Allen School News

In the spring of 2020, people took to the streets — and to the tweets — in protest after a white police officer murdered George Floyd, a Black man, in Minneapolis by kneeling on his neck and back for over nine minutes. Black Lives Matter, a movement spawned seven years earlier following the shooting death of Trayvon Martin, an unarmed Black teenager, in Florida and the killer’s subsequent acquittal, emerged as the online and offline rallying cry against police brutality and racist violence perpetrated against Black people. 

Following Floyd’s death, Twitter users flooded the social media platform with hundreds of millions of posts expressing a range of emotions concerning Black Lives Matter and the campaign for racial justice. A team led by Allen School professor Yulia Tsvetkov sought to facilitate a greater understanding of the connection between those emotional expressions and the narrative surrounding Black Lives Matter and its supporters — and how that connection could also shed light on the role of social media messaging in online activism and large-scale social movements, more generally. To that end, the researchers applied recent advances in natural language processing to analyze the content of 34 million original English-language tweets about BLM posted in 2020 between May 24 and June 28 to identify the prevailing emotions expressed on social media about the movement and associated protests. 

The researchers shared their findings in a paper published this week in the Proceedings of the National Academy of Sciences (PNAS) — findings that counter a harmful yet persistent narrative about the emotional tenor of the BLM movement and the people behind it.

“While we identified high levels of anger and disgust across all posts in our dataset, what jumped out at us was the prevalence of positive emotions in posts containing pro-BLM hashtags such as #BlackLivesMatter, #JusticeforFloyd and #NoJusticeNoPeace, and correlating with on-the-ground protests,” said co-lead author and Stanford University postdoc Anjalie Field, who worked on the project as a visiting UW student and Ph.D. candidate at Carnegie Mellon University. “Positive emotions like hope and optimism are more prevalent in posts with explicitly pro-BLM hashtags than other subsets of the data, which contradicts the stereotype of BLM supporters as promoting anger and outrage.”

To perform their analysis, Field, Tsvetkov and their collaborators developed a neural classification model based on the six core emotions identified by psychologist Paul Ekman: anger, disgust, fear, surprise, positivity — what Ekman refers to as “joy” — and sadness. Consistent with prior work applying Ekman’s taxonomy, the researchers treated each category as a superset of finer grained emotions such as the aforementioned hope and optimism (positivity), disapproval and rage (anger), vigilance and apprehension (fear), confusion and curiosity (surprise), and grief and remorse (sadness). Because neural models trained on a pre-collected data set may perform poorly when used to infer meaning from text gathered in a different domain, the team opted for a domain adaptation approach. By combining task-adaptive pre-training with few-shot learning techniques, their methodology permits the re-use of existing annotated datasets to train the model on different domains, rather than going through the effort and expense of collecting and annotating new datasets. They trained their model on two large, pre-existing social media datasets annotated for expressed emotions and adapted it to tweets relating to Black Lives Matter.

As Field noted, anger was the prevailing emotion identified by the model, which detected its presence in 40% or more of the tweets posted over the course of the month. Disgust and positivity alternated as the second-most prevalent emotions, with surprise, sadness and fear rarely approaching 10%. When the researchers analyzed the subset of 6.5 million tweets containing pro-BLM hashtags, they found that positivity consistently outweighed the other categories starting around four days in — on some days, more than double the negative emotions. 

Drilling down into the dataset according to date enabled the team to identify instances where emotions spiked, presumably in connection with events. For example, anger and sadness peaked in tweets with pro-BLM hashtags in the days following Floyd’s death and prior to the first weekend of protests. Positivity, meanwhile, rose in the days leading up to that weekend and afterward became the most frequently expressed emotion through the rest of the month. Positivity peaked on the Juneteenth holiday, present in 60% of tweets carrying pro-BLM hashtags. By analyzing location data, the researchers also found that the volume of tweets expressing positive emotions positively correlated with users’ proximity to on-the-ground protests.

Such rich analysis from the team’s adaptive neural model contrasts with that of conventional social science analyses, which typically rely on more rigid lexicon-based approaches using a set list of words associated with an emotion to determine whether content reflects that emotion.  

“Lexicon-based models are easy to use, but they aren’t particularly good at capturing broader connotations or adapting to new contexts,” explained co-lead author Chan Young Park, a visiting UW student who is pursuing a Ph.D. from CMU. “For example, one popular model connotes the word ‘police’ with the terms ‘fear,’ ‘positive’ and ‘trust’ — emotions that are unlikely to factor into protests against police brutality. Our framework offers a more robust method for extracting accurate social meaning from text data that can be adapted to different contexts and language varieties.”

The meaning the team extracted from posts using its computational model is consistent with findings from previous social psychology studies; while moral outrage and anger can prompt people to become involved in social movements, positive emotions are necessary to sustain that involvement over time.

“Words and emotions are powerful tools for online activism. Emerging NLP techniques are also powerful tools that can help us understand how those words and emotions contribute to building and sustaining social movements,” said Tsvetkov. “In doing so, they can help us also to dispel negative stereotypes about marginalized communities that lead to physical, social and economic harm.”

Additional contributors to the paper include co-lead author and then-student Antonio Theophilo, who recently earned his Ph.D. from the Institute of Computing at the University of Campinas in Brazil, and Jamelle Watson-Daniels, a Ph.D. student at Harvard University and Director of Research at Data for Black Lives.

Read the full paper here, and a related story by CMU here. Read more →

For people around the world, technology eases the friction of everyday life: bills paid with a few clicks online, plans made and sometimes broken with the tap of a few keys, professional and social relationships initiated and sustained from anywhere at the touch of a button. But not everyone experiences technology in a positive way, because technology — including built-in safeguards for protecting privacy and security — isn’t designed with everyone in mind. In some cases, the technology community’s tendency to develop for a “default persona” can lead to harm. This is especially true for people who, whether due to age, ability, identity, socioeconomic status, power dynamics or some combination thereof, are vulnerable to exploitation and/or marginalized in society.

Researchers in the Allen School’s Security & Privacy Research Lab have partnered with colleagues at the University of Florida and Indiana University to provide a framework for moving technology design beyond the default when it comes to user security and privacy. With a $7.5 million grant from the National Science Foundation through its Secure and Trustworthy Cyberspace (SaTC) Frontiers program, the team will blend computing and the social sciences to develop a holistic and equitable approach to technology design that addresses the unique needs of users who are underserved by current security and privacy practices.

“Technology is an essential tool, sometimes even a lifeline, for individuals and communities. But too often the needs of marginalized and vulnerable people are excluded from conversations around how to design technology for safety and security,” said Allen School professor and co-principal investigator Franziska Roesner. “Our goal is to fundamentally change how our field approaches this question to center the voices of marginalized and vulnerable people, and the unique security and privacy threats that they face, and to make this the norm in future technology design.”

To this end, Roesner and her collaborators — including Allen School colleague and co-PI Tadayoshi Kohno — will develop new security and privacy design principles that focus on mitigating harm while enhancing the benefits of technology for marginalized and vulnerable populations. These populations are particularly susceptible to threats to their privacy, security and even physical safety through their use of technology: children and teenagers, LGBTQ+ people, gig and persona workers, people with sensory impairments, people who are incarcerated or under community supervision, and people with low socioeconomic status. The team will tackle the problem using a three-prong approach, starting with an evaluation of how these users have been underserved by security and privacy solutions in the past. They will then examine how these users interact with technology, identifying both threats and benefits. Finally, the researchers will synthesize what they learned to systematize design principles that can be applied to the development of emerging technologies, such as mixed reality and smart city technologies, to ensure they meet the privacy and security needs of such users.

