Dr. Batjargal Dolgorsuren is an Associate Professor at the Mongolian University of Science and Technology (MUST), specializing in Graph Theory, Big Data, and Bioinformatics. She earned her Ph.D. in Computer Science from Kyung Hee University, South Korea (2019), focusing on graph compression and sparsification. Passionate about streaming/dynamic graphs, shortest path computation, and large-scale data analysis, she actively contributes to research in social networks, transportation systems, and bioinformatics. A dedicated IEEE member, she has a strong background in programming, data mining, and web development. Beyond academics, she enjoys reading, writing poetry, traveling, and swimming. 🌍💻📊
Publication Profile
Education & Experience 
Education 📚
✅ Ph.D. in Computer Science – Kyung Hee University, South Korea (2014-2019)
✅ Master’s in Computer Science – MUST, Mongolia (2010-2012)
✅ Bachelor’s in Computer Science (Software Engineering) – MUST, Mongolia (2006-2010)
Work Experience 🏢
👩🏫 Associate Professor – MUST, Mongolia (2022-Present)
👩🏫 Senior Lecturer – MUST, Mongolia (2021-2022)
👩🏫 Lecturer – MUST, Mongolia (2019-2021)
💻 Computer Science Teacher – MUST, Mongolia (2010-2014)
👩💻 Software Developer – BestSoft LLC, Mongolia (2011-2012)
📊 Training Teacher – Election Committee, Mongolia (2012)
🖥️ Scientific Programming Trainee – Cardiff University, UK (2013)
Suitability Summary
Dr. Batjargal Dolgorsuren, a distinguished computer scientist and researcher, is a highly deserving recipient of the Best Researcher Award for her pioneering contributions to graph theory, big data analytics, and bioinformatics. Her extensive work on graph compression, shortest path computation, and graph sparsification has significantly advanced the efficiency of data processing in large-scale and dynamic networks. As an Associate Professor at the Mongolian University of Science and Technology (MUST), she has made remarkable strides in both academic research and practical applications, reinforcing her position as a leading expert in graph-based data analysis.
Professional Development 
Dr. Dolgorsuren is committed to continuous learning in computer science, big data, and graph theory. She has expertise in graph mining tools (Giraph, GraphStream, Neo4j), big data analytics (R, SPSS, MapReduce), and programming languages (Python, PHP, Java). She has also worked on data visualization, system analysis, and AI-driven analytics. As an IEEE member, she collaborates on global research, contributing to dynamic graph algorithms and network analysis. She has attended specialized training, including scientific programming at Cardiff University, and has developed several projects in graph compression and web development. 🌐📊💡
Research Focus 
Dr. Dolgorsuren specializes in Graph Theory, Big Data, and Bioinformatics, with a keen interest in graph compression, sparsification, and shortest path algorithms. She explores efficient methods for summarizing large graphs, particularly in social networks, transportation systems, and biological data. Her work involves streaming and dynamic graph analysis, cut sparsifiers, and time-evolving community structures. She also develops algorithmic solutions for online/offline shortest path computation, optimizing large-scale network analysis. Her research is crucial in applications such as biological compound mapping, internet traffic optimization, and big data processing. 🔍📈🖥️
Awards & Honors 🏆
🏅 IEEE Member – Contributing to cutting-edge research in Graph Theory & Big Data
🏅 Best Research Paper Contributions – Published in reputable international journals
🏅 Scientific Programming Training – Cardiff University, UK
🏅 Scholarship for Ph.D. studies – Kyung Hee University, South Korea
🏅 Top Academic Performance – Ph.D. GPA: 4.06/4.3, Master’s GPA: 3.84/4.0
🏅 Recognition for Teaching Excellence – MUST, Mongolia
Publication Top Notes
📌 Faster compression methods for a weighted graph using locality sensitive hashing | Information Sciences 421 | 📅 2017 | 🔗 21 citations
📌 StarZIP: Streaming graph compression technique for data archiving | IEEE Access 7 | 📅 2019 | 🔗 16 citations
📌 EM-FGS: Graph sparsification via faster semi-metric edges pruning | Applied Intelligence 49 | 📅 2019 | 🔗 7 citations
📌 Similarity estimation for large-scale human action video data on Spark | Applied Sciences 8 (5) | 📅 2018 | 🔗 5 citations
📌 Lpami: A graph-based lifestyle pattern mining application using personal image collections in smartphones | Applied Sciences 7 (12) | 📅 2017 | 🔗 5 citations
📌 Extracting top-K interesting subgraphs with weighted query semantics | 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) | 📅 2017 | 🔗 3 citations
📌 SP2 spanner construction for shortest path computation on streaming graph | Proceedings of the Sixth International Conference on Emerging Databases | 📅 2016 | 🔗 3 citations