Kaarthik Sundar
Staff Scientist at Los Alamos National Laboratory
Los Alamos, New Mexico, United States
Summary
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Senior🎓
Top SchoolKaarthik Sundar is a Staff Scientist at Los Alamos National Laboratory with 11 years of experience applying combinatorial optimization, approximation algorithms, and optimal control to real-world engineering problems. He combines deep algorithmic expertise with production-grade coding in C++, Python, Java and Julia, and routinely leverages solvers like CPLEX, Gurobi and Ipopt to turn mathematical models into scalable solutions. His contributions to the widely used PowerModels.jl include implementing convex-hull relaxations for trilinear terms and optimality-based bound tightening, reflecting a knack for bridging advanced theory and practical power-system optimization. Educated with a PhD and MS from Texas A&M, he has a track record in mixed-integer programming for vehicle routing and sensor networks and an unusual strength in exploiting physical structure to accelerate algorithms. Based in Los Alamos, he blends research rigor with hands-on software development to deliver robust optimization tools for high-impact scientific problems.
11 years of coding experience
12 years of employment as a software developer
B.E., Electrical and Electronics Engg., B.E., Electrical and Electronics Engg. at College of Engineering Guindy, Anna UniversityDoctor of Philosophy (PhD), Mechanical Engineering, Doctor of Philosophy (PhD), Mechanical Engineering at Texas A&M University
Stackoverflow
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Github Skills (11)
network10
pow10
opt10
julia10
optimization10
data-structure9
algorithm9
data-structures9
algorithms9
dockers3
docker3
Programming languages (8)
Github contributions (5)
lanl-ansi/PowerModels.jl
Nov 2017 - Sep 2018
A Julia/JuMP Package for Power Network Optimization
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 commits, 9 PRs, 11 pushes in 10 months
Contributions summary:Kaarthik primarily contributed to the development and refinement of the `powermodels.jl` Julia package, focusing on power network optimization. Their work involved implementing and testing new formulations, specifically the QCWRTriPowerModel, and its related functionalities such as convex hull relaxation of trilinear terms. The user also addressed bug fixes and optimized existing code, including the QC-OTS formulation, along with adding new functionality for optimality-based bound tightening (OBBT).
jumppower-networkoptimizationoptimal-power-flowpower
kaarthiksundar/GasLib
Aug 2022 - Feb 2023
GasLib data parser
Contributions:34 commits, 6 PRs, 37 pushes in 6 months
data-parserparser
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