The robust impact that the Allen School and the University of Washington have in contributing to accessible technology was recognized at the 22nd International ACM SIGACCESS Conference on Computer and Accessibility (ASSETS 2020) held virtually last month. Researchers from the Allen School and the UW contributed to the Best Student Paper, Best Artifact and the Best Paper.
A team led by UW Human Centered Design & Engineering alumna and Carnegie Mellon University postdoc Cynthia Bennett earned the Best Student Paper Award for Living Disability Theory: Reflections on Access, Research and Design. The paper was co-written by lead author Megan Hoffman, a Ph.D. student at CMU, along with Allen School professor Jennifer Mankoff and City University of New York professor Devva Kasnitz. The paper emphasizes the importance of integrating disability studies perspectives and disabled people into accessibility research.
Top left to right: Mankoff, Froehlich, Jain Bottom left to right: Patel, Ngo, Nguyen
In the paper, the authors correlate personal experiences with theoretical experiences. They found that while accessibility research tends to focus on creating technology related to impairment, without including disability studies — which seeks to understand disability and advocate against ableist systems — accessibility research isn’t as inclusive as its intended purpose. From their research and personal experiences, the authors exemplify how disability is often mired in ableism and oversimplified. They urge disability researchers to commit to recognizing and repairing ableism; study disability beyond diagnosis; incorporate a disability studies perspective that includes disabled voices; and incorporate reflexive, interpretivist study as a regular and essential practice.
“It was so inspiring to learn from and be part of the team writing this paper,” said Mankoff, who leads the Allen School’s Make4All group. “More than anything it showed me that the next generation of scholars are already leading the way in defining what matters in our scholarship.”
Members of the Allen School also contributed to the Best Artifact: SoundWatch, a smartwatch app for d/Deaf and hard-of-hearing people who want to be aware of nearby sounds. The creators are Allen School Ph.D. student and lead author Dhruv Jain, Pratyush Patel and professor Jon Froehlich; undergraduates Hung Ngo and Khoa Nguyen; HCDE professor and Allen School adjunct professor Leah Findlater; Ph.D. student Steven Goodman; and research scientist Rachel Grossman-Kahn.
SoundWatch is an app for Android smartwatches that uses machine learning to alert users of sounds like nearby fire alarms and beeping microwaves, making their environment more accessible. Soundwatch identifies the sound and alerts the user with a friendly buzz along with information about the sound on the screen of the watch.
“This technology provides people with a way to experience sounds that require an action — such as getting food from the microwave when it beeps. But these devices can also enhance people’s experiences and help them feel more connected to the world,” said Jain in a recent UW News release. “I use the watch prototype to notice birds chirping and waterfall sounds when I am hiking. It makes me feel present in nature. My hope is that other d/Deaf and hard-of-hearing people who are interested in sounds will also find SoundWatch helpful.”
“The University of Washington has been a leader in accessible technology research, design, engineering, and evaluation for years,” said iSchool professor and Allen School adjunct professor Jacob O. Wobbrock, who, along with Mankoff, serves as founding co-director of the UW’s Center for Research and Education on Accessible Technology and Experiences (CREATE).”This latest round of awards from ACM ASSETS is further testament to the great work being done at the UW. Now, with the recent launch of CREATE, our award-winning faculty and students are brought together like never before, and we are already seeing the great things that come of it.”
Congratulations to all of the ASSETS 2020 award recipients!
Robots have traditionally been deployed for dull, dirty or dangerous tasks. What if robots instead could be used to support the sophisticated and iterative work of domain experts such as chemical engineers or synthetic biologists?
A University of Washington research project led by Allen School adjunct faculty member and Human-Centered Design and Engineering professor Nadya Peek and Allen School and Electrical and Computer Engineering professor Josh Smith, “NRI: FND: Multi-Manipulator Extensible Robotic Platforms,” received a $700,000 grant from the National Science Foundation’s (NSF) National Robotics Initiative 2.0: Ubiquitous Collaborative Robots (NRI-2.0) program.
“The tools we propose to develop include a family of open-source, replicable, extensible, parametrically-defined co-bots that will enable experts to iteratively develop automated processes and experiments,” Peek said. “This grant will help us develop hardware and software for authoring, running, and verifying automated workflows.”
Smith’s lab has developed an ultrasonic manipulator that allows a robot to pick up small objects without touching them. The grant will allow the researchers to combine this new ultrasonic manipulator with Peek’s open source multi-tool motion platforms, including Jubilee.
“Non-contact manipulation can allow robots to pick up small objects and powders, which is currently challenging for robots,” Smith said. “Non-contact manipulation can also help maintain sterility, which could be useful in surgical settings, and any time we are concerned about spreading pathogens.”
The integrated robotic system will allow end-users to develop automated workflows for domain specific tasks. The researchers are designing their system to be customizable and extensible. In particular, the robotic systems they develop are fabricatable, meaning that they can be made with easily sourced parts or parts made using low cost digital fabrication tools such as 3D printers. This means that even when the domain experts create highly sophisticated interactive and automated workflows, their experimental setups can easily be reproduced by other scientists.
In honor of the National First-Generation College Celebration on November 8, our latest Allen School spotlight highlights some of our own first-gen community members. Approximately 20 percent of the school’s undergraduate student body is the first in their family to pursue a four-year degree. Each is an academic trailblazer, navigating their way through the entire college experience as the first in their family to pursue a bachelor’s degree. Some are still finding their footing with the help of Gen1, a new student group for first-gen students, while others have been there, done that. These folks were eager to share what they have learned along the way.
Jun Hu, undergraduate student
Jun Hu
Jun Hu is a transfer student from Everett Community College originally from Canton, China. He moved to the United States in 2007, when he was 18. His interest in computer science began when his uncle, a computer software engineer, stayed with his family while Hu was in high school. When he moved to the U.S., Hu joined the Navy. While serving in the military, his interest in computer science grew as he noticed the constraints of the programs he was using and wanted to do something about it.
Allen School: As a first-generation student in a new country, how did you navigate through the college system?
