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Allen School’s Michael Ernst receives ACM SIGSOFT Outstanding Research Award for contributions that have revolutionized software engineering

Allen School professor and alumnus Michael Ernst (Ph.D., ‘00) has had a distinguished research career that spans more than two decades and includes multiple, influential contributions to programmer productivity through software analysis, testing and verification. In recognition of his impressive body of work, Ernst was recently recognized with the 2020 Outstanding Research Award from the Association for Computing Machinery’s Special Interest Group on Software Engineering (ACM SIGSOFT). 

Since he returned to his alma mater in 2009 to take up a faculty position with the Allen School’s Programming Languages and Software Engineering (PLSE) group, Ernst has advanced the state of the art in the field on multiple fronts and earned many accolades along the way. The latest of these, SIGSOFT’s most prestigious award, recognizes his enduring impact on the theory and practice of software engineering.

“Michael Ernst is one of the most accomplished software engineering researchers. His work has led to many tools that are applied daily to real life software products,” said SIGSOFT Chair Tom Zimmermann, Senior Principal Researcher at Microsoft. “His Daikon tool was revolutionary and an elegant solution that impacted the research community in testing, verification, and programming languages.”

Daikon is a software system that dynamically detects likely program invariants. Ernst and his co-authors first presented Daikon in the 1999 paper, “Dynamically discovering likely program invariants to support program evolution,” while he was a Ph.D. student with fellow graduate student Jake Cockrell (M.S., ’99), Allen School alumnus and UCSD professor William G. Griswold (Ph.D., ’91), and the late Allen School professor David Notkin. In the paper, Ernst and his collaborators described novel techniques, such as tracing variables of interest, for enabling programmers to easily identify program properties that must be preserved when modifying code. The team’s approach initiated an important new line of research, earned the 2013 Impact Paper Award, and continues to be relevant more than two decades later.

Another research highlight of Ernst’s impressive career so far was a paper he co-authored with Carlos Pacheco of Google, and Shuvendu Lahiri and Thomas Ball of Microsoft Research called “Feedback-directed random test generation” in 2007. In the paper, Ernst and his colleagues presented a test generation technique and corresponding tool, Randoop, which generates tests for programs written in object-oriented languages such as Java and .NET. The technique put forward by the team generates one test at a time, executes the test, and classifies it as a normal execution, a failure, or an illegal input. Based on this information, the technique biases the subsequent generation process to extend good tests and avoid bad tests. Ernst and the team earned the Most Influential Paper Award from the International Conference on Software Engineering (ICSE) in 2017, and Randoop remains the standard benchmark against which other test generation tools are measured.

A year after his ICSE recognition, Ernst received a 2018 Impact Paper Award from the International Symposium on Software Testing and Analysis (ISSTA) for the 2008 paper, “Practical pluggable types for Java.” With this project, Ernst and his co-authors set out to address a well-known verification problem for programmers working in Java: while type checking could prevent many bugs, it couldn’t prevent enough bugs. To address this, Ernst and his colleagues developed the Checker Framework, a scalable tool for creating expressive and concise pluggable type systems for Java. The Checker Framework incorporated a type system component for defining type qualifiers and their semantics, and a compiler plug-in that enforces the semantics. It is also backward-compatible and integrates easily with standard development environments and tools, making it useful to programmers seeking to write bug-free code as well as type-system designers looking to evaluate and deploy their type systems. At the time of publication, Ernst was a faculty member at MIT, working with researcher Jeff H. Perkins, graduate student Matthew M. Papi, and undergraduates Mahmood Ali and Telmo Luis Correa Jr.

Among his many other accolades, Ernst earned a 2019 ISSTA Impact Paper Award for “HAMPI: a solver for string constraints,” a 2011 European Conference on Object-Oriented Programming (ECOOP) Best Paper Award and a total of nine ACM Distinguished Paper Awards. He was the recipient of the inaugural John Backus Award (2009) — created by IBM to honor mid-career university faculty members — and was elected a Fellow of the ACM in 2014. 

“Michael is an exceptional software-engineering researcher and his continual momentum to produce research has advanced the field and has made some incredible real-world impacts,” said professor Magdalena Balazinska, director of the Allen School. “In addition to his research, he’s been an admirable influence on our next generation of computer scientists and engineers. We’re grateful he is a part of the Allen School faculty.”

