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ASU Engineering | Lalitha Sankar

Lalitha Sankar

ls_photo_dec2015Lalitha Sankar is an Associate Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University. She was an Assistant Professor at ASU from 2012 to 2016. Prior to that she was a Research Scholar in the Department of Electrical Engineering at Princeton University working with H. Vincent Poor. She was also a Science and Technology Teaching and Research Fellow supported by the Council on Science and Technology at Princeton University.

She graduated with a Ph.D from  Rutgers University, where she worked with Narayan Mandayam while collaborating with Gerhard Kramer (then at Bell Labs). Prior to that, Sankar was a Senior Member of Technical Staff at AT&T Shannon Labs, Florham Park, NJ, where she worked on design, development and prototyping of next-generation wired and wireless systems such as multi-band software radios and DSL modems. This was preceded by a year developing signal processing algorithms for the first digital camera prototype developed at Polaroid Corporation Engineering R&D in Cambridge, MA. Lalitha has a master’s degree from the Department of Electrical Engineering at the University of Maryland, Baltimore County and a bachelor’s degree in engineering physics is from the Indian Institute of Technology, Bombay, India.

Lalitha Sankar is a recipient of the 2014 NSF CAREER award. She received the best paper award from the IEEE Globecom 2011 for her paper on side-information privacy with R. Tandon and H. V. Poor. For her doctoral work, she received the 2007-2008 Electrical Engineering Academic Achievement Award from Rutgers University. For a more detailed CV, please click here.

News

Research interests

  • Cyber-security, privacy, and the big data problem in the smart grid
  • Privacy in Large Datasets (Databases), Smart Grid, Healthcare Records, Social Networks
  • Game-theoretic models for Privacy
  • Information Secrecy in Wireless Networks
  • Network Information Theory
  • Relaying and User-Cooperative Communications
  • Resource Allocation for Wireless Networks
  • Cooperative Game Theory as applied to Wireless Networks

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Contact

Lalitha Sankar
Assistant Professor

Arizona State University
School of Electrical, Computer, and Energy Engineering

Engineering Research Center

551 E. Tyler Mall, Room 585,

Tempe, AZ 85281 [map]

Phone: 480-965-4953
lalithasankar@asu.edu

Publications & Patents

Discoveries related to the study the utility and privacy in databases.

Teaching

Overview of courses and topics taught at Arizona State University and as a Princeton CST Fellow.

Collaborators

Working together to take research to the next level.

Towards Real-Time Integration of Synchrophasor Data

Deployment of synchrophasor infrastructure is occurring at an exceptionally fast rate in the US power grid; especially at the transmission and the sub-transmission networks. The world’s first three-phase phasor measurement unit (PMU)-only linear state estimator has been developed and is running successfully at Dominion Virginia Power. However, the data obtained from PMUs has been primarily used for forensics analysis in the past; i.e., after an undesirable event has occurred. This PSERC S-74 project is a step towards the utilization of PMU data in near-real-time environment. The main focus of this project is to develop algorithms that can distinguish normal system operations from anomalous system behavior using synchrophasor data; and consequently, enhance situational awareness for operational decision making.

A Class of Parameterized Loss Functions for Classification: Optimization Tradeoffs and Robustness Characteristics

We analyze the optimization landscape of a recently introduced tunable class of loss functions called α-loss, α∈(0,∞], in the logistic model. This family encapsulates the exponential loss (α=1/2), the log-loss (α=1), and the 0-1 loss (α=∞) and contains compelling properties that enable the practitioner to discern among a host of operating conditions relevant to emerging learning methods. Specifically, we study the evolution of the optimization landscape of α-loss with respect to α using tools drawn from the study of strictly-locally-quasi-convex functions in addition to geometric techniques. We interpret these results in terms of optimization complexity via normalized gradient descent.

High-Dimensional Spatio-Temporal Data Science for a Resilient Power Grid: Towards Real-Time Integration of Synchrophasor Data

The potential real-time situational awareness enabled by phasor measurements has been impeded by the massive scale of the time-series PMU data and have limited its use to passive, post-event forensics. The Institute meets this need for PMU-based real-time decision-making by examining five critical problems: (i) ensure data quality against bad, missing, or stale data; (ii) exploit the fine granularity of PMU data to track real-time changes in network parameters; (iii) detect, identify, localize, and visualize oscillation and failure events; (iv) assess and visualize cybersecurity threats and countermeasures specific to PMUs; and (v) create synthetic PMU datasets for testing and validation.

Relay and User Cooperative Networks

While cooperation can increase diversity in wireless networks, the tradeoff at every node of using resources for self vs. cooperative transmissions can make dedicated relays desirable. In my doctoral work, I compare the diversity gains achieved by inter-user...

Network Information Theory

The capacity region of multi-terminal networks remains a long-standing open problem, with the exception of a few classes of networks. With increasing demand for wireless data networks, developing fundamental performance limits is both essential and imperative to...

Information Security in Relay Networks

In contrast to and in addition to the traditional link layer cryptographic schemes, a relay can exploit the noisy physical channel to assist in ensuring the secrecy of data packets from untrusted eavesdropping nodes. In [1], we develop optimal source and relay...