Jun Hu: I went to college in China very briefly after high school — though the college experience was quite different compared to the states. Here you have so much freedom both socially and academically. I was quite lost. I did not know my education plan and just how life goes in general. Luckily, my adviser gave me a degree plan and it cleared up a bit. That was the first time I went to college after I moved to the states. After that, I took several classes when I was in the Navy, and the experience I learned from that helped me a lot. I was enrolled at Everett Community College but decided to transfer to the UW. I chose to do this because the CSE program in the Allen School is decent and there are a lot of resources and extracurricular activities to help you succeed.
Allen School: What does being a first-gen student mean to you?
JH: As a first-generation student, I felt it could be challenging sometimes because there is not much information you can obtain from your parents about college life in general. It is just a new experience that you have to go through on your own and figure things out along the way.
Allen School: What is your favorite part about being in the Allen School?
JH: It has been a pleasure to work with the advising team from the Allen school. Despite having been to several colleges, transferring to a new university is still difficult because each school has its own system on handling registration and navigating around the campus. Luckily, my adviser has been working with me since I got accepted so it made the transaction much easier. The teaching assistants and office hours in my classes are extremely helpful for me this quarter because the difficulty of the classes is much higher and online learning has created more barriers for me.
Allen School: What advice do you have for future first-gen students?
JH: The most important advice I have is to reach out for help. You are not alone in this process, there is a lot of help and opportunity for students to succeed. Most of the schools even have programs for first-gen students. The academic adviser is your friend. They will provide you some pathway when you are not sure what to do. The tutoring center is a fun place to hang out. You are able to get help with your school work and maybe meet some new friends that are also studying your field. Also, don’t be afraid to make mistakes and go explore new things. Join a student organization or be part of the student government and get involved. College is not only a place to obtain higher education, but also a place to know more about yourself. You will be surprised that you have more talents than you thought. By doing something you may find new goals and figure out what you really want to do.
Eman Mustefa, undergraduate student
Eman Mustafa
As a child of immigrants, Eman Mustefa, a sophomore from Federal Way, Washington, said she was expected to go to college. Her mother, who briefly attended community college, supported her in every way she could, despite not knowing much about the college process in the United States. Growing up in western Washington, Mustefa knew as early as second grade that she wanted to attend the UW. With an interest in technology and computers, and a few hours of programming under her belt from attending “Hour of Code” days in high school, her choice was to enroll in the Allen School.
Allen School: As a first-generation student, how did you navigate through the college system?
Eman Mustefa: I was very overwhelmed at first because I had not thought much about applying to college until the very end of my junior year. It felt like all of my other classmates had been prepping since the first day of high school, and I hadn’t done that at all. Luckily I had a really strong support system. My cousin was an instrumental part during my college application process. She was in her last year of college and was able to guide me to some of the resources that helped her and revise most of my essays. My aunt and uncle were also very supportive and along with my mom, they all took turns dropping me off and picking me up at every tour, testing location and interview. Although my mom knew she couldn’t help me with many parts of the process, she helped in any way that she could.
Allen School: What does being a first-gen student mean to you?
EM: Being a first-generation graduate means thanking my family for all the support and encouragement that they have given to me throughout the years, and showing them it was all worth it. It means showing others from first-generation backgrounds that it is possible and you don’t have to be a “traditional computer science student” in order to succeed in the field.
Allen School: What is your favorite part about being in the Allen School?
EM: My favorite part about working and being at the Allen School is hearing about everyone’s various backgrounds. I think in many people’s minds when we hear computer science we think about a monolith but in reality, each person that goes here is so different.
Allen School:What advice do you have for future first-gen students?
EM: I would tell them that even though it may feel like you are alone, you are not. There are so many different communities and people in the Allen School that are here for you, GEN1, a club I founded with a couple of my friends last year for first-generation college students, was made for that very reason. So know that you have a support system, even when it feels tough and it’s impossible, they are there to cheer you on to keep going.
Joyce Zhou, master’s student
Joyce Zhou
As a high school student, Joyce Zhou was accepted into the UW Academy, an early university admission opportunity for 10th grade students in Washington. They enjoyed their computer science classes in high school and had a great Advanced Placement computer science teacher, so it was only natural for them to continue their studies at the UW. After a successful undergraduate experience, they decided to continue as a graduate student in the Allen School’s fifth-year master’s program, working with professor Daniel Weld in the Lab for Human-AI Interaction.
Allen School: As a first-gen student, how did you navigate through the application and enrollment process?
Joyce Zhou: Because I got into the UW Academy, I was lucky not to have to navigate the entire college search process, just the requirements for applying to UW only. That still meant taking the SAT/ACT and writing several application essays, though. The Robinson Center provided lots of materials that described what to expect from the process. My parents, being Chinese, are super familiar with the idea of preparing for exams so I was set on test prep and high school grades, but for personal statement writing I used a lot of online guides.
Once I actually got into college, I learned from other people in my Academy cohort how to handle coursework. Online forums like Reddit were also really good for answering questions about undergraduate research and how to get involved. Unfortunately, I never got as familiar with the social part of college — such as networking and transitioning to adulthood — so I’m still struggling today with that. There’s been tons of helpful people along the way: high school teachers, advisers, TAs and resident assistants.
Allen School: What is your favorite part about being in the Allen School?
JZ: The people! Maybe I’m biased because I’m a grad student now, but I love that within the school it’s such a casual environment. There’s the openness between instructors, TAs, and students, tiny fun things that people pin up on lab windows or room number cork boards, events preceded by friendly jokes. Also the part where the actual people I know and talk frequently with are cool and do cool things.
Allen School: What advice do you have for future first-gen students?
JZ: Get to know people, as much of a wide range of people as possible — fellow first-gen students, grad students, advisers — and build or join some sort of friend support group. Also, don’t be afraid to ask questions or lurk on online forums as well, if there’s a question you have about how college works it’s almost certain someone has answered it online before.
