Integrating deep learning and machine learning models to simulate soil heterotrophic respiration and rhizosphere priming effect in Duke Forest
Published by
Bioresource Technology
Summary
(To be submitted; Internal review)
About
A highly accomplished Research Associate with a Ph.D. in Engineering, specializing in the application of advanced machine learning and deep learning techniques to complex problems in biological systems engineering, bioinformatics, and computational biology. Proven expertise in developing predictive models for biomass conversion efficiency, identifying disease-related genetic markers, and optimizing industrial processes, backed by 14 peer-reviewed journal publications and significant grant funding. Seeking to leverage strong analytical, modeling, and research skills to drive innovation in a leading R&D or academic institution.
Work
University of Wisconsin-Madison and USDA Forest Products Laboratory
|Research Associate
Madison, WI, US
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Summary
Deploys machine learning algorithms to predict biomass handling properties and conversion efficiencies, develops models for biomass characteristics, and optimizes cellulose nanomaterial production.
Highlights
Deployed advanced machine learning algorithms to accurately predict biomass handling properties and conversion efficiencies.
Developed sophisticated machine learning models to analyze biomass characteristics, handling techniques, pretreatment processes, and fuel conversion, enhancing process understanding.
Leveraged experimental data and hyperspectral images from forest and crop residues to validate and refine predictive models.
Optimized and scaled up the analysis of cellulose nanomaterial production through advanced machine learning models.
Jeonbuk National University
|Research Assistant
Jeonju, South Korea, Korea (Republic of)
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Summary
Developed computational models for bioinformatics and computational biology using deep and machine learning techniques, focusing on disease identification and genetic marker analysis.
Highlights
Developed advanced computational models for bioinformatics and computational biology, leveraging deep and machine learning techniques to address complex biological problems.
Participated in identifying genes associated with neurodegenerative diseases and disorders by applying artificial intelligence tools and techniques.
Assisted in developing deep learning models to accurately identify cancer-associated DNA methylation markers, contributing to early detection research.
The University of Azad Jammu and Kashmir (UAJK)
|Lab Engineer/Instructor
Muzaffarabad, Pakistan, Pakistan
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Summary
Conducted undergraduate lab sessions and designed lab manuals for electrical engineering courses, ensuring compliance with Higher Education Commission (HEC) guidelines.
Highlights
Conducted undergraduate lab sessions for 7 diverse electrical engineering courses, including Data Structures, Digital Signal Processing, and Electronic Circuit Design.
Designed and updated lab manuals for all assigned courses, ensuring alignment with Higher Education Commission (HEC) guidelines and enhancing student learning outcomes.
Participated in Pakistan Engineering Council accreditation visits, maintaining and improving standard operating procedures (SOPs) for departmental labs.
Sprintech Packaging Pvt. Ltd.
|Assistant Manager Electrical
Lahore, Pakistan, Pakistan
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Summary
Contributed to automating a seven-color rotogravure printing machine and oversaw the installation, commissioning, and maintenance of electrical and production units.
Highlights
Contributed to the automation of a seven-color rotogravure printing machine, significantly enhancing operational efficiency and output.
Oversaw the successful installation, commissioning, and troubleshooting of PLC and HMI interfaces.
Led the installation and commissioning of advanced bag-making machines, optimizing production capabilities.
Managed shifts for operation and maintenance (OM), utilities, and production units, ensuring seamless workflow and minimizing downtime.
AJK Power Development Organization
|Assistant Engineer Electrical
Muzaffarabad, Pakistan, Pakistan
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Summary
Supervised shift operations and oversaw the installation and maintenance of electromechanical equipment for hydropower plants.
Highlights
Supervised shift operations for the 3.0 MW Qadirabad Hydel Power Station, ensuring efficient load management, synchronization, monitoring, and maintenance of electromechanical equipment using SCADA.
Oversaw the installation of electromechanical equipment at the 3.2 MW Rehra Hydropower Plant, contributing to project completion and operational readiness.
