Prof Dr. Naoufal Lakhssassi | Prediction Award | Best Researcher Award

Prof Dr. Naoufal Lakhssassi, Hampton University, United States

Dr. Naoufal Lakhssassi is an Assistant Professor at the School of Biological Sciences, Hampton University, where he leverages his expertise in plant genetics and genomics to enhance crop resistance and seed quality. With a Ph.D. in Cell and Molecular Biology from the University of Malaga, Spain, and a background in biomolecular engineering and microbiology, Dr. Lakhssassi has a distinguished career in plant research. His work has been recognized with several awards, including the Inventor of the Year Award from Southern Illinois University. His research focuses on developing soybean varieties with improved disease resistance and nutritional content. Dr. Lakhssassi holds multiple patents related to soybean genetics and has secured significant funding from agencies like the United Soybean Board and USDA-NIFA for projects aimed at advancing agricultural biotechnology.

Professional Profile:

ORCID

Summary

Dr. Naoufal Lakhssassi’s innovative research, significant patents, successful grant applications, prestigious awards, and robust teaching background make him an excellent candidate for the Best Researcher Award. His contributions to plant genetics and biotechnology align with the criteria typically considered for such an award, showcasing both depth and breadth in his field.

Education

PhD in Cell and Molecular Biology, Plant Genetics, and Genomics
Department of Biochemistry and Molecular Biology, University of Malaga (UMA), Spain
2007-2011
Grade: Excellent (Cum-Laude)
Master in Biomolecular Engineering and Microbiology
Department of Microbiology, Biochemistry and Molecular Biology, University of Malaga (UMA), Spain
2008-2010
Diploma of Advanced Studies (Doctorado) in Cell and Molecular Fundamentals of Living Creatures
Department of Microbiology, Plant Pathology, and Plant-Pathogen Interaction, University of Malaga (UMA), Spain
2005-2007
Bachelor Degree in Biotechnology
Department of Biology, Abdelmalek Essaâdi University (UAE), Tangier, Morocco
2001-2005

Work Experience

Associate Scientist
Southern Illinois University (SIU), Carbondale
Department of Plant Soil and Agricultural Systems, College of Agricultural Sciences
May 2018 – Present
Research Assistant
Southern Illinois University (SIU), Carbondale
Department of Plant Soil and Agricultural Systems, College of Agricultural Sciences
January 2018 – May 2018
Post-Doctoral Associate
Southern Illinois University (SIU), Carbondale
- Department of Plant Soil and Agricultural Systems, College of Agricultural Sciences
  October 2012 – March 2016
- Department of Environmental and Civil Engineering, College of Engineering
  April 2016 – September 2016
- Department of Plant Soil and Agricultural Systems, College of Agricultural Sciences
  October 2016 – September 2017

Publication top Notes:

Detection and Classification of Cannabis Seeds Using RetinaNet and Faster R-CNN

Potential of Sorghum Seeds in Alleviating Hyperglycemia, Oxidative Stress, and Glycation Damage

Genomic Regions and Candidate Genes for Seed Iron and Seed Zinc Accumulation Identified in the Soybean ‘Forrest’ by ‘Williams 82’ RIL Population

Soybean gene co-expression network analysis identifies two co-regulated gene modules associated with nodule formation and development

Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage

Dr. Mehrdad Kaveh, K. N. Toosi University of Technology, Iran

Mehrdad Kaveh is a highly qualified expert in Surveying Engineering (GIS), holding a Ph.D. and M.Sc. from K.N. Toosi University of Technology, along with a B.Sc. from Babol Noshirvani University of Technology. His academic achievements include outstanding GPAs throughout his educational journey. He has contributed significantly to the field through several conference papers focusing on topics like landslide risk zoning, healthcare GIS, and urban transportation networks. With extensive teaching experience, Mehrdad has instructed ArcGIS and Optimization Algorithms at various universities, demonstrating his proficiency in GIS software and algorithmic optimization. His research spans diverse areas such as crisis management, machine learning implementation, SAR image processing, and spatial database design in Java. Proficient in ArcGIS, QGIS, GAMS, and programming languages like Matlab, Java, Python, and SQL, Mehrdad Kaveh combines academic excellence with practical skills in spatial analysis, optimization, and deep learning applications in environmental modeling.

Research and Practical Experience:

Application of GIS and RS in crisis management and urban planning.

Health GIS and healthcare network management.

GIS and multi-criteria decision analysis.

Simulation and implementation of machine learning and meta-heuristic algorithms.

Feature selection and classification of SAR images (image processing).

Multi-objective optimization problems simulation and implementation.

Design of spatial databases in Java.

Air pollution modeling using deep learning algorithms.

Publication top Notes:

A crossover-based multi-objective discrete particle swarm optimization model for solving multi-modal routing problems

TDMBBO: a novel three-dimensional migration model of biogeography-based optimization (case study: facility planning and benchmark problems)

Optimal Band Selection Using Evolutionary Machine Learning to Improve the Accuracy of Hyper-spectral Images Classification: a Novel Migration-Based Particle Swarm Optimization