Sometimes it can be hard to find just the right words to help someone who is struggling with mental health challenges. But recent advances in artificial intelligence could soon mean that assistance is just a click away — and delivered in a way that enhances, not replaces, the human touch.
In a new paper published in Nature Machine Intelligence, a team of computer scientists and psychologists at the University of Washington and Stanford University led by Allen School professor Tim Althoff present HAILEY, a collaborative AI agent that facilitates increased empathy in online mental health support conversations. HAILEY — short for Human-AI coLlaboration approach for EmpathY — is designed to assist peer supporters who are not trained therapists by providing just-in-time feedback on how to increase the empathic quality of their responses to support seekers in text-based chat. The goal is to achieve better outcomes for people who look to a community of peers for support in addition to, or in the absence of, access to licensed mental health providers.
Ashish Sharma (left) and Inna Lin
“Peer-to-peer support platforms like Reddit and TalkLife enable people to connect with others and receive support when they are unable to find a therapist, or they can’t afford it, or they’re wary of the unfortunate stigma around seeking treatment for mental health,” explained lead author Ashish Sharma, a Ph.D. student in the Allen School’s Behavioral Data Science Lab. “We know that greater empathy in mental health conversations increases the likelihood of relationship-forming and leads to more positive outcomes. But when we analyzed the empathy in conversations taking place on these platforms on a scale of zero for low empathy to six for high empathy, we found that they averaged an expressed empathy level of just one. So we worked with mental health professionals to transform this very complex construct of empathy into computational methods for helping people to have more empathic conversations.”
HAILEY is different from a general-purpose chatbot like ChatGPT. As a human-AI collaboration agent, HAILEY harnesses the power of large language models specifically to assist users in crafting more empathic responses to people seeking support. The system offers users just-in-time, actionable feedback in the form of onscreen prompts suggesting the insertion of new empathic sentences to supplement existing text or the replacement of low-empathy sentences with more empathic options. In one example cited in the paper, HAILEY suggests replacing the statement “Don’t worry!” with the more empathic acknowledgment, “It must be a real struggle!” In the course of conversation, the human user can choose to incorporate HAILEY’s suggestions with the touch of a button, modify the suggested text to put it in their own words and obtain additional feedback.
Unlike a chatbot that actively learns from its online interactions and incorporates those lessons in their subsequent exchanges, HAILEY is a closed system, meaning all training occurs offline. According to co-author David Atkins, CEO of Lyssn.io, Inc. and an affiliate professor in the UW Department of Psychiatry and Behavioral Sciences, HAILEY avoids the potential pitfalls associated with other AI systems that have recently made headlines.
“When it comes to delivering mental health support, we are dealing with open-ended questions and complex human emotions. It’s critically important to be thoughtful in how we deploy technology for mental health,” explained Atkins. “In the present work, that’s why we focused first on developing a model for empathy, rigorously evaluated it, and only then did we deploy it in a controlled environment. As a result, HAILEY represents a very different approach from just asking a generic, generative AI model to provide responses.”
Adam Miner (left) and David Atkins
HAILEY builds upon the team’s earlier work on PARTNER, a model trained on a new task of empathic rewriting using deep reinforcement learning. The project, which represented the team’s first foray into the application of AI to increase empathy in online mental health conversations while maintaining conversational fluency, contextual specificity, and diversity of responses, earned a Best Paper Award at The Web Conference (WWW 2021).
The team evaluated HAILEY in a controlled, non-clinical study involving 300 peer supporters who participate in TalkLife, an online peer-to-peer mental health support platform with a global reach. The study was conducted off-platform to preserve users’ safety via an interface similar to TalkLife’s, and participants were given basic training in crafting empathic responses to enable the researchers to better gauge the effect of HAILEY’s just-in-time feedback versus more traditional feedback or training.
The peer supporters were split into two groups: a human-only control group that crafted responses without feedback, and a “treatment” group in which the human writers received feedback from HAILEY. Each participant was asked to craft responses to a unique set of 10 posts by people seeking support. The researchers evaluated the levels of empathy expressed in the results using both human and automated methods. The human evaluators — all TalkLife users — rated the responses generated by human-AI collaboration more empathic than human-only responses nearly 47% of the time and equivalent in empathy roughly 16% of the time; that is, the responses enhanced by human-AI collaboration were preferred more often than those authored solely by humans. Using their 0-6 empathy classification model, the researchers also found that the human-AI approach yielded responses containing 20% higher levels of empathy compared to their human-only generated counterparts.
In addition to analyzing the conversations, the team asked the members of the human-AI group about their impressions of the tool. More than 60% reported that they found HAILEY’s suggestions helpful and/or actionable, and 77% would like to have such a feedback tool available on the real-world platform. According to co-author and Allen School Ph.D. student Inna Lin, although the team had hypothesized that human-AI collaboration would increase empathy, she and her colleagues were “pleasantly surprised” by the results.
“The majority of participants who interacted with HAILEY reported feeling more confident in their ability to offer support after using the tool,” Lin noted. “Perhaps most encouraging, the people who reported to us that they have the hardest time incorporating more empathy into their responses improved the most when using HAILEY. We found that for these users, the gains in empathy from employing human-AI collaboration were 27% higher than for people who did not find it as challenging.”
An example of how HAILEY assists peer supporters to incorporate more empathic language in their responses to people seeking mental health support.
According to co-author Adam Miner, a licensed clinical psychologist and clinical assistant professor in Stanford University’s Department of Psychiatry and Behavioral Sciences, HAILEY is an example of how to leverage AI for mental health support in a safe and human-centered way.
“Our approach keeps humans in the driver’s seat, while providing real-time feedback about empathy when it matters the most,” said Miner. “AI has great potential to improve mental health support, but user consent, respect and autonomy must be central from the start.”
Tim Althoff
To that end, the team notes that more work needs to be done before a tool like HAILEY will be ready for real-world deployment. Those considerations range from the practical, such as how to effectively filter out inappropriate content and scale up the system’s ability to provide feedback on thousands of conversations simultaneously and in real-time, to the ethical, such as what disclosures should be made about the role of AI in response to people seeking support.
“People might wonder ‘why use AI’ for this aspect of human connection,” Althoff said in an interview with UW News. “In fact, we designed the system from the ground up not to take away from this meaningful person-person interaction.
“Our study shows that AI can even help enhance this interpersonal connection,” he added.
