Automatic gender recognition (AGR) is a subfield of facial recognition that has recently been scrutinized for bias in the form of misgendering and erasure against various identity groups in our society. Recent studies have found that several commercial AGR classifiers (from Microsoft, IMB, Face++) are biased against women and darker-skinned people as well as gender non-binary people. In this work, we investigate and quantify AGR classifier bias against transgender people by developing and evaluating three different convolutional neural networks (CNN): using images of cisgender individuals, using images of transgender individuals, and using images of both cisgender and transgender individuals. We find that the cisgender trained classifier is 91.7% accurate when evaluated on cisgender people, but only 68.9% accurate when evaluated on transgender people, with the worst performance on trans men with 38.6% precision. We also find that the classifier trained on the combined dataset performs nearly as well as and occasionally outperforms both other classifiers when evaluated on their own datasets, highlighting potential methods for avoiding overfitting. Additionally, we visualize how the classifiers differ by obscuring different parts of the face. Overall, the disparities of accuracy between each classifier demonstrate the degree to which they are impacted by the composition on their dataset and highlight the possibility for commercial AGR classifiers potential to misgender trans people, in particular, transgender men, at a high rate.

In the general population, adherence to a daily routine is linked with well-being.This appears to be the case to an equal if not greater extent among individuals with schizophrenia. Individuals with schizophrenia-spectrum disorders who consistently engage in activities that typically occur in a routine – e.g. employment, education, healthy sleep, social connections, and physical activity – enjoy an array of physical and mental health benefits. The study of adherence to routine has been limited by the use of retrospective scales, which are common in clinical research. These measures require respondents to provide estimates of the amount and frequency of behaviors over weeks or months. Such estimates are insufficiently granular to assess adherence to routine. The present study aims to examine the relationship between behavioral stability and symptoms in schizophrenia. Our team previously deployed a multi-modal mobile assessment system in a sample of individuals with schizophrenia for twelve months. In this study we revisit those data to develop models that quantify within-day adherence to routine among individuals with schizophrenia. We operationalize adherence to routine as defined in an individual’s behavioral stability, or the extent to which their behaviors detected by sensors stay stable from one day to the next during the study period. The present study has four main aims: whether (1) passively sensed behavioral stability can be quantified, (2) whether it is associated with symptoms and dysfunction in schizophrenia, (3) whether the addition of behavioral stability improves predictions of symptoms and dysfunction, and (4) whether this behavioral stability metric might be associated with future risk of poor outcomes.

Software engineering is a known difficult task that spans a wide variety of problems; as such, an important skill for seasoned software developers is problem solving. Current software engineering research focuses on building tools that support software development processes, but very little research has been done to assist developers with learning programming strategies. Yet, previous research has shown that developers using explicit programming strategies (i.e. procedures in problem solving that were verbally described) were objectively more successful at code design and debugging tasks. In this research project, we extend the previous work in understanding how programming strategies may be used at scale, and whether it is a potentially effective way of improving developer productivity. We propose a novel platform composed of a repository of explicit programming strategies across various programming activities, such as debugging, design, and testing. Developers will be able to search, use, create, and provide feedback on programming strategies on this platform, which will require innovations in defining how explicit programming strategies are searched and indexed. With this explicit strategy sharing platform, we want to understand the experiences of developers who are strategy seekers or givers as well as their motivations using the platform. For strategy seekers, we would like to understand their experience in using strategies, as well as how the feedback process may evolve strategies. For strategy givers, we would like to understand their experience in writing strategies and why they do it. After building the platform, we will deploy the platform in a classroom setting that relates to software engineering, and allow students to use the platform organically. Then, we will perform user interviews and data analysis to evaluate our research question.

The goal of this research is to bridge the gap of stereotypes between homeless adults and other community members. In order to do this, we propose a pen pal application where homeless adults can write and exchange letters with adults who are not homeless. The application provides unique card templates and a pen pal request system that matches two users together based on common interests. By building relationships through customizable letters, our goal is to create understanding and reduce stereotypes between these groups of people. The method we are using to create this application is Value Sensitive Design. Specifically, we are seeking to address the values of storytelling and community, while supporting the interests of the key stakeholders, namely homeless adults, other community members, and Operation Nightwatch. Because we are partnering with Operation Nightwatch, a nonprofit organization that assists the homeless, we are constantly in contact with them, seeking to develop the pen pal application for their organization. However, the main focus are the users of the application so we are making sure to include them in the design process allowing them to share their stories and build relationships through this application. We have interviewed the stakeholders with semi-structured interviews, completed data analysis, analyzed potential stakeholder value tensions and the challenges that may come up, designed wireframes, created a high fidelity prototype, and conducted formative user testing. The main features of the app are: (1) Getting matched to a pen pal based on a profile; (2) Choosing a letter template and writing a letter; and (3) Receiving mail in the inbox. We have kept this application very simple: The focus is on sharing stories and catalyzing new understandings. We are designing and coding the application of the working prototype for the research symposium.