Elise deGoede Dorough, staff
Elise deGoede Dorough
Elise deGoede Dorough, originally of Sumner, Washington, is the Allen School’s director of graduate student services. Her grandparents, who immigrated from the Netherlands, met in western Washington and started TulipTown, a tulip bulb farm in Mt. Vernon. While her father went into the family business after high school, he wanted deGoede Dorough and her brother to have other options and encouraged them to go to college. She relied on teachers and peers to coach her through which classes to pick and which standardized tests to take. In the end, the UWwas the only school she applied to, as it was the only in-state school she was interested in attending.
Allen School: Did being a first-gen student influence your career?
Elise deGoede Dorough: I think it did influence my career. As an undergraduate, the UW was so big to me. When I look back, I realize that I needed more support but really didn’t know where to find it. Especially as a pre-major, I didn’t know who I could talk to about which major to pursue and how I should be preparing for different career paths. I also worked nearly full-time as a server in a restaurant from the summer of my freshman year all through the remainder of college, so I didn’t have a ton of extra time for any kind of exploration. My freshman year I ended up earning a 4.0 in my psychology class so I eventually chose that as my major without ever speaking to a staff adviser or a faculty member.
I was an introvert and maybe because of that I always first tried to find the information I needed online. So I read up on the course requirements, processes for declaring a major, etc. and submitted the paperwork. By the time I graduated I had probably only seen a staff adviser about three times in total. At that point, I had no idea that becoming an academic adviser, or working in student affairs in general, was a possible career path. I spent some years after college as a manager in the same restaurant I worked in through college, before I realized that I missed the rhythm of the academic year and I missed being on campus. I didn’t know what I would go back to school for, so instead I applied to fill in temp staff roles around campus. It was through those experiences that I realized working as a staff member for the UW was a possibility for me. Eventually I took an admissions and recruiting position at another college and went back to school for my Master’s in Education at the UW. Near the end of that program I was hired as an undergraduate adviser at the Allen School, and 10 years later here I am.
Allen School: What does being a first-gen student mean to you?
E dD: I would say opportunity and freedom to choose. Some people ask me why I didn’t go into the family business, and the answer is not because it wasn’t a good option. My dad and my younger brother, after going to college himself, are farming together now. I believe that our parents’ insistence that we both go to college and then make the choice of whether to join the family business or not was a good one. It gave us more time to learn what was out there and what we enjoyed doing. And it gave us the knowledge that we were in control of our path.
Allen School: What does working at the Allen School mean to you?
E dD: It might seem odd because I don’t have a background in computer science, but the Allen School is where I’ve really felt at home on the UW campus. I enjoyed my time as a student, and as I mentioned I love the rhythm of the academic year. But I never felt I had a specific home at UW even within the departments where I earned my degrees. I always felt anonymous and a little bit like an outsider, unsure of where my value or contributions were. But in CSE, as we called it when I joined, I have found ways to really make an impact, support students, and find fulfillment in that work.
Allen School: What advice do you have for future first-gen students?
E dD: Ask people where the resources are! I was so introverted and I thought I had to figure it out on my own. But there are people everywhere — peers, staff, faculty — who are more than willing to hear your questions and point you in the right direction. It can be very intimidating at first and students often apologize to me for asking questions. But, really, it’s my job to help and helping in that way is why I’m here.
Zachary Tatlock, professor
Zachary Tatlock
Allen School professor Zachary Tatlock grew up in a small southern Indiana town, where both of his parents also grew up. His father worked as a machinist and, after raising four children, his mother worked as a special education aide. His parents were supportive of his choice to go to college, but it was made clear that he would have to work hard and get a good scholarship. He did, which enabled him to enroll in Purdue University. Paging through the university’s booklet of majors, he chose computer science because it “sounded kind of like math but where you got to actually tinker and make things.”
Allen School: Why did you decide to continue your education and also become an educator yourself?
Zachary Tatlock: At Purdue I made some truly spectacular friends who had seemingly been programming since shortly after they started walking, while I knew relatively zip. I was again lucky that the community at Purdue was extremely supportive and helped a kid like me get up to speed. After my first year, I wanted to give back somehow, so I started TAing lab sessions for the introductory Java programming course. That went pretty well, and after a couple semesters I was put in charge of all the labs. I got to hire the friends who had helped me during my early struggles and we spent the next couple of years having a blast trying out all kinds of experiments, making new lab material and teaching it, and holding extra office hours late at night before project deadlines. I just couldn’t imagine leaving the academic computer-science community after only four years, so I decided to pursue a Ph.D. I wanted to make a career of hacking on interesting, diverse projects with brilliant friends from all over the world and helping share the joy of programming with students.
I was inspired by superb mentors like my undergraduate advisor, Suresh Jagannathan, who taught me that who you work with is more important than any particular project details, and my utterly amazing Ph.D. advisor, Sorin Lerner, taught me that academic research goes best when you put students first.
Allen School: What does being a first-gen student mean to you?
ZT: Being a first-generation student has meant bridging cultures, and more so the further I’ve gone down the academic path. Folks back home often don’t have much context to appreciate what researchers are doing with their hard-earned tax money; it can be difficult to convincingly justify why you and your friends worked hard for a year and then stayed up late for two weeks straight to make a piece of software that primarily exists just to be evaluated in a research paper. On the academic side, research colleagues often don’t have much context to appreciate the culture and norms of rural towns or so-called flyover states. In practice, it’s usually been best to just listen to folks and occasionally ask patient questions to help them hear what their ideas about “the other side” might sound like from a different perspective. Getting a college education has also provided other important, though more prosaic, benefits, like improving my ability to help support my family.
I also know that I have been unaccountably fortunate to end up with the career I’m in. I hope to pay it forward and smooth the way for other students who may not have had a clear path towards college or computer science.
Allen School: What do you like most about working at the Allen School?
ZT: The students are the very best part of the Allen School. Their enthusiasm, creativity, and incredible work ethic make every day a joy. I am also lucky to have some amazing colleagues who continue to challenge us all to grow and get better year after year. I love that I get to tackle new problems, meet new people, and learn new skills almost every day — this is a job that will never get boring. The Allen School culture weaves all these pieces together in a vibrant, close-knit community that cares a lot about young people; though Seattle is not a small town the Allen School feels like home.