Since he first arrived at the University of Washington in 2007, Allen School professor Luis Ceze has worn many hats: teacher, mentor, researcher, entrepreneur, venture investor. As of this week, he can add Fellow of the Association for Computing Machinery to that list after the organization bestowed upon him its most prestigious level of membership for “contributions to developing new architectures and programming systems for emerging applications and computing technologies.”
A computer architect by training, Ceze has been at the forefront of an expanding vision of the future of computation — and challenging the computer architecture community to rethink what a computer even is, thanks in part to some nifty research at the intersection of information technology and biology. His work also has extended to reimagining the hardware/software stack and embracing the emerging capabilities of machine learning.
“I’m motivated by the question of how we can build new programming models with and for future technologies and applications,” said Ceze, the inaugural holder of the Edward D. Lazowska Endowed Professorship at the Allen School. “There is so much untapped potential in drastically improving efficiency, enabling new types of applications, and making use of new hardware and device technology. From machine learning to automated hardware/software to molecular programming, we are in the midst of a new computing revolution.”
Ceze has played a significant role in enabling that revolution, having broken new ground with his work on DNA-based data storage and computing. As co-director of the Molecular Information Systems Lab, Ceze has teamed up with Allen School colleagues, Microsoft researchers and synthetic DNA supplier Twist Bioscience on an ambitious series of projects that demonstrate synthetic DNA’s potential as a data storage medium, developing a process for converting those digital 0s and 1s into the As, Ts, Cs and Gs of DNA — and then, crucially, back again — that combined advances in biotechnology with computational techniques such as error encoding schemes.
“Life has produced this fantastic molecule called DNA that efficiently stores all kinds of information about your genes and how a living system works — it’s very, very compact and very durable,” Ceze explained in a UW News release in 2016. “This is an example where we’re borrowing something from nature — DNA — to store information. But we’re using something we know from computers — how to correct memory errors — and applying that back to nature’s ‘device technology.’ “
Since their initial paper appeared at the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Ceze and his MISL collaborators have set a new record for the amount of data stored in DNA, demonstrated the ability to perform random access to selectively retrieve stored files and convert them back to digital format, and developed a method for performing content-based similarity search of digital image files stored in DNA — moving past an initial focus on DNA’s prospects as an archival storage medium to, as Ceze observed at the time, “pave the way for hybrid molecular-electronic computer systems.” The team also built a prototype of an automated, end-to-end system for encoding data in DNA.
“Our initial work on DNA data storage helped motivate and inform U.S. government research investment in this space, and then it expanded to other directions,” Ceze said. “And it was brought about by a collaborative team involving computer system architects, molecular biologists, machine learning engineers, and others. What we have in common is a curiosity and an excitement about what computing can learn from biology, and vice versa. Not many computer science schools have their own wet lab!”
Ceze didn’t need a wet lab for his other innovation: TVM, short for Tensor Virtual Machine, a flexible, efficient, end-to-end optimization framework for deploying machine learning applications across a variety of hardware platforms. Developed by a team that combined expertise in computer architecture, systems and machine learning, TVM bridged the gap between deep learning systems optimized for productivity and various hardware platforms, each of which are accompanied by their own programming, performance and efficiency constraints. TVM would allow researchers and practitioners to rapidly deploy deep learning applications on a range of systems — from mobile phones, to embedded devices, to and specialized chips — without having to sacrifice battery power or speed.
“Efficient deep learning needs specialized hardware,” Ceze noted at the time. “Being able to quickly prototype systems using FPGAs and new experimental ASICs is of extreme value.”
Ceze and his collaborators later teamed up with Amazon Web Services to build upon the TVM stack with the NNVM — short for Network Virtual Machine — compiler for deploying deep learning frameworks across a variety of platforms and devices. A year after TVM’s initial release, the team introduced the Versatile Tensor Accelerator, or VTA, an open-source customizable deep-learning accelerator for exploring hardware-software co-design that enables researchers to rapidly explore novel network architectures and data representations that would otherwise require specialized hardware support.
The team eventually handed off TVM to the non-profit Apache Software Foundation as an incubator project. Ceze subsequently co-founded a company, OctoML, that builds upon and uses the Apache TVM framework to help companies deploy machine learning applications on any hardware, reducing effort and operational costs. To date, the UW spinout — for which Ceze serves as CEO — has raised $132 million from investors and currently employs more than 130 people, with the majority in Seattle and the rest spread across the U.S. and abroad.
Before delving into deep learning accelerators and DNA synthesizers, Ceze made his mark in approximate computing. Combining aspects of programming languages, compilers, processor and accelerator architectures, machine learning, storage technologies, and wireless communication, Ceze and his colleagues developed a principled approach for identifying permissible tradeoffs between the correctness and efficiency of certain applications, such as those for search and video, to achieve significant energy savings in exchange for minimal sacrifices in output quality.
Their initial contributions revolved around EnerJ — referred to as “the language of good-enough computing” — is a Java extension that enables developers to designate which program components should yield precise or approximate results to achieve performance savings and then check the quality of output and recompute or reduce the approximation as warranted. The team also developed a pair of hardware innovations in the form of an instruction set architecture (ISA) extension that provided for approximation operations and storage along with a dual-voltage microarchitecture, called Truffle, that enabled both approximate and precise computation to be controlled at a fine grain by the compiler. Ceze and his colleagues subsequently proposed a new technique for accelerating approximate programs using low-power neural processing units and dual mechanisms for approximate data storage that improves the performance and density while extending the usable life of solid-state storage technologies such as Flash.
In addition to his roles at the Allen School and OctoML, in his “free time” Ceze is also a venture partner at Madrona Venture Group and chairs their technical advisory board. Madrona funded OctoML and his first startup, Corensic, that was spun out of the UW in 2008. Before his ascension to ACM Fellow, Ceze shared the ACM SIGARCH Maurice Wilkes Award from the ACM Special Interest Group on Computer Architecture with MISL co-director and Allen School affiliate professor Karin Strauss, senior principal research manager at Microsoft. He is the co-author of multiple Best Papers and IEEE Micro Top Picks and holds a total of 29 patents based on his research. To date, he has guided 23 Ph.D. students as they earned their degrees on their way to launching careers in academia or industry.
“Computing is an extremely rich field of intellectual pursuit, and it is especially exciting now with the convergence of abundant computing resources, new AI techniques, and the ability to interact with natural systems from the molecular level all the way to the cognitive level,” said Ceze. “I’m honored by this recognition and am extremely grateful to all my Ph.D. advisees and collaborators for contributing so much to the work and to my career!”
