Due to the constant need for connectivity, radio-frequency (RF) circuits will be of upmost importance in applications developed for the Internet of Things (IoT), the fifth-generation (5G) broadband technology and electronic health (eHealth) monitoring. However, the design of RF circuits in nanometric technologies for IoT/5G/eHealth applications is becoming extraordinarily difficult due to the high complexity and demanding performances of such circuits/systems. The need for high performance, low power, low voltage and low area circuits is immense and traditional design methodologies based on iterative, mostly manual, processes are unable to meet such challenges. Consequently, current EDA tools are getting out-of-date because they were developed to support that kind of traditional methodologies. Also, the short time-to-market demanded by nowadays IoT/5G/eHealth applications is creating a design gap, thus leading to a productivity decrease in the deployment of such IoT/5G/eHealth applications. In this framework, the focus of the SYSTEMIC-RF (Automated synthesis methodology for reliable RF integrated circuits) project is to develop a new design methodology that allows optimization-based synthesis approaches of RF circuits, where the circuit sizing and layout are treated in a complete and automated integrated fashion, in order to achieve fully optimal designs in much shorter times than traditional approaches. Moreover, the methodology will also take into account circuits' and systems' time-zero and time-dependent variability, and will be integrated in a state-of-the-art EDA tool in order to ease its usability. This EDA tool will definitely help RF designers to meet the very demanding specifications of IoT/5G/eHealth applications in a reasonable time.

Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines. From a machine learning perspective, many problems in NLP can be characterized as structured prediction: they involve predicting structurally rich and interdependent output variables. In spite of this, current neural NLP systems ignore the structural complexity of human language, relying on simplistic and error-prone greedy search procedures. This leads to serious mistakes in machine translation, such as words being dropped or named entities mistranslated. More broadly, neural networks are missing the key structural mechanisms for solving complex real-world tasks requiring deep reasoning. This project attacks these fundamental problems by bringing together deep learning and structured prediction, with a highly disruptive and cross-disciplinary approach. First, I will endow neural networks with a "planning mechanism" to guide structural search, letting decoders learn the optimal order by which they should operate. This makes a bridge with reinforcement learning and combinatorial optimization. Second, I will develop new ways of automatically inducing latent structure inside the network, making it more expressive, scalable and interpretable. Synergies with probabilistic inference and sparse modeling techniques will be exploited. To complement these two innovations, I will investigate new ways of incorporating weak supervision to reduce the need for labeled data. Three highly challenging applications will serve as testbeds: machine translation, quality estimation, and dependency parsing. To maximize technological impact, a collaboration is planned with a start-up company in the crowd-sourcing translation industry.