Allen School: What advice do you have for future first-gen students?
ZT: Pay attention and be persistent! If you keep your wits about you and just keep going no matter how bad things look, you will often succeed. Even if you don’t win a particular battle, you will know that you have done your best and you will have learned some valuable lessons. I know that all sounds trite, but the fact is that many people who don’t make it just over-worry and overthink themselves out of success. Another thing to always remember is that you belong: even though many of your fellow students will have had very different upbringings and opportunities, college is especially for people like you.
We are grateful for the many contributions our first-generation students, staff and faculty have made to our campus and community!
Ximing Lu (left) and Sanjana Chintalapati were recognized during the virtual Diversity in Computing celebration hosted by the Allen School last week
Kicking off the fall quarter by celebrating diversity in computing has become an Allen School tradition. This year the celebration went virtual, with around 80 people logged on to honor students who embody the school’s commitment to diversity and excellence and to hear from members of the community who participated in the Grace Hopper Celebration, the world’s largest gathering of women technologists, and the ACM Richard Tapia Celebration of Diversity in Computing, which brings together people of all backgrounds, abilities and genders in computing.
Allen School director Magdalena Balazinska welcomed participants to the celebration by highlighting some of the programs the Allen School has invested in to increase diversity, equity and inclusion, For example, the school has partnered with the College of Engineering’s STARS program and AccessCSForAll in order to nurture promising talent to bring to the Allen School and the field of computing. Balazinska also highlighted three student-led groups focused on supporting a diverse and inclusive community: Minorities in Tech (MiT), Gen1 and Q++. Following Balazinska’s remarks, professor Tadayoshi Kohno, the school’s associate director for diversity, equity and inclusion, spoke about new high school and summer programs the school is creating to find more students having diverse backgrounds and experiences. Other actions include conducting faculty searches at more diverse research institutions and posting open staff positions on more inclusive job posting websites.
During the diversity celebration, in a panel discussion hosted by Les Sessoms, The Allen School’s recruitment and retention specialist, Ph.D. student Samia Ibtasam in the Information and Communication Technology for Development Lab, and undergraduates Ximing Lu and Marisa Radensky talked about their initial interest in studying computer science and about their positive experiences at the conferences. Ibtasam said it was an inspiring experience to see other students like her represented at Grace Hopper, explaining that she felt like she was home. Each of the panelists felt the conferences opened up new opportunities that they didn’t know were available to them.
In addition to serving on the panel, Lu was recognized with the Lisa Simonyi Prize. Longtime friends of the school Lisa and Charles Simonyi established the scholarship to recognize and support students who exemplify excellence, leadership, and diversity. Lu, who is a double major in computer science and statistics, is a highly accomplished researcher. She has worked with Allen School and Electrical & Computer Engineering professor Linda Shapiro on the automated classification of cancer biopsy images; Allen School professor Yejin Choi on natural language generation; and Allen School professor Kevin Jamieson and Materials Science & Engineering professor Mehmet Sarikaya on neural network approaches for molecular analysis. She has also been a software developer with the Avionics Team of the UW Society for Advanced Rocket Propulsion, and with the UW Sensors, Energy and Automation Laboratory.
“Ximing has had a busy undergraduate career, earning a high GPA taking both undergraduate and graduate classes, interning, and working on several research projects,” Shapiro said. “I’m happy to see her hard work is paying off.”
“Ximing is hands down the best and the most promising undergraduate student I’ve ever seen or worked with over the past six years since I arrived at the University of Washington,” said Choi. “Not only is she extremely fast and technically strong, which I’ve seen before a few times, but she’s bursting with creative ideas, which is very rare. She also has enormous energy and enthusiasm for research at all levels of execution, which is even less common.”
Sanjana Chintalapati, a junior studying computer science, was awarded the Allen AI Outstanding Engineer Scholarship for Women and Underrepresented Minorities from the Allen Institute for Artificial Intelligence (AI2). The scholarship was created to encourage students from underrepresented groups to excel in computer science and engineering and become leaders and role models in their fields.
“Sanjana discovered something we try to emphasize: that success in computer science is much more dependent on committing oneself to constant learning and much less dependent on having a natural knack for it,” Balazinska said.
Chintalapati is interested in accessibility and is currently working on multiple projects that use computing to assist people with disabilities, including an app that assists disabled users who are using transit stations. The app is designed to alert users when an elevator is out of service that would prevent them from accessing the train or bus. Allen School professor emeritus Oren Etzioni, the CEO of AI2, said during the program that in an imposing field of applicants for the scholarship, Chantalapati stood out. The selection committee could tell how passionate she is about using technology for good, which Etzioni noted aligns well with the mission of AI2.
Thanks to the Simonyis and AI2 for supporting diversity and excellence, and thanks to everyone who logged on to celebrate the people who are making our school and our field a more welcoming destination for all. And congratulations to Ximing and Sanjana!
For more about the Allen School’s efforts to advance diversity in computing, please visit our website.
Many people have had the experience of being poked in the back by those annoying plastic tags while trying on clothes in a store. That is just one example of radio frequency identification (RFID) technology, which has become a mainstay not just in retail but also in manufacturing, logistics, transportation, health care, and more. And who wouldn’t recognize the series of black and white lines comprising that old grocery-store standby, the scannable barcode? That invention — which originally dates back to the 1950s — eventually gave rise to the QR code, whose pixel patterns serve as a bridge between physical and digital content in the smartphone era.
Despite their near ubiquity, these object tagging systems have their shortcomings: they may be too large or inflexible for certain applications, they are easily damaged or removed, and they may be impractical to apply in high quantities. But recent advancements in DNA-based data storage and computation offer new possibilities for creating a tagging system that is smaller and lighter than conventional methods.
That’s the point of Porcupine, a new molecular tagging system introduced by University of Washington and Microsoft researchers that can be programmed and read within seconds using a portable nanopore device. In a new paper published in Nature Communications, the team in the Molecular Information Systems Laboratory (MISL) describe how dehydrated strands of synthetic DNA can take the place of bulky plastic or printed barcodes. Building on recent developments in nanopore-based DNA sequencing technologies and raw signal processing tools, the team’s inexpensive and user-friendly design eschews the need for access to specialized labs and equipment.
