Allen School News

Fall in Seattle is signified by the sight of trees turning from green to rust, the sound of raindrops striking rooftops, and the energy infusing the University of Washington campus as students embark upon a new journey of intellectual and personal exploration. For a group of graduating Allen School Ph.D. students, this quintessential autumn ritual carries an added significance as they look forward to their new careers as faculty members at universities across the country and beyond.

Meet the 11 outstanding scholars who are set to extend the Allen School’s impact through teaching, research, and service:

James Bornholt: University of Texas at Austin

James Bornholt will join the computer science faculty at the University of Texas at Austin next fall after spending a year as an applied scientist in Amazon Web Services’ Automated Reasoning Group. Bornholt completed his Ph.D. working with professors Emina Torlak, Dan Grossman, and Luis Ceze on research spanning programming languages, systems, and architecture as a member of the Allen School’s UNSAT and Programming Languages & Software Engineering (PLSE) groups. Bornholt earned a Best Paper Award at OSDI 2016 for Yggdrasil, a toolkit enabling programmers to write file systems with push-button verification, and a Distinguished Artifact Award at OOPSLA 2018 for SymPro, a novel technique for diagnosing performance bottlenecks in solver-aided code through symbolic evaluation. Bornholt was also an early contributor to the development of an archival storage system for digital data in synthetic DNA as part of the Molecular Information Systems Lab, a collaboration between the University of Washington and Microsoft Research. The project was selected as an IEEE Micro Top Pick in computer architecture for 2016.

Tianqi Chen: Carnegie Mellon University

Tianqi Chen will join the faculty of Carnegie Mellon University in 2020 after spending a year at UW spinout OctoML. He completed his Ph.D. working with Allen School professors Carlos Guestrin, Luis Ceze, and Arvind Krishnamurthy as a member of both the MODE Lab and interdisciplinary SAMPL group. Chen’s research encompasses machine learning and multiple layers of the system stack. He was one of the principal architects of the TVM framework, an end-to-end compiler stack designed to bridge the gap between deep learning and hardware innovation by enabling researchers and technologists to rapidly deploy deep learning applications on a range of devices without sacrificing power or speed. The Allen School team transitioned TVM to the non-profit Apache Software Foundation as an Apache Incubator project earlier this year. Chen also co-created XGBoost, an open-source, end-to-end tree boosting system that is designed to be highly efficient, flexible, and portable and which has been widely adopted by industry, and Apache MXNet, an open-source, deep learning framework that supports flexible prototyping and production that was adopted by Amazon Web Services.

Eunsol Choi: University of Texas at Austin

Eunsol Choi will join the faculty of the University of Texas at Austin next fall after spending a year as a visiting research scientist at Google AI in New York City. Choi completed her Ph.D. as a member of the Allen School’s Natural Language Processing group working alongside professors Luke Zettlemoyer and Yejin Choi. Her research focuses on methods of extracting and querying information from text, particularly structured representations of human information such as scientific findings, historical facts, and opinions using natural language questions. Choi was lead author of  multiple papers on this topic, including a novel framework for coarse-to-fine question answering that matched or outperformed existing models while scaling to longer documents, and a new dataset for exploring question answering in context (QuAC) that draws upon 14,000 information-seeking dialogs between teacher and student. Choi also contributed to an analysis of the linguistic patterns of news articles and political statements to determine whether content is trustworthy, unreliable, or satirical.

Jialin Li: National University of Singapore

Jialin Li accepted a faculty position at the National University of Singapore after completing his Ph.D. in the Computer Systems Lab, where he worked with Allen School professors Arvind Krishnamurthy and Tom Anderson and affiliate professor Dan Ports of Microsoft Research. Li builds practical distributed systems that combine strong consistency with high performance. One example is Arrakis, a project that earned a Best Paper Award at OSDI 2014. Arrakis is a new operating system that separates the OS kernel from normal application execution to allow applications access to the full power of the unmediated hardware. Li and his colleagues later received a Best Paper Award at NSDI 2015 for Speculative Paxos, a new replication protocol for distributed systems deployed in the data center that employs a new primitive, Multi-Order Multicast (MOM), to achieve significantly higher throughput and lower latency than the standard Paxos protocol. Li was lead author of a subsequent paper that introduced Network-Ordered Paxos (NOPaxos), a system for dividing replication responsibility between the network and protocol layers using another new primitive, Ordered Unreliable Multicast (OUM). NOPaxos achieves replication in the data center without the performance cost of traditional approaches.

Dominik Moritz: Carnegie Mellon University

Dominik Moritz is currently a research scientist at Apple and will join the faculty of CMU’s Data Interaction Group next year. Moritz recently completed his Ph.D. working with Allen School professor Jeffrey Heer and iSchool professor Bill Howe as a member of the Interactive Data Lab and the Database Group. His research focuses on the development of scalable interactive systems for data visualization and analysis. Moritz was a member of the team that developed Vega-Lite, a high-level grammar for rapidly generating interactive data visualizations that earned a Best Paper Award at InfoVis 2016. He subsequently received another Best Paper Award at InfoVis 2018 for Draco, an extension of Vega-Lite that offers a constraint-based tool for building visualizations. Draco formalizes guidelines for visualization design while permitting trade-offs based on user preferences. Moritz also co-created user-centered tools such as Pangloss, which applies optimistic visualization to enable interactive, exploratory data analysis of approximate query results, and Falcon, a web-based system for optimizing latency-sensitive interactions such as brushing and linking that eliminates costly precomputation and enables cold-start exploration of large-scale datasets.

Rajalakshmi Nandakumar: Cornell University

Rajalakshmi Nandakumar will join the faculty of Cornell University next spring as a member of the Jacobs Technion–Cornell Institute at Cornell Tech. Nandakumar earned her Ph.D. working with professor Shyam Gollakota in the Allen School’s Networks & Mobile Systems Lab, where she focused on the development of mobile health applications and novel interaction technologies leveraging the Internet of Things. Her projects included ApneaApp, a mobile app that employed active sonar technology to detect signs of sleep apnea that was commercialized by ResMed as part of the publicly available SleepScore app, and SecondChance, a mobile app for detecting signs of opioid overdose that was presented in the journal Science Translational Medicine and is being commercialized by Sound Life Sciences Inc. She and her colleagues also earned a Best Paper Award at SenSys 2018 for µLocate, a low-power wireless localization system for subcentimeter sized devices. During her time at the Allen School, Nandakumar was recognized with a Paul Baran Young Scholar Award from the Marconi Society, a Graduate Student Innovator of the Year Award from UW CoMotion, and a GeekWire feature as “Geek of the Week.”

