Allen School News

The Allen School is thrilled to introduce nine outstanding educators and researchers who have joined or will soon join our faculty in the current academic year. The new arrivals strengthen our leadership in areas such as software engineering, networking, machine learning, and computer science education while enabling us to expand into exciting new territory spanning cryptography, computational design for manufacturing, and data science for human health and well-being.

Meet the latest additions to our scholarly community and discover how they will contribute to the University of Washington’s reputation for educational excellence and leading-edge innovation:

Tim Althoff, data science for health and well-being

Tim Althoff will arrive at the Allen School in January 2019. Althoff’s research focuses on leveraging the detailed sensor and social data resulting from people’s interactions with smart devices and social networks to address pressing societal challenges. To that end, he develops novel computational methods for modeling human behavior in order to generate actionable insights into people’s health and well-being. Althoff’s work, which combines elements of data science, social network analysis, and natural language processing, is highly interdisciplinary and has applications that extend beyond computing to fields such as medicine and psychology.

For one project, Althoff worked with colleagues in engineering and medicine to perform a global analysis of physical activity using smartphone date for more than 700,000 people in 111 countries. The goal of the study was to better understand physical activity patterns and identify factors related to gender, health, income level, and the built environment that contribute to “activity inequality” — and lead to more than 5 million deaths each year. The team’s analysis, which was published in the journal Nature, was the largest-ever study of human physical activity of its kind, representing 68 million days of data covering billions of individual steps. In another study, Althoff and collaborators at Microsoft Research examined the impact of the popular mobile game Pokémon Go on physical activity levels by drawing upon data from 32,000 users of the Microsoft Band fitness tracker. The researchers concluded that mobile gaming apps like Pokémon Go have the potential to more effectively increase activity levels among low-activity populations than health-focused apps or other existing interventions.

In addition to his focus on physical wellness, Althoff has explored how computing can help combat mental illness, a major global health issue that affects more than 43 million adults in the United States alone. While psychotherapy and counseling are important tools in the treatment of mental health issues, researchers have lacked sufficient data on which conversation strategies are most effective. To address this shortcoming, Althoff and his colleagues employed natural language processing techniques to produce the most extensive quantitative analysis of the linguistic aspects of text-based counseling conversations to date. By using sequence-based conversation models, message clustering, word frequency analyses, and other methods, the team was able to identify the conversation strategies most associated with successful patient outcomes. The resulting paper earned Althoff and his co-authors Best Paper at the annual conference of the International Medical Informatics Association (IMIA 2017).

Althoff earned his Ph.D. in computer science from Stanford University, where he was a member of the Stanford InfoLab, Stanford Mobilize Center, and the group behind the Stanford Network Analysis Project (SNAP). He previously received his bachelor’s and master’s degrees in computer science from the University of Kaiserslautern, Germany. His work has been covered by The New York Times, The Wall Street Journal, The Economist, the BBC, CNN, and many others.

René Just, software engineering and security

Former Allen School postdoc René Just returned to UW this fall from the University of Massachusetts Amherst, where he was a faculty member in the College of Information and Computer Sciences. Just’s research focuses on advancing software correctness, robustness, and security; it spans static and dynamic program analysis, mobile security, empirical software engineering, and applied machine learning. He is particularly interested in the development of novel techniques for automated testing and debugging that scale to real-world software systems. He also develops research and educational infrastructures with a focus on reproducibility and comparability of empirical research.

After earning his bachelor’s degree in computer science from the Cooperative State University Heidenheim, Just spent nearly two years in industry as a software design engineering for German company Fritz & Macziol. He returned to academia to earn his master’s and Ph.D. from the University of Ulm in Germany before taking up a position as a postdoctoral research associate at UW, where he worked with Allen School professor Michael Ernst in the Programming Languages & Software Engineering (PLSE) group.

Just’s research to advance the state of the art in software testing and debugging already has had an extensive impact within the software engineering community and earned him three Distinguished Paper Awards from the Association for Computing Machinery. For one award-winning project, Just and his collaborators studied the relationship between artificial faults, called mutants, and real faults, and the suitability of mutants for software testing research. This foundational work, which has received more than 260 citations in four years, provides strong empirical support for the use of mutants but also identifies inherent limitations. Just’s Major mutation framework played an important role in this project as it enables efficient mutation analysis of large software systems and fundamental research involving hundreds of thousands of mutants. For another award-winning project, Just and his collaborators analyzed the effectiveness and limitations of three popular automated test generation tools on more than 350 real-world faults. Their evaluation revealed that even using the most advanced techniques, fewer than 20 percent of the generated test suites detected a fault, and 15 percent of all generated tests were flaky—they failed randomly or generated false-positive warnings.

Just’s interest in the reproducibility of software engineering research led him to develop Defects4J, a first-of-its-kind repository of reproducible, isolated, and annotated faults coupled with a framework for experimentation and extension. Like the Major mutation framework, Defects4J is widely used in software engineering research and in undergraduate and graduate software engineering courses around the world. Since its inception in 2014, Defects4J has been referenced more than 300 times and has been used in the evaluation of multiple award-winning papers at top-tier research conferences.

Huijia “Rachel” Lin, cryptography

Rachel Lin is one of two new hires set to bring exciting new expertise to the Allen School in the field of cryptography starting in January 2019, after more than four years as a faculty member at the University of California, Santa Barbara. Lin’s research in cryptography, as well as at its intersection with security and theoretical computer science, aims to weaken or even supplant our reliance on trust-based approaches to securing confidential data — for example, trust between the corporate client and cloud service provider, or between citizens and government agencies — to cryptographically-enforced approaches that provide stronger privacy and integrity guarantees.

While current systems are fallible in that trust can be eroded or lost, that is not the only challenge; the risk of a single point of failure producing a large-scale privacy breach is ever-present. Lin is interested in reducing or removing this risk using techniques such as program obfuscation, wherein programs are rendered unintelligible while their functionality is maintained, and secure multiparty computation that enables a set of mutually distrustful entities to compute a function on their corresponding private data without revealing the data in the clear. Among the hurdles to advancing such alternatives to trust-based security is proving their feasibility based on well-studied, reliable, computational hardness assumptions, such as the hardness of factoring integers — proof of which Lin has already made significant progress. This is particularly true of her work in indistinguishability obfuscation (IO), a promising avenue of cryptography research for which she earned a CAREER Award from the National Science Foundation (NSF) last year. In a series of projects, she has succeeded in simplifying the algebraic structures needed to construct IO. In one example, Lin established a connection between IO, one of the most advanced cryptographic objects, with the pseudorandom generator (PRG), one of the most basic and familiar cryptographic primitives to prove that constant-degree, rather than high-degree, multilinear maps are sufficient for obfuscating programs. She subsequently refined this work to reduce the degree of multilinear maps required to construct IO from more than 30 to a grand total of three — a crucial step toward her goal of achieving IO from bilinear maps or other standard, well-studied objects, such as lattices.