“Molecular tagging is not a new idea, but existing methods are still complicated and require access to a lab, which rules out many real-world scenarios,” said lead author Kathryn Doroschak, a Ph.D. student in the Allen School. “We designed the first portable, end-to-end molecular tagging system that enables rapid, on-demand encoding and decoding at scale, and which is more accessible than existing molecular tagging methods.”
Instead of radio waves or printed lines, the Porcupine tagging scheme relies on a set of distinct DNA strands called molbits — short for molecular bits — that incorporate highly separable nanopore signals to ease later readout. Each individual molbit comprises one of 96 unique barcode sequences combined with a longer DNA fragment selected from a set of predetermined sequence lengths. Under the Porcupine system, the binary 0s and 1s of a digital tag are signified by the presence or absence of each of the 96 molbits.
“We wanted to prove the concept while achieving a high rate of accuracy, hence the initial 96 barcodes, but we intentionally designed our system to be modular and extensible,” explained MISL co-director Karin Strauss, senior principal research manager at Microsoft Research and affiliate professor in the Allen School. “With these initial barcodes, Porcupine can produce roughly 4.2 billion unique tags using basic laboratory equipment without compromising reliability upon readout.”
Although DNA is notoriously expensive to read and write, Porcupine gets around this by presynthesizing the fragments of DNA. In addition to lowering the cost, this approach has the added advantage of enabling users to arbitrarily mix existing strands to quickly and easily create new tags. The molbits are prepared for readout during initial tag assembly and then dehydrated to extend shelf life of the tags. This approach protects against contamination from other DNA present in the environment while simultaneously reducing readout time later.
Another advantage of the Porcupine system is that molbits are extremely tiny, measuring only a few hundred nanometers in length. In practical terms, this means each molecular tag is small enough to fit over a billion copies within one square millimeter of an object’s surface. This makes them ideal for keeping tabs on small items or flexible surfaces that aren’t suited to conventional tagging methods. Invisible to the naked eye, the nanoscale form factor also adds another layer of security compared to conventional tags.
The Porcupine team: (top, from left) Kathryn Doroschak, Karen Zhang, Melissa Queen, Aishwarya Mandyam; (bottom, from left) Karin Strauss, Luis Ceze, Jeff Nivala
“Unlike existing inventory control methods, DNA tags can’t be detected by sight or touch. Practically speaking, this means they are difficult to tamper with,” explained co-author Jeff Nivala, a research scientist at the Allen School. “This makes them ideal for tracking high-value items and separating legitimate goods from forgeries. A system like Porcupine could also be used to track important documents. For example, you could envision molecular tagging being used to track voters’ ballots and prevent tampering in future elections.”
To read the data in a Porcupine tag, a user rehydrates the tag and runs it through a portable Oxford Nanopore Technologies’ MinION device. To demonstrate, the researchers encoded and then decoded their lab acronym, “MISL,” reliably and within a few seconds using the Porcupine system. As advancements in nanopore technologies make them increasingly affordable, the team believes molecular tagging could become an increasingly attractive option in a variety of real-world settings.
“Porcupine is one more exciting example of a hybrid molecular-electronic system, combining molecular engineering, new sensing technology and machine learning to enable new applications.” said Allen School professor and MISL co-director Luis Ceze.
In addition to Ceze, Doroschak, Nivala and Strauss, contributors to the project include Allen School undergraduate Karen Zhang, master’s student Aishwarya Mandyam, and Ph.D. student Melissa Queen. This research was funded in part by the Defense Advanced Research Project Agency (DARPA) under its Molecular Informatics Program and gifts from Microsoft.
In the winning Test of Time paper, Heer and co-author Edward Segel explored how visual data enhances journalistic storytelling and studied design strategies for narrative visualization. The paper helped to frame and advance research into the use of visualization for journalistic reporting and storytelling. Since then, it has been widely cited and influential in the fields of both visualization and data-driven journalism.
Fascinated by the growing use of visualizations in online journalism, Heer and Segel built a catalog of examples to identify distinct genres of narrative visualization. The two characterized the design differences and messaging and found that many samples could be more dynamic with the help of more sophisticated online tools — including those that allow interactive exploration by the reader.
When the paper was originally published, Heer was a professor of computer science at Stanford University and Segel was a master’s student. Together, they created a comprehensive framework of design strategies for narrative visualization.
“We wanted to better understand the innovative work of data journalists and designers whose insights we hoped to give further reach with our paper,” Heer said. “From the framework of our research, we found promising yet under-utilized approaches to integrating visualization with other media, and the potential for improved user interfaces for crafting data stories.”
Heer had already started to develop a series of robust tools for producing interactive visualizations on the web. As a graduate student, he helped to create Prefuse, one of the first software frameworks for information visualization, and Flare, a version of Prefuse built for Adobe Flash that was partly informed by his work in animated transitions. This latest research with Segel focused on a central concern in the design of narrative visualizations: the balance between author-driven elements that provide narrative structure and messaging, and reader-driven elements that enable interactive exploration and social sharing. This work helped to identify successful design practices that guided the development of new narrative visualization tools.
Since joining the Allen School faculty in 2013, Heer has worked on a suite of complementary tools for data analysis and visualization design built on Vega, a declarative language for producing interactive visualizations. These tools include Lyra, an interactive environment for generating customized visualizations, and Voyager, a recommendation-powered visualization browser. In 2017 he was recognized with the IEEE Visualization Technical Achievement Award and the ACM Grace Murray Hopper Award for his significant technical contributions early in his career.
Vega led to Vega-Lite, a project that earned Heer and Moritz — now a professor in the Human-Computer Interaction Institute at Carnegie Mellon — a Best Paper Award at InfoVis 2016 along with their collaborators. Vega-Lite is a high-level grammar for rapid and concise specification of interactive data visualizations. The goal was to enable non-programmers to create sophisticated visualizations that can be generated automatically. That project and others formed the basis of Moritz’s 2019 dissertation, which made a number of contributions spanning formal languages, automatic reasoning for visualization design, and novel approaches for scaling interactive visualization to massive datasets for which he was honored at this year’s VIS conference.
