Sybille Légitime

Sybille Légitime, Kaustubh Prabhu, Devin McConnell et al.

Opioids are an effective analgesic for acute and chronic pain, but also carry a considerable risk of addiction leading to millions of opioid use disorder (OUD) cases and tens of thousands of premature deaths in the United States yearly. Estimating OUD risk prior to prescription could improve the efficacy of treatment regimens, monitoring programs, and intervention strategies, but risk estimation is typically based on self-reported data or questionnaires. We develop an experimental design and computational methods that combine genetic variants associated with OUD with behavioral features extracted from GPS and Wi-Fi spatiotemporal coordinates to assess OUD risk. Since both OUD mobility and genetic data do not exist for the same cohort, we develop algorithms to (1) generate mobility features from empirical distributions and (2) synthesize mobility and genetic samples assuming an expected level of disease co-occurrence. We show that integrating genetic and mobility modalities improves risk modelling using classification accuracy, area under the precision-recall and receiver operator characteristic curves, and $F_1$ score. Interpreting the fitted models suggests that mobility features have more influence on OUD risk, although the genetic contribution was significant, particularly in linear models. While there exist concerns with respect to privacy, security, bias, and generalizability that must be evaluated in clinical trials before being implemented in practice, our framework provides preliminary evidence that behavioral and genetic features may improve OUD risk estimation to assist with personalized clinical decision-making.

Sybille Légitime, Harini Suresh

The overreaches of mainstream social media platforms have been extensively reported and studied. For activist communities, these platforms pose risks of surveillance, censorship, or erasure. Decentralized social networks (DSNs) serve as alternative online spaces that appear to prioritize values such as user privacy, free speech, and community control. However, the decentralized ecosystem is vast and complex, making it difficult for communities to understand how to best use these platforms for their organizing aims. We aim to fill this gap by proposing a conceptual framework for navigating the DSN landscape that defines core activist community needs -- minimal overhead, community building and reach, on- and off-line safety, and operational sustainability -- and links them to concrete platform affordances such as resource efficiency, interoperability, and data ownership. We apply the framework to (1) evaluate and compare the sociotechnical tradeoffs of two contemporary DSNs (Mastodon and Bluesky), (2) understand broader community configurations that emerge across different DSN infrastructures and their implications for collective action, and (3) explore how two distinct activist communities facing infrastructural and political constraints might use the framework to find platforms that align with their needs. We conclude by reflecting on the theoretical promises of DSNs and the structural conditions that shape and constrain participation across them.

Nhuong V. Nguyen, Sybille Legitime

Extreme events are occurrences whose magnitude and potential cause extensive damage on people, infrastructure, and the environment. Motivated by the extreme nature of the current global health landscape, which is plagued by the coronavirus pandemic, we seek to better understand and model extreme events. Modeling extreme events is common in practice and plays an important role in time-series prediction applications. Our goal is to (i) compare and investigate the effect of some common extreme events modeling methods to explore which method can be practical in reality and (ii) accelerate the deep learning training process, which commonly uses deep recurrent neural network (RNN), by implementing the asynchronous local Stochastic Gradient Descent (SGD) framework among multiple compute nodes. In order to verify our distributed extreme events modeling, we evaluate our proposed framework on a stock data set S\&P500, with a standard recurrent neural network. Our intuition is to explore the (best) extreme events modeling method which could work well under the distributed deep learning setting. Moreover, by using asynchronous distributed learning, we aim to significantly reduce the communication cost among the compute nodes and central server, which is the main bottleneck of almost all distributed learning frameworks. We implement our proposed work and evaluate its performance on representative data sets, such as S&P500 stock in $5$-year period. The experimental results validate the correctness of the design principle and show a significant training duration reduction upto $8$x, compared to the baseline single compute node. Our results also show that our proposed work can achieve the same level of test accuracy, compared to the baseline setting.