Allen School News

Professor Hanna Hajishirzi, a professor in the Natural Language Processing group and director of the Allen School’s H2Lab, and Yin Tat Lee, a professor in the Theory of Computation group, have been named 2020 Sloan Research Fellows by the Alfred P. Sloan Foundation. The program recognizes early-career scientists in the United States and Canada who are nominated and judged by their peers based on their creativity, leadership, and achievements in research.

“I am thrilled that the Sloan Foundation has honored Hanna and Yin Tat for their outstanding work on fundamental problems that have broad relevance and potential for impact,” said professor Magdalena Balazinska, director of the Allen School. “Hanna is working at the leading edge of artificial intelligence to transform the way we conceive of and build AI systems that touch people’s everyday lives, from education and media, to financial services and scientific documents. And Yin Tat is doing groundbreaking — even audacious — work that pushes past decades-old limits of computing to create faster, better solutions to a range of modern-day problems.”

Hanna Hajishirzi

Hajishirzi, who joined the Allen School faculty in 2018 and is also an AI research fellow at the Allen Institute for Artificial Intelligence (AI2), addresses foundational problems in natural language processing, artificial intelligence, and machine learning. Her goal is to develop general-purpose algorithms that can represent, comprehend, and reason about diverse forms of data efficiently and on a large scale. Hajishirzi’s research spans multiple domains, including representation learning, question answering, knowledge graphs, and applications such as conversational dialogue and knowledge extraction from unstructured text.

“Enormous amounts of information are available online in multiple forms across diverse resources; for example, in news articles, web pages, textbooks and technical documents,” explained Hajishirzi. “An important challenge in AI is how to represent and integrate diverse resources to facilitate further comprehension and reasoning. It is the right time to address this challenge at large scale and in real-world settings, using a unified representation that combines the best features of deep neural models and symbolic formalisms.”

Hajishirzi is among the pioneers in designing novel, end-to-end neural models for question answering and reading comprehension. One of her key contributions is Bi-Directional Attention Flow for Machine Comprehension, or BiDAF, which is a deep neural model for end-to-end question-answering about text and diagrams that has been widely adopted in academia and industry. Hajishirzi and her collaborators designed the system to be both scalable and modular, thus enabling its use with multiple modalities and knowledge bases. Hajishirzi is also among the first to address the problem of understanding scientific articles and data across multiple modalities, such as diagrams, math and geometry word problems.  For example, she led the development of DyGIE, a system for enabling knowledge extraction from computer science and biomedical scientific papers. She also led the GeoS project, the first automated system for solving geometry word problems that can answer SAT geometry test questions on a par with the average American 11th grade student. More recently, Hajishirzi devised a new interpretable neural model for solving math problems, MathQA, that maps word problems to operation programs, and DenSPI and DecompRC, systems for real-time and multi-hop question answering that achieves state-of-the-art results by decomposing compositional questions into simpler sub-questions.

Hajishirzi has garnered numerous accolades for her research. She received an Allen Distinguished Investigator Award in AI for her work on the Spoon Feed Learning (SPEL) framework that combined principles of child education and machine learning to enable computers to interpret diagrams. Hajishirzi later earned a Google Faculty Research Award for her efforts to develop practical, scalable methods for open-domain question answering. She has also received an Amazon Research Award, a Bloomberg Data Science Award, and a Best Paper Award from the Special Interest Group on Discourse and Dialogue (SIGDIAL). Hajishirzi regularly publishes at the top conferences in the field, including the annual meeting of the Association for Computational Linguistics (ACL), the Conference on Empirical Methods in Natural Language Processing (EMNLP), and the Conference on Computer Vision and Pattern Recognition (CVPR).

Yin Tat Lee

Lee, who joined the Allen School faculty in 2017 and is also a visiting researcher at Microsoft Research AI, combines ideas from continuous and discrete mathematics to produce state-of-the-art algorithms for solving optimization problems that underpin the theory and practice of computing. His work encompasses multiple domains, including convex optimization, convex geometry, spectral graph theory, and online algorithms.

“From machine learning and experiment design, to route planning and medical imaging, convex optimization is used everywhere,” Lee said. “My group develops new techniques and algorithms to optimize faster, with the goal to design a universal optimization algorithm without compromising performance.”

Lee has already expanded convex optimization techniques to break long-standing running time barriers for a variety of problems. For example, he and his colleagues presented a new general interior point method that yielded the first significant improvement in linear programming in more than 20 years and a new algorithm for approximately solving maximum flow problems in near-linear time. Lee also has demonstrated the applicability of optimization techniques to an even broader class of problems than previously was considered feasible, devising a faster cutting plane method that improved the running time for solving classic problems in both continuous and combinatorial optimization. More recently, Lee contributed to a pair of new algorithms that achieve optimal convergence rates for optimizing non-smooth convex functions in distributed networks. That same year, Lee contributed to a total of six papers that appeared at the Symposium on Theory of Computing (STOC 2018) — a record high for an individual researcher at the conference.

