Runyu was admitted to Zhejiang University through a 3+2 program and joined at IOSNEL in Fall 2023 as a Master’s student. During her studies, she published one review article and one research article in Q1 journals. In Fall 2024, she began her PhD at IOSNEL and her research focuses on evaluating coffee and biochar using spectral techniques. She is a recipient of the Jonathan Baldwin Turner (JBT) Fellowship in recognition of her outstanding academic record.
1. Zheng, R., Jia, Y., Ullagaddi, C., Allen, C., Rausch, K., Singh, V., Schnable, J. C., & Kamruzzaman, M. (2024). Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi- country corn kernels via NIR spectroscopy. Food Chemistry, 140062. Link
2. Zheng, R., & Kamruzzaman, M. (2023). Applications of hyperspectral imaging in the coffee industry: Current research and future outlook. Applied Spectroscopy Reviews, 1-25. Link
3. Zheng, R., & Kamruzzaman, M. (2025). Near-infrared spectroscopy for microalgae studies: A comprehensive review of applications and outlooks. Algal Research, 104074. Link
4. Zheng, R., & Kamruzzaman, M. (2026). Characterization of coffee residues and derived biochar via Fourier-transform infrared spectroscopy: Current status and outlook. Critical Reviews in Analytical Chemistry, 1–23. Link
5. Zheng, R., & Kamruzzaman, M. (2026). Explainable AI for hyperspectral imaging in food quality decision support: Interpretability, reliability, and future directions. Critical Reviews in Food Science and Nutrition, 1–21. Link
Lisa joined IOSNEL in Spring 2021 as an undergraduate researcher. She completed ABE 397 (Independent Research) and co-authored a publication in Food Control based on her project. During her undergraduate studies, she also published two first-author articles in Q1 journals, one in Current Research in Food Science and another in Food Composition and Analysis. She received several scholarships, was consistently on the Dean’s List, and was recognized as a James Scholar. After earning her bachelor’s degrees in Agricultural and Biological Engineering (ABE) and Chemistry, she started a direct PhD program in Fall 2023 at IOSNEL as an Illinois Distinguished Fellow. Her current research focuses on nanozyme engineering and the development of nanozyme-based portable biosensors for detecting agricultural toxic molecules.
1. Wu, Q., & Kamruzzaman, M. (2024). Advancements in nanozyme-enhanced lateral flow assay platforms for precision in food authentication. Trends in Food Science and Technology. Link
2. Wu, Q., Oliveira, M. M., Achata, E. M., &; Kamruzzaman, M. (2023). Reagent-free detection of multiple allergens in gluten-free flour using NIR spectroscopy and multivariate analysis. Journal of Food Composition and Analysis, 119, 105274. Link
3. Wu, Q., Mousa, M. A., Al-qurashi, A. D., Ibrahim, O. H., Abo-Elyousr, K. A.,
Rausch, K., &; Kamruzzaman, M. (2023). Global calibration for non targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics. Current Research in Food Science, 100483. Link
4. Wu, Q., da Silva Ferreira, M. V., & Kamruzzaman, M. (2026). Fully portable smartphone-integrated device coupled with nanozyme-based assay for sensitive detection of TBHQ in edible oils. Food Chemistry, 518, 149610. Link
5. Wu, Q., & Kamruzzaman, M. (2025). Organic polymer-based C-A-Fe nanozyme for dual-sensitive colorimetric detection of Hg²⁺ and Cr⁶⁺. ACS Applied Nano Materials, 8(23), 12210–12221. Link
6. He, H.-J., da Silva Ferreira, M. V., Wu, Q., Karami, H., & Kamruzzaman, M. (2025). Portable and miniature sensors in supply chain for food authentication: A review. Critical Reviews in Food Science and Nutrition, 65(20), 3966–3986. Link
Yaqi Hu is a PhD student at IOSNEL, where she conducts research in advanced biosensing technologies for food safety applications. Her work focuses on nanozyme-based sensors for mycotoxin detection, with an emphasis on developing sensitive, reliable, and practical sensing strategies. She is particularly interested in translating nanotechnology innovations into real-world solutions that enhance food safety and agricultural quality monitoring.
