Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

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Dr. Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

Associate Professor at University of Guilan, Rasht, Iran

Dr. Hemad Zareiforoush is an Assistant Professor at the Department of Biosystems Engineering, University of Guilan, Rasht, Iran, where he has been contributing to both academic and practical advancements in biosystems engineering since 2015. With a focus on agricultural machinery, automation, and quality inspection systems, his work bridges engineering and food science, particularly in areas like computer vision, image processing, and renewable energy applications. His research is highly interdisciplinary, combining mechanical engineering principles with computational intelligence for improving the agricultural industry’s efficiency.

Profile

Google Scholar

Education

Dr. Zareiforoush’s educational background is robust, with a PhD in Mechanical and Biosystems Engineering from Tarbiat Modares University in Tehran, Iran, completed in 2014. His academic excellence is evident in his GPA of 17.84 out of 20. He earned his MSc in Mechanical Engineering of Agricultural Machinery at Urmia University in 2010, where he graduated with a remarkable GPA of 19.29 out of 20. Earlier, Dr. Zareiforoush obtained his BSc in the same field from Urmia University in 2007, graduating with a GPA of 15.75 out of 20. He also attended a specialized governmental high school for excellent pupils, where he focused on mathematics and physics, graduating with a GPA of 18.71 out of 20.

Experience

Since joining the University of Guilan in 2015, Dr. Zareiforoush has been teaching various courses, including Engineering Properties of Food and Agricultural Products, Renewable Energy, and Measurement and Instrumentation Principles. His practical experience spans various engineering disciplines, with a particular emphasis on instrumentation, automation in agriculture, and food quality monitoring. Notably, his research has led to the development of innovative systems for rice quality inspection using computer vision and fuzzy logic. Additionally, he has been involved in numerous projects related to agricultural machinery, renewable energy, and automation for optimizing food production processes.

Research Interests

Dr. Zareiforoush’s research interests lie at the intersection of biosystems engineering, computational intelligence, and food science. He is particularly interested in computer vision applications for food quality inspection, using advanced image processing techniques to enhance product quality and safety. His work also explores hyperspectral imaging and spectroscopy for monitoring the quality of food materials. Another key area of his research is the application of machine learning algorithms for modeling and classifying food products based on their quality attributes. Additionally, he is involved in renewable energy applications in agriculture, focusing on solar-assisted drying systems and energy-efficient food processing methods.

Awards

Dr. Zareiforoush has received several prestigious awards throughout his academic career. He was honored with the Iran Ministry of Science, Research, and Technology Scholarship in 2012 and the National Elite Scholarship by the Iran National Foundation for Elites (INFE) in 2011. His exceptional academic performance earned him the title of “Best Student” at Urmia University in 2009. Additionally, he has been recognized as a “Talented Student” at Tarbiat Modares University and ranked 1st among MSc students in his department.

Publications

Dr. Zareiforoush has published several influential papers in high-impact journals. Some of his notable publications include:

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features. Journal of Food Measurement and Characterization, 14: 1402–1416, Cited by: 43.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features. Plant Methods, 16:153, Cited by: 25.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2021). Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared-assisted continuous hybrid solar dryer. Food Science & Nutrition (JCR), 9(1), 532-543, Cited by: 12.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2016). Design, Development, and Performance Evaluation of an Automatic Control System for Rice Whitening Machine Based on Computer Vision and Fuzzy Logic. Computers and Electronics in Agriculture, 124: 14-22, Cited by: 67.

Soodmand-Moghaddam, S., Sharifi, M., Zareiforoush, H. (2020). Mathematical modeling of lemon verbena leaves drying in a continuous flow dryer equipped with a solar pre-heating system. Quality Assurance and Safety of Crops & Foods, 12(1): 57-66, Cited by: 30.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2015). Qualitative Classification of Milled Rice Grains Using Computer Vision and Metaheuristic Techniques. Journal of Food Science and Technology (Springer), 53(1): 118-131, Cited by: 45.