Lee’s work has earned him multiple Best Paper and Best Student Paper awards at premier conferences in the field, including the IEEE Symposium on Foundations of Computer Science (FOCS), the ACM-SIAM Symposium on Discrete Algorithms (SODA), and the Conference on Neural Information Processing Systems (NeurIPS 2018). Last year, he earned a Microsoft Research Faculty Fellowship in recognition of his efforts to advance the field of theoretical computer science for real-world applications. In 2018, the Mathematical Optimization Society awarded Lee the A.W. Tucker Prize, which recognizes the best doctoral thesis in optimization in the prior three years, for his work on faster algorithms for convex and combinatorial optimization. That same year, he received a CAREER Award from the National Science Foundation to build upon that work and overcome multiple obstacles to optimization.

“To receive a Sloan Research Fellowship is to be told by your fellow scientists that you stand out among your peers,” Adam F. Falk, president of the Alfred P. Sloan Foundation, said in a press release. “A Sloan Research Fellow is someone whose drive, creativity, and insight makes them a researcher to watch.”

Hajishirzi and Lee are among four University of Washington researchers to watch in this latest group of Fellows, which includes Kyle Armour, a professor in the School of Oceanography and Department of Atmospheric Sciences, and Jacqueline Padilla-Gamiño, a professor in the School of Aquatic and Fishery Sciences.

A total of 37 current or former faculty members at the Allen School have been recognized through the Sloan Research Fellowship program. Recent honorees include Shayan Oveis Gharan, who was recognized last year for his work on solutions to fundamental NP-hard counting and optimization problems; Maya Cakmak, for her contributions to robotics; Ali Farhadi and Jon Froehlich, for their research in artificial intelligence and human-computer interaction, respectively; and Emina Torlak, for her work in computer-aided verification and synthesis.

View the complete list of 2020 Sloan Research Fellows here and read the Sloan Foundation press release here. Read a related UW News release here.

The UW Reality Lab has launched a new incubator where students can develop innovative projects in augmented and virtual reality (AR/VR) with guidance and resources from lab faculty and staff. The Reality Lab, which launched two years ago, allows researchers to focus on the pursuit of leading-edge research and educating the next generation of innovators in this growing field. The incubator gives students a space to work on AR/VR projects while fostering a community of collaboration and organic mentorship. It also supports the greater UW community in AR/VR research and allows the novel research taking place in the incubator to be shared with the whole world. 

“We select projects and teams for the incubator with the goal of having every project ultimately be released to the community in the form of research results or an application,” said John Akers, director of research and education in the lab. “Projects can either be in support of other groups in the greater UW community, such as other labs and departments, or student motivated projects based on ideas they are committed to developing fully.” 

According to Ira Kemelmacher, professor of computer science and director of the UW Reality, Lab, one of the biggest challenges in AR/VR adoption is content and experiences creation. 

“We are opening the incubator to allow undergraduate researchers to team up, come up with fresh ideas, and invent the future of AR/VR. We started by teaching a series of AR/VR capstones where teams of students came up with application ideas and implementations, in an amazing variety of fields from visualization of homelessness to cooking in AR,” she said. “In capstones they only have 10 weeks to bring their ideas into life. Due to its high popularity in the undergrad community and successful results, we decided to open an incubator that will allow more time for development. We believe undergrads have an immense potential in creating breakthroughs in AR/VR technology and our incubator encourages them to do exactly that, while getting advising, state of the art hardware, and full support from us and our collaborators.”

At the moment, the incubator is hosting two projects. One is led by Max Needle, a Ph.D. student in the Department of Earth and Space Sciences. Needle’s research is in rocks bending. He flew a drone around a strip mine in Pennsylvania that features a large folded rock layer, as well as several fossils and lots of faults. He was then able to generate a high-resolution 3D model of the mine from the drone photos, with the goal of developing immersive geological adventure and educational experiences. 

“The strip mine is a field-trip destination for many university geology classes, however, like many exquisite exposures of geologic structures, there are geographical and physical limiting factors,” Needle said. “To overcome obstacles related to access, my group at the Reality Lab Incubator is developing a virtual field trip through the strip mine. The geology-specific tools that we develop for VR with the Reality Lab, as well as the format and gameplay, can be put in a pipeline to enhance VR experiences of other geologic sites that have been mapped digitally in 3D.

His work will open new doors for teaching geology, and who has access to field geology.

“The incubator is great, so far,” said Andrew Wang, a second year Allen School student working with Needle. “All of the necessary tools are available. Having these tools so accessible will help us debug and further develop our knowledge in VR/AR technology.”

The other active incubator project is led by an Allen School senior and explores how different forms of locomotion mechanics in virtual reality can create emergent gameplay. He is working to see if some of these modes could reduce the simulator sickness some people feel in VR.

According to Akers, the incubator hopes to increase projects in the future as the process process for how projects are accepted and teams are composed is solidified. Learn more about the incubator and how to get involved on their website. 

Sometimes a ring symbolizes a promise, sometimes it shows a person’s birth month or mood, and sometimes it’s a statement about their taste in jewelry. But thanks to researchers in the Allen School’s Ubicomp Lab, a ring can now do a lot more.