1. Hu, Y., & Kamruzzaman, M. (2026). Nanozyme-based biosensing strategies for aflatoxin detection: Recent advances and perspectives. Food Chemistry, 150770. Link
Md. Shahinur Alam is a PhD student in the Department of Agricultural and Biological Engineering at the University of Illinois Urbana-Champaign. He joined IOSNEL in Fall 2026. His research focuses on developing spectral-robotic systems by integrating hyperspectral imaging, spectroscopy, artificial intelligence, and robotics for agricultural applications. He earned his BSc degree in Mechatronics Engineering from Rajshahi University of Engineering & Technology (RUET), where he ranked at the top of his class. Before joining UIUC, he served as a faculty member in the Department of Farm Power and Machinery at Bangladesh Agricultural University.
1. Alam, M. S.
Md. Al-Mamun Provath is a PhD student in the Department of Agricultural and Biological Engineering at the University of Illinois Urbana-Champaign. He joined IOSNEL in Fall 2026. His research focuses on reconstructing hyperspectral images from RGB images using deep learning. He earned his BSc degree in Computer Science and Engineering from Chittagong University of Engineering and Technology (CUET), where he ranked at the top of his class. Before joining UIUC, he served as a faculty member in the Department of Computer Science and Engineering at CUET.
1. Provath, M. A-M.
He is a PhD candidate in Statistics and Agricultural Experimentation at ESALQ/USP and a Visiting Scholar at University of Illinois Urbana-Champaign, in the Department of Agricultural and Biological Engineering (ABE). His research integrates Machine Learning, Spatial Statistics, and Applied Statistics to address complex challenges in agricultural systems. He has a strong focus on developing data-driven approaches that bridge theory and real-world applications. Currently, his work centers on Spatial Machine Learning and Topoclimatic Zoning, aiming to transform data into actionable insights for the agricultural sector.
1. Werllerson Nascimento
Marciano Oliveira was a visiting PhD student at IOSNEL for six months as a PhD candidate from UNICAMP, Brazil. His research focused on using near-infrared (NIR) spectroscopy to predict impurities in cocoa shell powder. During his time at IOSNEL, he published two articles as lead author.
1. Oliveira, M. M., Ferreira, M. V. S., Kamruzzaman, M., & Barbin, D. F. (2023). Prediction of impurities in cocoa shell powder using NIR spectroscopy. Journal of Pharmaceutical and Biomedical Analysis Open, 2, 100015. Link to DOI
2. Oliveira, Mv., Badaró. A. T., Esquerre, C. A., Kamruzzaman, M. Barbin, D. F. (2023). Handheld and benchtop vis/NIR spectrometer combined with PLS regression for fast prediction of cocoa shell in cocoa powder. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 298, 122807. Link to DOI
Ayesha Syed got her PhD from the University of Agriculture Faisalabad, Pakistan. She joined IOSNEL as a visiting scholar in 2023. Her research focused on the application of near-infrared (NIR) spectroscopy combined with machine learning techniques to classify and predict total soluble solids (TSS) in sugarcane stems.
Dr. Khaliduzzaman earned his PhD in Bio-Sensing Engineering from Kyoto University, Japan. During his doctoral studies, he focused on non-destructive optical sensing techniques and imaging technologies for egg and poultry industry. Following his PhD, he served as a JSPS Postdoctoral Fellow at Kyoto University. As a postdoctoral researcher at IOSNEL, his work focused on hyperspectral imaging to enhance quality control and efficiency in the egg and poultry industries through the integration of advanced sensing and machine learning.
1. Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M (2025). Detection of early dead embryos using hyperspectral imaging system. 2025 International Poultry Scientific Forum, January 27-28, 2025, Atlanta, GA
Dong Hoon Lee completed his PhD in Agricultural and Biological Engineering in December 2024 and is now a proud alumnus of our lab. During his doctoral journey from August 2022 to December 2024, he made remarkable contributions organic compound based nanozyme research for food and agricultural sensing. He authored several high-impact publications with a cumulative journal impact factor of 69.8. His work appeared in prestigious journals such as Trends in Chemistry (IF: 14), Chemical Engineering Journal (IF: 13.4), Current Opinion in Food Science (IF: 9.6), and Food Chemistry (IF: 8.5), reflecting both the quality and significance of his research. His dedication, scientific curiosity, and collaborative spirit greatly enriched my group, and we look forward to seeing his continued success in the next chapter of his career.
