Manuel Ladron de Guevara

I am a fifth year PhD candidate advised by Ramesh Krishnamurti, Daragh Byrne, and Jun-Yan Zhu (committee member) at the Computational Design department at Carnegie Mellon University, where I work on computer vision and machine learning. I'm a research scientist intern at Adobe Research under Daichi Ito, Jose Echevarria, Yannick Hold-Geoffroy, Yijun Li, and Cameron Smith. I am interested in deep learning for image synthesis, deep painting and multimodal machine learning. I work as a Studio Instructor at the School of Architecture at Carnegie Mellon University, and I am a co-founder of CRAIDL.

Previously, I worked as a research scientist intern at Adobe Research under Aaron Hertzmann, and Matt Fisher. Prior to joining the PhD program, I did a Masters in Robotic Fabrication at CMU under Jeremy Ficca. I obtained my B-Arch and M-Arch in Architecture at the Polytechnic University of Catalonia with honors in my thesis project.

Email: manuelr [at] andrew [dot] cmu [dot] edu

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Design Intents Disentanglement: A Multimodal Approach for Grounding Design Attributes in Objects
Manuel Ladron de Guevara, Alexander Schneidman, Daragh Byrne, Ramesh Krisnamurti,
[Paper] [Code]
Bubble2Floor: A Pedagogical Experience With Deep Learning for Floor Plan Generation
Pedro Veloso, Jinmo Rhee, Ardavan Bidgoli, Manuel Ladron de Guevara,

MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation
George Cazenavette, Manuel Ladron de Guevara,
Preprint, 2021
Artistic style in robotic painting; a machine learning approach to learning brushstroke from human artists
Ardavan Bidgoli, Manuel Ladron de Guevara, Cinnie Hisiung Jean Oh, Eunsu Kang
EEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020
[Paper] [Presentation] [Video] [Code]
Multimodal Word Sense Disambiguation in Creative Practice
Manuel Ladron de Guevara, Christopher George, Akshat Gupta, Daragh Byrne, Ramesh Krisnamurti,
IEEE International Conference on Machine Learning and Applications , 2020
Multi-resolution in architecture as a design driver for additive manufacturing applications
Manuel Ladron de Guevara, Christopher George, Daragh Byrne, Ramesh Krisnamurti,
International Journal of Architectural Computing (IJAC), 2020
A multi-resolution design methodology based on discrete models
Manuel Ladron de Guevara, Luis Borunda, Ramesh Krisnamurti,
CAAD Futures, 2019
Robotic free-oriented additive manufacturing technique for thermoplastic lattice and cellular structures
Manuel Ladron de Guevara, Luis Borunda, Jeremy Ficca Daragh Byrne, Ramesh Krisnamurti,
Optimized infill in additive manufacturing of building components
Luis Borunda, Manuel Ladron de Guevara, Pavel Aguilar Jesus Anaya
IV International Conference on Technological Innovation in Building, 2019


Carnegie Mellon University, Pittsburgh, Pennsylvania
Ph.D. in Computational Design | 2018 - Present ,
Carnegie Mellon University, Pittsburgh, Pennsylvania
Masters in Advanced Architectural Design | 2017 - 2018 ,
Polytechnic University of Catalonia, Barcelona, Spain
B.Arch - M.arch in Architecture | 2007 - 2013 ,

Relevant Coursework

10-601 - Machine Learning | Fall 2019 Instructor: Matt Gormley

10-737 - Creative AI | Fall 2019 Instructor: Eunsu Kang & Jean Oh

10-403 - Deep Reinforcement Learning | Spring 2020 Instructor: Katerina Fragkiadaki
11-785 - Introduction to Deep Learning | Spring 2020 | Instructor: Bhiksha Raj

11-611 - Natural Language Processing | Spring 2020 | Instructor: Alan W. Black & David R. Mortensen

11-777 - Multimodal Machine Learning | Fall 2020 | Instructor: Louis-Philippe Morency

11-747 - Neural Networks for NLP | Spring 2021 | Instructor: Graham Neubig

16-726 - Learning-Based Image Synthesis | Spring 2021 | Instructor: Jun-Yan Zhu

16-824 - Visual Learning & Recognition | Fall 2021 | Instructor: Jun-Yan Zhu

Website source from Jon Barron .