reputation as a highly regarded researcher in multiple technical fields, a creative and open collaborator across many disciplines and an accomplished, caring mentor. She has been working in these fields for 48 years, has written nearly 300 research papers and has supervised the Ph.D. theses of 48 doctoral students

Shapiro’s research is in computer vision with related interests in image and multimedia database systems, artificial intelligence — search, reasoning, knowledge representation and learning — and applications in medicine and robotics. Her contributions in graph-based matching, computer-aided-design model-based vision, image retrieval, and medical image analysis have been fundamental, leading up to her recent work in facial expression recognition, cancer biopsy analysis, 3D face and head analysis and reconstruction, and object segmentation in videos. Her research has led to collaborations with medical doctors and engineers in a variety of fields from many institutions of higher education.

While Shapiro’s research career itself is highly impressive, her connection with her students is equally profound.

“Linda puts her students and their interests first. She is committed to helping them think broadly about research and personal goals and to strategically choose projects that further those goals,” said Magdalena Balazinska, professor and director of the Allen School. “This is demonstrated by her ability to define and promote new collaborations across groups, departments, and  institutions, which help her students gain needed domain knowledge and technical expertise.”

In fields with few women pursuing Ph.D.’s, Shapiro has recruited and advised 22 female students. She is committed to increasing diversity in the College and has mentored undergraduates via the Distributed Research Experiences for Undergraduates  (DREU) program, a highly selective national program whose goal is to increase the number of people from underrepresented groups that go to graduate school in the fields of computer science and engineering, for 15 years.

Shapiro earned a B.S. in mathematics from the University of Illinois (‘70), and an M.S. (‘72) and Ph.D. (‘74) in computer science from the University of Iowa, before joining the computer science faculty at Kansas State University in 1974. In 1979 she served on the CS faculty of Virginia Polytechnic Institute and State University for five years, then spent two years as the director of Intelligent Systems at Machine Vision International in Ann Arbor, Michigan, before joining the UW faculty in what was then known as the Department of Electrical Engineering. She joined the  Allen School four years later. 

Shapiro is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the International Association of Pattern Recognition. She is the recipient of several Best Paper Awards and Honorable Mentions from the International Association of Pattern Recognition and received a Best Paper Award at the 2012 International Conference on Medical Image Computing and Computer Assisted Intervention on medical content-based image retrieval.

Jon Froehlich

Jon Froehlich

The College honored Froehlich for his innovative and creative approaches to supporting remote learning and research during the pandemic — and for going above and beyond, in big and small ways, to support his community during this time.

Froehlich is the director of the Allen School’s Makeability Lab, where faculty and students design, build and study interactive tools and techniques to address pressing societal challenges. He also serves as the associate director of the Center for Research and Education on Accessible Technology (CREATE), an interdisciplinary center at the UW focused on making technology accessible and making the world more accessible through technology. 

The courses Froehlich teaches and the research he conducts fundamentally depend on physical resources and learning experiences; even so, he did not let the pandemic stop him from offering the same quality of education to his students wherever they happened to be. Froehlich transformed his physical computing courses to virtual platforms and, along with Ph.D. student Liang He, assembled and mailed hardware kits for the courses to his students’ homes. He created a fully interactive website with tutorials and videos using a green screen in his home office, which he turned into a virtual teaching studio. Froehlich’s passion for teaching and dedication to making the experience better for his students were appreciated and noted by his students in their course evaluations. 

In addition to his commitment to effectively teach his students regardless of their location, Froehlich also served as a strong mentor to them in a time when many felt isolated from family and friends. 

“During this time, Jon has prioritized mental and physical health in his group and purchased additional resources to equip his graduate students’ homes with the equipment they require, including 3D printers, soldering irons, chairs, and computers,” said Balazinska. “Moreover, to enable students to continue their research agendas, he helped secure multiple remote internships for his students and has weekly one-on-ones with each student and “game hours” with his group to help his students relax, socially interact, and continue to bond during the pandemic.”

Froehlich has also been committed to helping colleagues improve their virtual classrooms. He co-created an international working group, “Teaching physical computing remotely,” which meets to discuss challenges and solutions for teaching physical and other computational craft courses online. He’s also co-chairing the 2022 International ACM SIGACCESS Conference on Computers and Accessibility and is committed to making the future of the conference a place not only for those with physical or sensory disabilities but for those with chronic illnesses, caretaking responsibilities or other commitments that prevent physical travel.

