As a researcher, Johnson aims to create battery-less micro robots designed to operate autonomously for prolonged periods of time, implementable as the sensory notes in a swarm algorithm. To achieve this, he plans to leverage the properties of structures like leaf-out origami to create origami robots. These have potential applications in many environments requiring both low-power and small-scale devices, like in the deployment of space rovers for interplanetary exploration.
“As a first year graduate student, Kyle is leading embedded systems and robotic research that span aerospace, electrical and mechanical engineering and computer science,” said Gollakota. “The miniaturized robotic systems he is building, if successful, are creative and more importantly useful for achieving wireless sensor deployment at a scale that has not been possible before.”
In addition to his research, Johnson has been a leader in working towards improving diversity, equity and inclusion in academia and technology fields. For the past three years, Johnson has taught middle and high school students how to code and hardwire a multitude of different sensors and devices, demonstrating to students that they can apply the technical concepts that they learn in class towards solving real-world problems.
In the summer of 2019, Johnson traveled to Cape Town, South Africa to study racism, education and development. There, he learned from high school math and physics teachers about creating lesson plans and teaching to diverse students before eventually teaching some of those classes himself. At the University of South Africa, Johnson shared his experiences during the “Trauma, Educational Exclusions and Survival: Examining Global Student Experience & Resilience” workshop at an academic development symposium and encouraged those at the university to be more engaged in public schools to better prepare students for higher education.
Upon his return to the United States, Johnson co-founded the student group A Vision for Electronic Literary & Access (AVELA) to provide more opportunities for underrepresented students to pursue their interests at a university level. Johnson and the other AVELA members accomplish this by representing the populations that they aim to support while leading workshops, camps and other forms of community outreach. AVELA has partnered with Seattle Public Schools, the Kent School District, the National Society of Black Engineers, Seattle MESA, The Urban League of Metropolitan Seattle, InspireOne, and the city of Seattle to teach engineering workshops and create lesson plans catered towards aspiring, underrepresented students. The organization is in the process of earning a 501(c) status; as a nonprofit, AVELA can apply for grants and additional funding.
Over the past year, Johnson has worked to help change policies and job descriptions at the University of Washington to make them more equitable and created tools for students to more quickly report racism, sexism and other discriminatory actions. He also co-founded the Black Student Graduate Association (BSGA).
““The BGSA focuses on giving UW’s Black graduate students the space to relax, network and share experiences,” Johnson said. “As the 100-member organization expands, I hope more Black graduate students will find solace in a community of their peers.”
Ten Allen School students earned recognition from the National Science Foundation (NSF) as part of its latest round of Graduate Research Fellowship Program (GRFP) awards, which honor outstanding students who are pursuing full-time research-based degrees with the potential to produce innovative contributions in science and engineering.
Since 1952, this prestigious competition has provided support for graduate education in NSF-supported STEM (science, technology, engineering and mathematics) disciplines. The Allen School honorees — eight Ph.D. students and two undergraduate students — were recognized in the “Comp/IS/Engr” or other computing-related categories.
Johnson’s research focuses on structural properties in systems, like the bistability demonstrated in leaf-out origami, to create low-power and small-scale robots optimized for resource constrained applications. He utilizes various reinforcement learning algorithms to simulate the locomotion of structures in open-source physics engines to discover energy-efficient control systems for structures with complex energy landscapes. He aims to create battery-less micro robots that are designed to operate autonomously for prolonged periods of time, be quickly self-adapting and be implementable as the sensory notes in a swarm algorithm. Origami robots have potential applications in many environments requiring both low-power and small-scaled devices, like in the deployment of space rovers for interplanetary exploration.
Second year Ph.D. student Maruchi Kim, who also works with Gollakota, received a fellowship for his research at the intersection of human-computer interaction (HCI) and wireless audio systems.
Kim’s work aims to take audio technology beyond noise-canceling and transparency, and expand upon existing hearing aids and earbuds to augment the sound environment. His goal is to interactively attenuate, amplify and decorate specific sound sources of his own choosing, using noninvasive neural signals. Kim will integrate ideas from neural engineering and deep learning and embed them into a novel wireless audio system capable of user-selectable audio amplification and attenuation. This technology could ultimately make its way into hearing aids and audio wearables to give people a more enriched hearing experience via a natural amplification of sounds that they want to hear.
