‘Rational data processing in complex networks’ with Santiago Mazuelas | Qualcomm

Title: Rational data processing in complex networks

Abstract: Data has been shown to be an extremely useful resource in many fields including communications, information technology, weather forecasting, and economics. Data processing techniques developed under paradigms such as machine learning, data science, and Bayesian inference are enabling many critical applications. In each specific application, data can reduce the uncertainty on the consequences of possible actions and hence improve decision making. For instance, certain key words in an email reduce the uncertainty regarding email usefulness and, hence, improve spam filtering. Rational decision making that is consistent with preferences among consequences requires a probabilistic processing of data.  Such probabilistic framework for data processing encounters several technical difficulties including 1) modeling of complex data, 2) approximation of intractable computations, and 3) efficiency in high dimensions. In this talk, I will provide an overview of such probabilistic framework and describe techniques to overcome the above-mentioned difficulties. In addition, I will show the practical benefits of the techniques presented through the case study of network localization and navigation.

Keywords: probabilistic processing, machine learning, positioning, distributed processing, information coupling, graphical models, belief propagation, density estimation

Speaker Bio: Santiago Mazuelas has been a Staff Engineer at Qualcomm Corporate Research and Development since Mar 2014. Prior to joining Qualcomm, he was a Postdoctoral Fellow and Associate in the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT) from Sep 2009 to Mar 2014 . He previously worked from 2006 to 2009 as a researcher and project manager in a Spanish technological center as well as a junior lecturer in the University of Valladolid. His general research interest is the application of mathematics to solve engineering problems, currently his work is primarily focused on statistical signal processing, machine learning, and data science.

Dr. Mazuelas is Associate Editor for the IEEE Communications Letters, and has served as Co-chair for the Symposiums on Wireless Communications at the 2014 IEEE Global Communications Conference (Globecom) and at the 2015 IEEE International Conference on Communications (ICC). He has been a member of the Technical Program Committee (TPC) for numerous conferences including the IEEE Globecom in 2010-2017, IEEE ICC in 2013-2017, IEEE VTC in 2012 and 2015, and ICUWB in 2011, and 2013-2017. He received the Best Student Prize in Telecommunications Technical Engineering from University of Valladolid in 2006, the Best Doctorate Thesis Award from University of Valladolid in 2011, and the Young Scientist Prize from the Union Radio-Scientifique Internationale (URSI) Symposium in 2007. His papers received the IEEE Communications Society Fred W. Ellersick Prize in 2012, and Best Paper Awards from the IEEE ICC in 2013, the IEEE ICUWB in 2011, and the IEEE Globecom in 2011.