The latest in smart technology, the AuraRing is a ring and wristband combination with high-fidelity input tracking. The combination is a magnetic tracking system designed to report precise finger movement. 

“We’re thinking about the next generation of computing platforms,” said Allen School alumnus and co-lead author Eric Whitmire (Ph.D. ‘19), now a research scientist at Facebook Reality Labs. “We wanted a tool that captures the fine-grain manipulation we do with our fingers — not just a gesture or where your finger is pointed, but something that can track your finger completely.”

The ability to track a finger enables freeform and subtle input for wearable platforms like smartwatches and augmented and virtual reality headsets. AuraRing enables applications like object manipulation, drawing, sliding, swipe-based text input and hand pose reconstruction because of its absolute, continuous tracking with millimeter-level accuracy. Due to a high bandwidth and data rate, AuraRing is also capable of detecting taps of various intensities which enables new kinds of always-available ambient interfaces. 

The system, which is worn on the index finger, consists of a single transmitter coil tightly wrapped around a 3D-printed ring and a wristband with three embedded sensor coils that measure the resulting magnetic field. Using these measurements, the wristband tracks the absolute position and orientation of the ring in real-time, making free-form drawing, handwriting short text messages, controlling games and moving virtual objects with mixed reality headsets possible.  

AuraRing is also a low-power, battery operated device that generates an oscillating magnetic field around the hand. By focusing on short-range tracking over distances between 10 and 15 centimeters, the system is less susceptible to using up too much power and encountering environmental interference.

“To have continuous tracking in other smart rings you’d have to stream all the data using wireless communication. That part consumes a lot of power, which is why a lot of smart rings only detect gestures and send those specific commands,” said co-lead author Farshid Salemi Parizi, a Ph.D. student in electrical and computer engineering. “But AuraRing’s ring consumes only 2.3 milliwatts of power, which produces an oscillating magnetic field that the wristband can constantly sense. In this way, there’s no need for any communication from the ring to the wristband.” 

With these minimal, low-power electronics, AuraRing can operate for about a day on self-contained batteries and therefore has the potential to do a lot more.  

“Because AuraRing continuously monitors hand movements and not just gestures, it provides a rich set of inputs that multiple industries could take advantage of,” said professor Shwetak Patel, who holds a joint appointment in the Allen School and the Department of Electrical & Computer Engineering. “For example, AuraRing could detect the onset of Parkinson’s disease by tracking subtle hand tremors or help with stroke rehabilitation by providing feedback on hand movement exercises.” 

AuraRing was developed with support from the UW Reality Lab.The team, which has open-sourced the hardware designs and algorithms for their work, published their findings in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. To learn more, watch the group’s video and check out the UW News release and coverage by KING 5 News and VentureBeat.   

February 1 marked five years since the passing of professor Gaetano Borriello. Gaetano famously applied to only one program – ours – when he entered the academic job market in 1988 after earning his Ph.D. from the University of California, Berkeley. He spent the next 27 years on the Allen School faculty, six of those valiantly fighting the cancer that would eventually take him from us. Gaetano is one of several leaders, and dare we say, legends, whom the Allen School community lost in the last decade. As a new decade dawns, the memories of those who knew and worked with Gaetano haven’t faded.

“We continue to feel Gaetano’s loss every day. When I moved into the director’s office last month, one of my first thoughts was that now I will be near Gaetano’s bench, which is just across Sylvan Grove from my window,” said Magdalena Balazinska, professor and director of the Allen School. “It makes me feel like Gaetano is still with us — and I do believe his wisdom and compassion continue to influence our decisions and actions in a very positive way.

“It’s not the same without Gaetano,” she continued, “but we will continue to build upon his legacy by working to create a more inclusive community here within the Allen School and pushing state-of-the-art technologies to benefit people and communities around the world.”

Here, members of the extended Allen School family pay tribute to a friend, mentor and colleague whose presence is still very much felt by members of our community — and whose impact endures both here on campus and across the globe.

Gaetano’s vision:

“Not all research ideas go beyond the lab, so it’s a testament to the power of Gaetano’s vision that the tools he created are in active use in every country in the world,” said Allen School alumnus Yaw Anokwa (Ph.D., ‘12), who worked with Gaetano on the Open Data Kit project and continues to support the deployment of ODK via his company, Nafundi.

“I wish he could see how much impact he has had, and I wish I could still ask for his guidance,” continued Anokwa. “I miss him. A lot.”

“Gaetano stands out to me as a visionary and as an educator,” said professor Richard Anderson of the Allen School’s Information and Communication Technology for Development Lab, which is building on Gaetano’s legacy in ICTD research. “He was able to look 10 years ahead and develop high impact applications of emerging technology, and he loved to recount all the times people told him his approaches would never be practical.

“Throughout his career, he put students above all else,” Anderson continued, “both as an advisor and mentor of graduate and undergraduate students, and as a teacher in Computer Engineering.”