One of those contributions was Draco, an open-source, constraint-based system that formalized guidelines for visualization design and their application in visualization tools. The system, which earned Moritz and his colleagues a Best Paper Award at InfoVis 2018, offers a one-stop shop for researchers and practitioners to apply and test a set of accepted design principles and preferences and to make adjustments to their visualizations based on the results. To expand the application of user-friendly visualization tools to larger datasets, Moritz introduced Pangloss, which enables analysts to interactively explore approximate results pending completion of long-running queries. Pangloss generates visualizations based on samples while queries are ongoing, with the ability to detect and correct errors later. Moritz followed that up with Falcon, a web-based system that supports real-time exploration of billion-record datasets by enabling low-latency interactions across linked visualizations.
Moritz’s interactive systems for visualization and analysis have seen widespread adoption in the Python and JavaScript communities.
Our first undergraduate student spotlight of the new academic year features Nayha Auradkar, a junior from Sammamish, Washington who is majoring in computer science with a minor in neural computation and engineering. Auradkar currently serves as chair of the University of Washington chapter of the Association for Computing Machinery for Women (ACM-W), working to cultivate a strong, supportive community of women in the Allen School. In her leadership role, she hopes to increase programming, engagement and awareness among the organization’s members while building relationships with other minority students pursuing education in technology-related fields.
Allen School: Congratulations on becoming chair of ACM-W! What interested you in the position?
Nayha Auradkar: I believe that diversity in tech is essential for innovation and for building equitable communities. When we bring together people from various backgrounds, we gain new ideas and approaches when solving problems so that everyone, regardless of their background, benefits from technology.
I joined ACM-W my freshman year because I am passionate about diversity in tech and ensuring that women of all backgrounds are able to reach their full potential. In my sophomore year, I served as the public relations officer of ACM-W. This year, I was elected as chair. It is an honor to be leading an organization with such an important and powerful mission. I love the community of strong, supportive, and inspiring women I have met along the way.
Allen School: What are your goals for the group during your tenure as chair?
NA: As chair, my goals are to lead and strengthen the ACM-W community by creating opportunities to enable more active involvement of CSE in ACM-W. We recently created a membership system to make ACM-W more of a close-knit affinity group, and we have incorporated regular member meetings and socials into our event timeline. Additionally, we are launching committees to create more leadership opportunities within ACM-W.
Another goal I have is to recognize the intersectionality of being a woman in tech with other aspects of identity, such as a disability and race. This is important to discuss when talking about diversity, so we don’t leave behind groups that are marginalized the most. We are working on incorporating intersectionality themes into our quarterly diversity discussion events.
Allen School: How are you handling the challenges of organizing remote meetings and programs during a pandemic?
NA: Building community remotely and welcoming new students is especially challenging, which is why we are working to create more ACM-W member events and activities like member meetings, social events, and committee leadership positions.
Something I have really emphasized is making our events as accessible as possible. This is critically important, especially in a virtual world, and should be established as a norm.
Allen School: Why did you choose to study computer science?
NA: I chose to study CS because of the positive impact it can have on so many different disciplines. I am particularly interested in using CS in fields like neural engineering as well as applying CS to create technology that supports accessibility and inclusion for people with disabilities.
Allen School: What do you enjoy most about the Allen School?
NA: The people! I know I can always walk into the labs and find a friendly face. The advisers are willing to talk to and support students in anything. I feel a strong sense of community in the Allen School.
I also enjoy the rigor of the classes and the incredible research opportunities. Faculty at the Allen School are leaders in their fields, and I am grateful for the opportunity to learn from such accomplished and passionate individuals.
Allen School: Speaking of research opportunities, what kind of projects are you working on in the Make4all Lab, and why did you choose that lab in particular?
NA: As someone with a disability, I am deeply passionate about creating technologies that support accessibility and inclusion. I have had a stutter, a neurological condition, for my whole life, and my experiences as a person who stutters have shaped my interests and have allowed me to have empathy for others with similar experiences. Working in the Make4all Lab, led by Professor Jennifer Mankoff, has given me experience in Human-Computer Interaction (HCI) studies and accessible technology. I hope to carry this knowledge to wherever I end up going, so I can have a positive impact on accessibility efforts.
The project I am currently working on with my mentors Kelly Mack and Megan Hofmann is an HCI study focusing on quantitative and qualitative analysis of features of personal protective equipment designed in response to the COVID-19 pandemic.
Allen School: You also had the opportunity to intern at JP Morgan Chase over the summer. How was that experience?
NA: My intern team worked on JP Morgan’s Tech for Social Good team, and we built a web application to support a computer science education nonprofit. I loved having the opportunity to make a measurable impact on a nonprofit that supports underrepresented minorities in tech. I also worked with financial applications of technology, such as data visualization in investment banking and security in asset management. It was interesting to see so many applications of CS to areas I previously did not know much about.
The Allen School and ACM-W community is lucky to have a thoughtful, inclusive leader like you, Nayha. Thanks for all that you do!
Top row from left: Cory Doctorow, Scott Aaronson, Kunle Olukotun; bottom row from left: Brad Calder and Sarita Adve
Save the dates! Another exciting season of the Allen School’s Distinguished Lecture Series kicks off on Oct. 29. Join us to hear experts in technology activism, quantum computational supremacy, multi-chip processing, cloud infrastructure, and computer architecture.
All lectures with the exception of the November 19th Lytle Lecture, will be live streamed on the Allen School’s YouTube channel at 3:30 p.m. Pacific Time on the presentation date.
Cory Doctorow, an author of science-fiction and non-fiction books, a journalist and technology activist, will deliver a talk on Oct. 29 about “Early-Onset Oppenheimers.” His presentation will highlight the liberatory power technology workers have in what they design, build and share with the public, and how to convince these workers to use their power to deliver the same liberation to their users, rather than confiscating their users’ freedom.