The researchers have no intention of imposing solutions on marginalized and vulnerable communities; a core tenet of their proposal is direct consultation and collaboration with affected people throughout the duration of the project. They will accomplish this through both quantitative and qualitative research that directly engages communities in identifying their unique challenges and needs and evaluating proposed solutions. The team will apply these insights as it explores how to leverage or even reimagine technologies to address those challenges and needs while adhering to overarching security and privacy goals around the protection of people, systems, and data.

The team’s approach is geared to ensuring that the outcomes are relevant as well as grounded in rigorous scientific theory. It’s a methodology that Roesner, Kohno, and their colleagues hope will become ingrained in the privacy and security community’s approach to new technologies — but they anticipate the impact will extend far beyond their field.

“In addition to what this will mean in terms of a more inclusive approach to designing for security and privacy, one of the aspects that I’m particularly excited about is the potential to build a community of researchers and practitioners who will ensure that the needs of marginalized and vulnerable users will be met over the long term,” said Kohno. “Our work will not only inform technology design, but also education and government policy. The impact will be felt not only in the research and development community but also society at large.”

Kohno and Roesner are joined in this work by PI Kevin Butler and co-PIs Eakta Jain and Patrick Traynor at the University of Florida, co-PIs Kurt Hugenberg and Apu Kapadia at Indiana University, and Elissa Redmiles, CEO & Principal Researcher at Human Computing Associates. The team’s proposal, “Securing the Future of Computing for Marginalized and Vulnerable Populations,” is one of three projects selected by NSF in its latest round of SaTC Frontiers awards worth a combined $24.5 million. The other projects focus on securing the open-source software supply chain and extending the “trusted execution environment” principle to secure computation in the cloud.

Read the NSF announcement here and the University of Florida announcement here. Read more →

After graduating from the University of Washington in December with degrees in computer science and chemical engineering, Allen School alum Isaiah Lemmon (B.S., ‘21) took on a software engineering role at Amazon Web Services. There, he intends to put his education to work advancing energy efficient solutions for the datacenter, inspired in part by his experience as an undergraduate researcher during his time at UW. That experience, combined with his rigorous coursework and achievements inside and outside of the classroom, recently earned him a 2022 Dean’s Medal for Academic Excellence from the College of Engineering — affirming his decision to pursue research across multiple disciplines to prepare himself to make a positive difference in the world.

“I’m honored to have been chosen for this award, and I’m really excited about the opportunity the combination of these two degrees has opened to me,” Lemmon said. “The amount of applicable knowledge and skills that different engineering disciplines have to offer each other is incredible, and I’d strongly encourage other students to pursue interdisciplinary research for that exposure.”

From his early days as an undergraduate student, Lemmon displayed a talent and passion for research and discovery. He applied both to great effect working with professor Jim Pfaendtner, chair of the UW Department of Chemical Engineering, on projects that enabled him to explore his dual interests in molecular science and computation. One such project involved studying interactions between titanium dioxide and water at the solid/liquid interface via ab initio molecular dynamics, for which Lemmon performed programming and data analysis using the UW’s HYAK supercomputing cluster.

“Isaiah approached me when he was fresh out of high school, and I quickly realized that he was an incredibly rare student,” said Pfaendtner. “Before long, Isaiah was a regular in our research group and made impressive gains on several projects related to catalysis, interfacial phenomena and ab initio molecular dynamics. However, it was in his work as a de facto software engineer for my team, during his third year of study, that we really saw Isaiah shine.”

At that time, Pfaendtner was eager to keep Lemmon engaged with his lab by supporting him in his quest to combine his knowledge and skills from both majors. He decided to hire Lemmon to assist lab members with turning their prototype software into usable products that could be deployed in the real world. In one instance, Lemmon rewrote a complex piece of reaction engineering software in Python from scratch to make it more robust and useful for a broader range of projects in the lab. He subsequently collaborated with then-Ph.D. student Sarah Alamdari to make modifications to a community software package that enabled Pfaendtner’s group to make their algorithms more widely available within the research community. Lemmon’s mentor characterized this work as evidence that he had already achieved a level of ability and independence more akin to that of a fourth or fifth  year Ph.D. student than an undergraduate.

Lemmon also did not shy away from tackling advanced coursework, including material that would typically be the preserve of graduate students. For example, he excelled in Pfaendtner’s Mass Transfer and Separations course and Allen School professor Tom Anderson’s Distributed Systems course — two offerings considered by students to be among the most difficult and intellectually challenging within their respective majors. In his final quarter at the UW, Lemmon enrolled in a new and similarly challenging Datacenter Systems course that explores the technologies that go into the construction and operation of large-scale datacenters — described by Anderson as “among the most complex systems that people have ever built.” Even so, Lemmon was undaunted by the subject matter. For the open-ended class project, he and his partners built a system for mapping the wiring layout for a datacenter that surprised and delighted their professor.

“As part of their project, Isaiah and his partners built a truly impressive visualization tool that showed how the different layers of switches could be organized as the size of the datacenter scaled up,” Anderson recalled. “Even though Isaiah was still just an undergrad, I thought his work was among the most ambitious and most interesting in a class of 80 students.”

“For me, it was my passion for programming and clean energy that led to where I am — finding a meaningful way to overlap those fields has been my dream,” Lemmon said. “I’m still trying to find the exact niche I want to settle in and am not yet sure if that will be in industry or academia, but my hope is some combination of the two so I can continue to do impactful research.”

Lemmon is one of two students in the College of Engineering honored with a Dean’s Medal this year; the other is Taylor Juenke, a student in the Department of Materials Science & Engineering. Learn more about the 2022 Dean’s Medalists here.

Congratulations, Isaiah! Read more →

The Association for Computing Machinery’s Special Interest Group on the Management of Data honored Allen School professor Dan Suciu with its 2022 Edgar F. Codd Innovations Award in recognition of his “lasting contributions to the foundations of novel data management trends.” The award recognizes a member of the ACM SIGMOD community who has made enduring and highly significant contributions to the development, understanding or use of databases and database systems over the course of their career. In the same week he was recognized for his influential body of work, Suciu also collected a Best Paper Award for his latest contribution — an indication that he has no intention of resting on his laurels.

Throughout his career, Suciu has shown a propensity for drawing deep connections between logic, database theory, algorithms and systems. According to Dan Olteanu, professor and head of the Data Systems and Theory Group at the University of Zurich, Suciu’s approach to advancing new data management paradigms has also set a new standard for research in the field.

“Dan Suciu is the most prominent researcher bridging database systems and theory,” said Olteanu. “His numerous contributions to the theory and practice of databases have fundamentally transformed a wide range of areas of research, such as semistructured data, data security, querying unreliable and inconsistent sources, probabilistic databases, data pricing, distributed and parallel query processing, and causality inference. They established clean formal foundations and practical and elegant data processing techniques. They further changed how younger generations of computer science researchers, including myself, approach research problems, and we relentlessly strive to meet the bar set by his work.”

Suciu, who holds the Microsoft Endowed Professorship in the Allen School, has been setting the bar high ever since he joined the University of Washington nearly 22 years ago. For example, in a paper appearing at the 2002 Symposium on Principles of Database Systems (PODS) Suciu and then-student Gerome Miklau (Ph.D., ‘05), now a faculty member at the University of Massachusetts Amherst, examined the complexity of containment and equivalence for a core fragment of the XPath query language for XML applications. The fragment in question covers attributes that are frequently applied in practice, specifically queries that contain branching, label wildcards and can express descendant relationships between nodes. Whereas prior work had established that efficient containment algorithms exist for any combination of two of those, Suciu and Miklau established — to their own surprise — that the problem of checking containment of all three is coNP-complete. Based on their findings, the duo designed an efficient containment algorithm capable of running in polynomial time for several cases with practical significance that involve all three. Their paper was singled out for its impact with the PODS Alberto O. Mendelzon Test of Time Awards in 2012 — one of two PODS Test of Time Awards that Suciu has received in his career so far.

