Allen School News

Allen School professor Martin Tompa, an early and leading expert in computational molecular biology and a beloved mentor to undergraduate and graduate students, retired from the Allen School at the end of June. Tompa’s long and distinguished career spans an array of computer science research, including computational complexity, algorithms, and computational molecular biology. And then, of course, there’s his mastery of Schnapsen — the 300+ year old card game with an avid following in Europe and, thanks to Tompa, in the halls of the Allen School.

As an undergraduate at Harvard University in the early 70s, Tompa wanted to study computer science. It wasn’t offered as a major at Harvard at the time so he graduated in 1974 with a degree in applied mathematics. He still took every opportunity he could to take classes in computing and conducted undergraduate research with Thomas Cheatham, an early pioneer of extensible programming languages. As a computer science Ph.D. student at the University of Toronto, Tompa’s research focused on theoretical computer science, work that he continued at the University of Washington after he was hired onto the faculty in 1978. 

“I first met Martin when, in the fall of 1977, I was on sabbatical leave at the University of Toronto where he was a graduate student. I was thrilled when he decided to come to UW when he finished up his Ph.D. in 1978,” Richard Ladner, Allen School professor emeritus, said. “Through all these years he has been a valued colleague and friend. I especially appreciated Martin at faculty meetings where he would be a stickler to make sure our decision processes were fair and unbiased. Martin is also a wonderful game player in social games like contract bridge and his favorite, Schnapsen.”

In 1984, Tompa won an inaugural Presidential Young Investigator Award for his work in computing foundations. The next year, Tompa and his wife Anne McTiernan and two daughters left Seattle for New York, where McTiernan earned her medical degree. From 1985 to 1989, Tompa was on the staff of the IBM Research Division at the Thomas J. Watson Research Center and became manager of its Theory of Computation Group. 

Four years after their departure for the east coast, McTiernan was offered a residency position at UW Medicine and they were thrilled to return to Seattle. Tompa was welcomed back to the CSE department’s Theory of Computation research group.  He switched gears in 1998 to study computational molecular biology. 

“I enjoyed working in both theoretical computer science and computational molecular biology,” Tompa said. “There’s something wonderful about theoretical computer science — mathematics is so clean and proving things is wonderful. Molecular biology has been really exciting over the last few decades and it was exciting to learn about it. It was great fun to work with biologists and write programs to help with their analyses.”

Tompa was interested in the structure of DNA and learning the functions of its various parts. He worked on comparative sequence analysis, comparing the DNA sequences of multiple organisms to see what they had in common and where they differ.

“It was a lot of work to switch fields but also a lot of fun ― for the first several years it felt like I was a student again,” Tompa recalled. “Larry Ruzzo was also learning about it, so we would attend classes and seminars together, ask a lot of stupid questions and learn a lot from each other. It’s one of the beauties of being in an academic job, you have the freedom to, say, give up your research area, become a student again and change your field of research. You couldn’t do that in most jobs and, in the end, the change paid off. Larry and I learned a lot in the area and made some good contributions.”

Allen School professor Larry Ruzzo joined the UW faculty in 1977, just one year before Tompa’s arrival. The two were collaborators in theoretical computer science and the first Allen School faculty members to work in computational molecular biology. Tompa said working with Ruzzo was one of the best aspects of his academic career, as they basically built their careers together. 

“Martin and I basically ‘grew up’ together academically, and worked closely together on a variety of projects, including curriculum development, co-taught courses, service on each other’s student’s supervisory committees and many joint papers, Ruzzo said. “Martin is an academic gem — a delightful colleague, a brilliant researcher, caring and inspiring advisor, and a gifted classroom teacher.”

Members of the Allen School’s Computational and Synthetic Biology group collaborate with biologists on a wide range of computational problems that will ultimately enable them to better understand complex biological systems. Tompa’s research in this area has been extensive, particularly in motifs in genomic sequences, where his work helped biologists understand the mechanisms that regulate how, when, where, and at what rate genes express their products. An important aspect of this challenge is the identification of binding sites in the genome for the proteins involved in such regulation.  Finding those binding sites is where sequence motifs come in.

In 2001, Tompa, who was also an adjunct professor in the Department of Genome Sciences by then, and graduate student Jeremy Buhler, now a professor at Washington University in St. Louis, wrote “Finding motifs using random projections.” The paper introduced a novel randomized algorithm called PROJECTION for the discovery of short sequence motifs such as protein binding sites. The duo’s approach remedied weaknesses observed in existing motif discovery algorithms and solved difficult motif challenge problems. Its impact was so great that it earned a Test of Time Award from Research in Computational Molecular Biology in 2013. 

While Tompa published many papers in both theoretical computation and computational molecular biology, he took the greatest pleasure from his collaborations with interdisciplinary researchers.

“I was very proud of any paper that was really about biology,” he said. “Co-authoring with experimental molecular biologists allowed me to be a part of their discovery process. But I was also really proud of the papers I wrote with CSE graduate students, discovering new algorithms and solving problems in biology.”

Saurabh Sinha (Ph.D. ‘02), now a professor of computer science at the University of Illinois at Urbana-Champaign, co-authored several papers on motifs in genomic sequences as a graduate student with Tompa and has fond memories of working in his research group. Everything, from socials at Tompa’s house to the patience and time Tompa took to help Sinha edit his papers to maximize their chance of acceptance, meant a lot to him.

“The more diffuse memory I have is of how much respect he showed his students, and how much trust he had in us, and how much independence he gave us in doing our work while also keeping an eye on the progress and providing key technical advice at the right junctures,” Sinha said. “Also, it was from him that I learned the lessons of academic and research integrity, and the importance of prioritizing means over ends.”

Amol Prakash (Ph.D. ‘06) published several papers on comparative genomics with Tompa before going on to become the founder and CEO of Optys.

“Martin was the best mentor I could have imagined. It was an honor to be his student. There are so many things that I learned from him that reflect on my thinking and writing style,” Prakash said. “I chose a non-academic career, but I am sure if I had students to advise, I would have tried to emulate a lot of what I learned from his mentoring me. I wish him the best of times in the next phase.”

Tompa admits that, while researching and studying new disciplines was a favorite part of his career, it was the teaching that gave him the most joy. Six years ago he gave up half of his appointment as an experiment in retirement. In a  move that is almost unheard of, he opted to give up research rather than teaching. Since then, Tompa has spent all of his university time teaching undergrads and loved every minute of it. It’s evident that his joy has made a positive impact on his students, and they made sure it was celebrated. In 1998 and 1999, Tompa earned back to back UW ACM Teaching Awards, the recipient of which is chosen each year by undergraduate students in the Allen School.

“In the days when lots of faculty teach using prepared PowerPoint slides, Martin has a unique way of engaging his classes and making sure that the material feels fresh,” Allen School professor Paul Beame said. “Instead of slides, Martin writes everything out on the board or using a data projector, developing each point in the lecture in interaction with the class as he goes along. Powerpoint slides can feel stale and rehearsed, Martin’s unique method makes the class feel dynamic. I marvel at how he can do it.”

Several of Tompa’s students were happy to share their best memories of their time learning from Tompa, and most of them talked about his patience, mentorship and his favorite game, Schnapsen.

Tompa learned Schnapsen from his father, who grew up in Vienna and taught him the popular Austrian card game when he was a child. Schnapsen is a two-person game that has some similarities with another well-known game, Bridge. While he enjoyed the game as a child, Tompa admits he hadn’t played it for quite some time until 2011, when two former graduate students, Dick Garner (Ph.D. ‘82) and Jeff Scofield (Ph.D. ‘85), came to him looking for a good card game they might implement on an iPhone in OCaml. Martin told them he had just the game.  As it turned out, Schnapsen was the ideal game for the smaller screens of early iPhones. When Garner and Scofield sent him an early version of their app to test out, it beat Tompa far more often than not. 