The Computing Research Association recently honored Allen School undergraduates Michael Duan and Anas Awadalla as part of its Outstanding Undergraduate Researcher Awards program for 2023. The annual program highlights exceptional undergraduate students from across North America for their contributions to the computing field.
“It’s really cool that I got a chance to be considered among other innovative and hardworking undergraduates,” Duan said. “Their work is extremely inspiring to me, so I’m glad I got an opportunity to share my work alongside them.”
Duan was first author on both projects. The first focused on automated sidewalk accessibility assessment with crowdsourced data. Together, Duan and his co-authors investigated using computer vision methods to determine and label the presence of accessibility features such as curb ramps in urban scenery — findings, he said, that can assist urban planners, disability advocates and city governments in designing smarter, more inclusive infrastructure.
The second project explored data visualization related to disability advocacy and urban planning. With the sheer amount of ever-increasing datasets, better ways of visualizing that data are needed. To tackle this problem, Duan and his collaborators introduced Sidewalk Gallery, an interactive, filterable gallery of more than 500,000 crowdsourced sidewalk accessibility images across seven cities in two countries. The innovative interface allows users to browse and cull these images for different accessibility problem types, severity levels and more, providing a visualization aid and a teaching tool for urban design.
“Most datasets are geared primarily towards computer vision,” Duan said. “For both researchers and the general public, visualization tools are critical to exposing and understanding data, broadening reach and lowering barriers to use.”
“I am happy to get this distinction and to have my research recognized,” said Awadalla, a senior computer science major. “Research has been the most fulfilling experience during my time at UW.”
In his most recent research project, Awadalla and his co-authors investigated how NLP practitioners can use specific training methods to improve the robustness of their QA systems. Though research in the domain has increased, the community lacks a shared framework from which to evaluate robustness. Citing this challenge, Awadalla and his collaborators conducted a large empirical study to better understand and assess methods that generalize with more reliability.
Awadalla also collaborated on a project improving the reliability of AI-powered diagnostic and health screening tools. With unseen data, machine learning models can behave unpredictably, raising safety concerns in a field where errors can prove fatal. The research team demonstrated this flaw by using publicly available deep learning models and datasets. Then they created an interpretable confidence score for users, Awadalla said, to assess the compatibility of their dataset with a trained model. Their findings were integral in building consumer-friendly and trustworthy AI applications that can help patients and healthcare providers make more-informed decisions.
Raul Villanueva, an undergraduate in the UW Department of Electrical & Computer Engineering, was also recognized by CRA as a finalist.
Congratulations to Michael, Anas and Raul! Read more →
Allen School researchers continue to push the boundaries of artificial intelligence (AI) and in building innovative image-text models. At the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), several members from the Allen School, along with researchers from the Allen Institute for AI (AI2), earned recognition for their work in advancing their respective fields.
Allen School undergraduate Matt Deitke, professor Ali Farhadi, affiliate professors Ani Kembhavi, director of computer vision at AI2, and Roozbeh Mottaghi, research scientist manager at Meta AI, and their collaborators at AI2 won an Outstanding Paper Award for ProcTHOR: Large-Scale Embodied AI Using Procedural Generation, which investigated scaling up the diversity of datasets used to train robotic agents. The paper was among 13 selected for Outstanding Paper Recognition out of the 2,672 papers accepted to the conference — a high bar, given that NeurIPS received more than 10,000 total submissions.
Matt Deitke
“We are delighted to have received the Outstanding Paper Award at NeurIPS 2022 for our work on ProcTHOR,” said Deitke, first author on the paper. “It is great recognition from the AI community, and it motivates us to continue pushing the boundaries of AI research. The award is a reflection of the work of our team and the research communities fostered at the Allen School at UW and AI2.”
In addition to Deitke, Farhadi, Kembhavi and Mottaghi, contributors to ProcTHOR include AI2 technical artist Eli VanderBilt, research engineers Alvaro Herrasti and Jordi Salvador, research scientists Luca Weihs and Kiana Ehsani (Ph.D., ‘21) and game designer Winson Han, and DexCare director of data science Eric Kolve. Kolve and Mottaghi were at AI2 for the duration of the project.
The open-source ProcTHOR introduces a new framework for procedural generation of embodied AI environments. Prior to its introduction, artists had to manually design spaces such as simulated 3D houses or build environments via 3D scans of real structures. Each approach carried drawbacks, namely due to their cost, time-intensive nature and inability to scale. If the team trained a robot only on 100 houses designed by the artists, Deitke said, it would perform well in those environments but not generalize when placed in a house it had never seen before.
“In ProcTHOR, we took on a difficult challenge of attempting to generate 3D houses from scratch,” Deitke said. “The generated houses can then be used to massively scale up the amount of training data available in embodied AI.”
With ProcTHOR, the robots demonstrated robust generalization results. Instead of 100 houses, the team sampled 10,000, showing the power of ProcTHOR’s data.
The implications of such findings are manifold. Household robots have shown potential to aid in a number of tasks around the home, providing assistance to many, including individuals with disabilities and the elderly. For example, tasks such as cooking and cleaning could be delegated to the AI. Research has also explored how companion robots can assist with social interaction, motivating their owners to exercise, remind them about appointments or even talk about the weather and news.
They’re all aspects the team has taken into consideration as it looks to the future. Already, ProcTHOR has received interest from a number of companies, researchers and architects who see its potential to reimagine the AI landscape.
“It is truly inspiring that ProcTHOR is able to enable work across many areas of AI,” Deitke said.
“We are glad to see work in developing open source datasets and models recognized by the NeurIPS community,” Schmidt said. “We are particularly happy as the LAION-5B dataset is a grassroots community effort and we believe that this kind of collaboration is essential to driving progress in machine learning.”
Before LAION-5B, which contains 5.85 billion CLIP-filtered image-text pairs, no datasets of this size were made publicly available, creating a bottleneck wherein related research flowed through a small number of industrial research labs. While multimodal machine learning has progressed rapidly in recent years, large companies and labs such as Google and OpenAI have driven much of it.