Nanavati is interested in developing models of human behavior and integrating them into robot planning to enable smooth human-robot collaboration. As robots are increasingly deployed in dynamic and uncertain human environments, situations will arise that they are not equipped to handle. For example, consider an office robot. Due to limitations in hardware and computation, environmental uncertainties, or a lack of domain knowledge, the robot might get lost, get knocked over, etc. Nanavanti’s aim is to develop mathematical models and frameworks that enable a robot to effectively ask for help. This would require it to reason about humans’ help-giving behaviors and its own capacities to autonomously determine who, when and how to ask for help. Doing so will enable robots to adapt to dynamic and unexpected scenarios by switching between being autonomous and collaborative.
Ruth explores applications of computing tools to improve the quality and accessibility of health care. He aims to apply computer and engineering principles to expand access to ischemic stroke screening, specifically carotid artery stenosis and systemic embolism. Ruth will accurately extract pulse waveforms from smartphone video of the face and develop computer vision and signal processing advancements, building algorithms to compute pulse onset and peak delay for variable heart rate and noisy signals. He will also build a wearable ultrasound sensor that will require high precision timing, safe power management and embedded processing. After graduating this June with a degree in computer engineering and bioengineering, Ruth will enter the computer science Ph.D. program at Stanford University in the fall.
Strandquist’s research is in analyzing electrocorticography (ECoG) recordings of natural behaviors. These recordings can be used in neural decoding, which can enable brain computer interfaces to restore critical functions such as communication and mobility to people with neurological diseases. Strandquist found that while state-of-the-art neural decoders have significant potential, they are primarily tested on clean data obtained in controlled laboratory conditions. In reality, neural activity in freely-behaving humans is unstructured and non-stationary over time, which can impede brain computer interfaces from being deployed for use outside the lab. Her aim is to design a pipeline for real-time neural decoding of natural human behaviors from unstructured data that is viable for long-term use.
Weinberger’s work is at the intersection of machine learning and medicine. Single cell RNA sequencing (scRNA-seq) technologies offer an unprecedented opportunity to effectively characterize cellular level heterogeneity in health and disease, thereby opening a new avenue to gain insights into poorly understood diseases such as Alzheimer’s. Unfortunately, the high-dimensional nature of scRNA-seq data makes artificial intelligence (AI) models trained on it prone to overfitting, which can lead to spurious, unreproducible findings. To solve this, Weinberger is working on an AI framework called RISE (robust, interpretable single-cell embeddings). Once trained, RISE models can be used off-the-shelf by non-AI experts for arbitrary downstream tasks.
Second year Ph.D. student Ishan Chatterjee received an honorable mention from the NSF recognizing his work bridging hardware and HCI with Patel in the UbiComp Lab.
Chatterjee focuses on interface and sensing technologies to unlock smarter, more natural or more efficient experiences. For instance, in the case of frontline workers whose workflows are inherently hands-on and spatial rather than desk-based, mixed reality (MR) computing interfaces can help meet their needs. His goal is to use them to make the workflows of frontline workers more productive, more intuitive and more collaborative. His aim is to employ human-centered design processes to address frontline worker scenarios in design and manufacturing and generalize the approach through low-code authoring tools to reduce the barrier to creating MR applications. Chatterjee is also developing lightweight sensor systems and technologies to use in hands-on and social contexts for workers in people-facing roles.
La Fleur’s research focuses on learning to predict mutants that can disrupt protein-protein interactions (PPIs), which is an important step towards identifying potential pathogenic variants in the human genome, to advance our understanding of disease such as cancer and improve the well-being of individuals and society. Variation in genetic coding sequences can result in missense mutations in protein amino acids, potentially disrupting PPIs and hindering or destroying normal protein functions. Le Fleur has found current deep learning models perform poorly when predicting PPI disruptive missense mutations from non-disruptive mutations. She aims to develop surface feature input PPI models for predicting human protein interactions using available interactome scale datasets such as the Human Reference Interactome (HuRI).