In another paper that combined rigorous theoretical evaluation with real-world concerns — in this case, those of every individual who has purchased a product or service or surfed online — Suciu and his collaborators introduced a framework for the pricing of private data. This work, published in 2017, conceived of a market balancing the interests of individuals in safeguarding their personal information with the sometimes incompatible interests of companies and organizations seeking to extract value from said data for the purposes of offering more personalized services, targeting their marketing to specific interests, and direct sale to third parties. To find that elusive balance, Suciu, Miklau, and co-authors Chao Li and Daniel Yang Li drew from and expanded upon elements of differential privacy and data markets to construct a framework in which a “market maker” acts as an intermediary between individual data owners and institutional data buyers. In this scenario, the market maker responds to queries from the latter and sets prices to compensate the former based on a variety of factors, including the amount of perturbation — or noise — in the data, the option to source data from less expensive query sources, and individuals’ own risk tolerance for potential privacy loss. Suciu and the team earned a Best Paper Award at the 16th International Conference on Database Theory (ICDT 2013) for this work.

Around the same time, against the backdrop of the rapid rise in cloud computing and big data, Suciu tackled the algorithmic aspects of parallel data processing over large-scale distributed systems such as the MapReduce framework and UW’s own Myria system. Working with colleague Paul Beame of the Allen School’s Theory of Computation group and former student Paraschos Koutris (Ph.D., ‘15), now a faculty member at the University of Wisconsin-Madison, Suciu introduced a new theoretical model, Massively Parallel Computation (MPC), that separates the cost of computation from that of communication. By focusing the cost of parallel processing exclusively on the latter based on the amount of communication and the number of communication rounds, Suciu and his collaborators led a paradigm shift in how the community analyzed the complexity of distributed large-scale data queries — from run-time or the number of disk input/output operations, to the amount of data being reshuffled while maintaining server-load balance. Suciu and Koutris subsequently applied the MPC in a comprehensive survey of algorithms for different data processing tasks in collaboration with Semih Salihoglu, a faculty member at University of Waterloo.

In addition to publishing nearly 300 conference or journal papers, Suciu has contributed to multiple highly-cited books in data management. One of those, published in 2011, was an authoritative work on probabilistic databases he co-authored with Olteanu; former student Christopher Ré (Ph.D., ‘09), now a faculty member at Stanford University; and Christoph Koch, a faculty member at the École Polytechnique Fédérale de Lausanne. In the book, Suciu and his collaborators put forward novel representation formalisms and query processing techniques for modeling and processing probabilistic data used in information extraction, scientific data management, data cleaning, and other use cases that involve large volumes of uncertain data.

Three of Suciu’s aforementioned student collaborators — Miklau, Ré and Koutris — earned the ACM SIGMOD Doctoral Dissertation Award working with him. According to his UW Database Group colleague Magdalena Balazinska, Suciu’s impact in advancing new paradigms in data management is rooted not only in his vision and leadership in bridging theory and practice, but also in his devotion to developing the next generation of researchers to carry that work forward.

“As a database researcher myself, I have appreciated Dan’s approach to breaking new intellectual ground while offering a path to practical implementation,” said Balazinska, professor and director of the Allen School. “He has a knack for setting new directions for theoreticians to explore while also guiding engineers in the actual development of systems aligned with emerging trends. Not only is Dan a leader in the database research community, but he is a wonderful mentor to all who have the privilege of studying with him in addition to being a treasured colleague and friend.”

In his award talk at the SIGMOD/PODS conference in Philadelphia, Pennsylvania earlier this month, Suciu credited one of his own early mentors, Val Tannen, with “teaching me, and teaching me how to teach” and setting him on the path to his life’s work. It was Tannen who first introduced Suciu, back when he was a college student in Romania, to a new kind of mathematics that featured lattices, category theory and universal algebras — elements that Suciu found compelling and “strangely relevant” to his newfound passion for programming. Suciu later followed Tannen to the University of Pennsylvania, where he began his career in database research as a Ph.D. student working to redesign query languages grounded in mathematics — collecting the first of his many conference paper awards, at ICDT 1995, in the process.

Fast forward 27 years later, and Suciu collected his latest Best Paper Award, this time from PODS, for his work on “Convergence of Datalog over (Pre-) Semirings.” In the winning paper, Suciu and his co-authors — Mahmoud Abo Khamis and Hung Q. Ngo at RelationalAI, Reinhard Pichler at TU Wien, and Allen School Ph.D. student Remy Wang  — make progress on an open problem related to enabling recursive queries beyond Boolean space as required by modern data processing and tensor computations that power applications ranging from program analysis and machine learning, to graph algorithms and linear algebra. To enable this progress, the team introduced datalog^o, a powerful language for expressing recursive computations over general semi-rings. 

This latest accolade brings Suciu’s career tally at influential database, data management, and related conferences to six Best Paper or Distinguished Paper Awards, three Best Demo Awards, and five Test of Time or Influential Paper Awards.

Congratulations, Dan! Read more →

After two successive years of asynchronous, online tributes to graduates, the Allen School finally welcomed members of the classes of 2020, 2021 and 2022 to an in-person graduation celebration to mark the culmination of their academic journeys at the University of Washington. An estimated 3,000 faculty, staff, family and friends converged on Hec Edmundson Pavilion at the Alaska Airlines Arena on June 10th to honor the graduates’ achievements and recognize the impact that an Allen School education can have — not just on the lives of those who crossed the stage, but also on the world at large.

“We are counting on you”

Magdalena Balazinska, professor and director of the Allen School, acknowledged the immense challenges overcome by this group of graduates to reach this milestone. She expressed her pride in all that they had accomplished during their time at the school—and her belief in what they can accomplish in the future.

“Today, we at the Allen School are thrilled to join your families and friends in celebrating this momentous achievement. But tomorrow, we are counting on you,” said Balazinska. “The world needs your generation with your passion, your technical skills, your energy, and your commitment to your community and to the world. You now have the skills and knowledge to make the world a better place.

“Go out there, be brave, be kind, and do great things.”

Next stop: the north pole

Longtime friend to the Allen School Ron Howell, retired CEO of the Washington Research Foundation, joined in the celebration as the featured graduation speaker. In candid remarks, he shared the lessons he learned from his personal journey navigating severe depression for decades while building and maintaining a successful career in technology.

Howell used the metaphor of a sphere, the surface of which represented the various feelings, or “affects,” that people might experience throughout their lives. The very top of this sphere — the north pole — is a place of optimism, calmness, confidence, competence and resilience, while the south pole is home to the opposite. Howell offered the graduates his roadmap to the north pole. Some of the directions took the form of practical advice — eat right (“mostly vegetables”), exercise, meditate — while others served as reminders to extend grace to themselves as well as those they encounter along the way.

“Practice kindness and gratitude and forgiveness….and start with yourself,” he advised the graduates. “The stories that you tell yourself, and the voices that you use to tell yourself things, matter.”

Howell’s parting words were, perhaps, particularly relevant to those about to enter an industry characterized by constant striving. “Your success is not as important as you. You are more important than your success. Remember that,” he said.

“I’ll see you at the north pole.”

Making the world a better place

One of the Allen School’s graduation traditions is recognizing accomplished alumni who exemplify the qualities of technical excellence, leadership and service. While the pandemic disrupted these plans over the past two years, professor Ed Lazowska, the Bill & Melinda Gates Chair Emeritus at the Allen School, was finally able to reprise his customary role in the program to recognize 2020 recipients Emma Brunskill and Kirk Glerum, and 2022 recipients Brad Calder and Heather Underwood.

“You’re joining a long line of men and women who have built on their Allen School education to go out and make the world a better place,” Lazowska informed the graduates.