LAION-5B addresses exactly this hurdle by introducing the first public billion-scale image-text dataset that is suitable for training state-of-the-art multimodal models. In addition, LAION-5B also provides a starting point for researchers working to improve image-text datasets. Furthermore, the team showed that LAION-5B could replicate the performance of the original CLIP models using the OpenCLIP library, which was also developed by Allen School researchers. The authors also created a web interface to browse the dataset, which makes it easy to audit — one example being finding toxic content that the automatic filters missed.
“Moreover, we hope that the community collaborates to further improve public training datasets so that we can together mitigate bias and safety concerns in our widely used machine learning datasets,” Wortsman said. “We have already performed content analysis and filtering for LAION-5B, but there is still much work to be done. Open and transparent datasets are an important step towards safer and less biased models.”
Mitchell Wortsman
Other researchers have already built upon LAION-5B with projects such as Stable Diffusion or Imagen, which both generate images from a text description.
“We believe that future projects will continue along these directions to develop more capable multimodal machine learning models,” Schmidt said. “LAION-5B also presents a unique opportunity to study the influence of the data on the behavior of multimodal models. Finally, we hope that researchers will build upon LAION-5B to develop the next generation of open datasets.”
Stability AI lead cloud architect Richard Vencu and Stability AI artist Katherine Crowson also contributed to the project as co-authors, as well as Google machine learning engineer Romain Beaumont, UC Berkeley undergraduate research assistant Cade Gordon, Hugging Face computer vision developer Ross Wightman, Jülich Supercomputing Centre postdoctoral researcher Mehdi Cherti, TU Darmstadt University Ph.D. student Patrick Schramowski and Technische Universität München researcher Robert Kaczmarczyk.
Allen School professor Tadayoshi Kohno has devoted his career to advancing security, privacy and safety in multiple industries, from cars to cardiac defibrillators, while also advancing thoughtful and inclusive approaches to technology design. Meanwhile, his colleague Rajesh Rao contributed to the progress of brain-computer interfaces from science fiction to actual science by demonstrating their very real potential to help people with neurological injury or disease. What they both have in common is the honor of being named a 2023 IEEE Fellow by the world’s largest technical professional association focused on advancing technology to benefit humankind.
Tadayoshi Kohno: Putting the brakes on security and privacy threats
IEEE Fellow Tadayoshi Kohno’s contributions to security and privacy span multiple domains, from the automobile and medical device industries, to electronic voting and mixed reality.
IEEE honored Kohno for “contributions to cybersecurity” — an apt reference to Kohno’s broad influence across a variety of domains. As co-director of the Allen School’s Security and Privacy Research Lab and the UW Tech Policy Lab, Kohno has explored the technical vulnerabilities and societal implications of technologies ranging from do-it-yourself genealogy research, to online advertising, to mixed reality.
His first foray into high-profile security research, as a Ph.D. student at the University of California San Diego, struck at the heart of democracy: security and privacy flaws in the software that powered electronic voting machines. What Kohno and his colleagues discovered shocked vendors, elections officials, and other cybersecurity experts.
“Not only could votes and voters’ privacy be compromised by insiders with direct access to the machines, but such systems were also vulnerable to exploitation by outside attackers as well,” said Kohno. “For instance, we demonstrated that a voter could cast unlimited votes undetected, and they wouldn’t require privileged access to do it.”
After he joined the University of Washington faculty, Kohno turned his attention from safeguarding the heart of democracy to an actual heart when he teamed up with other security researchers and physicians to study the security and privacy weaknesses of implantable medical devices. They found that devices such as pacemakers and cardiac defibrillators that rely on embedded computers and wireless technology to enable physicians to non-invasively monitor a patient’s condition were vulnerable to unauthorized remote interactions that could reveal sensitive health information — or even reprogram the device itself. This groundbreaking work earned Kohno and his colleagues a Test of Time Award from the IEEE Computer Society Technical Committee on Security and Privacy in 2019.
“To my knowledge, it was the first work to experimentally analyze the computer security properties of a real wireless implantable medical device,” Kohno recalled at the time, “and it served as a foundation for the entire medical device security field.”
Kohno and a group of his students subsequently embarked on a project with researchers at his alma mater that revealed the security and privacy risks of increasingly computer-dependent automobiles, and in dramatic fashion: by hacking into a car’s systems and demonstrating how it was possible to take control of its various functions.
“It took the industry by complete surprise,” Kohno said in an article published in 2020. “It was clear to us that these vulnerabilities stemmed primarily from the architecture of the modern automobile, not from design decisions made by any single manufacturer … Like so much that we encounter in the security field, this was an industry-wide issue that would require industry-wide solutions.”
Those industry-wide solutions included manufacturers dedicating new staff and resources to the cybersecurity of their vehicles, the development of new national automotive cybersecurity standards, and creation of a new cybersecurity testing laboratory at the National Highway Transportation Safety Administration. Kohno and his colleagues have been recognized multiple times and in multiple venues for their role in these developments, including a Test of Time Award in 2020 from the IEEE Computer Society Technical Committee on Security and Privacy and a Golden Goose Award in 2021 from the American Association for the Advancement of Science. And most importantly, millions of cars — and their occupants — are safer as a result.
Kohno has journeyed into other uncharted territory by exploring how to mitigate privacy and security concerns associated with nascent technologies, from mixed reality to genetic genealogy services. For example, he and Security and Privacy Research Lab co-director Franziska Roesner have collaborated on an extensive line of research focused on safeguarding users’ security and privacy in augmented-reality environments. The results include ShareAR, a suite of developer tools for safeguarding users’ privacy while enabling interactive features in augmented-reality environments. They also worked with partners in the UW Reality Lab to organize a summit for members of academia and industry and issue a report exploring design and regulatory considerations for ensuring the security, privacy and safety of mixed reality technologies. Separately, Kohno teamed up with colleagues in the Molecular Information Systems Lab to uncover how vulnerabilities in popular third-party genetic genealogy websites put users’ sensitive personal genetic information at risk. Members of the same team also demonstrated that DNA sequencing software could be vulnerable to malware encoded into strands of synthetic DNA, an example of the burgeoning field of cyber-biosecurity.
Kohno’s contributions to understanding and mitigating emerging cybersecurity threats extend to autonomous vehicle algorithms, mobile devices, and the Internet of Things. Although projects exposing the hackability of cars and voting machines may capture headlines, Kohno himself is most captivated by the human element of security and privacy research — particularly as it relates to vulnerable populations. For example, he and his labmates recently analyzed the impact of electronic monitoring apps on people subjected to community supervision, also known as “e-carceration.” Their analysis focused on not only the technical concerns but also the experiences of people compelled to use the apps, from privacy concerns to false reports and other malfunctions. Other examples include projects exploring security and privacy concerns of recently arrived refugees in the United States, with a view to understanding how language barriers and cultural differences can impede the use of security best practices and make them more vulnerable to scams, and technology security practices employed by political activists in Sudan in the face of potential government censorship, surveillance, and seizure.
