Ned Richardson aka E.P.Richardson

Bio/Education

Born Boston, MA, US 1959

BA in Creative Writing Residential College, University of Michigan circa 1982; Studied at Art Students League, NYC (1991) and various continuing education programs for drawing and painting techniques since then. Self taught: Image Magick and Photoshop digital image creation and manipulation, open source shell scripting. Online courses in Python and Creative Applications of Deep Learning (2018).

My work is not in any one tradition. Influences range from the conceptual art of Lawrence Weiner and poems and objects of Carl Andre to the comic art of George Herriman, cellular automata, and beyond. In Boston around 2000 I learned the Mische oil and egg tempera painting technique which led to a brief foray into miniature landscape painting. That world seemed too contained so I expanded focus to include text based drawings, digital and video explorations, as well as continuing 2D ( and slightly 3D) work in various media. An early background in stage carpentry and lighting led to my strong connection with the work of Samuel Beckett. Beckett is an ongoing influence and informs the name lightblack, an LLC I created initially for video and film co-productions with the late Michael H. Shamberg. lightblack ceased being an active production company with Michael’s illness though we continued to collaborate as his health allowed, up until the time of his death in 2014. During that time I was very involved in his care. lightblack continues as a container for my own efforts.

I moved to Vermont with my family in 2013. We live surrounded by forest. Around 2017 I began to see the forest as a network, an idea borne out by subsequent reading. It occurred to me that since the forest/network is often considered a landscape, digital and technological networks might be considered a landscape as well. I tried to see these landscapes as one. In 2018 I managed to learn enough Python to tinker with source code for a Generative Adversarial Network (GAN) which I found on Github. GAN generated images, using training sets assembled from my own work and photos was the basis of much of my work from 2018-2022. With the explosion of image generators and AI my attention shifted to coding and sequence, as well as the process of learning, perception, and building a ‘model.’ I have always been drawn to the intersection of things: human/non-human, biological/technological, as well as combining new and old technologies – AI, slide rules, stereograms, digital media, painting - to name a few.