Publications

Abstract: Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment. Federated learning has been viewed as a promising solution for collaboratively training machine learning models among multiple parties while...

Abstract: Social network platforms have enabled large-scale measurement of user-to-user networks such as friendships. Less studied is user sentiment about their networks, such as a user’s satisfaction with their number of friends. We surveyed over 85,000 Facebook users about how satisfied they were with their number of friends on Facebook,...

Abstract: Affective computing is broadly applied to decision making systems ranging from mental health assessment to employability evaluation. The heterogeneity of human behavioral data poses challenges for both model validity and fairness. The limited access to sensitive attributes (e,g., race, gender) in real-world settings makes it more difficult to mitigate...

Abstract: Mobile health systems predict health conditions based on multimodal signals. Users are often reluctant to provide their health status over privacy concerns. It is challenging to make health predictions without sufficient historical data from the users. In this paper, we propose a user-based collaborative filtering mobile health system. The...

Abstract: As algorithmic decision making systems are increasingly used in high-stake scenarios, concerns have risen about the potential unfairness of these decisions to certain social groups. Despite its importance, the bias and fairness of multimodal systems are not thoroughly studied. In this work, we focus on the multimodal systems designed...

Abstract: Machine learning models are at the foundation of modern society. Accounts of unfair models penalizing subgroups of a population have been reported in domains including law enforcement, job screening, etc. Unfairness can spur from biases in the training data, as well as from class imbalance, i.e., when a sensitive...

Abstract: Since the outbreak in China in late 2019, the novel coronavirus (COVID‐19) has spread around the world and has come to dominate online conversations. By linking 2.3 million Twitter users to locations within the United States, we study in aggregate how political characteristics of the locations affect the evolution...

Abstract:Affective states are associated with people’s mental health status and have profound impact on daily life, thus unobtrusively understanding and estimating affects have been brought to the public attention. The pervasiveness of wearable sensors makes it possible to build automatic systems for affect tracking. However, constructing such systems is a...

Abstract:Wearable sensors (smart watches, health/fitness trackers, etc.) are experiencing an explosion in popularity. Their pervasiveness allows for effective data collections to quantify human behavior in natural settings, enriching traditional behavioral science research opportunities. In this paper, we focus on the problem of affect estimation from sensor-generated data, whereas ground truth...

Abstract: Cyberbullying is a major issue on online social platforms, and can have prolonged negative psychological impact on both the bullies and their targets. Users can be characterized by their involvement in cyberbullying according to different social roles including victim, bully, and victim supporter. In this work, we propose a...

Abstract: Hospitals are high-stress environments where workers face a high risk of occupational burnout due to a mix of imbalanced schedules, understaffing, and emotional stress. In this paper, we propose a computational framework to infer the latent psychological makeup and traits of hospital workers. We apply machine learning models to...

Abstract: According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform...

Abstract: Two-factor authentication (2FA) popularly works by verifying something the user knows (a password) and something she possesses (a token, popularly instantiated with a smart phone). Conventional 2FA systems require extra interaction like typing a verification code, which is not very user-friendly. For improved user experience, recent work aims at...

Abstract: Collaborative filtering plays an important role in online recommender systems, which provide personalized services to consumers by collecting and analyzing their rating histories. At the same time, such personalization may unfavorably incur privacy leakage, which has motivated the development of privacy-preserving collaborative filtering (PPCF) mechanisms. Most previous research efforts...

Abstract: Data confidentiality and availability are of primary concern in data storage. Dispersal storage schemes achieve these two security properties by transforming the data into multiple codewords and dispersing them across multiple storage servers. Existing schemes achieve confidentiality and availability by various cryptographic and coding algorithms, but only under the...

Abstract: Online social networks have becoming increasingly popular, where users are more and more lured to reveal their private information. This brings about convenient personalized services but also incurs privacy concerns. To balance utility and privacy, many privacy preserving mechanisms such as differential privacy have been proposed. However, most existent...

Abstract: Biometric identification is to reliably and effectively identify an individual of interest, where a pre-established database of biometric records is scanned with the unknown individual's biometric sample to look for a close enough match. This has recently been aided with cloud computing, where the database owner achieves higher efficiency...

Abstract: With the unique characteristics of low power consumption and low cost, IEEE 802.15.2 standard is widely used in the modern wireless networks. It can provide the lowest 0.006% of duty ratio to reduce power consumption, and offer real-time service for the node through the guarantee time slots (GTS) mechanism....