“I chose to specialize in computer security and privacy because I care about people. I wanted to safeguard people against the harms that can result when computer systems are compromised,” Kohno said. “To mitigate these harms, my research agenda spans from the technical — that is, understanding the technical possibilities of adversaries as well as advancing technical approaches to defending systems — to the human, so that we also understand people’s values and needs and how they prefer to use, or not use, computing systems.”
In addition to keeping up with technical advancements that could impact privacy and security, Kohno is also keen to push the societal implications of new technologies to the forefront. To that end, he and colleagues have investigated a range of platforms and practices, from the development of design principles that would safeguard vulnerable and marginalized populations to understanding how online political advertising contributes to the spread of misinformation, to educate and support researchers and developers of these technologies. He has also attempted to highlight the ethical issues surrounding new technologies through a recent foray into speculative and science fiction writing. For example, his self-published novella “Our Reality” explores how mixed reality technologies designed with a default user in mind can have real-world consequences for people’s education, employment, access to services, and even personal safety.
“It’s important as researchers and practitioners to consider the needs and concerns of people with different experiences than our own,” Kohno said. “I took up fiction writing for the joy of it, but also because I wanted to enable educators and students to explore some of the issues raised by our research in a more accessible way. Instead of identifying how a technology might have gone wrong, I want to help people focus from the start on answering the question, ‘how do we get this right?’”
Rajesh Rao: Engineering new possibilities through brain-computer interfaces
IEEE Fellow Rajesh Rao is a pioneer in brain-computer interfaces and computational modeling for advancing neuroscience and artificial intelligence.
Rao was elevated to IEEE Fellow for “contributions to brain-computer interfaces and computational modeling.” As a holder of the Cherng Jia and Elizabeth Yun Hwang Professorship in the Allen School and UW Department of Electrical & Computer Engineering, director of the Neural Systems Lab and co-director of the Center for Neurotechnology, Rao has been instrumental in advancing the fields of computational neuroscience and neural engineering over the past decade while helping to bring BCIs into the mainstream.
Rao’s early research focused on the application of Bayesian modeling to understand prediction and learning in the brain. In 1999, he and his Ph.D. advisor, Dana Ballard, introduced predictive coding, a hierarchical neural network model of how the visual cortex is constantly predicting and learning a model of the world by minimizing prediction errors. As the first to explain the phenomenon known as “endstopping” — the scenario in which a neuron ceases responding to a stimulus once it has extended beyond the neuron’s classical receptive field — the duo’s work has been highly influential in both neuroscience and artificial intelligence (AI), anticipating the importance of prediction in recent AI models such as transformers.
Rao subsequently built upon that work by developing models for attention, planning as inference, robotic imitation learning, and more. He was the first to apply approximate Bayesian inference for an arbitrary hidden Markov model in a recurrent neural network commonly used to model the cerebral cortex. Using an orientation discrimination task and a visual motion detection task, Rao demonstrated that his approach produces neural dynamics that emulate the response of decision-making neurons in the brain. He later combined Bayesian inference and the theory of partially observable Markov decision processes (POMDPs) with temporal difference learning algorithms to produce a model of decision-making under uncertainty — akin to how animals choose when to switch from information gathering to overt action to maximize future expected reward in the face of incomplete knowledge. For a well-known motion discrimination task, Rao demonstrated that his model neurons exhibited responses similar to those of the primate lateral intraparietal cortex.
“Predictive coding and Bayesian modeling of brain function are just two examples of the extremely fruitful ways in which computer science can help advance neuroscience and vice versa, leading to synergistic interactions between the two fields,” Rao said. “Another way is coupling computers directly to brains using brain-computer interfaces or BCIs.”
Leading a multidisciplinary research team at CNT with fellow Hwang Professor and co-director Chet Moritz, Rao helped develop new bi-directional BCIs — implantable devices that can both interpret brain signals and use this information to stimulate areas of the brain and spinal cord to help people regain function lost due to spinal cord injury, stroke, or other neurological conditions. Such devices also have the potential to promote neuroplasticity in regions of the brain or spinal cord that have been damaged — essentially assisting the networks of neurons in these regions to repair themselves.
“When Christopher Reeve sustained a spinal cord injury due to a fall from his horse, his brain circuits were still intact and able to form the intention to move, but unfortunately the injury prevented that intention from being conveyed to the spinal cord,” Rao explained to UW News in 2015, citing the original Superman actor’s paralysis as an example of how those connections are severed. “Our implantable devices aim to bridge such lost connections by decoding brain signals and stimulating the appropriate part of the spinal cord to enable the person to move again.”
In addition to advancing technologies that would restore the connections between an individual’s brain and their limbs, Rao has also explored techniques for creating new connections between multiple people’s brains. In 2013, he and another UW colleague, Andrea Stocco, a professor in the UW Department of Psychology and researcher in the Institute for Learning & Brain Sciences, offered the first demonstration of human brain-to-brain communication over the internet. During the experiment, Rao directed Stocco via brain signals to move his finger to hit a target on a video game from the other side of the UW campus. Their method of remote communication involved a non-invasive combination of electroencephalography, which recorded Rao’s silent instruction, and transcranial magnetic stimulation, which transmitted that instruction to Stocco’s brain.
A year after achieving this milestone, the researchers replicated their demonstration — this time involving multiple pairs of participants. Alluding to the Vulcan mind-meld from Star Trek and the premise of the movie Avatar, “we realized that we had all the equipment we needed to build a rudimentary version of this technology,” the duo wrote in Scientific American. “Along with other scientists, we are now learning to bypass traditional modes of communication and swap thoughts directly between brains.”
There were limitations to their initial proof of concept; it was restricted to communication between two people, and the second could only passively receive information. In 2019, Rao and his collaborators addressed that problem head-on with BrainNet, which expanded the capabilities of their brain-to-brain interface to enable a group of people to collaborate remotely to solve a task — in this case, a Tetris-like game — by both sending and receiving information.