In 2018, Ernst was formally recognized for his work as an educator and mentor with the CRA-E Undergraduate Research Faculty Mentoring Award from the Computing Research Association. The award recognizes faculty who provide exceptional mentorship and support to student researchers.

“I’m passionate about mentoring because doing research as an undergraduate changed the course of my career and my life,” Ernst said at the time. “I love making new discoveries — and I get a vicarious thrill from helping others to experience that same wonder.”

Congratulations, Mike!

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Vikram Iyer receives Marconi Society Young Scholar Award after creating a buzz with bio-inspired wireless technologies

No one could accuse Ph.D. student Vikram Iyer of just winging it. Since his arrival at the University of Washington, Iyer has advanced ground-breaking innovations in low-power wireless communication and computation to expand the Internet of Things, from 3D-printable wireless objects capable of storing and transmitting data, to insect-scale platforms that provide a bug’s eye view of the world. As a sign of just how his ideas have taken flight, today Iyer was named one of three recipients of the Paul Baran Young Scholar Award by the Marconi Society.

“By creating low cost, mobile IoT devices that can help answer questions and solve problems in any environment, Vikram’s work supports the Marconi Society’s mission of bringing the opportunity of the network to everyone,” said internet pioneer Vint Cerf, Chair of the Marconi Society, in a press release. “We are proud to welcome him to the Marconi family.”

The award recognizes innovative young engineers who show extraordinary technical acumen, creativity and promise for creating tomorrow’s information and communications technologies to support a digitally inclusive society. The honor came as no surprise to Iyer’s advisor, Allen School professor Shyam Gollakota, who recognized early on that his student would be a highflier.

”Vikram is a one-of-a-kind creative interdisciplinary researcher who is also humble,” said Gollakota. “He develops creative solutions that are at the intersection of hardware, software and biology. In so doing, he transforms what was once science fiction into reality.”

Iyer, a student in the UW Department of Electrical & Computer Engineering, began working with Gollakota in the Networks & Mobile Systems Lab in 2015. Among their first projects together was a collaboration with Allen School and ECE professor Joshua Smith of the Sensor Systems Laboratory on an ultra-low power system to provide wireless connectivity for implantable devices. Interscatter — short for intertechnology backscatter — employs a technique called backscatter communication to convert Bluetooth transmissions to WiFi and ZigBee signals over the air using commodity devices. As part of that project, Iyer and his collaborators created the first prototype contact lens antenna and an implantable neural recording interface capable of communicating directly with smartphones and smart watches. The team earned a Best Paper Award from the Association for Computing Machinery’s Special Interest Group on Data Communications (ACM SIGCOMM).

More recently, Iyer, Gollakota and their colleagues teamed up with professor Sawyer Fuller of the UW Mechanical Engineering Department’s Autonomous Insect Robotics (AIR) Lab to enable new wireless robotic technologies to take flight. The result was Robofly, the world’s first wireless fly-sized drone to achieve liftoff. Unlike previous insect-scale drones, Robofly does not require a wire to the ground to supply power and control signals — a significant achievement on the path toward autonomous robot flight. The team’s bio-inspired design featured dual flapping wings driven by a pair of piezoelectric actuators and directed by a lightweight microcontroller, which issues a series of pulses mimicking the action of a biological fly’s wings. An onboard photovoltaic cell converts light from a laser beam into electricity to power the onboard components without the need for heavy batteries, while the first sub-100 milligram boost converter and piezo driver sufficiently boosts the voltage to enable RoboFly’s ascent. 

While news of Robofly’s exploits took off, Iyer recognized that there are limitations to what a drone insect could do. For one thing, robotic liftoff was difficult to achieve. And commercial drones are limited in how long they can fly uninterrupted.

“This made me wonder, rather than building a system that mimics an insect, could we augment live insects with sensing, computing and communication functionalities to create a mobile IoT platform?” Iyer explained. “We could use this platform to study micro-climates on large farms, answer questions about insects’ behavior or collect air quality data at a more granular level than by using a handful of stationary sensors.”

