Javier Carnerero Cano

Javier Carnerero Cano

PhD Candidate

Imperial College London

About Me

Welcome! I am Javier, a Telecommunications Engineer and PhD Candidate in the Department of Computing at Imperial College London. My current interests are (not limited to) adversarial machine learning, aiming to investigate the security of machine learning algorithms (with special focus on data poisoning attacks); applications of machine learning in security; bilevel optimisation problems; and Generative Adversarial Networks (GANs). I am part of the Resilient Information Systems Security (RISS) Group under the supervision of Prof Emil C. Lupu and Dr Luis Muñoz González.

I am passionate about travelling. I am eager to learn, analytical, deliberative and achiever. I am always interested in challenges and open to new collaborations. Drop me an email or DM me on LinkedIn to connect!

Interests

  • Adversarial Machine Learning
  • Machine Learning for Security
  • Bilevel Optimisation
  • Generative Adversarial Networks

Education

  • MRes in Multimedia, Machine Learning and Communications, 2017

    Universidad Carlos III de Madrid

  • MEng in Telecommunications Engineering, 2017

    Universidad Carlos III de Madrid

  • BEng in Telecommunications Engineering, 2015

    Universidad Carlos III de Madrid

Experience

 
 
 
 
 

Teaching Assistant

Imperial College London (Department of Computing)

Oct 2019 – Present London, United Kingdom
Assisting in lab sessions and tutorials and marking coursework in:

 
 
 
 
 

Research Assistant

Imperial College London (Department of Computing)

May 2018 – Present London, United Kingdom
Researching the security of machine learning: analysing the vulnerabilities of machine learning algorithms, designing effective attacks and evaluating their impact, and proposing defences that can help these algorithms to be more robust to adversaries.
 
 
 
 
 

Data Engineer

Santander Global Tech (Area of Big Data and Business Intelligence Solutions)

Nov 2017 – Feb 2018 Madrid, Spain
Hired by the Data Science Talent Program at Santander Group. Carried out ETL tasks in the data lake of the company:

  • Study and optimisation of a PoC, written in Java language, for the extraction of information from a database warehoused in Hive/Impala (through Hue), and its subsequent transmission, processing and visualisation in Kibana. Other involved technologies were: Apache Maven, Apache Spark, MapReduce, and HDFS.
  • Collaboration in a data migration project: execution, validation and optimisation of processes and MySQL queries to ETL data warehoused in Hive/Impala (through Hue), according to a pre-established mesh (Control-M).
 
 
 
 
 

Research Assistant

Universidad Carlos III de Madrid (Department of Signal Theory and Communications)

Feb 2016 – Oct 2017 Madrid, Spain

Research in communications, radio frequency, antennas, and passive sensors.

We proposed a novel low-cost and portable IoT reader for passive wireless electromagnetic sensors. An interesting application is the remote measurement of harmful substances. Up to our knowledge, it was the first wireless reader of passive electromagnetic sensors including IoT functionalities ("An IoT Reader for Wireless Passive Electromagnetic Sensors").

As a further step, we designed a novel contactless sensing system composed of a metamaterial-inspired sensor and a reader antenna, in order to detect substances in short distances (< 1 cm) in real time. This led to a low-cost, replaceable, battery-free and fully passive solution. Moreover, thanks to the short-reading range, the sensor avoids external interferences and undesired radiations ("A Contactless Dielectric Constant Sensing System Based on a Split-Ring Resonator-Loaded Monopole").

On the other hand, we collaborated in an R&D project with an important Spanish telecommunications company (Prodetel, S.A.). Concretely, we designed an innovative multiband feeder for aeronautic telemetry applications.

Skills

Machine Learning

80%

Adversarial Machine Learning

80%

Deep Learning

70%

Python

70%

NumPy

80%

Scikit-learn

60%

PyTorch

80%

TensorFlow

50%

Keras

40%

MATLAB

80%

Java

40%

C

40%

LaTeX

70%

SQL

40%

RF and Antenna Engineering

70%

RADAR

70%

International Experience

80%

Spanish

100%

English

80%

Contact

  • j (dot) cano (at) imperial (dot) ac (dot) uk
  • 180 Queen's Gate, London, SW7 2AZ
  • DM me