“I couldn’t fathom what it was doing, and I wanted to understand how it was beating me. So I started taking notes on each game,” Tompa recalled. “They had done a marvelous job, developing a program that allowed the computer in the phone to explore a lot of possible moves and look ahead quite far in the game.” 

Schnapsen is the perfect pastime for computer scientists, and Tompa began a blog about the winning strategies he learned from their app, employing  concepts such as expected value and other aspects of probability theory. He turned the blog into a book, “Winning Schnapsen,” the definitive guide to mastering the centuries-old game that enjoys a popular following in Europe.

Tompa also incorporated Schnapsen into his CSE 312 course on the foundations of computing, using it as a running example to illustrate various concepts from probability theory. This inspired a group of students who took the course to establish a UW Schnapsen Club. Varun Mahadevan (B.S. ‘17), a teaching assistant for four quarters of the course, fondly remembers the experience and the opportunity to learn and play Schnapsen. 

“It was an absolute pleasure working with him,” Mahadevan said. “He had the most unique flavor for the class where he uses Schnapsen as a teaching tool for combinatorics. It was from this that we formed a Schnapsen club and we’d meet every Friday in a conference room on the 5th floor of the Allen Center and play cards and chat.”

One of those who fondly remembers these weekly meetups is Alex Tsun (B.S., ‘18), currently a graduate student at Stanford University.

“I remember when I first ‘met’ Martin during CSE 312 lecture more than five years ago in Winter 2015. He was so excited to be teaching probability and to share his love of Schnapsen with as many people as he could,” Tsun said. “He invited us, as he always does for his classes, to Schnapsen club on Fridays, at which I eventually became a regular. I’m not sure if he remembers this, but I first met him on the first Friday and challenged him to a game of Schnapsen. I believe I actually won my first game against him, probably by luck, and he promptly wiped me out in a second game, and in future weeks. I really enjoyed his class.”

According to Beame, when students talk about Tompa, Schnapsen is prominently mentioned, in addition to his sheer joy in teaching. 

“In teaching CSE 312, which introduces CSE students to the many aspects of counting and probabilistic reasoning that are so essential in machine learning, modern algorithms, and other areas of computer science, Martin has found ways to integrate Schnapsen in the course material and homework assignments,” Beame said. “While the use of familiar games like poker is common in such courses, because this is such a new game to the students, they cannot rely on the familiar and have to think outside the box, developing real new understanding along the way.” 

While students and colleagues alike will miss seeing Tompa regularly in the halls of the Allen School, Tompa says he’s excited for this new chapter of his life. He’s looking forward to spending more time with his three grandchildren in Seattle and continuing his research on his family’s genealogy. He’s working on a book about his parents and their families, who lived in central Europe in the 1930s. Their families had to flee Europe when the Nazis came to power and Tompa has been writing about what happened to them. 

Although he will no longer be in the classroom, he will still have a direct impact in the lives of many students.

“On the whole, I would say that CSE has always valued its educational mission, but amidst the competing demands on everyone’s time for teaching, research, and service, I can’t think of any of my colleagues who has more consistently prioritized the needs of students, at all levels,” Ruzzo said. “Martin really cares, it shows, and he pushed all of us to do the same.”

Congratulations, Martin, enjoy your well-deserved retirement! 

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Allen School professor Byron Boots was recognized with an Early Career Award at the Conference on Robotics: Science & Systems (RSS 2020) for his work spanning machine learning, artificial intelligence and robotics. Each year, the RSS Foundation selects one or more early career researchers for the honor based on their outstanding research accomplishments and demonstrated potential to advance the field of robotics. Boots, who directs the Allen School’s Robot Learning Laboratory, focuses on the development of theory and systems that tightly integrate perception, learning and control to enable new capabilities in motion planning, high-speed navigation, robot manipulation, and more.

Among Boots’ major contributions to date was the Gaussian Process Motion Planner (GPMP), an efficient, gradient-based algorithm that represents motion planning as continuous-time trajectories. He and his colleagues designed GPMP to address limitations associated with two strategies that, at the time, represented the state of the art in motion planning. The first, sampling-based algorithms, tend to require post-processing or the addition of optimal planners that are computationally inefficient in responding to high-dimensional problems with challenging constraints; the second, trajectory optimization algorithms, require fine discretization to integrate cost information under certain constraints and are themselves costly to rerun when faced with changing conditions. GPMP overcame such computational inefficiencies while maintaining smoothness in the result. It also served as the foundation for GPMP2, an extensible algorithm that treats the problem of motion planning as one of probabilistic inference. Under this approach, GPMP2 uses factor graphs to compute a more efficient solution. Boots and his collaborators extended GPMP2 to an incremental algorithm, iGPMP2, capable of efficiently replanning trajectories as conditions change. This combined work earned the team Paper of Year from the International Journal of Robotics Research in 2018. 

The following year, Boots and his colleagues presented a novel online learning-based framework for model predictive control, a powerful technique for optimizing control tasks in dynamic environments. As part of that work, they devised a new algorithm, Dynamic Mirror Descent Model Predictive Control (DMD-MPC), that can be applied to a variety of settings and cost functions. The team’s approach, which earned Best Student Paper and was a finalist for Best Systems Paper at RSS 2019, was yet another example of Boots’ keen interest in advancing robot learning in ways that combine new levels of flexibility with increased efficiency.

“Machine learning offers huge potential for robots to learn dynamically by interacting with their environments instead of requiring any new functionality to be hand-designed by engineers. But that level of flexibility and adaptability can come at a high cost,” Boots explained. “Machine learning algorithms are notoriously data-hungry as well as computationally expensive. My goal is to leverage a mix of machine learning and prior knowledge to accelerate robot learning for real-world applications while making the process more efficient and scalable.”

One of those real-world applications Boots is particularly keen to accelerate is high-speed navigation. He recently secured a grant from the United States Army Research Laboratory as part of its Scalable, Adaptive and Resilient Autonomy (SARA) program aimed at expediting research in autonomous mobility and maneuverability in complex, unknown and adversarial environments. The grant will support Boots’ work, alongside Allen School colleagues Dieter Fox and Siddhartha Srinivasa and collaborators at the Georgia Institute Technology, to develop new capabilities in perception, planning and model predictive control that will enable autonomous ground vehicles (AGVs) to operate safely and fluidly under dynamic conditions involving a variety of obstacles and terrain.

Boots joined the Allen School faculty in 2019 after five years as a professor at Georgia Tech’s School of Interactive Computing. He was no stranger to the University of Washington, having previously completed a postdoc working with Fox in the Allen School’s Robotics and State Estimation Lab after earning his Ph.D. from Carnegie Mellon University. Boots has published nearly 100 peer-reviewed papers and his work has earned recognition at many of the top conferences in the field, including RSS, the International Conference on Machine Learning (ICML), International Conference on Robotics & Automation (ICRA), International Conference on Artificial Intelligence and Statistics (AISTATS), Conference on Neural Information Processing Systems (NeurIPS).

“It was already clear during his postdoc at UW that Byron would become a trailblazer in robotics and machine learning,” said Fox. “The RSS Early Career Award is only given to a very small group of the most innovative and influential researchers in robotics, and I can’t think of anybody more deserving of this honor than Byron.”

Boots and his fellow Early Career Award recipients — Luca Carlone of the Massachusetts Institute of Technology and Jeannette Bohg of Stanford University — were formally honored during the RSS 2020 conference held online this week. 

Congratulations, Byron!