Doctorow, who created craphound.com, champions liberalizing copyright laws and co-founded the UK Open Rights Group. He works in digital rights management, file sharing and post-scarcity economics. He has written more than 20 books and has published an extensive collection of short stories and essays. He is a contributing writer to Wired magazine, The New York Times Sunday Magazine, The Globe and Mail, Asimov’s Science Fiction magazine, and the Boston Globe. Doctorow is the former European Director of the Electronic Frontier Foundation and is an MIT Media Lab research affiliate, a visiting professor of computer science at Open University, and a visiting professor of practice at the University of North Carolina’s School of Library and Information Science.
Nov. 19: Scott Aaronson, David J. Bruton Centennial Professor of Computer Science and founding director, Quantum Information Center at the University of Texas at Austin
Scott Aaronson is the Department of Electrical and Computer Engineering’s 2020 Lytle Lecturer and will deliver a talk on Nov. 19 about “Quantum Computational Supremacy and its Applications.” He will discuss Google’s first-ever demonstration of Quantum Computational Supremacy, which is a 53-qubit programmable superconducting chip called Sycamore. His talk will address questions about the chip, such as what problem Sycamore solved, how to verify the outputs using a classical computer, and how confident researchers are that the problem is classically hard — especially in light of subsequent counterclaims by IBM and others.
Aaronson focuses on the capabilities and limitations of quantum computers as well as computational complexity in general. His most recent work aims to demonstrate the quantum computing speedup that future technologies will help to create. In addition to his research, Aaronson writes about quantum computing for Scientific American, The New York Times, and on his own popular blog, Shtetl-Optimized. Prior to joining UT Austin, Aaronson spent nine years as a professor in electrical engineering and computer science at MIT. During that time he wrote his first book, Quantum Computing Since Democritus, about deep ideas in math, computer science and physics.
The Lytle Lecture will be broadcast via Zoom. When available, the link will be posted here.
Dec. 3: Kunle Olukotun, Cadence Design Systems Professor in the School of Engineering and professor of electrical engineering and computer science at Stanford University
Known as the father of the multicore processor and the leader of the Stanford Hydra chip multiprocessor (CMP) research project, Kunle Olukotun has been a trailblazer in processor design. He founded Afara Websystems, a company that built high-throughput, low-power multicore processors for server systems, saving power and space for data centers which was subsequently acquired by Sun Microsystems. Olukotun is actively involved in research in computer architecture, parallel programming environments and scalable parallel systems, and he currently co-leads the Transactional Coherence and Consistency project aimed at making parallel programming accessible to average programmers. He has designed multicore CPUs and GPUs, transactional memory technology and domain-specific languages programming models. He also directs the Stanford Pervasive Parallelism Lab (PPL) which focuses on the use of heterogeneous parallelism in all application areas using domain specific languages.
Dec. 10: Brad Calder, vice president of Product and Engineering of Technical Infrastructure and Cloud at Google
Allen School alumnus (B.S. ‘91) Brad Calder is the Vice President of Product and Engineering of Technical Infrastructure and Cloud at Google. There, he oversees the computer, networking, storage, databases, and data analytics services to provide customers more ways to connect to Google’s cloud computing services. Prior to joining Google, Calder was a vice president of engineering at Microsoft Azure and was on the founding team that started Azure in 2006. Before that, Calder was a tenured professor in the University of California, San Diego’s Department of Computer Science and Engineering, where he published over 100 papers in the areas of systems, architecture and compilers and co-directed the High Performance Processor Architecture and Compilation Lab.
Feb. 11: Sarita Adve, Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign
With research interests that span computer architecture, programming languages, operating systems and applications, Sarita Adve has devoted her career to advancing innovation at the hardware-software interface. Adve co-developed the memory models for C++ and Java programming languages, based on her work in data-race-free (DRF) models, and has made significant contributions to cache coherence, hardware reliability, and the exploitation of instruction-level parallelism (ILP) for memory level parallelism. She also led the design of one of the first systems to implement cross-layer energy management as well as the development of the widely used RSIM architecture simulator. Her current research focuses on scalable system specialization and approximate computing. Adve was the first woman of South Asian origin to be named a fellow of the Association for Computing Machinery, and was the first woman to earn a career award for computer architecture research when she received the ACM SIGARCH Maurice Wilkes Award.
For more details and future updates, be sure to check out our Distinguished Lecture Series page. And please plan to join us online!
Yin Tat Lee, a professor in the Allen School’s Theory of Computation group and visiting researcher at Microsoft Research, has earned a Packard Fellowship for Science and Engineering for his work on faster optimization algorithms that are fundamental to the theory and practice of computing and many other fields, from mathematics and statistics, to economics and operations research. Each year, the David and Lucile Packard Foundation bestows this prestigious recognition upon a small number of early-career scientists and engineers who are at the leading edge of their respective disciplines. Lee is among just 20 researchers nationwide — and one of only two in the Computer & Information Sciences category — to be chosen as members of the 2020 class of fellows.
“In a year when we are confronted by the devastating impacts of a global pandemic, racial injustice, and climate change, these 20 scientists and engineers offer us a ray of hope for the future,” Frances Arnold, Packard Fellowships Advisory Panel Chair and 2018 Nobel Laureate in Chemistry, said in a press release. “Through their research, creativity, and mentorship to their students and labs, these young leaders will help equip us all to better understand and address the problems we face.”
Lee’s creative approach to addressing fundamental problems in computer science became apparent during his time as a Ph.D. student at MIT, where he earned the George M. Sprowls Award for outstanding doctoral thesis for advancing state-of-the-art solutions to important problems in linear programming, convex programming, and maximum flow. Lee’s philosophy toward research hinges on a departure from the conventional approach taken by many theory researchers, who tend to view problems in continuous optimization and in combinatorial, or discrete, optimization in isolation. Among his earliest successes was a new general interior point method for solving general linear programs that produced the first significant improvement in the running time of linear programming in more than two decades — a development that earned him and his collaborators both the Best Student Paper Award and a Best Paper Award at the IEEE Symposium on Foundations of Computer Science (FOCS 2014). Around that same time, Lee also contributed to a new approximate solution to the maximum flow problem in near-linear time, for which he and the team were recognized with a Best Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA 2014). The following year, Lee and his colleagues once again received a Best Paper Award at FOCS, this time for unveiling a faster cutting plane method for solving convex optimization problems in near-cubic time.
