Jonathan Celaya Email & Phone Number

Mclean, Va, Us

State College, Pennsylvania, United States

My graduate research focuses on applying methods from operations research to improve the resilience of critical infrastructure - particularly the power grid - while considering the interactions of the infrastructures of multiple sectors. To date, I have worked as part of a team using statistical learning methods to classify potential future climate conditions which present high risk to power system operations, with the intent of identifying mitigation measures via stochastic capacity expansion modeling.

Albuquerque, New Mexico, United States

As a graduate intern at Sandia, I've rapidly developed the programming skills in Pyomo (Python) to implement bilevel and disjunctive mixed-integer optimization models for applications including electricity infrastructure planning. In addition, I have performed analysis on model outputs with the goal of informing DoE and DoD decision-makers. As part of my internship's professional development program, I have presented on this work and on technical papers to audiences including non-domain experts and internal technical staff members.

State College, Pennsylvania Area

In this role, I performed work across the Operations Research spectrum. Some highlights of my work include leading the design, implementation (in GAMS) and analysis of a large, dynamic-network, mixed-integer resource allocation problem, constructing object-oriented Discrete Event Simulations in MATLAB and Python, and assisting in the development and implementation of a novel risk assessment framework. Additional projects included collaborative software development and analysis, as well as algorithm construction in MATLAB and Simulink. Results of each project were used to inform sponsor investment and operational decisions.

Houston, Texas Area

Senior Capstone project in the Department of Computational and Applied Mathematics at Rice University. Under the supervision of Dr. Beatrice Rivière (Rice) and Dr. Kai Sun (British Petroleum), I worked in a team of three senior undergraduates to design and provide an efficient solution for a mixed-integer programming model to maximize the profit of an electric power generator. To solve this problem, my team converted physical constraints on the power generator into mathematical constraints in our MIP model, and tested potential algorithmic solutions. Ultimately, we elected to design and implement a dynamic programming algorithm in Python to provide a fast, heuristic solution.

Houston, Texas Area

Under the supervision of Drs. Andrew Schafer and Boris Brimkov (Rice), I independently conducted research in algorithms and graph theory. For this research, I utilized combinatorial and integer programming techniques to develop new, efficient algorithms to solve two inverse optimization problems, each on two classes of graphs. To assist my research, I used MATLAB to test the efficiency of my algorithms and determine if and what parts of the algorithms could be improved.

Berkely, Ca

In conjunction with two other undergraduates, I worked under the direction of a full professor and postdoctoral student to derive a closed-form solution for a previously-unsolved problem at the intersection of abstract algebra and graph theory. During this project, we utilized Python to run simulations of our problem and identify relevant patterns in the data. We isolated a potential answer from this data, and ultimately proved that it was a correct solution. In addition, during this project we were required to submit weekly oral and written reports of our progress, and were given instruction in how to communicate mathematics to audiences of any (or no) mathematical background.