Pavel Panchekha: University of Utah

Pavel Panchekha joined the University of Utah faculty after earning his Ph.D. working with professors Michael Ernst and Zachary Tatlock in the Allen School’s PLSE group. Panchekha’s research focuses on mechanical reasoning and synthesis for domain specific languages, including floating-point numerical programs and web page layout code. He and his colleagues earned a Distinguished Paper Award at PLDI 2015 for Herbie, a tool for finding and fixing floating-point problems. Herbie automatically rewrites floating-point expressions to eliminate numerical rounding errors and improve the accuracy of programs. Panchekha was also a major contributor to the Cassius Project, a framework for reasoning about web page layouts that offers tools for verification, synthesis, and debugging based on an understanding of how web pages render. As part of that project, Panchekha led the development of VizAssert, which verifies the accessibility of page layouts across a range of possible screen sizes, browsers, fonts, and user preferences.

Aditya Vashistha: Cornell University

Aditya Vashistha is joining Cornell University’s Department of Information Science this fall after completing a stint as a visiting researcher at Microsoft. Vashistha, who has the distinction of having earned the 600th Ph.D. granted by the Allen School, completed his degree working with professor Richard Anderson in the Information & Communication Technology for Development (ICTD) Lab. His research focuses on the development and deployment of novel computing systems for people with disabilities or low literacy and residents of rural communities, including the first-ever analysis of the use of social media platforms by low-income people in India, which earned a Best Student Paper Award at ASSETS 2015, and Sangeet Swara, a voice forum that relies on community moderation to disseminate cultural content in rural India that earned a Best Paper Award at CHI 2015. He and his collaborators later earned an Honorable Mention at CHI 2017 for Respeak, a low-cost, voice-based speech transcription system that provides dignified digital work opportunities in low-resource settings. Vashistha’s work, which earned him a Graduate Student Researcher Award from the UW College of Engineering, so far has reached an estimated 220,000 people in Africa and southern Asia.

Doug Woos: Brown University

Doug Woos joined the Brown University faculty as a lecturer focused on introductory computer science and courses in systems and programming languages. He recently earned his Ph.D. working with Allen School professors Tom Anderson in the Computer Systems Lab and Michael Ernst and Zachary Tatlock of the PLSE group. In his research, Woos applies techniques from programming languages to systems problems, with a focus on new approaches for verifying and debugging distributed systems. He was a member of the team behind the award-winning Arrakis operating system and co-led the development of Verdi, a novel framework for the formal verification of distributed systems using the Coq proof assistant that supports fault models ranging from idealistic to realistic. Woos and his colleagues later used Verdi to achieve the first full formal verification of the Raft consensus protocol, a critical component of many distributed systems. He also led the development of Oddity, a graphical interactive debugger for distributed systems that combines the power of traditional step-through debugging with the ability to perform exploratory testing.

Mark Yatskar: University of Pennsylvania

Mark Yatskar will take up a faculty position at the University of Pennsylvania next fall after completing his time as an AI2 Young Investigator. He completed his Ph.D. working with Allen School professors Luke Zettlemoyer in the NLP group and Ali Farhadi of GRAIL on research that uses the structure of language to advance new capabilities in computer vision. Yatskar was a member of the team that developed ImSitu, a situation recognition tool that uses visual semantic role labeling to move computers beyond simple object or activity recognition. ImSitu is designed to achieve a more human-like understanding of how participants and objects interact in a scene, enabling computers to predict what will happen next. Yatskar has also studied ways to reduce gender bias in machine learning datasets. For example, he and his collaborators earned a Best Long Paper Award at EMNLP 2017 for presenting Reducing Bias Amplification (RBA), a technique for calibrating the outputs of a structured prediction model to avoid amplifying gender biases ingrained in image labels incorporated into training datasets.

Danyang Zhuo: Duke University

Danyang Zhuo will join the faculty of Duke University next fall after completing a postdoc in the RISE Lab at the University of California, Berkeley. Zhuo recently completed his Ph.D. working with professors Tom Anderson and Arvind Krishnamurthy in the Allen School’s Computer Systems Lab. His research spans operating systems, distributed systems, and computer networking, with an emphasis on improving the efficiency and reliability of infrastructure and applications in the cloud. One of his early contributions was Machine Fault Tolerance (MFT), a new failure model that improves the resilience of data center systems against undetected CPU, memory and disk errors. Zhuo was lead author of a paper presenting Slim, a low-overhead container overlay network that improves the performance of large-scale distributed applications. Slim manipulates connection-level metadata to enable network virtualization to improve throughput and reduce latency. He also led the development of CorrOPT, a system for mitigating packet corruption in data center networks that was shown to reduce corruption losses by up to six orders of magnitude and improve repair accuracy by 60% compared to the current state of the art.

“Whether our graduates are heading to academia or industry, we are extremely proud of their past achievements and ongoing contributions to our field,” said Allen School director Hank Levy. “But I am thrilled that so many of our outstanding alumni this year will be guiding the next generation of students in using computing to make a positive impact on society. It is exciting to see so many former students become faculty colleagues who will extend the reach of the Allen School and University of Washington around the world.”

In addition to the 11 alumni of the Allen School’s Ph.D. program, two graduates with strong ties to the program also went on to faculty positions. Edward Wang, a graduate of UW’s Department of Electrical & Computer Engineering, recently joined the faculty of the University of California, San Diego. Wang recently earned his Ph.D. working with professor Shwetak Patel in the Allen School’s Ubicomp Lab on new sensing techniques for detecting and monitoring health conditions using mobile devices. Sarah Chasins, meanwhile, spent the past several years embedded in the Allen School and working with professor Rastislav Bodik while completing her Ph.D. from the University of California, Berkeley, where Bodik was previously a faculty member. Chasins, whose research is aimed at democratizing programming and developing tools that make it easy to automate programming tasks, will join the Berkeley faculty next fall.

