Skip to main content

#MemoriesInDNA portrait project blends DNA technology and art to memorialize pioneering scientist Rosalind Franklin

Portrait of Rosalind Franklin against background of tiny images
Portrait of Rosalind Franklin by Seattle artist Kate Thompson. Dennis Wise/University of Washington

British scientist Rosalind Franklin, who spent the early 1950s researching the structure of DNA at King’s College London, should have won the Nobel Prize. She very well may have, except that her untimely death from ovarian cancer at the age of 37 meant that the Nobel Committee, which does not award posthumously, did not even consider her. For it was Franklin, not the famous scientific duo Watson and Crick, who captured the first image proving the shape of deoxyribonucleic acid — better known as DNA, the building block of all life.

At the time, Franklin was applying her expertise in x-ray crystallography to determine the structure of DNA in collaboration with King’s College Ph.D. student Raymond Gosling. The researchers captured an image of moistened DNA fibers using x-ray diffraction techniques and equipment refined by Franklin herself. The so-called Photo 51, which revealed a helical shape consisting of two strands, and other unpublished data from Franklin’s lab would find their way into the hands of fellow scientists James Watson and Francis Crick at Franklin’s alma mater, the University of Cambridge. The material confirmed the three-dimensional structure of DNA as a double helix — a structure the pair would race to describe in a paper published in April 1953 in the journal Nature.

Although Franklin would publish her own paper co-authored with Gosling on the double helix in the same issue, it was Watson and Crick, along with Franklin’s King’s College colleague Maurice Wilkins, who went on to share the 1962 Nobel Prize in Physiology or Medicine and thus ensure their places in the pantheon of scientific achievement. The contributions of Franklin, who had passed away 4 years before the Nobel announcement, would only begin to be more widely appreciated decades later. In any event, the Nobel Prize can only be shared by up to 3 individuals, so we will never know whether Franklin, had she lived long enough, would have received her due. Given the limitation on sharing the prize and the attitudes toward women in science at that time — and her own colleagues’ attitudes toward Franklin, in particular — it seems unlikely.

More than 60 years after Watson and Crick laid eyes on Photo 51, Allen School professor Luis Ceze met Seattle-based multimedia artist Kate Thompson in a bar not far from the University of Washington campus. Ceze co-directs the Molecular Information Systems Laboratory, a partnership between UW and Microsoft that is exploring synthetic DNA’s potential as a long-term storage solution and computational platform for digital data. He had already crossed paths with Thompson on campus, where she was doing an artist’s residency in the Nemhauser Lab focused on evolutionary biology. Ceze was intrigued by what she told him about her work, which is focused on making science visual.

Action shot from above of artist Kate Thompson painting Rosalind Franklin's portrait over backdrop of images
Kate Thompson at work in her studio. Mary Bruno

For more than a year, he and his colleagues had been collecting images submitted by people around the world as part of the #MemoriesInDNA project under the tagline “What do you want to remember forever?” After meeting Thompson, Ceze became interested in exploring a way to ensure that the world would remember Franklin and her contributions forever — and he had a particularly fitting medium in mind.

“Rosalind Franklin was largely responsible for uncovering the structure of DNA, nature’s own perfected storage medium. Her work opened up a whole new avenue of scientific research and discovery for which, to this day, she does not really get the credit that she deserves,” Ceze said. “We had this massive collection of image files signifying what people want to preserve for posterity, and this new storage method. So we thought, why not use them to demonstrate the science we’ve been working on while paying tribute to the scientist who started it all?”

The idea he and Thompson hashed out over glasses of wine was elegantly simple. The lab would work with Thompson to create a piece of art to commemorate Franklin that incorporates the very medium that she revealed to Watson, Crick, and the world: DNA. To be precise, the medium would be synthetic DNA containing thousands of copies of images people had voluntarily contributed to advance a new wave of molecular systems research.

“I was fascinated by the idea of honoring this brilliant but mostly forgotten woman using the same material that should have made her famous in her own lifetime,” said Thompson, who took on the role of artist-in-residence at MISL after meeting with Ceze. “Until recently, her legacy to science and the world went largely unnoticed. I hope this project helps to ensure it will not be ignored.”

Closeup of a portion of Rosalind Franklin's face painted over tiny images
Dennis Wise/University of Washington

The result of Thompson’s collaboration with the MISL is now on display in the Bill & Melinda Gates Center for Computer Science & Engineering on the UW Seattle campus. The work, which measures 40 inches high by 30 inches wide and was created using acrylic ink on archival paper, took nearly 8 months from conception to completion. It is one of three original copies Thompson produced at the behest of the lab.