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Professor Sham Kakade, a member of the Allen School’s Machine Learning and Theory of Computation groups, received a Test of Time Award at the International Conference on Machine Learning (ICML 2020) for his work on “Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design.” In the winning paper, which was originally presented at ICML 2010, Kakade and his colleagues established the first sublinear regret bounds for Gaussian process (GP) optimization in the nonparametric setting. The team’s work was lauded by the machine learning community for its technical depth and for its enduring impact on both theory and practice.

Kakade, who holds a joint faculty appointment in the Allen School and the University of Washington’s Department of Statistics and is also a senior data science fellow at the eScience Institute, co-authored the paper while a professor at the University of Pennsylvania. His collaborators on the project included Niranjan Srinivas, a Ph.D. student at the California Institute of Technology; Andreas Krause, a professor at CalTech at the time; and Matthias Seeger, then a faculty member at the Universität des Saarlandes in Germany. Together, the team set out to address an open question in machine learning: how to optimize an unknown, noisy function that is expensive to evaluate while minimizing sampling.

“We were interested in finding a principled framework for addressing the problem of Bayesian optimization, and we realized that one way to formalize this was through the theory of the sequential design of experiments,” explained Kakade. “One that I was particular excited about was how we could provide a sharp characterization of the learning complexity through a novel concept we introduced, the ‘information gain.’” 

The question has numerous applications in both laboratory and real-world settings, from determining the optimal control strategies for robots, to managing transportation and environmental systems, to choosing which advertisements to display in a sponsored web search. To answer the challenge, Kakade and his colleagues united the fields of Bayesian optimization, bandits and experimental design. The team analyzed GP optimization as a multi-armed bandit problem to offer up a novel approach for deriving cumulative regret bounds in terms of maximal information gain. In the process, they succeeded in establishing a novel connection between GP optimization and experimental design. 

By applying a simple Bayesian optimization method known as the Gaussian Process Upper Confidence Bound (GP-UCB) algorithm, the team demonstrated that they could obtain explicit sublinear regret bounds for a number of commonly used covariance functions. In experiments using real-world network sensor data, Kakade and his collaborators showed that their approach performed as well or better than existing algorithms for GP optimization which are not equipped with regret bounds. In the decade after the researchers unveiled their results, Bayesian optimization has become a powerful tool in machine learning applications spanning experimental design, hyperparameter tuning, and more. The method, proof techniques, and practical results put forward by Kakade and his colleagues have been credited with sparking new research directions and subsequently enriching the field of machine learning in a variety of ways.

Since the paper’s initial publication, Srinivas joined 10x Genomics as a computational biologist after completing a postdoc at the University of California, Berkeley, while Krause moved from CalTech to the faculty of ETH Zürich in Switzerland. Seeger is now a principal machine learning scientist at Amazon. The team is being formally recognized during the ICML 2020 conference taking place virtually this week. 

Read the award citation here, and the research paper here. 

Congratulations to Sham and his co-authors!

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Jeong Joon (JJ) Park, a Ph.D. student working with Allen School professor Steve Seitz in the Graphics and Imaging Laboratory (GRAIL) has been named a 2020 Apple Ph.D. Scholar. Park was recognized in the “Artificial Intelligence/Machine Learning” category for his focus on 3D reconstruction and understanding. Apple selects its scholars based on their innovative research, record as thought leaders and collaborators in their fields, and unique commitment to take risks and push the envelope in machine learning and AI.

Park’s research focuses on recovering the underlying 3D properties of environments from photos and depth sensing. His work spans computer vision, deep learning, and augmented and virtual reality (AR/VR).

“3D computer vision can be very useful in many exciting areas of 3D applications, including VR/AR — visiting new places virtually, or robotics — using them to navigate and pick up stuff,” Song said. “I work on visually realistic reconstructions and study what is the most suitable representation for 3D geometry and appearance.” 

Park enjoys the work because it is an intersection of various fundamental areas that interest him, including machine learning, physics, computer vision and graphics. In his most recent paper, “Seeing the World in a Bag of Chips,” he and co-authors Alexsander Holynski and Seitz address problems of novel view synthesis and environment reconstruction from hand-held RGBD sensors. The team developed a method for modeling highly specular objects, inter-reflections and fresnel effects to enable surface light field reconstruction of the environment with the same input used to reconstruct the shape alone. The results can be seen in this video, where Park films a bag of chips and then reconstructs a high-resolution image of the surrounding room, including lights, furniture, windows — even trees and people seen through the windows. The team presented its work at the Computer Vision and Pattern Recognition 2020 Computer Vision and Pattern Recognition (CPVR 2020) conference in June, and the project was also featured in Scientific American and WIRED. This helps to create more realistic 3D views in VR and AR.

In another paper, “Deepsdf: Learning continuous signed distance functions for shape representation,” Park introduces a new way of representing shape using neural networks. The new representation enables computers to more effectively learn 3D shapes prior from large datasets, enabling applications in vision, graphics and robotics. That work was among the candidates for Best Paper at CVPR 2019. This technique can be used for applications in AR/VR for indoor scene geometry reconstruction, inferring 3D shape of humans from an image, robotics for object grasping, or autonomous-driving for predicting shape of cars around the driver. Previously, in his paper “Surface light field fusion” published at the 2018 International Conference on 3D Vision, Park shows how to scan highly reflective objects with an RBGD sensor, using infrared dot patterns themselves to recover specular coefficients.

“While most of my students look to me to choose research directions in the beginning, JJ was driving his own research program from the start,” said Seitz. ”He’s passionate about scene modeling and rendering and wants to solve this problem for real. Large scale, general objects, and real systems that work for real users. And it’s been thrilling to watch him make major inroads on solving this problem with a series of strong technical papers.”

Park is looking forward to the opportunities the program will afford him.

“I’m very happy to be supported by Apple,” he said. “The company is already taking initiatives in augmented reality and other research areas and I expect to see synergy between our research.”

Congratulations, JJ — and thanks to Apple for generously supporting student research! 

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Earlier this spring, the National Science Foundation recognized nine Allen School student researchers as part of its 2020 Graduate Research Fellowship competition. The honorees — seven Ph.D. students and two undergraduate students — were recognized in the “Comp/IS/Engr” category for their potential to make significant contributions to science and engineering through research, teaching, and innovation. Each of them already has amassed an outstanding track record of pursuing high-impact research in their respective areas, including theoretical computer science, systems, machine learning, computational neuroscience, security and privacy, robotics, and more.

“Allen School Ph.D. students represent the future of high quality research and innovation,” said professor Anna Karlin, associate director for graduate studies at the Allen School. “Their creativity and scholarly excellence is perfectly exemplified by our NSF GRFP honorees.”

Nathan Klein

Fellowship recipient Nathan Klein is a second-year Ph.D. student who works with Karlin and professor Shayan Oveis Gharan in the Allen School’s Theory of Computation group.

Klein focuses on the design of efficient algorithms that yield near-optimal solutions to fundamental NP-hard problems that underpin the theory and practice of computing. His current project aims to find a better approximation algorithm for the Traveling Salesperson Problem (TSP). The TSP is applicable to a large class of planning and decision problems with a variety of real-world applications, from transportation routing, to genome sequencing, to computer chip design. Recently, Klein and his collaborators presented the first sub-3/2 approximation algorithm for what is conjectured to be the most difficult case of TSP — making tangible progress in their quest to improve upon a result that has stood for more than 40 years. Through this work, Klein hopes to advance tools and techniques that will yield new insights into a broad array of optimization problems.

Jialin Li

Second-year Ph.D. student Jialin Li earned a fellowship for her work with professor Tom Anderson in the Computer Systems Lab on a new operating system that will provide performance guarantees for containers in cloud-based services.