Gaetano’s humanity:

“I knew Gaetano from my role as technical staff at CSE, overlapping with him for almost two decades,” said Scott Rose, who retired from the Allen School in 2015. “Gaetano was an exceptionally smart engineer, but what made him stand out from the crowd were his humor and his humanity — he was a genuine man of the people, without an arrogant bone in his body and with no appetite for status. He just wanted everybody to have the opportunity to do their best, to live with dignity, and he’d bend over backwards to make sure they did. 

“Students at any level, staff, colleagues — they were all part of Gaetano’s community of peers,” Rose continued. “I hope that I make my exit with the same dignity that he did. I miss him greatly, and at his advice, yeah, I get my regular colonoscopies.”

Gaetano’s devotion to students and staff also stuck with Crystal Eney, Director of Student Services at the Allen School.

“Gaetano was thoughtful, warm, passionate, entertaining and extremely student-focused. He always put students’ needs first. He was their advocate and their mentor. He would give them tough love when they needed it, but he was also a great listener and problem solver,” recalled Eney. “It wasn’t just the students who looked up to him — it was the staff, too. He treated us like equals, and was always there to say ‘good job’ or ‘nice work.’

“In this day and age when everything seems to move at lightning speed, Gaetano was one of those people who would stop by to ask how your day was going, and it wasn’t just a mindless pleasantry,” she noted. “He cared deeply about others, and he is so very deeply missed.”

Gaetano’s relationship with staff was such that he could bring people back into the fold after they had left — in the case of Kay Beck-Benton, the Allen School’s director of external relations, eight years after.

“I loved working with Gaetano,” said Beck-Benton, who started at the Allen School as a program manager before leaving UW for a series of startups. “So much so, that he is a major reason why I returned to — and currently work in — the Allen School. Gaetano was an advocate and mentor for staff and for students, as well as being passionate about research and teaching.

“No matter who you were or what position you held in the school, Gaetano made you feel welcome in his circle,” she concluded.

Gaetano’s mentorship:

Given how devoted he was to students, it is no surprise that mentorship and setting students up for success would be a recurring theme for those who remember Gaetano. 

“If you asked Gaetano what should be the UW’s top priority, he would have one answer: the students,” said Allen School Ph.D. candidate Waylon Brunette, who began working with Gaetano as an undergraduate researcher nearly 20 years ago. “His focus and dedication to students pervaded his views on education, research, and service, and he measured his own success based on his students’ success.

“Gaetano was great at finding students who might have a few rough edges, like me, and helping us to realize that we were actually diamonds in the rough,” Brunette continued. “Mentoring was fundamental to who Gaetano was as a person, and I have tried to model my own mentorship on his shining example.”

This sentiment was echoed by another former student, Scott Hauck (Ph.D., ‘95), who now holds the Gaetano Borriello Professorship for Educational Excellence in UW’s Department of Electrical & Computer Engineering.

“I know that advisors can have a big impact on people’s careers, but Gaetano was truly a role model to me,” Hauck said. “I saw in him someone who combined impactful research, careful mentoring, and a passion for education, and have tried to be as graceful and compelling a faculty member as he was. I often think about what Gaetano would have done when trying to structure my own teaching.

“I also try to put the students first, just as Gaetano did for me,” Hauck continued. “And I do still find myself wandering over to Sylvan Grove to sit on Gaetano’s bench sometimes and commune with him a bit.”

Gaetano also served as a mentor and role model to his faculty colleagues — whether intentional or not.

“Out of everything I can think of that has made me a better mentor and advisor over my 17 years at UW, being next door to Gaetano’s office for about five of those years, in a building with fairly thin walls, is near the top of the list,” observed professor Dan Grossman, vice director of the Allen School. “When I needed thoughtful advice on something, I would ask Gaetano. Nowadays, I try to guess the advice he would give me if he were still here.

“Gaetano was the full package — an excellent researcher who evolved his focus over his career, a gifted teacher, an award-winning mentor, and always one to carry more than his share of service,” Grossman continued. “He was also humble, self-aware, and grateful — a true role model for me and many others.”

Gaetano’s legacy:

Gaetano’s ability to educate and inspire those around him ensures his legacy will endure beyond the impact of the technologies he created. It’s a legacy that his former students and colleagues aim to expand through their own work.

“I would not be where I am today without Gaetano. As I learn how to advise Ph.D. students myself, I become more and more grateful for the incredible advocate and champion that Gaetano was for me, and for all his students,” remarked Allen School alumna Nicola Dell (Ph.D., ‘15), who is now a faculty member at Cornell Tech.

“I can’t believe that he has been gone five years,” she said. “I also can’t count the times I’ve wished I could call him up and ask for his advice. He is truly missed. I hope every day that he would be proud of the work we continue to do, that he continues to inspire.”

“Gaetano’s impact is felt every day, at UW and around the world,” said professor Ed Lazowska, who holds the Bill & Melinda Gates Chair in Computer Science & Engineering in the Allen School. “Scott Hauck, who was advised by Gaetano and his close colleague Carl Ebeling, now holds the Gaetano Borriello Professorship for Educational Excellence at UW. Open Data Kit, a project Gaetano launched with several of his students, sees ever-expanding adoption for mobile data collection around the world — an expansion supported by Nafundi, a company co-founded by two of Gaetano’s former Ph.D. students, Yaw Anokwa and Carl Hartung. And ODK-X is building on that legacy, guided by Gaetano’s faculty colleague Richard Anderson and alumni who previously worked with Gaetano.