Froehlich joined the Allen School, his alma mater, as a professor in 2017. Before that, he was a professor of computer science at the University of Maryland, College Park. He previously earned his M.S. in Information and Computer Science at the University of California, Irvine.  

Froehlich has earned a Sloan Research Fellowship and an NSF CAREER Award and has published more than 70 scientific peer-reviewed publications, including seven Best Papers and eight Best Paper Honorable Mentions. His team’s paper on Tohme earned a place on ACM Computing Reviews’ “Best of Computing 2014” list. 

Congratulations to Della, Linda and Jon! 

June 7, 2021

Allen School researchers discover medical AI models rely on “shortcuts” that could lead to misdiagnosis of COVID-19 and other diseases

Chest x-ray
Source: National Institutes of Health Clinical Center*

Artificial intelligence promises to be a powerful tool for improving the speed and accuracy of medical decision-making to improve patient outcomes. From diagnosing disease, to personalizing treatment, to predicting complications from surgery, AI could become as integral to patient care in the future as imaging and laboratory tests are today. 

But as Allen School researchers discovered, AI models — like humans — have a tendency to look for shortcuts. In the case of AI-assisted disease detection, such shortcuts could lead to diagnostic errors if deployed in clinical settings.

In a new paper published in the journal Nature Machine Intelligence, a team of researchers in the AIMS Lab led by Allen School professor Su-In Lee examined multiple models recently put forward as potential tools for accurately detecting COVID-19 from chest radiography (x-ray). They found that, rather than learning genuine medical pathology, these models rely instead on shortcut learning to draw spurious associations between medically irrelevant factors and disease status. In this case, the models ignored clinically significant indicators in favor of characteristics such as text markers or patient positioning that were specific to each dataset in predicting whether an individual had COVID-19. 

According to graduate student and co-lead author Alex DeGrave, shortcut learning is less robust than genuine medical pathology and usually means the model will not generalize well outside of the original setting.

Portrait of Alex Degrave
Alex DeGrave

“A model that relies on shortcuts will often only work in the hospital in which it was developed, so when you take the system to a new hospital, it fails — and that failure can point doctors toward the wrong diagnosis and improper treatment,” explained DeGrave, who is pursuing his Ph.D. in Computer Science & Engineering along with his M.D. as part of the University of Washington’s Medical Scientist Training Program (MSTP). 

Combine that lack of robustness with the typical opacity of AI decision-making, and such a tool could go from potential life-saver to liability.  

“A physician would generally expect a finding of COVID-19 from an x-ray to be based on specific patterns in the image that reflect disease processes,” he noted. “But rather than relying on those patterns, a system using shortcut learning might, for example, judge that someone is elderly and thus infer that they are more likely to have the disease because it is more common in older patients. The shortcut is not wrong per se, but the association is unexpected and not transparent. And that could lead to an inappropriate diagnosis.”

The lack of transparency is one of the factors that led DeGrave and his colleagues in the AIMS Lab to focus on explainable AI techniques for medicine and science. Most AI is regarded as a “black box” — the model is trained on massive data sets and spits out predictions without anyone really knowing precisely how the model came up with a given result. With explainable AI, researchers and practitioners are able to understand, in detail, how various inputs and their weights contributed to a model’s output.

Portrait of Joseph Janizek
Joseph Janizek

The team decided to use these same techniques to evaluate the trustworthiness of models that had recently been touted for what appeared to be their ability to accurately identify cases of COVID-19 from chest radiography. Despite a number of published papers heralding the results, the researchers suspected that something else may be happening inside the black box that led to the models’ predictions. Specifically, they reasoned that such models would be prone to a condition known as worst-case confounding, owing to the paucity of training data available for such a new disease. Such a scenario increased the likelihood that the models would rely on shortcuts rather than learning the underlying pathology of the disease from the training data.

“Worst-case confounding is what allows an AI system to just learn to recognize datasets instead of learning any true disease pathology,” explained co-lead author Joseph Janizek, who, like DeGrave, is pursuing a Ph.D. in the Allen School in addition to earning his M.D. “It’s what happens when all of the COVID-19 positive cases come from a single dataset while all of the negative cases are in another.

“And while researchers have come up with techniques to mitigate associations like this in cases where those associations are less severe,” Janizek continued, “these techniques don’t work in situations you have a perfect association between an outcome such as COVID-19 status and a factor like the data source.” 