Lin and her collaborators have demonstrated IO’s potential to be a powerful tool with multiple applications, including an alternative approach to designing fully homomorphic encryption without relying on lattices or the untested circular security assumptions, and ensuring the adaptive integrity and adaptive privacy of delegating Random Access Memory (RAM) computation to a cloud. She has also focused on advancing the state of the art in zero-knowledge proofs, non-malleability, and multi-party computation. On the latter, Lin earned a Best Paper Award at EUROCRYPT 2018 for achieving new levels of efficiency in multi-party computation protocols through the introduction of a novel framework for garbled interactive circuits, among other innovations.

Before joining the UC Santa Barbara Faculty, Lin spent two years as a postdoctoral researcher at MIT. She earned her master’s and Ph.D. in computer science, with a minor in applied mathematics, from Cornell University and her bachelor’s in computer science from Zhejiang University in China.

Ratul Mahajan, networking

Allen School alumnus Ratul Mahajan (Ph.D., ’05) will return to his alma mater in January as a faculty member after spending 13 years in industry as a researcher and entrepreneur. During that time, Mahajan has focused on the development of new architectures, systems, and tools for making cloud networks more efficient, agile, and reliable — first during more than a decade at Microsoft Research in Redmond, and more recently, as Cofounder and CEO of Seattle-based startup Intentionet. Mahajan — a self-described “computer systems researcher with a networking focus” — draws inspiration and techniques from multiple domains, including machine learning, theory, human-computer interaction, formal verification, and programming languages. He has applied these techniques to a variety of research questions in the areas of Internet measurement, wireless networks, mobile systems, smart home systems, and network verification.

Cloud computing is a particularly compelling area of exploration for Mahajan — not only for the way it has transformed computing infrastructure on a practical level, but its suitability as a vessel for realizing new research ideas in real-world systems. One of those ideas is centralized resource allocation, a concept that turns the typical distributed approach to allocating resources across computer networks on its head. Aiming to move beyond the inefficiency and rigidity of the traditional model, Mahajan and his colleagues developed their software-driven wide area network (SWAN) to enable the centralized allocation of bandwidth in global networks connecting multiple datacenters in the cloud. SWAN was designed to achieve high efficiency, while satisfying policies such as preferential treatment for priority services, by taking a global view of traffic demand and rapidly analyzing and updating the network’s forwarding behavior in response. The researchers devised a novel technique to prevent transient congestion during demand-driven reconfigurations by leveraging a small amount of scratch capacity on the network links. In subsequent work, the team expanded the concept to the delivery of online services via an integrated infrastructure through Footprint, and introduced a network “operating system,” Statesman, that mediates between multiple, independently running applications to achieve a target state which preserves network-wide safety and performance invariants.

Mahajan and his colleagues borrowed from the programming languages and formal verification communities in building a set of sophisticated analysis and synthesis tools for simplifying network configuration tasks and eliminating configuration errors — the primary contributor to the dreaded network outage. They began with Batfish, a tool that enables general analysis of a network’s configuration based on its packet-forwarding behavior. The team followed that up with a pair of tools designed to move network configuration beyond testing to verification: abstract representation of control plane (ARC) for rapidly analyzing correctness under arbitrary failures, and efficient reachability analysis (ERA) for performing efficient network reachability analyses of various incarnations of a network using a symbolic model of the network control plane. To simplify the underlying task of generating correct configurations in the first place, Mahajan and his colleagues built Propane, the first system capable of generating border gateway protocol (BGP) configurations that are provably correct. Propane, which earned the team Best Paper at SIGCOMM 2016, was followed by Propane/AT, a configuration-synthesis system that relies on abstract topologies to support network evolution.

Before enrolling in the Allen School’s Ph.D. program, where he was advised by professors Tom Anderson and David Wetherall, Mahajan received his bachelor’s degree from the Indian Institute of Technology in Delhi. He has earned numerous accolades for his research; in addition to the Best Paper for Propane, Mahajan has been recognized by ACM SIGCOMM with a Best Student Paper, Rising Star, and Test of Time awards; the William R. Bennett prize by the Institute of Electrical and Electronics Engineers (IEEE), Best Paper by the Haifa Verification Conference (HVC), Best Dataset Award by the Internet Measurement Conference (IMC); and the Microsoft Significant Contribution Award for his work on the company’s cloud computing infrastructure.

Sewoong Oh, machine learning

Sewoong Oh will bring his expertise in machine learning to the Allen School in January after six years on the faculty of the Department of Industrial and Enterprise Systems at the University of Illinois at Urbana-Champaign. Oh focuses his research at the intersection of theory and practice, with an emphasis on the development of new algorithmic solutions for machine learning applications using techniques drawn from information theory, coding theory, applied probability, stochastic networks, and optimization.

One of the topics that Oh has been keen to explore is the rise of social-media sharing via anonymous messaging platforms. Spurred by an interest in how people use such platforms to support freedom of expression and ensure personal safety, Oh and his colleagues developed a set of novel techniques for safeguarding users’ anonymity against adversaries attempting to uncover the source of potentially sensitive messages online. The researchers developed a novel messaging protocol, adaptive diffusion, for which they earned the Best Paper Award at the Association of Computing Machinery’s International Conference on Measurement and Modeling of Computer Systems (ACM SIGMETRICS 2015). Adaptive diffusion rapidly spreads anonymous messages on an underlying contact network, such as a network of phone contacts or Facebook friends. Oh and his team demonstrated that a perfect obfuscation of the source is guaranteed when the communication graph is an infinite regular tree. They went a step further with the development of preferential attachment adaptive diffusion (PAAD), a new family of protocols to counteract adversarial attempts to identify a message source through statistical inference in real-world social networks. Oh then turned his attention from preserving individuals’ anonymity in a crowd to leveraging the wisdom of the crowd, receiving an NSF CAREER Award to advance the algorithmic foundations of social computing and explore how the data generated by online communities can be harnessed to address complex societal challenges.

