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University of Washington researchers create a buzz with Living IoT system that replaces drones with bees

Bumblebee wearing the Living IoT backpack while foraging on a flower.

A team of researchers in the Networks & Mobile Systems Lab led by Allen School professor Shyam Gollakota and the Autonomous Insect Robotics (AIR) Laboratory led by Mechanical Engineering professor Sawyer Fuller have designed a new mobile platform that combines sensing, computation, and communication in a package small enough to be carried by a bumblebee. Dubbed Living IoT, the system allows nature to take its course while enabling new capabilities in agricultural and environmental monitoring.

Living IoT’s reliance on biological, rather than mechanical, flight presents new opportunities for continuous sensing without having to repeatedly recharge power-hungry batteries throughout the day. However, it did present some novel challenges for the team, not least of which was how create a form factor small enough and light enough to ride on the back of a bee that could still power data collection and communication.

“We decided to use bumblebees because they’re large enough to carry a tiny battery that can power our system, and they return to a hive every night where we could wirelessly recharge the batteries,” explained Vikram Iyer, a Ph.D. student in Electrical & Computer Engineering and co-primary author on the research paper.

The resulting design, which incorporates an antenna, envelope detector, sensor, microcontroller, backscatter transmitter, and rechargeable battery, weighs in at a minuscule 102 milligrams — roughly half of a bumblebee’s potential payload and less than the maximum weight the team determined the insect could carry without interfering with takeoff or controlled flight.

Vikram Iyer at the UW's urban farm with a one of the Living IoT bumblebees

“The rechargeable battery powering the backpack weighs about 70 milligrams,” noted Allen School Ph.D. student Rajalakshmi Nandakumar in a UW News release. “So we had a little over 30 milligrams left for everything else, like the sensors and the localization system to track the insect’s position.”

The battery offers seven hours of uninterrupted data collection time before it has to be recharged. As the bees fly around, their onboard sensors collect data such as temperature, humidity, and light intensity and store it for later upload back at the hive. That upload happens wirelessly using backscatter communication, a technique honed by members of the Networks & Mobile Systems Lab to enable a range of IoT applications.

To enable location-based sensing and data tracking in the absence of flight control, the researchers came up with a novel approach for self-localization that relies on passive operations in place of power-hungry radio receiver components. Instead, strategically positioned access point (AP) radios broadcast signals that are received by the bees as they go about their business. Using changes in the signal amplitude extracted from the onboard envelope detector, the team is able to determine the insect’s angle relative to each AP at various points throughout the day and triangulate its position to localize the sensor data. According to Allen School Ph.D. student Anran Wang, the system can detect the bee’s position within 80 meters of the antennas — roughly three-quarters of the length of a football field.

For the moment, the system is limited to roughly 30 kilobytes of data storage. If that can be expanded to include tiny cameras live streaming information about the condition of crops in the field or even the bees themselves, the notion of using live insects in place of drones for smart agriculture and other applications could really take off.

“Having insects carry these sensor systems around could be beneficial for farms because bees can sense things that electronic objects, like drones, cannot,” explained Gollakota. “With a drone, you’re just flying around randomly, while a bee is going to be drawn to specific things, like the plants it prefers to pollinate. And on top of learning about the environment, you can also learn a lot about how the bees behave.”

The team will present its research paper at MobiCom 2019, the Association for Computing Machinery’s 25th International Conference on Mobile Computing and Networking.

Read the UW News release here and visit the Living IoT project page here. Check out coverage in GeekWire, NBC MachCNBCTechCrunch, Digital TrendsViceMIT Technology ReviewNew Atlas, FuturityEngadget, Futurism, InverseForbesKOMO News, KUOW, The Seattle Times, Seattle PIBusiness Insider, Digital Journal, and IEEE Spectrum.

Photo credits: Mark Stone/University of Washington

December 13, 2018

MISL researchers earn Best Student Paper Award for designing and demonstrating content-based media search directly in DNA

Kendall Stewart onstage

Kendall Stewart presents a system for content-based media search in DNA

Researchers in the Molecular Information Systems Lab (MISL) have taken another step forward in their quest to develop a next-generation data storage system with the introduction of new mechanisms for content-based similarity search of digital data stored in synthetic DNA. The team, which includes researchers from the University of Washington and Microsoft, took home the Best Student Paper Award in recognition of its work from the 24th International Conference on DNA Computing and Molecular Programming (DNA 24) in October.