Congratulations and best wishes to all of our newly-minted faculty colleagues — see you on the conference circuit!

Benjamin Lee, a second-year Ph.D. student in the Allen School’s Artificial Intelligence group working with professor Daniel Weld, has been named a 2020 Innovator in Residence by the Library of Congress. Now in its second year, the Innovator in Residence program aims to enlist artists, researchers, journalists, and others in developing new and creative ways of using the library’s digital collections.

During his residency, Lee will apply deep learning to enable the automatic extraction and tagging of photographs and illustrations contained in the more than 15 million newspaper scans comprising the library’s Chronicling America collection. His goal is to produce interactive visualizations, searchable by topic, that will make the content more accessible to users and support cultural heritage research.

“A primary motivation behind my project is to excite the American public by demonstrating the possibilities of applying machine learning to library collections,” Lee explained in an interview posted on the library’s blog. “Given the widespread enthusiasm about machine learning, this project could draw new people to the Library of Congress’s digital collections, as well as excite the Library’s regular users about emerging technological advances. My hope is that this project could also inspire members of the public to start their own coding projects involving the Library of Congress’s digital collections.”

Lee is no stranger to combining technology and culture, having first developed an interest in digital humanities as an undergraduate at Harvard College. That led to a year-long fellowship at the United States Holocaust Memorial Museum, where he used machine learning to enable new ways for users and researchers to search the archives of the International Tracing Service. His journey into this line of research was a deeply personal one, inspired by his grandmother who survived Auschwitz-Birkenau Concentration Camp during the Holocaust.

Lee previously earned a Graduate Research Fellowship from the National Science Foundation to support his work at the Allen School on explainable artificial intelligence and human-AI interaction. He is one of only two Innovators in Residence named by the library this year; the other, Brian Foo, is a data visualization artist at the American Museum of Natural History who plans to make interesting and culturally relevant material from the library’s audio and moving image collections more accessible to the public by embedding it into hip hop music.

Read the Library of Congress press release here, and an interview with Lee and Foo here. Learn more about the Innovator in Residence program here.

Congratulations, Ben!

This month’s Allen School Undergrad Spotlight features Eunia Lee, a third year, direct admission computer science major from Sammamish, Washington. Lee is an Allen School Ambassador and chair of the University of Washington’s chapter of the Association for Computing Machinery (ACM). Through her service, she aims to show young women in high school and those just starting out in computer science that they can be leaders in the tech world.

Allen School: Why did you choose to major in computer science?

Eunia Lee: I never thought about pursuing computer science. Throughout high school I was interested in chemistry and biology, so I planned to pursue something in either field. During my junior year I took an introductory computer science class — mainly to fulfill a graduation requirement. Something about the projects and the concepts stood out to me more than in any other class before. Even outside of the classroom, I would be thinking about a problem I was stuck on so that, as soon as I got to a computer, I could try out different solutions. When I eventually was admitted to UW, I was given the amazing opportunity to come to the Paul G. Allen School as a direct admit. Soon after I arrived, I knew I made the right choice.

Allen School: What do you find most enjoyable about being an Allen School student?

EL: There’s a lot I love about the Allen School and its community — the courses, the opportunities and the labs. Ultimately, I think what makes these aspects so special are the people behind them. The past courses I’ve taken have had such amazing lecturers and teaching assistants that truly make learning a great experience. I used to be someone who didn’t like talking during office hours or in a small classroom, but now I feel comfortable asking questions and even making mistakes. The Allen School advising team and staff are always working incredibly hard and care so much about our program, which is evident in many ways. Also, my peers are such impressive people who amaze me with their accomplishments and passions every day. These people make me excited to be a part of such a vibrant community!

Allen School: What inspired you to volunteer with the Allen School Ambassadors, and what keeps you active in the program three years later?

EL: When I first came to UW, I heard about the Ambassadors. It sounded like a perfect opportunity for me, because I loved to tutor others when I was in high school and wanted to share my experiences with computing. With the group, I’ve been a part of various events like teaching elementary students how to program mini robots and leading processing workshops in the Allen Center on Saturdays throughout the year.

For me personally, it’s amazing to see high school students I have met at career fairs or workshops eventually become students at the Allen School. Up until college, I never had any female role models in computer science. One of my goals for our outreach is to actively change that experience for other women who may have never considered computing for their future.

Allen School: What do you hope to accomplish through your leadership role at ACM? 

EL: With the encouragement of fellow ambassadors who were involved in ACM, I decided to apply to be a social event coordinator my freshman year. Although at times it was crazy and busy, the memories I made and the people I met made it worth the ups and downs. That experience led me to apply to be chair for the current school year.

As a member of the ACM leadership team, I enjoy being able to drive what kind of events we have and the purpose behind them. In the beginning of my time at UW, I struggled to navigate and balance all of my interests. To help students who might share that feeling, ACM has lots of programs and events to guide students in many aspects. Our hope with ACM is that we provide resources to all students, and I highly encourage my peers to come out to events that sound exciting to them.

Allen School: Who or what inspires you in the Allen School? 

Many of my peers are doing amazing things in research, internships, classes or volunteering. When I first came to the Allen School, I was unsure of what it meant to be a woman studying computer science and how my identity could impact the way I was perceived by others. There were times where I felt unsure of whether I was meant to pursue a future in the tech industry, but because of those around me, I continued and I’m so thankful for these people. My CSE 142 teaching assistant, Ivy, former lead ambassador Katherine, previous ACM officers Allison, Cheng, Silin, and Yegee, and my go-to internship guide Puja are just some of the upper-class people who cheered me on from the beginning. I hope that I can pass on their knowledge to the newer members of our community throughout the rest of my time here!

