¡Hola! I am Fernando

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About Me

I am a graduate student at the University of Wisconsin-Madison's Department of Industrial and Systems Engineering. My research lies at the intersection of machine learning and operations research to improve healthcare operations.

Before coming to Madison, I obtained my bachelor's degree in industrial engineering from the University of Puerto Rico-Mayaguez (UPRM). During my undergraduate years at the UPRM, I conducted research on the applications of machine learning and operations research for healthcare delivery and transportation systems. In addition, I worked for a year as a researcher in the Clinical Machine Learning Group at MIT. Outside of academics, I worked as an analytics and data science intern at NASA Glenn Research Center and Amgen Manufacturing Limited.

In the summer of 2022, I started to work as a graduate research assistant at the University of Wisconsin-Madison. I am collaborating with the Analytics for Human Development Lab, Zayas-Caban Lab, and UW Health on a project to improve the operations of healthcare systems using machine learning and operations research.

Email me at: facostaperez@wisc.edu

Outreach and Professional Activities

With the Puerto Rican flag at MIT during my research internship

Participating in NASA Glenn Research Center outreach events

Math tutoring at public high schools in Puerto Rico

Designing interactive puzzles to teach operations research ideas to middle school and high school students

Impacting community high schools in Puerto Rico with engineering workshops

My last day as an intern at NASA Glenn Research Center

Presenting my research at the University of Puerto Rico

Receiving the Dwight D. Eisenhower Undergraduate Research Fellowship

In The Media

Mathematical modeling helps hospitals efficiently assign patients to the right space - College of Engineering - University of Wisconsin-MadisonA hospital’s emergency department swells with patients, a chunk of whom will need to be admitted to the facility’s inpatient unit. But decision-makers in emergency care face a dilemma: When...

2023 SCOT INFORMS scholarship recipients announcedProgram is aimed at expanding participation in operations research, management science, and analytics research for those from underrepresented backgrounds.

Zayas-Caban industrious in efforts to open STEM doors - College of Engineering - University of Wisconsin-MadisonWhen Gabriel Zayas-Caban arrived at the University of Wisconsin-Madison as an assistant professor of industrial and systems engineering in 2017, he was already familiar with the campus. His sister, Teresa,...

ROCS: Una aplicación colegial que garantiza un estacionamientoUn junte interdisciplinario entre los departamentos de Ingeniería Civil y Agrimensura (INCI), Ingeniería Eléctrica y Computadoras (INEL/ICOM), Ingeniería de Software e Ingeniería Industrial (ININ) del Recinto Universitario de Mayagüez (RUM), busca desarrollar una aplicación que ayudaría en la situación de falta de estacionamiento en el campus.

Congrats to my mentors/colleagues from the UPRM who transformed our theoretical operations research project into a practical case study on managing the parking scarcity situation at the University of Puerto Rico.

New paper on precision oncology by Aiforgood | MIT Jameel Clinic posted on the topic | LinkedInPrecision oncology is a paradigm shift in cancer treatment, but it requires careful consideration of genetic, clinical, ethical, and logistical factors. These factors can make treatment decisions complex for physicians, especially when clinical trials cannot account for every patient scenario. Clinical decision support systems (CDSSs) offer an alternative path to precision oncology and some CDSSs use machine learning to help physicians make treatment decisions, but there are very few currently being used in clinical practice due to challenges in clinical validation and implementation. While physicians are now exploring the use of ML to make treatment decisions, there is little research on how clinicians interact with ML-based systems in conjunction with current standard-of-care evidence from randomized controlled trials (RCTs), which are considered the "gold standard" for study designs. In a new paper, Zeshan Hussain, Barbara Lam, Fernando A. Acosta-Pérez, Irbaz Bin Riaz, Maia Jacobs, Andrew Yee, and MIT Jameel Clinic - AI & Health PI David Sontag designed a CDSS that displays data from both RCTs and ML models to evaluate physicians' confidence with using the CDSS, how they perceived the ML model's reliability, and how they used that data to decide treatment options for patients with multiple myeloma. Paper: https://lnkd.in/e43UN4Hf #aiforgood #aiforhealth #precisiononcology #healthcareinnovation

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