Containers are a lightweight computing model that offers a platform-independent way of packaging application dependencies; as such, they have been widely adopted in industry for building microservice-based applications. While existing operating systems provide functional support for containers, they fall short of providing the performance guarantees necessary for satisfying service-level agreements. This typically leads application developers to request more container resources than required, which wastes energy and resources. Li is designing a new operating system using the Rust low-level programming language that will monitor container performance and intelligently reallocate resources based on container loads, thus increasing resource utilization while offering performance guarantees.

Ashlie Martinez

Fellowship winner Ashlie Martinez is a second-year Ph.D. student in the Computer Systems Lab working with professor Tom Anderson and affiliate professor Irene Zhang of Microsoft Research to develop a user space file system for distributed storage applications.

Recent advances in storage technologies have significantly increased storage capacity while speeding up  input/output (I/O) by orders of magnitude. While storage technologies have evolved to the point where they can service requests in microseconds, developers’ approach to storage, generally speaking, has not — for the most part, they continue to regard I/O as a slow operation best done through an operating system’s file system. The Storage Performance Development Kit (SPDK) is available to bypass the kernel and speed up I/O, but it is difficult to integrate into existing software as the API exposes raw storage devices instead of a file system. To overcome these challenges and improve the performance of today’s distributed storage applications, Martinez is building a kernel-bypass file system, or KBFS, that combines a generic API with strong consistency guarantees. Using this approach, she aims to reduce developer effort while making KBFS faster and easier to maintain compared to existing OS file systems.

Josh Pollock

Josh Pollock, an undergraduate majoring in computer science, works with professor Zachary Tatlock in the Allen School’s Programming Languages and Software Engineering (PLSE) group. He received a fellowship based on his research at the intersection of programming languages and visualization.

Pollock started his undergraduate research career in verification and formal methods, specifically the development of computerized proof assistants that take advantage of the correspondence between type theory and mathematical logic. As part of this work, Pollock prototyped a compiler between the Coq and Lean proof assistants. He subsequently contributed to Relay, a compiler for machine learning frameworks, as a member of the Allen School’s multidisciplinary SAMPL group. Expanding his interests to include principles of human-centered research, Pollock is designing Sidewinder, a framework for creating visualizations of program execution to help students and developers understand program semantics. Sidewinder employs formal abstract machine definitions to produce complete, continuous, and customizable program semantics visualizations. Pollock aims to build upon this work while pursuing a Ph.D. at MIT starting this fall.

Kimberly Ruth

Graduating senior Kimberly Ruth received a fellowship based on her work in the Security and Privacy Research Lab with professors Tadayoshi Kohno and Franziska Roesner.

As an undergraduate, Ruth has focused on addressing security and privacy issues associated with emerging augmented reality (AR) technologies that can have a profound impact on users’ perception of the world. In her early work, Ruth focused on mitigating the risks of buggy or malicious output in AR applications that could endanger user safety by enabling the operating system to constrain undesirable output. She subsequently helped conduct a user study to understand concerns around multi-user AR. More recently, Ruth led the development of ShareAR, a tool for developers of AR applications to enable secure sharing of multi-user content. Going forward, Ruth sees the next step in this line of work to be designing a multi-user sharing protocol at the platform level that would mediate cross-app as well as cross-user interactions. Ruth looks forward to pursuing her Ph.D. at Stanford University in the fall.

Zöe Steine-Hanson

First-year Ph.D. student Zöe Steine-Hanson earned a fellowship for her research in computational neuroscience with professors Rajesh Rao and Bingni Brunton. Steine-Hanson is working on the development of a new, generalizable brain-computer interface (BCI) using deep learning and transfer learning techniques.

Currently, even the most advanced BCIs require the collection of significant training data on a single human subject, and the majority of BCI research takes place in a laboratory rather than in naturalistic settings. These factors hinder the ability to generalize state-of-the-art BCIs for people’s everyday use. To address this problem, Steine-Hanson is training a deep neural network on electrocorticography (ECoG) and video data collected from multiple human subjects. By applying techniques from transfer learning, she aims to reduce the amount of training data required for each new subject by leveraging the knowledge collected from previous subjects. Her ultimate goal is to improve quality of life for individuals living with neurological impairments through the use of next-generation BCI technologies in real-world settings.

Nick Walker

Fellowship recipient NickWalker is a second-year Ph.D. student working with professor Maya Cakmak in the Human-Centered Robotics Lab. Walker’s research focuses on human-robot communication with the aim of enabling any user to customize a robot to meet their needs.

Previously, Walker developed techniques for improving natural language interfaces within a robot’s existing capabilities. These included the creation of embodied language learners that can acquire understanding of simple words and leveraging neural models to compensate for variations in phrasing of natural language commands. Walker plans to build upon this past work by leveraging language to enable a robot to perform completely new tasks; to that end, he has turned his attention to the development of natural language programming techniques that will address a variety of robotics use cases. As part of this work, Walker plans to explore questions around people’s perceptions of robot agency and who bears responsibility for a robot learner’s mistakes, in anticipation of a time when home robots will be the personal computers of a future generation.

Matthew Schmittle

Second-year Ph.D. student Matthew Schmittle earned an honorable mention for his work with professor Siddhartha Srinivasa in the Personal Robotics Lab on the use of online learning methods to enable lifelong learning in robots.

Schmittle’s latest project focuses on improved techniques for imitation learning (IL), an approach to training dynamical systems that leverages expert feedback and demonstrations rather than requiring the hand-tuning of reward functions. IL offers an advantage over reinforcement learning in robotics, where real-world execution can be expensive or dangerous, due to its greater sample efficiency. However, most IL algorithms demand optimal state action demonstrations, which can be challenging even for experts. An alternative is to employ corrective feedback, in which users dispense with full demonstrations in favor of making adjustments during robot execution. This approach is easier for a teacher to provide but tends to be noisy and each teacher and task may require different feedback. To overcome this challenge, Schmittle recognizes robots must be able to learn from a variety of feedback and makes the following key insight: the teacher’s policy is latent, and their feedback can be modeled as a stream of loss functions. Based on this insight, he proposes a new corrective feedback meta-algorithm that can learn from a variety of noisy feedback across different tasks, teachers, and environments.

Caleb Ellington

Caleb Ellington, a senior double-majoring in computer science and bioengineering, has pursued undergraduate research in the Baker Lab working with Ph.D. candidate Nao Hiranuma. Ellington earned an honorable mention for his work on machine learning techniques to improve the design of new therapeutics.

Recombinant protein therapeutics have emerged as an area of huge potential in medical research due to their universal biocompatibility and high specificity. They are also significantly harder to design compared to small-molecule drugs, which has caused their development to lag. Inspired by what he encountered as an intern at Nepal’s Annapurna Neurological Institute and Dhulikhel Hospital — where computing and 3D printing are used to produce imaging and surgical tools quickly and inexpensively — Ellington intends to explore the potential for computer science to speed up the design of new protein therapeutics. Specifically, he proposes to leverage advances in generative deep convolutional neural networks (DCNNs), which are capable of inferring and correcting data, to the design of protein-ligand interactions. His approach is based on a hypothesis that, under the right conditions, generative models are powerful enough to create entirely new proteins based on a target binding region — a potential breakthrough in protein design that could yield effective new treatments for a variety of diseases. Ellington will pursue this research as a Ph.D. student in computational biology at Carnegie Mellon University.

In addition to the Allen School honorees, students from other UW departments were also recognized by the NSF in the “Comp/IS/Engr” category. Ph.D. students Steven Goodman and Sharon Heung in the Department of Human-Centered Design & Engineering both received fellowships, while fellow HCDE student Andrew Beers and Electrical & Computer Engineering undergraduate Kyle Johnson earned honorable mentions.

Congratulations to all — you make the Allen School and UW proud!

