cv: April 4, 2018
webpage: December 7, 2020
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 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.
Between November 2017 and February 2018 I worked on an open-source python library called partial_dependence in collaboration with Josua Krause and Enrico Bertini.
In October 2017 I graduated at the Master (MSc) of Data Science from Sapienza University of Rome and my thesis is available here.
In August 2016 I wrote my thesis in the research team of Enrico Bertini, associate professor at New York University, Tandon School of Engineering.
- Rivelo - visual analytics tool for machine learning explanations.
- partial_dependence - python package for machine learning visualization.