Undergraduate Millicent Li received an honorable mention for her work with Patel in the UbiComp Lab on tools to support mental health.
Li’s research encompasses the realm of HCI, mobile computing and natural language processing. Her goal is to make inferences about an individual’s stress level by applying NLP and speech processing techniques to predict stress in day-to-day conversational settings. Li plans to develop a framework for predicting stress and analyze the efficacy of NLP paired with speech processing for the task in order to create wearables for quick mental health analysis. She hopes that the project will help shape future research into interdisciplinary approaches for smartphone sensing in large-scale mental health analysis for diverse populations. After graduation, she will work as an AI Resident at Facebook AI Research before starting her Ph.D. in computer science at Northeastern University in fall 2022.
Wei’s research focuses on usability of computer security and privacy (S&P). She is investigating the role gender plays in how people conceptualize S&P measures, particularly when it comes to everyday behaviors like avoiding scams, creating passwords and sharing information on social media. By analyzing stereotyped themes present in the literature, as well as empirically measuring gender stereotypes in the field, she will identify gendered differences in S&P knowledge, behaviors or threat models. This work combined with her prior research into systemic forces in other contexts — individuals reusing the same passwords for online accounts, and their privacy helplessness in the face of massive online data collection — will further her goal of equity in security and privacy and develop a user-empowering approach for conducting and sharing her research.
Recent Allen School bachelor’s alumni Siena Dumas Ang (B.S.,’17), Belinda Zou Li (B.S.,’19), Jenny Liang (B.S., ’21) and Harrison Kwik (B.S.,’18) also earned fellowships. Ang, currently a second year Ph.D. student at Princeton University, earned a fellowship in the “Life Sciences” category for her work in computational biology. She previously worked with professor Hannaneh Hajishirzi in AI before focusing on DNA data storage with Microsoft and the Molecular Information Systems Lab. Li, a first year Ph.D. student at Massachusetts Institute of Technology, previously worked with professor Luke Zettlemoyer in NLP. Liang, who will be a Ph.D. student at Carnegie Mellon University in the fall, worked with professor Amy Ko in the Code and Cognition Lab, as did fellow honoree Kwik, who is currently a second year Ph.D. student in the Technology and Social Behavior program at Northwestern University. Kwik earned a fellowship in the “STEM Education and Learning Research” category.
Allen School alumni Ben Evans (B.S.,’18, M.S.,’19) and Spencer Peters (B.S.,’19) received honorable mentions. Evans worked with professor Sham Kakade and Ph.D. student Aravind Rajeswaran on an off-policy reinforcement algorithm for machine learning. He is now a first year Ph.D. student at New York University. Peters previously worked on improving phasing algorithms with professor Walter Ruzzo and is currently a second year Ph.D. student at Cornell University.
In addition to the Allen School honorees, students from other UW departments were also recognized by the NSF in the “Comp/IS/Engr” category. In the Department of Human Centered Design & Engineering, Ph.D. students Neille-Anne Herrera Tan, Emma Jean McDonald and Jay Little Cunningham received fellowships, as well as iSchool Ph.D. students Nicole Simone Kuhn and Anastasia Schaadhart and University of Washington Tacoma Ph.D. student Kyle Bittner. Akeiylah Sammon DeWitt in HCDE was recognized with an honorable mention.
Earlier this spring, the National Science Foundation recognized nine Allen School student researchers as part of its 2020 Graduate Research Fellowship competition. The honorees — seven Ph.D. students and two undergraduate students — were recognized in the “Comp/IS/Engr” category for their potential to make significant contributions to science and engineering through research, teaching, and innovation. Each of them already has amassed an outstanding track record of pursuing high-impact research in their respective areas, including theoretical computer science, systems, machine learning, computational neuroscience, security and privacy, robotics, and more.
“Allen School Ph.D. students represent the future of high quality research and innovation,” said professor Anna Karlin, associate director for graduate studies at the Allen School. “Their creativity and scholarly excellence is perfectly exemplified by our NSF GRFP honorees.”
