J. de Curtò y DíAz

I've had numerous research appointments, namely at the Department of Computer Science and Engineering at CUHK and at Carnegie Mellon. As well as at the EE and CS Departments at City University of Hong Kong.

I was with the Laboratory of Computer Vision at ETH Zürich. Previously I completed my Master of Science (with distinction) in EE and CS at City University of Hong Kong and at the School of Computer Science at Carnegie Mellon. I developed my thesis at the ML Dept. and at the Robotics. At City University of Hong Kong, I received several awards: the Top Achiever 2015, MS Internship Sponsorship 2014 and MS Entrance Scholarship 2013/14.

I hold a 5-year degree in Engineering of Telecommunication from Universitat Autònoma de Barcelona and Universitat Politècnica de Catalunya. I also worked as Research Scientist at CELLS ALBA Synchrotron. I had near perfect top nationwide scores in the university entrance examinations and baccalaureate.

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I'm broadly interested in computer vision and learning. I develop models that extract high-level information of the world to assist robots and automated systems such as self-driving cars. Representative publications are highlighted.

We introduce a new real-time pipeline for SLAM and VIO in the context of planetary rovers. We leverage prior information of the location of the lander to propose an object-level SLAM approach that optimizes pose and shape of the lander together with camera trajectories of the rover. As a further refinement step, we propose to use techniques of interpolation between adjacent temporal samples; videlicet synthesizing non-existing images to improve the overall accuracy of the system.

We introduce tools to handle planetary data from the mission Chang’E-4 and present a framework for Neural Style Transfer using Cycle-consistency from rendered images. The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the first unmanned rover of America to be on the Moon.

Dr. of Crosswise proposes a new architecture to reduce over-parametrization in Neural Networks. It introduces an operand for rapid computation in the framework of Deep Learning that leverages learned weights.

In order to boost network convergence of DCGAN and achieve good-looking high-resolution results we propose a new layered network, HDCGAN, that incorporates current state-of-the-art techniques for this effect.

Recognitions:
State-of-the-art in synthetic image generation on CelebA 128x128 (MS-SSIM). 2017.
State-of-the-art in synthetic image generation on CelebA 64x64 (FID). 2017.

We propose a novel approach to address the problem of Simultaneous Detection and Segmentation. We use an efficient and accurate procedure that exploits the feature information of the hierarchy using Locality Sensitive Hashing.

McKernel introduces a framework to use kernel approximates in the mini-batch setting with Stochastic Gradient Descent (SGD) as an alternative to Deep Learning.

Master of Science.
City University of Hong Kong. Carnegie Mellon.

5-year Degree in Engineering of Telecommunication.
Universitat Autònoma de Barcelona. Universitat Politècnica de Catalunya.

From Catalunya (Regne d'Espanya); in Pittsburgh, Hong Kong and Zürich. My last name is 'De Curtò', 'DíAz' is the surname of my mother; 'i/y' is a conjunction that literally means 'and' in Catalan/Spanish. 'De Curtò' signifies 'to cut' in Latin. A tribute to some of my family, ancestors and mentors: De Curtó i Bel (my father): passionate about cars, mechanics and painting. De Curtó i Berengué (my grandpa): he survived the civil war and protected the family until just after I was born. Bel i Bosch (my grandma): she knew beforehand I was the chosen one. Supported my education and all my crazy endeavors without asking for an explanation, she truly believed in me. Masterson (my teacher of English from age 7 until 17, and onwards a real supporter and a second mother): from Edinburgh (Scotland). She has taught me how to be a good Briton.

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