Felice House: A Painters Technique: Stepping through an Oil Portrait
Portrait painters have been developing techniques to create expressive and realistic renderings for centuries. In this talk, portrait painter Felice House will step through and demonstrate the process she uses to create portraits. From transparent underpainting to final impasto brush mark, she will share the methods, materials and stages of her paintings. Along the way she will reflect on how her process both aligns with, and differs from more classical methods. By outlining and demonstrating, the audience will gain a more thorough understanding of one painter’s methods and materials.
Felice House is a figurative painter who strives, through her portraits of women, to provide a counterpoint to the passive female representations found in art historical tradition and culture at large. Her work endeavors to challenge stereotypes and empower her audience, women in particular, to change their preconceived notions of gender and power.
Felice House has exhibited in museums and galleries across the country, as well as internationally. Her paintings are represented in both private and public collections, including The Booth Museum of Western Art, New Mexico State University, American Campus Communities, Austin City Limits Music Festival, and Prentice Women’s Hospital. House’s 2017 solo exhibition at the Leeds College of Art in Leeds, UK was featured on the BBC news, in Vice Magazine’s The Creators Project, This is Colossal, Upworthy, Refinery29, Boing Boing, Fubiz, El Diario (Spain) and Tabi Labo (Japan).
House received a BFA from the Nova Scotia College of Art and Design, an MS from Texas A&M University in computer graphics and an MFA in painting from the University of Texas. In between her other schooling, she spent time studying classical painting and portraiture at the Schuler School of Fine Art in Baltimore, MD. She is an Assistant Professor in the Visualization Department at Texas A&M University.
Tim Davis: Math, Matrices, and Music
We all know what music *sounds* like, but if you could see a whole piece of music drawn in a single artistic visualization, what would it *look* like?
In his day job, Davis creates mathematical algorithms for solving huge sparse matrix problems. His solvers are widely used in industry, academia, and government labs. For example, every photo on the planet in Google StreetView is placed in its proper position by his codes. He also curates a vast collection of matrices, so that he and others in his field can test their methods on real-life problems.
In collaboration with Yifan Hu at Yahoo! Labs, these matrices are converted into images via a physics simulation. The primary purpose of these visualizations is to understand the relationships in the matrix and how various solvers behave on different problems. But the images also happen to be stunningly beautiful, and they have caught the eye of the popular press (Geeky Science Problems Double as Works of Art, FastCompany).
The matrices in Davis’ collection have nothing to do with music, but a few years ago the organizers of the London Electronic Arts Festival came across the images and gave him a challenge: “These pictures are amazing … could you create similar images from sound bites?”
Davis’ first thought was “Music?! That’s crazy; these are matrices, not music.” Taking up the challenge, however, Davis constructed a mathematical algorithm for converting an entire piece music into a sparse matrix, capturing time and frequency, and remapping them into a new domain of space and color. His method captures a visual essence of an entire piece of music in a single image. The heavy regular beat of jazz and electronic music converts into simple elegant meshes, like a fish net. Complex orchestral works convert into dazzlingly complex fuzzy structures. Davis’ art appeared on billboards around London as the theme art for the Festival. In this seminar, Davis will present his music artwork and how it came to be created. You can preview his portfolio at notesartstudio.com, and browse his sparse matrices at http://faculty.cse.tamu.edu/davis/matrices.html .
Tim Davis is a Professor in the Computer Science and Engineering Department at Texas A&M University. His primary scholarly contribution is the creation of widely-used sparse matrix algorithms and software. As an NVIDIA Academic Partner, he is creating a new suite of highly-parallel sparse direct methods that can exploit the high computational throughput of recent GPUs. He was elected in 2013 as a SIAM Fellow, in 2014 as an ACM Fellow, and in 2016 as an IEEE Fellow. He serves as an associate editor for the ACM Transactions on Mathematical Software, the SIAM Journal on Scientific Computing, and the Journal of Parallel and Distributed Computing. Tim is a Master Consultant to The MathWorks, and the primary author of x=A\b in MATLAB when A is sparse.