Model-free Reinforcement Learning (RL) has recently demonstrated its great potential in solving difficult intelligent tasks. However, developing a successful RL model requires an extensive model tuning and tremendous training samples. Theoretical analysis of these RL methods, more specifically policy optimization methods, only stay in a simple setting where the learning happens in the policy space. This project attempts to advance the analysis of the policy optimization methods to a more realistic setting in the parameter space.

MatchWork enables non-profit employment support organizations to support marginalized people to find meaningful employment opportunities. This includes people with physical and mental challenges, veterans, new immigrants and refugees.

In recent years, deep learning has led to unprecedented advances in a wide range of applications including natural language processing, reinforcement learning, and speech recognition. Despite the abundance of empirical evidence highlighting the success of neural networks, the theoretical properties of deep learning remain poorly understood and have been a subject of active investigation. One foundational aspect of deep learning that has garnered great intrigue in recent years is the generalization behavior of neural networks, that is, the ability of a neural network to perform on unseen data.

Occupational stress conveys risk of Posttraumatic Stress Disorder (PTSD).

Compilers are large software projects consisting of many separate but common components like code generators, garbage collectors, and runtime diagnostic tools, to name but a few. Historically compiler developers have had to write each of these components from scratch. The Eclipse OMR project was created to provide generic components for use in new compilers and language runtime environments. OMR has enough flexibility to accommodate a wide range of programming languages without sacrificing performance, portability, or robustness.

Against a backdrop of alarming privacy breaches at social media, banking, and government websites, the European Union will see the General Data Protection Regulation (GDPR) come into force as of May 25 2018. GDPR will drastically increase fines for data loss and breach, introduces new requirements to obtain individual consent for data use, and defines new responsibilities for data processors and controllers.

In this project, we are interested in device-free methods that passively sense, monitor, and track people’s indoor presence, location, and movement using off-the-shelf Wi-Fi-enabled devices. We use information extracted from the physical layer of wireless links to detect and interpret human presence, location, and physical activities. The current design and implementation of Wi-Fi-based systems exhibit some temporal inconsistencies and limitations due to the complexity of the wireless signal propagation in indoor environment and the challenging nature of human's behavior itself.