Afef Najjari | Bioinformatics | Women Researcher Award

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Assist. Prof. Dr. Afef Najjari | Bioinformatics | Women Researcher Award

Assist. Prof. Dr. Afef Najjari | Faculty of sciences of Tunisia/University of Tunis el Manar | Tunisia

Dr. Afef Najjari is an Assistant Professor in Biomedical Engineering, specializing in Bioinformatics at the Faculty of Sciences of Tunis. Her academic journey is marked by deep engagement in microbial genomics, with a particular focus on extremophilic microorganisms from the Tunisian desert. Dr. Najjari has authored over 17 peer-reviewed research papers and 2 book chapters, collaborating on international projects that explore genomics, environmental microbiology, and bioremediation. She has taught bioinformatics and genomics at institutions like ISBST and has mentored numerous master’s and PhD students. Her work contributes to understanding microbial diversity and biotechnological applications, particularly in arid ecosystems. With active involvement in teaching, supervision, and research, Dr. Najjari is a role model for young women entering STEM fields in Tunisia and beyond.

Publication Profile: 

Scopus

Education:

Dr. Najjari holds advanced degrees in Biological Sciences and Bioinformatics, culminating in a Ph.D. focused on microbial genomics and environmental microbiology. Her academic foundation combines traditional biology with computational and data-driven sciences. Though specific degree names or institutions aren’t listed, her roles in research and teaching suggest completion of doctoral training in a relevant biological discipline, followed by postdoctoral experience or academic training in genomics. Her integration into faculty positions at institutions such as the Faculty of Sciences of Tunis and ISBST further implies strong academic credentials, likely earned through nationally or internationally accredited programs. She effectively blends her biological background with technical bioinformatics applications, enabling interdisciplinary teaching and research.

Experience:

Dr. Najjari has over a decade of experience in academia, research, and mentorship. Since 2014, she has taught Genomics and Bioinformatics at ISBST and currently at the Faculty of Sciences of Tunis. She has supervised 11 graduate students across master’s and PhD levels. Her research centers on microbial genomics, metataxonomics, pan-genomics, and bioremediation, particularly in extreme environments like saline and geothermal oases. As an Assistant Professor in Biomedical Engineering, she skillfully integrates molecular biology with computational science. Beyond publishing in leading journals, she engages in collaborative, interdisciplinary research projects. Her dual role as educator and researcher allows her to train future bioinformaticians while advancing knowledge in microbial adaptation, iron uptake, and heavy metal remediation.

Research Focus:

Dr. Najjari’s research explores the genomic and functional diversity of extremophiles, focusing on halophilic archaea and bacteria from desert and saline ecosystems in Tunisia. She applies bioinformatics pipelines to understand microbial adaptation to harsh conditions, including iron uptake, oil degradation, and biopolymer production. Her projects use pan-genome analysis, 16S rRNA metataxonomics, and functional genomics to identify microbial traits valuable in bioremediation, such as flocculation of heavy metals and pesticides. Her work advances the understanding of environmental microbiomes and their industrial and ecological potential. Recently, she has extended her research to include the gut microbiome of honeybees and livestock genomics, contributing to biodiversity preservation. Her focus is timely and relevant, addressing global environmental challenges with sustainable biotechnological solutions.

Publications Top Notes:

  1. Genome and pan-genome analysis of Psychrobacillus sp. – Front. Microbiol.,

  2. Physiological and genomic insights into halophilic archaeon Natrinema altunense – Genetica,

  3. 16S rRNA phylogenetic diversity of archaeal communities in Saharan halite salts – Biology (Basel),

  4. Metataxonomics of Tunisian phosphogypsum using five bioinformatics pipelines – Genomics,

  5. Flocculating heteropolysaccharide–protein from haloarchaea for heavy metals removal – Environmental Technology,

  6. Genome analysis of Halomonas desertis G11 for oil degradation and biosurfactant production – Genomes,

  7. Pseudomonas rhizophila S211 for pesticide bioremediation and plant growth – Front. Microbiol.,

  8. Microbiota of whitefly Bemisia tabaci via 16S rDNA sequencing – Microbiology Research,

  9. Genome-wide selection scans in fat- vs. thin-tailed North African sheep – Animal Genetics,

  10.  Metataxonomic analysis of halophiles in geothermal oases of southern Tunisia – FEMS Microbiol. Lett.,

Conclusion:

Dr. Afef Najjari is a highly suitable candidate for the Research for Women Researcher Award. Her innovative research in bioinformatics and microbial genomics, particularly in extreme and underexplored ecosystems of Tunisia, not only advances scientific knowledge but also addresses pressing environmental challenges. Her dedicated teaching and supervision record, combined with her technical expertise, exemplify academic excellence and leadership. While expanding her engagement in women-focused STEM initiatives and international research programs could further amplify her impact, her existing contributions already position her as a key figure in North African science. Recognizing Dr. Najjari with this award would celebrate her scientific achievements and inspire broader participation of women in high-impact research fields.

Shima Shafiee | Cell Structure Analysis | Best Researcher Award

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Dr. Shima Shafiee | Cell Structure Analysis | Best Researcher Award

Dr. Shima Shafiee, Razi University, Iran

Shima Shafiee is an accomplished Iranian researcher specializing in computer systems architecture and bioinformatics, with a strong focus on machine learning applications in biological data analysis. She recently earned her Ph.D. in Computer Engineering from Razi University, where she focused on predictive modeling of protein-peptide binding interactions. Currently under consideration at the IDEL Lab, Shahid Bahonar University of Kerman, Shima has authored numerous national and international publications. With a rich background in algorithm optimization and artificial intelligence, her research stands at the intersection of computational biology, deep learning, and evolutionary algorithms. Shafiee’s work has contributed to the development of predictive tools in bioinformatics, such as DP-site and SPPPred, and she consistently ranks at the top of her academic cohort. Her ability to integrate traditional computer engineering concepts with advanced biological research makes her a notable candidate for the Best Researcher Award.