Klein focuses on the design of efficient algorithms that yield near-optimal solutions to fundamental NP-hard problems that underpin the theory and practice of computing. His current project aims to find a better approximation algorithm for the Traveling Salesperson Problem (TSP). The TSP is applicable to a large class of planning and decision problems with a variety of real-world applications, from transportation routing, to genome sequencing, to computer chip design. Recently, Klein and his collaborators presented the first sub-3/2 approximation algorithm for what is conjectured to be the most difficult case of TSP — making tangible progress in their quest to improve upon a result that has stood for more than 40 years. Through this work, Klein hopes to advance tools and techniques that will yield new insights into a broad array of optimization problems.
Second-year Ph.D. student Jialin Li earned a fellowship for her work with professor Tom Anderson in the Computer Systems Lab on a new operating system that will provide performance guarantees for containers in cloud-based services.
Containers are a lightweight computing model that offers a platform-independent way of packaging application dependencies; as such, they have been widely adopted in industry for building microservice-based applications. While existing operating systems provide functional support for containers, they fall short of providing the performance guarantees necessary for satisfying service-level agreements. This typically leads application developers to request more container resources than required, which wastes energy and resources. Li is designing a new operating system using the Rust low-level programming language that will monitor container performance and intelligently reallocate resources based on container loads, thus increasing resource utilization while offering performance guarantees.
Fellowship winner Ashlie Martinez is a second-year Ph.D. student in the Computer Systems Lab working with professor Tom Anderson and affiliate professor Irene Zhang of Microsoft Research to develop a user space file system for distributed storage applications.
Recent advances in storage technologies have significantly increased storage capacity while speeding up input/output (I/O) by orders of magnitude. While storage technologies have evolved to the point where they can service requests in microseconds, developers’ approach to storage, generally speaking, has not — for the most part, they continue to regard I/O as a slow operation best done through an operating system’s file system. The Storage Performance Development Kit (SPDK) is available to bypass the kernel and speed up I/O, but it is difficult to integrate into existing software as the API exposes raw storage devices instead of a file system. To overcome these challenges and improve the performance of today’s distributed storage applications, Martinez is building a kernel-bypass file system, or KBFS, that combines a generic API with strong consistency guarantees. Using this approach, she aims to reduce developer effort while making KBFS faster and easier to maintain compared to existing OS file systems.
Pollock started his undergraduate research career in verification and formal methods, specifically the development of computerized proof assistants that take advantage of the correspondence between type theory and mathematical logic. As part of this work, Pollock prototyped a compiler between the Coq and Lean proof assistants. He subsequently contributed to Relay, a compiler for machine learning frameworks, as a member of the Allen School’s multidisciplinary SAMPL group. Expanding his interests to include principles of human-centered research, Pollock is designing Sidewinder, a framework for creating visualizations of program execution to help students and developers understand program semantics. Sidewinder employs formal abstract machine definitions to produce complete, continuous, and customizable program semantics visualizations. Pollock aims to build upon this work while pursuing a Ph.D. at MIT starting this fall.
As an undergraduate researcher, Ruth has focused on addressing security and privacy issues associated with emerging augmented reality (AR) technologies that can have a profound impact on users’ perception of the world. In her early work, Ruth focused on mitigating the risks of buggy or malicious output in AR applications that could endanger user safety by enabling the operating system to constrain undesirable output. She subsequently helped conduct a user study to understand concerns around multi-user AR. More recently, Ruth led the development of ShareAR, a tool for developers of AR applications to enable secure sharing of multi-user content. Going forward, Ruth sees the next step in this line of work to be designing a multi-user sharing protocol at the platform level that would mediate cross-app as well as cross-user interactions. Ruth looks forward to pursuing her Ph.D. at Stanford University in the fall.
First-year Ph.D. student Zöe Steine-Hanson earned a fellowship for her research in computational neuroscience with professors Rajesh Rao and Bingni Brunton. Steine-Hanson is working on the development of a new, generalizable brain-computer interface (BCI) using deep learning and transfer learning techniques.
