2023-24 Courses

All Publications

  • Sex-Specific Vulnerability to Externalizing Problems: Sensitivity to Early Stress and Nucleus Accumbens Activation Over Adolescence. Biological psychiatry Borchers, L. R., Yuan, J. P., Leong, J. K., Jo, B., Chahal, R., Ryu, J., Nam, A., Coury, S. M., Gotlib, I. H. 2024


    Exposure and sensitivity to early life stress (ELS) are related to increased risk for psychopathology in adolescence. While cross-sectional studies have reported blunted nucleus accumbens (NAcc) activation in the context of these associations, researchers have not yet assessed the effects of ELS on developmental trajectories of activation. We examined whether trajectories are affected by stress and the moderating role of biological sex in predicting vulnerability to symptoms of psychopathology.Adolescents (n=173) completed three assessments at two-year intervals across puberty (ages 9-18 years). At baseline we assessed objective ELS and stress sensitivity using the Traumatic Events Screening Inventory for Children. At all timepoints we assessed NAcc activation using the Monetary Incentive Delay task and externalizing, internalizing, and total problems using the Youth Self-Report. We correlated NAcc trajectories (extracted using linear mixed effects models) with ELS and stress sensitivity, and conducted multivariate regression analysis to examine the interaction of NAcc trajectories and biological sex in predicting symptoms of psychopathology.Symptoms increased over adolescence. Stress sensitivity, but not objective ELS, was associated with decreasing trajectories of NAcc activation. Biological sex interacted with NAcc trajectories to predict psychopathology: boys, but not girls, with decreasing NAcc activation had more severe externalizing problems in adolescence. These findings were replicated in the putamen and caudate but not in the medial prefrontal cortex or control brain regions.NAcc activation may be a sex-specific marker of externalizing problems in adolescence. Efforts to reduce stress sensitivity may help to decrease symptoms of psychopathology in adolescent boys.

    View details for DOI 10.1016/j.biopsych.2024.01.011

    View details for PubMedID 38272286

  • Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences of the United States of America Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J. R., Jurafsky, D., Goel, S. 2020


    Automated speech recognition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text, have become increasingly widespread, powering popular virtual assistants, facilitating automated closed captioning, and enabling digital dictation platforms for health care. Over the last several years, the quality of these systems has dramatically improved, due both to advances in deep learning and to the collection of large-scale datasets used to train the systems. There is concern, however, that these tools do not work equally well for all subgroups of the population. Here, we examine the ability of five state-of-the-art ASR systems-developed by Amazon, Apple, Google, IBM, and Microsoft-to transcribe structured interviews conducted with 42 white speakers and 73 black speakers. In total, this corpus spans five US cities and consists of 19.8 h of audio matched on the age and gender of the speaker. We found that all five ASR systems exhibited substantial racial disparities, with an average word error rate (WER) of 0.35 for black speakers compared with 0.19 for white speakers. We trace these disparities to the underlying acoustic models used by the ASR systems as the race gap was equally large on a subset of identical phrases spoken by black and white individuals in our corpus. We conclude by proposing strategies-such as using more diverse training datasets that include African American Vernacular English-to reduce these performance differences and ensure speech recognition technology is inclusive.

    View details for DOI 10.1073/pnas.1915768117

    View details for PubMedID 32205437

  • A university map of course knowledge. PloS one Pardos, Z. A., Nam, A. J. 2020; 15 (9): e0233207


    Knowledge representation has gained in relevance as data from the ubiquitous digitization of behaviors amass and academia and industry seek methods to understand and reason about the information they encode. Success in this pursuit has emerged with data from natural language, where skip-grams and other linear connectionist models of distributed representation have surfaced scrutable relational structures which have also served as artifacts of anthropological interest. Natural language is, however, only a fraction of the big data deluge. Here we show that latent semantic structure can be informed by behavioral data and that domain knowledge can be extracted from this structure through visualization and a novel mapping of the text descriptions of elements onto this behaviorally informed representation. In this study, we use the course enrollment histories of 124,000 students at a public university to learn vector representations of its courses. From these course selection informed representations, a notable 88% of course attribute information was recovered, as well as 40% of course relationships constructed from prior domain knowledge and evaluated by analogy (e.g., Math 1B is to Honors Math 1B as Physics 7B is to Honors Physics 7B). To aid in interpretation of the learned structure, we create a semantic interpolation, translating course vectors to a bag-of-words of their respective catalog descriptions via regression. We find that representations learned from enrollment histories resolved courses to a level of semantic fidelity exceeding that of their catalog descriptions, revealing nuanced content differences between similar courses, as well as accurately describing departments the dataset had no course descriptions for. We end with a discussion of the possible mechanisms by which this semantic structure may be informed and implications for the nascent research and practice of data science.

    View details for DOI 10.1371/journal.pone.0233207

    View details for PubMedID 32997664