The team trained multiple deep convolutional neural networks on radiography images from a dataset that replicated the approach used in the published papers. They tested each model’s performance on an internal set of images from that initial dataset that had been withheld from the training data and on a second, external dataset meant to represent new hospital systems. The found that, while the models maintained their high performance when tested on images from the internal dataset, their accuracy was reduced by half on the second, external set — what the researchers referred to as a generalization gap and cited as strong evidence that confounding factors were responsible for the models’ predictive success on the initial dataset. The team then applied explainable AI techniques, including generative adversarial networks (GANs) and saliency maps, to identify which image features were most important in determining the models’ predictions. 

Figure from paper showing three x-ray images paired with color-coded saliency maps indicating weight of factors in model prediction
The team used explainable AI to visualize the image factors that influenced neural network models’ predictions of COVID-19 status based on chest radiography. Here, saliency maps reveal the models’ tendency to emphasize diagnostically irrelevant features such as laterality tokens, image corners or the patient’s diaphragm in addition to — or instead of — the lung fields when making their predictions.^

When the researchers trained the models on the second dataset, which contained images drawn from a single region and was therefore presumed to be less prone to confounding, this turned out to not be the case; even those models exhibited a corresponding drop in performance when tested on external data. These results upend the conventional wisdom that confounding poses less of an issue when datasets are derived from similar sources — and reveal the extent to which so-called high-performance medical AI systems could exploit undesirable shortcuts rather than the desired signals.

Despite the concerns raised by the team’s findings, DeGrave said it is unlikely that the models they studied have been deployed widely in the clinical setting. While there is evidence that at least one of the faulty models – COVID-Net – was deployed in multiple hospitals, it is unclear whether it was used for clinical purposes or solely for research.

“Complete information about where and how these models have been deployed is unavailable, but it’s safe to assume that clinical use of these models is rare or nonexistent,” he noted. “Most of the time, healthcare providers diagnose COVID-19 using a laboratory test (PCR) rather than relying on chest radiographs. And hospitals are averse to liability, making it even less likely that they would rely on a relatively untested AI system.”

Janizek believes researchers looking to apply AI to disease detection will need to revamp their approach before such models can be used to make actual treatment decisions for patients.

“Our findings point to the importance of applying explainable AI techniques to rigorously audit medical AI systems,” Janizek said. “If you look at a handful of x-rays, the AI system might appear to behave well. Problems only become clear once you look at many images. Until we have methods to more efficiently audit these systems using a greater sample size, a more systematic application of explainable AI could help researchers avoid some of the pitfalls we identified with the COVID-19 models,” he concluded.

Janizek, DeGrave and their AIMS Lab colleagues have already demonstrated the value of explainable AI for a range of medical applications beyond imaging. These include tools for assessing patient risk factors for complications during surgery, which appeared on the cover of Nature Biomedical Engineering, and targeting cancer therapies based on an individual’s molecular profile, as described in a paper published in Nature Communications.

Su-In Lee holding pen with coffee mug and laptop
Su-In Lee

“My team and I are still optimistic about the clinical viability of AI for medical imaging. I believe we will eventually have reliable ways to prevent AI from learning shortcuts, but it’s going to take some more work to get there,” Lee said. “Going forward, explainable AI is going to be an essential tool for ensuring these models can be used safely and effectively to augment medical decision-making and achieve better outcomes for patients.”

The team’s paper, “AI for radiographic COVID-19 detection selects shortcuts over signal,” is one of two from the AIMS Lab to appear in the current issue of Nature Machine Intelligence. Lee is also the senior and corresponding author on the second paper, “Improving performance of deep learning models with axiomatic attribution priors and expected gradients,” for which she teamed up with Janizek, his fellow M.D.–Ph.D. student Gabriel Erion, Ph.D. student Pascal Sturmfels, and affiliate professor Scott Lundberg (Ph.D., ‘19) of Microsoft Research to develop a robust and flexible set of tools for encoding domain-specific knowledge into explainable AI models through the use of attribution priors. Their framework supports the widespread adoption of techniques that will improve model performance and increase computational efficiency in AI for medicine and other areas of applied machine learning. 

Also see a related GeekWire article here.