Publication Profile: 

Google Scholar

Strengths for the Award:

  1. Strong Academic Foundation
    Dr. Shafiee has a stellar academic record, graduating first in her Ph.D. class at Razi University with a CGPA of 3.77 and a thesis grade of 3.98, under the supervision of respected experts in computer engineering and bioinformatics.

  2. Innovative Interdisciplinary Research
    Her research bridges computer systems architecture, machine learning, and bioinformatics, with notable contributions to protein-peptide binding prediction, a critical domain in drug discovery and computational biology.

  3. High-Impact Publications
    She has published in IEEE/ACM Transactions, Applied Soft Computing, and Methods, reflecting both quality and visibility in international forums. Tools like SPPPred and DP-site demonstrate her practical impact in bioinformatics.

  4. Research Originality and Versatility
    Dr. Shafiee has developed hybrid models combining genetic programming, support vector machines, and deep learning, with practical tools and open-source contributions.

  5. Early Recognition and Outreach
    She has been active in academic dissemination since 2015, with selected papers in national and international conferences, showing early promise and consistency.

  6. Teaching and Mentorship
    Through her roles as a lecturer at multiple institutions, she has contributed to academic growth at the grassroots level.

Areas for Improvement:

  1. International Collaboration & Visibility
    While her publication quality is strong, Dr. Shafiee could expand her global visibility through collaborations with international research labs, EU Horizon, or NIH-funded projects.

  2. Post-Ph.D. Grant Applications
    She could benefit from applying for independent research grants or postdoctoral fellowships to lead projects that could shape the future of AI in biology.

  3. Open-Source Software and Data Availability
    While her models are impactful, increased accessibility via open-source repositories (e.g., GitHub) would boost reproducibility and encourage broader adoption.

  4. Industry Impact Metrics
    More emphasis on industry collaborations, patents, or application of models in clinical/biotech settings would enhance translational impact.

Education:

Shima Shafiee completed her Ph.D. in Computer Engineering (2016–2024) from Razi University, specializing in Computer Systems Architecture. Her dissertation titled “Application of learning-based models in predicting of protein-peptide binding interactions” earned her a thesis grade of 3.98/4.00 and an overall CGPA of 3.77. She worked under the guidance of Dr. Abdolhossein Fathi and Dr. Ghazaleh Taherzadeh, focusing on bioinformatics using deep learning, ensemble learning, and evolutionary algorithms. Prior to her Ph.D., she was ranked third in her Master’s program (2015). Shafiee’s educational background is rooted in computational problem-solving, algorithm development, and cross-disciplinary research involving biological data. Her consistent academic excellence and high-ranking performance culminated in her being recognized as the top Ph.D. student in 2025, a testament to her dedication and scholarly capabilities. Her education blends rigorous theory with innovative applied research, making her exceptionally well-prepared for high-impact contributions in academia and industry.

Experience:

Shima Shafiee’s experience spans both academic and applied computer engineering roles. She began her journey with an internship at Kimia Pardaz Pars Company (2013). Between 2015 and 2016, she served as a lecturer for computer fundamentals at Fajr High School and Al-Zahra Seminary School in Jiroft, where she taught introductory computer science to pre-university students. These experiences highlight her foundational teaching skills and outreach to educational institutions in her community. Her major academic contribution began during her Ph.D., where she collaborated with IDEL Lab and contributed to developing tools like SPPPred and DP-site, combining genetic programming, support vector machines, and deep learning to predict protein-peptide binding regions. Her experience uniquely blends educational outreach, algorithmic development, and publication-driven research in machine learning, optimization, and computational biology, reflecting her versatility and impact across the scientific and academic spectrum.

Awards & Honors:

Shima Shafiee has earned multiple distinctions recognizing her academic and research excellence. In 2015, she was named the third-place student in her Master’s program, demonstrating early academic excellence. Her continuous dedication to research and scholarship led her to be recognized as the first-place student in her Ph.D. program in 2025. One of her papers was selected at the 2nd International Congress of Electrical Engineering, Computer Science, and Information Technology (2015), highlighting the innovation and relevance of her early research in optimization algorithms. Her high publication output, including appearances in top-tier venues like IEEE/ACM Transactions on Computational Biology and Bioinformatics and Applied Soft Computing, reflects a consistent standard of excellence. These honors collectively showcase her as a standout figure in her field, with both academic and applied contributions acknowledged at national and international levels.

Research Focus:

Shima Shafiee’s research lies at the intersection of machine learning, bioinformatics, and computational systems engineering. Her primary focus is the prediction of protein-peptide binding interactions using intelligent algorithms such as genetic programming, ensemble models, and deep learning techniques. She has proposed several innovative hybrid models combining sequence-based and structure-based features to identify critical interaction residues. Her doctoral thesis and publications have led to the development of tools like SPPPred and DP-site, which aid in biological sequence analysis, with applications in drug discovery, protein function prediction, and biomedical engineering. Shafiee also has a strong background in optimization algorithms, especially particle swarm optimization (PSO), applied to computationally intensive problems like bin packing. Her ability to blend theoretical computing with practical biological data analysis makes her contributions valuable to both computational scientists and biologists, and positions her as a leading candidate for research recognition awards in AI and bioinformatics.