Iyer with his advisor, Shyam Gollakota, unveiling 3D-printed wireless smart objects in 2017

To explore the idea, Iyer set up an amateur beekeeping operation in a room in the Paul G. Allen Center on campus. The result was Living IoT, a mobile platform that combines sensing, computation, and communication packaged into a tiny wireless backpack light enough to be carried by a bumblebee. The entire system — antenna, envelope detector, sensor, microcontroller, backscatter transmitter, and rechargeable battery — weighed in at just 102 milligrams, or around half a bumblebee’s potential payload. Because the system did not need to power flight, only data collection, the team could keep the weight down by designing the system to transmit data and recharge the battery when the bee returned to the hive each day.

For his latest project, which was recently published in Science Robotics, Iyer’s insect subjects kept their feet on the ground. Building off of the previous work with bees, Iyer and his collaborators created a new wireless backpack containing a tiny, steerable video camera operated via Bluetooth. This time, they fitted their system on two species of beetle to demonstrate the potential for insect-scale robotic vision. Dubbed “BeetleCam,” the system emulates a real bug’s energy-efficient approach to gathering visual information, which relies on head motion independent of its body, while a built-in accelerometer prolongs the battery life by allowing the system to capture images only when the beetle is in motion. Weighing in at a mere 250 milligrams, or roughly half the payload the insects can carry, the system enables the beetles to freely navigate terrain and climb trees.

The team used what it learned to design the world’s smallest power-autonomous terrestrial robot with vision — proving, once again, that good things really do come in small packages.

“This is the first time that we’ve had a first-person view from the back of a beetle while it’s walking around. There are so many questions you could explore, such as how does the beetle respond to different stimuli that it sees in the environment?” Iyer said in a UW press release. “But also, insects can traverse rocky environments, which is really challenging for robots to do at this scale. So this system can also help us out by letting us see or collect samples from hard-to-navigate spaces.”

Iyer and a bumblebee demonstrate Living IoT

Iyer is the second UW student — and second from Gollakota’s lab — to earn this prestigious award. His labmate and frequent collaborator, Rajalakshmi Nandakumar (Ph.D. ‘19), now a faculty member at Cornell Tech, was honored in 2018 for her work on mobile apps for detecting life-threatening health issues. In addition to Iyer, the Marconi Society recognized two other researchers with 2020 Young Scholar Awards: Yasaman Ghasempour at Rice University (soon joining the Princeton University faculty) for her work on efficient, ultra-high speed network connections for next-generation IoT, and Piotr Roztocki at Canada’s Institut National de la Recherche Scientifique (INRS) for his work on scalable quantum resources for “future-proofing” telecommunications network security. The honorees were selected by an international panel of engineers drawn from leading universities and companies.

“Our Young Scholars are the braintrust that will put the speed, security and applications of next generation networks into the hands of billions,” said Cerf.

View Iyer’s Marconi Society profile here, and learn more about the 2020 Young Scholar Awards here. Watch a conversation between Iyer and Marconi Fellow Brad Parkinson here, and check out Iyer’s Geek of the Week profile on GeekWire here.

Congratulations, Vikram!

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Allen School’s Robert Minneker imagines a new way to detect and treat Parkinson’s disease with computer vision

Robert Minnekar

Our latest student spotlight features Vancouver, Washington native Robert Minneker, who graduated in June with degrees in biomedical engineering and computer science and is continuing his studies in the Allen School’s fifth-year Master’s program. Using the skills they gained as undergraduates, Minneker and two friends created Tremor Vision, a web-based tool to help diagnose and treat Parkinson’s disease. The creators — Minneker, Allen School alumna Janae Chan (B.S. ‘19) and informatics graduate Drew Gallardo — were runners up in the 2020 Microsoft Imagine Cup

Allen School: Congratulations on your success in the Imagine Cup! What was the experience like, particularly as it was virtual this year?

Robert Minneker: Thank you so much! I still can’t believe how far we made it. The Imagine Cup was an unforgettable experience. I learned so much about what it takes to pitch your idea in a concise and convincing way and what it takes to get a product to market and in the hands of those it’s intended for. I definitely think it would’ve been more fun to be in the same room as Drew and Janae pitching our idea to the judges, but being virtual was the right call given the circumstances. I really appreciate that Microsoft was flexible with the competition and continued it despite COVID-19. None of us had been to the Imagine Cup before and had no idea what to expect. It is one of the most memorable and influential experiences of my undergraduate career and something I will never forget.