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Allen School professor Adriana Schulz and adjunct professor Nadya Peek are among the 35 “Innovators Under 35” recognized by MIT Technology Review as part of its 2020 TR35 Awards. Each year, the TR35 Awards highlight early-career innovators who are already transforming the future of science and technology through their work. Schulz, a member of the Allen School’s Graphics & Imaging Laboratory (GRAIL) and Fabrication research group, was honored for her visionary work on computer-based design tools that enable engineers and average users alike to create functional, complex objects. Peek, a professor in the Department of Human-Centered Design & Engineering, was honored in the “Inventors” category for her work on modular machines for supporting individual creativity. Schulz and Peek are also among the leaders of the new cross-campus Center for Digital Fabrication (DFab), a collaboration among researchers, educators, industry partners, and the maker community focused on advancing the field of digital fabrication.

Schulz develops novel tools, from algorithms to end-to-end systems, that bridge the gap between ideas and implementation. Schulz’s approach is based on the premise that design should be informed by how objects will perform once they are built, and that users have the opportunity to balance multiple, potentially conflicting tradeoffs as part of the design process. To that end, Schulz has focused on developing interactive software that enables users to explore variations of their design with instant performance feedback and to efficiently gauge the impact of various design compromises to arrive at the optimal choice for their desired functionality.

“3D printers are radically transforming the automotive and aerospace industries. Whole-garment knitting machines allow automated production of complex apparel. Electronics manufacturing using flexible substrates enables a new range of integrated products for consumer electronics and medical diagnostics,” Schulz observed. “These advances demonstrate the potential for a new economy of on-demand production of objects of unprecedented complexity and functionality.”

By combining new computational tools with the proliferation of these new fabrication technologies, Schulz aims to help usher in that new economy. She is also keen to democratize design and production in order to extend the benefits of this brave, new digital manufacturing revolution to the masses. 

”Digital fabrication technologies can be used to not only increase productivity but also to dramatically improve the quality of the products themselves, from consumer goods to medical applications,” Schulz explained. “But beyond the commercial impact, what I am really excited about is the potential to enable anyone to create anything, regardless of their background or individual needs. My goal is to empower people to shape the objects and environments around them to be more accessible, sustainable, and inclusive.”

A recent example of her approach is Carpentry Compiler, a project for which she teamed up with members of the Allen School’s Programming Languages & Software Engineering (PLSE) group and the Department of Mechanical Engineering. Carpentry Compiler leverages abstractions — which revolutionized computing by decoupling hardware from software development — to optimize the production of customized carpentry items. The tool enables users to specify a high-level geometric design that is automatically compiled into low-level hardware instructions for fabricating the parts. This approach optimizes for accuracy, fabrication time, and materials to improve sustainability of the fabrication process while reducing costs.

Lately, Schulz has turned her attention to applying digital fabrication techniques to meeting urgent needs in response to COVID-19. When the pandemic hit, Schulz and other DFab members came together to harness the UW’s fabrication capabilities to rapidly respond to a shortage of critical personal protective equipment (PPE) for frontline health care workers. As part of this effort, Schulz co-led the design and iteration of a low-cost medical gown that can be fabricated from readily available plastic sheeting — specifically, two-millimeter thick U-Line brand sheeting often used as a high-quality painter’s drop cloth — with the aid of a CNC vinyl cutter.

As they iterated their designs with their collaborators at UW Medicine, Schulz and the team quickly learned that they had to optimize for a very different set of parameters than what they were accustomed to working with. For example, their design had to provide the required level of protection while simultaneously allowing for freedom of movement. The wearer also needed to be able to quickly and easily remove a used gown without contaminating themselves or others in the process.

“Adriana’s work on the medical gown and other projects reflect her collaborative spirit and her great ingenuity and intuition when it comes to designing to optimize for user needs and preferences,” observed professor Magdalena Balazinska, director of the Allen School. “By creating tools that enable people to quickly and easily understand various tradeoffs between design decisions and performance, Adriana is creating an exciting new paradigm in computer-aided manufacturing. Her creativity and energy have been transformative to the Allen School. We feel fortunate to have her as a colleague and are proud to see her recognized.”

Schulz joined the University of Washington faculty in 2018 after earning her Ph.D. from MIT. It was there that she honed her approach to computational design for manufacturing while collaborating on projects such as InstantCAD, which enables users to quickly and easily gauge performance tradeoffs associated with changing a mechanical shape’s geometry, and AutoSaw, a template-based system for robot-assisted fabrication to enable mass customization of carpentry items. She also co-led the development of Interactive Robogami, which offers a framework for creating 3D-folded robots out of flat sheets.

Peek, who also joined the UW faculty in 2018 after earning her Ph.D. and completing a postdoc at MIT, directs the Machine Agency lab. Peek develops systems that lower the threshold to deploying precise computer-controlled processes and empower domain experts in a variety of fields to use automation without machine design expertise. Her goal is to extend the benefits of automation — precision and speed — to low-volume manufacturing, scientific exploration, and creative problem solving. For example, she led the development of Jubilee, an open-source tool changing machine that enables researchers to develop workflows for fabrication, material exploration, and other applications and which can be built using a combination of 3D-printed and readily available parts.

Peek’s early work advanced the concept of object-oriented machine design. She established the Machines that Make project to design modular machine components that could be assembled by non-experts into different configurations and directly controlled. Another of her projects, Cardboard Machine Kit, has been used by thousands of people worldwide to make hundreds of different machines. More recently, Peek has turned her attention to the development of production systems for digital fabrication in architecture and construction, automated experiment generation and execution in chemical engineering, and robotic farming of aquatic plants.

“Both Nadya and Adriana are incredibly talented researchers who are adept at synthesizing advances spanning multiple domains to realize their vision,” said Shwetak Patel, a professor in the Allen School and Department of Electrical & Computer Engineering who earned a TR35 in 2009 for his work on energy and health sensing. “They are each transforming in fundamental ways how we think about design, fabrication, and production, and their work has quickly helped to establish the UW as a hub of digital fabrication innovation.”

In addition to Schulz and Peek, another 2020 TR35 honoree has a strong Allen School connection. Undergraduate alumna and former postdoc Leilani Battle (B.S., ’11), now a member of the computer science faculty at the University of Maryland, College Park, was honored for her work on interactive and predictive data exploration tools that enable scientists and researchers to work more efficiently. Battle worked with Balazinska in the UW Database Group as an undergraduate and completed her postdoc working with professor Jeffrey Heer in the Allen School’s Interactive Data Lab. In between, she earned her master’s and Ph.D. from MIT.

Previous Allen School TR35 honorees include professor Franziska Roesner in 2017, for her work on security and privacy of augmented reality; professors Shyam Gollakota and Kurtis Heimerl in 2014, for their work on battery-free communication and community-based wireless, respectively; adjunct professor and current HCDE chair Julie Kientz in 2013, for her work on software to support health and education; adjunct professor and Global Health faculty member Abie Flaxman in 2012, for improvements in measuring disease and gauging the effectiveness of health programs; professors Jeffrey Heer and Shwetak Patel in 2009 for their work in data visualization and sensor systems, respectively; and professor Tadayoshi Kohno in 2007, for his work on emerging cybersecurity threats. Allen School alumni previously recognized by TR35 include Jeff Bigham, Adrien Treuille, Noah Snavely, Kuang Chen, and Scott Saponas.

Read MIT Technology Review’s TR35 profile of Schulz here, the profile of Peek here, the profile of Battle here, and the full list of TR35 recipients here. Read the related HCDE story here.

Congratulations, Adriana, Nadya, and Leilani!

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Gabriel Erion, an M.D.-Ph.D. student currently doing his Ph.D. research with Allen School professor Su-In Lee in the Artificial Intelligence for Medicine and Science (AIMS) Lab, has earned the prestigious Ruth L. Kirschstein National Research Service Award from the National Institutes of Health. The Kirschstein-NRSA, also known as the F30, was created to enhance research and clinical training of promising predoctoral students who are enrolled in a combined M.D.-Ph.D. training program and plan to pursue careers as physician-scientists. 