* Image sourced from the National Institutes of Health (NIH) Clinical Center and used with permission: Wang, X. et al. “ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. https://nihcc.app.box.com/v/ChestXray-NIHCC

^ Figure 2a (bottom) adapted with permission from Winther, H. et al. COVID-19 image repository. figshare. https://doi.org/10.6084/m9.figshare.12275009

June 1, 2021

Anna Karlin receives ACM Paris Kanellakis Theory and Practice Award

Collage of award recipients' portraits with ACM logo
Top, from left: Yossi Azar, Andrei Broder and Anna Karlin; bottom, from left: Michael Mitzenmacher and Eli Upfal

Professor Anna Karlin and a team of collaborators have received the ACM Paris Kanellakis Theory and Practice Award from the Association for Computing Machinery for their work on balanced allocations, also known as the “power of two choices.” Karlin, a member of the Allen School’s Theory of Computation group, first introduced the balanced allocations paradigm with co-authors Yossi Azar of Tel Aviv University, Andrei Broder of Google Research, and Eli Upfal of Brown University at the 1994 ACM Symposium on Theory of Computing (STOC) in what the ACM has described as “elegant theoretical results” that continue to have a demonstrable impact on the practice of computing. 

The team’s paper considers a basic “balls-in-bins” problem: It has long been known that when n balls are tossed one at a time into n bins, independently and uniformly at random, the expected value of the maximum load is about log(n)/loglog(n). Karlin and her colleagues showed that balancing the allocation of the balls by first selecting not one, but two bins at random, and then placing the ball in the bin that has the lesser load between the two reduces the expected maximum load across all the bins to loglog(n) — an exponential improvement.

Fellow honoree Michael Mitzenmacher of Harvard University later significantly extended these initial results.

“Since bins and balls are the basic model for analyzing data structures, such as hashing or processes like load balancing of jobs in servers, it is not surprising that the power of two choices that requires only a local decision rather than global coordination has led to a wide range of practical applications,” the ACM observed in a press release. “These include i-Google’s web index, Akamai’s overlay routing network, and highly reliable distributed data storage systems used by Microsoft and Dropbox, which are all based on variants of the power of two choices paradigm.”

Karlin’s Allen School colleague, Paul Beame, recalls that it was “stunning and unexpected” that such a simple change in the algorithm would yield such a dramatic improvement — one that has proven to have an enduring impact within the field.

“One can easily ensure balanced allocations if one has prior knowledge about the pattern of requests or some form of centralized control but, without that, finding good balance becomes difficult. The typical solution before the work of Anna and her co-authors allocated each request based on a single random choice of a resource. However, unless the system is vastly over-provisioned, this will result in a somewhat bad overall imbalance,” explained Beame. “What Anna and the team showed is that a simple adjustment — making two random choices rather than just one, and keeping the better of the two — results in an exponential improvement in the overall balance, effectively at most a small constant for all reasonable data sizes.

“In the last couple of decades, their paper has led to much follow-on research,” Beame noted. “The ‘power of two choices’ has since become one of the essential new paradigms in the design of dynamic data structures and algorithms and is regularly taught in graduate courses worldwide.”

Prior to winning the ACM Paris Kanellakis Award, Karlin recently became the first Allen School faculty member to be elected to the National Academy of Sciences for career contributions to algorithms and algorithmic game theory. She is one of two Allen School faculty members to be recognized with ACM technical awards this week, as Shyam Gollakota received the ACM Grace Murray Hopper Award for his work on wireless sensing and communication. 

Read the ACM announcement here, and learn more about the Paris Kanellakis Theory and Practice Award here.

Congratulations, Anna!

May 26, 2021

Shyam Gollakota wins ACM Grace Murray Hopper Award

Portrait of Shyam Gollakota

Allen School professor Shyam Gollakota received the ACM Grace Murray Hopper Award from the Association for Computing Machinery for “contributions to the use of wireless signals in creating novel applications, including battery-free communications, health monitoring, gesture recognition, and bio-based wireless sensing.” Each year, the ACM Grace Murray Hopper Award honors an early-career professional in computing who has made a major technical or service contribution to the field before the age of 35. 

As director of the Allen School’s Networks & Mobile Systems Lab, Gollakota advances big ideas in compact, energy-efficient form factors to expand the Internet of Things (IoT). Among his earliest contributions was ambient backscatter, a groundbreaking technique he pioneered with Allen School colleague and electrical engineering professor Joshua Smith. Backscatter essentially produces power out of thin air by harvesting television, WiFi, and other wireless signals to enable battery-free computation and communication. The team later refined and expanded their work to enable transmissions over greater distances and via embedded devices with long-range backscatter, and even produced a prototype of the world’s first battery-free cellphone. Gollakota also showed how backscatter communication could be accomplished without electronics with the introduction of 3D printed smart objects. The researchers started a venture-backed company, Jeeva Wireless, to commercialize their work.