Oh is also interested in advancing the state of the art in training algorithms for generative adversarial networks (GANs). GANs are leading-edge techniques for training generative models to produce realistic examples of images and texts using competing neural networks. They are particularly promising in image and video processing and for dialogue systems and chatbots. One of their shortcomings, however, is that they tend to produce samples with little diversity — even when trained on diverse data sets. Oh and his colleagues set out to better understand and mitigate this phenomenon, known as mode collapse, by examining GANs through the lens of binary hypothesis testing. This novel perspective yields a formal mathematical definition of mode collapse that allows one to represent a pair of distributions — the target and the generator — as a two-dimensional region. The analysis of these mode collapse regions leads to a new framework, PacGAN, which naturally penalizes generator distributions with more mode collapse during the training process. With PacGAN, the discriminator makes decisions based on multiple “packed” samples from the same class — either real or artificially generated — rather than treating each sample as a single input. This idea of packing is shown to be fundamentally related to the concept of mode collapse, with a packed discriminator inherently penalizing mode collapse. The researchers’ approach, which can be applied to any standard GAN, enables the generator to more readily detect a lack of diversity and combat mode collapse without requiring the significant computational overhead or delicate fine-tuning of hyperparameters associated with previous approaches.

Oh received the ACM SIGMETRICS Rising Star Research Award in recognition of his outstanding early-career contributions matrix factorization, statistical learning, and non-convex optimization. Before joining the UIUC faculty, Oh was a postdoctoral researcher in MIT’s Laboratory of Information and Decision Sciences (LIDS). He earned his Ph.D. in electrical engineering from Stanford University and his bachelor’s in electrical engineering from Seoul National University in Korea.

Hunter Schafer, computer science education

Hunter Schafer joined the Allen School faculty this fall as a full-time lecturer. The appointment was a homecoming for Schafer, who graduated from the Allen School’s combined bachelor’s/master’s program this past spring. During the course of his studies, Schafer served as a part-time lecturer, teaching assistant (TA), and head TA for multiple sections of our introductory computer science courses.

As head TA for CSE 143, the follow-on to our popular introductory programming course, Schafer coordinated roughly 30 to 40 TAs per quarter. In this role, he helped to develop course goals, coordinated TA staff meetings, shared teaching best practices, and trained TAs in the grading of assignments — all in addition to handling his own quiz sections. In addition to playing a vital role in introducing hundreds of fellow students to computer science, Schafer served as a TA for CSE 373, the Allen School’s upper-division course on data abstractions and algorithms for non-CSE majors.

Outside of his teaching duties, Schafer spent time in industry as a software development intern. While at Socrata, he was responsible for creating a tool in Python for classifying the semantic meaning of a column in a dataset that improved the user experience and could be extended to other classification problems. He also worked as part of a team defining the Python libraries needed to support the company’s work on diverse machine learning problems. As an intern at Redfin, Schafer developed a tool to assist realtors in presenting information contained in a potential buyer’s offer in a dynamic and visually appealing way.

Foreshadowing his future career path, last summer Schafer went to the head of the class as the lecturer for CSE 143 that quarter, overseeing a class of more than 100 students and a staff of seven TAs. That fall, while working towards his master’s degree, Schafer led an honors discussion section of CSE 142, introducing his students to topics in machine learning, data visualization, and online privacy to supplement their programming lessons. Throughout his time as an instructor, Schafer has aimed to help students see the relevance of computer science in their day to day lives in addition to introducing them to concepts of interest beyond core programming.

Adriana Schulz, computational design for manufacturing

Adriana Schulz, who arrived at the Allen School this fall after earning her Ph.D. at MIT, focuses on computational design to drive the next great wave of manufacturing innovation. Drawing upon her expertise in computer graphics and inspired by recent hardware advances in 3D printing, industrial robotics, and automated whole-garment knitting, Schulz develops novel software tools that empower users to create increasingly complex, integrated objects.

At its core, Schulz’s work aims to democratize design and manufacturing through computation. To that end, she has produced numerous tools and data-driven algorithms to render the process more efficient and accessible to people of different skill levels, including those without domain expertise, while optimizing performance. For example, Schulz and her colleagues devised a way to interpolate parametric data from Computer Aided Design (CAD) models — nearly ubiquitous in professional design and manufacturing — and incorporated it into an interactive tool, InstantCAD, that enables users to quickly and easily gauge how changes to a mechanical shape’s geometry will impact its performance without the time-consuming and computationally expensive operations required by traditional CAD tools. Schulz and her fellow researchers also developed an algorithm and interactive visualization tool for exploring multiple, sometimes conflicting, design and performance trade-offs — enabling designers to efficiently evaluate and navigate such compromises. The team presented both projects at the International Conference & Exhibition on Computer Graphics and Interactive Techniques (ACM SIGGRAPH).

Schulz has applied a similar approach to empower novice users to create their own functioning robots, flying drones, and even custom-built furniture. As a member of MIT’s Computational Fabrication Group, she co-led the development of Interactive Robogami, a framework for creating robots out of flat sheets that can be folded into 3D structures — reminiscent of origami, the Japanese art of paper folding. The system enables users to compose customized robot designs from a database of 3D-printable robot parts and test their functionality via simulation before moving ahead with fabrication. She also contributed to an interactive system for designing and fabricating multicopters. With its intuitive interface, the system enables even novice users to assemble a working aerial drone design while exploring tradeoffs between criteria such as size, payload, and battery usage. More recently, Schulz was part of the team that designed AutoSaw, a template-based system that enables the design and robot-assisted fabrication of custom carpentry items — an approach that could help usher in a new era of mass customization.

Before her arrival at MIT, Schulz earned her master’s degree in mathematics from the National Institute of Pure and Applied Mathematics and her bachelor’s in electronics engineering from the Federal University of Rio de Janeiro in Brazil. She is the co-author of the book Compressive Sensing and her work has been featured in Wired, TechCrunch, MIT Technology Review, New Scientist, IEEE Spectrum, and more.

Stefano Tessaro, cryptography

Stefano Tessaro will join the Allen School faculty in January after spending more than five years on the faculty of the University of California, Santa Barbara, where he holds the Glen and Susanne Culler Chair in Computer Science. Tessaro brings expertise in a variety of topics related to the foundations and applications of cryptography and its connections to disciplines such as theoretical computer science, information theory, and computer security. Together, he and fellow newcomer Rachel Lin will build upon the Allen School’s existing strengths in security and theory to expand into this exciting new area of research.