Publications Top Notes: 

  • 🧠 SPPPred: sequence-based protein-peptide binding residue prediction using genetic programming and ensemble learning (IEEE/ACM TCBBS, 2022)

  • 🔍 Prediction of protein–peptide-binding amino acid residues regions using machine learning algorithms (CSICC, 2021)

  • 🧬 Combination of genetic programming and SVM-based prediction of protein-peptide binding sites (Journal of Computing and Security, 2021)

  • 🧪 Prediction of protein–peptide binding residues using classification algorithms (IEEE Bioengineering Conf, 2020)

  • 🧠 A Review of the Uses of AI in Protein Research (Peptide Science Conf, 2019)

  • 🤖 DP-site: dual deep learning method for protein-peptide interaction site prediction (Methods, 2024)

  • 🧬 Protein-peptide interaction region prediction using generative sampling & ensemble DL (Applied Soft Computing, 2025)

  • 🧠 Comparing classification vs. segmentation predictors in protein-peptide interaction (CSICC, 2025)

  • 🧬 Leveraging structure-based and learning-based predictors in protein-peptide interaction (ICCKE, 2024)

  • 📘 Application of learning-based models in protein-peptide binding (Ph.D. Dissertation, 2024)

Conclusion:

Dr. Shima Shafiee is a highly suitable candidate for the Best Researcher Award based on her academic excellence, interdisciplinary research achievements, and consistent contributions to the fields of artificial intelligence and bioinformatics. Her ability to bridge computer science and biological challenges has resulted in meaningful and applicable solutions. She has displayed originality, depth, and foresight in her work, developing novel methods that align with modern computational biology trends.

Bilal Ahmad MIr | Microbial Cell Biology | Best Researcher Award

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Mr.Bilal Ahmad MIr | Microbial Cell Biology | Best Researcher Award

Mr.Bilal Ahmad MIr | Jeonbuk National University | South Korea

Bilal Ahmad Mir is a dedicated Ph.D. scholar at the NSCL Lab, Jeonbuk National University, South Korea, with a strong focus on artificial intelligence, machine learning, and computational sciences. Born on May 7, 1993, Bilal has a diverse academic and research background encompassing data science, deep learning, computational biology, and chemistry. He combines technical acumen with innovative thinking to solve real-world scientific problems. Fluent in English, Urdu, and Kashmiri, Bilal’s research is published in leading international journals. He is well-versed in programming languages such as Python, R, MATLAB, and Java, and excels in cloud computing technologies. His scholarly contributions span predictive modeling, neural networks, and intelligent systems. His enthusiasm for technological advancements and interdisciplinary research positions him as a strong candidate for prestigious research awards, reflecting both his scientific rigor and passion for discovery.

Publication Profile:

Google Scholar

✅ Strengths for the Award:

  1. Interdisciplinary Expertise:
    Bilal’s work spans artificial intelligence, deep learning, computational biology, and chemistry, reflecting strong interdisciplinary depth. He has applied advanced ML models like CNNs, LSTMs, and GRUs across bioinformatics and synthetic chemistry, showing his adaptability and scientific creativity.

  2. Research Publications:
    He has published in high-impact journals such as Journal of Molecular Biology, Computational Biology and Chemistry, and Sustainability. These works demonstrate novelty and real-world relevance, e.g., sustainable solar energy prediction and enhancer identification in genomics.

  3. Technical Proficiency:
    Bilal is proficient in multiple programming languages (Python, R, MATLAB, Java, etc.) and research tools, which enhances his capability to design, implement, and optimize advanced computational models.

  4. Academic Progression:
    His academic journey from a B.Sc. through MCA to a Ph.D. in South Korea demonstrates commitment to continuous learning and global academic engagement.

  5. Early Research Experience:
    His MCA project on real-time facial recognition using Raspberry Pi and GSM modules showed practical innovation, integrating software and hardware for applied AI.

⚠️ Areas for Improvement:

  1. Citation and Impact Metrics:
    While Bilal has strong publications, more details on citations, h-index, or conference presentations would strengthen his profile for global competitive awards.

  2. Leadership in Projects:
    Future applications should highlight any mentoring, project leadership, or grant involvement, which are important indicators of research independence.

  3. Community Contribution:
    Participation in open-source contributions, academic societies, or organizing workshops/seminars would further showcase his community engagement and outreach efforts.

  4. Formal Language Polishing:
    Refinement in presenting his resume/CV with consistent formatting and professional tone would improve the impression in award submissions.

🎓 Education:

Bilal Ahmad Mir began his academic journey with a B.Sc. in Mathematics, Electronics, and IT from Sri Pratap College, Srinagar, graduating with 60% in 2016. He then pursued an MCA (Master of Computer Applications) at the Islamic University of Science and Technology, Awantipora, where he excelled in courses like algorithms, AI, ML, data structures, and cloud computing, graduating with a CGPA of 7.76/10 in 2019. He is currently enrolled as a Ph.D. scholar at Jeonbuk National University, South Korea, in the Department of Electronics and Information Engineering. His doctoral work at the NSCL Lab integrates deep learning, computational chemistry, and molecular biology, contributing to high-impact publications. His solid academic foundation and continued pursuit of knowledge equip him with the interdisciplinary expertise necessary to tackle complex computational and AI challenges in life sciences and beyond.

🧪 Experience:

Bilal’s academic and research journey spans across domains of intelligent systems, AI, and computational biology. During his MCA, he completed a dissertation on a real-time “Intelligent Face Recognition System” using Raspberry Pi and Eigenface recognition, integrating image processing with GSM modules. As a Ph.D. researcher at NSCL Lab in South Korea, he has been involved in multiple projects focusing on neural networks, such as CNNs, LSTMs, and GRUs, for bioinformatics and organic chemistry applications. His hands-on experience in deep learning, data preprocessing, and predictive modeling has resulted in several peer-reviewed journal publications. He is proficient in Python, MATLAB, R, and Java and is experienced with research tools used for analyzing genetic and chemical data. Bilal’s versatility across both hardware (e.g., Raspberry Pi) and software research platforms positions him as a highly capable and adaptable scientist in the interdisciplinary field of AI-powered scientific research.

🏆 Awards and Honors:

Bilal Ahmad Mir has received multiple accolades that highlight his academic potential and creative engagement in both academic and extracurricular domains. He secured the 1st rank in a national-level quiz competition organized during the Digital India Week in 2015, reflecting his strong grasp of technical knowledge and current affairs. During his post-graduate studies, he was honored with the title of “Mr. Fresher” for the MCA batch of 2016 at the Islamic University of Science and Technology, recognizing his leadership and interpersonal qualities. His growing contribution to impactful scientific research has earned him recognition among academic peers. With peer-reviewed publications in top-tier journals and ongoing contributions to AI-driven biological and chemical modeling, Bilal is on a trajectory of continued academic success. These honors reflect both his intellect and his dedication to continuous learning and innovation, making him a strong contender for prestigious awards such as the Best Researcher Award.

