Ms. Havva Mehralitabar | Biophysics | Women Researcher Award

Ms. Havva Mehralitabar, Tarbiat modares university, Iran

Dr. Havva Mehralitabar Firozjah, born on December 2, 1985, is a distinguished biophysicist specializing in molecular dynamics simulations, peptide design, and nanotechnology. Currently a researcher at Sari Agricultural Sciences and Natural Resources University, Iran, she has made significant contributions to cancer research, bacterial and viral infections, and peptide-based therapeutic designs. With a strong academic foundation, Dr. Mehralitabar’s Ph.D. in Biophysics from Tarbiat Modares University centered on self-assembling peptide nanofibers, laying the groundwork for her innovative approaches in neural stem cell differentiation. Her work is marked by numerous high-impact publications and collaborations in computational biology and molecular simulations. As a researcher and educator, she has excelled in both wet lab and computational biology skills, mentoring students and contributing to interdisciplinary scientific advancements.

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Summary of Suitability for the Research for Women Researcher Award

Havva Mehralitabar Firozjah is an outstanding candidate for the Research for Women Researcher Award based on her impressive academic background, diverse research experience, and contributions to science. With a Ph.D. in Biophysics from Tarbiat Modares University and a focus on molecular dynamics, she has worked on pioneering projects in cancer, bacterial and viral infections, and nanotechnology. Her research skills, particularly in computational biology and molecular dynamics simulations, are extensive, covering critical areas like protein modeling, molecular docking, and bioactive peptide design.

Education

Dr. Havva Mehralitabar Firozjah’s academic journey is marked by excellence and dedication to biophysics and molecular biology. She earned her Ph.D. in Biophysics (2013-2018) from Tarbiat Modares University, Tehran, where her thesis on self-assembling alkylated peptide nanofibers provided a unique perspective on neural differentiation. Before this, she completed her M.Sc. in Biophysics (2009-2012) at the Institute for Advanced Studies of Basic Sciences (IASBS), Zanjan, where she investigated the thermodynamic interactions between Doxorubicin and DNA. Dr. Mehralitabar also holds a B.Sc. in Plant Biology (2004-2008) from Alzahra University, Tehran. Her research focus spans molecular dynamics simulations and computational biology, and she has developed deep expertise in both theoretical and practical applications of biophysics, laying a strong foundation for her groundbreaking research in peptide-based therapeutic designs and biomolecular structures.

Experience

Dr. Havva Mehralitabar Firozjah has accumulated extensive research experience across various scientific domains. She is currently a researcher (2020-2023) at Sari Agricultural Sciences and Natural Resources University, focusing on in silico studies of lactoferrin mechanisms in cancer and bacterial infections. Prior to this, she served as a consultant (2018-2020) at Hakim Sabzevari University, guiding M.Sc. students in molecular dynamics simulations. As an adjunct professor (2019-2022), she taught advanced courses in system biology and molecular docking methods, further shaping young scientists. Her experience in both wet labs and computational biology makes her a versatile scientist, skilled in molecular simulations, peptide design, and biophysics research. Through multiple collaborations and supervisory roles, Dr. Mehralitabar has played a key role in advancing research in the field of molecular biology and nanotechnology.

Awards and Honors

Dr. Havva Mehralitabar Firozjah’s contributions to biophysics have earned her numerous awards and recognitions. Notably, she secured 6th place in the National Ph.D. Entrance Exam (2013) out of 120 participants, showcasing her academic prowess. She has also served as a reviewer for the esteemed Journal of Computer in Biology and Medicine (2020-2022), underlining her expertise in computational biology. During her academic tenure, Dr. Mehralitabar received several honors for her innovative research in molecular dynamics simulations and peptide nanotechnology, including recognition from Tarbiat Modares University for her exceptional Ph.D. thesis on alkylated-peptide nanofibers. Additionally, her active participation in conferences and workshops has led to several poster presentations, contributing to her standing as a thought leader in her field. Her relentless pursuit of scientific excellence continues to make a significant impact on the biophysics research community.

Research Focus

Dr. Havva Mehralitabar Firozjah’s research primarily revolves around molecular dynamics simulations, peptide design, and nanotechnology. Her work focuses on exploring the interactions between peptides and biological molecules to develop innovative therapeutic strategies. One of her most notable projects includes in silico studies of lactoferrin mechanisms, aiming to address cancer, bacterial, and viral infections. She also specializes in self-assembling peptides for neural stem cell differentiation, demonstrating the potential of biomaterials in regenerative medicine. Dr. Mehralitabar’s research extends into the structural dynamics of proteins, molecular docking, and simulations to understand how molecular structures influence biological functions. With extensive expertise in computational biology and hands-on molecular biology techniques, her work integrates theoretical approaches with practical lab results, making strides in drug design and biomolecular engineering.

