Skip to content
View shamsbasir's full-sized avatar
24 followers · 6 following
  • Quinstreet
  • North Carolina

Achievements

Achievement: StarstruckAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

Achievements

Achievement: StarstruckAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

Block or report shamsbasir

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
shamsbasir/README.md

Hi 👋, I'm Shams Basir

A passionate senior machine learning engineer at Quinstreet

shamsbasir

shamsbasir

  • 🔭 I’m working on leveraging large langauge models (LLMs) to develop intelligent AI agents that can interact with external data sources to perform complex tasks in healthcare.

  • 💬 Ask me about generative AI, deep learning, machine learning, computational fluid dynamics.

  • 📫 How to reach me shamsbasir@gmail.com

  • ⚡ Fun fact I have two B.Sc degrees in aerospace engineering and civil engineering

Connect with me:

shamsulhaqbasir

Languages and Tools:

aws bash c cplusplus docker git kubernetes linux matlab mysql pandas photoshop python pytorch scikit_learn seaborn sqlite tensorflow

shamsbasir

 shamsbasir

shamsbasir

Pinned Loading

  1. PECANNs PECANNs Public template

    Forked from HiPerSimLab/PECANN

    Physics and Equality Constrained Artificial Neural Networks (PECANNs)

    Jupyter Notebook 1 2

  2. investigating_mitigating_failure_modes_in_pinns investigating_mitigating_failure_modes_in_pinns Public

    This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Networks(PINNs)"

    Jupyter Notebook 16 5