The winning paper describes an end-to-end DNA-based architecture for content-based similarity search of stored media — in this case, image files. The MISL team’s contribution includes a novel neural-network-based sequence encoder trained on more than 30,000 images from the Caltech256 image dataset, and a laboratory experiment demonstrating the technique on a set of 100 test images.

Instead of encoding and storing the complete image files, the researchers concentrated on building a database for storing and retrieving their associated metadata. Each image’s encoded DNA strand includes a “feature sequence” representing its semantic features, as well as an “ID sequence” pointing to the location of the complete file in another database.

By adapting a technique from the machine learning community called “semantic hashing” to work with DNA sequences, the team designed an encoder to output feature sequences that react more strongly with the feature sequences of similar images. This enables a molecular “fishing hook”: when a molecule representing a query image is added to the database, similar images react with and stick to the query. The resulting query-target pairs can then be extracted using standard laboratory techniques like magnetic bead filtration.

Diagram of the training methodology showing training images of binoculars and a sailboat.

The training methodology used for the sequence encoder. A neural network translates images into DNA sequences, which are compared for similarity. If the sequences are similar but the images are not – or vice versa – the neural network is updated to increase its accuracy.

According to Allen School Ph.D. student and lead author Kendall Stewart, this type of efficient, content-based search mechanism will be key to unlocking DNA’s potential as the digital storage medium of the future.

“We’re approaching a time when zettabytes of data will be produced each year — a daunting challenge that requires us to think beyond the current state of the art,” Stewart explained. “Our approach takes advantage of DNA’s near-data processing capabilities while borrowing from the latest machine learning techniques to present one possible solution for the indexing and retrieval of content-rich media.”

Stewart’s co-authors on the work include Microsoft researcher Yuan-Jyue Chen, MISL members David Ward and Xiaomeng “Aaron” Liu, Allen School and Electrical & Computer Engineering professor Georg Seelig, Allen School professor Luis Ceze, and Microsoft senior researcher and Allen School affiliate professor Karin Strauss.

The team validated its design in wet-lab experiments using 100 target images and 10 query images, with 10 similar images for each query image included in the target set. The results, Stewart said, were “moderately successful,” with visually similar files accounting for 30% of the sequencing reads for each query, despite comprising only 10% of the total database.

The researchers believe their approach can be generalized to databases containing any type of media. While it would be a challenge to scale such a system to larger and more complex datasets, the project opens up a promising new avenue of exploration around DNA-based data processing and content-based search.

Read the research paper here and learn more about MISL’s work here.

Congratulations to Kendall and her entire team!

 

December 10, 2018

Allen School’s Yin Tat Lee earns Best Paper Award at NeurIPS 2018 for new algorithms for distributed optimization

Yin Tat LeeA team of researchers that includes professor Yin Tat Lee of the Allen School’s Theory of Computation group has captured a Best Paper Award at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018). The paper presents two new algorithms that achieve optimal convergence rates for optimizing non-smooth convex functions in distributed networks, which are commonly used for machine learning applications to meet the computational and storage demands of very large datasets.

Performing optimization in distributed networks involves trade-offs between computation and communication time. Recent progress in the field has yielded optimal convergence rates and algorithms for optimizing smooth and strongly convex functions in such networks. In their award-winning paper, Lee and his co-authors extend the same theoretical analysis to solve an even thornier problem: how to optimize these trade-offs for non-smooth convex functions.

Based on its analysis, the team produced the first optimal algorithm for non-smooth decentralized optimization in the setting where the slope of each function is uniformly bounded. Referred to as multi-step primal dual (MSPD), the algorithm surpasses the previous state-of-the-art technique — the primal-dual algorithm — that offered fast communication rates in a decentralized and stochastic setting but without achieving optimality. Under the more challenging global regularity assumption, the researchers present a simple yet efficient algorithm known as distributed randomized smoothing (DRS) that adapts the existing randomized smoothing optimization algorithm for application in the distributed setting. DRS achieves a near-optimal convergence rate and communication cost.

Lee contributed to this work in his capacity as a visiting researcher at Microsoft Research and a faculty member at the Allen School. His collaborators include lead author Kevin Scaman, a research scientist at Huawei Technologies’ Paris-based machine learning lab; Microsoft senior researcher Sébastien Bubeck; and researchers Francis Bach and Laurent Massoulié of Inria, the French National Institute for Research in Computer Science and Automation. Members of the team will present the paper later today at the NeurIPS conference taking place this week in Montreal, Canada.

