Cristina González

B.Sc. Computer Science

B.Sc. Biomedical Engineering

Universidad de los Andes

I am a Ph.D. Candidate in Engineering and a researcher at the Center for Research and Formation in Artificial Intelligence (CINFONIA) supervised by Prof. Pablo Arbeláez at Universidad de Los Andes in Bogotá, Colombia. My main interests include visual recognition, biomedical image analysis, egocentric vision and vision & natural language processing. I am participating in Ego4D!

ci.gonzalez10@uniandes.edu.co
Google scholar
github.com/cigonzalez
Cristina González's picture

Publications

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Kristen Grauman et al.

CVPR, 2022 (Oral presentation).

Project page

Panoptic Narrative Grounding

Cristina González, Nicolás Ayobi, Isabela Hernández, José Hernández, Jordi Pont-Tuset and Pablo Arbeláez

ICCV, 2021 (Oral presentation).

Project page

Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge

Tobias Roß et. al.

Medial Image Analysis, 2021.

PharmaNet: Pharmaceutical discovery with deep recurrent neural networks

Paola Ruiz, Natalia Valderrama, Cristina González, Laura Daza, Carolina Muñoz-Camargo, Juan Carlos Cruz, and Pablo Arbeláez

PLOS ONE, 2021.

Project page

SIMBA: Specific Identity Markers for Bone Age Assessment

Cristina González*, Maria Escobar*, Felipe Torres, Laura Daza, Gustavo Triana and Pablo Arbeláez

MICCAI, 2020.

Project page

ISINet: An Instance-Based Approach for Surgical Instrument Segmentation

Cristina González*, Laura Bravo* and Pablo Arbeláez

MICCAI, 2020.

Project page

Hand Pose Estimation for Pediatric Bone Age Assessment

Maria Escobar*, Cristina González*, Felipe Torres Figueroa, Laura Daza, Gustavo Triana and Pablo Arbeláez

MICCAI, 2019 (Oral presentation).

Project page

An Empirical Study on Global Bone Age Assessment

Felipe Torres, Cristina González, Maria Escobar, Laura Daza, Gustavo Triana and Pablo Arbeláez

SIPAIM, 2019.

Project page

Experience

Ph.D Research Assistant, Center for Research and Formation in Artificial Intelligence (CINFONIA)

Universidad de los Andes, Bogotá

  • Member of the Biomedical Computer Vision (BCV) group at Uniandes led by Prof. Pablo Arbeláez.
  • Applying computer science skills in biomedical imaging problems.
  • Working on teams to develop automated solutions to biomedical problems.
  • Writing paper submissions for international conferences.
August 2021 - Present

Undergraduate Research Assistant, Biomedical Computer Vision

Universidad de los Andes, Bogotá

  • Member of the Biomedical Computer Vision (BCV) group at Uniandes led by Prof. Pablo Arbeláez.
  • Applying computer science skills in biomedical imaging problems.
  • Working on teams to develop automated solutions to biomedical problems.
  • Writing paper submissions for international conferences.
Jan 2018 - May 2021

Deep Learning Research Intern

Adobe Research

  • Intern at the Deep Learning Lab advised by Fabian Caba.
  • Applying deep learning algorithms in video analysis problems.
  • Working on teams to develop automated solutions to video analysis problems.
Jun 2020 - Sept 2020

Undergraduate teaching assistant

Universidad de los Andes, Bogotá

In carge of assisting students during midterms and office hours, designing and grading midterms, designing review exercises for each class and review lessons.

  • Analysis and Processing of Biomedical Images
  • Scientific Programming
  • Algorithm and Object-Oriented Programming I
  • Freshman students' Support Program
Jan 2017 - Dec 2018

Awards

Adobe Research Women-in-Technology Scholarship 2020

Adobe Research

Recognize outstanding undergraduate female students anywhere in the world who are studying computer science

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2020

Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019

Laura Bravo*, Cristina González* and Pablo Arbeláez

Endoscopic Vision Challenge, MICCAI 2019

1st place:

  • Multiple Instance Detection task.
  • Normalized Surface Dice Generalization ranking of the Multiple Instance Segmentation task.

2nd place:

  • Dice Similarity Coefficient Generalization ranking of the Multiple Instance Segmentation task.
  • Dice Similarity Coefficient Robustness ranking of the Multiple Instance Segmentation task.
  • Normalized Surface Dice Robustness ranking of the Multiple Instance Segmentation task.

Our performance shows that our method was the most robust of the competition.

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Oct 2019

Skills

Pytorch

Python

Matlab

Java

Javascript

SQL