Emma Brunskill (B.S., ‘00)

Emma Brunskill earned bachelor’s degrees in Computer Engineering and Physics from the UW before earning a Rhodes Scholarship to Oxford University, where she obtained a master’s degree in neuroscience on her way to earning her Ph.D. from MIT. Brunskill subsequently completed a postdoc at University of California Berkeley before joining the faculty of Carnegie Mellon University. She later moved to Stanford University, where her research focuses on the development of robust artificial intelligence systems for use in health care and education that make good decisions despite being inherently data-limited.

“Emma has already amassed an amazing collection of accomplishments and contributions, and unlike me, has decades of them still ahead of her,” Lazowska said with a smile.

Kirk Glerum (B.S., ‘83)

“Customers hate crashes.” Those words, and what followed them in a two-page memo Kirk Glerum wrote back in 1998, would end up revolutionizing software reliability. Glerum was a member of the Microsoft Office team, which he joined a few years after graduating from the UW with degrees in Computer Science and Mathematics. With web-based communication becoming more common, he envisioned a way to save future customers from the sight of the dreaded dialog box by introducing a crash reporting system to enable the company’s developers to issue fixes. Glerum’s solution was, Lazowska noted, “a game-changer for Microsoft which ultimately became common across the industry.”

Glerum spent 22 years at Microsoft. Now “happily retired,” he and his family have supported his alma mater in multiple ways, including creation of the Glerum Family Endowed Scholarship and Glerum Family Endowed Fellowship to support Allen School undergraduate and graduate students who demonstrate financial need.

Brad Calder (B.S., ‘91)

Brad Calder is another who earned degrees in both Computer Science and Mathematics from the UW, after which he went on to earn his Ph.D. from the University of Colorado at Boulder. From Digital Equipment Corporation’s Western Research Lab, to the University of California San Diego faculty, to Microsoft and, now, Google, “Brad has truly done it all,” observed Lazowska. 

Before leaving academia for industry, Calder graduated 14 Ph.D. students and published over 100 research papers on systems, architecture and compilers. At Microsoft, Calder was a member of the team that founded the company’s Azure cloud project in 2006. Now, at Google, he serves as Vice President and General Manager of Google Cloud Platform and Technical Infrastructure. In this role, he oversees product and engineering for hardware, compute, networking, storage, databases, and data analytics.

Heather Underwood (B.S., ‘09)

Heather Underwood is a prime example of the almost limitless paths open to holders of a computer science degree. Her research has spanned computer science, global health, human-centered design, education and international development. While working on her Ph.D. at CU Boulder advised by fellow Allen School alum John Bennett (Ph.D., ‘88), Underwood developed PartoPen, a digital pen application used to monitor maternal labor and reduce birthing complications in the developing world. After serving on the faculty of CU Boulder and as executive director of Denver BioLabs, she served as a Biodesign Fellow at Stanford University on the way to becoming a full-fledged medical device and health technology entrepreneur.

Currently CEO of EvoEndo, which has developed a safer and more affordable single-use alternative to sedated endoscopy, “Heather, like all of our Alumni Impact Award recipients, is changing the world,” Lazowska concluded.

Recognizing the next generation of leaders and innovators…

Outstanding Senior Awards

The Allen School recognized two graduating students with Outstanding Senior Awards for demonstrating superior scholarship, leadership potential and the ability to create and apply new knowledge in the field of computing.

Honoree Joseph Schafer earned a bachelor’s degree in Computer Science with a minor in Ethics. As a social media data analyst in the UW Center for an Informed Public, Schafer has made significant contributions to qualitative and quantitative research to understand and respond to the spread of mis/disinformation on social media on topics related to politics, elections and the COVID-19 pandemic. Schafer also served as a teaching assistant (TA) and as an officer of the Student Advisory Council. He recently earned a Graduate Research Fellowship from the National Science Foundation to continue his research as a Ph.D. student in UW’s Department of Human Centered Design & Engineering.

Recent Computer Engineering graduate Peter Michael (B.S., ‘21) was honored for his research contributions at the intersection of computer graphics, vision, and machine learning, with a particular emphasis on machine learning-based virtual green screen algorithms — similar to those used in Zoom’s background replacement feature — through a year-long collaboration between UW and Runway ML. He also served as a TA for courses focused on all three subjects and in linear algebra. Michael, who completed his bachelor’s degree last fall and went on to earn his fifth-year master’s from the Allen School in just two quarters, will pursue a Ph.D. at Cornell University next year.

Undergraduate Service Awards

The Undergraduate Service Awards recognize outstanding graduating seniors who have taken an active role in building community by contributing to Allen School activities and events. This year, the school honored four graduates for their service.

Nayha Auradkar was described as “fiercely dedicated to making the communities around her more inclusive.” After serving as president of Huskies Who Stutter, Auradkar founded and led Ability, an Allen School student group focused on connecting students with disabilities and raising awareness of accessibility issues. She also spent nearly two and a half years as chair of ACM-W focused on cultivating a community for women in computing.

Neha Jagathesan “goes above and beyond in everything she does.” This holds particularly true for her work with the Allen School’s Diversity & Access team, for which she has served as an ambassador and lead ambassador helping to build high school students’ interest in computing, and her contributions to the school’s five-year strategic plan for increasing diversity, equity, inclusion and access throughout our community.

Lucy Jiang is known as a “phenomenal student leader” who has taken on many roles in service to the Allen School community. As chair of the UW student chapter of the Association for Computing Machinery (ACM), she oversaw the organization’s first-ever fundraisers to support local causes such as the University District Food Bank, Code.org, and Seattle Children’s Hospital. She also represented the student voice on the school’s Diversity Committee and Curriculum Committee.

Aerin Malana co-founded and chaired GEN1, an Allen School student group that offers support for those who are the first in their families to pursue a U.S. bachelor’s degree. “Committed to bringing visibility and fostering community” among first-generation students, Malana helped surface a college experience that, too often, can be an invisible one. She has also served as an officer of ACM-W and handled logistics for the DubHacks collegiate hackathon.

Best Honors Thesis Award

Each year, the Allen School recognizes a graduating senior who has completed an outstanding independent research project working with a faculty advisor as part of the requirements for Allen School honors. The 2022 Best Honors Thesis Award went to Lucy Jiang for “Flipping the Script: Co-Designing Systems to Support Blind and Low Vision Audio Description Scriptwriters,” completed under the supervision of professor emeritus Richard Ladner. Audio description is an essential function for making videos accessible to BLV audiences, but those audiences are typically excluded from the AD creation pipeline. Jiang conducted interviews and user studies with BLV audiences and AD writers to understand the barriers to accessibility and then applied those insights to the design of AccessbleAD, a system for making AD writing more accessible to BLV users.

…And honoring those who inspired them

Bob Bandes Memorial Award

The Bob Bandes Memorial Award recognizes outstanding teaching assistants (TAs) in the Allen School for their contributions to the classroom and their fellow students’ success. This year, the school selected three winners and three honorable mentions out of approximately 200 TAs nominated by faculty and the students who learned from them.

Winner Shreya Jayaraman has served as a TA for a total of 8 quarters for Foundations of Computing I and II, as both an undergraduate and a graduate student in the Allen School’s BS/MS program. According to one faculty member who worked with her, “Shreya was one of the best TA’s I’ve ever worked with: knowledgeable, responsible, and a natural teacher.”

Fellow winner Soham Pardeshi has been a TA as both an undergraduate and a graduate student in the BS/MS program for a near-record 14 quarters spanning five different courses: Computer Programming II, Intermediate Data Programming, Foundations of Computing I, Software Design and Implementation, and Introduction to Algorithms. According to one student, “Soham is by far the best TA I have had at UW. It is obvious he cares about the wellbeing and success of his students.”