“Humans are social beings who communicate with each other to cooperate and solve problems that none of us can solve on our own,” Rao said in a UW News release at the time. “We wanted to know if a group of people could collaborate using only their brains. That’s how we came up with the idea of BrainNet: where two people help a third person solve a task.”
In a series of experiments, participants averaged 81.25% accuracy in completing the task set for them, with the receiver learning to trust the instructions from the more reliable sender. The work pointed to the potential for humans to engage in collaborative problem-solving via a “social network” of brains.
Recently, Rao has returned his attention to his earlier, career-defining research to take advantage of new developments in the field while continuing his research in BCIs.
“This is an exciting time to be working at the intersection of computer science and neuroscience,” said Rao. “Inspired by recent advances in AI, my group is currently working on new versions of predictive coding, called dynamic and active predictive coding, to better understand brain function while also suggesting new brain-inspired AI models. In the area of BCIs, we have proposed a new type of BCIs called brain co-processors that use artificial neural networks to interact with biological brains to restore or augment human function.”
Rao and Kohno are among four University of Washington professors elevated to the status of IEEE Fellow for 2023. They are joined by Uri Shumlak, a professor in the William E. Boeing Department of Aeronautics & Astronautics who was recognized for “research of sheared flow stabilization of the Z pinch for fusion energy,” and Yinhai Wang, a professor in the Department of Civil & Environmental Engineering who was recognized for “contributions to traffic sensing, transportation data science, and smart infrastructure systems.”
Two years of conducting business, school, and socialization online during the COVID pandemic exposed the extent to which unequal access and inadequate connectivity is a barrier for people in both rural and urban communities. For a team led by Allen School professor Kurtis Heimerl, closing the gap in access to internet connectivity had been the focus of earlier research in far-off locales. With the pandemic highlighting connectivity challenges closer to home, the researchers turned their focus to what they could do to help the estimated 5% of the population that remains unconnected in Seattle and neighboring communities in the Puget Sound region.
The team — which includes Ph.D. students Esther Jang and Matthew Johnson and postdoc Spencer Sevilla — aims to address the problem by supporting the creation and expansion of community networks (CNs). CNs are community-built and managed infrastructure that offer tailored access to that community. While CNs have been successfully deployed on a small scale, large-scale adoption is impeded by both technical and non-technical issues that include the cost of technology, limited unlicensed spectrum, and difficulty scaling with non-technical partners. The team’s approach was twofold: first, they needed to implement the required technological infrastructure and second, they required community partners to make the goal of increased connectivity a reality.
Their efforts were recently recognized as the Best Overall Winner in the Proof of Concept Category in the 2022 IEEE Connecting the Unconnected Challenge, a global competition that aims to identify research projects that bridge the digital divide in innovative ways and offer technological solutions to improve connectivity for unconnected and under-connected people to facilitate the expansion of online marketplaces and provide opportunities to engage with increasingly online services for health, education and economic progress. The Allen School-led project, “Scaling the Seattle Community Network with dAuth and the Teaching Network,” introduces an innovative approach to core network design and highlights the establishment of seven functional CN stations in Seattle, Tacoma and other local municipalities. Likewise, this project showcases the success of building community partnerships through research and a Digital Stewards Trainee Program to extend internet connectivity to people who are marginalized and who live in low-income areas.
The new core network design utilizes an open source, community-based federated trust model that allows for seamless scaling of the network with incremental trust between partner organizations. This facilitates authentication and authorization for granting access on a serving network. In this way networks do not need to have preexisting relationships. A wide range of off-the-shelf devices without modification can be supported under this design.
The network is designed to tolerate temporary failure of one of the partner organization’s networks without the overall system losing functionality, as well as the means to operate with the presence of malicious nodes that exist outside of the user’s home network without affecting the user’s security. The team built the system with existing hardware created for standardized 4G and 5G networks that does not require a system-specific upgrade from the manufacturer.
To date, Heimerl’s team has successfully deployed CN stations at the Seattle Filipino Community Center, the main branch of Tacoma Public Libraries, Garfield High School in Seattle’s Central District, Franklin High School in Seattle’s Mount Baker neighborhood, the Oromo Cultural Center in Seattle’s Rainier Beach neighborhood, the Skyway Library and SURGETacoma. Related to this, the team has engaged with their partner community organizations through digital stewardship, vocational training and digital literacy curriculum development.
“Our theory of change revolves around the coupling of core technical advances that place power and control in the hands of community members with capacity building to bolster communities’ ability to manage and maintain those technologies,” explained Heimerl. “We believe this holistic vision will dramatically reshape the local telecommunications ecosystem and produce sustainable innovations that impact both industry and communities where people have lacked adequate internet access.”
The initial stages of this work culminated in the founding of the non-profit Local Connectivity Lab, which would form the basis for the Seattle Community Network and would deploy the necessary LTE infrastructure to make the project come to fruition. The lab has entered into multiple partnerships in the communities it serves, including but not limited to Seattle Community College, Seattle Public Libraries, Seattle Public Schools, Tacoma Public Library, Tacoma Cooperative Network, Black Brilliance Research Project, Althea and the Filipino Community of Seattle.
Starting in high school, Suchin Gururangan felt the pull of research. A summer internship piqued his curiosity. Projects in his university’s neuroscience lab fed it further.
But “life happens,” as he put it, and graduate school took a backseat for the time being. He jumped into industry, first in venture capital, then in cybersecurity. Following graduation from the University of Chicago, he moved to the Boston area. Even after landing in Seattle to join a startup, the former researcher still had questions he wanted to answer through an academic lens.
“Throughout my journey in industry, I had always had some lingering desire to revisit my early days of research,” Gururangan said. “Which was always so exciting and fulfilling to me.”
When the startup folded, Gururangan decided to rekindle those interests, pursuing his master’s in the computational linguistics (CLMS) program. The timing couldn’t have been better.
“It was too late for most application deadlines,” he said. “I applied on a whim with the intention of coming back into industry afterwards. I got into the program and then the rest was history!”
Gururangan eventually joined professor Noah Smith’sARK group in natural language processing (NLP) and nurtured his passion for research besides lasting friendships. While working as a predoctoral young investigator with the Allen Institute for AI, he grew a lot as a scholar, he said, crediting his collaborators and mentors for encouraging his academic pursuits.