“Other alumni have gone on to faculty positions at other universities, where they are building on Gaetano’s legacy in technology for development,” Lazowska continued. “Gaetano may not be physically with us, but five years on, he is everywhere.”

Following Gaetano’s passing, tributes poured in from colleagues and collaborators around the world, including primatologist and wildlife conservationist Jane Goodall, the International Red Cross, IEEE, PATH, and others. Read more here.

Behavioral studies in labs on university campuses are overwhelmed with participants who are WEIRD: western, educated, and from industrialized, rich and democratic countries. They are usually college students participating in the studies for class credit. 

In an effort to expand these studies to non-WEIRD people too, virtual labs like LabintheWild and Amazon’s Mechanical Turk, open up the study to anyone with access to the internet. These labs give researchers a broader glimpse at the way people think and behave from young to old, around the globe, with diverse cultural beliefs and geographical locations. But, researchers still hesitate to rely too much on these virtual labs because they need a more controlled environment.

Allen School researchers have come up with a tool to allow for more control. The Virtual Chinrest “enables remote, web-based psychophysical research at large scale, by accurately measuring a person’s viewing distance through a 30-second task,” according to the lead author and Allen School Ph.D. student, Qisheng Li.

She said that online studies in psychophysical experiments allow researchers to analyze human perception and performance. Study participants in labs often need to rest their chins in a certain location to control the exercise, making sure each performer is viewing the test from the same place. 

“We don’t know how far people in any online environment sit from their computers and we don’t know how big their display of the test is,” said Allen School professor Katharina Reinecke, co-founder of LabintheWild. “The virtual chinrest can monitor both the resolution on the screen and the physical distance from the monitor so researchers have more control over the online studies.”

To first calculate a participant’s display, participants are asked to place a credit card-sized card on the screen and adjust the slider on the screen to fit the credit card. That allows the researchers to calculate the pixel density on the monitor.

To measure the user’s distance from his or her monitor, there is also a blind spot task. Testers are asked to focus on a black square on the screen with their right eye closed, while a red dot repeatedly sweeps from right to left. They must hit the spacebar on their keyboards whenever it appears that the red dot has disappeared. That allows researchers to determine the distance between the center of the black square and the center of the red dot when it disappears from eyesight and understand how far the participant is from the monitor.

In an online test of the Virtual Chinrest on LabintheWild that included 1153 participants, Reinecke’s team was able to replicate and extend the results of previous in-lab studies to prove that the Virtual Chinrest can allow psychophysical studies to be done online, allowing for more diverse participant samples. 

Reinecke and her collaborators presented Virtual Chinrest in a recent paper published in the research journal Nature’s Scientific Reports. Additional authors include professor Sung Jun Joo of Pusan National University and professor Jason D. Yeatman of Stanford University. 

From computer science to linguistics and kickboxing to baking, this month’s undergraduate student spotlight, Noelle Merclich, is driven to creating a welcoming environment in the Allen School, serving others and always being kind and compassionate. During a month when people are making and struggling to stick to new resolutions, the Maple Valley, Wa., native and junior computer science major resolved long ago to do her best and try new things every day. 

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

Noelle Merchlich: After taking a couple years of programming in high school, I realized I really enjoyed the puzzle solving nature of it and how computer science impacts nearly every other possible career. Also my dad constantly jokes about how his return on investment for my college education is my ability to pay for him to take trips around the world, so the financial stability doesn’t hurt either. 

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

NM: Definitely the people. Some of my favorite memories of the last few years include playing card games in the labs until 2 a.m. after barely finishing an assignment before the deadline and baking a surprise birthday cake in the residence halls. My experience at UW wouldn’t be the same without the friends I’ve met in the Allen School, and I know I definitely wouldn’t have survived most finals weeks without their help.

Allen School: What do you like about being a TA for the CSE Startup course and Direct Admission seminars? 

NM: I’ve had the opportunity to be a TA for CSE Startup for three years now. It’s been exciting to see how the course has changed and grown over time. I enjoy working with the instructor, Lauren Bricker, and undergrad adviser Leslie Ikeda to improve the curriculum to best fit the needs of our students during their first experience in college and at the Allen School. I appreciate how I’m able to help the curriculum evolve along with my own experiences at UW. As for the DA seminar, I like how I’m able to help a larger scale of students with their transition to the Allen School. Realistically, my experience in computer science has always been a positive one since my parents have always supported my aspirations. And it’s never seemed abnormal to be a woman in the industry since my first two computer science teachers were women. However, I realize this is definitely not how everyone is initially exposed to the field, so I try to use my role in the DA seminar to help make each incoming student feel as though they have a place in computer science in whatever way I can.

Allen School: Why did you choose to minor in linguistics? 