Tessaro is particularly interested in the concept of provable security, in which formal definitions of threat models enable rigorous security proofs. To that end, he often pursues theoretical advances that enable the application of provable security to real-world cryptography. For example, his work uses techniques from complexity theory, information theory, combinatorics and probability theory to study the effective security of methods for encryption and authentication, to yield new and improved mechanisms for password protection, and to explore tradeoffs between input/output efficiency and security for cloud computing applications.

Throughout his research, Tessaro’s goal has been to identify problems and solutions that blend practical value with theoretical depth. This principle is evident in his pioneering work — for which he earned an NSF CAREER Award — that seeks to reconcile the need for provable security guarantees with real-world efficiency demands. In a paper that earned the Best Paper Award at EUROCRYPT 2017, Tessaro and co-authors presented security proofs for practical schemes to protect passwords against attacks using custom-made hardware, overcoming technical barriers in characterizing the power of memory-limited adversaries. He also developed new information-theoretic techniques to analyze algorithms in symmetric cryptography. This work is the closest that theory researchers have come to understanding why basic cryptographic building blocks like the Advanced Encryption Standard (AES) — the most widely used cryptographic algorithm — withstand a broad class of attacks. Tessaro also studies and formalizes emerging threats, such as large-scale attackers (e.g., state actors) leveraging their ability to collect vast amounts of Internet traffic. He proved, for example, that core components of Internet-scale protocols like Transportation Layer Security (TLS) are well designed to resist such attacks. Tessaro’s work does not only deal with proofs; he recently surfaced attacks against standards for Format-Preserving Encryption, a widely adopted tool for data protection in the financial industry.

Last year, Tessaro was recognized with an Alfred P. Sloan Research Fellowship for his contributions to the theoretical foundations of cryptography — contributions that extend to their practical application to large-scale systems in the form of efficiency tradeoffs in searchable symmetric encryption (SSE) and the first oblivious storage system that is secure under concurrent access. Prior to his arrival at UC Santa Barbara, Tessaro held postdoctoral research positions at MIT and the University of California, San Diego. He earned his master’s and Ph.D. in computer science from ETH Zurich in Switzerland.

Brett Wortzman, computer science education

Brett Wortzman is no stranger to the Allen School community, having previously taught multiple sections of our introductory courses on a part-time basis and served as an instructor for our DawgBytes summer camps.

Wortzman began his career more than a decade ago as a software engineer in the technology industry after earning his bachelor’s in computer science from Harvard University. He soon discovered that he preferred the classroom to the conference room, however, and decided to make the leap to education. This fall, he is introducing a new class of UW students to the wonders of computer science as a full-time faculty member.

After earning his master’s in education from UW, Wortzman embarked on his new career by teaching computer science and mathematics at Issaquah High School. There, he was responsible for growing the school’s lone Advanced Placement computer science class — which originally reached a grand total of 16 students — into a robust program encompassing four unique computer science courses divided into eight sections and serving more than 200 students annually. His successful efforts earned him an Inspirational Teachers award from the Allen School last year.

In addition to his formal teaching duties, Wortzman has served in a variety of roles for TEALS, an organization that aims to increase youth access to computer science by partnering with K-12 teachers and schools to build sustainable CS education programs. He is also active in the Puget Sound Computer Science Teachers Association and is an avid organizer of educator meet-ups to build community and share best practices.

The latest round of new arrivals follows the addition of Hannaneh Hajishirzi, whose expertise spans artificial intelligence, natural language processing, and machine learning, this past summer. This talented cohort builds upon the Allen School’s recent success in bringing recognized leaders and rising stars to UW and the Seattle region, including roboticist Sidd Srinivasa and computer engineer Michael Taylor, human computer interaction experts Jennifer Mankoff and Jon Froehlich, machine learning researcher Kevin Jamieson, and theoretical computer scientist Yin Tat Lee.

Welcome, one and all, to the Allen School family!

Frequent readers of the blog may be familiar with our Undergraduate Spotlight, an occasional feature in which we highlight an Allen School student who represents the next generation of innovators and leaders in the field of computing. For our latest feature, we shine the spotlight on a group of students who are among the first in their families to attend college as part of a nationwide celebration of the contributions that first-generation students make to our campus communities.

Meet Allen School undergraduates Shariya Ali, Simplicio DeLeon, and Dilraj Devgun — three trailblazers who are on their way to academic success.

Shariya Ali

Shariya Ali is a junior majoring in Computer Science at the Allen School, where she serves as a member of the CSE Student Advisory Council representing the voices of undergraduate and master’s students on issues ranging from diversity and social responsibility to student wellness. Ali arrived at UW by way of the San Francisco Bay area, where she was born after her family immigrated to the United States from Suva in the Fiji Islands. As both a mother and full-time college student, she is determined to make the most of her opportunity at the Allen School and looks forward to mentoring other young women and supporting a more diverse and inclusive technology community.

Allen School: What does being a first-generation student mean to you?

Shariya Ali: Being a first-generation student means that I won’t have to struggle to provide for my family like my mom struggled to provide for me. She had to put in so much work and make so many sacrifices to give me a good life, but for most of my childhood that still meant we lived paycheck to paycheck. My mother has since passed away, and graduating from UW will be the best way I can honor her memory.

Allen School: What advice do you have for future first-gen students?

SA: My advice to future first generation students is to stop listening to other people’s opinions about your life. If you have a dream, go for it. It’s your life, and it’s up to you to make it the life you want. If I had listened to everyone about my chances of being admitted to the Allen School, I would have been too scared to move to Seattle and I wouldn’t be here today. Never let your fears dictate your life, and always believe in yourself!

Allen School: What is your favorite part about being an Allen School student?

SA: My favorite part about being an Allen School student is seeing the surprised faces people make when I tell them my major. People have an image in their minds about what software engineers should look like, and I love being an example of someone outside of that mold. I hope I can become an inspiration to other young women and show them they can be whatever they want to be.

