As an MLOps developer, simulation specialist, and research engineer with over 6 years of experience, I specialize in developing innovative data-driven solutions. My tenure as a postdoc at NC Inc. was marked by leveraging machine learning and synthetic data from finite element simulations for thermal stress analysis in pipe bends, directly addressing industry challenges. In my current role at Arcurve Inc., I utilize cloud services (e.g., Azure and AWS) to develop/maintain end-to-end machine-learning pipelines. Additionally, my personal project 'DocsGPT,' a RAG-based querying app using LangChain and OpenAI's API, demonstrates my proficiency in implementing solutions using generative AI. Let's connect on LinkedIn.
-
Arcurve Inc.
- Calgary
- https://www.linkedin.com/in/farhad-davaripour/
Pinned Loading
-
GenAI_Applications_in_Pipeline_Engineering
GenAI_Applications_in_Pipeline_Engineering PublicThis repo provides examples of using Generative AI applications to streamline the design of gas pipelines.
Jupyter Notebook 1
-
AI_Applications_in_Pipeline_Engineering
AI_Applications_in_Pipeline_Engineering PublicThis repository uses machine learning to map pipeline anomalies, predict future depths, and fill missing data to improve pipeline integrity management.
Jupyter Notebook 1
-
CFRP_Reinforced_HDD_overbend
CFRP_Reinforced_HDD_overbend PublicThis project employs machine learning and synthetic dataset to predict the peak equivalent stress imposed on a CFRP wrapped HDD overbend
Python
-
Stanford-CS229-Spring2023-Notes
Stanford-CS229-Spring2023-Notes PublicCS229 course notes from Stanford University on machine learning, covering lectures, and fundamental concepts and algorithms. A comprehensive resource for students and anyone interested in machine l…
-
If the problem persists, check the GitHub status page or contact support.