🔬 Research Focus:

Bilal Ahmad Mir’s research focus lies at the confluence of artificial intelligence, deep learning, and life sciences. He applies cutting-edge machine learning techniques—particularly CNNs, LSTMs, and GRUs—to computational biology and chemistry, aiming to solve intricate molecular problems. His key research areas include enhancer identification, RNA modification prediction, and retrosynthetic pathway modeling. Through deep learning architectures and stacked ensemble models, he enhances the accuracy of biological predictions and synthesis pathway generation. His recent work also explores sustainable energy research, applying AI to predict recombination losses in perovskite solar cells. Bilal’s interdisciplinary work is distinguished by its practical application to genomics, cheminformatics, and renewable energy, blending technical rigor with scientific curiosity. His aim is to use AI not only for theoretical insights but also for impactful innovations in healthcare, sustainable energy, and synthetic biology. This makes him a versatile and forward-thinking researcher in the modern AI landscape.

📚 Publication Titles Top Notes:

  1. 🧬 Improving enhancer identification with a multi-classifier stacked ensemble model – Journal of Molecular Biology, 2023

  2. 🔄 Sb-net: Synergizing CNN and LSTM networks for uncovering retrosynthetic pathways in organic synthesis – Computational Biology and Chemistry, 2024

  3. 🔋 Toward Sustainable Solar Energy: Predicting Recombination Losses in Perovskite Solar Cells with Deep Learning – Sustainability, 2025

  4. 🧪 GRU-Based Prediction of RNA 5-Hydroxymethylcytosine Modifications – 정보 및 제어 논문집

🧾 Conclusion:

Bilal Ahmad Mir is a highly promising and emerging researcher in the AI-bioinformatics interface. His dedication to interdisciplinary research, proven publication record, and hands-on approach to complex problems make him a strong candidate for the Best Researcher Award. With ongoing contributions, especially in deep learning for biology and sustainable energy, and with slight enhancements in scientific communication and visibility, he is on a trajectory toward impactful global research leadership.

M. A. El-Shorbagy | Cell Structure Analysis | Best Researcher Award

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Prof. M. A. El-Shorbagy | Cell Structure Analysis | Best Researcher Award

Prof. M. A. El-Shorbagy, Prince Sattam Bin Abdulaziz University, Saudi Arabia

Prof. Mohammed Abd El-Rahman El-Shorbagy Hassan is a distinguished academic specializing in engineering mathematics and optimization. Born in Egypt on March 4, 1982, he currently serves as a Professor in the Department of Mathematics at the College of Science and Humanities Studies, Prince Sattam Bin Abdulaziz University, Saudi Arabia. With a career spanning over a decade in higher education and applied research, Prof. El-Shorbagy has contributed significantly to the fields of numerical optimization, artificial intelligence, and multiobjective programming. He is widely published and recognized for his scholarly books and high-impact journal articles. His work integrates mathematical rigor with real-world engineering applications. In addition to teaching and mentoring, he actively participates in international conferences and collaborative research projects. Known for his hybrid approaches combining swarm intelligence with classical methods, Prof. El-Shorbagy stands as a prominent figure in optimization and computational engineering.

Publication Profile: 

Google Scholar

✅ Strengths for the Award:

  1. Extensive Research Output:
    Prof. El-Shorbagy has a substantial publication record that includes books, high-impact journal articles, and international conference proceedings. His research appears in prestigious outlets such as Scientific Reports, Renewable Energy, and Materials, with multiple papers exceeding 100 citations.

  2. Strong Interdisciplinary Focus:
    His work bridges mathematics, artificial intelligence, and engineering, showcasing versatility. He effectively combines theoretical frameworks (e.g., optimization theory) with practical applications in power systems, materials science, and fluid dynamics.

  3. Innovative Methodologies:
    Known for hybridizing Particle Swarm Optimization, Genetic Algorithms, and Trust Region methods, he has demonstrated an innovative approach to solving complex, real-world optimization problems.

  4. Global Academic Impact:
    His presence in international conferences across Egypt, France, and Saudi Arabia highlights his global engagement. His work on swarm intelligence and nanofluid modeling reflects both theoretical significance and technological relevance.

  5. Authorship of Academic Books:
    Author of three technical books with Lambert Academic Publishing, which extend his research to a broader academic audience, enhancing educational and professional value.

⚠️ Areas for Improvement:

  1. Greater Industry Collaboration:
    While his academic output is exceptional, engaging in more industry-linked projects (e.g., smart grids, AI in civil infrastructure) could boost the applied relevance of his work.

  2. Editorial and Review Roles:
    Taking on editorial positions in leading journals or acting as a regular peer reviewer could enhance his visibility and influence in the academic publishing landscape.

  3. Funding and Grants:
    Participation in or leadership of internationally funded research programs (such as Horizon Europe or NSF collaborations) would strengthen his research infrastructure and international profile.

🎓 Education:

Prof. El-Shorbagy earned his Ph.D. in Engineering Mathematics from the Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Egypt, in July 2013. His doctoral thesis, “A Study of Some Numerical Optimization Methods to Solve Multiobjective Mathematical Programming Problems”, focused on advanced optimization algorithms. Prior to this, he completed his M.Sc. in February 2010 at the same institution, where he presented a thesis titled “Hybrid Particle Swarm Optimization Algorithm for Multiobjective Optimization”. Both degrees reflect his specialization and depth in engineering mathematics with emphasis on numerical methods and artificial intelligence. Throughout his academic formation, he was mentored by prominent researchers in the fields of computational mathematics and optimization. His educational background has laid a solid foundation for his subsequent contributions to academia, allowing him to merge theoretical principles with cutting-edge applications in engineering and data-driven modeling.

