Alejandro Carderera

Ph.D. student in Machine Learning

Georgia Institute of Technology


I am a second year Ph.D. student in Machine Learning at the Georgia Institute of Technology, working with Prof. Sebastian Pokutta. My work is currently aimed at designing novel convex optimization algorithms with solid theoretical convergence guarantees and good numerical performance.

Prior to joining the Ph.D. program I worked at HP as an R&D Systems Engineer for two years. I obtained a bachelor of science in Industrial Engineering from the Universidad Politécnica de Madrid and a Master of Science in Applied Physics from Cornell University.


  • Convex Optimization
  • Theoretical Machine Learning
  • Causal Inference


  • Ph.D. in Machine Learning, 2022 (Expected)

    Georgia Institute of Technology

  • MS in Applied Physics, 2016

    Cornell University

  • BSc in Industrial Engineering, 2014

    Universidad Politécnica de Madrid


Second-order Conditional Gradient Sliding - Preprint

Constrained second-order convex optimization algorithms are the method of choice when a high accuracy solution to a problem is needed, …

Locally Accelerated Conditional Gradients - AISTATS 2020

Conditional gradients constitute a class of projection-free first-order algorithms for smooth convex optimization. As such, they are …

Recent & Upcoming Talks

Breaking the Curse of Dimensionality (Locally) to Accelerate Conditional Gradients

Locally Accelerated Conditional Gradients

Locally Accelerated Conditional Gradients



Quantitative Analyst - Summer Associate

J.P. Morgan

Jul 2020 – Aug 2020 New York, USA.

Graduate Research Assistant

Georgia Institute of Technology

May 2019 – Present Atlanta, USA.

Graduate Teaching Assistant

Georgia Institute of Technology

May 2019 – Aug 2018 Atlanta, USA.

R&D Systems Engineer


Jul 2018 – Aug 2016 Barcelona, Spain

Graduate Research Assistant

Cornell University

Jul 2016 – Jul 2015 Ithaca, USA.