Akshay Sharma
Machine Learning Engineer at Lyft
California, United States
Summary
🤩
Rockstar🎓
Top SchoolAkshay Sharma is a Machine Learning Engineer with 11 years of software and research experience, currently building ML and RL-driven pricing optimization at Lyft after earlier roles designing cloud-native systems at Intuit and contributing to scientific ML at Julia Computing. He holds an MS in Computer Science from UMass Amherst, where he worked on NLP research and taught systems for data science, and has hands-on AWS administration and data science experience supporting university infrastructure. Akshay’s open-source work includes initiating NeuralPDE.jl—now an official SciML package—where he prototyped neural solvers for differential equations and helped integrate them into the DiffEq ecosystem. He combines production-grade engineering (Java Spring, React, AWS Lambdas) with applied research in convolutional and physics-informed neural nets, and has a track record of automating developer workflows that saved significant triage time. Based in California, he’s an AI and open-source enthusiast who enjoys turning research prototypes into robust, deployable systems.
11 years of coding experience
6 years of employment as a software developer
Full Stack Web Development Computer Soft, Full Stack Web Development Computer Soft at Free Code CampSenior Secondary Science Mathematics, Senior Secondary Science Mathematics at Step By Step High SchoolBachelor of Technology (B.Tech.) Information Technology, Bachelor of Technology (B.Tech.) Information Technology at National Institute of Technology KarnatakaMaster of Science - MS Computer Science, Master of Science - MS Computer Science at University of Massachusetts AmherstSecondary Education Distinction Basic sciences and Mathematics, Secondary Education Distinction Basic sciences and Mathematics at Maheshwari Public School
English, Hindi
Github Skills (13)
neural-network10
machine-learning10
differential-equations10
optimizer10
knexjs10
knn10
optimizers10
optim10
adam10
ordinary-differential-equations9
scientific-machine-learning9
julia9
partial-differential-equations8
Programming languages (7)
Github contributions (5)
SciML/NeuralPDE.jl
Mar 2017 - Dec 2017
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Role in this project:
ML Engineer
ML EngineerContributions:30 commits, 26 pushes, 4 branches in 8 months
Contributions summary:Akshay's contributions primarily revolve around implementing and refining a neural network model for solving ordinary and partial differential equations. They started with a prototype using Knet, explored different loss functions and optimizers, and subsequently modularized the code. The user integrated ForwardDiff for gradient calculations and incorporated test code. The user also works on refactoring the code into the DiffEq ecosystem.
simulationscientific-machine-learningpartial-differential-equationsdifferentialode
akaysh/SpiderMonkey.jl
Apr 2020 - Dec 2021
A Julia package for Spider Monkey Optimization.
Contributions:25 commits, 5 PRs, 23 pushes in 1 year 8 months
julia-packageoptimization-algorithmsspideroptimizationmonkey
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