Chief Data Scientist & Group Leader
Chief Data Scientist & Group Leader

Biography

Dr. Halappanavar is a chief data scientist at PNNL, where he serves as the  group leader of the Data Science and Machine Intelligence group and as the Computer Science subsector lead for DOE's Advanced Scientific Computing Research (ASCR) program. He also holds a joint appointment as adjunct faculty in computer science at the School of Electrical Engineering and Computer Science at Washington State University in Pullman. His research has spanned multiple technical foci and includes combinatorial scientific computing, parallel graph algorithms, artificial intelligence and machine learning, and the application of graph theory and game theory to solve problems in application domains, such as scientific computing, power grids, cybersecurity, and life sciences. He co-authored a book on design of parallel graph algorithms on shared-memory architectures and has authored over 170 technical publications for peer-reviewed journals, conferences, and workshops. He is a member of the Society for Industrial and Applied Mathematics (SIAM), and a senior member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE).

Research Interests

  • Artificial Intelligence and Machine Learning, Graph Algorithms, Parallel Computing, Scientific Computing

Disciplines and Skills

  • Combinatorial and Graph Algorithms
  • Parallel Computing
  • Artificial Intelligence and Machine Learning
  • Combinatorial Scientific Computing
  • Application Domains such as Computational Biology, Chemistry, and Cybersecurity

Education

  • Doctor of Philosophy in Computer Science, Old Dominion University
  • Master of Science in Computer Science, Old Dominion University
  • Master of Business Admin in Business Admin & Management, Karnatak University
  • Bachelor of Engineering in Industrial/Production Eng, BLDE PG Halakatti Col Eng&Tech

Affiliations and Professional Service

  • SIAM
  • Senior Member, ACM
  • Senior Member, IEEE

Awards and Recognitions

  • MIT/Amazon Graph Challenge Champions for "TriC: Distributed-memory Triangle Counting by Exploiting the Graph Structure," IEEE High Performance Extreme Computing Conference (2020)
  • Amazon Graph Challenge Innovation Award for “Scaling and Quality of Modularity Optimization Methods for Graph Clustering,” IEEE High Performance Extreme Computing Conference (2019)
  • Amazon Graph Challenge Innovation Award for “Direction-Optimizing Label Propagation Algorithm,” IEEE High Performance Extreme Computing Conference (2019)
  • Best Poster Award for “Cyber-Based Interdependent Infrastructure Network Resilience Analysis,” Society for Risk Analysis (2019)
  • Amazon Graph Challenge Student Innovation Award for "Scalable Community Detection Using Vite," IEEE High Performance Extreme Computing Conference (2018)
  • Best Conference Papers Award for “Synthetic Power Grids from Real World Models,” IEEE Power & Energy Society (PES) General Meeting (2018)
  • Best Paper Award in the Attack and Disaster track for “Quantitative Assessment of Transportation Network Vulnerability with Dynamic Traffic Simulation Methods," IEEE Conference on Technologies for Homeland Security (2017)
  • DARPA/Amazon Graph Challenge Champions for "Scalable static and dynamic community detection using Grappolo," IEEE High Performance Extreme Computing Conference (2017)
  • Best Paper Award for “On Stable Marriages and Greedy Matchings,” SIAM Workshop on Combinatorial Scientific Computing (2016)
  • Best Paper Award in the Cyber Security track for "Quantifying Mixed Uncertainties in Cyber Attacker Payoffs," IEEE Conference on Technologies for Homeland Security (2015)
  • Best Paper Award in the Cyber Security track for "Towards A Theory of Autonomous Reconstitution of Compromised Cyber-Systems," IEEE Conference on Technologies for Homeland Security (2013)
  • Old Dominion University's Office of Graduate Studies University Graduate Fellowship (2005 – 2006)

