Ashwin Kayyoor

Dr. Ashwin Kayyoor is a Senior Principal Researcher at Symantec Research Labs. He obtained his Ph.D. in Computer Science from University of Maryland, College Park under the guidance of Prof. Amol Deshpande and Prof. Jimmy Lin. The title of his Ph.D. thesis was "Minimizing Resource Consumption through Workload Consolidation in Large-scale Distributed Data Platforms". He holds bachelors degree in Computer Science from Indian Institute of Information Technology, Allahabad, India.

He enjoys applying graph theoretical concepts, machine learning, and NLP to solve data science and in general large-scale data management systems problems. He is also interested in designing and building scalable systems for large-scale data analytics. To date, he has published several research papers in top tier conferences and filed numerous patents in the area of data analytics, storage and security.

Previously, through several internships and research positions, he has worked on problems that span a variety of research areas such as Database Systems, Graph Analytics, Data Markets, Information Extraction, Knowledge Management, Security, Artificial Intelligence, Parallel Computing, ML, NLP.

Selected Academic Papers

  • Utility-Driven Graph Summarization
    K. Ashwin Kumar, Petros Efstathopoulos
    To appear at the 45th International Conference on Very Large Database (VLDB 2019)

    In this work, we present a novel approach to summarize a complex graph driven by the objective of maximizing the utility of the calculated graph summary. Subsequently, we propose a utility-driven summarization algorithm, that allows a user to query a graph summary with a specified utility value.

  • Lean On Me: Mining Internet Service Dependencies From Large-Scale DNS Data
    Matteo Dell'Amico, Leyla Bilge, Ashwin Kayyoor, Petros Efstathopoulos, Pierre-Antoine Vervier
    In Proceedings of the 33th Annual computer Security Applications Conference (ACSAC 2017)

    To assess the security risk for a given entity, and motivated by the effects of recent service disruptions, we perform a large-scale analysis of passive and active DNS datasets including more than 2.5 trillion queries in order to discover the dependencies between websites and Internet services.

  • Efficient Routing for Cost Effective Scale-out Data Architectures
    Ashwin Narayan, Vuk Markovic, Natalia Postawa, Anna King, Alejandro Morales, K. Ashwin Kumar, Petros Efstathopoulos
    In Proceedings of the IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS'16)

    In the context of large-scale data architectures, we propose an efficient technique to speedup the routing of a large number of real-time queries while minimizing the number of machines that each query touches (query span).