🧑‍🏫 Experience:

Prof. El-Shorbagy has amassed over 15 years of academic and research experience. He currently holds the position of Professor in the Department of Mathematics, College of Science and Humanities Studies, at Prince Sattam Bin Abdulaziz University in Saudi Arabia. His career began in Egypt, where he contributed to both teaching and research at Menoufia University. Over the years, he has developed and delivered undergraduate and postgraduate courses in mathematics, optimization techniques, numerical methods, and computational intelligence. In parallel, he has maintained an active research agenda, collaborating with global institutions and publishing extensively in top-tier journals. He is well-versed in supervising theses and projects related to AI-based optimization methods and engineering simulations. Prof. El-Shorbagy is also a frequent speaker at international conferences and has been part of organizing committees, reviewer boards, and technical panels—proving his leadership in academic and scientific communities.

🔬 Research Focus:

Prof. El-Shorbagy’s research revolves around optimization theory and its practical application to engineering and mathematical models. His primary focus is on developing and enhancing numerical methods for solving multiobjective optimization problems using artificial intelligence. Key techniques in his portfolio include Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Trust-Region Methods, and hybrid models that integrate local search strategies. Recently, he has extended his expertise to smart materials, nanofluids, energy optimization, and AI-driven modeling in civil and mechanical engineering systems. His interdisciplinary approach bridges theory and practice, using computational models to solve complex real-world problems such as wind turbine placement, reactive power compensation, and fluid dynamics. Prof. El-Shorbagy’s research is not only high in theoretical impact but also has broad industrial and environmental relevance, making him a valuable contributor to applied sciences and engineering optimization domains.

📰 Publications Top Notes:

  • 🧬 Integrating PSO with GA for Solving Nonlinear Optimization Problems, JCAM, 2011

  • 🐝 Local Search-Based Hybrid PSO for Multiobjective Optimization, Swarm & Evolutionary Computation, 2012

  • 💧 Darcy Ternary-Hybrid Nanofluid Flow with Induction Effects, Micromachines, 2022

  • ⚗️ Green Synthesis of ZnO–CuO Nanoparticles and Cytotoxicity, Scientific Reports, 2021

  • 🧱 Glass Fiber-Reinforced Concrete: Mechanical and Microstructure Analysis, Materials, 2022

  • 🌬️ Optimization of Wind Turbines Siting Using GA-Based Local Search, Renewable Energy, 2018

  • 🔥 Hybrid Nanofluid Flow with Hall Current and Chemical Reaction, Alexandria Eng. Journal, 2022

  • 🌴 Coconut Fiber Reinforced Concrete: State-of-the-Art Review, Materials, 2022

  • ⚡ MHD Stagnation Point Flow with Joule Heating and Convective Effects, Case Studies in Thermal Eng., 2021

  • ♻️ Concrete with Waste Glass and Recycled Aggregate Substitution, Materials, 2022

🧾 Conclusion:

Prof. Mohammed Abd El-Rahman El-Shorbagy Hassan is a highly suitable candidate for the Best Researcher Award. His consistent and high-quality contributions to engineering mathematics and optimization, impactful publication record, and innovative research approaches mark him as a leading scholar in his domain. By expanding his engagement with industry and international grant opportunities, he could further elevate his already outstanding academic profile. He exemplifies the blend of research excellence, innovation, and practical relevance that such an award seeks to recognize.

illych alvarez | Cell-Cell Communication | Best Researcher Award

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Dr. illych alvarez | Cell-Cell Communication | Best Researcher Award

Dr. illych alvarez, Escuela superior Politecnica del litoral, Ecuador

Illych Ramses Alvarez Alvarez is a mathematician, professor, and researcher from Guayaquil, Ecuador, specializing in chaos theory, artificial intelligence, and applied mathematics. With a rich background in academia and educational innovation, he has played a vital role in advancing active learning in mathematics at the Escuela Superior Politécnica del Litoral (ESPOL), where he currently teaches and conducts research. He has also taught at the Polytechnic University of Valencia, Spain. Dr. Alvarez is widely published in prestigious journals, focusing on dynamic systems, fuzzy logic, numerical simulations, and biomedical modeling. His work bridges complex theoretical concepts and practical applications in areas such as diabetes treatment, heat transfer, and mortality analysis. An active contributor to scientific communities, he is a frequent keynote speaker, reviewer, and track chair at international conferences including LACCEI. His dedication to cross-disciplinary collaboration and mathematical education makes him a prominent figure in Latin American scientific research.

Publication Profile: 

Orcid

✅ Strengths for the Award:

  1. 🌍 International Academic Recognition
    Dr. Alvarez holds advanced degrees from respected institutions in Spain, Cuba, and Ecuador, and teaches both locally and abroad (ESPOL and Polytechnic University of Valencia).

  2. 🔬 Interdisciplinary Research Impact
    His research bridges pure and applied mathematics, with contributions to fields like biomedical engineering (e.g., insulin delivery), materials science, control systems, and fuzzy logic.

  3. 📚 High-Quality and Diverse Publications
    With numerous peer-reviewed journal articles and conference papers indexed in Scopus and published in reputable outlets (Elsevier, Springer, Wiley), his academic output is substantial and impactful.

  4. 🎤 Active Role in Academic Community
    He has served as a keynote speaker, reviewer, committee member, and research track chair at major international events like LACCEI, REDU, and ICCSCM.

  5. 📈 Innovation in Education
    A recognized innovator in teaching methodologies, he led the design of active learning strategies and B-learning models, demonstrating a commitment to educational reform.

  6. 🔎 Societal Relevance of Research
    His work includes applied studies in child mortality analysis and educational equity, aligning mathematical research with real-world social impact.

⚠️ Areas for Improvement:

  • 🌐 Expand Global Collaborations
    While active in Latin America and Europe, broader partnerships across Asia or North America could elevate the visibility and global reach of his work.

