Ladner (center) with late friend and colleague David Notkin’s daughter, Emma (left), and widow Cathy Tuttle (not pictured: son Akiva).
Richard Ladner, professor emeritus of the Allen School and a recognized leader in accessible technology research and advocacy, accepted the Harrold and Notkin Research and Graduate Mentoring Award this week from the National Center for Women & Information Technology (NCWIT). The award, which recognizes faculty who combine outstanding research contributions with excellence in mentoring the next generation of researchers while promoting diversity in the field, is named in memory of the late Georgia Tech professor Mary Jean Harrold and Allen School professor David Notkin, who served as chair of what was then the University of Washington Department of Computer Science & Engineering from 2001 to 2006. Both Harrold and Notkin passed away after battles with cancer in 2013.
“Mary Jean and David were guiding lights when it comes to prioritizing student mentorship and diversity alongside technical excellence,” Ladner said. “The connection between this award and my good friend and colleague makes this recognition particularly meaningful. It is humbling to be recognized in this way by my peers in the computing community.”
Of the 30 students Ladner has advised or co-advised on their way to earning their Ph.D., 43 percent are women. One of those former students, Cornell Tech professor Shiri Azenkot (Ph.D., ‘14), credits Ladner with providing a supportive environment for people of all genders. “I would look forward to our research group meetings, in which all the students (Ph.D. and undergraduate) and collaborating faculty members would come together to discuss their current research,” Azenkot told NCWIT. “Our group included at least 80 percent women (sometimes more), and our meetings offered a relaxed and friendly place to discuss research and other activities.”
The Harrold and Notkin Research and Graduate Mentoring Award recognizes Ladner’s commitment to outstanding research, graduate mentoring, and diversity.
A strong proponent of gender diversity throughout his career, Ladner has also made it his mission to ensure that ensuring access for people with disabilities is part of every conversation about diversity and inclusion in the computing field. While he devoted the first three decades of his faculty career to theoretical computer science — and made fundamental contributions to complexity theory, parallel computing, and more during that time — Ladner came to the realization that accessibility, not just algorithms, was one of his true passions as both a researcher and a leader. His shift in focus played a significant role in UW’s emergence as a center of accessible technology education and research, and helped expand how his own field thinks about diversity.
“Part of my job as an accessibility researcher is to change people’s mindset about disability,” Ladner explained in a 2017 article looking back on his career. “It presents challenges, yes, but it’s not necessarily a tragedy. It is part of the diversity of life. Our role as researchers and technologists is to embrace this diversity and make sure we reflect that in our work.”
Ladner’s commitment to mentorship has extended to the undergraduate level, where he has advised more than 100 students engaged in research. As his Allen School colleague, Ed Lazowska, attests, that commitment has extended beyond his own program, as Ladner often has tapped into his vast network of colleagues and friends to offer students outside the UW a path into research. “Richard has personally helped place 50 students with disabilities in undergraduate research internships around the nation since 2011,” Lazowska noted. “In the placement process, he phoned each student to try to understand their scholarly interests and abilities, to help find a suitable mentor the student’s summer research experience.”
Ladner at NCWIT’s 2019 Summit on Women and IT held in Nashville, Tennessee earlier this week.
Ladner has also championed a number of initiatives to engage more students with disabilities in computer science education and careers, including breaking down barriers at the K-12 level. Earlier this year, Code.org and the Computer Science Teachers Association named Ladner a Computer Science Champion in recognition of his work on AccessCSForAll, an initiative that provides curriculum and professional development tools to help teachers in making computer science accessible to students with disabilities in their high school classrooms. AccessCSForAll was an outgrowth of his leadership of AccessComputing, a program Ladner co-founded with frequent collaborator Sheryl Burgstahler, Director of the UW’s Do-IT Center, to increase the participation of people with disabilities in postsecondary computing education and careers by offering resources, mentoring, funding, and networking opportunities. Previously, Ladner led the Summer Academy for Advancing Deaf and Hard of Hearing in Computing, which provided deaf and hard of hearing students from around the country the opportunity to take computing courses for credit and connect with industry professionals who were themselves deaf or hard of hearing.
Nine Allen School students were recently honored for excellence in computing-related research by the National Science Foundation as part of the agency’s 2019 Graduate Research Fellowship competition. The NSF Graduate Research Fellowship Program — the oldest fellowship program of its kind — recognizes and supports outstanding graduate students pursuing research in designated science, technology, engineering, and mathematics disciplines. The goal of the GRFP is to assist recipients in becoming lifelong leaders who will contribute to science and engineering education and innovation while advancing the nation’s technological infrastructure, security, and societal well-being.
Over the past five years, the NSF has recognized 46 Allen School student researchers for excellence in the “Computer and Information Science and Engineering” category through the GRFP competition. Read on to discover how the 2019 honorees are helping to shape the future of computing through their work in artificial intelligence, machine learning, natural language processing, computational biology, robotics, security and privacy, ubiquitous computing, and theoretical computer science.
Chen’s research interests lie at the intersection of technology and crime, physical safety, and at-risk populations. Her recent work has focused on technology and survivors of human trafficking. Chen just wrapped up a study in which she interviewed victim service providers (VSPs) to expose how technology can be utilized to re-victimize survivors of trafficking and understand how VSPs mitigate these risks as they interact with and support survivors. As a result of this work, Chen and her collaborators propose privacy and security guidelines for technologists who wish to partner with VSPs to support and empower trafficking survivors. The study will be presented at the upcoming USENIX Security Symposium in August.
Lee’s research spans explainable artificial intelligence and human-AI collaboration. He is particularly interested in the development of explainable, interactive recommender systems that will impart greater understanding and control to users, given that current systems tend to be opaque and offer only limited explanation for why they issued a particular result. To that end, Lee and Weld have partnered with the Semantic Scholar team at the Allen Institute for Artificial Intelligence (AI2) to examine explainable AI in the context of research paper recommendations issued by academic search engines. As part of this project, Lee and his collaborators are exploring how enabling users to act on explanations via more granular up-and-down ratings and natural language feedback could improve the relevance and quality of recommendation results.
