nonlinear optimization, machine learning, control theory, and pattern recognition
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Jet Propulsion Laboratory
Data Scientist
June 19, 2026 – Present
DRL_DistributedRL
February 13, 2019 – February 15, 2019
DRL_DistributedRL — GitHub repository
View ProjectDRL_continuous_control
January 12, 2019 – February 13, 2019
DRL_continuous_control — GitHub repository
View ProjectDOGS
November 18, 2016 – March 6, 2017
This is a derivative-free global optimization package
View ProjectKriging_in_c
September 1, 2016 – July 26, 2018
This is an efficient implementation of Dacefit package and Gaussian Processes in C.
View Projectdelta-Dogs-Lambda
August 15, 2016 – December 20, 2016
Delaunay-based Derivative-free Optimization via Global Surrogates with linear feasible domain leveraging general lattice. Based on Delta-DOGS (Lambda)
View Projectsubinterpolation
March 22, 2016 – August 15, 2016
This work is introducing a new concept called subinterpolation instead of interpolation which the values of search function model needs to be exact at the evaluated points. Instead the value of search function needs to be in the boundary less than real value of objective function and greater than the minimum of evaluated points. This property allow us to integrate the derivative information in the optimization algorithm while the algorithm explores globally it can be looked locally. In this project, first we introduce the subinterpolation and its properties; then, the interpolation idea will be integrated in the delaunay based optimization algorithm and the convergence properties will be analyzed. At the end, some examples will be shown to see the performance of subinterpolation versus using interpolation strategy.
View ProjectCultural Fit Analysis
The candidate's projects are predominantly personal and research-oriented, focusing on advanced mathematical optimization and machine learning. While this demonstrates strong technical depth, the lack of diverse project types (e.g., team-based, industry-specific, open-source contributions beyond personal repos) makes it difficult to fully assess cultural fit for a collaborative, product-focused environment. The experience level is listed as 0, which contradicts the current role at JPL, suggesting a data inconsistency or a very recent entry into the professional field despite advanced project work. This discrepancy impacts the cultural fit assessment as it's unclear if the candidate has experience navigating typical corporate structures or team dynamics.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a strong focus on technical implementation and research, but there is no information regarding collaboration, communication, or problem-solving approaches in a team setting.