Gollakota has also tapped into the sensing capabilities of smartphones to develop a series of health screening and monitoring tools in collaboration with clinicians at UW Medicine. These include a non-invasive app for detecting fluid in the ear — a symptom of ear infection and one of the most common reasons for visits to the pediatrician — and one for detecting obstructive sleep apnea that was subsequently commercialized by ResMed. He also showed how smartphones can be powerful tools for tackling broader public health crises with Second Chance, an app that employs sonar to monitor a person’s breathing and movements for signs of a potential opioid overdose. Gollakota and his colleagues have since explored how other smart devices, such as Amazon’s Alexa smart speaker, can be used for home health monitoring. To that end, he and his team have developed new smart speaker skills to detect if a person has an irregular heart rhythm or is experiencing a cardiac emergency and also to monitor a baby’s breathing during sleep. He co-founded two companies, Sound Life Sciences and Wavely Diagnostics, to commercialize this work.

A common thread running throughout Gollakota’s research is his creativity in addressing real-world problems while pushing the limits of what was previously thought possible through technology.

“Simply put, Shyam is amazing — he is easily the most creative person I have ever met,” said Allen School professor Thomas Anderson. “He repeatedly invents and builds prototypes that, before you see them demonstrated, you would have thought impossible. I do not know of any junior faculty member, in any area of computer science, whose work has had greater practical impact on our understanding of how to build useful systems.”

Lately, those systems have bridged the digital and natural worlds in Gollakota’s efforts to enable wireless sensing and computation to take off — literally as well as figuratively. In a series of projects that can be described as “living IoT,” Gollakota and his colleagues attached sensors to bees to demonstrate a system for wireless data transmission with applications in agriculture and environmental monitoring; outfitted a beetle with a steerable robotic camera that can be used to track moving objects and live-stream images to a smartphone; and developed a lightweight sensor that can be transported to hard-to-reach locations by moths in a step towards creating the Internet of Biological Things. Gollakota also has taken inspiration from nature to build insect-sized robots capable of wireless flight in a collaboration with mechanical engineering professor Sawyer Fuller.

“His work has revolutionized and re-imagined what can be done using wireless systems and has a feel of technologies depicted in science fiction novels,” the ACM said of Gollakota in a press release.

The ACM Grace Murray Hopper Award is accompanied by a monetary prize of $35,000, which Gollakota has opted to donate in support of LGBTQIA+ students in the Allen School.

“We are extremely proud of Shyam and his research group. They produce incredibly creative and innovative technology that also strives to address fundamental environmental and societal problems,” said Magdalena Balazinska, professor and director of the Allen School. “On a personal level, I am proud to call Shyam a colleague and am very happy to see him recognized with this award not only for his technical contributions, but also for his service as a mentor and role model for future innovators in our field.”

Gollakota is the second Allen School faculty member to receive the Grace Murray Hopper Award. The ACM previously recognized professor Jeffrey Heer in 2017 for his work on leading-edge visualization tools for exploring and understanding data.

Read the ACM announcement here, and learn more about the Grace Murray Hopper Award here. In addition, see our story on professor Anna Karlin receiving the ACM Paris Kanellakis Theory and Practice Award, also announced today, for her work on balanced allocation or the “power of two choices.”

Congratulations, Shyam!

May 26, 2021

UW recognizes three Allen School undergraduates in the Husky 100

Husky 100 banner

Allen School undergraduates Nayha Auradkar, Melissa Birchfield and Raida Karim have been selected for the 2021 class of the Husky 100. Each year the program honors 100 University of Washington students across its three campuses who are making the most of their time as Huskies to have a positive impact on the UW community. 

Nayha Auradkar

Nayha Auradkar

Nayha Auradkar is a junior from Sammamish, Washington, majoring in computer science with a minor in neural computation and engineering. She aims to create technology that supports accessibility and inclusion for people with disabilities. Her work with Jennifer Mankoff in the Make4all Lab in human-computer interaction (HCI) and accessible technology complements her goals. Outside of the lab, Auradkar is the chair of the UW chapter of the Association for Computing Machinery for Women (ACM-W), working to cultivate a strong, supportive community of women in the Allen School, and is president of Huskies Who Stutter. She previously served as the outreach director of the Society of Women Engineers

“Technology can benefit the world in limitless and profound ways — even more so when everyone’s voice is heard,” Auradkar said. “Throughout my time as a Husky I have worked towards advocating for more equitable communities in tech and beyond.”