Nelson Liu, natural language processing
Undergraduate Nelson Liu earned a fellowship for his work to improve the generalizability and robustness of natural language processing systems with professor Noah Smith of the Allen School’s Natural Language Processing group.
In a recent project undertaken with Smith, research scientists Matt Gardner and Matthew E. Peters of AI2, and Harvard SEAS/MIT CSAIL postdoc Yonatan Belinkov, Liu and Smith examined the linguistic knowledge implicitly encoded within contextualized word vectors by assessing their ability to predict a broad range of linguistic features of their input text. In other work with Smith and former Allen School postdoc Roy Schwartz of AI2, Liu proposes a new approach for characterizing the lack of robustness in NLP methods — a first step towards disentangling failures of models from deficiencies within their training datasets. Liu and his collaborators will present papers on both projects at the upcoming conference of the North American chapter of the Association for Computational Linguistics (NAACL 2019). Liu will continue his research as a Ph.D. student at Stanford University this fall.
Niyaz is interested in designing graph-based motion planning algorithms for robots deployed in highly challenging and constrained environments, particularly in surgical and manufacturing scenarios. His current work focuses on interleaving these algorithms with gradient-free optimizers to improve the setups of difficult motion planning problems. He is also a strong believer in public education and intends to become a teaching professor after completing his degree. Niyaz previously earned an Honorable Mention in the NSF GRFP competition while an undergraduate student at the University of California, Berkeley.
Nicholas Nuechterlein, machine learning
Ph.D. student Nicholas Nuechterlein earned a fellowship for his research at the intersection of machine learning and medicine with Allen School professor Linda Shapiro and professor Tara Madhyastha of the UW Radiology Department.
Nuechterlein develops ML models for analyzing medical resonance imaging (MRI) and genomic data to improve outcomes for patients with glioblastoma multiforme (GBM), an aggressive form of brain cancer. Current ML methods tend to be ineffective when faced with the high-dimensional, heterogeneous, and incomplete data sets typically associated with GBM. Nuechterlein has developed an automatic segmentation algorithm to scale the analysis of GBM patient datasets containing advanced MRI sequences capable of shedding light on the tumor microenvironment. His goal is to develop an interpretable ML classifier able to distinguish between true progression of GBM tumors, which require immediate, aggressive changes in treatment, and pseudoprogression, which indicates the current treatment is effective. The results of this work will prevent patients from undergoing unnecessary surgeries and could be extended across other medical imaging domains to improve the standard of care.
Tang explores the capabilities of “quantum-inspired” classical sublinear-time sampling algorithms to understand where quantum machine learning can and cannot revolutionize data analysis and machine learning practice. She was previously recognized among Forbes’ “30 Under 30” in science for developing an algorithm enabling a classical computer to solve the “recommendation problem” in roughly the same time that a quantum computer can, exponentially faster than previous algorithms. Tang is also interested in extension complexity, a line of research in which she and Lee aim to prove that certain problems are hard for the linear systems solvers and SDP solvers frequently used in practice.
Whitehill’s research involves the development of novel sensing systems that leverage creative approaches to signal processing and machine learning, with a particular interest in systems having applications in health and wellness. For example, two of Whitehill’s current projects involve using sound as the sensing medium to better track patient’s pulmonary health. One project involves the use of a deep neural network to identify users by their cough, while the other determines a user’s deviation from their baseline lung functionality based on their speech. In the future, Whitehill hopes to apply his expertise to improving disease diagnosis and monitoring in the developing world.
Erin Wilson, computational biology
Fellowship winner Erin Wilson is a second-year Ph.D. student working in Computational and Synthetic Biology with professors Georg Seelig of the Allen School and Department of Electrical & Computer Engineering, Mary Lidstrom of Microbiology and Chemical Engineering, and David Beck of Chemical Engineering and the eScience Institute.
Wilson’s research spans the intersection of genetics, data science, and sustainability. Her work is largely inspired by her previous experience at biotech companies Amyris and Zymergen, who engineer microorganisms such as yeast and bacteria into tiny, biological factories that can sustainably produce everyday molecules. This is accomplished by editing the microorganisms’ genomes to convert renewable feedstocks (sugar) or waste streams (methane) into a new desired target molecule such as medicine, biofuel, or other molecules found in nature. Wilson’s current research focuses on applying computational methods to better understand the “genetic grammar” underlying how these microorganisms control gene expression and use these insights to more efficiently engineer them for sustainable molecule production.
Peter West, natural language processing
Second-year Ph.D. student Peter West received an honorable mention from NSF in recognition of his research in NLP, particularly where it intersects with questions of cognition, information theory, and machine learning.
West works with Allen School professor Yejin Choi in the xlab, where he aims to answer fundamental questions about how statistical models relate to language and cognition. For his current project, he applies concepts from information theory to summarize sentences unsupervised, without requiring human-written examples. West, whose work is funded by a postgraduate fellowship from Canada’s Natural Sciences & Engineering Research Council (NSERC), has previously conducted research on microfluidic sensors, computational biology, auction simulation, and synthetic biology — experience which continues to shape his approach to his latest research.
Two recent Allen School bachelor’s alumni — Emily Allaway (B.S., ’18) and Ryan Benmalek (B.S., ’17) — were also recognized as part of this year’s NSF GRFP competition. Allaway, currently a graduate student at Columbia University, earned a fellowship for her research in NLP. As an undergraduate, Allaway worked with professor Yejin Choi and graduate students Hannah Rashkin and Maarten Sap of the Allen School’s NLP research group. Benmalek, a second-year graduate student at Cornell University, received a fellowship for his work encompassing computer vision and NLP. During his time at the University of Washington, Benmalek worked with Choi and Allen School professor Ali Farhadi. In addition to the Allen School recipients, Ph.D. student Jenna Register of the UW’s Information School earned a fellowship for her work in human-computer interaction.
When Computer Science major Mitali Palekar graduates next month, she can start a new chapter as a software engineer at LinkedIn knowing that she has made a difference in the community she has called home for the past four years. An accomplished student researcher, mentor and campus leader, Palekar has combined her love of programming and a passion for diversity to elevate the voices of students around her and help build a more welcoming and inclusive community within the field of computing.