Simplicio DeLeon

Simplicio DeLeon is a junior majoring in Computer Engineering at the Allen School. Hailing from Harrah, Washington, DeLeon is an alumnus of the Washington State Academic RedShirt (STARS) program, which helps first-generation students and those from low-income and underserved backgrounds to navigate the transition to college-level engineering and computer-science coursework. DeLeon recently completed an Explorer Internship at Microsoft, where he was a member of the Business Application Platform Team. DeLeon is active in the Society of Hispanic Professional Engineers, the UW Salsa Club, and Husky ADAPT, a program that supports accessible design and play in collaboration with the Allen School’s Taskar Center for Accessible Technology and Mechanical Engineering’s Ability & Innovation Lab.

Allen School: What does being a first-generation student mean to you?

Simplicio DeLeon: To me, being a first-gen student means doing my best in school in order to pay back for all of the sacrifices my parents made for me.

Allen School: What advice do you have for future first-gen students?

SD: Everything might be really new and difficult but hang in there. You are capable and it’ll be worth it.

Allen School: What is your favorite part about being an Allen School student?

SD: My favorite part is being able to explore such a vast field. There are so many opportunities to check out different parts of computer science like the Internet of Things, artificial intelligence, and cloud computing. We have really talented and knowledgeable staff who you can talk to in order to learn more or find ways to explore further. My other favorite part of the Allen School is the people. There are so many different clubs and student groups here that I think everyone should look into. Aside from that, it’s always great to meet people either in classes, in the Allen Center atrium, or in the labs.

Dilraj Devgun

Dilraj Devgun is in his final year at UW, where he is majoring in Computer Science at the Allen School and pursuing a minor in Mathematics. Devgun has served as a teaching assistant for the Allen School’s introductory courses since 2017 and spent the past summer as a software engineering intern at Microsoft. Outside of the classroom, Devgun has engaged in systems research alongside Allen School professor Tom Anderson and served as lead iOS developer for the Stroke Research Team at the UW Medical Center. As a high school student in Bellevue, Washington, he co-founded Clockwork Development, LLC, an iOS app development company. Originally from Bracknell, England in the United Kingdom, Devgun has also lived in Canada and the U.S. states of Florida and Georgia.

Allen School: What does being a first-generation student mean to you?

Dilraj Devgun: Being a first-generation student isn’t something I think about on a daily basis now, but when I first started at UW and I was still trying to find my community, it was a defining factor about how I viewed myself in relation to everyone around me. I was left to rely largely on myself. I felt like I didn’t belong, and to this day I still shy away from telling anyone the full story about my upbringing; however, I wouldn’t trade the experience for any other. My father never finished high school because he left India as a refugee from the Sikh massacre. My Mum, having dealt with a hearing disability since birth, also never had much of an education — which left a large burden on me, as the environment I grew up in treated me with hostility. Education was largely seen as an escape, but we don’t have the guidance many others have. I feel proud to be where I am and to me the title of a first-generation student is a humbling fact since it gives me the time to reflect on who I am and where I came from.

Allen School: What advice do you have for future first-gen students?

DD: Don’t compare yourself to anyone around you. Focus on yourself and put in the work. Put aside pride and any lack of privilege, because hard work doesn’t discriminate. Everyone has to put in the effort and some people just have to work harder than the rest, but as long as you’re focused on your passion you can outwork anyone around you. There’s no guarantee of success, but the only thing you have control over is to do your best, so don’t give anything less than your best.

Allen School: What is your favorite part about being an Allen School student?

DD: I owe so much to the Allen School. They took a chance on me and offered me an education and community that I feel so proud to say that I’m a part of. Firstly, I love the classes that I take — I always learn something new each quarter. In addition to the education, the Allen School has provided opportunities for me to explore what my specific passion is within the field and to give back to other students in the form of teaching or mentoring. That is something I am really happy I have the chance to do.

“As a first-generation student myself, I know how transformative higher education can be for these students and their families,” said Allen School Director Hank Levy. “I also know that it can be challenging to overcome any obstacles along the way. We are thrilled to count Shariya, Simplicio, Dilraj and the many other first-gen students at the Allen School as members of our extended family and appreciate the diverse experiences and talents they bring to our campus and our field.”

Learn more about the National First-Generation College Celebration here.

From left: Team members Halden Lin, Dominik Moritz, and Jeffrey Heer accept the Best Paper Award from committee member Niklas Elmqvist.

A team of researchers in the Allen School’s Interactive Data Lab (IDL) collected the Best Paper Award at InfoVis 2018 for Draco, an open-source, constraint-based system that formalizes guidelines for visualization design and their application in visualization tools. Draco provides 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.

“There is a robust, ongoing line of research devoted to understanding how people interpret visualizations, but that guidance is ever-changing and tends to make its way very slowly into practical application,” said Allen School Ph.D. student Dominik Moritz, lead author on the paper. “Draco formalizes that knowledge into a tool that enables researchers and practitioners to build effective visualizations that are grounded in the latest research, while offering the flexibility to make design trade-offs based on user preference.”

Draco encodes the building blocks of visualization, such as input data and visualization type, into a set of logical facts based on an extension of Vega-Lite, a high-level language for describing interactive visualizations. It combines that with an encoding of accepted design principles and preferences as a set of hard and soft constraints that govern the appearance of those facts. The system employs a constraint solver to reason about visualization design based on the encoded guidelines and user inputs. While Draco requires that hard constraints — such as the use of a valid mark type like bar, line, point, etc. — must be satisfied, the soft constraints are assigned varying weights based on visual perception experiments and generally accepted best practices. This allows for a degree of flexibility for the user to make design trade-offs based on their own preferences or the conventions of their particular field.

A graphic recording of Dominik Moritz’ presentation on Draco completed live at the conference by artist Benjamin Felis

The constraint-based approach has another advantage in that it allows Draco to actively assist the user by recommending and ranking visualization designs based on a query, or partial specification, in which the user has left some attributes of their desired visualization blank. The user can also test their visualizations in Draco, which will alert them to violations of the encoded guidelines “similarly to how the automatic spell-check function operates in a word processing program,” Moritz explained. The user can then decide whether and how to adjust their graphic based on the severity of the violation. As people’s preferences and formal design theory evolve, the team envisions the design community adding new constraints — and adjusting the relative weights of existing ones — to incorporate the latest best practices.

Moritz’ co-authors on the paper include fellow Ph.D. students Chenglong Wang and Greg Nelson, fifth-year master’s student Halden Lin, Allen School professor Jeffrey Heer, iSchool professor and Allen School adjunct professor Bill Howe, and former Allen School postdoc Adam Smith, who is now a faculty member at the University of California, Santa Cruz. The team presented Draco at VIS 2018, part of the Institute of Electrical and Electronics Engineers’ annual IEEE VIS conference that brings together researchers in Scientific Visualization, Information Visualization, and Visual Analytics, in Berlin, Germany last month.

