Welcome!
I'm Bodruzzaman Khan, an Aspiring Researcher

I am a self-motivated interdisciplinary explorer. My research specializes in sustainability within agricultural and environmental systems. I employ machine learning (ML), remote sensing, and data-driven approaches to study pressing issues in agri-environment domains such as land use change, climate change, resource management, and nature-based solutions. I strongly believe in crafting domain-specific ML models that meet reliability, generalizability, and novelty rather than straightforward implementations.
With a strong foundation in agricultural and environmental engineering, I focus on doing research to develop innovative solutions that enhance agricultural productivity and promote environmental conservation. I aim to make meaningful contributions to global sustainability efforts, fostering resilience and efficiency in both agricultural and environmental practices.

Goals for PhD

I aspire to contribute to cutting-edge research that advances sustainable practices in agriculture and environmental management. My goal is to leverage machine learning and remote sensing technologies to improve resource efficiency, enhance environmental health, and develop innovative solutions for tackling pressing challenges.

My Research

Research Focus

My key research focuses on sustainability in both agriculture and the environment, aiming to make meaningful contributions to global challenges.

Latest Research

Title: Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification.
Journal: Scientific Reports (Nature Portfolio)

Sponge City

Designed a sustainable flood mitigation approach using machine leraning, remote sensing and hydrologic-hydraulic simulation. This research was recognised by the Ministry of Science and Technology, Bangladesh.

Scientific Machine Learning

Ongoing research on energy systems using SciML.This is a collaboration work with the researchers from Columbia University in The City of New York and University of Science and Technology of China.

Custom Vision Transformer

Ongoing research on object detection using advanced customized ViT framework.This is a collaboration work with the researchers from Sylhet Agricultural University.

Spatio-temporal Hybrid Model

Ongoing research on outage prediction using Hybrid DL.This is a collaboration work with the researchers from Cornell University and Columbia University in The City of New York.

Study on Climate Change

A manuscript (collaboration work with Sylhet Agricultural University) has been submitted to the journal of Computers and Electronics in Agriculture on the study of climate change on rice production.

ST-GUI: A GUI for Automatic Soil Textural Classification

A practical application of Python in developing user-friendly software solutions, enabling efficient and accurate analysis of soil classes for professionals.

Geospatial Study with GEE

I have done several practical projects related to remote sensing and spatial analysis where I used the Google Earth Engine platform. Along with GEE, I regularly use other spatial tools like GIS and ArcGIS.

3d & 2D Design

I use professional design programs, such as AutoCAD and SkechUp, for 2D and 3D drawing tasks.

Fluid Simulation with PINNs

Using physics-informed neural networks (PINNs), I was able to model the complex interactions that govern fluid behavior around rotating bodies.

Interpretable AI

I have modeled an interpretable machine learning approach for land cover classifications. This interpretability is crucial for the reliability of ML in practical cases.

Profile Overview

Research Interests

My research interests lie at the intersection of machine learning, sustainable agriculture, and environmental management.
I aim to explore innovative methodologies for addressing climate change and resource management.

Academic Background

Master’s Degree in Agricultural Construction and Environmental Engineering
CGPA: 3.962/4 | Completion Year: 2024
Bachelor’s Degree in Agricultural Engineering and Technology
CGPA: 3.846/4 | Completion Year: 2022

Research Methodologies

I employ wide range methodologies, including applied machine learning, hierarchical deep learning, explainable AI, geospatial analysis, hydrological simulation, remote sensing, image classification, spatio-temporal analysis, and time-series forecasting.

Technical Skills

Proficient in Python, with extensive real-life experience in deep learning, supervised and unsupervised learning, statistical analysis, engineering design and modeling, and geospatial technologies including ArcGIS, QGIS, and Google Earth Engine.

Innovative Technologies

Engage with emerging technologies such as AI, computer vision, and deep learning to enhance data-driven decision-making in sustainable agriculture and environmental management, fostering innovative solutions for global environmental challenges.

Collaborations and Networking

Active participation in research collaborations and academic networks to foster interdisciplinary approaches towards global agri-environmental issues. I am open to opportunities for Novel studies, as I aspire to contribute to impactful research that addresses global challenges.