  • 📢 Enhanced Science Communication
    More engagement with popular science outlets, policy forums, or public-facing platforms would help communicate his research to non-specialist audiences and stakeholders.

  • 🎯 Focused Thematic Consolidation
    Given the wide range of topics, a deeper focus or a flagship research theme could enhance long-term branding and scholarly identity.

🎓 Education:

Illych Alvarez holds a Ph.D. in Mathematics from the Polytechnic University of Valencia in Spain, where he explored advanced topics in dynamical systems and mathematical modeling. His academic journey also includes a Master’s in Mathematical Sciences with a focus on Numerical Mathematics from the University of Havana, Cuba. Additionally, he earned a Master’s in Mathematics Teaching from the Escuela Superior Politécnica del Litoral (ESPOL) in Ecuador, underscoring his commitment to mathematics education. Complementing his technical expertise is a Bachelor’s degree in Education Sciences from Universidad Metropolitana del Ecuador, which equipped him with pedagogical tools for effective teaching. This combination of theoretical depth, computational skills, and instructional knowledge enables Dr. Alvarez to operate at the intersection of education and scientific innovation. His educational path reflects both a local and global perspective on mathematics, fostering a blend of research rigor and educational leadership.

💼 Experience:

Dr. Illych Alvarez’s professional journey spans over two decades, blending teaching, research, and academic leadership. He began his career in secondary education, serving as Head of Mathematics and Academic Coordinator at renowned institutions such as Liceo Naval de Guayaquil and Liceo Los Andes. Transitioning to higher education, he became a key figure at ESPOL, where he serves as Professor and Researcher, curriculum designer, and workshop instructor. At ESPOL, he led the Active Learning Mathematics Program and has taught foundational and advanced mathematics courses. His international experience includes teaching at the Polytechnic University of Valencia. Dr. Alvarez has also made his mark in global academic communities, contributing as a keynote speaker, scientific reviewer, and track chair at numerous conferences including LACCEI. His combined experience in both grassroots education and advanced research positions him as a comprehensive academic leader committed to both knowledge generation and knowledge dissemination.

🔬 Research Focus:

Dr. Illych Alvarez’s research spans dynamical systems, chaos theory, numerical simulations, and artificial intelligence, with an emphasis on applied mathematics. His work explores complex phenomena in set-valued and fuzzy dynamical systems, often integrating numerical methods to visualize abstract mathematical behavior. A unique dimension of his research is its interdisciplinary application—his recent studies include numerical modeling of chemo-fluidic oscillators for diabetes treatment, showcasing the practical reach of theoretical mathematics. He also investigates recurrence and transitivity in dynamic environments and applies control theory to epidemiological and demographic models. Furthermore, his interest in mathematics education has led him to develop and assess innovative B-learning and inverted classroom methodologies. This dual focus on theoretical rigor and pedagogical innovation distinguishes his contributions to both science and society. Dr. Alvarez’s research continues to evolve toward multiscale modeling and computational methods, making significant strides in both academic and applied contexts.

📚 Publications Top Notes:

  • 📘 Advanced Numerical Modeling and Simulation of Hydrogel‐Based Chemo Fluidic Oscillator for Enhanced Insulin Delivery System in Diabetes Treatment

  • 📘 Recurrence in Collective Dynamics: From the Hyperspace to Fuzzy Dynamical Systems

  • 📘 Advanced Extensions and Applications of Transitivity and Mixing in Set‐Valued Dynamics With Numerical Simulations and Visual Insights

  • 📘 Advanced Extensions and Applications of Transitivity and Mixing in Set-Valued Dynamics with Numerical Simulations and Visual Insights (SSRN)

  • 📘 Heat Transfer Problem Solving Techniques in Materials Engineering: A Numerical Approach and Practical Applications

  • 📘 Recurrence in Collective Dynamics: From the Hyperspace to Fuzzy Dynamical Systems (arXiv)

  • 📘 Advanced Numerical Analysis and Simulation of a Chemo-Fluidic Oscillator: Comparative Study of Numerical Methods and Robustness Evaluation

  • 📘 A New B-Learning Methodology for Teaching Differential Integral Calculus in a School of Engineering

  • 📘 Optimal Exponentially Weighted Moving Average Of T² Chart

  • 📘 A New Inverted Class Methodology Applied as a Pilot Program to Students Aspiring to Enter an Ecuadorian University

  • 📘 Application of Control Charts to Detect Anomalies in Child Mortality in Ecuador

📝 Conclusion:

Illych Ramses Alvarez Alvarez demonstrates excellence in both research and education, with a dynamic profile that integrates theoretical innovation, real-world application, and pedagogical leadership. His impactful publications, international engagement, and interdisciplinary expertise make him a highly suitable and competitive candidate for the Best Researcher Award. His work exemplifies the integration of mathematics with societal needs and educational advancement, aligning perfectly with the core values of academic excellence and innovation.

Waseem Haider | Bioinformatics | Best Paper Award

Published on by Cell Biologist

Dr. Waseem Haider | Bioinformatics | Best Paper Award

Dr. Waseem Haider , COMSATS University Islamabad , Pakistan

Dr. Waseem Haider is an Associate Professor of Bioinformatics at COMSATS University Islamabad, Pakistan, with over 25 years of teaching and research experience. He holds a Ph.D. from the University of Illinois at Urbana-Champaign, USA. As the founder and CEO of Next Gen. Solutions, a data analysis training and consultation company, he has trained over 1500 researchers in various fields, including DNA/RNA sequencing, public health data analysis, and machine learning applications. Dr. Haider’s expertise spans bioinformatics, molecular biology, and data science, utilizing tools such as R, SAS, Python, and PERL. His significant contributions include high-impact research on genetic analysis, disease monitoring, and agricultural genomics. He is passionate about applying computational methods to solve complex biological problems, enhancing public health, and advancing biotechnology.