Education
Jeonbuk National University
Jeonju, South Korea, Korea (Republic of)
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Doctor of Philosophy
Electronics and Information Engineering
Abasyn University Islamabad Campus
Islamabad, Pakistan, Pakistan
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Master of Science
Electrical Engineering
Ghulam Ishaq Khan Institute of Science and Technology
Swabi, Pakistan, Pakistan
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Bachelor of Science
Electronic Engineering
Awards
USDA-DOE Cooperative Agreement Support
Awarded By
U.S. Department of Agriculture Forest Service and Department of Energy
Secured funding under Cooperative Agreement No. 22-CO-11111137-017 and DOE Award No. DE-EE0008911 for collaborative research with the University of Georgia and University of Wisconsin.
Forestry Nanotechnology Research Grant
Awarded By
U.S. Endowment for Forestry and Communities, Inc., and P3Nano
Awarded for Project No. 21-00288, supporting research in forestry nanotechnology.
AFRI Competitive Research Grant
Awarded By
USDA National Institute of Food and Agriculture
Received grant funding under Grant No. 2020-68012-31881 for competitive research.
Brain Korea (BK-21) Scholarship
Awarded By
National Research Foundation of Korea
Awarded for doctoral studies in Engineering, supporting research under Grant Numbers: 2017M3C7A1044816 and 2020R1A2C2005612.
Publications
Near-infrared hyperspectral imaging-driven machine learning models for predicting southern pine residue thermochemical conversion efficiency
Published by
Energy and AI
Summary
(To be submitted; Internal review)
Advancing Biomass Handling and Logistics: Machine Learning Models for Predicting Corn Stover Bulk Flow Properties.
Published by
ASABE Annual International Meeting
Summary
Oral Presentation at Sheraton Centre Toronto Hotel, Toronto, Ontario.
Application of machine learning to predict the products yield of lignocellulosic biomass fast pyrolysis process
Published by
Bioresource Technology
Summary
(To be submitted; Internal review)
Machine learning for predicting and optimizing corn stover hydrolysis conversion efficiency
Published by
Applied Energy
Summary
(Submitted)
Deep learning enabled process design and decision-making for lignocellulosic biomass slow pyrolysis
Published by
Renewable and Sustainable Energy Reviews
Summary
(Submitted)
Model interpretability vs model complexity: comparing partial least squares and artificial neural network wood property near infrared spectroscopy models.
Published by
International Union of Forest Research Organizations (IUFRO) Division 5 Conference
Summary
Oral Presentation in Cairns, Queensland, Australia.
Identification of protein-ligand binding residue having variable length sequences using deep learning.
Published by
2019 Korean Electrical Society on Information and Control Conference (CICS)
Summary
pp. 266-267.
Languages
English
Skills
Machine Learning Frameworks
Tensorflow, Scikit-Learn, Biopython.
Programming Languages
Python, Matlab, C.
Data Analysis & Modeling
Machine Learning, Deep Learning, Bioinformatics, Computational Biology, Hyperspectral Imaging, Predictive Modeling, Statistical Analysis.
Engineering & Systems
Biological Systems Engineering, Electrical Engineering, Electronic Engineering, SCADA, PLC, HMI Interfaces, Electromechanical Equipment, Biomass Conversion, Cellulose Nanomaterials.
Research & Development
Experimental Design, Data Interpretation, Grant Writing, Publication, Academic Mentoring, Peer Review.
Projects
Machine Learning Framework for Biomass Handling and Conversion
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Summary
Developing a machine learning-based modeling framework to relate biomass tissue properties with handling and conversion performances.
P3Nano: Advancing Commercialization of Cellulosic Nanomaterials
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Summary
A project focused on advancing the commercialization of cellulosic nanomaterials.
Mid-Atlantic Sustainable Biomass Consortium for Value-Added Products
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Summary
Developing value-added products from biomass within the Mid-Atlantic region.
AI-driven Modeling of Microbial Environment Interactions
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Summary
Utilizing AI for modeling microbial environment interactions to support sustainable biomass ecosystems.
AI-driven Gene Identification for Neurological Diseases
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Summary
Identification of genes causing natural intelligence and brain diseases using artificial intelligence.
Deep Learning Model for Cancer-causing DNA Methylation
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Summary
Development of a deep learning model to identify cancer-causing DNA methylation markers.