A 2019 Husky 100 honoree and former TUNE House scholar, Palekar was most recently featured as GeekWire’s Geek of the Week for her many achievements during her time as a University of Washington student. These include serving as a past president and adviser to the UW chapter of the Society of Women Engineers, peer adviser and mentor for students in the Allen School, College of Engineering, and Interdisciplinary Honors Program, and a research assistant in the Allen School’s Security and Privacy Research Lab. She has also taken multiple opportunities to apply what she has learned through internships at Facebook, LinkedIn, Uber, and Stripe, and as a contributor to Bottomline, a compensation analytics startup that advanced to the Sweet 16 of the regional Dempsey Startup Competition hosted by the UW’s Buerk Center for entrepreneurship.
While she enjoys tackling technical challenges and exploring entrepreneurial opportunities, Palekar is particularly eager to use her position and experience to smooth the way for others. “I hope to play my role in improving representation, inclusion and belonging of underrepresented minorities in technology, ensuring that we continue to hone different sorts of talent and perspectives in tech,” Palekar told GeekWire. “Computer science can be for everyone.”
Palekar goes on to say that she draws inspiration from her parents for their hard work coupled with kindness, and from the “strong, fearless and passionate young women” among her friends, roommates and fellow technologists. They would, no doubt, be eager to return the compliment.
Professor Yin Tat Lee of the Allen School’s Theory of Computation group has been named a 2019 Microsoft Research Faculty Fellow. Since 2005, Microsoft has used its Faculty Fellowship program to recognize promising, early-career researchers whose exceptional research talent makes them emerging leaders in their fields. Lee, who joined the University of Washington faculty in 2017, is one of only five recipients selected by Microsoft this year from universities and colleges across North America.
Lee was recognized for his research in theoretical algorithms, convex optimization, convex geometry, spectral graph theory, and online algorithms — work that Microsoft Research considers among some of the most exciting areas of computer science research today. “While I devote my energy on theoretical algorithmic research such as how to solve some decades- or even centuries-old problems faster, I am often surprised how much these ideas can be used to solve difficult modern problems,” Lee told the company. “Theoretical computer scientists are like particle physicists, instead of understanding the limits of physics, we understand the limits of computation. Although it looks impractical for outsiders, it often leads to important useful results.”
This focus on advancing the theoretical underpinnings of computation with real-world applications has been a recurring theme in Lee’s early work. Since joining the University of Washington faculty three years ago, Lee has earned earned a Best Paper Award at the Conference on Neural Information Processing Systems (NeurIPS 2018) for presenting new algorithms for achieving optimal convergence rates for optimizing non-smooth convex functions in distributed networks, and a CAREER Award from the National Science Foundation for his work on more efficient algorithms for solving convex and other optimization problems. As part of that project, Lee aims to advance the scientific community’s understanding of the relationship between convex geometry and optimization algorithms and improve upon current optimization techniques, the results of which will have broad impact across the sciences and in many other fields. Last summer, Lee collected the A.W. Tucker Prize from the Mathematical Optimization Society for his Ph.D. thesis exploring how to combine and improve upon existing optimization techniques to produce faster algorithms for solving a range of problems underpinning the theory and practice of computing.
The Faculty Fellowship comes with an unrestricted annual gift of $100,000 for two years to enable recipients like Lee to pursue their breakthrough research. Lee and his fellow honorees were selected as part of a rigorous, multi-tier process based on their pursuit of cutting-edge research, a demonstrated ability to communicate complex concepts, and the skills required to turn their ideas into impact. Past fellowship winners include Allen School faculty members Shwetak Patel (2011), Luis Ceze (2009), and Magdalena Balazinska (2007).
Professor emeritus Hellmut Golde in Sylvan Grove in 2003
The Allen School family is mourning the loss of our friend and colleague Hellmut Golde, who passed away earlier this month after a brave battle against cancer. As one of the founding members of the Computer Science Group at the University of Washington and leader of the team that developed the wildly successful VAX Pascal compiler, Golde’s legacy includes the emergence of the Paul G. Allen School of Computer Science & Engineering as an education and research powerhouse and the emergence of the Seattle region as a hub of computing innovation.
Golde, who grew up in Germany, joined the UW faculty in 1959 as a professor in the Department of Electrical Engineering after earning his Ph.D. from Stanford University. He was one of the founders of the Computer Science Group in 1967, the precursor to the Department of Computer Science, formed in 1974, which evolved into the Department of Computer Science & Engineering in 1989 and eventually into the Paul G. Allen School in 2017. In those early days, Golde played a crucial role in establishing the culture for which the Allen School is widely known — one of friendship, support, and community.
A few years after he helped lead the creation of the Computer Science Group, Golde became director of the Computer Science Laboratory, the research and educational facility that hosted a couple of aspiring computer scientists, Paul Allen and Bill Gates, in the days before they set out to put a computer on every desktop. Back then, the pair would sneak into the lab and “borrow” computer time, as Gates would later describe it. Golde would cement his place in campus lore by famously expelling Allen and Gates from the lab.
“Dear Mr. Allen….”
Delivering his rebuke via a polite yet sternly worded letter, Golde directed Allen to “turn in your keys and terminate your activities” owing to offenses such as “you removed the acoustic coupler from Dr. Hunt’s office without authorization and without leaving at least a note.” At the celebration of the naming of the Allen School more than 40 years later, Allen astonished Golde and the rest of the audience by pulling out the letter and reading it in its entirety as he reminisced about his long-standing affection for UW. Allen subsequently framed two copies of Golde’s letter as mementos for the school and for Golde, while we attempted to make amends for the decades-old rebuke by gifting Allen an acoustic coupler purchased on eBay.
Professor emeritus Richard Ladner, who joined the department in 1971, fondly recounted the ease with which Golde bridged the divide in their academic interests and became not only a colleague but lifelong friend. “Hellmut was one of the first people I met when I interviewed for a faculty position at the University of Washington. We were miles apart in terms our academic interests, me a mathematician and he an electrical engineer,” Ladner recalled. “Nonetheless, we hit it off because of his infectious sense of humor and overall kindness. Some of my fondest memories of days with Hellmut are skiing with him and his family at Alpental, Mount Baker, Stevens Pass, and Big Sky. It was always hard for me to keep up with him on the slopes.”