Last but not least, winner Allie Pfleger, who is about to enter the Allen School’s BS/MS program, is also about to enter her 7th quarter as a TA this summer. As an undergraduate, she assisted with multiple quarters of Foundations of Computing I, Systems Programming and The Hardware/Software Interface. According to one of her former students, “She deserves more than this award, she deserves part of my degree when I eventually earn it!”

The Honorable Mention recipients were Dylan Hartono, Abhishek Shah, and Nicolas Suhardi. Students who studied with Hartono, an undergraduate TA for five quarters, lauded him for being “efficient, thorough, knowledgeable, and kind.” Shah, a student in the BS/MS program, has served as a TA for four quarters and was recognized for going the extra mile to help students while offering “an engaging and welcoming learning experience.” Suhardi, an undergraduate who has assisted with five quarters of the Introduction to Programming course, was recognized for doing his best to help students develop “a thorough understanding and love of CS.”

ACM Teaching Awards

Lucy Jiang, president of UW ACM, announced the recipients of the ACM Teaching Awards highlighting faculty excellence. This year, the students broke from tradition by opting to recognize three faculty members for their contributions to the student experience in the Allen School.

Teaching professor and Allen School alum Hunter Schafer (B.S., ‘16, M.S., ‘18) was honored for his contributions to 100-level courses, including Introduction to Computer Programming and Intermediate Data Programming. “Students both within the Allen School and from other majors appreciate his dedication to making computer science more approachable.”

Teaching professor Robbie Weber (Ph.D., ‘20) is another alum who, despite not having taught for very long, has already made an impact. In 300-level courses such as Foundations of Computing I and II and Data Structures and Parallelism, Weber is known for having “crystal clear and enjoyable lectures that students are excited to attend.”

Finally, UW ACM recognized professor Tadayoshi Kohno, the Allen School’s Associate Director for Diversity, Equity, Inclusion and Access and co-director of the Security & Privacy Research Laboratory. In addition to his commitment to creating a welcoming and supportive environment, Kohno is credited for making the topics in his 400-level courses “accessible and entertaining…he has even re-inspired students’ love for computing as a field.”

During the program, the Allen School also took the opportunity to recognize in person the award recipients from 2020 and 2021. The Allen School awarded approximately 700 degrees in the 2021-2022 academic year.

Congratulations to our award recipients and all of our graduates! Please keep in touch! Read more →

Professor Shayan Oveis Gharan, a member of the Allen School’s Theory of Computation group, was named a 2022 Simons Investigator by the Simons Foundation for his innovative approach to fundamental problems in algorithm design and combinatorial optimization. The Simons Investigator Award is designed to support outstanding theoretical scientists in mathematics, physics, astrophysics and computer science as they pursue creative new research directions, mentor junior scientists, and provide leadership within their respective fields.

Oveis Gharan is perhaps best known in theoretical computer science circles for his devotion to the Traveling Salesperson Problem — a devotion that has propelled him to achieve state-of-the-art results on a core research question in combinatorial optimization with practical applications in many other domains. His impact stretches back to his time as a Ph.D. student, when he worked with his advisor, Stanford University professor Amin Saberi, and McGill University professor Mohit Singh to produce the first improved approximation algorithm for the special case of metric TSP called graphic TSP in 35 years. Their simple yet powerful algorithm, which drew upon ideas from probability theory, graph theory and polyhedral theory, earned the Best Paper Award at the 52nd annual IEEE Symposium on Foundations of Computing (FOCS 2011).

After his arrival at the University of Washington in 2015, Oveis Gharan continued to make progress on his favorite problem. After a decade of effort that began during his Ph.D., he and Allen School colleague Anna Karlin and Ph.D. student Nathan Klein managed to extend the 2011 result and design the first improvement over the Christofides algorithm for any metric in more than 40 years. In addition to the temporal significance, the result was hugely symbolic: Oveis Gharan and his co-authors had devised the first algorithm capable of producing a solution that costs less than 50% above the optimum. The team’s achievement earned the Best Paper Award at the 53rd annual Symposium on Theory of Computing (STOC 2021). 

Despite the seriousness of the subject — after all, the TSP is one of the problems underpinning the field — it’s evident that Oveis Gharan genuinely enjoys the challenge. And his collaborators enjoy it right along with him.

“Shayan is technically brilliant and intellectually fearless. He tackles only the hardest and most fundamental problems. These are problems that have remained open for decades, despite the attention of numerous researchers. And time and again, to my and other colleagues’ amazement, he makes progress on those problems,” said Karlin. “I cannot resist adding that he is also the most enthusiastic, generous and fun collaborator one could imagine working with.”

Oveis Gharan’s progress on fundamental — and fundamentally hard — problems extends beyond TSP. One of his recent results that has spurred a flurry of follow-on work within the theoretical computer science community resolved a two-decades-old open problem concerning the sampling of independent sets of a graph up to the computational complexity threshold. In the paper “Spectral Independence in High-Dimensional Expanders and Applications to the Hardcore Model,” Oveis Gharan and his co-authors, Allen School Ph.D. student Kuikui Liu and Stanford University professor Nima Anari, showed for the first time that Glauber dynamics — an algorithmic tool used in statistical physics for modeling ferromagnetism — mixes in polynomial time for any graph, not just a subset of graphs, up to the phase-transition threshold, after which the problems become computationally intractable. After much follow-on work, another team subsequently built on their result to finally show that one can sample an independent set of a graph, up to the phase-transition threshold, in near-linear time — essentially, in the same time as sorting the vertices of the graph. Although the work is not yet two years old, having first appeared at FOCS 2020, it has already inspired more than 50 follow-on papers and was subsequently featured in the SIAM Journal of Computing published by the Society for Industrial and Applied Mathematics.

The above result built on previous work by the team in conjunction with UW Mathematics professor Cynthia Vinzant — at the time, a faculty member at North Carolina State University — that linked the analysis of Markov chains and the field of high dimensional expanders. In one of a series of papers to advance the theory of completely log-concave polynomials to study the combinatorial structure of matroids, they presented the first fully polynomial randomized approximation scheme (FPRAS) for counting the bases of a matroid. This novel approach, which is based on a simple two-step Markov Chain Monte Carlo process, enables the sampling of random spanning forests in a graph to estimate the reliability polynomial of any matroid. In the same paper, Oveis Gharan and his co-authors also proved the 30-year-old Mihail-Vazirani conjecture that the bases exchange graph of any matroid has edge expansion of at least 1. The team earned a Best Paper Award at STOC 2019 for their contributions, which have real-world applications for network reliability, data transmission, and more.

“Shayan is at the forefront of a series of exciting discoveries that are advancing our understanding of the foundations of computing. The impact he’s already made, so early in his career, is astonishing,” said Allen School colleague James R. Lee, who was named a Simons Investigator in 2017. “His application of algebraic and spectral methods to algorithm design and combinatorial optimization is expanding the mathematical and algorithmic toolbox for our entire community. And along the way, he’s making progress on some of the most significant and longest-standing problems in the field.”

Oveis Gharan’s designation as a Simons Investigator is the latest in a string of accolades that he has accumulated since his arrival at the Allen School, including the EATCS Presburger Award from the European Association for Theoretical Computer Science in 2021 and a Sloan Research Fellowship in 2019.

Learn more about the 2022 Simons Investigators here.

Congratulations, Shayan! Read more →

According to the World Health Organization, more than 430 million people around the globe live with a disabling hearing loss — including 34 million children, the majority of whom experience hearing loss that is due to preventable causes. Nearly 80% of people with disabling hearing loss live in low- or middle-income countries where they may encounter barriers to routine medical care, which includes screening for ear disorders to support early detection and intervention.