Viewed at a distance, the work is clearly a portrait of Franklin. Thompson reproduced her likeness from an old black and white photograph, combining soft, dark brush strokes with a mosaic of nearly 2,000 meticulously arranged images — the majority of which measure just ⅞ inch square — from the #MemoriesInDNA collection. The artist arranged the latter with the help of a macro she wrote for the purpose, which sifted through the images and sorted them by tonal values.

The images that make up Franklin’s face are contained within a larger collection that serves as a colorful backdrop for the subject of the painting. Up close, the images themselves come into sharper relief; if they linger long enough, viewers can make out the contents of the individual photos in detail. Each image depicts a person, place or object someone wants remembered in perpetuity.

But Thompson didn’t just paint on the images; she painted with them, infusing Franklin’s likeness with the actual data files ensconced in their microscopic storage medium. Using roughly half of the trove of more than 10,000 photos submitted as part of #MemoriesInDNA, MISL researchers first converted the digital image files — around 325 megabytes of data — into the As, Ts, Cs and Gs of DNA and encoded them into synthetic DNA ordered from Twist Bioscience. Thompson then took the vial of DNA furnished by the lab, which consisted of a mere 1.5 milliliters of liquid, and mixed the contents with black acrylic ink along with a binding substance that would help it adhere to the paper. She combined yet more of the image-laden DNA with a clear acrylic hardener, which she used to coat the finished piece.

“I painted several practice portraits using plain ink first,” the artist recalled with a laugh. “I didn’t want to mess up when it came time to use the real thing. Ink is relatively inexpensive but specially encoded DNA from a lab, not so much!” 

The process the lab and artist followed to turn digital photos into a DNA-infused painting. Kate Thompson

Before Thompson could pick up her brush, lab members David Ward, Bichlien Nguyen, Xiaomeng Liu, and Jeff Nivala conducted experiments to ensure that, in the latter’s words, “the science was as rigorous as the art.” First and foremost, they needed to establish that no chemical reaction would occur between the synthetic DNA and acrylic medium when Thompson mixed the two in her studio. The team also wanted to be confident that, once mixed and applied to paper, the DNA could be subsequently retrieved from the material. The outcome is both a work of art and an artifact of science.

“Not that I’m suggesting you do this — in fact, please don’t! — but if you were to scrape a little bit of the portrait off, with the right equipment you could retrieve the data and convert it back from DNA molecules to digital 0s and 1s,” explained MISL co-director Karin Strauss, principal research manager at Microsoft Research and affiliate professor at the Allen School. “This portrait is not only preserving Franklin’s memory but preserving the data as well, in a form that will be accessible to future generations.”

To increase the likelihood that the data contained in the work will, indeed, be accessible for generations to come, the team built in a high degree of redundancy. Nguyen estimates it took 30 minutes to amplify copies of each image file using polymerase chain reaction, or PCR.

“Because DNA as a storage medium is so dense, we were able to provide Kate with around a trillion copies of each image to mix into the paint,” explained Nguyen, a senior researcher at Microsoft Research who oversaw the DNA storage process. “That way, we can be certain that we will be able to retrieve all of the data — even if a portion of one set of images is somehow lost or damaged, we still have many back-ups.”

This is not the first time that the MISL team has applied its science to the arts. The lab previously partnered with Twist Bioscience to preserve significant cultural and historical artifacts in DNA as a way of demonstrating its potential for archival data storage and retrieval, including iconic musical performances at the Montreux Jazz Festival, the top 100 books of Project Gutenberg, the Universal Declaration of Human Rights in 100 languages, and the non-profit Crop Trust’s entire seed database. Along the way, the team set a new record for the amount of digital data stored in and successfully retrieved in DNA that appeared in a peer-reviewed journal, presented new techniques for random access, developed a new platform for microfluidics automation for DNA data storage at scale, and demonstrated the world’s first end-to-end automated system for encoding digital data in DNA.

Members of the team with the finished portrait, left to right: Luis Ceze, David Ward, Bichlien Nguyen, Kate Thompson, and Karin Strauss (not pictured: Xiaomeng Liu, Jeff Nivala). Dennis Wise/University of Washington

Members of the public may view the portrait of Franklin on the ground floor of the Bill & Melinda Gates Center, at the base of the Anita Borg Grand Stairway. The lab is also exploring the potential to exhibit the artwork in additional locations in the future to reach an even wider audience. 