Golde later stepped in to serve as acting chair of the department from 1976 to 1977. It was a period of transition for our program, and Golde wasn’t given much of a choice in the matter — the first department chair, his dear friend Jerre Noe, had declared himself a sabbatical after coming to the realization that if he didn’t leave town, he might wind up being chair for life. At the time, the department had a grand total of 11 faculty and had graduated 25 students.
Hellmut Golde served as acting chair of UW CSE from 1976 to 1977
As Golde was completing his time as chair, Ed Lazowska arrived to join the UW faculty one week after completing his Ph.D. — and just one week before the start of the school year. His new colleagues tried to reassure him that he didn’t need to worry, because “the course you’ve been assigned has been taught for many years by Hellmut Golde. He’s the best teacher in the department. Ask him for his notes.” Unfortunately for Lazowska, “Hellmut was such a good teacher that those ‘notes’ consisted of a single page, with a single line for each day of the course specifying the topic to be covered that day,” Lazowska laughed. “Let’s just say that my course evaluations were not as good as Hellmut’s had been.”
Although Golde’s time at the helm was short-lived, he had an outsize impact on the program beyond the recruitment of Lazowska and Ladner — each of whom went on to have an outsize impact of their own on the growth of our program and on our emergence as a leader in accessible computing, respectively. In October 1977, Digital Equipment Corporation (DEC) introduced the VAX-11/780 computer, which became the mainstay of many computer science departments and companies nationwide. UW managed to acquire an early VAX on the understanding that Golde and a group of graduate students would write a VAX compiler for the Pascal programming language, which was widely used for introductory programming.
“DEC was willing to give Hellmut a portion of sales for the VAX Pascal compiler because they greatly underestimated the demand for Pascal and the market for the VAX computer in education,” explained Allen School Director Hank Levy, who was a member of DEC’s VAX design and development team at the time. “Partly as a result of the compiler that Hellmut and his team built, the VAX become highly successful in educational institutions, resulting in more than $1 million in royalties flowing from DEC to the department — a not insignificant sum in those days. These funds were crucial in allowing the department to grow during difficult financial times on campus. It was Hellmut’s foresight, technical acumen, and generosity which allowed the department to preserve its momentum — and later, to climb into the ranks of the top 10 computer-science programs in the nation.”
It appears that Hellmut neglected to return a Selectric typewriter that he borrowed in 1976. We trust that UW’s Equipment Inventory Office is on the case!
The VAX-11/780 computer on display in the Allen Center atrium stands as a monument to those days, when this single computer could support the entire department’s computing needs.
As Ladner noted, Golde enjoyed a good joke — even if it came at his own expense. He was particularly amused at people’s inability to reckon with the pronunciation of his German name. “Hellmut used to display on his office door a montage of address labels from letters people sent him on which his name had been butchered,” recalled Lazowska. “He would say ‘Hellmut Golde’ on the phone, and often the person on the other end wouldn’t get it quite right. My personal favorite was the time someone addressed a letter to ‘Hal McGoldy.’”
Always a leader and forever a friend, Hellmut retired from UW as emeritus professor in 1992 but remained actively engaged with his Allen School family. We miss him greatly.
Four Allen School undergraduates are among the 2019 class of the Husky 100, a program that recognizes students from across the University of Washington’s three campuses who are making the most of their Husky Experience — both inside and outside of the classroom. This year’s honorees from the Allen School exemplify a commitment to academic excellence, campus leadership, and service to the community that are the hallmarks of a Husky 100 student.
Caleb Ellington
Caleb Ellington is originally from Austin, Texas but proclaims he “couldn’t be more proud to be a Husky” as he looks to apply his talents at the intersection of biology and computing in developing communities.
After he graduates with degrees in Bioengineering and Computer Science next year, Ellington hopes to build on his past experience as a biomedical engineering ambassador to hospitals across Nepal by helping to develop emergency services in that country. Having completed internships at Boulder, Colorado-based Muse Biotechnology and at Amazon in Seattle during his studies, Ellington is also interested in pursuing entrepreneurial opportunities focused on therapeutics manufacturing.
“I came to UW with a mission to improve health care and medicine using tools available to everyone,” Ellington said.
Alison Ng
Alison Ng of Woodinville, Washington will graduate this year with a degree in Computer Engineering. A leader inside the Allen School as well as out, Ng chairs the CSE Student Advisory Council, which represents the undergraduate and master’s student voices within the school, and serves as an officer of the UW chapter of the Society of Women Engineers, an organization that aims to expand educational and career opportunities for women in technical fields.
Ng also represents her fellow students as a member of the Allen School Diversity Committee and serves as a peer adviser helping to guide other CSE majors and prospective students toward academic success. She is currently in her second quarter as a teaching assistant for the Allen School’s Introduction to Digital Design course.
“The University of Washington has provided me with the opportunity to explore my passion for computer engineering and to develop the technical skills necessary to thrive in the industry,” Ng said.
Eugene Oh
Eugene Oh of Federal Way, Washington is pursuing degrees in Computer Science, with a concentration in Data Science, and Social Work.
During his time at UW, Oh has assisted current and prospective CSE majors as a peer adviser and also served as a teaching assistant for the Allen School’s Freshman Direct Admit seminar — in which he focused on the topic of social good — and Computer Science Principles course. When he is not honing his technical skills at a local technology company or as undergraduate research assistant, Oh devotes himself to volunteer service at the Roots Young Adult Shelter and mentoring students at Kent-Meridian High School through the UW Dream Project.
“My time at the UW has taught me to continually push my growing edge in all the spaces that I occupy,” said Oh. “I move forward from the UW hoping to combine my interests in education and technology to work toward empowering the youth of tomorrow.”
Mitali Palekar
Mitali Palekar will graduate this year with a Computer Science degree with Interdisciplinary Honors. Palekar, who hails from Cupertino, California, describes her Husky Experience as one of transformation from “naive freshman” into an engineer, leader, and advocate.