Allen School: What is Tremor Vision, and how did the three of you come to be a team and bring it to fruition for the Imagine Cup?

RM: Tremor Vision began during DubHacks 2019, the University of Washington’s annual hackathon. I skateboarded with Drew pretty often — we actually met at a skatepark a few years ago — and quickly realized he shared a love for programming. We have been good friends ever since. Janae and I both studied BioE and CS, so we shared classes and were interns at Sage Bionetworks at the same time. We got pretty close. I knew both of them, but they didn’t know each other, and I knew they were both going, so we kind of just formed a team from there. 

When we got to the hackathon, we had no idea what we were going to work on. Janae and I, both being BioE majors, wanted to do something related to health care. Drew is quite passionate about telehealth, so we started looking for open problems in that area that we thought we could tackle, particularly using machine learning. After some digging, we came across the challenges associated with detection and tracking of Parkinson’s and thought we would be able to propose a solution. We played around with many ideas and many approaches that night, and by the morning we had a working computer vision model that could detect Parkinson’s from a patient’s hand-drawn spiral, with a nice user interface to display the results to their physician. This was our first time at a hackathon, so we definitely didn’t know what to expect and especially didn’t expect to make something that worked and that was so well received by the judges and our fellow participants. 

One of the prizes we won at DubHacks was the Azure Champ Prize for our use of Azure services, and part of that included fast-track entry to the Imagine Cup competition. None of us had heard of the competition before DubHacks but decided to apply since they showed interest in our project and believed in us. We definitely thought we would be quickly screened out of the competition and did not think we would go as far as we did!

Allen School: How has a top performance in the Imagine Cup impacted the future of Tremor Vision?

RM: We are still working on Tremor Vision. We have a minimum viable product developed and are currently participating in the NSF I-CORPS program via UW CoMotion focused on customer discovery. We plan to complete user testing and have a version ready for market by the end of the year.

Allen School: In addition to your success with Tremor Vision, you are continuing your studies for a fifth year in the B.S./M.S. program. What piqued your interest in computer science initially?

RM: I decided to study in the Allen School after completing most of the bioengineering major and taking CSE 142/143 as my engineering electives and participating in computational research — specifically, biomedical informatics. I quickly realized the potential that computing would have on health and medicine in the coming years and decided that I would really enjoy being a part of it. After seeing firsthand in my research how artificial intelligence and machine learning could transform health care, I knew I needed to become more knowledgeable in the area. Computer science seemed like the logical choice given the school’s strengths and faculty research interests.

Allen School: What do you enjoy most about being in the Allen School?

RM: The quality of the courses and the resources available to students were invaluable. Everything from the TA program to computer labs, career fairs, and the opportunities posted via the undergraduate blog. I felt as if I was part of a school that was doing its best to help me succeed. 

Read more about Tremor Vision on Microsoft’s blog

Congratulations on the success of Tremor Vision, Robert! The Allen School is excited to see how it — and you — progress over the next year. 

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UW establishes Senosis–Paul G. Allen Endowed Professorship in Computer Science & Engineering

Julie Kientz and Shwetak Patel
Julie Kientz and Shwetak Patel

The University of Washington has established a new endowed professorship, the Senosis–Paul G. Allen Endowed Professorship in Computer Science & Engineering, through the generosity of professors Shwetak Patel and Julie Kientz. The $1 million endowment, which was made possible in part by the acquisition of Patel’s mobile health startup Senosis Health by Google in 2017, will support recruitment and retention of Allen School faculty who pursue high-impact research aimed at solving meaningful, real-world problems for the benefit of society.

Patel directs the UW’s Ubiquitous Computing (UbiComp) Lab and holds the Washington Research Foundation Entrepreneurship Endowed Professorship in the Allen School and the Department of Electrical & Computer Engineering. Kientz serves as chair of the Department of Human-Centered Design & Engineering, where she directs the Computing for Healthy Living and Learning (CHiLL) Lab, and is an adjunct professor in the Allen School and the Information School. The pair joined the UW faculty in 2008 after earning their Ph.D.s from the Georgia Institute of Technology. Since arriving in Seattle, Patel and Kientz have applied their expansive view of computing to explore solutions for health care, education, environmental sustainability, and more.