Moritz and Heer were also part of the team that developed Vega-Lite, the visualization language that underpins Draco which earned a Best Paper Award at InfoVis 2016. For more on Draco, check out the Interactive Data Lab’s Medium post here, and read the research paper here.

Way to go, team!

The Paul G. Allen School of Computer Science & Engineering is proud to participate in this weekend’s tribute to Mr. Allen. We re-commit ourselves to fulfilling his vision.

The UW-led Curious Minded Machine team, clockwise from top left: Sidd Srinivasa, Maya Cakmak, Dieter Fox, and Leila Takayama

A team of researchers led by professor Siddhartha “Sidd” Srinivasa of the Allen School’s Personal Robotics Lab is contributing to an ambitious new project to better understand human curiosity and how that principle can be applied to robot learning. The initiative, Curious Minded Machine, was launched by Honda Research Institute USA to support academic research that will advance artificial cognition by instilling curiosity in intelligent systems — with the ultimate goal of enabling robots to continuously and independently acquire new knowledge and capabilities for the benefit of humankind.

Srinivasa and Allen School professors Maya Cakmak, director of the Human-Centered Robotics Lab, and Dieter Fox, head of the Robotics & State Estimation Lab, will apply their combined expertise in robot manipulation, human-robot interaction, programming by demonstration, and robot perception to develop a mathematical model of curiosity inspired by the concept of child learning through exploration. In collaboration with professor Leila Takayama of the University of California, Santa Cruz — an expert in the psychology of human-robot interaction — they will test their model via implementation in physical systems and through user studies. The group plans to deploy its “curiosity engine” in two kinds of robots: a social robot that engages with people in its environment, to explore the impact on human-robot interaction; and a manipulator robot that engages with objects, to determine its effect on tasks involving manipulation and control.

In addition to overcoming the technical challenges, Srinivasa foresees having to grapple with questions that get at the heart of what it means to be human — and how the emergence of Curious Minded Machines might alter the way in which we relate to our robot counterparts. “What is curiosity? Can we build a rich mathematical model that makes a robot curious?” Srinivasa wondered during an interview with UW News. “Will a curious robot be accepted more? Will we be more tolerant of its mistakes?”

Cakmak, for one, hopes that will be the case, and that curiosity will not only make robots more adaptable and better at their jobs but also more appealing to people. Aside from these practical considerations, Cakmak and her colleagues are interested in discovering whether the lifelong benefits of human curiosity — the ones that accrue beyond the task at hand — can also be transferred to robots. “Humans are intrinsically rewarded by new information even when that information is not necessarily applicable,” she noted, “but curiosity has long-term benefits. We would like to give robots similar benefits for being curious.”

Srinivasa and his colleagues are interested in whether curiosity will enable robots to independently acquire new knowledge and capabilities. Dennis Wise/University of Washington

The University of Washington-led team will receive $2.7 million over three years from the Honda Research Institute to support its work as part of a Network of Excellence that also includes Massachusetts Institute of Technology and the University of Pennsylvania. Each of the partner institutions is tackling a different but complementary challenge; MIT will focus on establishing a causal theory of sensor percepts that will enable intelligent systems to predict future percepts and the effects of future actions, while the University of Pennsylvania team aims to mimic biological learning to aid robots in acquiring representations of the surrounding world and actions.

“Our ultimate goal is to create new types of machines that can acquire an interest in learning and knowledge, and the ability to interact with the world and others,” Soshi Iba, principal scientist at Honda Research Institute USA, explained. “We want to develop Curious Minded Machines that use curiosity to serve the common good by understanding people’s needs, empowering human productivity, and ultimately addressing complex societal issues.”

Read more about today’s announcement in the institute’s press release here and the UW News story here. To learn more about the initiative, visit the Curious Minded Machine website here. Check out a related article by GeekWire here.

Researchers in the Allen School and University of Washington’s Department of Electrical & Computer Engineering were recognized this week with the IMWUT Vol 1. Distinguished Paper Award for their 2017 paper, “LoRa Backscatter: Enabling the Vision of Ubiquitous Connectivity.” The award, which was announced during the Ubicomp 2018 conference in Singapore, recognizes outstanding research contributions published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.

Long-range backscatter is the first system of its kind to enable low-cost, wide-area connectivity for a range of objects and devices while consuming 1000x less power than existing technologies. Until now, devices capable of communicating over long distances were bulky and consumed significant amounts of power, whereas the communication range for lighter, less power-hungry devices was short. Long-range backscatter offers the best of both worlds: a light-weight form factor that requires mere microwatts of power that is also capable of transmitting data over a distance of 2.8 kilometers. It manages this by reflecting radio frequency (RF) signals onto sensors that, in turn, synthesize and transmit data to a receiver for decoding, using chirp spread spectrum (CSS) modulation to amplify the signals over longer distances. Other noteworthy technical contributions include the first backscatter harmonic cancellation mechanism to combat sideband interference, and a link-layer protocol that enables multiple devices to share the spectrum.

The project represented a significant breakthrough in the effort to embed connectivity into everyday objects to enable a range of new applications, from smart agriculture to personalized medicine. It earned the notice of Paul Allen, who highlighted long-range backscatter as one of 10 innovations “making the world a better place” to have emerged from the Allen School during its first year. The project was featured on Allen’s blog back in March to coincide with the anniversary of the founding of the school that bears his name.

The system builds on previous work on backscatter led by Allen School professor Shyam Gollakota, director of the Networks & Mobile Systems Lab, and Allen School and ECE professor Joshua Smith, head of the Sensor Systems Laboratory. ECE Ph.D. alumnus Vamsi Talla and Allen School Ph.D. student Mehrdad Hessar are co-primary authors of the paper. Additional contributors include ECE Ph.D. students Bryce Kellogg and Ali Najafi. UW spinout Jeeva Wireless, where Talla now serves as chief technology officer, is commercializing the technology.

For more on this project, see the original UW News release here and a related blog post here. Visit the project website here.

Congratulations to the entire team!

