Carlos Quintero-Peña

Carlos Quintero-Peña

PhD Student in Computer Science

Rice University

Kavraki Lab


I am a second-year PhD student in Computer Science at Rice University, working under the supervision of Lydia Kavraki and Anastasios Kyrillidis. I am honored to have received a Fulbright Scholarship in 2019 to start my doctoral studies.

I am interested in understanding how to plan safe motions for robots in unstructured environments. My goal is to provide robots with the capabilities of autonomously deciding and acting in environments that may have noisy or incomplete information. To this end, I have explored the use of optimization and data-driven models that enable the robots to perform complicated tasks safely and reliably.

Before joining Rice, I was an instructor and researcher at Universidad de los Andes and Universidad Santo Tomás where I thaught courses mainly in Electronic Engineering, Robotics and Machine Learning. During this time I had the chance to join the RoboCup community by becoming part of the STOx’s team of the Small Size League (2014-2017), serving as TC member in 2018 and by co-founding the SinfonIA team of the @Home Social Standard Platform League in 2019.

Download my resumé.

  • Robotics
  • Optimization
  • Machine Learning
  • PhD in Computer Science, 2024 (expected)

    Rice University

  • MSc in Computer and Electronic Engineering, 2011

    Universidad de los Andes

  • BSc in Electronic Engineering, 2009

    Universidad de los Andes




Can Theoretical Algorithms Efficiently Escape Saddle Points in Deep Learning?

Review of optimization algorithms that can escape saddle points in Deep Learning and some experimental results

Inverse Kinematics Robo Picasso

Solving the inverse kinematics problem for a FANUC S-500 robot and using it to draw a Mickey Mouse

Motion Planning with incomplete scene information

A Fetch robot doing Motion Planning and executing a trajectory when the scene representation may be incomplete

Neural Network Pruning - A review

Review of methods to prune neural networks

Robust Motion Planning under Sensing Uncertainty

Optimization-based motion planning algorithm capable of incorporating sensing uncertainty for a variety of noise models