During this season of gratitude, we are particularly thankful to students like Eunia. Her commitment to giving back to our community and guiding the next generation makes her an exceptional role model for current and future Allen School students!

Every fall, the Allen School’s Industry Affiliates program hosts a research showcase to highlight the ways in which our faculty and student researchers are advancing the frontiers of computing. The day-long event features sessions devoted to various topics in computing and culminates in an open house and poster session that gives our industry partners, alumni, and friends an opportunity to learn more about the latest innovations emerging from Allen School labs.

Among the many highlights of the 2019 Research Showcase, which was held Wednesday in the Paul G. Allen Center and Bill & Melinda Gates Center on the University of Washington’s Seattle campus, was a keynote by professor Tim Althoff. Althoff, who joined the Allen School faculty last year, combines techniques from data mining, social network analysis, and natural language processing to generate actionable insights about people’s physical and mental health.

For example, Althoff is pursuing ground-breaking research that aims to use data generated by people’s everyday behavior to better understand the level and variance of physical activity of populations around the world. As part of this work, he found that the inequality of physical activity within a country is a predictor of obesity rates. Althoff believes that such insights can inform how our environment influences our behavior and health, and in the future could support the data-driven design of cities.

“This research is uniquely enabled by the massive digital traces generated by wearables and mobile devices,” explained Althoff. “It revealed the existence of a health inequality that we were previously unaware of.”

For another project, Althoff analyzes online search engine interactions to gauge the impact of sleep on cognitive performance in the workplace and among athletes. He is also exploring a data-driven approach to mental health counseling to identify the most effective conversational strategies to support peer-to-peer counseling and improve client outcomes. 

In addition to Althoff’s talk, the program included in-depth sessions in which participants had an opportunity to explore the latest developments across a variety of domains, including data management, programming languages and software engineering, robotics, systems, augmented and virtual reality, ubiquitous computing, machine learning, deep learning for natural language processing, and the intersection of computation and biology. At the end of the day, Allen School leadership and representatives of Madrona Venture Group announced the recipients of the 14th annual Madrona Prize and the People’s Choice Award — a tradition in which we celebrate the innovative contributions of our student researchers with prizes and public bragging rights.

This year’s grand prize winner, CoAI: Cost-Aware Artificial Intelligence for Health Care from the Allen School’s Laboratory of Artificial Intelligence for Medicine and Science (AIMS) led by Professor Su-In Lee, was chosen by Madrona Venture Group for combining excellence in research with the potential for commercial success. CoAI is a machine learning method for making cost-sensitive predictions in clinical settings that maintains or improves accuracy while dramatically reducing the time it takes to predict a variety of patient outcomes. The team, which includes Lee, Allen School Ph.D./M.D. students Gabriel Erion and Joseph Janizek, and Drs. Carly Hudelson and Nathan White of UW Medicine, developed CoAI to integrate with existing machine learning packages with just a few lines of code to improve patient care when it comes to time-sensitive clinical prediction tasks in all areas of medicine.

Madrona also recognized three runners-up that also exemplify high-quality research combined with commercial potential:

AuraRing: Precise Electromagnetic Finger Tracking via Smart Ring, from the UbiComp Lab, by Electrical & Computer Engineering Ph.D. students Farshid Salemi Parizi and Alvin Cao; Allen School alumnus Eric Whitmire (Ph.D., ‘19), now a research scientist at Facebook Reality Labs; Allen School Ph.D. student Ishan Chatterjee; GIX master’s student Tianke Li; and professor Shwetak Patel, who holds a joint appointment in the Allen School and Department of Electrical & Computer Engineering

Molecular Tagging with Nanopore-orthogonal DNA Strands, from the Molecular Information Systems Lab, by Allen School Ph.D. students Katie Doroschak and Melissa Queen; Chemistry undergraduate Karen Zhang; Allen School master’s student Aishwarya Mandyam (B.S., ‘19); research scientist Jeff Nivala; Allen School affiliate professor Karin Strauss, Principal Research Manager at Microsoft Research; and Allen School professor Luis Ceze.

HomeSound: Exploring Sound Awareness in the Home for People Who Are Deaf and Hard of Hearing, from the Makeability Lab, by Allen School Ph.D. students Dhruv Jain and Kelly Mack; Human-Centered Design & Engineering Ph.D. student Steven Goodman; professor Leah Findlater of the Department of Human-Centered Design & Engineering; and Allen School professor Jon Froehlich.

Calling the Allen School showcase “one of the highlights of our year,” Madrona managing director Tim Porter said, “The Allen School at the UW is an incredibly important resource for our region and as the school has grown and actively attracted researchers from many different areas, we have seen the breadth and depth of innovation grow.”

HomeSound also took home the coveted People’s Choice Award, which is voted on by attendees at the open house as their favorite poster or demo of the evening. The runner-up for People’s Choice was ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks. The team behind ALFRED spans the Allen School’s Robotics and Natural Language Processing groups, including Allen School Ph.D. students Mohit Shridhar and Daniel Gordon; Allen School postdoc Jesse Thomason; former postdoc Yonatan Bisk, currently a visiting researcher at Microsoft; Winson Han and Roozbeh Mottaghi of the Allen Institute for Artificial Intelligence; and Allen School professors Luke Zettlemoyer and Dieter Fox.

“Our students and faculty aim for real-world impact, and it really shows in the presentations we saw this week,” said Hank Levy, director of the Allen School. “We’re pleased that so many of our industry partners could join us to learn about the exciting developments happening in our labs — developments that not only will advance our field, but also have the potential to improve millions of people’s lives. I want to thank Madrona Venture Group, in particular, for their friendship and support to the school and our students throughout the years.”

This is the 14th year in which Madrona has formally recognized student research with commercial potential emerging from the Allen School.

Read more in the Madrona press release here, and check out GeekWire’s coverage of Althoff’s keynote here and the poster session here. See a complete list of past Madrona Prize winners here, and learn more about the Allen School’s Industry Affiliates program here.

Thanks to Madrona and to all of our industry partners, alumni and friends who showed up yesterday in support of our students, and congratulations to the winners — see you next year!