Melissa Birchfield

Melissa Birchfield

Melissa Birchfield is a senior also from Sammamish, Washington, majoring in computer science with UW Interdisciplinary Honors. Birchfield worked as a teaching assistant for several CS and Human Centered Design & Engineering (HCDE) courses and has led outreach projects through Alternative Spring Break. She has contributed to research as part of the Programming Languages & Software Engineering (PLSE) group and worked with Ph.D. student Eunice Jun  in the Interactive Data Lab to facilitate statistical analysis for end-users. She has also explored the intersection of HCI and machine learning in HCDE’s Inclusive Design Lab. She is an anti-human trafficking advocate and author of “Data for Dignity: Leveraging Technology in the Fight Against Human Trafficking” examining the role of data in cross-sector collaboration to combat human trafficking.

“My time at UW has challenged me to embrace new experiences while clarifying my heart for outreach and innovation,” Birchfield said. “As an educator, researcher, author and anti-trafficking advocate, I pursue ways to bridge gaps in communities by leveraging technology to open unexplored opportunities.”

Raida Karim

Raida Karim

Raida Karim is a fourth year computer science major from Dhaka, Bangladesh. She’s a researcher working with Allen School professor Maya Cakmak in the Human-Centered Robotics Lab. Karim is focused on meeting critical human needs and achieving social good, which has inspired her to create technologies like a robot that measures stress levels in teens and develops some therapeutic, intervening techniques to help them. She served as a program lead in Mentor Power for Success in the Office of Minority Affairs & Diversity, a student leader in the UW chapter of the Association for Computing Machinery, and worked as a teaching assistant in the Allen School’s direct admit seminar. Off campus, she was an intern at Cisco and is currently an X-Force Fellow sponsored by the National Security Innovation Network. 

“My personal background as a woman of color and an immigrant allows me to embrace all differences in humanity,” Karim said. “I hope my visibility and persistence demonstrates that hard work, determination and self-worth are keys to tackling odds in engineering, when there are no familiar faces or legacies.”

View the 2021 Class of the Husky 100 here.

Congratulations, Nayha, Melissa and Raida! 

May 25, 2021

Allen School alumni and friends honored with College of Engineering Diamond Awards

Dadgar, Hashimoto, Israel
Left to right: Dadgar, Hashimoto, Israel

Allen School alumni Armon Dadgar (B.S. ‘11) and Mitchell Hashimoto (B.S. ‘11) and long-time Allen School friend and University of Washington alumnus Allen Israel (B.S. ‘68 Mechanical Engineering, MBA ‘71, J.D. ‘78) have been honored with 2020 Diamond Awards from the College of Engineering. Dadgar and Hashimoto were recognized with the Early Career Achievement Award, given to outstanding graduates who have made exceptional professional contributions to engineering through research, teaching or service within the first 10 years of their careers. Israel earned the Dean’s Award for extraordinary contributions to the advancement of engineering.

Dadgar and Hashimoto are the co-founders of HashiCorp, which helps companies to address fundamental challenges related to infrastructure, applications, networking and security in the cloud. The company’s software offerings, including Consul, Terraform and Vault, have become hugely popular tools for automating processes that are used by organizations worldwide. The early vision for HashiCorp — now a $5.2 billion company — was originally hatched when the two were Allen School undergraduates.

Dadgar and Hashimoto initially met while collaborating on a research project. Afterward, Dadgar reached out to Hashimoto to see if he wanted to work on something fun outside of the classroom.

“We were passionate about creating something people needed,” Hashimoto said. “What makes our company unique is that it’s open-source and free. But we never expected to make a career out of it, let alone start a successful company. We are still surprised at how far we’ve come.”

Hashimoto said they loved the idea of automating repetitive tasks. Their plan was to build a program based around cloud infrastructure that would enable developers to eliminate some of the more tedious manual processes needed in cloud computing through a self-service automation tool.

“When Mitchell and I started HashiCorp, our mission was really focused on building great products that we ourselves would enjoy using,” Dadgar said. “It was about solving an immediate problem that we had personally experienced, and we didn’t have a clear business plan going into it. It has been an incredible journey at HashiCorp, especially figuring out how to transition from making tools people like into being a company that organizations depend on.”

Their creation grew from two students working in a basement on campus in their spare time, to young college graduates working from IKEA desks in Dadgar’s living room, to a business with more than 1,300 employees that serves some of the biggest companies in the world. The duo had agreed to give the business a year to take off before selling the desks and taking traditional jobs as software engineers. There was no need to sell the desks.

“I think it’s safe to say it has exceeded our wildest imaginations. From an adjustment phase, I like to joke that every quarter I need to figure out what my job is again,” Dadgar said. “Given our growth, our roles continue to evolve dramatically. In the beginning, Mitchell and I would write code full time – we personally authored many of our initial products. I don’t think I’ve written any software for at least three years now, but instead spend much more time on hiring, customers, partners, and running the business.”

