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.