Tympanometry is a test of middle ear function that can be used in conjunction with otoscopy and other tests to diagnose middle ear disorders that, if left untreated, could lead to complications such as permanent hearing loss. Although it is a relatively simple test to perform, there are barriers for widespread use in resource-constrained communities — not only are they not designed for portability, but they carry a price tag ranging from $2,000 to $5,000 each.

In a paper published today in Nature Communications Medicine, researchers at the University of Washington’s Paul G. Allen School of Computer Science & Engineering, UW Medicine and Seattle Children’s present a lightweight alternative to conventional tympanometry devices that is also lighter on the wallet. News of the team’s system, which turns a smartphone into a handheld tympanometer, should be music to the ears of those working in public health.

“Conventional desktop tympanometry is expensive, bulky, and requires a source of wall power, which makes it less than ideal for use in mobile clinics and rural communities. Consequently, in some areas, people may have to travel long distances to obtain a test — if they are able to travel, that is,” said lead author Justin Chan, a Ph.D. student in the Allen School. “Our open-source system is inexpensive, portable, easy to use, and works with any Android smartphone.”

During tympanometry, a probe is inserted into the ear canal to alter the air pressure and measure the mobility of the tympanic membrane and ossicular chain. The resulting tympanogram is analyzed to determine if there is a buildup of fluid behind the eardrum — also known as middle ear effusion — or other conditions that may require treatment.

The UW-designed system follows a similar approach, albeit packaged in a smaller form factor. A rubber-tipped probe for insertion into the ear canal is connected to a 3D-printed housing that contains the electronic components, which are connected directly to the smartphone via a headphone jack. Those components include a printed circuit board with a tiny speaker and microphone that sends and receives the audio signal, and a stepper motor connected to a syringe and plunger for altering the air pressure inside the ear canal during the test.

When the probe is inserted into the patient’s canal, the smartphone software automatically detects when a seal is established. At that point, the motor automatically — and gently — moves the plunger of the syringe, guided by feedback from an onboard pressure sensor, while the microphone emits a 226 Hz audio tone into the canal. Once the test cycle is complete, the onboard microcontroller instantly transmits the recorded acoustic reflections and pressure data to the smartphone via a built-in wireless Bluetooth radio to generate a tympanogram in real time.

The software can be programmed on the smartphone to adjust testing parameters such as pressure limits, pressure speed, audio frequency and volume, and the entire hardware configuration, minus the smartphone, can be assembled for around $28. The team is making the code freely available to anyone interested in building their own device.

“We designed our system to be safe and comfortable for the patient as well as economical and easy to use. For example, the probe rests lightly yet securely in the patient’s ear without any force applied, and it’s compatible with the same rubber ear tips already used with conventional tympanometers,” noted co-senior author Shyam Gollakota, director of the Networks & Mobile Systems Lab and the Torode Family Professor in the Allen School. “We also built in a fail-safe mechanism — which we haven’t yet needed — that will automatically terminate the test in the unlikely event of a pressure sensor malfunction to avoid large changes in pressure within the ear.”

The researchers evaluated the system on a group of patients scheduled to undergo tympanometry with audiologists at Seattle Children’s. The patients, who were between one and 20 years of age, were each screened using the smartphone system and a commercial tympanometer. The audiologists tested a total of 50 patient ears. A separate panel of audiologists were then asked to classify each of the 100 tympanograms generated by the tests into Liden and Jerger classifications — a scale used to describe the level of mobility of the tympanic membrane that could indicate a middle ear disorder — to compare how the smartphone-based test measured up to the existing standard.

“For the clinical study, we directly compared the results of our smartphone-based tympanometry to the commercial device,” explained co-senior author Dr. Randall Bly, an assistant professor in UW Medicine’s Department of Otolaryngology – Head and Neck Surgery and a pediatric otolaryngology specialist at Seattle Children’s. “There was significant agreement — roughly 86% — between the results of the two screening methods. Most importantly, when there was an abnormal finding such as a Type B tympanogram, there was 100% agreement. Our goal was to develop an accessible device that can accurately assess the middle ear, providing clinicians critical diagnostic information. These results show promise towards achieving this goal.”

Additional studies will be needed to determine efficacy for screening infants under one year of age.

Other co-authors of the paper include Ali Najafi, Ph.D. student in the UW Department of Electrical & Computer Engineering; Mallory Baker and Julie Kinsman, audiologists at Seattle Children’s; Dr. Susan Norton, professor emeritus in UW Medicine’s Department of Otolaryngology – Head and Neck Surgery who served as chief of Audiology Programs and Research at Seattle Children’s; and Lisa Mancl, affiliate faculty member in the UW Department of Speech & Hearing Sciences.

Read the paper in Nature Communications Medicine here, and visit the project website here. Watch a demo video here. Read a related article in STAT News here.

All photos: Dennis Wise/University of Washington Read more →

Each year, the University of Washington’s College of Engineering recognizes alumni and friends who have made outstanding contributions to the field of engineering through its Diamond Awards. Among the 2022 honorees are two Allen School alumni whose cutting-edge research has helped steer the future of computer networking with far-reaching impacts: Stefan Savage (Ph.D., ’02), recipient of the Distinguished Achievement in Academia Award, and Justine Sherry (B.S., ‘10), recipient of the Early Career Award.

Stefan Savage: Distinguished Achievement in Academia

Those who say “crime doesn’t pay” might have crossed paths with University of California San Diego professor Stefan Savage — or at least, come across the results of his work. It was Savage who led a team of researchers in demonstrating how to disrupt the global web of electronic criminals by analyzing the economic, as well as technical, aspects of the problem. Before he helped to untangle the connections between email spam and the financial services used to monetize it, attempts to combat such networks amounted to a game of “whack a mole”: shut down one domain name, for example, and the criminals could easily and cheaply switch to another.

Savage and his colleagues developed an infrastructure to track on the order of hundreds of millions of spam emails, which enabled them to piece together the entire value chain of these illicit enterprises. This led them to discover a weak link: payment processing. Unlike much of the computing network infrastructure that the criminals relied on, the handful of financial institutions hosting their merchant accounts and processing the credit card payments that allowed them to profit off of their activities were not so easy or inexpensive to replace. Banks, law enforcement, the International Anti-Counterfeiting Coalition, and regulatory agencies such as the U.S. Food and Drug Administration and Federal Trade Commission have since applied the lessons learned through Savage’s work to combat online drug trafficking and the sale of counterfeit goods.

Savage was also instrumental in advancing the field’s understanding of the scope and mechanics of internet denial of service attacks, worms and other malware — along with effective countermeasures. For example, he co-led the development of a technique called backscatter analysis that uses packets sent from the victim in response to spoofed packets from an attacker to measure the number, duration and focus of denial of service attacks. He and his team then applied the technique to provide the first-ever estimate of denial of service activity worldwide. According to Allen School professor Ed Lazowska, Savage’s contributions to the safety and security of modern computing cannot be overstated.

“Stefan is the most creative person working in the hugely important fields of network security, privacy, and reliability today,” said Lazowska, who is the Bill & Melinda Gates Chair Emeritus at the Allen School. “He has an uncanny ability to ask exactly the right question, devise exactly the right methodology to explore that question, propose exactly the right solution, and see that solution through to impact.”

That uncanny ability was on full display when Savage and Allen School professor Tadayoshi Kohno co-led a team of researchers in exploring how the increasing computerization of automobiles introduced the potential for new security threats. After showing how they could physically infiltrate a vehicle’s onboard networks to gain control of critical systems, Savage and his collaborators followed up by demonstrating how the same systems were also vulnerable to remote wireless attacks — including, at one point, using a laptop to interfere with a car’s braking system as it cruised down an abandoned airstrip. 