In addition to serving as an Allen School peer adviser helping her fellow students navigate their own Husky Experience, she has been active in the UW chapter of the Society of Women Engineers as a senior adviser and past president. Palekar has also taken the opportunity to apply her technical skills as an undergraduate research assistant in the Allen School’s Security and Privacy Research Lab and through internships with multiple technology companies in Seattle and Silicon Valley.
“I have developed a passion for building products and communities that uplift the voices of people around me,” Palekar said.
“Through the Husky Experience, students discover their passions in life and work,” said UW Provost Mark Richards. “They become independent thinkers and leaders. They gain the skills they need to prepare for rewarding careers in industry, community and in life. That’s exactly what each of these 100 students is doing.”
This is the fourth year the UW has recognized students through the Husky 100 program. Past honorees from the Allen School include 2018 recipients Amanda Chalfant, Aishwarya Mandyam, Melissa Medsker (Galloway), and Kimberly Ruth; 2017 recipients Camille Birch and Kelsie Haakenson; and 2016 recipients Krittika D’Silva, Victor Farkas, Karolina Pyszkiewicz and Sarah Yu.
Congratulations to Caleb, Alison, Eugene, and Mitali on this well-deserved recognition — and thank you for devoting your time and talents to helping your fellow students and the community!
Allen School Ph.D. candidate Kira Goldner has received a Mathematical Sciences Postdoctoral Research Fellowship from the National Science Foundation to advance her work on algorithmic mechanism design for social good. Goldner, who works with professor Anna Karlin in the Allen School’s Theory of Computation group, will take up a postdoc position hosted by professor Tim Roughgarden at Columbia University following her graduation from the University of Washington this spring.
Throughout her studies, Goldner has often focused on theoretical problems that have real-world applications across multiple domains. She launched her research career by focusing on revenue maximization within mechanism design. In one high-profile project, Goldner tackled the so-called FedEx Problem, which deals with determining how to sell a variety of shipping options in order to maximize revenue for FedEx while meeting the expectations on the part of the customer in terms of service quality and cost. The Fedex Problem is a fundamental one that has implications in multiple market sectors, from shipping, to internet service, to cloud computing. Goldner and her collaborators designed the optimal revenue-maximizing auction for situations in which prior information on consumer values are known, contributing new algorithmic techniques and a greater understanding of mechanism design beyond single-parameter settings.
With algorithms playing an increasingly prominent role in systems that impact people’s day-to-day lives, Goldner became interested in algorithmic mechanism design for social good. She is particularly keen to explore how the techniques she honed on classic theoretical problems can be applied in domains such as health care and labor markets to maximize outcomes for both society and individuals — despite participants acting in their own self-interest. Such systems have tangible, sometimes high-stakes effects on people’s physical well-being and economic prospects, as well as on the welfare of society as a whole, via the allocation of resources, the setting of policies, and the regulation of activities to achieve certain outcomes. For instance, the goal of a health care system would be to align providers’ incentives with the dual (and sometimes dueling) goals of minimizing costs while maximizing patient health. Online job recruiting systems should be geared toward identifying optimal employer-employee matches while mitigating discriminatory hiring practices. Goldner aims to bring mathematical formality to these and other pressing social issues, applying her expertise in algorithmic mechanism design and game theory to develop new theoretical approaches for meeting objectives that are motivated by the social good.
To this end, Goldner has also been sharing her vision with the research community. In the fall of 2016, she co-founded the Mechanism Design for Social Good (MD4SG) initiative. That effort spawned a collection of events that Goldner also has co-organized — including an annual workshop, an online research group, and more — and inspired industry-funded grants on the topic. In December 2017, she delivered an invited tutorial on mechanism design for social good at the Conference on Web and Internet Economics.
“Since her arrival at the Allen School, Kira has done outstanding research, for example, studying problems like the FedEx Problem that are helping to advance our understanding of theoretical principles with real-world implications. She is currently tackling problems in health care and labor markets using the tools of algorithmic mechanism design; here the work has the potential to contribute directly to the well-being of society,” said Karlin. “Kira has repeatedly demonstrated outstanding technical expertise in tackling very interesting — and difficult — problems. Other qualities that particularly stand out are her creativity and vision in defining new and interesting research questions and the enthusiasm and thoughtfulness with which she questions standard assumptions.”
Goldner previously was recognized for her work with a Microsoft Research Ph.D. Fellowship and a Google Anita Borg Scholarship. She also was named a finalist for the Facebook Fellowship and earned two Honorable Mentions in the annual NSF Graduate Research Fellowship competition.
Researchers in the Molecular Information Systems Laboratory are exploring new capabilities in digital microfluidics, programming, and automation to advance DNA data storage
For the past three years, researchers in the Molecular Information Systems Laboratory (MISL) have been on a mission to store the world’s digital data in DNA. A partnership between the University of Washington and Microsoft, the lab has already sparked the imagination of artists, archivists, scientists, and the public with its vision to move beyond traditional data storage media, inspired by the very building blocks of life — what Allen School professor Luis Ceze refers to as “nature’s own perfected storage medium.”
Now the team, co-led by Ceze and Microsoft Principal Researcher Karin Strauss, is ramping up the innovation — and the scientific “wow” factor — with a series of new projects that have opened up exciting new avenues for exploration at the intersection of biology and computer science. One of those projects, described in detail in a new paper published in the journal Nature Communications last week, may be the clearest indicator yet that a DNA-based storage system is not only an intriguing option for solving the world’s data crunch, but also a practical one.
“Once we outlined a DNA storage system, we began contemplating the practical considerations,” said Strauss, who is also an affiliate professor in the Allen School. “The first milestone was figuring out random access within a single pool of DNA molecules mixed together, to retrieve only the data we want and avoid the time and expense of sequencing what we don’t. Our next challenge was to figure out how to take full advantage of DNA’s incredible density and resiliency while automating as many stages of the process as possible. Our latest step shows how to physically organize multiple DNA pools and retrieve them with liquid droplets controlled digitally.”