NM: My interest in linguistics began after giving a presentation on Noam Chomsky for a psychology class. When I took Ling 200: Intro to Linguistic Thought,  I was really fascinated by the universality of how we break down language, so I took a few more classes in the linguistics department. I committed to completing my minor because over the past two years I’ve developed an interest in natural language processing, and understanding concepts like how syntax and semantics work together to form meaning is helpful with that.

Allen School: What are some of your favorite experiences or activities at the UW?

NM:  I started taking kickboxing classes my freshman year because I’ve always thought it seemed really fun. Now it’s become a way for me to punch my stress away. Also, before I decided on pursuing computer science I thought I would go to culinary school to become a pastry chef. It’s safe to say that I don’t just like desserts, I love them. I’m constantly trying out different bakeries around campus and Seattle to find the best macarons, tres leches, or anything sweet. I highly recommend Cubes and Le Panier.

Allen School: Who or what inspires you?

NM: My grandmother, Rosa. In spite of all the drama and tragedy she faced throughout her life, she maintained such kindness and compassion for everyone. When I was about 14, I remember helping her make homemade spaghetti and meatballs to give to the construction workers doing renovations on her neighbor’s house at the end of the block. Although she passed away at the end of my freshman year, she has always motivated me to be more kind, patient, and helpful to those around me. Her example is part of why I’m so passionate about computer science outreach. I try to find ways to connect students to computer science who normally wouldn’t have the resources to get started themselves. As a result, when one of the Allen School advisers sent out an application for HCDE’s alternative spring break group, I jumped at the chance. For two years I’ve had the incredible opportunity of being part of a team that created curricula for teaching introductory programming concepts to middle and high school students in rural Neah Bay, WA. It was definitely one of the more challenging and fulfilling college experiences I’ve had.

We are inspired by Noelle’s contributions to the Allen School and her outreach work! 

Tree-based machine learning models are among the most popular non-linear predictive learning models in use today, with applications in a variety of domains such as medicine, finance, advertising, supply chain management, and more. These models are often described as a “black box” — while their predictions are based on user inputs, how the models arrived at their predictions using those inputs is shrouded in mystery. This is problematic for some use cases, such as medicine, where the patterns and individual variability a model might uncover among various factors can be as important as the prediction itself.

Now, thanks to researchers in the Allen School’s Laboratory of Artificial Intelligence for Medicine and Science (AIMS Lab) and UW Medicine, the path from inputs to predicted outcome has become a lot less dense. In a paper published today in the journal Nature Machine Intelligence, the team presents TreeExplainer, a novel set of tools rooted in game theory that enables exact computation of optimal local explanations for tree-based models. 

While there are multiple ways of computing global measures of feature importance that gauge their impact on the model as a whole, TreeExplainer is the first tractable method capable of quantifying an input feature’s local importance to an individual prediction while simultaneously measuring the effect of interactions among multiple features using exact fair allocation rules from game theory. By precisely computing these local explanations across an entire dataset, the tool also yields a deeper understanding of the global behavior of the model. Unlike previous methods for calculating local effects that are impractical or inconsistent when applied to tree-based models and large datasets, TreeExplainer produces rapid local explanations with a high degree of interpretability and strong consistency guarantees.

“For many applications that rely on machine learning predictions to guide decision-making, it is important that models are both accurate and interpretable — meaning we can understand how a model combined and weighted the various input features in predicting a certain result,” explained lead author and recent Allen School alumnus Scott Lundberg (Ph.D., ‘19), now a senior researcher at Microsoft Research. “Precise local explanations of this process can uncover patterns that we otherwise might not see. In medicine, factors such as a person’s age, sex, blood pressure, and body mass index can predict their risk of developing certain conditions or complications. By offering a more robust picture of how these factors contribute, our approach can yield more actionable insights, and hopefully, more positive patient outcomes.”

Lundberg and his colleagues offer a new approach to attributing local importance to input features in trees that is both principled and computationally efficient. Their method draws upon game theory to calculate feature importance as classic Shapley values, reducing the complexity of the calculation from exponential to polynomial time to produce explanations that are guaranteed to always be both locally accurate and consistent. To capture interaction effects, the team introduces Shapley Additive Explanation (SHAP) interaction values. These offer a new, richer type of local explanation that employs the Shapley interaction index — a relatively recent concept in game theory — to produce a matrix of feature attributions with uniqueness guarantees similar to Shapley values.

This dual approach enables separate consideration of the main contributions and the interaction effects of features that lead to an individual model prediction, which can uncover patterns in the data that may not be immediately apparent. By combining local explanations from across an entire dataset, TreeExplainer offers a more complete global representation of feature performance that both improves the detection of feature dependencies and succinctly shows the magnitude, prevalence, and direction of each feature’s effect — all while avoiding the inconsistency problems inherent in previous methods.

In a clinical setting, TreeExplainer can provide a global view of the dependency of certain patient risk factors while also highlighting variabilities in individual risk. In their paper, the UW researchers describe several new methods they developed that make use of the local explanations from TreeExplainer to capture global patterns and glean rich insights into a model’s behavior, using multiple medical datasets. For example, the team applied a technique called local model summarization to uncover a set of rare but high-magnitude risk factors for mortality. These are inputs such as high blood protein that are shown to have low global importance, and yet they are extremely important for some individuals’ mortality risk. Another experiment in which the researchers analyzed local interactions for chronic kidney disease revealed a noteworthy connection between high white blood cell counts and high blood urea nitrogen; the team found that the model assigned higher risk to the former when it was accompanied by the latter.

