M Arshad Zahangir Chowdhury

Rensselaer Polytechnic Institute, Troy, NY 12180.

chowdm6ATrpi.edu.

2023 -
I started my academic career at RPI. My research focus is on smart sensors, design and optimization, and applied machine learning. I teach primarily Elements of Mechanical Design. I also teach Machine Learning for Engineering, Fluid Dynamics Laboratory, Thermal and Fluids Laboratory, and Engineering Economics.
2018 - 2023
I received my PhD from Rensselaer Polytechnic Institute in Mechanical Engineering advised by Prof. Matthew A. Oehlschlaeger.
2021 - 2022
I was a research aide at the Data Science and Learning Division at Argonne National Laboratory.
2016 - 2018
I obtained my Master's degree from then Indiana University Purdue University Indianapolis (present day Purdue University in Indianapolis), advised by Prof. M. Razi Nalim.
2014 - 2016
I served as a lecturer of mechanical engineering at Ahsanullah University of Science and Technology, Dhaka, Bangladesh.
2009 - 2014
I majored in Mechanical Engineering with a Bachelor of Science in Mechanical Engineering at Bangladesh University of Engineering and Technology.
Research

My research is focused on developing smart sensors integrated with AI. See below for some of my detail work during and post-PhD and one of the review I wrote for further details.

Deep Learning for Gas Sensing via Infrared Spectroscopy

In this project, A deep learning model is trained on simulated absorbance spectra to recognize infrared spectra of up to three component mixture in a broad 400-4000 1/cm wavenumber range. The proposed model is capable of highlighting important frequencies for multi-gas sensing and probable cause for misclassification within a user defined frequency range.

TSMC-Net : Deep Learning Mixture Component Identification

In this project, A deep learning model is trained on simulated absorbance spectra to recognize THz spectra of up to eight component mixture in 220-330 GHz frequency range. The workflow proposed is capable of highlighting important frequencies for multi-gas sensing and probable cause for misclassification by the model.

VOC-Net: Deep Learning Volatile Organic Compounds Classifier

In this project, A deep learning 1D-CNN based classifier is trained on simulated absorbance spectra to recognize VOC THz spectra in 220-330 GHz frequency range.

IR Spectra Classification

In this project, SVM models are trained on simulated absorbance spectra to recognize IR spectra in a broad frequency range.

X-Ray Fluoroscence Imaging ROI-Finder

Code for intelligently guiding X-ray fluorescence scans to regions of interest using a suite of ML-based clustering algorithms.

Rotational Spectra Classification

This code trains on simulated absorbance spectra to recognize measured THz spectra in the 220-330 GHz frequency range using several machine learning methods.

Students

Supervised Undergraduate Research Projects (URPs)

Ian Oehlschlaeger, Machine learning for sports analytics.

Michael Ahn, Generative design of machine elements.

Runhan Gu, Classify and autoencode gas spectral data to make a gas sensing digital twin.

Teaching

At Rensselaer Polytechnic Institute, Troy, New York

MANE 4030: Elements of Mechanical Design [Sum'23, Fall'23, Spring'24, Sum'24, Fall'24, Spring'25, Sum'25]

MANE 4962: Machine Learning for Engineering [Spring'23,Spring'24, Spring'25]

MANE 4910: Fluid Dynamics Laboratory

MANE 4740: Thermal and Fluids Engineering Laboratory

ENGR 4760: Engineering Economics

At Ahsanullah University of Science and Technology, Dhaka, Bangladesh

ME 201: Elements of Theory of Machine and Machine Design

ME 2201: Mechanics of Materials

ME 2110/ME 102/ME114: Mechanical Engineering Drawing I

ME 3204: Control Engineering Sessional

ME 3206: Heat Transfer Sessional

ME 4102: Heat Engines Sessional

ME 302: Fundamentals of Mechanical Engineering Sessional