By turning their students loose on a pair of Chevy Impalas, Savage and Kohno ultimately helped turn the entire automobile industry in a new direction and, as Savage himself put it, influenced “how products are built and how policies are written.” As part of those changes, manufacturers began hiring dedicated security teams, while regulatory bodies established new security standards and devoted resources to addressing emerging threats. It is yet another example of how Savage’s combination of curiosity, creativity and technical excellence has had a tangible impact in the field of computer security and beyond — an impact that has earned not one but two Test of Time Awards from the IEEE Symposium on Security and Privacy recognizing the enduring influence of his electronic crime-fighting and automobile cybersecurity work.

“Stefan is somebody who very much embodies the values of the University of Washington,” said Geoffrey Voelker (Ph.D., ‘00), Savage’s UCSD colleague and fellow Allen School alum. “He is a world-class researcher who has had tremendous impact in his field. He’s also just an amazing person who is very generous and positively affects everybody he comes in contact with.”

Justine Sherry: Early Career

Justine Sherry may still be in the first decade of her faculty career at Carnegie Mellon University, but she has already established herself as a consummate researcher and sought-after thought leader when it comes to the design and implementation of networked systems that can handle the scale and complexity of the modern internet. Since graduating from the UW with bachelor’s degrees in computer science and international studies, she has produced a succession of groundbreaking contributions that have advanced network performance, reliability, security and fairness.

“Justine is among the very best young computer science researchers in the world,” said Dan Grossman, professor and vice director of the Allen School. “Her work is hard to pigeonhole because it spans much of modern networking, from algorithms, to measurements, to arguments on how to evaluate a network policy’s ‘goodness.’ Justine’s leadership among computer networking experts is impressive for someone of any age, and truly extraordinary for someone so early in their career.”

That extraordinary early leadership was evident in her Ph.D. research at the University of California, Berkeley, where she devised a method for managing network traffic in the cloud that came to be known as network function virtualization. Conventional enterprise network management relied on a collection of hardware devices dedicated to various functions, from intrusion detection to load balancing. The entire system was expensive to assemble, complicated to manage, difficult to scale, and vulnerable to security breaches. Sherry developed an architecture to support outsourcing middlebox functions to the cloud, accompanied by practical approaches for addressing concerns such as latency and fault tolerance to meet performance demands. Network function virtualization has grown into a $12 billion business — and is projected to triple over the next few years.

Sherry has also emerged as an expert in the theory and practice of congestion control, which is essential to maintaining fairness and reliability of internet services by managing demand for total available bandwidth. As part of this work, she and her team explore fundamental questions about what makes congestion control algorithms “good” — including what properties matter most under which conditions — and how to evaluate whether one algorithm is better than another. These issues are particularly salient as cloud operators’ congestion control algorithms, which are generally proprietary, affect the experience of billions of internet users. 

For example, traditional algorithms are designed to reduce the portion of bandwidth their service is using in proportion to the number of other services seeking a share. Using a combination of experiments and mathematical modeling, Sherry and her colleagues discovered that Google’s newly released BBR algorithm for YouTube consumed a fixed portion of available bandwidth regardless of the level of demand from other services — leading to one experiment where a single BBR connection took up 40% of the network, with 16 competing services attaining less than 4% each. She and the team developed an evaluation framework based on a metric of “harm” to evaluate new algorithms in relation to the status quo prior to deployment.

“Justine was described by one of her colleagues as being fearless, and that fearlessness shows itself in her problem formulations, in the questions that she asks.” said Christopher Ramming, senior director of research and innovation at VMWare. “It’s that combination of properties that makes her stand out.”

That fearlessness prompted Sherry and her collaborators to question the conventional approach to intrusion detection and prevention processing. The goal was to determine whether it was possible to use a single software server to manage these critical workloads — among the most demanding network functions — for networks on the order of 100 Gbps and involving hundreds of thousands concurrent connections and more than 10,000 rules. Sherry and her team demonstrated that it is not only feasible, but also practical, with Pigasus, a FPGA-first architecture capable of handling the majority of intrusion detection and prevention processing for a 100 Gbps network using five cores and a single FPGA. In addition to being more cost-effective, they demonstrated that their FPGA-first model consumes 38x less energy than existing CPU-based approaches. This work has opened up new research directions in energy-efficient large-scale computing and the performance of network-intensive computation on hybrid software/hardware platforms in the cloud.

Sherry, Savage and their fellow award recipients were formally honored at an event hosted by the College last month. Learn more about the 2022 Diamond Award recipients here.

Congratulations, Stefan and Justine, and thank you for being such wonderful ambassadors for UW, the College and the Allen School! Read more →

In 2020, four couples who are longtime friends of the Allen School joined forces to create a new endowed professorship fund named for professor Ed Lazowska to honor his wide-ranging impact on the field of computing, Washington higher education, and the technology community. They seeded the fund with a combined gift of $1 million, and then began inviting other friends and alumni of the school to contribute — quietly, at first, and then more openly once they had revealed their intentions to Lazowska himself to mark his 70th birthday. 

To date, more than 230 individuals, families or organizations have pledged a total of $4.7 million to the Endowed Professorships in Computer Science & Engineering in Honor of Edward D. Lazowska. Once those commitments are fully realized, the Lazowska Professorships will be one of the largest professorship funds at the University of Washington.

The enthusiastic response will come as no surprise to anyone who knows Lazowska, whose impact over his 45 years of service to the UW and the broader community has influenced everything from the direction of national technology policy, to a significant increase in the number of Washington high school students who can earn a degree in computer science. Along the way, he transformed the landscape for computing at UW — literally and figuratively — as one of the driving forces behind not one but two dedicated buildings and the elevation of the former Department of Computer Science & Engineering into the Paul G. Allen School of Computer Science & Engineering, with support from the late Paul Allen and Microsoft.

At a recent event celebrating the community of supporters who made the professorships possible, UW President Ana Mari Cauce observed that his leadership, vision and commitment helped mold the Allen School into one of the top computer science programs in the nation and in the world. But that is only part of his extraordinary legacy.

“It would really be impossible to overstate Ed’s impact on the lives of his students, his colleagues, our university, and actually the entire field of computer science,” Cauce said. “Ed’s efforts have transformed how we think about the power of computer science to innovate for greater equity, for economic opportunity, and for discovery.”

The idea for the Lazowska Professorships can be traced back to December 2019. That’s when Peter Lee, corporate vice president of research and incubations at Microsoft, and Allen School alumnus Jeff Dean (Ph.D., ‘96), a Google Senior Fellow and senior vice president of Google Research and Google Health, decided the best way to honor their friend and colleague’s legacy would be to enhance the Allen School’s ability to attract and retain other world-class faculty. Their original plan was to engage the community in the tribute, assemble the professorship fund, and host an in-person celebration to mark Lazowska’s 70th birthday the following summer. After the COVID-19 pandemic hit, the pair changed tack. They teamed up with two of Lazowska’s other longtime friends and associates, Microsoft emeritus researcher Harry Shum and Microsoft president and vice chair Brad Smith, to fund the first of what they hoped would be multiple professorships; instead of a cake and candles, Lazowska received the surprise news over Zoom.

After this inaugural Lazowska Professorship was awarded to Luis Ceze, co-director of the Molecular Information Systems Laboratory (MISL) and co-founder and CEO of Allen School spinout OctoML, the broader campaign began in earnest. The subsequent outpouring of support will ultimately enable the Allen School to award no fewer than four professorships bearing Lazowska’s name. The outcome is a testament to the high regard and deep affection for Lazowska and his stature in the local tech community — summed up by Allen School professor Hank Levy, who worked alongside him first as department chair and then inaugural director of the school, as “professor, leader, scientist, teacher, colleague and friend.” As Lazowska made clear to the assembled crowd, the feeling is mutual.