Now as a third-year Ph.D. student, Gururangan is continuing to channel his curiosity into solving real-world problems. He recently received a 2022-2023 Bloomberg Data Science Ph.D. Fellowship, which provides early-career researchers in the data science field with financial aid and professional support. Fellows also have the opportunity to engage in a 14-week summer internship in New York, during which they’ll further their research goals while also helping Bloomberg crack complex challenges in fields such as machine learning, information extraction and retrieval and natural language processing, among several others.
“I’m grateful to Bloomberg for recognizing the research that me and my collaborators have done,” Gururangan said. “And I’m really thankful to all my collaborators who have helped me bring these research directions to life; I’m really proud about the work we’ve done so far.”
During his internship at Bloomberg next year, Gururangan will work on models that adapt to constantly evolving text streams. He’ll focus on building rapidly updating language models on incoming streams of news and also on developing methods to preserve privacy in experts specialized to sensitive financial documents.
The work dovetails with his previous research on a new domain expert mixture (DEMix) layer for modular large language models and embarrassingly parallel training of large language models. Each tackles the issue of centralization: Instead of one centralized model trained on thousands of graphics processing units (GPUs), which is costly and can invite bias, a number of smaller models fills the gap. The researchers can asynchronously train these models to specialize in a particular domain. For instance, they may train the models, or experts, in parsing social media text or scientific documents. Designed with modularity in mind, the experts exhibit more flexibility on the fly — a boon for organizations such as Bloomberg, where the news cycle never sleeps.
“How do you efficiently update these models or these experts to incorporate that new information that’s coming in?” Gururangan said. “That’s a really hard problem. Especially with these bigger existing models.”
Centralization also can lead to ethical questions surrounding data curation. Gururangan is trying to help the data science community better understand and answer these queries through the lens of artificial intelligence (AI) and NLP.
“The way that data is selected for training these models is really centralized and only a few people have the power to select that data,” Gururangan said. “And so what happens when only a few people are able to do that? What we found is that people have their own ideologies about what makes good text, what makes for reasonable text for models to see and be exposed to, and that has important downstream biases that result.”
Gururangan was also part of the team that created RealToxicityPrompts, a dataset of 100,000 naturally occurring text prompts that helped evaluate the phenomenon of toxic language degeneration – or how pretrained neural language models can exhibit the biases and noxious content that can result from having only a few hands at the controls. Even with just a handful of harmful inputs, the language models could go rogue, generating toxic content with real consequences.
“We’ve been thinking a lot about how you redesign these models to be more decentralized and so more people have more power to shape what the data coverage is at these models,” Gururangan said. “That can inform what sorts of technical solutions you’re interested in and what the possibilities are.”
Those possibilities continue to drive Gururangan to seek out solutions using data. Being at the intersection of AI and ethics excites him, he said, and not only feeds his curiosity but also his desire to focus his research toward advancing social good.
“Much of my research involves understanding language variation in large datasets,” Gururangan said, “and I strongly believe that if we’re careful about and understand where our training data is coming from, the stronger and more reliable our language technologies will be.” Read more →
Each December, the Allen School invites prospective students and families to join us for a week-long celebration of Computer Science Education Week, a nationwide event that aims to inspire students, advance equity, and honor those who are contributing to the field and to society. After being compelled to go fully virtual due to the pandemic in 2020 and 2021, the school’s Diversity & Access Team was thrilled to offer a hybrid celebration this year.
Throughout the week, prospective students and families joined Allen School researchers, staff and students for virtual sessions devoted to a range of topics, from “a day in the life” of an Allen School major, to exploratory discussions of computing’s impact on society, to research talks and demos spanning artificial intelligence, computer security, robotics, accessibility, and more.
“CS Education Week is our largest outreach event of the year,” said Assistant Director for Diversity & Access Chloe Dolese Mandeville. “We know that it’s extremely important for future students to picture themselves in computing and the Allen School before they even step foot on campus as an undergraduate. This week is an opportunity for us to provide that picture for high school students — especially those who don’t have access to computing opportunities in their schools or communities.”
To that end, in addition to offering an overview of what it’s like to be an Allen School student, the team also organized sessions devoted specifically to highlighting the experience of students from underrepresented communities and those who are the first generation in their families to pursue a bachelor’s degree. The celebration culminated in a virtual Hour of Code followed by an in-person open house in the Allen Center and Gates Center on the University of Washington’s Seattle campus, where an estimated 250 people spent the day touring the labs, participating in interactive demos, talking to current students and researchers, and experiencing firsthand what it means to be part of the Allen School and the field of computing.
“It was an incredible experience to host students and their families on campus again after three years,” said Dolese Mandeville. “We loved the energy that students and their families brought to campus — taking photos with each other, getting to know our undergraduate students, and engaging in hands-on activities. This celebration is really about welcoming more students into the computing community and the Allen School community, too.”
Members of the Diversity & Access Team, including our student ambassadors and Changemakers in Computing mentors, were ready to welcome 250 students, family members and friends to the Allen School’s first open house since 2019.
Visitors assembled in the auditorium for an Allen School information session to learn about the student experience and the application process.
Students and their families explore the Gates Center.
Next stop: the Machine Learning Lab.
Speaking of learning, visitors learned about a LEGO simulator that is teaching machines to reason about structures the way kids do — by taking things apart and putting them back together again. (Bonus: The bricks are virtual, so there’s no danger of accidentally stepping on one!)
Tired: Viewing museum exhibits from a distance. Wired: Interacting with the exhibits at CS Education Week. Inspired: Experiencing what it’s like to be part of the Allen School community!
Changemakers in Computing mentors came prepared, because everyone loves swag (and candy)!
Taylor Swift concert? Nah — not to be “Mean,” but these students and their families are having “The Best Day” at CS Education Week! After finding some “Blank Space” in line at the Allen Center, they waited patiently to tour our UbiComp Lab and be “Enchanted” by some amazing research.
New album, just dropped! (Just kidding — these are members of the Allen School’s Diversity & Access Team taking over the CS Education Week photo booth. But they’re definitely rock stars in our book!)
Thanks to all of our presenters, students and visitors for making CS Education Week a tremendous success! See you next year!
The inaugural 100-member class includes students specializing in a number of disciplines across science, technology, mathematics and engineering. Operated and administered by Schmidt Futures, the Quad Fellowship is designed to spur research efforts and build ties among the next generation of STEM leaders.
Each participating country is represented by 25 Fellows, who are selected for their commitment to advancing innovation for positive social impact. Each Fellow will receive benefits such as funding, cross-cultural exchange through cohort-wide trips, mentorship and participation in regular virtual and in-person workshops on various themes, including the intersection of STEM and society, ethics and innovation, and emerging technologies.