Currently, even the most advanced BCIs require the collection of significant training data on a single human subject, and the majority of BCI research takes place in a laboratory rather than in naturalistic settings. These factors hinder the ability to generalize state-of-the-art BCIs for people’s everyday use. To address this problem, Steine-Hanson is training a deep neural network on electrocorticography (ECoG) and video data collected from multiple human subjects. By applying techniques from transfer learning, she aims to reduce the amount of training data required for each new subject by leveraging the knowledge collected from previous subjects. Her ultimate goal is to improve quality of life for individuals living with neurological impairments through the use of next-generation BCI technologies in real-world settings.hnologies in real-world settings.
Fellowship recipient NickWalker is a second-year Ph.D. student working with professor Maya Cakmak in the Human-Centered Robotics Lab. Walker’s research focuses on human-robot communication with the aim of enabling any user to customize a robot to meet their needs.
Previously, Walker developed techniques for improving natural language interfaces within a robot’s existing capabilities. These included the creation of embodied language learners that can acquire understanding of simple words and leveraging neural models to compensate for variations in phrasing of natural language commands. Walker plans to build upon this past work by leveraging language to enable a robot to perform completely new tasks; to that end, he has turned his attention to the development of natural language programming techniques that will address a variety of robotics use cases. As part of this work, Walker plans to explore questions around people’s perceptions of robot agency and who bears responsibility for a robot learner’s mistakes, in anticipation of a time when home robots will be the personal computers of a future generation.
Schmittle’s latest project focuses on improved techniques for imitation learning (IL), an approach to training dynamical systems that leverages expert feedback and demonstrations rather than requiring the hand-tuning of reward functions. IL offers an advantage over reinforcement learning in robotics, where real-world execution can be expensive or dangerous, due to its greater sample efficiency. However, most IL algorithms demand optimal state action demonstrations, which can be challenging even for experts. An alternative is to employ corrective feedback, in which users dispense with full demonstrations in favor of making adjustments during robot execution. This approach is easier for a teacher to provide but tends to be noisy and each teacher and task may require different feedback. To overcome this challenge, Schmittle recognizes robots must be able to learn from a variety of feedback and makes the following key insight: the teacher’s policy is latent, and their feedback can be modeled as a stream of loss functions. Based on this insight, he proposes a new corrective feedback meta-algorithm that can learn from a variety of noisy feedback across different tasks, teachers, and environments.
Caleb Ellington, a senior double-majoring in computer science and bioengineering, has pursued undergraduate research in the Baker Lab working with Ph.D. candidate Nao Hiranuma. Ellington earned an honorable mention for his work on machine learning techniques to improve the design of new therapeutics.
Recombinant protein therapeutics have emerged as an area of huge potential in medical research due to their universal biocompatibility and high specificity. They are also significantly harder to design compared to small-molecule drugs, which has caused their development to lag. Inspired by what he encountered as an intern at Nepal’s Annapurna Neurological Institute and Dhulikhel Hospital — where computing and 3D printing are used to produce imaging and surgical tools quickly and inexpensively — Ellington intends to explore the potential for computer science to speed up the design of new protein therapeutics. Specifically, he proposes to leverage advances in generative deep convolutional neural networks (DCNNs), which are capable of inferring and correcting data, to the design of protein-ligand interactions. His approach is based on a hypothesis that, under the right conditions, generative models are powerful enough to create entirely new proteins based on a target binding region — a potential breakthrough in protein design that could yield effective new treatments for a variety of diseases. Ellington will pursue this research as a Ph.D. student in computational biology at Carnegie Mellon University.
In addition to the Allen School honorees, students from other UW departments were also recognized by the NSF in the “Comp/IS/Engr” category. Ph.D. students Steven Goodman and Sharon Heung in the Department of Human-Centered Design & Engineering both received fellowships, while fellow HCDE student Andrew Beers and Electrical & Computer Engineering undergraduate Kyle Johnson earned honorable mentions.
Congratulations to all — you make the Allen School and UW proud!