Since his arrival at the University of Washington in 2017, Lee has continued to show his eagerness to apply techniques from one area of theoretical computer science to another in unexpected ways — often to great effect.
“Even at this early stage in his career, Yin Tat is regarded as a revolutionary figure in convex optimization and its applications in combinatorial optimization and machine learning,” observed his Allen School colleague James Lee. “He often picks up new technical tools as if they were second nature and then applies them in remarkable and unexpected ways. But it’s at least as surprising when he uses standard tools and still manages to break new ground on long-standing open problems!”
One of those problems involved the question of how to optimize non-smooth convex functions in distributed networks to enable the efficient deployment of machine learning applications that rely on massive datasets. Researchers had already made progress in optimizing the trade-offs between computation and communication time for smooth and strongly convex functions in such networks; Lee and his collaborators were the first to extend a similar theoretical analysis to non-smooth convex functions. The outcome was a pair of new algorithms capable of achieving optimal convergence rates for this more challenging class of functions — and yet another Best Paper Award for Lee, this time from the flagship venue for developments in machine learning research, the Conference on Neural Information Processing Systems (NeurIPS 2018).
Having earned a reputation as a prolific researcher — he once set a record for the total number of papers from the same author accepted at one of the top theory conferences, the ACM Symposium on Theory of Computing (STOC), in one year — Lee also has received numerous accolades for the quality and impact of his work. These include a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, a National Science Foundation CAREER Award, and the A.W. Tucker Prize from the Mathematical Optimization Society.
“Convex optimization is the workhorse that powers much of modern machine learning, and therefore, modern computing. Yin Tat is not only a pivotal figure in the theory that underpins our field, but also one of the brightest young stars in all of computer science,” said Magdalena Balazinska, professor and director of the Allen School. “Combined with his boundless curiosity and passion for collaboration, Yin Tat’s depth of knowledge and technical skill hold the promise for many future breakthroughs. We are extremely proud to have him as a member of the Allen School faculty.”
Lee is the fifth Allen School faculty member to be recognized by the Packard Foundation. As one of the largest nongovernmental fellowships in the country supporting science and engineering research, the Packard Fellowship provides $875,000 over five years to each recipient to grant them the freedom and flexibility to pursue big ideas.
Allen School professor Rachel Lin helped solve a decades-old problem of how to prove the security of indistinguishability obfuscation (iO)
Over the past 20 years, indistinguishability obfuscation (iO) has emerged as a potentially powerful cryptographic method for securing computer programs by making them unintelligible to would-be hackers while retaining their functionality. While the mathematical foundation of this approach was formalized back in 2001 and has spawned more than 100 papers on the subject, much of the follow-up work relied upon new hardness assumptions specific to each application — assumptions that, in some cases, have been broken through subsequent cryptanalysis. Since then, researchers have been stymied in their efforts to achieve provable security guarantees for iO from well-studied hardness assumptions, leaving the concept of iO security on shaky ground.
That is, until now. In a new paper recently posted on public archives, a team that includes University of California, Los Angeles graduate student and NTT Research intern Aayush Jain; Allen School professor Huijia (Rachel) Lin; and professor Amit Sahai, director of the Center for Encrypted Functionalities at UCLA, have produced a theoretical breakthrough that, as the authors describe it, finally puts iO on terra firma. In their paper “Indistinguishability Obfuscation from Well-Founded Assumptions,” the authors show, for the first time, that provably secure iO is constructed from subexponential hardness of four well-founded assumptions, all of which have a long history of study well-rooted in complexity, coding, and number theory: Symmetric External Diffie-Hellman (SXDH) on pairing groups, Learning with Errors (LWE), Learning Parity with Noise (LPN) over large fields, and a Boolean Pseudo-Random Generator (PRG) that is very simple to compute.
Previous work on this topic has established that, to achieve iO, it is sufficient to assume LWE, SXDH, PRG in NC0 — a very simple model of computation in which every output bit depends on a constant number of input bits — and one other object. That object, in this case, is a structured-seed PRG (sPRG) with polynomial stretch and special efficiency properties, the seed of which consists of both a public and a private part. The sPRG is designed to maintain its pseudo-randomness even when an adversary can see the public seed as well as the output of the sPRG. One of the key contributions from the team’s paper is a new and simple way to leverage LPN over fields and PRG in NC0 to build an sPRG for this purpose.
Co-authors Aayush Jain (left) and Amit Sahai
“I am excited that iO can now be based on well-founded assumptions,” said Lin. “This work was the result of an amazing collaboration with Aayush Jain and Amit Sahai, spanning over more than two years of effort.
“The next step is further pushing the envelope and constructing iO from weaker assumptions,” she explained. “At the same time, we shall try to improve the efficiency of the solutions, which at the moment is very far from being practical.”
More recently, Lin worked with Jain, Sahai, and UC Santa Barbara professor Prabhanjan Ananth and then-postdoc Christian Matt on a new method for constructing iO without multilinear maps through the use of certain pseudo-random generators with special properties, formalized as Pseudo Flawed-smudging Generators (PFG) or perturbation resilient generators (ΔRG). In separate papers, Lin and her co-authors introduced partially hiding functional encryption for constant-degree polynomials or even branching programs, based only on the SXDH assumption over bilinear groups. Though these works still assumed new assumptions in order to achieve iO, they offered useful tools and ideas that paved the way to the recent new construction.
Lin, Jain and Sahai aim to build on their latest breakthrough to make the solution more efficient so that it works not just on paper but also in real-world applications.
“These are ambitious goals that will need the joint effort from the entire cryptography community. I look forward to working on these questions and being part of the effort,” Lin concluded.