It is with great sadness that the faculty, staff, and students of the Paul G. Allen School of Computer Science & Engineering mark the passing of Paul Allen — pioneering innovator, generous philanthropist, and faithful friend. Mr. Allen was a visionary who opened up new frontiers and pushed the limits of scientific discovery. His connection to the University of Washington ran deep. Through his vision, his leadership and his generosity, he transformed our program, our campus, our region, and the world.

“Paul’s vision of the role of science and technology in society coupled scientific discovery with the quest for solutions to humankind’s greatest challenges,” said Allen School professor Ed Lazowska. “It led him to establish the Allen Institutes for Artificial Intelligence, Brain Science, and Cell Science — and to invest in us, the Paul G. Allen School. We are deeply saddened by his death, and we recommit ourselves to the pursuit of this vision.”

When the Allen School was announced in March 2017, Mr. Allen expressed optimism that we were entering a golden age of innovation in computer science. “I look forward to watching the new Paul G. Allen School of Computer Science & Engineering continue to make profound contributions both to the field and to the world,” he said. “I look ahead with anticipation to the advances that will continue to flow from the school — advances that I hope will drive technology forward and change the world for the better.”

We did not have nearly enough time to demonstrate how we would repay his faith, but we will continue to draw inspiration from his words and his belief in what the Allen School could achieve.

“Paul was a truly remarkable person who changed the world multiple times in his lifetime, and whose initiatives will continue to change the world for decades to come,” said Allen School Director Hank Levy. “We can only hope to follow his example, by searching for the most important scientific and societal challenges of our era and applying our energies to solving them.”

Mr. Allen was a giant. As we said on our very first day as the Allen School, his gift will inspire us to reach higher every day.

A team led by Allen School professor Su-In Lee and Ph.D. student Scott Lundberg has developed a machine-learning system that both predicts and explains why some patients are at risk for developing hypoxemia, a potentially dangerous drop in blood oxygen levels that can occur in people under general anesthesia. A growing number of predictive machine-learning models have shown high accuracy in medical applications, but understanding how they arrive at their predictions remains a challenge. The aptly-named Prescience analyzes factors specific to the patient and procedure that may presage hypoxemia and explains their impact on a patient’s risk in real time to aid anesthesiologists in preventing life-threatening complications during surgery. The project, which was developed in collaboration with physicians at UW Medicine, Seattle Children’s, and the Veterans Affairs Puget Sound Health Care System, is featured on the cover of the latest issue of Nature Biomedical Engineering.

Hypoxemia during surgery is associated with a range of adverse medical outcomes, including cardiac arrest, post-operative infection, decreased cognitive function, and more. While operating room personnel are able to continuously monitor a patient’s blood oxygen saturation with the aid of pulse oximetry, such data do not enable them to anticipate a hypoxemic event — only react to one that is in process. Prescience supplements pre-surgery and real-time patient data with minute-by-minute data from more than 50,000 past surgeries to reliably predict when hypoxemia is likely to occur and which combination of factors led to its prediction.

Because it can both anticipate and explain hypoxemia risk, Prescience represents a marked improvement over existing decision support systems — which tend to support interventions that are more reactive than proactive — and over uninterpretable machine learning solutions. By providing both predictions and explanations, this approach can help doctors to establish an appropriate level of trust in the model. It’s this difference, Lee says, that makes Prescience such a powerful tool to improve patient outcomes. “Modern machine-learning methods often just spit out a prediction result. They don’t explain to you what patient features contributed to that prediction,” Lee explained in a UW News release. “Our method opens this black box and actually enables us to understand why two different patients might develop hypoxemia.”

The “why” is determined via a complex combination of factors, including patient physiology, medical history, vital signs, ventilator settings, medication, and time. Prescience relies on feature-importance estimates to weigh the strength of each factor in formulating its prediction, which an anesthesiologist can use to determine the most appropriate intervention. The team tested the ability of anesthesiologists to anticipate hypoxemic events with and without the aid of Prescience and found that, using the system, they could correctly predict whether a patient was at risk nearly 80% of the time.

Prescience team members, left to right: Bala Nair, Su-In Lee, Monica Vavilala, and Scott Lundberg. Mark Stone/University of Washington

Extrapolating the results of their experiments to the roughly 30 million surgeries performed annually in the United States alone, the researchers found that using Prescience could double from 15% to 30% the proportion of hypoxemic events that could be anticipated and potentially prevented — the equivalent of 2.4 million incidents per year. Given that 20% of the predicted risk is driven by settings under an anesthesiologist’s control, Prescience could become an indispensable tool for achieving better post-operative outcomes for a significant number of patients. “Prescience doesn’t treat anyone,” Lundberg noted. “Instead it tells you why it’s concerned, which then enables the doctor to make better treatment decisions.”

Contributors to the paper presenting Prescience include Drs. Bala Nair and Monica Vavilala, and software engineer Shu-Fang Newman of UW Medicine’s Department of Anesthesiology & Pain Management; Dr. Mayumi Horibe of the Veterans Affairs Puget Sound Health Care System; and Drs. Michael Eisses, Trevor Adams, David Liston, Daniel King-Wai Low, and Jerry Kim of Seattle Children’s. Kim and Lee initially conceived of the project. The team is planning to make further refinements to both the system and the interface before Prescience can be deployed in operating rooms around the country.

For more on Prescience, read the Nature Biomedical Engineering paper, “Explainable machine-learning predictions for the prevention of hypoxaemia during surgery,” and the UW News release. Also see related coverage by GeekWire.

The research team, from left: Vikram Iyer, Shyam Gollakota, Jennifer Mankoff, Ian Culhane, and Justin Chan. Mark Stone/University of Washington

Last year, researchers in the Allen School’s Networks & Mobile Systems Lab unveiled a set of prototypes and schematics that represented the first 3D-printed objects capable of communicating over WiFi without built-in electronics. Now, those smart objects are about to get even smarter thanks to new built-in analytics that can wirelessly track and store data about their use — even when they are out of the range of WiFi.

The new system is the product of a collaboration between the original group, led by professor Shyam Gollakota, and the Allen School’s Make4All Group led by professor Jennifer Mankoff. Together, this multidisciplinary team demonstrated how 3D-printed items imbued with analytic capabilities could be used for a variety of applications to improve quality of life or potentially even save a life, from smart assistive devices that absorb feedback from the user, to smart pill bottles that record when a patient last took their medication.