The Quad governments announced the first cohort of Fellows last week.
“It is my great privilege to join the four Quad leaders in welcoming our inaugural cohort of Quad Fellows,” said Tony Woods, executive director of the Quad Fellowship. “The work that our Fellows do during and after their time as Quad Fellows will lead to bold, new inventions and generational breakthroughs in STEM that will enrich the globe for generations to come. At the same time, our scholars will help to build deeper ties between our four nations and renew the power and possibility of collaborative democracy.”
“I’m very grateful for the opportunity to build ties with other STEM professionals through the Quad Fellowship,” Johnson said. “I look forward to meeting a diverse group of students in and outside of my field, and learning how folks from various STEM backgrounds address current global issues. I also know that academia can get daunting, so I aspire to help grow a culture of support amongst the fellows through the highs and lows of our graduate school journeys.”
Orii is a second-year Ph.D. student who works with Allen School professor Richard Anderson in the Information and Communication Technology for Development (ICTD) Lab. She was named as one of the Quad Fellows representing Japan. Her research focuses on the development of human-centered technologies to address barriers associated with stigmatized health conditions, such as HIV and menopause, and improve health care quality and access for vulnerable communities in low-resource settings. Orii was previously recognized as one of MIT Technology Review’s Innovators Under 35 Japan for her work.
“I am very thankful and honored to have been selected into the inaugural cohort of the Quad Fellowship,” Orii said. “I am excited to join a community of researchers who are committed to channeling their skills and knowledge in STEM for social impact. The Quad Fellowship will play a key role in driving my research forward and advancing my mission to empower vulnerable populations and promote health equity. With the fellowship’s financial support, I can actively visit, engage with and collaborate with communities in low-income and low-resource settings on a global scale.
“As a Quad Fellow from Japan,” Orii continued, “I wish to encourage students, especially in my home country, to participate in socially conscious work and recognize that this is not only possible but also valuable.”
UW alum Dellen Behrend, now a master’s student in civil and environmental engineering at the University of California, Berkeley, was also part of the Japan cohort. While an undergraduate student researcher in the UW Biogeotechnics Lab, she focused on the upscaling of bio-cementation soil improvement. She graduated from UW with her bachelor’s in civil engineering in June.
Congratulations to Kyle, Lisa and Dellen! Read more →
When Allen School Ph.D. student Lisa Orii was in high school, she became involved with the work of Parasaiyo, a Japanese nonprofit that supports foster children in the Philippines in pursuing higher education. She began making annual visits to a foster home there over the summer, developing a bond with the children and staff. Between her trips, she helped organize related fundraising events back in her native Japan.
Later, as an undergraduate at Wellesley College, Orii began to think about how she could connect her academic interests with her humanitarian ones. Curiosity crystallized into research. A computer science and philosophy major, she saw the potential for human-centered technologies, especially where they were needed most.
“After discovering human-computer interaction, I thought about how I could apply the thinking and methods of this field to supporting vulnerable populations,” Orii said. “I was extremely excited to join the University of Washington’s Information and Communication Technology for Development (ICTD) Lab, where I found great researchers who have visions similar to mine.”
Orii continues to realize her visions of a better tomorrow. She was recently named one of MIT Technology Review’s Innovators Under 35 Japan, a distinction that recognizes researchers, entrepreneurs and activists under age 35 who are helping to solve challenges in a number of academic and industrial fields.
“I am extremely grateful and honored to receive MIT Technology Review’s Innovators Under 35 Japan Award. I am very excited to join this year’s and past years’ recipients whom I have admired and respected,” Orii said. “With this award, I wish to share with people in my home country the work that my peers and I do at the intersection of technology and society. I hope that this recognition informs more people in Japan that socially motivated work is not only valuable but also possible.”
Orii earned the award in the philanthropist category, which honors researchers who design technology to address injustice and empower vulnerable populations. On this front, Orii has conducted research in global health, data security and privacy, urban planning and more.
In one recent study, Orii and her collaborators at the University of Tokyo investigated how certain vocal characteristics can invite bias and proposed a system for practicing one’s own speaking style that is specifically tailored to individuals’ identities and improves their self-perception. Using interactive voice conversion software, participants manipulated the pitch and duration of their speech and listened to the modified playback. They also listened to recordings of “role model” speakers, evaluating vocal traits besides their capability to be good public speakers.
Orii’s team found that the combination of voice manipulation and model audio had the potential to improve self-perceptions of speech. Previous research has shown that gender biases can inform speech perceptions, with lower, more masculine voices being coded as more authoritative. Through their study, Orii and her collaborators sought to uplift the voices of women and gender minorities in the arena of public speaking.
“Being unable to speak in a natural voice can negatively affect a speaker’s perceptions of their speaking abilities and discourage them from actively speaking,” Orii said. “With this thought, I became interested in thinking about how technology could play a role in speakers’ self-perceptions of their speech performances, hoping that there could be a way for speakers to not only gain insight into their speaking but also better appreciate the way they speak.”
Orii has also completed field research in Malawi identifying areas of improvement in HIV data security and electronic medical records. Along with her advisor, Allen School professor Richard Anderson, and UW Department of Global Health professor Caryl Feldacker, Orii gathered the perspectives of healthcare providers, patients, government officials and others in developing educational programs related to HIV patient data and security.
“There is a lot of context that could only be fully captured through direct observations and interactions at the site,” Orii said. “From this experience, I learned the significance of working directly with stakeholders to address their concerns and needs. Through my conversations with them, I came to greatly appreciate the sophistication, consideration and care that these individuals had with respect to protecting HIV patients’ data. I am very thankful for the HIV clinic for welcoming me into their community and allowing me to do this research.”
In a separate ongoing project, Orii is exploring designs for recording and sharing menopause experiences across generations, promoting open communication via tools to support women’s mental and physical health. Another project in the works asks how technology that delivers accurate information about contraception influences the decision to use contraceptive methods.
Though her research has taken her far and wide, both in geographic and academic terms, the goal of her projects remains the same: helping those in need through technology.
“My research focuses on developing human-centered technologies to address the barriers associated with stigmatized health in low-income and low-resource settings,” Orii said. “By working with local partners, I aspire to advance technologies that improve quality of care and drive equitable access to health care for vulnerable communities.”