In addition to discerning these patterns, the researchers were able to identify population sub-groups that shared mortality-related risk factors and complementary diagnostic indicators for kidney disease using a technique called local explanation embeddings. In this approach, each sample is embedded into a new “explanation space” to enable supervised clustering in which samples are grouped together based on their explanations. For the mortality dataset, the experiment revealed certain sub-groups within the broader age groups that share specific risk factors, such as younger individuals with inflammation markers or older individuals who are underweight, that would not be apparent using a simple unsupervised clustering method. Unsupervised clustering also would not have revealed how two of the strongest predictors of end-stage renal disease — high blood creatinine levels, and a high ratio of urine protein to urine creatinine — can each be used to identify a set of unique at-risk individuals and should be measured in parallel. 

Beyond revealing new patterns of patient risk, the team’s approach also proved useful for exercising quality control over the models themselves. To demonstrate, the researchers monitored a simulated deployment of a hospital procedure duration model. Using TreeExplainer, they were able to identify intentionally introduced errors as well as previously undiscovered problems with input features that degraded the model’s performance over time.

“With TreeExplainer, we aim to break out of the so-called black box and understand how machine learning models arrive at their predictions. This is particularly important in settings such as medicine, where these models can have a profound impact upon people’s lives,” observed Allen School professor Su-In Lee, senior author and director of the AIMS Lab. “We’ve shown how TreeExplainer can enhance our understanding of risk factors for adverse health events.

“Given the popularity of tree-based machine learning models beyond medicine, our work will advance explainable artificial intelligence for a wide range of applications,” she said.

Lee and Lundberg co-authored the paper with joint UW Ph.D./M.D. students Gabriel Erion and Alex DeGrave; Allen School Ph.D. student Hugh Chen; Dr. Jordan Prutkin of the UW Medicine Division of Cardiology; Bala Nair of the UW Medicine Department of Anesthesiology & Pain Medicine; and Ronit Katz, Dr. Jonathan Himmelfarb, and Dr. Nisha Bansal of the Kidney Research Institute.

Learn more about TreeExplainer in the Nature Machine Intelligence paper here and the project webpage here.

A team of researchers that includes Allen School Ph.D. student Aditya Kusupati has developed a new low-power real-time solution for mote-scale (tiny sensor with a weak microprocessor) radar-based intruder detection. Their work has enabled the first end-to-end deep learning solution for radar classification and won the “Best Paper Runner-Up Award” at the Association for Computing Machiner’s 6th International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys 2019) last month.

With the rapid growth of the Internet of Things sensors, there is an increased need for more sophisticated but efficient sensors. In their paper, Kusupati and the team uses a low cost, Arm Cortex M3 processor, which has only 96 KB of RAM for a more cost and energy-efficient solution.

“Imagine a situation inside a wildlife reserve, secluded from the modern world, with intermittent network connectivity and vast expanses to monitor,” said Kusupati. “You would ideally want to catch poachers, or intruders in a general setting, as soon as possible using minimal energy as the battery on your radar is limited since you don’t have electricity in the reserve. The technique we developed is the first deep learning based real-time solution that’s at least three times faster and more accurate than the existing state-of-the-art methods.”

The techniques proposed in the paper were also put to use to create a demonstration which was additionally presented at the conference. The solution proposed is hierarchical and so computationally very efficient while the models generated are tiny enough to fit and work in 96 kilobytes of RAM.

“The applications are varied and in the case of a more general use, it could be used as an inexpensive and non-intrusive intruder detection system for smart cities and even enable smart lights and more,” said Kusupati.  

Aditya worked on this while at Microsoft Research India before joining the Allen School. Collaborators include Ohio State University Ph.D. students Dhrubojyoti Roy, Sangeeta Srivastava and professor Anish Arora, Pranshu Jain, a Ph.D. student in IIT Delhi and Manik Varma, senior principal researcher at Microsoft Research India.

To learn more, read the research paper here.

In this month’s Undergrad Spotlight, we check in with a student who may need no introduction to gaming fans. Kevin Ryoo — a second year Allen School student who transferred from Highline College — built a career as a world champion gamer before deciding to study computer science. In fact, according to Ryoo, playing games all day every day inspired his academic pursuits to learn how to design and build software. As his education advanced, his interest in computer vision, machine learning and artificial intelligence has grown. Ryoo has an upcoming internship with Shopify, after Tweeting about needing an internship. Shopify’s CEO Tobias Lütke saw it, and knowing about Ryoo’s professional gaming career, offered him an internship on the spot.  

Allen School: When did you start gaming, and how did you become so successful at it? 

