Prof. Jihwan Ha | Systems Biology | Best Researcher Award 

Assistant Professor, at Pukyong National University, South Korea.

Dr. Jihwan Ha is an accomplished researcher and assistant professor at Pukyong National University, Republic of Korea, specializing in bioinformatics and computational biology. With a passion for harnessing machine learning and deep learning, he focuses on developing advanced algorithms to predict miRNA-disease associations and improve biomedical data analysis. Dr. Ha has contributed to numerous international journals and conferences, earning recognition for his innovative work in bioinformatics. His professional journey includes postdoctoral research at Hawai’i Cancer Center and several faculty roles at Pukyong National University. Beyond academia, Dr. Ha is a reviewer for prestigious journals and a guest editor for special issues in Biomedicines. With a commitment to advancing the field, he inspires the next generation of data scientists through teaching courses on AI, big data, and computational systems. 🌟

Profile 

Scopus

ORCID

Education 🎓

• Yonsei University, Seoul, Republic of Korea
  Ph.D. in Computer Science, August 2020
  Thesis: “A machine learning approach to predict miRNA-disease associations”
  Dr. Ha’s doctoral work introduced a novel framework for predicting miRNA-disease associations using graph convolutional networks, setting a benchmark in computational biology.
• Yonsei University, Seoul, Republic of Korea
  M.S. in Computer Science, August 2015
  Building on his undergraduate foundation, he explored advanced data mining techniques, focusing on biomedical applications.
• Pusan National University, Busan, Republic of Korea
  B.S. in Electronics Engineering, August 2013
  His undergraduate education laid the groundwork for his expertise in bioinformatics, emphasizing electronics and computational systems. 🎉

Experience 💼

• Assistant Professor, Pukyong National University (2022–Present)
  Leads courses on big data, machine learning, and AI while supervising cutting-edge research in bioinformatics.
• Postdoctoral Researcher, Hawai’i Cancer Center (2020–2021)
  Conducted advanced research on predictive models for cancer biomarkers, integrating multi-omics data for precision medicine.
• Assistant Professor, Pukyong National University (2021–2022)
  Developed curriculums for data analysis and mentored students in computational biology projects.

Dr. Ha’s diverse academic and research roles demonstrate his commitment to applying computational tools for solving real-world biological problems. 🌟

Research Interests 🔬

Dr. Ha’s research bridges data science and biomedicine, focusing on:

• Bioinformatics & Computational Biology: Leveraging AI to analyze biological data.
• Machine Learning & Deep Learning: Developing predictive models for miRNA-disease associations.
• Recommender Systems: Applying data mining for personalized medicine.
• Data Mining: Extracting meaningful patterns from complex datasets.
  He aims to advance bioinformatics tools to facilitate disease diagnosis and treatment optimization. 🌱

Awards 🏆

• Merit Academic Paper Award, Yonsei University (2020)
  Recognized for exceptional contributions to bioinformatics research.
• Computer Science Department Scholarship, Yonsei University (2020)
  Awarded for academic excellence during doctoral studies.
• Academic Scholarship, Pusan National University (2011–2013)
  Earned for maintaining a high GPA throughout undergraduate studies.

Dr. Ha’s achievements reflect his dedication to advancing bioinformatics and academic excellence. 🎖️

Top Noted Publications 📚

Dr. Ha has authored impactful papers in top journals. Selected works include:

• Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association
  • Journal: Biomedicines
  • Published Year: 2025
  • Details: This paper presents a novel graph convolutional network combined with neural collaborative filtering to predict miRNA-disease associations. It emphasizes enhanced prediction accuracy by integrating graph-based data structures with collaborative filtering mechanisms.
  • Cited by: [To be updated based on citation databases]
  • Hyperlink: Link to Biomedicines Paper
• LncRNA Expression Profile-based Matrix Factorization for Predicting LncRNA-Disease Association
  • Journal: IEEE Access
  • Published Year: 2024
  • Details: This research introduces a matrix factorization approach leveraging LncRNA expression profiles for predicting disease associations. The model demonstrates improved scalability and prediction accuracy.
  • Cited by: [To be updated based on citation databases]
  • Hyperlink: Link to IEEE Access Paper
• Node2vec-based Neural Collaborative Filtering for Predicting miRNA-Disease Association
  • Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • Published Year: 2023
  • Details: The paper proposes a method combining Node2vec embeddings with neural collaborative filtering to predict miRNA-disease associations. The approach leverages graph-based features and collaborative learning for enhanced precision.
  • Cited by: [To be updated based on citation databases]
  • Hyperlink: Link to IEEE/ACM TCB Paper
• SMAP: Similarity-based Matrix Factorization Framework for Inferring miRNA-Disease Association
  • Journal: Knowledge-Based Systems
  • Published Year: 2023
  • Details: SMAP employs a similarity-based matrix factorization approach to infer miRNA-disease associations. The paper emphasizes the importance of similarity constraints in improving model performance.
  • Cited by: [To be updated based on citation databases]
  • Hyperlink: Link to Knowledge-Based Systems Paper

Conclusion

Dr. Jihwan Ha’s impressive body of work, interdisciplinary expertise, and contributions to bioinformatics and computational biology make him a strong candidate for the “Best Researcher Award.” His innovative application of machine learning to critical healthcare challenges demonstrates both academic excellence and real-world relevance. Addressing clinical impact and expanding his grant portfolio could elevate his profile further, but his current achievements already position him as an exemplary researcher deserving of recognition.