Publications

2025

  • Mattei, M., D. Soriano-Paños, M. Halappanavar, and A. Arenas. 2025. Structured interactions drive abrupt transitions in the spatial organization of microbial communities. Physical Review Research 7(4). doi:10.1103/g5bm-m9c4
  • Islam, K., Z. Mehrab, M. Halappanavar, H. Mortveit, S. Katragadda, D. Loftis, and M. Marathe. 2025. Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting. doi:10.1145/3748636.3764165
  • Ferracina, F., P. Beeler, M. Halappanavar, B. Krishnamoorthy, M. Minutoli, and L. Fierce. 2025. Learning to Simulate Aerosol Dynamics with Graph Neural Networks. ACS ES&T Air. doi:10.1021/acsestair.4c00261
  • Wang, Y., Y. Zhang, Z. Guo, H. Shomer, H. Han, T. Derr, N. Ahmed, M. Halappanavar, and J. Tang. 2025. Machine Learning on Graphs in the Era of Generative Artificial Intelligence. doi:10.1145/3711896.3737870
  • 2025. Leveraging Multimodal AI for Efficient Data Discovery in Wind Energy Research. doi:10.1145/3708035.3736038
  • 2025. Leveraging Language Modeling and Dynamic Social Network Analysis to recognize Patterns in the Spread of COVID-19 Misinformation Narratives on Social Media. doi:10.1145/3733155.3734908
  • 2025. Structural Validation of Synthetic Power Distribution Networks Using The Multiscale Flat Norm. International Journal of Computational Geometry & Applications. doi:10.1142/S0218195925500049
  • 2025. ScaWL: Scaling k-WL (Weisfeiler-Lehman) Algorithms in Memory and Performance on Shared and Distributed-Memory Systems. ACM Transactions on Architecture and Code Optimization. doi:10.1145/3715124
  • 2025. Predictive analytics of selections of russet potatoes. Crop Science. doi:10.1002/csc2.21432

2024

2023

  • Xiang, L., A. Khan, S. Ferdous, S. Aravind, and M. Halappanavar. 2023. cuAlign: Scalable Network Alignment on GPU Accelerators. doi:10.1145/3624062.3625129
  • Minutoli, M., M. Halappanavar, E. Aprà, P. El-Khoury, and N. Govind. 2023. 0BGRaman: Graph Network based Simulator for Forecasting Molecular Polarizability. doi:10.2172/2007881
  • D'Ambra, P., F. Durastante, S. Ferdous, S. Filippone, M. Halappanavar, and A. Pothen. 2023. AMG Preconditioners based on Parallel Hybrid Coarsening and Multi-objective Graph Matching. doi:10.1109/PDP59025.2023.00017

2022

  • Liu, X., A. Lumsdaine, M. Halappanavar, K. Barker, and A. Gebremedhin. 2022. Direction-optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis. ACM Journal of Experimental Algorithmics 27, 1-31. doi:10.1145/3564593
  • Gawande, N., S. Ghosh, M. Halappanavar, A. Tumeo, and A. Kalyanaraman. 2022. Towards scaling community detection on distributed-memory heterogeneous systems. Parallel Computing 111, 102898. doi:10.1016/j.parco.2022.102898
  • Sun, J., L. Yang, J. Zhang, F. Liu, M. Halappanavar, D. Fan, and Y. Cao. 2022. Self-supervised Novelty Detection for Continual Learning: A Gradient-Based Approach Boosted by Binary Classification. doi:10.1007/978-3-031-17587-9_9

2021

  • Acer, S., A. Azad, E. Boman, A. Buluç, K. Devine, S. Ferdous, N. Gawande, S. Ghosh, M. Halappanavar, A. Kalyanaraman, A. Khan, M. Minutoli, A. Pothen, S. Rajamanickam, O. Selvitopi, N. Tallent, and A. Tumeo. 2021. EXAGRAPH: Graph and combinatorial methods for enabling exascale applications. The International Journal of High Performance Computing Applications 35(6), 553-571. doi:10.1177/10943420211029299

2020

  • Jain, M., K. Gupta, A. Sathanur, V. Chandan, and M. Halappanavar. 2020. Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data. doi:10.2172/2203451

2017

  • Panyala, A., D. Chavarría-Miranda, J. Manzano, A. Tumeo, and M. Halappanavar. 2017. Exploring performance and energy tradeoffs for irregular applications: A case study on the Tilera many-core architecture. Journal of Parallel and Distributed Computing 104, 234-251. doi:10.1016/j.jpdc.2016.06.006