Zareiforoush, H., Komarizadeh, M.H., Alizadeh, M.R. (2010). Effects of crop-screw parameters on rough rice grain damage in handling with a horizontal screw auger. Journal of Food, Agriculture and Environment, 8(3): 132-138, Cited by: 19.

Conclusion

Dr. Hemad Zareiforoush’s academic and professional contributions significantly impact the fields of biosystems engineering, food science, and agricultural machinery. His work in developing intelligent systems for quality inspection and automation has improved agricultural productivity and food safety. His expertise in computational techniques, including fuzzy logic and machine learning, continues to shape the future of smart farming and food processing. With numerous awards, highly cited publications, and a track record of excellence, Dr. Zareiforoush is a leading figure in his field.

Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

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Assist. Prof. Dr. Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

Assistant Professor at Department of Electronic Engineering, Tsinghua University, China

Quanming Yao is a world-class researcher in the field of machine learning, holding the position of Assistant Professor in the Department of Electronic Engineering at Tsinghua University. With a strong academic background and extensive experience in deep learning, Yao’s research focuses on creating efficient and parsimonious solutions in machine learning, particularly in deep networks and graph learning. His work aims to enhance interpretability in AI models and has led to groundbreaking advancements, such as the development of EmerGNN, the first deep learning model that interprets drug-drug interaction predictions for new drugs. His contributions have significantly impacted both academia and industry, leading to the commercialization of his methods in the AI unicorn 4Paradigm.

Profile

Orcid

Education

Yao earned his Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology (HKUST) between 2013 and 2018. Prior to this, he completed his undergraduate studies at Huazhong University of Science and Technology, where he obtained a degree in Electronic and Information Engineering in 2013.

Experience

Before becoming an assistant professor at Tsinghua University in 2021, Yao worked as a researcher and senior scientist at 4Paradigm Inc. in Hong Kong, from June 2018 to May 2021. In his current academic role, he serves as a Ph.D. advisor, leading research in machine learning and AI, with a specific focus on making deep learning models more efficient and interpretable.

Research Interests

Yao’s research interests revolve around the concept of “parsimonious deep learning,” wherein he explores how simple solutions can lead to substantial improvements in machine learning models. His work is especially notable for its emphasis on automated graph learning methods, which has earned him first place in the Open Graph Benchmark, an equivalent to ImageNet in graph learning. He is also dedicated to the development of deep learning methods that provide interpretable results, particularly in domains like drug discovery, where his innovations have had a direct impact on creating a synthetic biology startup, Kongfoo Technology.

Awards

Yao’s exceptional contributions to the field of machine learning have earned him numerous prestigious awards. These include the Inaugural Intech Prize in 2024, the Aharon Katzir Young Investigator Award in 2023, Forbes 30 Under 30 in the Science & Healthcare Category (China) in 2020, and the Google Ph.D. Fellowship in 2016. He was also recognized as one of the World’s Top 2% Scientists in 2023, highlighting his influence in the global research community.

Publications

Yao has published over 100 papers in top-tier international journals and conferences, with a significant citation record (around 12,000 citations and an h-index of 36). His work includes several landmark papers, such as:

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network, Nature Computational Science, 2023.

AutoBLM: Bilinear Scoring Function Search for Knowledge Graph Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

Efficient Low-rank Tensor Learning with Nonconvex Regularization, Journal of Machine Learning Research (JMLR), 2022.

Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels, Advance in Neural Information Processing Systems (NeurIPS), 2018.

These papers showcase his innovative work in the areas of drug interaction prediction, knowledge graph learning, and robust training of deep neural networks, significantly impacting both theoretical and practical aspects of AI.

Conclusion

Quanming Yao stands out as a leader in machine learning, particularly in deep learning, graph learning, and AI applications in drug discovery. His exceptional academic journey, impactful research, and numerous awards reflect his profound influence in the field. Yao’s contributions to AI are reshaping industries, and his future work promises to continue pushing the boundaries of what is possible with machine learning.