Dr. Kais Iben Nassar | Machine Learning | Best Researcher Award

Dr. Kais Iben Nassar | Machine Learning | Best Researcher Award

Doctorate at University of Aveiro , Portugal

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Academic Background

Dr. Kais Iben Nassar is a researcher with a focus on Condensed Matter Physics and Computational Chemistry. He completed his PhD in Physics of Condensed Materials in 2022 through a joint program between the University of Aveiro, Portugal, and the University of Sfax, Tunisia. Dr. Nassar is renowned for his work in materials science, particularly in the study of 2D materials like MXenes and their applications in energy storage and catalysis.

Education

  • PhD in Physics of Condensed Materials
    Université de Sfax & Universidade de Aveiro (2022)
    Achieved with highest honors.
  • Master’s in Condensed Matter Physics
    Université de Sfax (2018)
    Graduated with distinction.
  • Fundamental License in Physics-Chemistry
    Université de Sfax (2016)
    Graduated with distinction.

Professional Experience

  • Postdoctoral Researcher
    Universidade de Aveiro, CICECO (2023 – Present)
    Focus on MXenes catalysts and computational chemistry.
  • Researcher
    Université de Sfax & Universidade de Aveiro (2018 – 2021)
    Conducted research on perovskites and materials science.
  • Invited Assistant Professor
    Université de Sfax (2021 – 2022)
    Taught and mentored students in physics and chemistry.

🔬 Research Interests

Dr. Nassar’s research interests encompass Condensed Materials Physics, nano-materials, computational chemistry, and machine learning. His work includes investigating the properties of 2D materials such as MXene, exploring their potential in energy storage, catalysis, and electronics. He is actively engaged in the preparation and characterization of new perovskite ceramics and the study of their structural, electrical, and magnetic properties. Dr. Nassar is also a member of the European Materials Acceleration Center for Energy (EU-MACE) under the COST Action CA22123.

 Publications

Tailoring of structural, morphological, electrical, and magnetic properties of LaMn1−xFexO3 ceramics
  • Authors: Thakur, P., Nassar, K.I., Kumar, D., Essid, M., Lal, M.
  • Journal: RSC Advances
  • Year: 2024
Structural, electrical properties of bismuth and niobium-doped LaNiO3 perovskite obtained by sol–gel route for future electronic device applications
  • Authors: Nassar, K.I., Benamara, M., Kechiche, L., Teixeira, S.S., Graça, M.P.F.
  • Journal: Indian Journal of Physics
  • Year: 2024
Investigating Fe-doped Ba0.67Ni0.33Mn1−xFexO3 (x = 0, 0.2) ceramics: insights into electrical and dielectric behaviors
  • Authors: Tayari, F., Iben Nassar, K., Algessair, S., Hjiri, M., Benamara, M.
  • Journal: RSC Advances
  • Year: 2024
Sol–gel synthesized (Bi0.5Ba0.5Ag)0.5 (NiMn)0.5O3 perovskite ceramic: An exploration of its structural characteristics, dielectric properties and electrical conductivity
  • Authors: Tayari, F., Iben Nassar, K., Benamara, M., Soreto Teixeira, S., Graça, M.P.F.
  • Journal: Ceramics International
  • Year: 2024
Study of Electrical and Dielectric Behaviors of Copper-Doped Zinc Oxide Ceramic Prepared by Spark Plasma Sintering for Electronic Device Applications
  • Authors: Benamara, M., Iben Nassar, K., Rivero-Antúnez, P., Serrà, A., Esquivias, L.
  • Journal: Nanomaterials
  • Year: 2024

Dr. Irsa Sajjad | Machine Learning | Best Researcher Award

Dr. Irsa Sajjad, Machine Learning, Best Researcher Award

Doctorate at Central South University, China

Profiles

Scopus

Google Scholar

🌍 Academic Background:

Dr. Irsa Sajjad is a Research Scholar at Central South University, Changsha, China, known for her expertise in hybrid choice modeling and machine learning. Her innovative research integrates deep learning and attention mechanisms, significantly advancing methodologies and applications in the field.

🎓 Education:

Dr. Irsa’s academic background is marked by advanced studies in machine learning and choice modeling, equipping her with a comprehensive understanding of both theoretical concepts and practical applications in her field.

👩‍🏫 Professional Experience:

Dr. Irsa has actively contributed to significant research projects, including developing novel hybrid choice models and Gaussian mixture models. She has collaborated with industry partners on machine learning applications and data visualization techniques and is currently publishing a book on advanced choice modeling.

🔬 Research Interests:

Dr. Irsa’s research interests center on Hybrid Choice Models (HCM), particularly those incorporating attention mechanisms, deep learning, and latent class analysis. Her work aims to enhance the accuracy and effectiveness of choice modeling by addressing complex data structures and improving analytical insights.

📖 Publications:

Advancing Covid-19 Data Modeling: Introducing a Neutrosophic Extension of Ramous Louzada Distribution
  • Authors: Al-Aziz, S.N., Sajjad, I., Dar, J.G., El Bagoury, A.A.-A.H.
  • Journal: International Journal of Neutrosophic Science
  • Year: 2023
Quantile regression-ratio-type estimators for mean estimation under complete and partial auxiliary information
  • Authors: Shahzad, U., Hanif, M., Sajjad, I., Anas, M.M.
  • Journal: Scientia Iranica
  • Year: 2022
Mathematical Simulation and Numerical Computation of the Temperature Profiles in the Peripherals of Human Brain during the Tepid Sponge Treatment to Fever
  • Authors: Aijaz, M., Dar, J.G., Almanjahie, I.M., Sajjad, I.
  • Journal: Computational and Mathematical Methods in Medicine
  • Year: 2022
Imputation based mean estimators in case of missing data utilizing robust regression and variance–covariance matrices
  • Authors: Shahzad, U., Al-Noor, N.H., Hanif, M., Sajjad, I., Muhammad Anas, M.
  • Journal: Communications in Statistics: Simulation and Computation
  • Year: 2022
A new family of robust regression estimators utilizing robust regression tools and supplementary attributes
  • Authors: Sajjad, I., Hanif, M., Koyuncu, N., Shahzad, U., Al-Noor, N.H.
  • Journal: Statistics in Transition New Series
  • Year: 2021