Every year Inria@SiliconValley launches a call for Post-Doc Fellowships.
After a tough competition, we are please to congratulate Melissa Ailem, Shuxia Tang and Georgios Bouloukakis who have been selected!
“Transfer and multi-modal learning of word representations”
Recently, word embedding models, e.g., word2vec, have attracted a lot of interest in several research communities, such as machine learning, natural language processing (NLP) and information retrieval. These models seek continuous representations of words that reflect various linguistic regularities between them. Modeling words in such a way has shown great improvements, in various NLP tasks, over the default representation of words as discrete and distinct symbols.
Despite the recent advance in learning distributed representation of words in a vector space, existing models remain far too limited to capture the complexity of text data as they are task-agnostic and fall short of modeling complex structures in languages. The objective is to combine statistical NLP and machine learning techniques to learn finer representations of words. In particular, we will address the following research questions: what are the right representations for structured data and how to learn them automatically? and how to use such representations in subsequent complex and structured prediction tasks in NLP?
To address the above-mentioned challenges, we plan to investigate two main directions. The first direction is in line with the use of multi-modal inputs, e.g., text, images, etc., to learn word embeddings. The second direction is to address the problem of learning good representations for low-resource languages by transferring knowledge from other (high-resource) languages.
“Smart cities: real-time decision making in traffic management”
Due to the complex road topologies and increasing number of vehicles on the roads especially in urban and suburban areas, many traffic problems arise such as traffic jams, car incidents and etc, resulting in large amounts of wasted time and energy. The call for intelligent traffic management and efficient transportation was made long ago, which is a coordinated task among city planners, policy makers and civil engineers. When considering the traffic management problems, researchers generally assume realistically that the city road network is well planned and the transportation policies are perfectly formulated. Under these assumptions, real-time traffic information, collected from various sources such as traffic sensors and video cameras, can be highly counted on. In order to improve the traffic management especially the traffic network flow, an emerging topic is to find intelligent transportation policies based on the traffic data. Although having sufficient information is surely beneficial for formulating on-road decisions, the numerous real-time information supply proposes a major challenge on making the decisions quickly enough or even real-time.
The postdoctoral research plan aims at developing systematic real-time decision-making procedures for the realistic traffic flows on large-scale road networks. In particular, this poses a problem of how to deal with the big data to make real-time decisions. It would be desirable if a systematic control or estimation algorithm could be derived for the class of traffic flow dynamic systems. Many researchers have devoted their efforts to this subject, but most of the results obtained so far are based on discretized PDEs. In the postdoctoral project, we aim to work directly on the continuum traffic flow models of PDE systems, and this would hopefully be one of the first works in this area to the best of our knowledge.
“Resilient and Interoperable Interactions for Emergency IoT Applications”