 As part of the UW EXP Study led by professor Jennifer Mankoff in the Allen School’s Make4all Lab, a team of researchers set out to measure how specific incidents of discrimination can affect people’s behavior in the short-term by analyzing the experiences of college students at the University of Washington with the help of mobile and wearable devices.

In a paper presented last week at the Association for Computing Machinery’s 22nd Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2019) in Austin, Texas, Mankoff and her collaborators analyzed the prevalence of discriminatory events and their association  with behaviors among UW freshmen over two academic quarters in 2018. Working with 209 student volunteers — 176 of whom completed the study — the team sought to quantify each separate experience of discrimination to learn more about the immediate reactions. The study used passive sensing data collected via Fitbit Flex 2 wearable devices that tracked participants’ daily activities like sleep and movement and AWARE, an app installed on their smartphones that tracked their location, when and how often their phone screens were unlocked, and the length of their calls. The sensing data was correlated with the results of periodic surveys in which the students self-reported instances of unfair treatment. The researchers aimed to engage female students in STEM-related fields, where gender discrimination is an ongoing problem, as well as minorities and first-generation students in engineering. 

By the end of the study, participants had reported experiencing a total of 448 discriminatory events. The researchers found that, on average, those that reported being discriminated against were more physically active, interacted with their phones more, and slept less on the day of the event. 

“It’s so hard to summarize the impact of something like this in a few statistics,” Mankoff said in a UW News release. “Some people move more, sleep more or talk on the phone more, while some people do less. Maybe one student is reacting by playing games all day and another student put down their phone and went to hang out with a friend. It’s giving us a lot of questions to follow up on.”

Mankoff said the survey didn’t capture all discrimination events, but was instead a snapshot of some of the students’ experiences. More than half of them reported undergoing at least one discrimination event; many of those experienced about five events during the six month study period. In addition to the data on sleep, physical activity, and communication, the team found that discrimination events were associated with increased depression and loneliness — though the impact appeared to be less among those who could count on better social support.

“We looked at objective measures of behavior to try to really understand how this experience changed students’ daily life,” explained Allen School Ph.D. student Yasaman Sefidgar, lead author of the paper. “The ultimate goal is to use this information to develop changes that we can make both in terms of the educational structure and individual support systems for students to help them succeed both during and after their time in college.”

The researchers hope that, by possessing a better understanding of the consequences of discrimination, this work will lead to more supportive policies focused on prevention, intervention, and student retention in higher education.

“These students are not just giving us data, which sounds like some abstract, unemotional term,” Mankoff said. “They are sharing deeply personal information with us. It’s very important to me that we honor that gift by finding ways to help that don’t place the responsibility to deal with discrimination all on the individual.”

In addition to Mankoff and Sefidgar, co-authors on the paper include Allen School Professor Tim Althoff; UW Information School Dean Anind Dey; Eve Riskin, associate dean of diversity and access for the UW College of Engineering; Paula Nurius, professor of the UW School of Social Work; Anne Browning, founding director of the UW Resilience Lab; Kevin Kuehn, a clinical psychology doctoral student at the UW; and University of Michigan doctoral student Woo Suk Seo.

To learn more, read the full publication, “Passively-sensed behavioral correlates of discrimination events in college students,” the related UW News release, and coverage by Inside Higher Ed and The College Post. 

The University of Washington values and honors diverse experiences and perspectives, strives to create welcoming and respectful environments and promotes access and opportunity. Students, faculty and staff that encounter or suspect incidents of bias are encouraged to report it. 

For students who are among the first in their families to attend college, the experience of navigating a four-year degree can be daunting. From decoding the campus lingo to overcoming imposter syndrome, the more than 250 undergraduates currently pursuing their bachelor’s degree as first-generation students in the Allen School are breaking down barriers and carving their own paths. We caught up with a few members of our community who are either finding their footing as first-generation students, or have been there, done that, and are happy to share what they learned in honor of the National First-Generation College Celebration taking place today at the University of Washington and across the nation.

Meet undergraduates Andres Eligio and Aaron Pham, graduate student Alyssa La Fleur, and academic adviser Chelsea Navarro — each with an inspirational story to tell about where they have been and where they are going as first-generation college students.

Andres Eligio

Andres Eligio is a freshman from Des Moines, Washington whose parents immigrated from Mexico in 1996 to provide a better life for their children. He is the first in his family to pursue an education after high school. Eligio credits the College Access Now (CAN) program as an important factor in his pursuit of a college degree. CAN helped him navigate and learn about colleges and the application process, while robotics, mathematics, and computer science classes in middle and high school solidified his interest in computer science.

Allen School: What does being a first-generation student mean to you?

Andres Eligio: To me it means facing the challenge of pursuing an education without having your parents’ support. It means working towards a goal which you can’t truly see. It can be scary to try and break away from what your parents have done. But I hope to fulfill all the work they did by coming to this country and take advantage of all the opportunities I am given. 

Allen School: What is your favorite part about being an Allen School student?

AE: My favorite part by far of the Allen School is how hard they work to make sure you feel like a part of the community. The faculty are very friendly and provide countless opportunities to make connections and learn more about being a UW student. I almost didn’t go to college. It took me a very long time to decide whether to continue my education after high school or work for my father’s landscaping company. I decided to try. I didn’t think I’d feel like I belonged, but I took part in the CSE Startup, a program for direct admit students that help them get used to life and classes at the UW. Before taking this course, I didn’t feel like I belonged. Now, having completed the program, I feel as if I am a part of the UW and more importantly, meant to be here in the Allen School. I feel confident about the rest of the school year and my success academically.

Allen School: What advice do you have for future first-gen students?

AE: I would tell them that I know it can be scary trying to pursue an education after high school. You can’t necessarily look to your parents for help, and they can only try to understand the struggle of finding the right college and choosing which field to study. Even though it seems hard and confusing, if you truly want to go to college you can do it. Look into your school or programs for support. Talk to counselors and friends. There will always be people to support you. It isn’t easy to leave your parents. Personally, it was really hard for me to leave. I worked hard to support my family, both in my dad’s business and in taking care of my brother. Many of you are in a similar situation, be it helping your parents or taking care of siblings. Your parents have worked really hard to provide you opportunities they didn’t have, and you should take full advantage of it.

