Research Associate, Biological Systems Engineering
Madison, US.
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
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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
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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)
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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.
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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
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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 PhilosophyElectronics and Information Engineering
Abasyn University Islamabad Campus
Islamabad, Pakistan, Pakistan
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Master of ScienceElectrical Engineering
Ghulam Ishaq Khan Institute of Science and Technology
Swabi, Pakistan, Pakistan
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Bachelor of ScienceElectronic Engineering
Awards
USDA-DOE Cooperative Agreement Support
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
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
USDA National Institute of Food and Agriculture
Received grant funding under Grant No. 2020-68012-31881 for competitive research.
Brain Korea (BK-21) Scholarship
National Research Foundation of Korea
Awarded for doctoral studies in Engineering, supporting research under Grant Numbers: 2017M3C7A1044816 and 2020R1A2C2005612.
Publications
Integrating deep learning and machine learning models to simulate soil heterotrophic respiration and rhizosphere priming effect in Duke Forest
Bioresource Technology
(To be submitted; Internal review)
Near-infrared hyperspectral imaging-driven machine learning models for predicting southern pine residue thermochemical conversion efficiency
Energy and AI
(To be submitted; Internal review)
Advancing Biomass Handling and Logistics: Machine Learning Models for Predicting Corn Stover Bulk Flow Properties.
ASABE Annual International Meeting
Oral Presentation at Sheraton Centre Toronto Hotel, Toronto, Ontario.
Application of machine learning to predict the products yield of lignocellulosic biomass fast pyrolysis process
Bioresource Technology
(To be submitted; Internal review)
Machine learning for predicting and optimizing corn stover hydrolysis conversion efficiency
Applied Energy
(Submitted)
Deep learning enabled process design and decision-making for lignocellulosic biomass slow pyrolysis
Renewable and Sustainable Energy Reviews
(Submitted)
Stacking based ensemble learning framework for identification of nitrotyrosine sites
Computers in Biology and Medicine
Volume 183, 109200. (Co-first author)
Utilization of synthetic near-infrared spectra via generative adversarial network to improve wood stiffness prediction
Sensors
Volume 24, no. 6, 1992. (Editor's Choice Article)
Fiber quality prediction using NIR spectral data: tree-based ensemble learning vs. deep neural networks
Wood and Fiber Science
Volume 55, no. 1, 100-115.
Model interpretability vs model complexity: comparing partial least squares and artificial neural network wood property near infrared spectroscopy models.
International Union of Forest Research Organizations (IUFRO) Division 5 Conference
Oral Presentation in Cairns, Queensland, Australia.
Identification of piRNA disease associations using deep learning
Computational and Structural Biotechnology Journal
Volume 20, 1208-1217.
Interpretable machine learning identification of arginine methylation sites
Computers in Biology and Medicine
Volume 147, 105767.
Identification of functional piRNAs using a convolutional neural network
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume 19, 1661-1669.
Prediction of RNA 5-Hydroxymethylcytosine modifications using deep learning
IEEE Access
Volume 9, 8491-8496.
Identification of Human Promoter using Convolutional Neural Network.
2021 International Conference on Artificial Intelligence (ICAI)
pp. 213-216. IEEE.
A CNN-based RNA N6-methyladenosine site predictor for multiple species using heterogeneous features representation
IEEE Access
Volume 8, 138203-138209. (Co-first author)
iIM-CNN: Intelligent identifier of 6mA sites on different species by using convolution neural network
IEEE Access
Volume 7, 178577-178583. (Co-first author)
Identification of protein-ligand binding residue having variable length sequences using deep learning.
2019 Korean Electrical Society on Information and Control Conference (CICS)