The results have paid off not just for the university and the Seattle region, but also for society at large. Their success — and Patel’s direct experience with the benefits of holding an endowed professorship — inspired the couple to pay it forward by supporting future faculty members who, they hope, will not so much follow in their footsteps as blaze their own trails.

“As the beneficiary of the WRF Entrepreneurship Professorship, I have appreciated firsthand the role of endowments in enabling faculty to be creative and flexible in research. This kind of support empowers you to think outside the box and challenge existing assumptions,” explained Patel. “Julie and I hope that this new professorship will offer future faculty members that same freedom to pursue innovation that truly pushes the envelope — and pushes technology in new and unexpected directions that will have a positive impact on people’s lives.”

Thinking outside the box and pushing the limits of technology have been hallmarks of both Patel’s and Kientz’s research. Already in their careers, each of them has made groundbreaking contributions and set new directions for research that have established the UW as a leader in human-computer interaction, digital health, accessible technology, low-power sensing, and mobile computing. 

Patel began his career focused on new capabilities for whole-home sensing. Recognizing that each appliance emits a distinct signal on a home’s electrical system, he and his students developed a technique for measuring power consumption at the individual device level with a single sensor. They later expanded their approach to monitoring water usage based on the pressure waves generated as each plumbing fixture is turned on and off. To commercialize this research, Patel and his team launched a company, Zensi, that was later acquired by Belkin. He followed that up with SNUPI Technologies, a company he and his collaborators started to commercialize a consumer-facing whole-home sensing system called WallyHome, which was subsequently acquired by Sears.

Inspired by the global growth in smartphone use, Patel began exploring how he and his team could leverage the increasingly sophisticated sensors built into today’s mobile devices to aid in the early detection and monitoring of disease — pioneering an entirely new field focused on mobile health sensing that has become even more compelling since the outbreak of the COVID-19 pandemic. By combining sensing data from inputs like the phone’s camera, microphone and accelerometer with new machine learning algorithms, Patel’s lab has worked with health care providers on a variety of health monitoring tools. Patel and a group of collaborators started Senosis Health with a view to commercializing their initial work in this space. The team also approached the Food and Drug Administration to obtain approval for the use of mobile apps in both clinical and at-home settings — a novel idea at the time, as the agency had no prior experience with mobile app development.

Julie Kientz and Shwetak Patel in Ph.D. regalia
Kientz and Patel earned their Ph.D.s from Georgia Tech before joining the UW faculty

Since Google’s acquisition of Senosis three years ago, Patel has built on this work as the leader of the company’s Seattle-based engineering team focused on mobile health technologies. He hopes that by leveraging the acquisition to establish a new professorship, more faculty will be inspired to unite academic research with entrepreneurial impact.

“Being an entrepreneur has helped me to identify research problems I wouldn’t have previously considered solely as an academic,” Patel explained last year when he received the ACM Prize in Computing — the premier mid-career award in the field of computer science — from the Association for Computing Machinery. “That experience opened up opportunities for me to venture down research paths I wouldn’t have otherwise thought about.”

In addition to supporting an entrepreneurial mindset and a more expansive view of the role of academic research, Patel and Kientz also hope that holders of the professorship will model a commitment to diversity and inclusion in computing through their teaching, outreach, and service.

“We need to design and develop  computing technology for all of society, not just a privileged subset. One of the ways we make sure we do that is to make the computing discipline representative of the people we are trying to serve,” Kientz said. “Shwetak and I feel strongly that our duty as educators and as researchers is to advance technologies and create an academic community that reflects a rich diversity of backgrounds and experiences. It’s important to us that the holders of this professorship also model these values.”

Kientz is keenly aware of the power of technology and education to be the great equalizer, and in more ways than one. Her research takes a human-centered approach to technology, combining ubiquitous computing, human-computer interaction, and informatics to design and study novel interactive technologies for health and education while also working to understand and reduce the burden technology places on the people who use it. It’s an approach that she honed early in her career, when she explored technology to support caregivers of young children in data-based decision-making. Rather than approach the topic solely as a computer scientist, though, Kientz spent significant time among the communities for whom she was designing — giving her firsthand insight into how technology could best support their work by, for example, making tracking childhood development data more fun and meaningful by linking it with sentimental mementos in a digital baby book and allowing it to be shared with family and friends. 