Members of the Molecular Information Systems Laboratory
In their latest paper, Strauss and her colleagues presented a system for achieving high-density data storage in synthetic DNA. By “high-density” they mean one full terabyte of data — the equivalent of 1,000 gigabytes — in a single spot of dehydrated DNA one millimeter in diameter, or roughly the size of a pinhead. Although the information density of DNA molecules is theoretically much higher than that, the team wanted to ensure the ability to retrieve specific data from a particular pool without having to sequence the entire pool.
The team arranged the spots of DNA on glass cartridges, with each cartridge capable of storing up to 50 terabytes based on current DNA storage techniques. Multiple glass cartridges can then be stacked in a space-saving vertical configuration — akin to the approach taken with existing magnetic tape or hard drive-based storage systems to conserve room, albeit much more compact.
“DNA must be in liquid form for sample preparation and sequencing, but isolating liquid samples can be a cumbersome process and requires separate vessels — which would sacrifice a significant amount of density,” explained lead author Sharon Newman, an alumna of UW Bioengineering who is currently pursuing a Ph.D. at Stanford University. “Our dry storage architecture enables us to store data with much higher density compared to other approaches, and allows for physical isolation and data retrieval without risking contamination from other samples.”
To retrieve the data, the researchers rehydrate the DNA with a droplet of water using a digital microfluidics (DMF) device. Newman and her colleagues took a keen interest in DMF technology, which is capable of manipulating liquids in very small quantities with higher precision than humans. By automating biological and chemical protocols with DMF, researchers can scale up the processes involved in implementing DNA data storage. The devices are particularly suited to DNA storage processing, but they have their limitations: not only do DMF platforms tend to be prohibitively expensive, but they are also inflexible, error-prone, and difficult to program.
Luis Ceze (left) examines an early prototype of PurpleDrop with Karin Strauss (center) and Sharon Newman
Recognizing that DNA data storage would remain a pipe dream as long as the combination of cost and complexity made it inaccessible to all but a handful of experts in resource-rich laboratories, a group of MISL researchers developed a low-cost, general-purpose DMF device for holding and manipulating the droplets of DNA, which they dubbed PurpleDrop, and a full-stack DMF automation platform known as Puddle. Together, the projects — which the team will present at the Association for Computing Machinery’s 24th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2019) this week — comprise a complete DMF programming system that will make microfluidic technology more accessible while simultaneously expanding its computational capabilities.
“Most of the work on microfluidics has focused on automating individual protocols, in which the device is given a fixed set of inputs, and manipulates them in a specified way to produce an output,” explained Allen School Ph.D. student Max Willsey. “Although this is an important component of wet-lab research, it limits the role of DMF to that of a microcontroller. We envision a more expansive and dynamic system that enables scientists to program complex protocols in Python or another language of choice while providing real-time error correction.”
Puddle’s dynamic approach to resource management sets it apart from existing techniques, which take a more static approach to microfluidic programming. Puddle is an application programming interface (API) that purposefully maximizes expressiveness and ease of use, in exchange for sacrificing some efficiency and ahead-of-time guarantees. This trade-off gives Puddle more flexibility, allowing both the system and the user to react to data from the fluidic domain. These data-driven decisions fall into three categories: protocol-level decisions, such as automatic replenishment of a liquid that has evaporated during an experiment; application-level decisions based on the protocol output, such as what experiment to run next; and execution-level decisions, such as error detection and correction. To enable the latter, the team employed computer vision and a small camera mounted on top of the DMF device. The camera functions as a multi-purpose sensor for detecting the location and volume of droplets to help Puddle decide if an error has occurred.
Members of the microfluidics research team, left to right: Bichlien Nguyen, Max Willsey, Ashley Stephenson, and Chris Takahashi
The built-in error detection enables the robust execution of the system on relatively cheap hardware, meaning the researchers could prioritize simplicity and accessibility in designing the PurpleDrop device. In addition to the camera, PurpleDrop features a Raspberry Pi 3B single-board computer, instead of a microcontroller, to drive the electronics, which enables it to function as a self-sufficient microfluidics platform. Costing about$300 assembled — orders of magnitude less than most fluidics systems — the design is also simple enough for many labs to put together on their own, without requiring access to a clean room.
“Cost considerations are one of the main sticking points when it comes to microfluidics,” noted Allen School research scientist and co-author Ashley Stephenson. “So as we look for ways to expand the capabilities, we want to ensure that scientists and practitioners will be able to access these innovations. This work can be used to advance not just DNA data storage but also many other areas of research, such as medicine.”
Because cost and complexity are probably the two biggest barriers to widespread adoption of DNA as a storage medium, it comes as no surprise that automation has emerged as a recurring theme in MISL’s work. Last month, the world said “hello” to the first fully automated, end-to-end system for storing digital data in synthetic DNA. Lab members took those five letters, represented by five bytes of data, and ran them through a fully functioning prototype incorporating the equipment required to encode, synthesize, pool, sequence, and read back the data — the majority of which, like PurpleDrop, was built using inexpensive, off-the-shelf components. And it performed the cycle without human intervention, which as senior research scientist Chris Takahashi pointed out, will be an advantage when it comes to DNA data storage in the wild.
“You can’t have a bunch of people running around a data center with pipettes,” pointed out Takahashi, lead author of a related paper published in Nature Scientific Reports. “It’s too prone to human error, too costly, and the footprint would be too large.”
The MISL team developed the first automated, end-to-end system for DNA data storage
Takahashi and his colleagues did not set out to demonstrate speed or even affordability at this stage; rather, they built the machine to show that end-to-end automation was possible. The team’s ultimate goal is to develop a system that resembles any other cloud-based storage service, to which end users would be able to upload their data to a storage center. The difference is, instead of staying in digital form, a customer’s data would be converted to the As, Ts, Cs, and Gs of DNA until it is needed again.
“We are developing an entirely new way of storing digital data from scratch, which means building all new hardware, platforms, and techniques,” said Ceze. “There is a lot more to it than solving the technical challenges related to converting those 0s and 1s to DNA molecules, and we have made significant progress in the last three years. With these latest results, we are building a bridge between computation and molecular biology and introducing exciting new capabilities that will benefit both fields.”
