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Name: Harish PanneerSelvam

Type: User

Company: Exergi Predictive LLC, NREL

Bio: Data Scientist working in an exciting startup developing physics-based ML software for energy prediction of Military electric and hybrid vehicles @ExergiPredict

Location: Denver, Colorado

Hi, I'm Harish Panneer Selvam, a Graduate Research Assistant and a Master's student in the Mechanical Engineering Department at the University of Minnesota Twin Cities. I completed my Bachelor's degree in Chemical Engineering from IIT Madras, India. Most of my undergraduate years were spent on the Formula SAE racecar team called Raftar Formula Racing, where I built three racecars ground up.

My current interests and projects are in the hybrid research space of data science and mechanical engineering. My work during my master's degree include using physics-informed interpretable artificial intelligence models in automotive applications like vehicle emissions and EV battery available energy predictions from vehicle onboard diagnostics data. I'm also investigating deep learning techniques to solve differential equations that govern real-world engineering systems.

I'm looking to collaborate on interdisciplinary projects that hope to bridge the gap between engineering and data science.

You can reach me on LinkedIn: https://www.linkedin.com/in/harish-panneerselvam/

Harish PanneerSelvam's Projects

aaai2020 icon aaai2020

Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary Results

appliedenergy2021 icon appliedenergy2021

This repository contains the test data and code used for the paper titled "Physics-Based Machine Learning Framework for Predicting NOx Emissions from Compression-Ignition Engine Powered Vehicles" that is submitted to the Applied Energy Journal

ensemblelearningsae icon ensemblelearningsae

This repository contains the code and datasets used for NOx prediction using physics-based ensemble learning models such as RandomForestRegression that is related to the upcoming paper: "Prediction of NOx Emissions from Compression Ignition Engines Using Ensemble Learning-Based Models with Physical Interpretability"

me2011-ta icon me2011-ta

Code using fuzzywuzzy python library for reading online class attendance for ME2011 course (University of Minnesota Twin Cities) from Zoom registration reports

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