But first, they had to find a way to perform room-scale sensing while registering a range of bi-directional and rotational movements. The team also needed an effective means of storing and retrieving the collected data even if the object does not maintain a WiFi connection while relying on plastic parts. “Using plastic for these applications means you don’t have to worry about batteries running out for your device getting wet,” Gollakota noted in a UW News release. “But if we really want to transform 3D-printed objects into smart objects, we need mechanisms to monitor and store data.”

The team began by building on previous, groundbreaking work from Gollakota and colleagues that successfully married mechanical gears and switches with the digital capabilities of backscatter communication. Backscatter enables devices to transmit data by reflecting ambient radio frequency (RF) signals that are decoded by a WiFi receiver. For this project, the researchers aimed to extend the transmission range of their first 3D-printed objects to room scale — a necessity if such devices are to be practical for everyday living. By applying interference cancellation techniques, which enabled the receivers to pick up weaker backscattered signals from farther away, the team demonstrated their devices could successfully transmit data from a distance of four meters.

In 3D-printed smart objects, a switch made of conductive plastic filament, not electronic components, is used to transmit the data when activated by the mechanical turning of a gear. The original design contained a uni-directional switch with a single antenna. But as Vikram Iyer, a Ph.D. student in the Department of Electrical & Computer Engineering who works with Gollakota, explained, they had to switch up their approach to sense bi-directional movement. “This time we have two antennas, one on top and one on bottom, that can be contacted by a switch attached to a gear,” he said. “So opening a pill bottle cap moves the gear in one direction, which pushes the switch to contact one of the two antennas. And then closing the pill bottle cap turns the gear in the opposite direction, and the switch hits the other antenna.”

A 3D-printed e-NABLE prosthetic device that collects and stores data about its use. Mark Stone/University of Washington

To determine the direction of movement, Iyer and his colleagues embedded an asymmetric code into the gear’s teeth. As the gear turns, the specific direction of movement is indicated via the encoded sequence — “like Morse code,” according to Allen School Ph.D. student Justin Chan.

The team, which also includes undergraduate Ian Culhane of the Department of Mechanical Engineering, aimed to produce “anywhere analytics” by enabling the devices to collect and store data over time even as the user moves in and out of WiFi range. To accomplish this, the researchers developed a mechanical data capture and storage mechanism that relies on a ratchet system. The system holds state as data is collected out of range of WiFi; when the device is once again within range, the press of a button releases the ratchet so it can wirelessly transmit the stored data. As proof of concept, the team designed a special insulin pen that employs the ratchet system to store a user’s dosage history, based on how far the syringe’s plunger has been depressed.

Having solved the technical issues, the team was interested in finding out whether its approach could benefit the users of a particular class of 3D-printed objects: customized prosthetic devices. While the growing popularity and affordability of 3D printing has the potential to lower barriers of access to such specialized equipment, there is no practical way to track what happens with the devices once they are with the user — and evidence suggests that the abandonment rate for assistive technologies could be as high as 75%. But armed with embedded analytics, technologies such as the e-NABLE prosthetic limb, which assists children with hand abnormalities, could potentially track frequency of use as well as finer-grained data on rotation angle and direction to paint a fuller picture of how users are – or aren’t – benefiting from these devices.

For Mankoff, who has done extensive work in this area, the combination of 3D printing and backscatter technology is an opportunity to not only get to the root of those statistics, but hopefully, to turn the numbers around. “This system will give us a higher-fidelity picture of what is going on,” Mankoff explained. “Right now we don’t have a way of tracking if and how people are using e-NABLE hands. Ultimately what I’d like to do with these data is predict whether or not people are going to abandon a device based on how they’re using it.”

The team will present its research paper at the Association for Computing Machinery’s Symposium on User Interface Software and Technology (UIST 2018) next week in Berlin, Germany.

For more on this project, read the UW News release here and visit the project page here. Also check out related stories by Engadget, MIT Technology Review, Silicon Republic, and Professional Engineering.

Irene Zhang (left) with award committee chair Emmett Witchel

Allen School alumna Irene Zhang (Ph.D., ’17) has been recognized with the 2018 Dennis M. Ritchie Doctoral Dissertation Award at the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI) taking place in Carlsbad, California. The award committee selected Zhang’s dissertation, “Towards a Flexible, High–Performance Operating System for Mobile/Cloud Applications,” for its breadth and potential to inspire future research.

Zhang’s thesis makes multiple contributions spanning mobile and cloud computing. Today’s applications have become incredibly difficult to write; no longer simple desktop programs, they are now surprisingly complex distributed systems with components spread across geographically and functionally diverse mobile devices and cloud servers. Zhang presents multiple systems that address the challenges of programming in this space.

The first of Zhang’s contributions, Sapphire, offers both a new methodology for writing distributed applications and a system supporting that methodology. The Sapphire system greatly simplifies programming for distributed applications by separating generic application logic from distributed deployment decisions, such as where data or computation should be located, what data should be cached or replicated, and what consistency guarantees are necessary.

Another system, named Diamond, is concerned with a relatively new property of modern applications: reactivity. Applications such as games and social networking expect changes to distributed state to be propagated automatically and instantly to other users and to durable storage, so that all users see the same values in the same order. With Diamond, changes to shared application variables on any device automatically cause those values to be made durable in the cloud, update the values on other devices sharing them, and trigger those devices to “react” to the changes by updating the user interface so the user quickly sees the change.

Finally, in TAPIR (“Transactional Application Protocol for Inconsistent Replication”), Zhang dissects the protocols used in today’s distributed storage systems to improve the performance of consistency management among replicas. By simplifying the replication protocol with a technique she calls “inconsistent replication,” Zhang is able to provide both lower latency and higher throughput on distributed storage systems without sacrificing transactional properties.

The Dennis M. Ritchie Award was created by the Association for Computing Machinery’s Special Interest Group in Operating Systems (ACM SIGOPS) to recognize and encourage creative research in software systems in honor of A. M. Turing Award winner Dennis Ritchie, who was a pioneer in operating systems theory and implementation of the UNIX operating system. The award is presented during alternating years at OSDI and the ACM Symposium on Operating Systems (SOSP).

Zhang, who earned her Ph.D. working with professors Arvind Krishnamurthy and Hank Levy and is now a researcher at Microsoft Research, is the second Allen School student to be acknowledged by the Ritchie Award. Alumna Roxana Geambasu (Ph.D., ’11) earned an Honorable Mention for her dissertation “Empowering Users with Control over Cloud and Mobile Data” in 2013, the first year in which the award was given.

Congratulations, Irene!