The University of Washington is one of about 50 schools around the country that have a Medical Scientist Training Program — an M.D.-Ph.D. program supported by federal funding from the NIH. The program can last eight or more years; students spend the first two years of their training in medical school, mostly in a classroom setting. They then transition to a Ph.D. and finish the entire degree before returning to medical school for two years of primarily hospital-based clerkships. After graduating, students often split their time between research and practicing medicine. Erion was the first MSTP student at the University of Washington to join the Allen School for a Ph.D. in Computer Science & Engineering.

Erion initially planned to study biology in college and then go to medical school, but his love for building computers inspired him to take math and computer science classes, too. He switched his major to applied math, but still wanted to combine that with experience in health care. 

“An M.D.-Ph.D. was one of the best ways I found to combine these fields, and the University of Washington had one of the best programs for someone with my interests,” Erion said. “Unlike many other schools, the UW program didn’t limit MSTP students to biology departments, and was supportive of my idea to do a math and computer science based Ph.D.”

Erion and Lee’s research on CoAI: Cost-Aware Artificial Intelligence for Health Care, in collaboration with Dr. Nathan White from the University of Washington Department of Emergency Medicine, helped him secure the fellowship. CoAI is a machine learning method for making cost-sensitive risk scores in clinical settings that maintains or improves accuracy while dramatically reducing the time it takes to predict a variety of patient outcomes.

Medical risk scores are used by doctors to help assess a patient’s risk of having a disease. There are hundreds of such risk scores, and they’re used for a wide range of purposes. Erion’s research focuses on three areas: predicting trauma patients’ risk of bleeding disorders, which is used by doctors to anticipate needs for resources like blood transfusions; predicting in-hospital death of patients in intensive care units, which is used to determine who needs the most attention from doctors and nurses; and predicting whether primary care patients will live for 10 years after their visit — an outcome which is not often assessed in clinics, but could be useful as a general indicator of patient health for primary care doctors. 

CoAI was created to help doctors with these time-consuming risk assessments by providing a general-purpose machine learning framework for building low-cost clinical risk scores. The F30 grant will enable Erion and his team to create methodological developments and experiments to demonstrate CoAI’s value in a range of important clinical scenarios. For example, predicting a trauma patient’s risk of bleeding disorders is tedious right now with the Prediction of Acute Coagulopathy of Trauma (PACT) score, but CoAI could make it more efficient. 

“The PACT score variables are some of the most time- and effort-intensive to gather, which we determined by surveying EMTs, paramedics, and nurses in our region. PACT requires blood pressure and heart rate measurements as input, as well as the Glasgow Coma Score, which is a 15-point scale that takes time and cognitive effort to calculate,” Erion explained. “We wanted to see if we could predict bleeding disorders as well as or better than PACT, while reducing the effort required for emergency medical service providers to gather necessary data for the score.”

This ease of use means the tool would be more likely to be deployed in practice, saving EMTs valuable time in the ambulance. The team conducted a survey that showed providers would prefer a risk calculator for which inputs would only take less than a minute to gather. 

“We showed that CoAI can actually provide more accurate predictions than the PACT score with only 50 seconds of data-gathering, which is almost 10 times less than the PACT score was calculated to take,” Erion said. “It does this by automatically determining which variables are easy to measure, for example, data from the dispatcher that is pre-provided, or data like age that take only seconds to gather. We also demonstrated similar performance improvements for the ICU and primary care datasets.”

Prior to Erion earning the NIH fellowship, he and the CoAI team captured the Madrona Prize at the Allen School’s 2019 Research Showcase.

Congratulations, Gabe! 

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Allen School professor Luis Ceze and affiliate professor Karin Strauss, a principal research manager at Microsoft, have earned the 2020 Maurice Wilkes Award from the Association for Computing Machinery’s Special Interest Group on Computer Architecture (ACM SIGARCH) for “contributions to storage and retrieval of digital data in DNA.” The award, which is named in honor of the pioneering British computer scientist who built the first operational stored-program computer, recognizes an outstanding contribution by a member of the computer architecture field within the first two decades of their professional career. Ceze and Strauss are the first recipients to share the award in its 22 year history.

“The Maurice WIlkes Award has always been an individual honor, but I think the award committee made the right choice in recognizing Karin and Luis together,” said Allen School professor Hank Levy, who recruited Ceze and Strauss to the University of Washington. “We are witnessing the emergence of an entirely new area of our field — molecular information systems — and Karin and Luis are at the forefront of this exciting innovation.”

Since 2015, Ceze and Strauss have co-directed the Molecular Information Systems Laboratory (MISL), a joint effort by the University of Washington and Microsoft to explore synthetic DNA as a scalable solution for digital data storage and computation. They achieved their first breakthrough the following spring, when they described an archival storage system for converting the binary 0s and 1s of digital data into the As, Ts, Cs, and Gs of DNA molecules. The team followed up that achievement by storing a record-setting 200 megabytes of data in DNA, from the Declaration of Human Rights in more than 100 languages to the music video for “This Too Shall Pass” by popular band OK Go.

The team would later publish the science behind this feat in the peer-reviewed journal Nature Biotechnology, along with a description of their technique for achieving random access using a library of primers that they designed and validated for use in conjunction with polymerase chain reaction (PCR). The latter was a crucial step in demonstrating the feasibility of a large-scale DNA-based digital storage architecture, since such a system would be cost- and time-prohibitive without building in the ability to quickly and easily find and retrieve specific data files without sequencing and decoding the entire dataset.

Last year, Ceze, Strauss, and their colleagues introduced the world to the first automated, end-to-end system for DNA data storage, encoding the word “hello” as five bytes of data in strands of DNA and recovering it. Their fully functioning prototype incorporated the equipment required to encode, synthesize, pool, sequence, and read back the data — all without human intervention. After demonstrating it was feasible to automate DNA data storage, they moved on to showing how it could be practical, too, unveiling a full-stack automated digital microfluidics platform to enable DNA data storage at scale. As part of this work, the lab designed a low-cost, general-purpose digital microfluidics device for holding and manipulating droplets of DNA, dubbed PurpleDrop, which functions as a “lab on a chip.” PurpleDrop can be used in conjunction with the team’s Puddle software, an application programming interface (API) for automating microfluidics that is more dynamic, expansive, and easier to use than previous techniques. Since then, the team has begun exploring new techniques for search and retrieval of data stored in DNA, such as content-based similarity search for images.

”Many people don’t envision a wet lab full of pipettes and DNA quantification, synthesis, and sequencing machines when they think of computer architecture,” Strauss admitted. “But that’s what makes this work so interesting, and frankly, thrilling. We, computer architects, get to work with an incredibly diverse group of brilliant researchers, all the way from coding theorists and programming languages, to mechanical and electrical engineering, to molecular biology and biochemistry. 

“Having done this research has only energized us further to continue working with our colleagues to push the boundaries of computing by demonstrating how DNA, with its density and its durability, offers new possibilities for processing and storing the world’s information,” she concluded.

In addition to pushing the boundaries of computer architecture, Strauss, Ceze, and the other MISL members have made an effort to push the limits of the public’s imagination and engage them in their research. For example, they collaborated with Twist Bioscience — the company that supplies the synthetic DNA used in their experiments — and organizers of the Montreux Jazz Festival to preserve iconic musical performances from the festival’s history for future generations to enjoy. In 2018, the lab enabled the public to take a more active role in its research through the #MemoriesInDNA campaign, which invited people around the world to submit their photos for preservation in DNA under the tagline “What do you want to remember forever?”

