Upcoming events

Epidemic processes can model anything that spreads. As such, they are a useful tool for studying not only human diseases, but also network attacks, chains of activation in the brain, the propagation of real or fake news, the spread of viral tweets, and other processes. In this talk, we investigate epidemics spreading on a graph in the presence of various forms of uncertainty. We present in particular a result about controlling the spread of an epidemic when there is uncertainty about who exactly is infected. We show first that neither algorithms nor results are robust to uncertainty. In other words, uncertainty fundamentally changes how we must approach epidemics on graphs. We also present two related results about learning the graph underlying an epidemic process when there is uncertainty about when people were infected or what infected them.

Despite recent advances in natural language processing and generation, communication between humans and machines is in still its infancy. Existing intelligent home and mobile assistant technologies excel at scripted tasks such as weather or news reports and music control, yet typically fail at more advanced personalization. This calls for a centralized repository for personal knowledge about each user, which will then be a distant source of background knowledge for personalization in downstream applications. Such personal knowledge repository will be beneficial as a reusable asset; it should be both explainable and scrutable, giving full control to the owning user on editing and sharing stored information with selected service providers.

In this talk, I will discuss our efforts on automated personal knowledge extraction. We can easily obtain personal knowledge of famous people from biographies or news articles, however, such resources are not available for ordinary users. Hence, we turn to the task of inferring personal attributes from users' utterances in conversations, e.g., guessing a person's occupation from "I was sitting the whole day in front of my computer today, trying to finish a grant proposal for my research." I will highlight our Hidden Attribute Models (HAM) to solve the problem, a neural architecture leveraging attention mechanisms and embeddings, as well as an ongoing work on its extension to address challenging attributes such as hobbies and travel preferences with wide sets of multi-faceted attribute values. Finally, I will present an outlook on what we can further infer from users' activities, particularly in relation with their mood and emotion.