Internet connectivity has reached 51% of the world’s population. A disproportionately large percentage of the unconnected live in small remote communities which are difficult to access and serve under the constraints of traditional centralized communications infrastructure. Community Networking, the act of deploying, operating, and maintaining networks by community members, is a promising approach to bringing sustainable Internet connectivity to these populations. In community networks, users make many of the day-to-day operational choices, including the mechanisms for sharing limited Internet links. In a rural community in Oaxaca, Mexico we explored the values and requirements the community would like to uphold when implementing a new 4G Internet-based network. We worked in partnership with Rhizomatica, a Oaxacan non-profit organization that supports rural communities in deploying community networks, to facilitate a series of workshops and interviews. During three workshops, 39 community members came together to create personas representative of network users which were used to evaluate a range of alternative congestion management models. In informal interviews, we asked community members questions about how they would like to use the Internet and how the community manages other shared resources such as water. Overall, the community expressed a desire for equal division of resources between individuals, prioritization of high-value applications, and preservation of individual privacy. Community leaders also expressed a desire for long-term tracking of network usage to observe the network's impact on the local way of life. The field work in Oaxaca led us to develop a new class of tools supporting communal network management. We are currently in the process of building software to support the network administration in managing the LTE network in Oaxaca and future community-run networks in Mexico and around the globe. We will deploy the preliminary version of the network management software this year.

Although DNA is best known as the molecule that encodes the genetic information of all living things, they can also be utilized as chemical building blocks. Using DNA as the building material of choice, I am working on constructing a complex DNA circuit, in specific, a binary neuron network that utilizes DNA strand displacement reactions to compute a winner-take-all or majority voting operation. Winner-take-all computation is just one type of competitive neural network model, mirroring the lateral inhibition and competition seen in biological neurons of the brain. DNA circuits are efficient at collecting and responding to information within a biochemical environment; processing information locally and producing specific outputs in response to changing environmental conditions. DNA strand displacement is the process by which two DNA strands that are partially or fully complementary hybridize to one another, thereby displacing one or more pre-hybridized strands. Strand displacement reactions are facilitated by a "toehold" domain, a region of exposed DNA on a double-stranded gate complex that is complementary to an input strand.The winner-take-all function can be broken down into sub-functions that use four distinct seesaw DNA gate motifs: weights, thresholding, annihilator, and catalytic amplifier. Additionally, we aim to combine spatial separation in microfluidic droplets with a stricter choice of network architecture to address previously seen issues of scalability. By isolating each computational primitive in droplets, DNA species can be re-used for all primitives of the same network layer. Recently, we have experimentally tested a 5-input neuron and used manual pipetting to simulate droplet operations. While the leak caused problems for patterns close to the decision boundary, we could successfully compute well-separated patterns. Next, we plan on optimizing the sequence design to reduce leak and scale up the network size by automating the network execution on a microfluidic droplet device.

 The internet has changed the norms for how we write and communicate. Many major technology companies communicate with their users in much more casual and conversational language than the formal written English taught in schools. For instance, contractions and sentence fragments are common, the word “like” often used in place of “such as”, and “info” often used instead of “information”. This casual writing style may help users view a company as more approachable, but they may also perceive the casualness as lacking professionality and trustworthiness. Trust and taking the right action are especially important in security-related interfaces, as a user’s security and privacy may be compromised if an interface fails to educate users on secure behaviors. Through an online quantitative study we will explore the effects that formality of language has in security-related prompts. These effects include: how a user understands a prompt, their perception of the prompt’s formality, and how likely they are to take the action the prompt suggests. We will also investigate how users perceive the formality of various major technology companies and whether these perceptions match how the companies actually communicate with users. As average-level formality is different for everyone, we will analyze our results across different demographics, such as education-level, age, and the country the person grew up in. Our goal is to measure the likelihood of a person to take an action based on the wording of a security prompt, the person’s sentiments towards the prompt, and whether these depend on the person’s demographics and the company with which they are interacting. Our work serves a practical purpose, that is, helping technology companies decide what tone they want to address users with to accomplish their goals. It also serves a more theoretical purpose, in furthering understanding in the intersection of human-computer interaction and security.

As information processing machines approach the nanoscale level, DNA has emerged as a powerful tool in molecular engineering systems. The specificity and programmability of its hybridization interactions offer flexible and fine-tuned control over reacting species. Among the DNA computing techniques used today, strand displacement circuits are highly popular, with potential applications ranging from disease diagnostics to DNA-based artificial neural networks. The fundamental mechanism of these circuits is the hybridization of a single-stranded DNA input strand to a double-stranded complex which triggers the release of a prehybridized output strand. When released, this output can be detected and used to characterize circuit behavior. The output strands of strand displacement circuits are typically read out using fluorescence spectroscopy. However, due to spectral overlap of traditional reporters (e.g. FAM, TAMRA, Cy5), the number of outputs that can be detected in parallel is severely limited. To address this, we present the use of nanopore sensing technology as an alternative readout device that enables highly scalable, real-time detection and quantification of DNA strand displacement circuits. We demonstrate dynamic sensing of an operating circuit within the flow cell of a commercially-available high-throughput nanopore sensor array (Oxford Nanopore Technologies’ MinION device) and show that strand capture frequency can be correlated to concentration, allowing for direct quantification of desired circuit elements. To investigate this reporter strategy’s multiplexing potential, we present a collection of ten orthogonal circuit output sequences (barcodes) that can be classified at the single-molecule level from raw nanopore signal data using machine learning, with the potential to scale to larger barcode sets. We conclude that nanopore-based detection of strand displacement circuits holds key advantages over fluorescence-based methods for real-time, multiplexed circuit readout on an inexpensive, portable sensor device.