Publication Top Notes

A combination of bioactive and nonbioactive alkyl-peptides form a more stable nanofiber structure for differentiating neural stem cells: a molecular dynamics simulation survey
Citations: 9
Abiraterone and D4, 3-keto Abiraterone binding to CYP17A1, a structural comparison study by molecular dynamic simulation
Citations: 3
The role of Wnt palmitoleylated loop conserved disulfide bonds in Wnt-frizzled complex structural dynamics: Insights from molecular dynamics simulations
Citations: 1
The physicochemical properties role of a functionalized alkyl-peptide in nanofibre formation and neural progenitor cells viability and survival
Citations: 1
In vitro study of the expression of autophagy genes ATG101, mTOR and AMPK in breast cancer with treatment of lactoferrin and in silico study of their communication networks and …

Dr. Soheila Kookalani, Cambridge University, United Kingdom

Soheila Kookalani is a distinguished Research Associate at the University of Cambridge, specializing in Civil and Structural Engineering. She has a profound expertise in steel reuse, the circular economy, life-cycle assessment, and the integration of digital twin technologies in construction. Her research focuses on leveraging artificial intelligence and machine learning to optimize structural designs, promoting sustainability in construction. Soheila holds a Ph.D. in Civil and Structural Engineering from Shanghai Jiao Tong University, where she pioneered research on GFRP elastic gridshells. With a commitment to environmental responsibility, she continuously explores innovative ways to integrate sustainable practices into building designs, aiming to revolutionize construction methodologies. She has numerous publications in leading journals, demonstrating her contribution to both academia and the industry. Her work emphasizes sustainable engineering practices that align with modern technological advancements, particularly in the realm of structural optimization and reuse strategies.

Summary of Suitability for the Research for Best Researcher Award

Soheila Kookalani’s innovative approach to structural engineering, coupled with her strong commitment to sustainability and integration of advanced technologies, positions her as a highly suitable candidate for the Research for Best Researcher Award. Her work has significant implications for the future of sustainable construction, making her a valuable asset to both academia and industry.

Education

Soheila Kookalani pursued her academic journey with a solid foundation in architectural and civil engineering. She earned her Bachelor of Science in Architectural Engineering from Azad University in Iran, where her thesis explored hybrid architecture in cinematic arts. She then advanced her studies with a Master of Science in Civil and Structural Engineering from Hohai University, China, focusing on the seismic performance of hybrid structures in high-rise buildings. Her academic journey culminated with a Ph.D. from Shanghai Jiao Tong University, China, where she focused on the structural optimization of GFRP elastic gridshells using machine learning techniques. Her doctoral research contributed to advancing knowledge in structural design and building sustainability. Throughout her studies, Soheila has consistently integrated innovative technologies, such as artificial intelligence and machine learning, into her research, making her an authority in the field of structural design and optimization for sustainable construction.

Experience

Soheila Kookalani has accumulated extensive experience in civil and structural engineering, with a primary focus on sustainable design practices. She currently serves as a Research Associate at the University of Cambridge, where she is engaged in a groundbreaking project on the reuse of structural steel in construction. Her experience spans across diverse areas, including the circular economy, life-cycle assessment, building information modeling (BIM), and digital twin technology. From 2018 to 2022, she conducted research on GFRP elastic gridshells as part of her Ph.D. at Shanghai Jiao Tong University. Prior to this, Soheila gained practical experience working on seismic performance evaluation of hybrid structures during her Master’s at Hohai University. Her expertise also extends to the application of artificial intelligence in optimizing structural designs, demonstrating her capacity to bridge the gap between theoretical research and practical application in sustainable construction.

Awards and Honors

Throughout her academic and professional career, Soheila Kookalani has been recognized for her outstanding contributions to civil and structural engineering. She has received several prestigious awards and honors, including recognition from leading engineering conferences and academic institutions. Her work on the integration of AI and machine learning in structural design has garnered international attention, earning her accolades for innovation in sustainable construction. Soheila has also been honored for her research on GFRP elastic gridshells, receiving commendations for excellence in structural optimization and sustainable design. Her role as a published author in top-tier engineering journals has further solidified her reputation as a leading researcher in the field. Additionally, Soheila’s contributions to the reuse of structural steel and her involvement in cutting-edge projects at the University of Cambridge have earned her numerous industry awards, highlighting her commitment to environmental responsibility and sustainable engineering practices.

Research Focus

Soheila Kookalani’s research is centered on the intersection of civil engineering, sustainability, and advanced technology. Her primary focus is on steel reuse, promoting a circular economy in construction through the reuse of structural components. She is also deeply involved in life-cycle assessments, aiming to reduce the environmental impact of construction projects. Her research integrates building information modeling (BIM) and digital twin technology to enhance the design, monitoring, and optimization of construction projects. Soheila is particularly interested in applying artificial intelligence (AI) and machine learning to optimize structural designs, especially in the context of GFRP elastic gridshells. Her work on generative AI in structural engineering seeks to streamline design processes and improve sustainability. By combining these advanced technologies, her research contributes to developing more efficient, eco-friendly building practices that align with global sustainability goals.

Publication Top Notes

BIM-based augmented reality for facility maintenance management
- Cited by: 24
Structural analysis of GFRP elastic gridshell structures by particle swarm optimization and least square support vector machine algorithms
- Cited by: 18
Shape optimization of GFRP elastic gridshells by the weighted Lagrange ε-twin support vector machine and multi-objective particle swarm optimization algorithm considering …
- Cited by: 14
An analytic approach to predict the shape and internal forces of barrel vault elastic gridshells during lifting construction
- Cited by: 14
Effect of Fluid Viscous Damper parameters on the seismic performance
- Cited by: 14