The developers-turned-business-leaders took a lot of risks in the beginning to see their vision for HashiCorp come to fruition.

“It wasn’t a smooth transition, we had a lot of ups and downs,” Hashimoto acknowledged. “We had stages where the company was doing well and not doing well. Personally, we’ve had to adapt and learn to recognize where we’re operating smoothly and providing value, and where we don’t enjoy and don’t provide any value.”

Hashimoto said he still enjoys the work and he’s driven by a desire to identify other areas where automated tools will make people’s lives easier and to continue building new products. Dadgar is also still 100%  committed to HashiCorp; with the company’s continued success, he and his husband, Josh Kalla, are interested in pursuing more philanthropic endeavors. They’ve already given more than $3.5 million to the UW’s Office of Minority Affairs & Diversity for student support.

“With any big success like HashiCorp, there is always a mix of hard work, opportunity, and luck that is necessary. We’ve been so fortunate in our lives, that now it is our turn to help create opportunities for others,” Dadgar said. “The OMA&D really was a great fit for our interests, because they focus on first-generation, underrepresented, and financially challenged students. Those are the groups we feel need to have more luck and opportunities, and so working with OMA&D to create a scholarship was a great fit.”

Dan Grossman, professor and vice director of the Allen School, said the combination of business and technical skills from such young alumni is especially notable. “Mitchell and Armon have demonstrated a unique combination of applying deep computer science, customer focus, and business skills to create a series of technologies, spanning a wide range of distributed systems solutions, that are pragmatic and loved by customers,” Grossman said. “And they’ve managed in a very short time to create a successful business based on these developments.”

While Dadgar and Hashimoto are in the early stages of their professional and philanthropic journeys, their fellow Diamond Award honoree, Allen Israel, has had a lifetime of impact on the lives of others.

After completing his UW undergraduate degree in Mechanical Engineering, Israel worked as an engineer at Boeing Commercial Airplanes before returning to his alma mater to earn his MBA followed by his J.D. After law school, Israel joined Foster Garvey PC, where he has spent more than 40 years practicing law, primarily in business and mergers and acquisitions. In 2013, he was named “Lawyer of the Year” in Seattle by Best Lawyers M&A.

In the course of his work, Israel represented nonprofit and individual clients in corporate, business, real estate and contract law. One of those individuals was Paul G. Allen, who Israel served as personal attorney from the early 1980s until Mr. Allen’s death in 2018. Among many other activities, Israel represented Mr. Allen in making many transformative gifts to the University of Washington — including the naming gift for the Paul G. Allen Center for Computer Science & Engineering, and the gift that enabled the creation of the Paul G. Allen School of Computer Science & Engineering.

“Allen has been instrumental in helping us to expand our leadership in the field and our impact around the world,” said Magda Balazinska, Professor and Director of the Allen School. “He played a central role in the construction of the Allen Center, our first permanent home, and in the creation of the Allen School on the occasion of our 50th anniversary. He also represents the Allen School on the University of Washington Foundation Board.”

Throughout his career, Israel has made time to champion and provide leadership in many areas of his alma mater. For nearly 25 years he served as a member of the College of Engineering Dean’s Visiting Committee, providing strategic counsel to four successive leaders. He also has served on the UW Law School Dean’s Advisory Committee and the Law School Foundation Board.

“Allen Israel has been a tremendous friend of the Allen School, the College of Engineering, and the University of Washington for many decades,” said Ed Lazowska, Professor and Bill & Melinda Gates Chair Emeritus in the Allen School. “He is an engineer, an MBA, a lawyer, and a role model.”

The College formally honored the 2020 Diamond Award recipients in a virtual celebration last week after having postponed the event due to the pandemic.

Congratulations to Allen, Mitchell and Armon, and thank you for your continued friendship to the Allen School and our students!

May 24, 2021

Shayan Oveis Gharan receives EATCS Presburger Award for groundbreaking contributions to the Traveling Salesperson Problem

Portrait of Shayan Oveis Gharan

Professor Shayan Oveis Gharan, a member of the Allen School’s Theory of Computation group, earned the 2021 Presburger Award for Young Scientists from the European Association for Theoretical Computer Science (EATCS) for his research on the Traveling Salesperson Problem (TSP). Each year, the EATCS bestows the Presburger Award on an early-career scientist who has made outstanding contributions in the field of theoretical computer science. In its unanimous selection of Oveis Gharan for this year’s honor, the award committee heralded his “creative, profound, and ambitious” work on a fundamental problem that has advanced scientists’ understanding of the design and analysis of algorithms.