Aaron Pham

Aaron Pham, a junior who transferred to the University of Washington in the spring, moved to the United States with his family in February of 2016. Born in Vietnam, he is the oldest child in his family and the first to go to college. His father worked for the U.S. Embassy in Vietnam; when the U.S. government gave him the opportunity to come to the States, the whole family moved to Washington, where Pham began his college career the following year at South Seattle College.

Allen School: What does being a first-generation student mean to you?

Aaron Pham: While it is such an honor, this also comes with challenges and responsibility. I will be a good example to my nephew and my younger cousins. It is a great motivation for me to keep learning, improving and pushing myself out of my own limits to become a successful student.

Allen School: What is your favorite part about being an Allen School student?

AP: My favorite part is the opportunities I have to connect with other friends and professors who also have a great passion for computer science. Studying and working in this environment not only improves my technical coding skills, but also guides me to become a person who wants to make impacts and contribute to the community and society by applying my knowledge and my passion for computer science. Being in the Allen School allows me to reach my full potential.

Allen School: What advice do you have for future first-gen students?

AP: Get involved at school, find your community, connect with a counselor, attend orientation activities, know where to get help on campus, and embrace who you are and don’t compare yourself to others. Everyone has their own weaknesses and strengths — so do you. No one is perfect. We are all here to learn and push ourselves out of our own limits. 

Alyssa La Fleur

Alyssa La Fleur, from Monroe, Washington, is a student in the Allen School’s full-time Ph.D. program. She fell in love with computational biology as an undergraduate and is now developing the skills to build a successful research career. La Fleur was homeschooled and attended a co-op before enrolling in Cascadia Community College through the Running Start program in high school. She graduated in the spring from Whitworth University with a triple major in math, bioinformatics and biochemistry. 

Allen School: What does being a first-generation student mean to you?

Alyssa La Fleur: It means that I will have greater job opportunities and financial security than my parents.

Allen School: What is your favorite part about being an Allen School student?

AL: So far, my favorite thing has been the friendly community and the diverse fields of study represented in it.

Allen School: What advice do you have for future first-gen students?

AL: Don’t be afraid to ask questions, even if you think they might be stupid. It’s also fine if you don’t know what questions you should be asking in the first place, as you are in a new environment and probably won’t realize what the gaps are in your knowledge right away. Also, if someone ever makes you feel uncomfortable when asking for help, there are plenty of other campus resources to use instead. I particularly recommend asking senior students in your major for advice.

Chelsea Navarro

Chelsea Navarro is an academic adviser at the Allen School focused on serving undergraduate students, including students transferring to UW from two-year colleges. As a first-generation student from San Diego, California, she credits services such as the Educational Opportunity Program (EOP) and Federal TRIO Programs in helping her begin her college career at Palomar Community College before transferring to San Diego State University, where she received her bachelor’s degree in sociology. The dedicated student affairs professionals and advisers that worked with her along the way inspired her to pursue a career in higher education. Navarro subsequently earned a Master’s of Education in student affairs from the University of California, Los Angeles and is proud of being a first-generation student. 

Allen School: What does being a first-generation student mean to you?

Chelsea Navarro: Growing up, my biggest ambition was to graduate from high school since it’s an accomplishment that not many people in my family are able to fulfill. My parents met as teenagers and had me when they were teens themselves. My father is a high school graduate and my mom dropped out of school when she was in middle school. I am the eldest of two daughters. One saying that has guided my practice is “remember why you started,” as so much of what I do is rooted in my higher education experience. I got into this field to help others and to hopefully be part of the support network that makes a student successful, like many advisers and faculty were for me when I was a student.

Allen School: What is your favorite part about working at the Allen School?

CN:  Working at the Allen School is an opportunity for me to continue to give back as faculty and advisers did for me. Part of my role as an undergraduate adviser at the Allen School is to work with our transfer students, which I absolutely enjoy doing. In many ways, it feels like I’ve come full circle and working at the Allen School is an amazing opportunity for me to continue to help others. 

Allen School: What advice do you have for future first-gen students?

CN:  When I first started at community college, I was incredibly lost and had a difficult time understanding university policies and interpreting my degree requirements: What is a credit? What happens in a lab? What is an associate’s degree? Is it okay to meet with professors? I’ll never forget the first time I met with an adviser and brought a huge list of questions with me to my appointment. I learned so much in those 30 minutes. Given my experience, my advice would be to encourage first-year, first-generation college students to be up front with the questions they want to have answered. It’s okay to admit that you feel lost and that you need help. As a first-generation student, I often felt like I was the only one going through the overwhelming experience of being the first in my family to go into higher education. Plus, I was scared to talk about my problems because in my mind, an administrator would notice and tell me that my greatest fear was true — they would say that I didn’t belong in higher education. Imposter syndrome is a difficult reality for many first-year, first-generation college students, so I encourage any students going through it to talk about it so they can get the support they need. 

We are grateful for the many contributions our first-generation students, faculty and staff have made to the Allen School community! Learn more about the National First-Generation College Celebration here, and activities celebrating UW’s first-generation community here.

Check out our student profiles from last year’s celebration here.

Anyone who believes the adage “Rome wasn’t built in a day” hasn’t met the members of the Allen School’s Graphics and Imaging Laboratory (GRAIL). Ten years ago, postdoc Sameer Agarwal, Ph.D. student Ian Simon, alumnus Noah Snavely, professor Steve Seitz, and affiliate professor Richard Szeliski of Microsoft Research demonstrated how to digitally reconstruct the Italian capital in 3D using the large cache of photos shared on the internet. Last week, the team was one of two recipients of the Helmholtz Prize recognizing papers from a decade ago that have had a significant impact on computer vision research at the International Conference on Computer Vision (ICCV 2019) held in Seoul, Korea.

