Prof. Desislava Ivanova | Quantum Computing | Best Researcher Award

Desislava Ivanova at Technical University of Sofia, Bulgaria.

Prof. Desislava Ivanova is a prominent Bulgarian computer scientist and expert in high-performance and quantum computing. As a professor and dean at the Technical University of Sofia, she has played a pivotal role in shaping the university’s research in informatics, parallel computing, and applied AI. Her academic journey is highlighted by international fellowships and research engagements in Germany, Italy, Spain, and the United States, including at Carnegie Mellon University.

Prof. Ivanova is an active member of several scientific communities, including IEEE Computer Society, Informatics Europe, and the Union of Automation and Informatics, and serves as AWS Coordinator and EuT+ Data Science Institute Coordinator for TU-Sofia. Her contributions span both foundational research and practical systems for healthcare and business informatics.

Professional Profile

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🎓 Education 

• Master’s Degree in Automation and Information Technologies, University of Chemical Technology and Metallurgy, Sofia, 2004

• Ph.D. in Computer Systems, Complexes and Networks, Faculty of Computer Systems and Control, Technical University of Sofia, 2014

💼 Experience 

Prof. Desislava Ivanova has built an illustrious academic career at the Technical University of Sofia, where she currently serves as a Professor and Dean of the Faculty of Applied Mathematics and Informatics. From 2014 to 2017, she held the position of Chief Assistant Professor in the Computer Systems Department. In 2018, she was promoted to Associate Professor and later became Dean in 2019. As of 2025, she holds the title of Full Professor, demonstrating a consistent trajectory of academic leadership and excellence.

Her global academic engagement is underscored by research collaborations and specializations at prestigious institutions, including Carnegie Mellon University (USA), Technical University of Kaiserslautern (Germany), CINECA Supercomputing Center (Italy), and University of Granada (Spain).

🔬 Research Interests

• Primary Areas:

  • High Performance Computing (HPC)

    • Supercomputer Architectures and Applications

    • System Area Networks

    • Parallel Programming and Algorithms

    • In-Situ Visualization

  • Bioinformatics

  • Machine Learning

  • Cloud Computing

  • Quantum Computing

• Additional Areas:

  • Information Systems for Health Care and Business Management

  • Component-Based Control Systems

  • Formal Methods (Verification & Validation)

Author Metrics

• Publications: Numerous peer-reviewed articles and technical reports in high-performance computing, parallel algorithms, and machine learning

• Collaborations: Active international research links with top institutions across Germany, Italy, Spain, and the USA

• Recognition:

  • DAAD Alumni

  • Member of Carnegie Mellon University’s Software Research Network

  • Recognized academic leader in HPC systems research in Eastern Europe

Top Noted Publications:

1. Ant Colony Optimization Applied for Multiple Sequence Alignment

• Authors: S. Tsvetanov, D. Ivanova, B. Zografov

• Journal: Biomath Communications, Volume 2, Issue 1, 2015

• Summary: This study introduces an ant colony optimization (ACO) algorithm tailored for multiple sequence alignment (MSA) in bioinformatics. Drawing inspiration from the foraging behavior of ants, the algorithm seeks optimal alignments of biological sequences. The authors demonstrate that their ACO-based approach achieves competitive alignment quality compared to traditional MSA methods, highlighting its potential in computational biology applications.

2. Intelligent Method for Adaptive In Silico Knowledge Discovery Based on Big Genomic Data Analytics

• Authors: Plamenka Borovska, Desislava Ivanova

• Conference: 44th International Conference on Applications of Mathematics in Engineering and Economics (AMEE’18), AIP Conference Proceedings 2048, 2018

• Summary: The paper presents an intelligent method for adaptive in silico knowledge discovery (KDD) leveraging big genomic data analytics, aimed at supporting precision medicine. The proposed method comprises two overlapping phases: a machine learning phase and an operational phase, both utilizing scientific analytics workflows. A software system architecture based on this method is proposed, with applicability illustrated through a conceptual model for personalized breast cancer diagnostics and therapy recommendations. The method’s effectiveness is validated through case studies involving gene finding and mutation detection.

3. In Silico Knowledge Data Discovery in the Context of IoT Ecosystem Security Issues

• Authors: Plamenka Borovska, Desislava Ivanova

• Conference: 46th International Conference on Applications of Mathematics in Engineering and Economics (AMEE’20), AIP Conference Proceedings 2333, 2021

• Summary: This paper explores the application of in silico knowledge data discovery methods within the context of Internet of Things (IoT) ecosystem security. The authors discuss the integration of big data analytics and machine learning techniques to identify and mitigate security vulnerabilities in IoT systems. The study emphasizes the importance of adaptive and intelligent data analysis frameworks to enhance the resilience and security of interconnected devices and networks.

4. Hybrid Parallel Multiple Sequence Alignment Based on Artificial Bee Colony on the Supercomputer JUQUEEN

• Authors: Plamenka Borovska, Veska Gancheva, Ivailo Georgiev, Desislava Ivanova

• Conference: 2017 European Conference on Electrical Engineering and Computer Science (EECS)

• Summary: The authors present a hybrid parallel implementation of multiple sequence alignment (MSA) using the Artificial Bee Colony (ABC) algorithm on the JUQUEEN supercomputer. By combining MPI and OpenMP parallelization techniques, the study achieves significant performance improvements in aligning biological sequences. The results demonstrate the scalability and efficiency of the hybrid approach, making it suitable for large-scale bioinformatics applications.

5. Usability Strategy and Guidelines for Building an Accessible Web Portal

• Authors: Desislava Ivanova, Daniel Mitev

• Conference: 46th International Conference on Applications of Mathematics in Engineering and Economics (AMEE’20), AIP Conference Proceedings 2333, 2021

• Summary: This paper outlines a usability strategy for developing an accessible web portal dedicated to Bulgarian cultural and historical heritage. The strategy is grounded in the Web Content Accessibility Guidelines (WCAG) 2.0 and incorporates usability studies involving assistive technologies. The authors provide comprehensive guidelines to ensure the portal is accessible to users with disabilities, aiming to create an inclusive digital experience for all visitors.

Conclusion:

Prof. Desislava Ivanova embodies the core values of the Research for Best Researcher Award—innovation, collaboration, academic leadership, and global impact. Her work intersects quantum computing, HPC, and AI-driven bioinformatics, positioning her as a pioneer in multidisciplinary computing research.

While deeper specialization in quantum-specific publications and expanded industry outreach could further elevate her profile, her proven record of impactful, interdisciplinary work fully merits recognition through this prestigious award.