Read the research paper here.

Congratulations, Yin Tat!

 

December 6, 2018

Allen School kicks off Computer Science Education Week with Code.org and the Computer Science Teachers Association

Hadi Partovi onstage to kick off Computer Science Education Week

Code.org’s Hadi Partovi kicks off the Computer Science Education Week celebration on the UW Seattle campus.

The Allen School teamed up with Code.org and the Computer Science Teachers Association (CSTA) on Monday to host the kickoff event marking the 2018 start of Computer Science Education Week and the largest-ever Hour of Code.  The event featured special guests Melinda Gates, co-chair and trustee of the Bill & Melinda Gates Foundation, and Brad Smith, president and chief legal officer of Microsoft.

Gates engaged in a question and answer session with a local student about her inspiration for pursuing computer science and how efforts to engage girls in computer science early are helping to address the gender gap in computing. In addition to giving a shout-out to her high school math teacher, Mrs. Susan Bauer, Gates acknowledged the efforts of the Allen School that have seen us increase our percentage of CS degrees awarded to women to nearly twice the national average. “This isn’t because of chance,” Gates noted. “It’s because they worked at it.”

Noting that she was lucky to have a teacher who introduced her to computer science 35 years ago, Gates hoped that today’s students wouldn’t have to rely on luck to discover computing thanks to programs like Code.org, Girls Who Code, and Black Girls Code. “Not every girl is going to have a Mrs. Bauer to introduce her to computing,” Gates said. “But if we build the right support system, she won’t need one.”

In addition to energizing the room full of teachers, administrators, and students about engaging in computer science learning, Gates joined Code.org co-founder and CEO Hadi Partovi and CSTA Executive Director Jake Baskin in celebrating the recipients of the Champions for Computer Science Awards. These awards recognize individuals and organizations that are working to make computer science education accessible to everyone — including 2018 honoree and Allen School professor emeritus Richard Ladner.

Richard Ladner accepting his award from Melinda Gates

Richard Ladner (left) accepts his Champions for Computer Science Award from Melinda Gates as Hadi Partovi (right) looks on

Ladner was named a Computer Science Champion based on his leadership in launching and growing AccessCSForAll, a set of curriculum and professional development tools that empower teachers and schools to make computer science accessible to K-12 students with disabilities. CSTA and Code.org credited Ladner and collaborators Andreas Stefik and the Quorum programming team, for their leadership in ensuring that the drive to increase CS educational opportunities is truly inclusive for all students. Happily, Ladner points out, the accessibility mindset is becoming more ingrained not just in education, but in industry, too — with more companies now focused on developing technologies and products that are built with accessibility in mind to serve the broadest community of users. “Accessibility is becoming mainstream,” Ladner said. “It’s not something one person does, it’s something everyone does.”

Smith — a longtime friend of the Allen School and a staunch advocate for computer science education, generally — ended the program on a high note by announcing Microsoft’s commitment of an additional $10 million to Code.org. The funding will support the organization’s efforts to make CS-related professional development available to teachers in every school, as well as its advocacy for state-level policies that will increase access to computer science education for K-12 students across the nation.

Allen School ambassadors welcome students and parents to the 2018 Computing Open House

Allen School ambassadors welcome a group of students and parents to the 2018 Computing Open House

While today marked the official kickoff, the Allen School began celebrating Computer Science Education Week early, at our Computing Open House this past weekend. The open house is an annual tradition that coincides with both the start of CS Education Week and the birthday of computer programming pioneer Grace Hopper. More than 500 local students and parents descended upon the Paul G. Allen Center for Computer Science & Engineering for tours, research demos, and talks by industry representatives on how their computer science education helped them to launch fulfilling careers creating technologies that benefit people around the world. Students at the open house had a chance to learn about computer security, program an Edison robot, construct their own messages in binary and DNA, and get hands-on with the latest smartphone-based apps for mobile health.

Thanks to everyone who joined us, whether in person or online, to support computer science education — and congratulations to Richard and the team at Quorum for their much-deserved recognition!

Learn more about CS Education Week here and the Hour of Code here. View all of the Champions for Computer Science honorees here.

