Santiago Mazuelas Franco

Ikerbasque Research Associate

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Information of interest

Santiago Mazuelas received the Ph.D. in Mathematics and Ph.D. in Telecommunications Engineering from the University of Valladolid, Spain, in 2009 and 2011, respectively.

He is currently an Ikerbasque Research Associate at the Basque Center for Applied Mathematics (BCAM). Prior to joining BCAM, he was a Staff Engineer at Qualcomm Corporate Research and Development from 2014 to 2017. He previously worked from 2009 to 2014 as Postdoctoral Fellow and Associate in the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). His general research interest is the application of mathematics to solve practical problems, currently his work is primarily focused on machine learning methods.

Dr. Mazuelas is Editor for the IEEE Transactions on Wireless Communications and was Area Editor (signal processing) for the IEEE Communications Letters from 2017 to 2022. He has served as Technical Program Vice-chair at the 2021 IEEE Globecom, and as Symposium Co-chair at several IEEE ICC and Globecom conferences. He received the Young Scientist Prize from the Union Radio-Scientifique Internationale (URSI) Symposium in 2007, and the Early Achievement Award from the IEEE ComSoc in 2018. His papers received the IEEE Communications Society Fred W. Ellersick Prize in 2012, the SEIO-FBBVA Best Applied Contribution in the Statistics Field in
2022, and Best Paper Awards from the IEEE ICC in 2013, the IEEE ICUWB in 2011, and the IEEE Globecom in 2011.

  • Efficient Learning of Minimax Risk Classifiers in High Dimensions 

    Bondugula, K.R.Autoridad BCAM; Mazuelas, S.Autoridad BCAM; Pérez, A.Autoridad BCAM (2023-08-01)
    High-dimensional data is common in multiple areas, such as health care and genomics, where the number of features can be tens of thousands. In such scenarios, the large number of features often leads to inefficient ...
  • Minimax Risk Classifiers with 0-1 Loss 

    Mazuelas, S.Autoridad BCAM; Romero, M.; Grunwald, P. (2023-07-01)
    Supervised classification techniques use training samples to learn a classification rule with small expected 0 -1 loss (error probability). Conventional methods enable tractable learning and provide out-of-sample ...
  • Indoor Localization System With NLOS Mitigation Based on Self-Training 

    Huang, Y.; Mazuelas, S.Autoridad BCAM; Ge, F.; Shen, Y. (2023-07-01)
    Location-awareness has become a fundamental requirement for multiple emerging applications with the rapid development of wireless technologies. The high-accuracy ranging enabled by ultra-wide bandwidth (UWB) signals is ...
  • Double-Weighting for Covariate Shift Adaptation 

    Segovia, J.IAutoridad BCAM; Mazuelas, S.Autoridad BCAM; Liu, A. (2023-07)
    Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates $x$) of training and testing samples $p_\text{tr}(x)$ and $p_\text{te}(x)$ are different but the label ...
  • Female Models in AI and the Fight Against COVID-19 

    Guerrero, C.Autoridad BCAM; Mazuelas, S.Autoridad BCAM (2022-11-01)
    Gender imbalance has persisted over time and is well documented in science, technology, engineering and mathematics (STEM) and singularly in artificial intelligence (AI). In this article we emphasize the importance of ...
  • Generalized Maximum Entropy for Supervised Classification 

    Mazuelas, S.Autoridad BCAM; Shen, Y.; Pérez, A.Autoridad BCAM (2022-04)
    The maximum entropy principle advocates to evaluate events’ probabilities using a distribution that maximizes entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for ...
  • Location Awareness in Beyond 5G Networks 

    Conti, A.; Morselli, F.; Liu, Z.; Bartoletti, S.; Mazuelas, S.Autoridad BCAM; Lindsey, W.C.; Win, M.Z. (2021-11-01)
    Location awareness is essential for enabling contextual services and for improving network management in 5th generation (5G) and beyond 5G (B5G) networks. This paper provides an overview of the expanding opportunities ...
  • Minimax Classification with 0-1 Loss and Performance Guarantees 

    Mazuelas, S.Autoridad BCAM; Zanoni, A.; Pérez, A.Autoridad BCAM (2020-12-01)
    Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate ...
  • General supervision via probabilistic transformations 

    Mazuelas, S.Autoridad BCAM; Pérez, A.Autoridad BCAM (2020-08-01)
    Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training samples. This ...
  • Probabilistic Load Forecasting Based on Adaptive Online Learning 

    Álvarez, V.Autoridad BCAM; Mazuelas, S.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2020)
    Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent ...
  • Soft information for localization-of-things 

    Conti, A.; Mazuelas, S.Autoridad BCAM; Bartoletti, S.; Lindsey, W.C; Win, M. (2019-11-01)
    Location awareness is vital for emerging Internetof- Things applications and opens a new era for Localizationof- Things. This paper first reviews the classical localization techniques based on single-value metrics, such ...
  • Crowd-Centric Counting via Unsupervised Learning 

    Morselli, F.; Bartoletti, S.; Mazuelas, S.Autoridad BCAM; Win, M.; Conti, A. (2019-07-11)
    Counting targets (people or things) within a moni-tored area is an important task in emerging wireless applications,including those for smart environments, safety, and security.Conventional device-free radio-based ...
  • Spatiotemporal information coupling in network navigation 

    Mazuelas, S.Autoridad BCAM; Shen, Y.; Win, Z. (2018-12)
    Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information ...
  • Soft range information for network localization 

    Mazuelas, S.Autoridad BCAM; Conti, A.; Allen, J.C.; Win, M.Z. (2018-06-15)
    The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on ...

More information

MRCpy: a library for Minimax Risk Classifiers 

MRCpy library implements minimax risk classifiers (MRCs) that are based on robust risk minimization and can utilize 0-1-loss.

Authors: Kartheek Reddy, Claudia Guerrero, Aritz Perez, Santiago Mazuelas

License: free and open source software