1. Lee, D. H., & Kamruzzaman, M. (2023). Eco-friendly, degradable, peroxidase-mimicking nanozyme for selective antioxidant detection. Materials Today Chemistry. 34, 101809. Link
2. Lee, D. H., & Kamruzzaman, M. (2023). Organic compound-based nanozymes for agricultural herbicide detection. Nanoscale, 15, 12954-12960. Link
3. Lee, D. H., & Kamruzzaman, M. (2024). Advancements in organic materials-based nanozymes for broader applications. Trends in Chemistry. Link
4. Lee, D.H., Ahmed, M.W., & Kamruzzaman, M. (2024). Nanoscale substance-integrated optical sensing platform for pesticide detection in perishable foods. Current Opinion in Food Science, 60, 101227. Link
5. Lee, D. H., & Kamruzzaman, M. (2025). Amino acid-based, sustainable organic nanozyme and integrated sensing platform for histamine detection. Food Chemistry, 142751. Link
6, Lee, D. H., & Kamruzzaman, M. (2025). Consolidated sustainable organic nanozyme integrated with Point-of-Use sensing platform for dual agricultural and biological molecule detection. Chemical Engineering Journal, 159560. Link
7. Lee, D. H., & Kamruzzaman, M. (2025). Second generation organic nanozyme for effective detection of agricultural herbicides. Advanced Sustainable Systems. 2401029. Link
8. Lee, D. H., & Kamruzzaman, M. (2025). Sustainable organic nanozyme with an integrated colorimetric sensing system for mycotoxin detection. ACS Applied Nano Materials. Link
Dr. Shigeru Ichiura, a PhD graduate from the United Graduate School of Agricultural Sciences at Iwate University, Japan, is currently a Project Lecturer at Yamagata University’s Advanced Research Center for Agri-Food Systems. He serves as the CEO of ViAR&E Corporation, leveraging his extensive experience in electrical engineering and technology development, which includes roles at Toshiba, Softbank, Motorola, and NVIDIA. He has focused his research on the application of AI and robotics in agriculture, including developing a robot for safflower harvesting, tracking chicken behavior, and estimating duck weight using AI techniques. At IOSNEL, Dr. Ichiura will work on gender detection of eggs.
Toukir joined IOSNEL in Fall 2021 as a direct PhD student after completing his undergraduate degree in Computer Science and Engineering from BUET. While the typical direct PhD program takes at least five years, he completed his journey in just 3 years and 7 months. His research focused on explainable artificial intelligence (XAI) and deep learning-based reconstruction of hyperspectral images for applications in the sweetpotato industry. Toukir’s work in hyperspectral imaging, XAI, and image reconstruction has demonstrated significant real-world impact. During his PhD, he published nine research articles as first author, eight of which appeared in Q1 journals. He also excelled academically, earning a perfect 4.0 CGPA. In parallel with his PhD, he pursued a concentration in Data Science Engineering (DSE), further strengthening his research capabilities. We are excited to see the lasting impact Toukir will continue to make in the fields of XAI and hyperspectral image reconstruction.
Asher Sprigler is an undergraduate Computer Engineering major at the Milwaukee School of Engineering who visited UIUC as an NSF REU student during the summer of 2024. He assisted with the data science and model building aspects of agricultural research. His main research focused on sexing and determining the fertility of chicken eggs using hyperspectral imaging and machine learning. During his time at IOSNEL, he published two research articles as a second author.
Xiuning (Belle) Kuang is a sophomore in the (iSchool) School of Information Science at the University of Illinois at Urbana-Champaign. She is a laboratory assistant in Dr. Kamruzzaman’s group.
Sreezan Alam, from the Department of Chemical and Biomolecular Engineering at the University of Illinois at Urbana-Champaign, joined IOSNEL through the NSF REU program during the summer of 2024 and worked on smart drying process monitoring using hyperspectral imaging (HSI). His research focuses on hyperspectral imaging analysis, monitoring food moisture content during drying, and applying machine learning algorithms for pattern analysis and predictive modeling. During his time at IOSNEL, he published two research articles as a second author.