The Nature Communications paper on high-density DNA data storage was co-authored by Newman, Stephenson, Willsey, Takahashi, Strauss, Ceze, and Microsoft researcher Bichlien Nguyen.
The ASPLOS paper on Puddle and PurpleDrop was co-authored by Willsey, Stephenson, Takahashi, Nguyen, Newman, Strauss, Ceze, high school intern Pranav Vaid, Allen School master’s students Michal Piszczek and Christine Betts, and Allen School alumnus Sarang Joshi (B.S., ‘18).
The Nature Scientific Reports paper on end-to-end automation of DNA data storage was co-authored by Takahashi, Nguyen, Strauss, and Ceze.
To learn more about the DNA data storage research, visit the MISL website here.
Allen School alumnus Krzysztof Gajos (Ph.D., ‘08) and professor Daniel Weld were recognized with the Most Impact Paper Award at the Association for Computing Machinery’s 24th International Conference on Intelligent User Interfaces (IUI 2019) last month for “SUPPLE: Automatically Generating User Interfaces.” Each year, the IUI organizing committee selects a past paper submitted to the conference that has had the greatest impact and visibility since its initial publication. This year’s winning paper, which Gajos and Weld originally presented at IUI 2004, presents a system for automatically rendering user interfaces based on device characteristics and usage patterns.
When the team began working on SUPPLE, even the most promising solutions for dealing with this challenge could not sufficiently handle the growing array of display sizes and types of interactions available. Many were unable to cope with situations in which the device constraints could not be anticipated in advance or cases in which the functionality had to be generated dynamically. Often, these tools required interface designers to hand-craft templates or explicitly and painstakingly identify which widgets to use and under what constraints — making the process both time-consuming and expensive. And none of them were geared toward addressing the needs of the user, especially the many people with physical disabilities who struggle to use interfaces crafted for an imaginary “average” user.
Gajos and Weld opted to define interface generation as a constrained decision-theoretic optimization problem. They then set about creating a model-driven solution capable of dynamically generating the “optimal” interface for each user given their physical capabilities and constraints such as device capabilities. Rather than specifying how certain features should be presented by an interface from the start — the approach followed by most existing tools — the team preferred to specify what functionality was intended by the interface and leave the decision on how it should be presented to the SUPPLE algorithm.
Jacob Wobbrock
As it became clear that the algorithm might provide revolutionary benefits to users with physical disabilities, Gajos and Weld teamed up with Jacob Wobbrock, a professor of human-computer interaction in the UW Information School and adjunct faculty member in the Allen School. Together, they added methods for quickly and automatically characterizing physical capabilities, and this enabled SUPPLE to generate one interface for a user with muscular dystrophy and a very different one for a person with cerebral palsy, because it learned that the former user had low strength and couldn’t move the pointing device more than small distances while the latter had impaired dexterity and required larger targets for accurate selections. The team’s studies showed that SUPPLE could dramatically increase the speed of users with motor impairments while simultaneously decreasing their error rate.
“We wanted every person, regardless of their physical abilities, to be able to easily manipulate computer interfaces,” explained Gajos, who is now a member of the faculty of Harvard University. “One can view the project as advancing the notion of Ability-based Design. This is important because the prevalent one-size-fits-all methodology inevitably leads to some form of discrimination.”
“Our initial objective with SUPPLE was to provide a consistent user experience across a range of online platforms and services, regardless of physical location or the type of device,” said Weld, a member of the Allen School’s Artificial Intelligence group. “By approaching interface generation as an optimization problem, we were able to move beyond generic design, instead creating personalized interfaces for people with different preferences and physical abilities.”
The system automatically selects the optimal elements to display in a particular interface from multiple widget libraries. In this case, the “optimal” SUPPLE rendering not only satisfies functional and device constraints, but also requires the least amount of user effort expressed as a cost function. To compute estimated cost of user effort, the team employed a trace-driven approach to model users’ typical interactions in addition to their tool for quickly eliciting performance profiles for users with physical disabilities.
To highlight the lasting value of the team’s contributions, IUI organizers invited an expert panel to reflect on the impact of SUPPLE on the intelligent user interface community at the 2019 conference. The panelists included Henry Lieberman of MIT CSAIL, Jeffrey Nichols of Google, and Simone Stumpf of the Centre for HCI Design at City, University of London.
University of Washington professor Shwetak Patel has been named the recipient of the 2018 ACM Prize in Computing from the Association for Computing Machinery. Patel, who holds a joint appointment in the Paul G. Allen School and the Department of Electrical & Computer Engineering and also leads a team at Google, is being honored by the ACM for “contributions to creative and practical sensing systems for sustainability and health.” The ACM Prize in Computing recognizes early or mid-career computer scientists whose research has had fundamental impact and broad implications and is among the highest honors bestowed in computer science — second only to the A. M. Turing Award, which is widely regarded as the “Nobel Prize of computing.”
“Despite the fact that he is only 37, Shwetak Patel has been significantly impacting the field of ubiquitous computing for nearly two decades,” ACM President Cherri M. Pancake said in a press release. “His work has ushered in new possibilities in many applications of ubiquitous computing for sustainability and health.”
Patel began his research career as an undergraduate at Georgia Tech, where he had the opportunity to work on the “Aware Home,” a project that aimed to envision the connected home of the future. The experience inspired Patel to focus his career on developing low-power sensing capabilities that transformed how we view technologies old and new — from the humble U-bend under your sink, to the basic electrical wiring in your home, to the latest smart devices. Even as an undergraduate, Patel knew that he wanted to make his mark as a faculty member in academia, where he would have the freedom to pursue his research interests without the constraints of working in industry. Only later would he discover the extent to which he could combine the two to great effect.
“Academic life is my intellectual playground,” Patel said. “Computing has so much potential to have a positive impact on society and I’ve been fortunate to be able to try new things through my research. I’m also passionate about getting the technology out there by working closely with industry and through commercialization.”
