Shima Shafiee | Cell Structure Analysis | Best Researcher Award

Published on by Cell Biologist

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

Published on by Cell Biologist

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.