 

December 3, 2018

Allen School team earns Best Paper Award at SenSys 2018 for new low-power wireless localization system

Vikram Iyer and Rajalakshmi Nandakumar holding their SenSys 2018 Best Paper Awards

Vikram Iyer (left) and Rajalakshmi Nandakumar, recipients of the SenSys 2018 Best Paper Award for µLocate

Researchers in the Allen School’s Networks & Mobile Systems Lab have developed the first low-power 3D localization system for sub-centimeter sized devices. The system, µLocate, enables continuous object tracking on mobile devices at distances of up to 60 meters — even through walls — while consuming mere microwatts of power. The team behind µLocate was recognized with a Best Paper Award at the recent Conference on Embedded Networked Sensor Systems (SenSys 2018) organized by the Association for Computing Machinery in Shenzhen, China — demonstrating once again that good things really do come in small packages.

Localization is integral to the Internet of Things (IoT). While WiFi radios and ultra-wideband radios are capable of fulfilling this function, their power needs are such that they can only operate for about a month using smaller coin cell or button cell batteries. On the other hand, radio frequency identification tags (RFID) require significantly less power and are a suitable size, but their limited range and inconsistent operation through barriers make them unsuitable for use in whole-home and commercial IoT applications.

“When it comes to tracking IoT devices, existing localization systems typically hit a wall due to their functional limitations or outsize power needs that make them impractical for real-world use.” observed Allen School Ph.D. student Rajalakshmi Nandakumar, lead author on the paper. “µLocate overcomes these barriers by eliminating the need for bulky batteries while enabling us to localize across multiple rooms using devices that are smaller than a penny.”

Nandakumar and her colleagues — Electrical & Computer Engineering Ph.D. student Vikram Iyer and Allen School professor Shyam Gollakota — developed a novel architecture that builds on the lab’s previous, pioneering work on long-range backscatter. That project introduced the first low-power, wide-area backscatter communication system using chirp spread spectrum (CSS) modulation. For this latest iteration, the team’s hardware design consists of an access point and a receiver that incorporates a low-power microcontroller equipped with a built-in oscillator. The access point transmits the CSS signal, which the receiver shifts by 1-2 MHz before backscattering it to the access point. The system is designed to operate concurrently across the 900 MHz, 2.4 GHz, and 5 GHz ISM radio bands; the access point extracts and combines the phase information from the backscattered signals across the spectrum to localize the target.

The team designed two diminutive receiver prototypes, each of which could last between five and 10 years running on two button cell batteries: a multi-band prototype capable of operating across the aforementioned ISM bands at longer range, and an even smaller single-band prototype that operates at 5 GHz for short-range applications. The researchers evaluated µLocate in an open field and an office building to test its performance in line-of-sight and walled settings, respectively, demonstrating its ability to return a location value within 70 milliseconds at ranges of up to 60 meters. They also deployed the system in multiple real-world scenarios, including several single- and multi-story residences and across multiple rooms in a hospital wing.

“This was our lab’s first foray into both wireless device localization and the design of tiny, programmable devices,” noted Gollakota. “We’re excited that our work in this new line of research is already being recognized within the sensing community.”

To learn more about µLocate, read the research paper here.

Way to go, team!

 

November 29, 2018

Allen School welcomes nine new faculty with expertise in cryptography, data science, machine learning, and more

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

TTim Althoff portraitim 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

René Just portraitFormer 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 portraitRachel 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

Ratul Mahajan portraitAllen 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 portrait

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 portraitHunter 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 portraitAdriana 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 portraitStefano 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 portraitBrett 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!

 

November 21, 2018

Allen School undergrads are blazing new trails as first-generation college students

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 AliShariya 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 DeLeonSimplicio 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.

 

November 8, 2018

Draco, a constraint-based model for formalizing principles of good visualization design, earns Best Paper at InfoVis 2018

Halden Lin, Dominik Moritz, Jeffrey Heer holding Best Paper Award certificate, Niklas Elmqvist

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.

Graphic depicting concepts and words from Dominik Moritz' presentation on Draco: Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

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!

 

November 5, 2018

Paul G. Allen, 1953-2018

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.

November 3, 2018

Allen School roboticists and Honda Research Institute are on a quest to create a Curious Minded Machine

Portraits of team members, clockwise from top left: Sidd Srinivasa, Maya Cakmak, Dieter Fox, Leila Takayama

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?”

Curious Minded Machine logoCakmak, 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.”

Sidd Srinivasa examining the positioning of a robot arm while members of his lab look on.

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.

 

October 25, 2018

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