Camila Hammel was an undergraduate student in Food Engineering at the University of São Paulo. She completed an internship at the University of Illinois at Urbana-Champaign, where she worked as a laboratory assistant in Dr. Kamruzzaman’s group under the supervision of Dr. Marcus Ferreira. She is passionate about learning and growth, and she demonstrated strong capabilities in conducting research related to food science.
Nathan completed his BSc in Materials Science and Engineering from UIUC and developed a strong interest in sustainable bioprocessing. He joined IOSNEL as a Master’s student and conducted research on NIR spectroscopy for authentication and adulteration detection in organic spices. His work was published in Food Composition and Analysis (Q1). He is currently working as a Junior Pilot Plant Specialist at the Integrated Bioprocessing Research Laboratory (IBRL).
Ocean Monjur earned his undergraduate degree in Computer Science and Engineering from the Islamic University of Technology and completed his M.S. in Agricultural and Biological Engineering at the University of Illinois Urbana-Champaign, where he was a member of IOSNEL. His research at IOSNEL focused on artificial intelligence applications in agriculture, particularly real-time process monitoring using hyperspectral imaging and deep learning-based reconstruction of hyperspectral images from RGB data. His work helped develop efficient, accessible, and scalable imaging solutions for agricultural and biological applications. He is currently pursuing a PhD in Computer Science at the University of South Florida.
Di Song joined IOSNEL after completing his MS at China Agricultural University, bringing prior research experience in crop phenotyping. During his time at IOSNEL, he focused on multi-scale crop growth assessment by integrating remote sensing technologies with machine learning algorithms. His work contributed to advancing precision agriculture through scalable, data-driven crop monitoring solutions.
1. Song, D., De Silva, K., Brooks, M. D., &; Kamruzzaman, M. (2023). Biomass prediction based on hyperspectral images of the Arabidopsis canopy. Computers and Electronics in Agriculture, 210, 107939. Link
2. Song, D., Wu, Q., &; Kamruzzaman, M. (2023). Appropriate use of chemometrics for feasibility study for developing low-cost filter-based multi-parameter detection spectroscopic device for meat proximate analysis. Chemometrics and Intelligent Laboratory Systems, 239, 104844. Link
3. Song, D., Ngumbi, E., Allen, C. M., & Kamruzzaman, M. (2026). Super-resolution-enhanced texture and vegetation index fusion for drone-based detection of corn growth status under waterlogging stress. International Journal of Remote Sensing, 47(12), 5011–5033. Link
4. Song, D., Sun, H., Ngumbi, E., & Kamruzzaman, M. (2025). Multispectral image reconstruction from RGB images for maize growth status monitoring based on a window-adaptive spatial-spectral attention transformer. Computers and Electronics in Agriculture, 239(Part B), 111062. Link
After completing his BSc and MS in Food Engineering from Bangladesh Agricultural University and an MSc in Food Science, Technology, and Business from KU Leuven (Belgium), Wadud joined IOSNEL in Fall 2022 and completed his PhD in just two years and seven months. His research focused on NIR spectroscopy and hyperspectral imaging for evaluating fertility, mortality, sex, and structural attributes of eggs, including shell thickness, shell strength, and yolk ratio. These advancements support the ongoing transformation of the egg industry toward Industry 4.0, where automation, real-time monitoring, and data-driven decision-making are essential for ensuring quality, efficiency, and sustainability. He also worked on the rapid detection of biomass composition using NIR spectroscopy. During his PhD, he published 10 first-author articles, 8 of which appeared in Q1 journals. Additionally, he co-authored six more articles as a second author, five of which were also published in Q1 journals. We look forward to seeing his lasting impact on hyperspectral imaging research in food and biological applications.
After his research experience at IOSNEL, he became a researcher, CEO, and Founder of We-Cre-8 Engineering, where he operates at the intersection of food engineering, chemistry, and advanced data technologies. His work focuses on the integration of smart sensing systems—including optical sensors (RGB, NIR, and NIR-HSI) and electronic nose technologies—with artificial intelligence, machine learning, and chemometrics. Through both research and consulting projects, he develops and implements innovative, lab-driven solutions tailored to enhance agricultural practices, food processes, and data-driven decision-making in the agri-food sector. Following this transition, he was elected a full member of Sigma Xi and was featured in Who’s Who in America, reflecting recognition of his contributions beyond his time at IOSNEL.