cv: April 4, 2018
webpage: October 17, 2019
Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations
Paolo Tamagnini, Josua Krause, Aritra Dasgupta and Enrico Bertini
In Workshop on Human-In-the-Loop Data Analytics (HILDA), 2017
Data scientist specialized in guided analytics applications
paolotamag [at] gmail [dot] com
Data scientist with research experience, fond of data visualization and machine learning, currently working for KNIME in Berlin.
In October 2017 I graduated in my master in Data Science in Rome at Sapienza University and my thesis is available here.
My thesis was related to an internship I started in August 2016 in the research team of Enrico Bertini, associate professor at New York University, Tandon School of Engineering.
In November 2017 I have been working on an open-source python library called partial_dependence in collaboration with Josua Krause and Enrico Bertini.
In May 2018 I moved to Berlin to work as a data scientist in the evangelism team of KNIME. My work at KNIME is variegated, but it is particularly focused on guided analytics and automated machine learning.
- Rivelo - visual analytics tool for machine learning explanations.
- partial_dependence - python package for machine learning visualization.