Early diagnosis and discovery of therapeutic drug targets are crucial objectives for the effective management of Alzheimer's Disease (AD). Current approaches for AD diagnosis and treatment planning are based on radiological imaging and largely inaccessible for population-level screening due to prohibitive costs and limited availability. Recently, blood tests have shown promise in diagnosing AD and highlighting possible biomarkers that can be used as drug targets for AD management. Blood tests are significantly more accessible to disadvantaged populations, cost-effective, and minimally invasive. However, biomarker discovery in the context of AD diagnosis is complex as there exist important associations between various biomarkers. Here, we introduce BRAIN (Biomarker Representation, Analysis, and Interpretation Network), a novel machine learning (ML) framework to jointly optimize the diagnostic accuracy and biomarker discovery processes to identify all relevant biomarkers that contribute to AD diagnosis. Using a holistic graph-based representation for biomarkers, we highlight their inter-dependencies and explain why different ML models identify different discriminative biomarkers. We apply BRAIN to a publicly available blood biomarker dataset, revealing three novel biomarker sub-networks whose interactions vary between the control and AD groups, offering a new paradigm for drug discovery and biomarker analysis for AD.

Sleep behavior significantly impacts health and acts as an indicator of physical and mental well-being. Monitoring and predicting sleep behavior with ubiquitous sensors may therefore assist in both sleep management and tracking of related health conditions. While sleep behavior depends on, and is reflected in the physiology of a person, it is also impacted by external factors such as digital media usage, social network contagion, and the surrounding weather. In this work, we propose SleepNet, a system that exploits social contagion in sleep behavior through graph networks and integrates it with physiological and phone data extracted from ubiquitous mobile and wearable devices for predicting next-day sleep labels about sleep duration. Our architecture overcomes the limitations of large-scale graphs containing connections irrelevant to sleep behavior by devising an attention mechanism. The extensive experimental evaluation highlights the improvement provided by incorporating social networks in the model. Additionally, we conduct robustness analysis to demonstrate the system's performance in real-life conditions. The outcomes affirm the stability of SleepNet against perturbations in input data. Further analyses emphasize the significance of network topology in prediction performance revealing that users with higher eigenvalue centrality are more vulnerable to data perturbations.

Cellular networks are ubiquitous entities that provide major means of communication all over the world. One major challenge in cellular networks is a dynamic change in the number of users and their usage of telecommunication service which results in overloading at certain base stations. One class of solution to deal with this overloading issue is the deployment of drones that can act as temporary base stations and offload the traffic from the overloaded base station. There are two main challenges in the development of this solution. Firstly, the drone is expected to be present around the base station where an overload would occur in the future thus requiring a prediction of traffic overload. Secondly, drones are highly constrained in their resources and can only fly for a few minutes. If the affected base station is really far, drones can never reach there. This requires the initial placement of drones in sectors where overloading can occur thus again requiring a traffic forecast but at a different spatial scale. It must be noted that the spatial extent of the region that the problem poses and the extremely limited power resources available to the drone pose a great challenge that is hard to overcome without deploying the drones in strategic positions to reduce the time to fly to the required high-demand zone. Moreover, since drone fly at a finite speed, it is important that a predictive solution that can forecast traffic surges is adopted so that drones are available to offload the overload before it actually happens. Both these goals require analysis and forecast of cellular network traffic which is the main goal of this project

Emotion prediction plays an essential role in mental health and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person's physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict happiness and stress. In addition to a person's physiological features, we also incorporate the environment's impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all the users. The construction of social networks does not incur additional cost in terms of EMAs or data collection from users and doesn't raise privacy concerns. We propose an architecture that automates the integration of a user's social network affect prediction, is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. Our extensive evaluation highlights the improvement provided by the integration of social networks. We further investigate the impact of graph topology on model's performance.