Publication Profile:

Orcid

Strengths for the Award:

  1. Impressive Academic and Research Track Record: Dr. Haider has a wealth of experience in teaching, supervising graduate and postgraduate research, and contributing significantly to the field of bioinformatics. His supervision of over 25 MS theses and 4 PhD candidates speaks to his leadership in academic research.

  2. Wide Range of Research Interests: His research interests are diverse, covering plant biology, molecular genetics, disease diagnostics, and public health. This breadth shows that Dr. Haider is not only an expert in bioinformatics but also contributes substantially to real-world challenges in genomics and medicine.

  3. High-Impact Publications: Dr. Haider has co-authored numerous publications in reputed international journals, including high-impact ones such as PLoS One, Frontiers in Plant Science, Plant Journal, and Genes. Many of these papers tackle important issues in agriculture, biotechnology, and human health.

  4. Strong Collaboration and Networking: His collaborations with international researchers and institutions, as evidenced by his referees and joint projects, reflect his established network within the global scientific community.

  5. Practical Contribution via Next Gen. Solutions (NGS): As the founder of NGS, Dr. Haider has trained over 1,500 researchers, indicating his commitment to the practical application of bioinformatics in Pakistan and beyond. His ability to translate complex bioinformatics concepts into hands-on training is a significant strength.

  6. Cutting-Edge Research Themes: Dr. Haider’s research on RNA-Seq, genome analysis, and machine learning, as well as the application of these techniques to cancer, public health, and agricultural improvements, positions him at the forefront of modern bioinformatics research.

  7. Multidisciplinary Expertise: His ability to apply bioinformatics techniques to diverse areas (e.g., plant genomics, cancer research, disease diagnostics) demonstrates his versatility as a researcher and innovator.

Areas for Improvement:

  1. Visibility and Citation Impact: While Dr. Haider has published extensively, some of his publications might not have achieved as wide a readership or citation count as others. Increasing the visibility of his research through strategic dissemination (e.g., social media engagement, academic platforms) could enhance its impact.

  2. Focus on Recent Advancements: Although Dr. Haider is clearly active in bioinformatics, certain emerging fields, such as AI-driven personalized medicine or novel data analysis techniques, might offer opportunities to expand the scope of his work. Engaging with these newer areas could help to further strengthen his contributions to the field.

  3. Mentorship and Further Collaboration: Despite his significant supervisory role, expanding his mentorship to include more international collaborations could bring fresh perspectives and international recognition to his work. More cross-disciplinary partnerships could lead to innovative breakthroughs.

Education:

Dr. Waseem Haider’s academic journey began with a Master of Computer Science from Arid Agriculture University Rawalpindi in 2008, followed by an M.Sc/M. Phil in Biochemistry and Molecular Biology from Quaid-e-Azam University, Islamabad, in 2002. His pursuit of advanced studies culminated in a Ph.D. in Bioinformatics from the University of Illinois at Urbana-Champaign, USA, in 2014. His educational background has allowed him to bridge the gap between biology, computational sciences, and bioinformatics, shaping his research career. Dr. Haider’s extensive academic foundation enables him to supervise graduate-level research and contribute valuable insights to both the scientific community and industry. His cross-disciplinary expertise supports his role as a mentor, guiding students in bioinformatics, molecular biology, and computational biology, and fostering innovation in these fields.

Experience:

Dr. Waseem Haider has an extensive academic career, serving as a faculty member at COMSATS University Islamabad, Pakistan, since 2005. Before this, he gained experience in college-level teaching, including at Cadet College Hassan Abdal, contributing to a 25-year career in teaching and research. In addition to his academic role, he is the founder and CEO of Next Gen. Solutions (NGS), a company specializing in data analysis training and consultation. Dr. Haider has trained over 1500 researchers in bioinformatics, molecular biology, and computational techniques. His company also conducts workshops and online training sessions, providing critical skills in DNA sequencing, RNA-Seq, and machine learning. With expertise in Linux, Unix, Python, R, and PERL, Dr. Haider has applied computational methods to solve a wide range of biological problems, with his work spanning genomic research, public health, and agricultural improvements.

Research Focus:

Dr. Waseem Haider’s research focuses on applying bioinformatics and computational biology to a variety of biological fields. He specializes in DNA and RNA sequencing technologies, including genome/exome sequencing, RNA-Seq, and metagenomic data analysis. His research also extends to the application of machine learning in cancer genomics, such as RNA-Seq data analysis in cancer, and deep learning techniques in medical imaging. Additionally, Dr. Haider explores genetic variations, particularly in plant biology, and uses transcriptomic data to understand the molecular mechanisms behind disease resistance and stress tolerance in plants like soybean and canola. His work contributes to public health, especially in the analysis of infectious diseases like hepatitis and cystic echinococcosis. He has also made significant contributions to the identification of genetic biomarkers in diseases like breast cancer and diabetes. Through these research endeavors, Dr. Haider is advancing bioinformatics tools and techniques to improve healthcare and agricultural outcomes.

Publications Top Notes: 

  1. Varietal Response of Lycopersicon esculentum L. to Callogenesis and Regeneration 🍅🧬
  2. Functional and Evolutionary Characterization of the CONSTANS Gene Family in Short-Day Photoperiodic Flowering in Soybean 🌱🌾
  3. Newcastle Disease as an Emerging Disease in Peacocks of Tharparker, Pakistan 🦚🦠
  4. Sero-Prevalence of Hepatitis B and C Virus from Rural Areas of Northern Punjab (Sargodha District), Pakistan 🦠💉
  5. Evolutionary Trajectories of Duplicated FT Homologues and Their Roles in Soybean Domestication 🌿🔬
  6. Knowledge, Attitudes, and Practices Related to Cystic Echinococcosis Endemicity in Pakistan 🐑💉
  7. Transcriptome-Enabled Network Inference Revealed the GmCOL1 Feed-Forward Loop and Its Roles in Photoperiodic Flowering of Soybean 🌾🧬
  8. Knowledge, Attitudes & Practices Regarding Rabies Endemicity Among the Community Members, Pakistan 🐕💉
  9. Morpho-Ecological Study of Freshwater Mollusks of Margalla Foothills, Pakistan 🦪🏞️
  10. Protein Quantification and Enzyme Activity Estimation of Pakistani Wheat Landraces 🌾🔬

Conclusion:

Dr. Waseem Haider is undoubtedly a strong candidate for the Best Paper Award. His deep expertise, innovative research, impactful publications, and significant contributions to training researchers make him a leading figure in bioinformatics. While there are opportunities to increase the visibility of his research, his dedication to solving pressing global problems through bioinformatics and molecular biology positions him as a highly deserving candidate for recognition in the scientific community.