“Our team was excited to partner with Kate on this project, which highlights an often forgotten figure who helped usher in the age of molecular storage,” Ceze said. “I hope the artwork itself, the science upon which it’s based, and the story of Rosalind Franklin will inspire people.”

Read more about Thompson’s work here, and learn more about the MISL’s work on DNA data storage here. Read the original #MemoriesInDNA announcement here. The lab members and the artist would like to express their appreciation to the thousands of people, spread out over 80 countries, who shared their personal photos as part of the #MemoriesInDNA project. The lab will continue to use the images as part of its research to advance DNA-based computation and image search.

Read more →

Hannaneh Hajishirzi and Yin Tat Lee named 2020 Sloan Research Fellows

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

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

Hanna Hajishirzi

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

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

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

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

Yin Tat Lee

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

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

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

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

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

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

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

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

Read more →

UW Reality Lab opens incubator to foster student innovation in augmented and virtual reality 

In the UW Reality Lab incubator

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

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

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

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

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

Team discussion in the UW Reality Lab incubator

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

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

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

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

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

Read more →

AuraRing puts the power of electromagnetic tracking system on your finger


With continuous tracking, AuraRing can pick up handwriting — potentially for short responses to text messages

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

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

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

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

The ring is a single transmitter coil tightly wrapped around a 3D-printed loop

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

The writstband is embedded with sensor coils that measure the ring’s magnetic field

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

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

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

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

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

Read more →

Five years on, remembering professor Gaetano Borriello

Gaetano Borriello portrait

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

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

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

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

Gaetano’s vision:

Four Ph.D. students in robes and caps with Gaetano Borriello
Gaetano (center) poses with newly-minted Ph.D. graduates (left to right) Brian DeRenzi, Benjamin Birnbaum, Yaw Anokwa, and Carl Hartung

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

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

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

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

Gaetano’s humanity:

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

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

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

Crystal Eney and Gaetano Borriello in front of Drumheller Fountain
Crystal Eney (left) poses with Gaetano in front of Drumheller Fountain on the UW Seattle campus

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

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

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

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

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

Gaetano’s mentorship:

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

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

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

Melissa Westbrook and Scott Hauck
Melissa Westbrook (left), Gaetano’s widow, recognizes Scott Hauck with the Gaetano Borriello Professorship for Education Excellence

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

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

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

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

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

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

Gaetano’s legacy:

Gaetano Borriello wears a trash bag to protect his clothes from cream pie
Gaetano’s affection for students extended to willingly taking a pie in the face at the ACM spring picnic

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

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

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

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

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

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

Read more →

With Virtual Chinrest, Allen School researchers aim to make online behavioral research less WEIRD

Knowing the distance between the center of display and the entry point of the blind spot area (s), and given that α is always around 13.5 degrees, the authors can calculate the viewing distance (d) as part of the Virtual Chinrest.

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

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

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

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

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

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

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

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

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

Read more →

Noelle Merclich works to make the Allen School experience a great one for incoming undergrads


Noelle Merclich

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

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

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

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

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

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

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

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

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

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

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

Allen School: Who or what inspires you?

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

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


Read more →

Seeing the forest for the trees: UW team advances explainable AI for popular machine learning models used to predict human disease and mortality risks

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

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

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

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

Diagram illustrating difference between "black box" and TreeExplainer models
Many predictive AI models are a “black box” that offer predictions without explaining how they arrived at their results. TreeExplainer produces local explanations by assigning a numeric measure of credit to each input feature, such as factors that contribute to mortality risk shown in the example above. The ability to compute local explanations across all samples in a dataset can yield a greater understanding of global model structure.

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

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

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

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

Portraits of AIMS Lab researchers with white block "W" on purple background
AIMS Lab researchers, top row from left: Su-In Lee, Scott Lundberg, and Gabriel Erion; bottom row, from left: Hugh Chen and Alex DeGrave

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

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

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

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

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

Read more →

Allen School’s Aditya Kusupati earns Best Paper Runner-Up at BuildSys 2019 for new low-power, deep learning algorithm for radar classification

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

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

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

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

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

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

To learn more, read the research paper here.


Read more →

Professional gaming inspired Allen School undergrad Kevin Ryoo to study computer science

Kevin Ryoo

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

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

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

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

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

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

Before a competition

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

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

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

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

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

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

Read more →

« Newer PostsOlder Posts »