More recently, the MISL team entered into a collaboration with Seattle-based artist Kate Thompson to pay tribute to another pioneering British scientist, Rosalind Franklin, who captured the first image revealing the shape of DNA. That tribute took the form of a portrait comprising hundreds of photos crowdsourced from people around the world as part of the #MemoriesInDNA campaign. The researchers encoded a selection of those photos in DNA, with redundancy, and turned the material over to Thompson. The artist then mixed it with the paint to infuse Franklin’s portrait with the very substance that she had helped reveal to the world. 

“We believe there are a lot of opportunities in getting the best out of traditional electronics and combining them with the best of what molecular systems can offer, leading to hybrid molecular-electronic systems,” said Ceze. “In exploring that, we’ve combined DNA storage with the arts, with history, and with culture.”

In addition to combining science and art, Ceze has also been keen to explore the combination of DNA computing and security. In a side project to his core MISL work, he teamed up with colleagues in the MISL and in the Allen School’s Security and Privacy Research Lab to explore potential security vulnerabilities in DNA sequencing software. In a highly controlled experiment, the researchers encoded an exploit in strands of synthetic DNA. They then processed the sample with software compromised by a known vulnerability to demonstrate that it is possible to infect a computer by malicious code delivered through DNA. Ceze also worked with a subset of that same team to understand how people’s privacy and security could be compromised via online genetic genealogy services — including, potentially, the spoofing of relatives who do not exist. 

“As the intersection of DNA and computing becomes more mainstream, it’s important to highlight these vulnerabilities,” Ceze explained. “We want to address any security issues before they can cause harm.”

Ceze joined the University of Washington faculty in 2007 after earning his Ph.D. from the University of Illinois at Urbana-Champaign. Early in his career, Ceze emerged as a leading proponent of approximate computing, which aims to dramatically increase efficiency without sacrificing performance. His work has blended operating systems, programming languages, and computer architecture to develop solutions that span the entire stack, from algorithms to circuits. While approximate computing most often focuses on computation itself, Ceze was keen to apply the principle to data storage. He teamed up with Strauss and other colleagues at UW and Microsoft to focus on what he calls “nature’s own perfected storage medium,” and the rest will go down in computer architecture history.

“Luis established himself as a leader in the architecture community when he took approximate computing from a niche idea to a mainstream research area,” said Josep Torrellas, Ceze’s Ph.D. advisor and director of the Center for Programmable Extreme-Scale Computing at the University of Illinois at Urbana-Champaign. “Since then, his contributions working alongside Karin on synthetic DNA for digital data storage have been nothing short of groundbreaking — encompassing an overall system architecture, decoding pipeline, fluidics automation, wet lab techniques and analysis, search capabilities, and more.

“Luis and Karin have advanced a completely new paradigm for computer architecture, and they did it in an impressively short period of time,” he continued. “I can’t think of anyone working in the field today who is more deserving of this recognition.”

Strauss, who also earned her Ph.D. from the University of Illinois at Urbana-Champaign working with Torrellas, joined Microsoft Research in 2009 after spending nearly two years at AMD. A major focus of her work over the past decade has been on making emerging memory technologies viable for use in mainstream computing. In 2013, Strauss contributed to a paper, along with Ceze and their Allen School and MISL colleague Georg Seelig, exploring new approaches for designing DNA circuits. That work challenged the computer architecture community to begin contributing in earnest to the development of this emerging technology. She led the charge within Microsoft Research, along with Douglas Carmean, to devote a team to DNA-based storage, leading to the creation of the MISL.

“Karin is a pioneering researcher in diverse areas of research spanning hardware support for software debugging and machine learning, main memory technologies that wear out, and emerging memory and storage technologies,” said Kathryn McKinley, a researcher at Google. “Her latest research is making DNA data storage a reality, which will revolutionize storage and computing. It is heartwarming to see the amazing research partnership of Karin and Luis recognized with this extremely prestigious award.”

The Maurice Wilkes Award is among the highest honors bestowed within the computer architecture community. Recipients are formally honored at the ACM and IEEE’s Joint International Conference on Computer Architecture (ISCA). This year, the community celebrated Ceze and Strauss’ contributions in a virtual award ceremony as part of ISCA 2020 online.

“We are tremendously proud of Luis and Karin! They are true visionaries and trail-blazers and their creativity never ceases to amaze me,” said professor Magdalena Balazinska, director of the Allen School. “I look forward to seeing the next exciting research results that will come out of their lab. Their work so far has definitely been very impactful, and I’m very happy they have been recognized with this prestigious award.”

Congratulations, Karin and Luis!

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Each year in June, the Allen School invites graduates and their friends and family from across the country and around the world to join in a celebration on the University of Washington’s Seattle campus. As COVID-19 precludes the traditional in-person celebration at present, the school is paying tribute to the 2019-2020 graduates online — including video messages from faculty wishing the graduates well as they embark upon the next stage of their academic or professional journeys.

Professor Magdalena Balazinska, director of the Allen School, kicked off the celebration by acknowledging the difficulties that graduates of the Class of 2020 have had to overcome to reach this milestone.

“Your generation has seen some great challenges. When the COVID-19 pandemic abruptly sent us off campus at the end of winter, you rose up to the challenge and completed the quarter,” she said. “You worked hard in difficult conditions all spring. And when it came time to stand up in the name of justice, you stood up. We are immensely proud of you.”

Balazinska’s sentiment was echoed by other Allen School professors featured in the video, including professor and vice director of the Allen School, Dan Grossman. “Your class will always have a special place in our hearts, and not to be melodramatic, a special place in history,” he said. “Please go out and make the world a better place for all of us.”

Grossman’s confidence in the future impact of the Class of 2020 is understandable, given their past achievements. The school recognized several of them as part of its online tribute for their academic excellence, research, teaching and service. 

Each year, the school selects four graduating bachelor’s students — two each in Computer Science and Computer Engineering — as recipients of the Outstanding Senior Award in recognition of their exceptional academic performance, contributions to the advancement of knowledge, and demonstrated leadership potential and good citizenship. The first of the 2020 honorees, Ethan Chau, is graduating summa cum laude with degrees in Computer Science with the data science option and in Linguistics. Chau worked with Allen School professor Noah Smith in the Natural Language Processing group on the development of new tools for representing languages for which data is scarce and conventional methods are ineffective. He also studied abroad at the prestigious Swiss Federal Institute of Technology (ETH Zurich). Chau will continue his studies at the Allen School as part of the Combined B.S./M.S. program.

Fellow Computer Science honoree Moe Kayali is graduating cum laude and will enter the Allen School’s full-time Ph.D. program this fall to work with the Database group. Kayali spearheaded the development of the latest version of the control software for the Manastash Ridge Observatory run by the UW Astronomy Department and earned both a Mary Gates Research Scholarship and an Honorable Mention in the Computing Research Association’s CRA Outstanding Undergraduate Researcher Awards competition. The CRA similarly honored Outstanding Senior Award recipient Pathirat Kosakanchit for her work with the Information and Communication Technology for Development (ICTD) Lab. Kosakanchit, who graduated magna cum laude with a degree in Computer Engineering in winter quarter, previously received the Outstanding Female Award from UW Society of Women Engineers and an Honorable Mention in the CRA Outstanding Undergraduate Researcher Awards competition. After completing her current internship at Qumulo, Kosakanchit will continue in the Allen School as a Master’s student in the Combined B.S./M.S. program.