The first industry Patel helped build was whole-home sensing for sustainability. Patel, whose work on the Aware Home led him to become a plumber and electrician in addition to a computer scientist, recognized that home systems such as the electrical wiring and plumbing could reveal fine grained information about power and water usage — so fine-grained, in fact, that he and his students figured out how to combine signal processing and machine learning to measure electricity usage at the individual device level, including televisions, lights, dishwashers, and more.
Patel and his students noted that each device places distinct “noise” on the home’s electrical system. This noise makes it possible to determine which device is in use and how much power is being consumed. Patel and his students applied a similar principle to monitor the home plumbing system by measuring pressure waves in the home’s plumbing as each faucet or fixture is turned on and off. “Your noise is our signal,” Patel would often say about this work.
Beyond the significance of the research findings, Patel demonstrated that sustainability sensing could be practical, too. His system required only a single device to be plugged into an outlet or connected to the plumbing in order to gather data on the entire system. Patel co-founded a startup company, Zensi, to commercialize this work — the first of several startups he would establish to push his research out into the marketplace. Zensi was subsequently acquired by Belkin, which opted to open its new WeMo Labs in Seattle with Patel serving as Chief Scientist in addition to his faculty position at UW.
As it turned out, Patel discovered that a home’s electrical system could be used to reveal a lot more than whether someone left the television on. That same system could be used like a whole-home antenna to transmit a variety of other data points that could provide an early indication of home hazards, such as elevated moisture levels inside the walls that indicate an appliance malfunction or leak. Patel and his collaborators developed a platform known as Sensor Nodes Utilizing Powerline Infrastructure, or SNUPI for short, that leveraged a network of ultra-low-power sensors deployed throughout the home to wirelessly transmit data, via the electrical circuit, to a base station.
“Sensors can collect information in real time, enabling a homeowner to get ahead of an issue. But if you put sensors throughout the home, you don’t want to have to keep replacing batteries,” Patel noted. “Our home monitoring sensors were designed to be embedded into the wall and last for decades — essentially enabling people to ‘set it and forget it.’”
As before, Patel co-founded a company, the aptly-named SNUPI Technologies, to commercialize the team’s results. SNUPI released a consumer product, WallyHome, that was later acquired by Sears.
By that time, Patel had already begun turning his focus from sensor systems covering an entire building to ones that fit into the palm of a hand. “I began noticing how people are constantly interacting with their phones, which contain increasingly sophisticated sensing capabilities through their cameras, microphones, accelerometers, and other features,” Patel recalled. “And I started wondering how we could use these touch points to monitor health and get ahead of conditions that would otherwise require more time-consuming, potentially invasive interactions.”
One of the first projects he worked on was designed to turn a mobile phone into a handheld spirometry device for measuring lung function. SpiroSmart and a related tool, SpiroCall, enabled people to use their phones to measure their lung function at home or on the go by simply blowing into the microphone. Patel and his students demonstrated that their tool, which like the home sensing systems combined signal processing with machine learning, could achieve acceptable medical standards for accuracy compared to commercial spirometry devices — without the time and expense of an in-person doctor’s visit. They also made use of the built-in camera to develop a series of apps to screen for a variety of medical conditions, including BiliCam for detecting infant jaundice; BiliScreen for detecting adult jaundice (known to be an early symptom of pancreatic cancer); and HemaApp for measuring hemoglobin levels in the blood to detect anemia and other conditions. And these are just a few examples of what Patel has been working on. He also commercialized some of these technologies, which were acquired by Google where he now leads a team.
“That device in your pocket or hands has so much potential, and we’ve only just begun to tap into what it can do for individuals and communities,” observed Patel. “I’ve begun thinking about mobile technologies in the context of not just domestic health, but global health. What can this technology do for communities where no landline infrastructure exists, or where a significant percentage of the population is illiterate? Mobile phones are the most ubiquitous computing platform in the world.”
Patel has begun to see the opportunity in action in collaboration working with local communities and the Bill & Melinda Gates Foundation. He and his team have deployed SpiroSmart and SpiroCall in clinics in India and Bangladesh, for example, while another tool developed in his lab, CoughSense, is being used to track the spread of tuberculosis in South Africa. Meanwhile, providers in Peru are using HemaApp as a non-invasive alternative to traditional blood tests to screen children for anemia. Patel is hopeful that these and other tools will soon be available to health care providers, government and non-profit agencies, and individual users across the globe.
Academia may be his playground — a place where he can test off-the wall ideas and collaborate with students and peers to push the boundaries of what technology can do — but Patel acknowledges that it’s his forays into industry that have enabled him to realize the impact of his research at scale. The time spent working on his startups has also made him a better researcher, he says, by broadening his view of what questions he could address through his work.
“Being an entrepreneur has helped me to identify research problems I wouldn’t have previously considered solely as an academic,” Patel explained. “That experience opened up opportunities for me to venture down research paths I wouldn’t have otherwise thought about.”
“Shwetak is an exceptional innovator who combines an insatiable curiosity with an unrelenting drive to produce research that has real-world impact,” said Hank Levy, Director of the Allen School. “Thanks to his technical excellence, breadth, and vision, we can now do things with sensors and smartphones that were unthinkable outside the confines of science fiction only a short time ago. Shwetak not only has expanded our understanding of what technology can do, but also created new companies and given rise to entirely new industries. And he has undertaken this amazing work all while staying true to our mission as educators and mentors of the next generation of computer scientists. I cannot think of anyone more deserving of this honor.”
This marks the first time a UW faculty member has received the ACM Prize in Computing, but not the first with a UW connection. Previous winners include Allen School alumni Jeff Dean (Ph.D., ‘96), a senior fellow at Google, and Stefan Savage (Ph.D., ‘02), a faculty member at the University of California, San Diego. The ACM Prize comes with a cash award of $250,000 from an endowment furnished by Infosys. Patel will be formally honored at the ACM’s annual awards banquet coming up on June 15th in San Francisco, California.
“I’m honored and humbled to be recognized by the ACM and my peers in this way,” Patel said. “My hope is that this award and the body of work it represents will inspire students to think broadly about the impact that they can have as computer scientists on people’s everyday lives and in the quest for solutions to our greatest public challenges. I would also like to acknowledge my hard working students whose dedication and passion really enabled all of this.”