“Shayan is a leader in the application of algebraic and spectral methods to classical problems in combinatorial optimization, and he’s the architect of a series of surprising and profound developments in the theory of algorithms,” said Allen School professor James Lee. “He exhibits a remarkably consistent ability to make progress on important problems that had remained open for decades.”

That progress began when Oveis Gharan was a Ph.D. student at Stanford University, where he and his collaborators produced an approximation algorithm that offered the first asymptotic improvement on TSP in the asymmetric case in three decades. It has culminated — so far, at least — in the first performance improvement on metric TSP in nearly half a century. In between, Oveis Gharan also contributed to the first improvement over Christofides’ 3/2-approximation for the symmetric graph case of TSP, first put forward in 1976, in what the Presburger Award committee describes as a “remarkable tour de force.” Over the course of his career, Oveis Gharan has continued to develop and expand the concept of “negative dependence” between the presence of edges in a certain distribution on random spanning trees of a graph — a tool he first applied to great effect in his initial contribution to TSP as a student — to push the field forward. 

For his latest milestone, Oveis Gharan worked with Allen School Ph.D. student Nathan Klein and faculty colleague Anna Karlin to devise an approximation algorithm capable of returning a solution that surpasses 50% of the optimum for the very first time. The team will receive a Best Paper Award at the Association for Computing Machinery’s upcoming Symposium on the Theory of Computing (STOC 2021) for their groundbreaking achievement. According to Karlin, Oveis Gharan stands out not only for his technical contributions, but also for the way he approaches his research.

“Shayan is an exceptionally brilliant, ambitious and fearless researcher,” said Karlin. “What blows my mind is that, on top of all of that, he is also one of the kindest, most generous people I know. Every meeting with Shayan and our students lifts my spirits because he brings such enthusiasm, warmth and positivity to his work.”

The central question of TSP — how to determine the shortest and most efficient route between multiple destinations and back to the starting point — is more than a theoretical problem. It has multiple real-world applications across a variety of domains, from planning and scheduling, to supply chain logistics, to microchip manufacturing. It also has provided an ideal vehicle for Oveis Gharan to apply his expertise in analysis, probability and combinatorics to push the theoretical limits of computation and enable progress in other fields while providing the inspiration and the tools for other young researchers to follow his lead.

“Shayan’s enthusiasm for what he does is infectious, and he has helped me gain a new appreciation of computer science. He communicates to his students a strong sense that we are not here just to solve problems but to learn, grow, and discover new ideas,” Klein said. “He also has a great ability to zoom out and synthesize. For example, during the TSP project I came to him with a mess of proofs of a dozen probabilistic lemmas we needed. He immediately recognized a common theme and extracted an elegant theorem that characterized all of these lemmas and more. This theorem ended up being quite useful in guiding our understanding for the remainder of the project.”

While the challenge of TSP may hold particular fascination for Oveis Gharan, it is not the only open problem on which he has made notable progress in recent years. In 2019, he earned a STOC Best Paper Award for his work with Allen School Ph.D. student Kuikui Liu and collaborators on the first fully polynomial randomized approximation scheme (FPRAS) for counting the bases of a matroid. Drawing from several seemingly unrelated areas of mathematics and theoretical computer science — namely Hodge theory for combinatorial geometries, analysis of Markov chains, and high dimensional expanders — the team applied a novel theory of spectral negative dependence to prove a conjecture by Mihail and Vazirani that had remained an open question for 30 years. As a follow-up, members of that team applied some of those same insights to sampling an independent set from the hardcore model. Their results addressed a 25-year-old open problem concerning the mixing time of Glauber dynamics by proving that, for any graph, they mix in polynomial time up to the tree uniqueness threshold.

Since his arrival at the University of Washington in 2015, Oveis Gharan has earned a Sloan Research Fellowship, a CAREER Award from the National Science Foundation, an ONR Young Investigator Award from the Office of Naval Research, and a Google Faculty Research Award. In 2016, Science News magazine named him one of “10 Scientists to Watch” for his work on TSP. Oveis Gharan will collect his latest honor virtually during the upcoming annual meeting of the EATCS, the International Colloquium on Automata, Languages and Programming (ICALP 2021), in July.

Read the EATCS announcement here, and learn more about the Presburger Award here.

Congratulations, Shayan!

May 20, 2021

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