At the time the paper was written, city-scale 3D reconstructions largely relied on data from structured sources such as satellite imagery from Google Earth or street-level imagery captured by a moving vehicle. These visual datasets are typically produced by cameras with consistent calibration at a regular sampling rate. They are also often accompanied by additional sensor data, such as GPS, which further simplifies the computation involved in reconstructing a location. By contrast, images from unstructured sources — those posted on Flickr and other photo sharing websites — tend to share none of those characteristics. A search for “Rome” on Flickr returned more than two million photos at the time the researchers embarked on their project, reflecting a variety of camera settings, angles, lighting conditions, and location information.

To overcome these challenges and tap into what they described as “extremely rich source of information about the world,” the researchers sought to leverage massive parallelism along with the massive redundancy found in large internet photo collections. The team employed a combination of parallel distributed matching and reconstruction algorithms to create a system that scales in line with both the size of the problem and the available computational resources. Using this approach and applying state-of-the-art techniques such as structure from motion (SfM), SIFT, vocabulary trees, bundle adjustments, and more, they reconstructed a 3D version of the Eternal City in less than a day from a trove of 150,000 images harvested from the internet — the first city-scale reconstruction produced from unstructured photo collections.

The Helmholtz Prize is awarded every other year by the IEEE Computer Society’s Technical Committee on Pattern Analysis and Machine Intelligence. The team originally presented its winning paper at ICCV 2009 in Kyoto, Japan. Since then, Agarwal and Simon (Ph.D., ‘11) have gone on to engineering positions at Google, while Snavely (Ph.D., ‘08) is a member of the computer science faculty at Cornell Tech and a researcher at Google Research in New York City. Seitz currently splits his time between the Allen School and Google, where he serves as the director of teleportation, while Szeliski is now a research scientist and founding director of the Computational Photography Group at Facebook Research.

To learn more, read the winning research paper here, and check out the project website here.

Congratulazioni to the entire team!

The strength and enduring impact of the Allen School and University of Washington’s contributions in accessible technology were on full display at the 21st International ACM SIGACCESS Conference on Computers and Accessibility, known as ASSETS, last month in Pittsburgh. Current or former Allen School researchers had a hand in three award-winning papers recognized at the conference, with a mix of current students, faculty, and alumni all represented. 

The Best Student Paper Award was granted to the paper “Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery.” The authors,  Allen School Ph.D. students Galen Weld and Esther Jang, undergrad Aileen Zeng, professors Kurtis Heimerl, in the Information and Communication Technology for Development Lab  and Jon Froehlich, founder of the Makeability Lab and University of Maryland Ph.D. student Anthony Li, wrote about the use of deep learning to automatically assess the accessibility of sidewalks found in online imagery.

In an effort to let the public know which sidewalks in any city are accessible to people with disabilities, researchers began using smartphone-based tools to capture sidewalk accessibility problems on a large scale. Users marked accessibility on a map using their smartphones. This solution was not without flaws‒not many regions are adopting the program, the areas they cover are small and the burden on users is too much: they must download an app, take a picture, annotate it and upload it. To improve upon those issues, researchers implemented machine learning and satellite imagery to decrease manual labor and costs. However, even this work has been impacted negatively by small training sets and less than stellar machine-learning. To improve the process even more, the Allen School team used residual neural networks modified to support image and non-image features and had bigger training sets based on a dataset of 300,000+ image-based sidewalk accessibility labels collected by Project Sidewalk in the Makeability Lab. The results described in the winning paper were more accurate than previous automated methods, and in some cases exceeded the accuracy of human-generated labels.

Members of the Allen School also contributed to the 2019 SIGACCESS ASSETS Paper Impact Award, granted to a paper at least 10 years old that has had a significant impact on computing and information technology addressing the needs of persons with disabilities. Allen School  alumnus Jeffrey Bigham (Ph.D., ‘09), Information School professor and Allen School adjunct professor Jacob Wobbrock, and Information School alumnus Shaun Kane (Ph.D. ‘11), earned the award for their paper, “Slide Rule: Making mobile touch screens accessible to blind people using multi-touch interaction techniques.”  

Before the iPhone popularized the touchscreen smartphone, users who were blind could rely on their sense of touch using a cell phone’s physical buttons. To enable these users to take advantage of the latest smartphone features, the team designed Slide Rule, an audio-based program that enables blind users to access touch screen applications through the use of gestures. Slide Rule was the first prototype to exhibit finger-driven screen-reading and a second-finger tap gesture, today called “split tap,” to trigger on-screen targets like links and buttons. Since the team published its paper, major manufacturers have incorporated these features into their commercial products.

Last but not least, recent Allen School graduate Danielle Bragg (Ph.D., ‘18), who worked with Allen School professor Richard Ladner as a student, was lead author of the paper that earned  this year’s ASSETS Best Paper Award. Bragg and her co-authors at Microsoft Research were recognized for their paper “Sign language recognition, generation and translation: An interdisciplinary perspective,” which presents the results of an interdisciplinary workshop where researchers discussed developing successful sign language recognition, generation and translation systems in fields such as computer vision, computer graphics, natural language processing, human-computer interaction and linguistics. 

Congratulations to all of the ASSETS 2019 award recipients! 

Researchers from the Allen School’s UNSAT group took home one of two Best Paper Awards and a Distinguished Artifact Award at the Association for Computing Machinery’s 27th Symposium on Operating Systems Principles (SOSP 2019) in Ontario, Canada last week. The winning paper, “Scaling symbolic evaluation for automated verification of systems code with Serval,” introduces a new framework for building automated verifiers for systems software.

Serval was developed by Allen School Ph.D. student and lead author Luke Nelson; alumnus James Bornholt (Ph.D., ‘19), now a faculty member at the University of Texas at Austin; Allen School professors Emina Torlak and Xi Wang; Columbia University professor Ronghui Gu; and Andrew Baumann, a principal researcher at Microsoft Research. Together, the researchers created a framework that overcomes several obstacles to scaling automated verification, including the developer effort required to write verifiers, the difficulty of finding and fixing performance bottlenecks, and limitations on their applicability to existing systems.

