Kimberly Villalobos Carballo

Contact: kimvc [at] mit.edu

Hello! I am currently an instructor at MIT, where I teach Machine Learning under a Modern Optimization Lens (MIT 15.095). I will be starting as an Assistant Professor at NYU Tandon School of Engineering next January 2025. My research integrates optimization and machine learning to develop data-driven algorithms that address practical challenges, such as working with small datasets, enhancing robustness and satisfying computational requirements. I am particularly passionate about healthcare applications, and a large part of my research has been inspired by collaborations with hospitals that aim to improve the quality of their services and operations.

Interests: Optimization (Robust, Stochastic, Convex and Non-convex, Discrete and Continuous, Multistage), Machine Learning, Data Multimodality, Healthcare Analytics, Applications in Medicine.

MIT Education:

  • Ph.D. in Operations Research, 2024
  • Bachelor of Science degree in Mathematics, 2019
  • Bachelor of Science degree in Computer Science, 2019
  • Minor in Statistics and Data Science, 2019

Papers Published

  • Holistic Deep Learning

    Dimitris Bertsimas, Kimberly Villalobos Carballo, Léonard Boussioux, Michael Lingzhi Li, Alex Paskov, Ivan Paskov

    Machine Learning (MACH), 2023

  • Integrated multimodal artificial intelligence framework for healthcare applications

    Luis R Soenksen, Yu Ma, Cynthia Zeng, Leonard Boussioux, Kimberly Villalobos Carballo, Liangyuan Na, Holly M Wiberg, Michael L Li, Ignacio Fuentes, Dimitris Bertsimas

    NPJ Digital Medicine (NPJDIGITALMED), 2022

  • From predictions to prescriptions: A data-driven response to COVID-19

    Dimitris Bertsimas, Leonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis, Alexandre Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, Cynthia Zeng

    Health Care Management Science, 2021

  • Do neural networks for segmentation understand insideness?

    Kimberly Villalobos, Vilim Štih, Amineh Ahmadinejad, Shobhita Sundaram, Jamell Dozier, Andrew Francl, Frederico Azevedo, Tomotake Sasaki, Xavier Boix

    Neural Computation, 2021

Papers Under Review

Collaborations

I have been lucky to work in partnership with several medical institutions to improve their healthcare operations.

  • Improving Hospital Operations with Patient Outcome Predictions.
  • Optimizing Elective Surgery Assignments.
  • Identifying Severe Events for Early Dispatch of Rapid Response Teams.
  • Reducing Length of Stay via Patient Flow Predictions.
  • Detecting Victims of Domestic Violence with Multi-Modal Data.

Honors & Awards

  • Winner, INFORMS’ William Pierskalla Best Paper Award 2020
  • Finalist, INFORMS Doing Good with Good OR Student Competition 2023
  • Winner, MIT-Pillar AI Collective Prize 2023
  • Winner, MIT Cognex Poster Competition 2022
  • Tau Beta Pi Honor Society 2018
  • Eta Kappa Nu Honor Society 2018
  • Young Talent Costa Rican Award 2014
  • Bronze Medal, 55th International Mathematical Olympiad 2014
  • Honorable Mention, 54th International Mathematical Olympiad 2013
  • Bronze medal, Asian Pacific Mathematics Olympiad 2013
  • Bronze medal, Iberoamerican Mathematical Olympiad 2013
  • Bronze medal, Iberoamerican Mathematical Olympiad 2012

Teaching Experience

--Graduate level (PhD, MSc, MBAn) course on ML using mixed-integer, robust, and convex optimization.

--Led recitations and weekly office hours, developed and graded assignments, supervised final projects.

--Class Size: 105; Student Evaluation Score: 6.9/7.0

--Graduate level (PhD, MSc, MBAn) course on theory, modeling, algorithms, and applications of robust optimization.

--Led recitations and weekly office hours, developed and graded assignments, supervised final projects.

--Class Size: 21; Student Evaluation Score: 6.7/7.0

--Master of Business Analytics course on using analytical tools to solve key business challenges.

--Evaluated project presentations, provided feedback and graded final project reports.

--Class Size: 78; Student Evaluation Score: NA

--Master of Business Analytics course on software tools such as R, Python, Julia and Git.

--Designed curriculum and created software workshops on data wrangling, visualization, machine learning, deep learning, version control, optimization.

--Average Class Size: 80; Student Evaluation Score: 7.0/7.0

--Executive Master of Business Analytics course on quantitative methods including data-mining, dynamic optimization, and simulation.

--Led recitations and weekly office hours, developed and graded assignments, supervised final projects.

--Class Size: 46; Student Evaluation Score: 7.0/7.0

Online course on quantitative methods including data-mining, dynamic optimization, and simulation

--Class Size: 844; Student Evaluation Score: NA