“The Allen School has been blessed to grow up alongside and in partnership with the region’s tech industry and its people, and it’s been a completely magical ride that has brought all of us to the forefront of this incredibly competitive field,” Lazowska said. “I really want this evening to be not about me, but about you: about your generosity, your loyalty and the investment that you — led by Peter and Jeff and Brad and Harry — have made in the Allen School.

“And I want it to be about the Allen School itself,” he continued. “The distance we’ve traveled, the potential for the future — and about the University of Washington and the region. We really are leaders, and we’re a true community. That’s been the most heartwarming thing about the 45 years I’ve spent here. This is a community in which we lift one another up.”

To learn more and view a list of donors, visit the Lazowska Professorships webpage here. To contribute to the Lazowska Professorships, visit the giving page here. Read our previous story on the Lazowska Professorship here, and a related GeekWire article here.

We are profoundly grateful to our founding donors — Peter and Susan Lee, Jeff Dean and Heidi Hopper, Harry Shum and Ka Yan Chan, and Brad Smith and Kathy Surace-Smith — for bringing the community together to pay tribute to Ed while supporting the Allen School. Thank you to all of our donors for your generosity and friendship!

Read more →

Allen School professor and alumna Leilani Battle (B.S., ‘11) is building a career out of building better ways to deal with data. Her research, which looks beyond conventional data management techniques to incorporate human behavior and preferences, enables analysts to spend more time engaging with the data they need, and less time searching and waiting for it to load. Recognizing the transformational impact and future potential of Battle’s work, the IEEE Computer Society’s Technical Committee on Data Engineering recently recognized her with its TCDE Rising Star Award for “contributions to interactive data-intensive systems for exploratory data analysis.”

Battle joined the Allen School faculty in the summer of 2020 after spending three years as a professor at the University of Maryland, College Park. The move was akin to a second homecoming; having earned her bachelor’s degree at the University of Washington, she later went on to complete a postdoc working with professor Jeffrey Heer in the Interactive Data Lab — of which she is now co-director — in between earning a Ph.D. from MIT and launching her faculty career. Shortly before her return to Seattle, Battle was named one of MIT Technology Review’s 35 Innovators under 35; according to Heer, she is pushing the state of the art across multiple areas of computer science.

“Leilani is transforming how people explore and analyze data on a massive scale through her pursuit of a deeper understanding of users’ goals, strategies and behaviors, which she then leverages to develop novel systems and optimization methods,” said Heer. “She has shown a remarkable ability to bridge several subfields, from databases to visualization to human-computer interaction. By combining them in new and interesting ways to provide practical tools for scientists and analysts, she is advancing all three.”

Since the early days of her research career — she got her start as an undergraduate research assistant in the UW Database Group — Battle has been interested in how people interact with data and ways to reduce friction in those interactions. One of her initial and highly influential contributions was ForeCache, a user-friendly tool that reduces lag time, or latency, in interactive data visualization systems following an approach that Battle describes as “behavior-driven optimization.” This optimization is driven by ForeCache’s built-in prediction engine, which enables more efficient data exploration by anticipating and prefetching the results of queries it deems the user is most likely to want next based on past interaction patterns. The system also adapts its predictions based on actual usage patterns over time to improve future performance. In their paper presented at the Association for Computing Machinery’s 2016 Conference of the Special Interest Group on Management of Data (SIGMOD), Battle and her co-authors highlighted ForeCache’s dramatic improvements in latency compared to systems that do not rely on prefetching — reducing lag time by up to 430% — and significant improvements in both latency (88%) and accuracy (25%) over existing state-of-the-art prefetching techniques.

“Modeling user behaviors during interactive data exploration enables us to predict what interactions analysts will want to perform in our visualization interface,” Battle explained. “We can then use these models to preemptively execute database queries ahead of users as they explore. This helps analysts to focus more on their data and less on latency issues.”

Battle was keen to understand just how influential those latency issues are — and what other elements may be at play. By carefully studying how users interacted with visualization interfaces in the performance of different tasks, she and her collaborators have debunked some of the prevailing wisdom around what and how various factors shape users’ experiences with such systems. 

For example, in a paper presented at EuroVis 2019 organized by the Eurographics Working Group on Data Visualization and IEEE Visualization and Graphics Technical Committee, Battle and Heer examined the impact of latency on exploratory visual analysis performed in Tableau. They compared their findings to the existing literature that reinforced the assumption that slower interfaces lead analysts to perform fewer interactions and gain fewer insights from their data. Battle and Heer discovered that, in practice, the issue is more nuanced; while latency does have an impact, it has been overstated relative to other factors such as the difficulty of the task users are trying to perform and the pacing of interactions based on “think time.” In another paper, this one appearing at the IEEE Information Visualization conference (InfoVis 2019), Battle and a multi-institutional team of researchers analyzed the impact of latency on visual search through a series of studies on Amazon Mechanical Turk. They found that while latency is a statistically significant predictor of user behavior under some conditions, in other cases, factors such as task type, task complexity and total interactions performed render latency virtually meaningless.

“What this research showed is that latency can have a more subtle and gradual effect than previously believed,” said Battle. “It’s still a factor, but it’s not the only one. This is a useful insight for designing better evaluations that reflect how people interact with these systems in the real world.”

Another previous belief that Battle subsequently turned on its head was the presumption that optimization schemes for well-researched use cases such as online analytical processing (OLAP) are also applicable to interactive scenarios. In a paper that appeared at SIGMOD 2020, she and her colleagues presented a novel benchmark for evaluating the suitability of database systems for supporting ad-hoc, real-time data exploration. Unlike other benchmarks for measuring database system performance, the new framework captures the cadence and flow of real users’ data explorations to more accurately reflect the dynamic nature of the associated queries.

“The patterns of queries we see issued to database systems are fundamentally different between the interactive and OLAP scenarios. The performance expectations are also different,” Battle noted. “Our paper provided the first empirical evidence of this mismatch, which had already been informally observed in the industry.”

Some of Battle’s latest work has focused on another potential mismatch, this time between users and designers of automated visualization recommendation systems. One of the selling points of these systems is that they alleviate some of the decision-making burden for analysts by suggesting which variables to focus on; by pointing users to the most salient visualizations and insights, the system speeds up the exploration process. In a paper published at the ACM Conference on Computer-Human Interaction (CHI 2021), Battle and her colleagues assessed how people actually use auto-generated visualizations to understand how user attitudes and expectations shape their results. While their findings partially backed up assumptions about users’ trust in algorithmically generated visualizations, the team pointed to a number of ways to make these systems more useful to different user profiles, including designing for different “foraging” patterns and taking into account the potential for biases about source and quality to influence the behavior of a subset of users. 

Despite the proliferation of visualization recommendation systems, there has been no way to rigorously evaluate their suitability for various tasks. That is, until last year’s VIS 2021 conference, where Battle and her co-authors earned a Best Paper Honorable Mention for presenting the first framework for fairly and rigorously comparing visualization recommendation systems. And in work that appeared at CHI 2022 earlier this month, Battle and her collaborators explored user preferences in a specific domain, public health, to understand what that category of analysts values most from visualization recommendation systems. She recently received a CAREER Award from the National Science Foundation to build on this line of work by developing new tools for evaluating how well different systems can help analysts meet their particular data exploration goals.

“By making it easier and faster for analysts to explore their data, recommendation systems can improve the accuracy and rigor of the insights they take away from these sessions,” said Battle. “But if we can’t formally compare them, then we have no idea whether new ones we build actually provide any benefits over the old ones. We also lack an empirical understanding of which systems are best suited to specific users’ goals. Both are essential for making data visualizations more relevant and more useful to people.”

Battle was formally honored by the TCDE community during the 38th IEEE International Conference on Data Engineering (ICDE 2022) held earlier this month in Kuala Lumpur, Malaysia.

Congratulations, Leilani! Read more →