Irena Roterman | Protein structure | Best Researcher Award

Published on by Cell Biologist

Irena Roterman | Protein structure | Best Researcher Award

Prof. Irena Roterman , Jagiellonian University – Medical College , Poland

Irena Roterman-Konieczna is a distinguished biochemist specializing in bioinformatics and protein structure. With a PhD in biochemistry from the Nicolaus Copernicus Medical Academy Krakow, she has held significant academic positions, including Professor of Medical Sciences at Jagiellonian University. Irena is recognized for her innovative contributions, particularly the fuzzy oil drop model, which emphasizes environmental influence on protein folding. She has published extensively, contributing to the understanding of protein dynamics and interactions. As a committed educator, she has guided numerous PhD students and served as the Chief Editor for the journal Bio-Algorithms and Med-Systems. Her work continues to impact the fields of protein folding, membrane proteins, and systems biology.

Publication Profile

Scopus

Strengths for the Award

Irena Roterman-Konieczna’s extensive academic background and innovative contributions to the field of bioinformatics and protein structure make her an exceptional candidate for the Best Researcher Award. Her pioneering work on the fuzzy oil drop model has provided critical insights into the environmental influences on protein folding. With a prolific publication record of 149 articles, she has consistently advanced the understanding of protein dynamics, particularly in membrane proteins and chaperonins. Additionally, her role as Chief Editor of the journal Bio-Algorithms and Med-Systems demonstrates her leadership in the scientific community. Her commitment to mentoring future researchers is evident through her advisory work with 15 PhD students, ensuring the continued growth of the field.

Areas for Improvement

While Irena’s contributions to theoretical models are significant, there may be opportunities to further integrate experimental validation into her research. Collaborating with experimentalists could enhance the practical applications of her models, particularly in understanding real-world protein behavior. Additionally, increasing outreach to interdisciplinary fields could broaden the impact of her research on medicine and biotechnology.

Education

Irena Roterman-Konieczna completed her basic education in theoretical chemistry at Jagiellonian University in 1974. She earned her PhD in biochemistry in 1984, focusing on the structure of the recombinant IgG hinge region at the Nicolaus Copernicus Medical Academy in Krakow. Following her doctoral studies, Irena undertook postdoctoral research at Cornell University from 1987 to 1989 in Harold A. Scheraga’s group, where she analyzed force fields in molecular modeling programs like Amber and Charmm. In 1994, she achieved habilitation in biochemistry at Jagiellonian University’s Faculty of Biotechnology and later attained the title of Professor of Medical Sciences in 2004. This strong educational foundation laid the groundwork for her extensive research and contributions to the field of biochemistry and bioinformatics.

Experience

Irena Roterman-Konieczna has a robust academic and research background spanning several decades. She has held key academic positions at Jagiellonian University, where she is currently a Professor of Medical Sciences. Irena’s postdoctoral research at Cornell University deepened her expertise in molecular modeling and protein interactions. Throughout her career, she has authored numerous publications and books, significantly advancing the understanding of protein folding and structure. As Chief Editor of the journal Bio-Algorithms and Med-Systems from 2005 to 2020, she played a vital role in disseminating research in the field. Additionally, she has supervised 15 PhD students, fostering the next generation of researchers. Irena’s collaborative efforts and advisory roles in various projects highlight her commitment to scientific advancement and education in biochemistry and bioinformatics.

Research Focus

Irena Roterman-Konieczna’s research centers on bioinformatics, particularly in understanding protein structure and dynamics. Her innovative fuzzy oil drop model explores the role of environmental factors in protein folding, proposing that external force fields influence hydrophobic core formation and overall structure. Irena investigates the effects of membrane environments on protein behavior, examining how hydrophobic factors can alter folding dynamics. Her work also delves into chaperonins and their role in facilitating proper protein folding under varying conditions. Additionally, she explores domain-swapping structures and their implications for complex formation in proteins. Irena’s research emphasizes the necessity of simulating external force fields in computational protein folding, integrating both internal and external interactions. Her contributions to systems biology and the development of quantitative models for protein behavior continue to advance the field, making significant impacts in both theoretical and practical applications.

Publications Top Notes

  • Chameleon Sequences─Structural Effects in Proteins Characterized by Hydrophobicity Disorder 🌊
  • Transmembrane proteins—Different anchoring systems ⚓
  • External Force Field for Protein Folding in Chaperonins─Potential Application in In Silico Protein Folding 💻
  • Structural features of Prussian Blue-related iron complex FeT of activity to peroxidate unsaturated fatty acids 🔬
  • Domain swapping: a mathematical model for quantitative assessment of structural effects 📊
  • Editorial: Structure and function of trans-membrane proteins 🧬
  • Model of the external force field for the protein folding process—the role of prefoldin 🌐
  • Role of environmental specificity in CASP results 📈
  • Ab initio protein structure prediction: the necessary presence of external force field as it is delivered by Hsp40 chaperone 🔍
  • Secondary structure in polymorphic forms of alpha-synuclein amyloids 🧪

Conclusion

Irena Roterman-Konieczna’s innovative research, leadership in academia, and dedication to mentorship position her as a strong contender for the Best Researcher Award. Her groundbreaking work in bioinformatics not only advances scientific understanding but also lays the groundwork for future discoveries in protein dynamics and interactions. Recognizing her contributions would not only honor her achievements but also inspire ongoing research in the field.