Kevin Ryoo: I started playing games professionally when I was 14 years old. I played a game called Warcraft 3. Even back then (in 2002), it had a match-making system where you get matched with a player at the same skill level. I kept winning and my ranking improved. Eventually I was ranked in the top 16 and my name was on the first page of players. Because of that, pro-gaming teams reached out to me with offers. That’s how I first got into it.

I think I was able to become successful in gaming because I had the motivation and dedication to do my best. I really liked the feeling of winning a game — I hated losing, it was stressful to me. So I practiced a lot and whenever I lost, I watched the replay of the game to learn from my mistakes. I repeated it again and again and as a result, I was able to become a champion in multiple competitions. I worked hard to become a gaming nerd. 

Allen School: How did you build a career in professional gaming, and how did it change your life?

KR: When I was 16 years old, I won a tournament called the World Cyber Games (WCG). It took place once a year and follows a structure similar to the Olympics, in which regional champions compete to represent their countries. In turn, those winners compete for international glory. By winning the tournament two years in a row, in 2005 and 2006, I became a world-renowned Esports gamer and was even inducted into the WCG Hall of Fame. After that, I quit gaming to finish high school. While waiting for my green card to go to college [Ryoo moved to the USA from Korea at the age of 16], I started to play Starcraft 2. As my ranking went up, I decided to do pro-gaming again. In Starcraft 2, I won a Blizzcon US Championship, which is my second biggest achievement, and I also got 2^nd and 3^rd place in Major League Gaming.

I really loved my pro-gaming life and learned a lot from it. I traveled the world, made new friends from interesting places and learned to appreciate humanity’s rich diversity. In addition to meeting gamers and fans on the road, I became a more effective communicator by becoming an online streamer on Twitch.tv. At any given moment, I would have roughly 7,000 people watching live on my Twitch streaming channel. I really enjoyed these sessions because, in addition to showcasing my craft, I could communicate with people from other countries, share opinions, and develop an appreciation for the things that make different groups unique and wonderful. Through this, I naturally became much more personable, social optimistic, and open-minded person. Unfortunately, I have no time to spend on gaming right now. But I am fine with it because I am motivated to study computer science, not gaming, at this moment.

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

KR: The Allen School has all the resources that students need. The professors are great, the TAs have a lot of office hours, there are tons of tech-talks from top companies, a great career fair that also includes the top companies, amazing advisers, a new Bill & Melinda Gates Center to study in, and the labs have impressive equipment. I always feel so fulfilled by these resources and I never feel alone.

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

KR: I am a member of the UW Association for Computing Machinery (ACM) and have been following and participating in the events. I will also be on the panel for the next Husky Gaming Expo.  

Check out Kevin’s Geek of the Week profile here and read more about him in The Daily. We are proud to have Kevin as a member of the Allen School community — FTW! 

Researchers at the University of Washington have created a digital tool to optimize the design and fabrication of woodworking projects by drawing inspiration from computing’s move to decouple hardware and software development. The resulting program, Carpentry Compiler, allows users to create a design, then find the best step-by-step, tool-specific instructions to bring the design to fruition. The compiler can generate and combine multiple fabrication processes to create one single design.

“To make a good design, you need to think about how it will be made,” senior author Adriana Schulz, a professor in the Allen School’s Graphics and Imaging Laboratory (GRAIL), explained in a UW News release. “Then we have this very difficult problem of optimizing the fabrication instructions while we are also optimizing the design. But if you think of both design and fabrication as programs, you can use methods from programming languages to solve problems in carpentry, which is really cool.”

Schulz and the team, which includes professor Zachary Tatlock and Ph.D. student Chandrakana Nandi of the Allen School’s Programming Languages & Software Engineering (PLSE) group, and Jeffrey Lipton,  an adjunct professor at the Allen School, considered that a design is a sequence of geometric construction operations while fabrication is a sequence of physical instructions. They found that by drawing ideas from modern computer systems, they could optimize fabrication processes to improve accuracy while reducing fabrication time and material costs. To do so, they created Hardware Extensible Languages for Manufacturing, or HELM. The system combines a high-level language for designing the project and a low-level language for the process of building it. The researchers also designed a compiler to map the designs to all of the available fabrication plans. They made the architecture design extensible so that new fabrication hardware can be added. 

Users of HELM can enter materials, parts and tools into the program, which then shapes the most optimal fabrication process based on what the carpenter has at the ready. 

The compiler explores all of the possible combinations of instructions and uses programming to find the best directions for the project. One program might have the best process to make the roof of a bird house, the other the best way to build the rest of the house. The compiler will combine them to find the best directions for the project while enabling the user to make various design tradeoffs to suit their needs and preferences.

“The future of manufacturing is about being able to create diverse, customizable high-performing parts,” Schulz said. “Previous revolutions have been about productivity mostly. But now it’s about what we can make. And who can make it.”

Additional co-authors include visiting doctoral student Chenming Wu from Tsinghua University, and Allen School postdoctoral researcher in GRAIL Haisen Zhao. The team presented this research last month at SIGGRAPH Asia in Brisbane, Australia.

To learn more, read the full publication, “Carpentry Compiler,” watch the group’s video, and check out the related UW News release and coverage by TechCrunch and Popular Woodworking.