The fourth and final Outstanding Senior Award went to Kimberly Ruth, who is graduating summa cum laude with degrees in Computer Engineering and Mathematics. For most of her undergraduate career, Ruth worked with members of the Allen School’s Security and Privacy Research Lab on new tools for safeguarding users of emerging augmented reality technologies. Her work earned her a CRA Outstanding Undergraduate Researcher Award, a Goldwater Scholarship, a Mary Gates Research Scholarship, and a Dean’s Medal for Academic Excellence from the College of Engineering. Her work also earned the Allen School’s Best Senior Thesis Award for “Understanding and Designing for Security and Privacy in Multi-User AR Interactions,” which she completed under the guidance of professors Tadayoshi Kohno and Franziska Roesner. As part of that work, Ruth led the development of ShareAR, a toolkit that helps app developers build in collaborative and interactive features without sacrificing user privacy and security. She will continue her research as a Ph.D. student at Stanford University in the fall.

The Allen School highlighted the work of two other graduating seniors with Best Thesis Award Honorable Mentions. Anand Sekar was recognized for his work on “Hardware Implementation of a Wireless Backscatter Communication Protocol for Brain-Controlled Spinal Interfaces,” supervised by professor Josh Smith, director of the Sensor Systems Laboratory, with postdoctoral researcher Laura Arjona. Guanghao Ye, who worked with Yin Tat Lee of the Allen School’s Theory of Computation group, was honored for “Fast Algorithm for Solving Structured Convex Programs.”

The Allen School also recognizes a group of graduating seniors each year for their exemplary service to their fellow students and contributions to the community through its Undergraduate Service Awards. The first of the 2020 recipients, Angela Eun, earned recognition for her mission-driven leadership and compassion as chair of the UW chapter of the Association for Computing Machinery’s Council on Women in Computing (ACM-W). During her time at the helm, Eun worked hard to recenter the organization’s focus on supporting, celebrating and advocating for women in computing. She also empowered her peers to develop their own leadership skills and encouraged them to tap into their strengths to serve the broader school community.  Honoree Jenny Liang is similarly passionate about empowering others and also about developing technology for social good. As vice chair of the Student Advisory Council, she served as an advocate for students’ needs to the school’s leadership and led important directives aimed at supporting student success and equity. She also contributed to research on community-owned LTE networks for underserved areas of Indonesia and Mexico.

Murathan Sarayli earned recognition for his commitment to ensuring transfer students have a positive experience in the Allen School in his roles as a TA for the transfer seminar and as the diversity representative on the Student Advisory Council. In selecting him for the award, the Allen School noted that Sarayli gave voice to a student experience that is often overlooked — a contribution that will have a lasting impact on the school and its students. Last but not least, recipient Savanna Yee served her fellow students both as a TA and a peer adviser. In the latter role, she helped hundreds of students understand the application process and navigate their studies. She also served on the Student Advisory Council and as an officer for ACM-W. In those roles, Yee organized several community events, including ones focused on failure and vulnerability to provide a platform for students to learn and grow.

In addition to honoring exemplary graduates, the Allen School also recognized outstanding TAs who have devoted themselves to promoting computer science education and serving their fellow students through the Bob Bandes Memorial Awards for Excellence in Teaching. The three winners were undergraduate Andrew Gies, a Computer Science and Theatrical Design major who spent five quarters as a TA in the Software Design and Implementation Course; Travis McGaha, a fifth-year student in the Combined B.S./M.S. program who was a TA for nine quarters spanning Computer Science Principles, Computer Programming II, The Hardware/Software Interface, Data Structures and Algorithms, and Systems Programming; and Batina Shikhalieva, an Electrical Engineering major served as a TA for five quarters in Computer Programming I and Computer Programming II. 

Bandes Award Honorable Mentions went to Allen School Ph.D. student Jialin Li, a Ph.D. student and TA for the Allen School’s Operating Systems course; Ph.D. student Chung-Yi Weng, a TA in Computer Graphics, and Tal Wolman, an Earth & Space Sciences major who was a TA for five quarters in the Web Programming course.

While unable to honor graduates with its traditional celebration this month, the Allen School plans to invite them back for a belated celebration when it is safe to gather as a group. Balazinska, for one, is already looking forward to reconnecting in person and hearing about the impact they have made with their education.

“I hope you will come back and visit us and let us know how far an Allen School degree has taken you,” Balazinska told the graduates via video. “When you come back to visit, I will not ask you if you have a big salary or if you have a big house. I will want to hear about how you have changed the world.”

View our online tribute to the Class of 2020 here, profiles of our graduating Ph.D.s here, and awards and special recognition here. The Allen School will award roughly 600 total degrees this year. Only the names of graduates who opted into taking part in the public tribute are listed online.

Congratulations to all of the members of the Allen School Class of 2020!

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To our extended Allen School community,

Traditionally, June marks a time of great joy and celebration at the Allen School, as we send off our graduates into the world to push the limits of innovation and apply computing for the benefit of humanity. If this were a normal year, we would be gathering with friends and families tomorrow in Hec Edmundson Pavilion on the University of Washington campus in Seattle to cheer on our bachelor’s and master’s recipients as they walked across the stage in their caps and gowns, and to honor our newly-minted Ph.D.s with a traditional hooding ceremony and hugs.

But at this particular moment, we are grappling not only with the scourge of COVID-19, but with another scourge that has taken its toll on members of our community and torn the very fabric of our society. We will find a way to celebrate with our graduates and families when it is safe to do so. But we also must recognize that, for Black families, COVID-19 is not the only public health threat that they need to worry about. And this fills us with sadness and with anger.

We are scientists, engineers, educators, administrators, managers, and counselors. We devote our professional lives to seeking answers and solving problems. It’s what we are trained to do, and what we are training the next generation to do. Usually, when we see a problem, we work out a solution and then we move on. But we can’t do that here. The problems we are aligned against — racism, police brutality, injustice — cannot be solved by an algorithm or an app. We can’t just fix it and move on.

In the face of these challenges, monumental in both scope and urgency, the most immediate thing in our power to do is to look within ourselves and within our own community. 

We acknowledge the pain and the trauma that people of color, individually and collectively, endure — and have endured for generations — at the UW and across the nation. As educational leaders and as human beings, we are outraged at the latest in a long list of examples of injustice and craven indifference toward members of the Black community. We will channel that outrage into our work to make our school, our discipline, and our society more compassionate and inclusive. We will seek out and amplify the voices of those in our community who need to be heard but are too often silenced or ignored. And as we make conditions immediately around our own community better, we believe those changes will spread more globally as persistent work and positive actions build upon each other.

We will take actionable steps to support our students of color and become better allies so that we can share in the burden and emotional labor of confronting racism while seeking to live up to our values around inclusiveness. The first of these will be a virtual community conversation event for Black students and/or students whose loved ones are part of the Black community this week, followed by a community-wide event on allyship over the summer. We wish to acknowledge the efforts of our undergraduate student leaders in moving these conversations forward and for supporting their peers and us as school leaders during this difficult time. They give us hope.

Over the medium and longer terms, we must turn our conviction — that computer science is a gateway to opportunity — into action that ensures that underrepresented minority students are fully exposed to its potential and given the resources and mentorship they need to excel in this field. As concrete steps in this direction, we will strengthen our efforts around recruiting and retaining a diverse faculty and staff and increasing representation of undergraduate and graduate students of color. We will launch a new high school mentorship and pipeline program for underrepresented high school students in Washington to cultivate their academic potential and interest in computer science. We will build on our participation in the FLIP Alliance, a partnership of our nation’s top computer science doctoral programs to increase the number of underrepresented minority students who become future professors and leaders in our field. We will continue to pursue computer-science research agendas and educational content that are ethical and inclusive of everyone, not just those who look like us. 

Last but not least, we will say their names. George Floyd. Breonna Taylor. Ahmaud Arbery. Manuel Ellis. There are more names — far too many more. To honor their memory, and because it is the right thing to do, we will call out racism and injustice when we see it. We will put in the work to help solve this problem and to bring about positive change. And we will start with ourselves.

–The Allen School Leadership Team

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