Since then, Kientz and her students have developed tools to help people improve their sleep quality, support physical fitness goals of people with visual impairments, support inclusive education, help parents teach their children to self-regulate screen time, and to monitor children’s developmental progress. Her work on the latter led her to work on the launch of startup, BrightSteps, to assist parents and caregivers in monitoring  children’s development and connecting them to resources.

The Senosis–Paul G. Allen Professorship leverages funds made available through the Paul G. Allen Professorship Matching Program. That program, which is supported by earnings of the endowment established by Mr. Allen and Microsoft upon creation of the school, provides a 1:2 match on individual gifts aimed at attracting and retaining exceptional faculty who will advance UW’s leadership in computer science education and research.

Shwetak Patel and Julie Kientz with mountains and trees in background
Patel and Kientz on a backpacking trip to the Enchantments last summer

The professorship is one of two UW initiatives announced today and funded with gifts from the couple. The other is a gift to Kientz’s home department to create the Kientz & Patel HCDE Student Emergency Support Fund. That gift will offer support to students facing near-term financial hardship, such as unexpected health care costs, car repairs, legal fees, emergency travel, and housing insecurity. The goal, Kientz explains, is to alleviate the burden for students who suddenly find themselves facing unexpected financial emergencies. 

“Students who cannot make a rent payment may struggle with housing security, or one unpaid bill can begin to collect fee upon fee, quickly making payment completely unattainable,” she said. “This can make the difference between being able to stay in school or have to drop out.”

“Julie and Shwetak are both superstars who have enriched our university, our community, and our field in countless ways,” said professor Magdalena Balazinska, Director of the Allen School. “Time and again, they have demonstrated in tangible ways how technology can help solve some of society’s most vexing problems — from addressing disparities in education and health care, to conserving natural resources for a more sustainable planet. They are also both dedicated members of our community, supporting our mission through their service roles as department chair and associate director for research and innovation. With these gifts, they are once again leading the way in showing how UW faculty are truly a force for good.”

The Senosis–Paul G. Allen Professorship will become the Shwetak N. Patel & Julie A. Kientz–Paul G. Allen Endowed Professorship when the couple eventually become emeritus faculty or retire from the university. The professorship is the third endowment in the Allen School created as the result of faculty entrepreneurial activity. In 2017, the Guestrin Endowed Professorship in Artificial Intelligence and Machine Learning was established following Apple’s acquisition of Carlos Guestrin’s machine learning startup Turi. And the establishment of the Washington Research Foundation Entrepreneurship Endowed Professorship — the professorship held by Patel — was related to the acquisition by Microsoft of Oren Etzioni’s startup Farecast.

Way to go, Shwetak and Julie — thank you for your leadership and your generous support of faculty and students!

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Allen School celebrates career of professor Martin Tompa, computational biologist, Schnapsen master, and mentor extraordinaire

Tompa in Sieg Hall, 1999. Photo credit: John Zahorjan

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.”

McTiernan and Tompa in Buffalo, 1976

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.

Tompa showing Husky pride, 1995

“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.

Some of the founding members of the UW Schnapsen Club, 2015

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. 

Tompa with Mahadevan and his family on graduation day, 2017

“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.”

Tompa and the TAs for CSE 312, 2017

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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Byron Boots earns RSS Early Career Award for contributions to robot learning

Portrait of Byron Boots

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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Sham Kakade earns Test of Time Award at ICML 2020 for novel optimization techniques that sparked new directions in machine learning research

Sham Kakade portrait

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 Park earns Ph.D. fellowship as part of the Apple Scholars program

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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Allen School students recognized for excellence in research by the National Science Foundation

National Science Foundation logo

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

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

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

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

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

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

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

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

Matthew Schmitze

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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Adriana Schulz and Nadya Peek earn TR35 Awards for their efforts to revolutionize fabrication and manufacturing while bridging the human-machine divide

Adriana Schulz
Adriana Schulz

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.

Schulz wearing a version of the DFab’s medical gown

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.”

Nadya Peek
Nadya Peek

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.”

Leilani Battle
Leilani Battle

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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