CV
Education
- B.S. in Computer Engineering, Louisiana State University, 2016
- M.S. in Algorithms, Combinatorics, and Optimization, Carnegie Mellon University, 2019
- Ph.D in Algorithms, Combinatorics, and Optimization, Carnegie Mellon University, 2022 (expected)
Research Interests
I am generally interested in online and approximation algorithms with a recent focus in incorporating machine learned predictions into algorithm design. I am also interested in designing scalable algorithms for problems in data analysis and machine learning
Publications
Working Papers
TODO
Work experience
- Summer 2021: Google Research Intern
Teaching
Service and leadership
- Reviewer for ICML 2021 and Neurips 2021
Skills
- Proficient with the following programming/scripting languages
- Python
- C/C++
- R
- Matlab
- Linux/Bash
Talks
A Framework for Parallelizing Hierarchical Clustering Methods
Conference proceedings talk at European Conference on Machine Learning (ECML-PKDD) 2019, Würzburg, Germany
Online Load Balancing via Learned Weights
Conference talk at Informs Annual Meeting 2019, Seattle, Washington, USA
Online Scheduling via Learned Weights
Conference proceedings talk at ACM-SIAM Symposium on Discrete Algorithms (SODA) 2020, Salt Lake City, Utah, USA
Combinatorial Optimization Augmented with Machine Learning
Seminar talk at Johns Hopkins University CS Theory Seminar, Virtual
A Scalable Approximation Algorithm for Weighted Longest Common Subsequence
Conference proceedings talk at International European Conference on Parallel and Distributed Computing (EURO-PAR) 2021, Virtual
Learnable and Instance Robust Predictions for Online Matching, Flows, and Load Balancing
Conference proceedings talk at European Symposium on Algorithms (ESA) 2021, Virtual