In advanced systems and applications, efficient task-oriented representation and acquisition of information are critical to allow new and future complex solutions. In recent years, it has been shown how data-driven methods can allow tremendous applications in many domains of science, industry, engineering,
and beyond. These new applications, e.g., streaming, autonomous vehicles, smart cities, IoT, learning applications, already have a significant impact on our society and could potentially change the lives of millions in the future. However, these solutions demand the usage of a massive amount of readily available data that can inform the design and operation of such applications. The data may be captured from several co-located and distributed sources and may be used to perform specific tasks, e.g., learning models, computing a function of the data, or informing a data-aided decision, with security and privacy constraints. This increased demand for network connectivity and high data rates necessitates efficient utilization of all possible resources via heterogeneous networks. In this talk, I will show how we can significantly improve the way we acquire, represent and transmit information by dragging Information Theory and Signal Processing into networks. In particular, I will present the following directions: reliable data acquisition over networks with delay and throughput guarantees, efficient data acquisition methods for tasks, security and privacy in advanced systems and computation in networks.
Alejandro Cohen is a post-doctoral associate at the Department of Electrical Engineering and Computer Science (EECS), Massachusetts Institute of Technology (MIT). He received B.Sc. from the Department of Electrical Engineering, SCE College of Engineering, Israel, in 2010 and M.Sc. and Ph.D. from the Department of Communication Systems Engineering, Ben-Gurion University of the Negev, Israel, in 2013 and 2018, respectively. From 2007 to 2014, he was with DSP Group, where he worked on voice enhancement and signal processing. From 2014 to 2019, he was with Intel, where he worked as a research scientist in the Innovation Group at Mobile and Wireless. His areas of interest are Information Theory, Signal Processing, and Networks. In particular, he is interested in wireless communication, security, network information theory and network coding, anomaly detection, coding, computation in networks, and speech enhancement.