Unlike previous push-button verification approaches, which support automation at the expense of generality by requiring the co-design of systems and verifiers, Serval provides an extensible infrastructure that enables developers to easily retarget verifiers to new systems, including those not originally designed for automated verification. To do this, it leverages Rosette, a solver-aided programming language for synthesis and verification, to “lift” an interpreter into a verifier — that is, transform a regular program to work on symbolic values.

Verifiers created using Serval inherit a number of vital optimizations from Rosette, including constraint caching, state merging, and partial evaluation. But Serval goes a step further by introducing new capabilities for identifying and repairing performance bottlenecks. Employing recent advances in symbolic profiling, which offers a systematic approach to discovering performance bottlenecks, the researchers built a catalog of common bottlenecks for automated verifiers that includes indirect branches, memory accesses, trap dispatching, and more. They then built into Serval a set of symbolic optimizations that eliminate such bottlenecks and improve performance by exploiting domain knowledge to produce solver-friendly constraints for a class of systems.

To demonstrate Serval’s utility, the team developed reusable, interoperable automated verifiers for four instruction sets — RISC-V,  X86-32, LLVM, and Berkeley Packet Filter (BPF) — and used them to uncover previously unknown bugs in existing, unverified systems such as Keystone and the Linux kernel. They also applied Serval to two systems, CertiKOS and Komodo, to demonstrate how previously verified systems can be retrofitted for automatic verification.

Read the full research paper here, and explore the Serval on the project website here.

Way to go, team!

Fall is back and so is the Allen School’s Undergrad Spotlight! This month’s student feature is Bellevue, Washington native Manoj Sarathy. Even before his arrival as part of the school’s expanded Direct to Major admissions program, the freshman computer science major was using machine learning to help environmental conservationists track and organize wildlife data. He was recently featured in the Seattle Times and on King 5 News for his work supporting wolverine recovery in Washington.  

Allen School: Why did you want to study computer science, and what made you choose the Allen School?

Manoj Sarathy: Like most high school seniors, I had a lot of interests but my work on applying machine learning to the field of environmental conservation showed me that computer science can be useful in essentially any field. I decided to study at the Allen School because of the connections the school has with all the major companies that are implementing machine learning — and I wanted to stay close to home in the beautiful Pacific Northwest.

Allen School: What do you enjoy most about being an Allen School student?

MS: I find the resources available to the Allen School’s undergraduate students to be extremely valuable. For example, the career fairs that took place earlier this month were very useful. I learned more about companies hiring in the computer science field.

Allen School: What activities and interests do you have outside of your studies?

MS: I have attended a meeting for the Society for Economic Restoration and will be attending some of their work parties to restore the campus. I am also interested in finding out more about Students Expressing Environmental Dedication (SEED). I hope to continue playing squash, a racquet sport, during my free time. One of the opportunities I gave up by coming to UW was playing for a varsity squash team, but I hope I can be in some kind of squash club here. 

Allen School: Why did you become a member of the Conservation Northwest while you were in high school?

MS: I wish I could say it was purposeful, but it was honestly an accident. I learned about the organization while doing some online research regarding environmental conservation in the Pacific Northwest for an environmental science class I was taking in high school. I really liked the work they do, like building wildlife overpasses and underpasses across I-90 and reintroducing fishers, a species that belongs in the same family as wolverines and which were wiped out in the Pacific Northwest by hunters. I reached out to the organization to learn about volunteer opportunities, and one thing led to another. At one time, I even printed t-shirts at home to raise funds for them and through that effort, met with some international conservation organizations.

One of the projects I became involved in was their camera trap project. Teams would hike up to areas where wildlife may be located to set up camera traps to observe predators and prey in that area. Conservation organizations use camera traps, but then have to spend a lot of time and work to classify the images. Involvement in that project led me to the idea of using machine learning to speed up that effort. 

Allen School: Is that when you began to work with Woodland Park Zoo’s senior conservation scientist, Robert Long?

MS: While working on my camera trap model, I quickly realized that I needed actual camera trap images from different cameras and angles to make my machine learning model accurate. I started writing to researchers who use camera traps and he was one of the few to respond immediately and generously offered his images to me to train my model. Luckily, he was in Seattle, and invited me to meet him at the Woodland Park Zoo. I have been working with him ever since. 

Allen School: How did you use machine learning to classify all of the images?

MS: Any machine learning system learns from input data. The better and the more varied the input data, the more accurate the machine learning system can be. Initially, I naively tried to use images from Google to train my machine learning model. I tried to create a model that distinguishes between species. When I tested the model with actual camera trap images, I quickly learned that the system was nearly useless because most images on the internet show animals in nearly ideal conditions, like with the background out of focus. Next, I found an online database used by prior researchers called “Snapshot Serengeti,” which has thousands of images of animals from Africa. Again, I found the lack of variety in the animals and vegetation to not be very useful for the camera trap images American conservationists were collecting.

I started writing to researchers and only a couple responded. Fewer still offered to share their images with me. I also learned through my discussions with them, and based on my own experience with Conservation Northwest’s camera trap project, that just separating images containing animals or humans from other images containing only background foliage would be immensely useful because researchers spend countless time looking at each false positive to make sure they are not missing anything. Distinguishing between animals and humans would also be very helpful. So I started building a model that classifies images into three categories: false positive, human, and animal. This enables volunteers to be more productive and efficient by prioritizing images for analysis. 

Allen School: Did you know how to do all of this before you started on the project? 

MS: Before I started working on my project, I knew next to nothing about coding and machine learning. I read as much as I could about machine learning and Google’s TensorFlow. I also needed to learn some Python programming to get it to work. Over time and through lots of failures and crashes, I slowly built a decent model. I don’t claim to understand how TensorFlow or machine learning frameworks actually work, but I hope to learn more about these topics in the Allen School! 

Allen School: Do you want to remain working in conservation after you finish your CS degree? 

MS: I genuinely enjoy the natural environment we are fortunate to have here in the Pacific Northwest. So I will definitely stay involved in environmental conservation, but I haven’t yet decided in what way or how I can make the most impact. Ask me that question again when I’m a senior, I may have a better idea.

We’re so excited to have a dedicated conservationist like Manoj as a member of the Allen School community. We are confident his innovation will change the world!