Bio

B.S. ​Indian Institute of Technology, Madras, 1984M.S. ​Duke University, 1985Ph.D. ​Duke University, 1988

For information related to the late Professor Veeraraghavan's work, please contact ece-dept-chair@virginia.edu.

Malathi Veeraraghavan's laboratory at UVA is called High-Speed Networks (HSN). Her research group currently works on network security, software-defined networks, and networks for smart/connected communities and industrial robotics. She has recently started working on precision oncology. She leads a large DARPA project for cyber hunting at scale using federated privacy-preserving machine-learning methods. She received her BTech degree from Indian Institute of Technology (Madras) in 1984, and MS and Ph.D. degrees from Duke University in 1985 and 1988, respectively. After a ten-year career at Bell Laboratories, she served on the faculty at Polytechnic University, Brooklyn, New York from 1999-2002, where she won the Jacobs award for excellence in education in 2002. She served as Director of the Computer Engineering Program at U.Va from 2003-2006. She holds 30 patents, has over 130 publications and has received six Best paper awards. She served as the Technical Program Committee Co-Chair for the High-Speed Networking Symposium in IEEE ICC 2013, as Technical Program Committee Chair for IEEE ICC 2002 and Associate Editor for the IEEE/ACM Transactions on Networking. She was General Chair for IEEE Computer Communications Workshop in 2000, and served as an Area Editor for IEEE Communication Surveys. She served as Editor of IEEE ComSoc e-News and as an Associate Editor of the IEEE Transactions on Reliability from 1992-1994.

Research Interests

  • Cyber Security
  • Computer Networks
  • Precision Oncology using Electronic Health Record (EHR) data analysis

Selected Publications

  • “A Dynamic Network Design for High-Speed Enterprise Access Links,” in Proc. of IEEE Globecom 2015, San Diego, US, Dec. 2015. Xiaoyu Wang, Malathi Veeraraghavan, Maite Brandt-Pearce, Takahiro Miyazaki, Naoaki Yamanaka, Satoru Okamoto and Ion Popescu.
  • “A High-Performance OpenFlow Software Switch,” The 2016 IEEE High Performance Switching and Routing (HPSR), pp. 86-92, June 2016. Reza Rahimi, Malathi Veeraraghavan, Yoshihiro Nakajima, Hirokazu Takahashi, Yusuke Nakajima, Satoru Okamoto, Naoaki Yamanaka
  • “A Cross-layer Design for Large Transfers in SDNs,” International Conference on Ubiquitous and Future Networks (ICUFN), 2016. F. Al-Ali M. Veeraraghavan
  • A Multi-domain SDN for Dynamic Layer-2 Path service. In Proceedings of the Fifth International Workshop on Network-Aware Data Management (NDM ’15). S. Tepsuporn, F. Al-Ali, M. Veeraraghavan, X. Ji, B. Cashman, A. J. Ragusa, L. Fowler, C. Guok, T. Lehman, and X. Yang. 2015.
Google Scholar

Courses Taught

  • Computer Networks
  • From Data to Knowledge

Featured Grants & Projects

  • US Ignite: Collaborative Research: Track 1: Industrial Cloud Robotics across Software Defined Networks

    PI: Malathi Veeraraghavan; Award: 1531065; $424,261.00; Started: August 24, 2015 Co-PIs: Shaun Edwards, SwRI, & Andrea Fumagalli, UTD

    Currently, industrial robots are cost-effective for repetitive and high-volume tasks such as welding and painting, but not for lower-volume, mixed-part production. The need for robotic part handling for unstructured industrial applications is diverse. In manufactured-goods distribution centers, where multiple bins are presented to an operator, a human is required to handle a range of parts that must be boxed and shipped. In the reclamation and recycling industry, humans sort waste streams of mixed products on conveyor belts. Assembly and kitting operations in manufacturing are termed robotic opportunities but they require a solution for handling many part types in the same work-cell. This project will research and integrate technologies to enable the use of industrial robots for low-volume mixed-part production tasks. The proposed solution will include 3D image sensors and high-speed flexible networking, cloud computing, and industrial robots. The inclusion of cutting-edge new software such as the Robot-Operating System Industrial (ROS-I) and Cloud Computing platforms offer excellent educational opportunities for both undergraduate and graduate students. The software developed in this project will be widely distributed to enable further innovations by other teams. The project objective is to develop cloud robotics applications that leverage high-performance computing and high-speed software-defined networks (SDN). Specifically, the target applications combine big-data analytics of sensor data (of the type collected from factory floors) with the control of industrial robots for low-volume, mixed-part production tasks. Cloud computers located at a remote facility relative to the factory floor on which industrial robots operate can be used for compute-intensive applications such as object identification from 3D sensor data, and grasp planning for the robots to perform object manipulation. The project methods will consist of (i) integrating ROS-I components and developing new software as required to transmit the 3D sensor data to remote computers, running the object identification and grasp planning applications, and returning robot instructions to the original site, (ii) running this software on geographically distributed compute clouds, (iii) collecting measurements and enhancing the software to meet real-time delay requirements. The technical challenge lies in meeting these stringent real-time requirements. For example, high-speed networks with the flexibility to connect arbitrary factory floors and datacenters are needed to transfer the 3D sensor data quickly to the remote cloud computers and to deliver the computed robot instructions(hence, SDN).