Sanjukta Bhowmick

Sanjukta Bhowmick is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas, part of the College of Engineering. Her research focuses on network analysis, graph algorithms, parallel computing, and scalable data systems. She is actively involved in collaborative projects such as LEGAS (Learning Evolving Graphs At Scale) and MLN-DIVE (Multilayer Network Data Infrastructure for Visualization and Exploration), emphasizing interdisciplinary applications like precision agriculture and cybersecurity.

Her work bridges theoretical foundations with practical implementations, addressing challenges in dynamic networks, community detection, and high-performance computing. Notable contributions include efficient algorithms for motif counting, centrality disruption analysis, and checkpointing systems optimized for GPU acceleration. She has co-authored numerous papers presented at conferences like SIAM Data Mining and HPC Symposium, showcasing her commitment to advancing computational methods for complex systems.

• Grants & Collaborations: Collaborative Research projects funded by NSF (SHF: Small, CSSI: CANDY), ANACIN-X framework for nondeterminism analysis, and multilayer network infrastructure initiatives.
• Key Themes: Network robustness, scalable parallel algorithms, graph-based machine learning, and interdisciplinary data-driven solutions.

Her research emphasizes real-world applications, such as optimizing variable rate spraying in agriculture and enhancing fault tolerance in distributed systems. She actively contributes to workshops and symposiums, promoting co-design approaches for deep learning accelerators and high-performance computing systems.