Vyas Sekar

Professor, Electrical and Computer Engineering Carnegie Mellon University

  • Pittsburgh PA

Vyas Sekar seeks to develop more rigorous foundations for securing tomorrow’s electric energy grid

Contact

Carnegie Mellon University

View more experts managed by Carnegie Mellon University

View all Experts

Biography

Vyas Sekar and fellow CMU researchers Lujo Bauer and Larry Pileggi are calling on the research and policy communities to develop more comprehensive and accurate grid evaluation frameworks and datasets, and for updating threat models and grid resiliency requirements to match cyber attackers realistic capabilities.

Sekar received his Ph.D. from the computer science department at Carnegie Mellon University in 2010. He earned his bachelor's degree from the Indian Institute of Technology Madras, where he was awarded the President of India Gold Medal. His work has been recognized with best paper awards at ACM SIGCOMM, ACM CoNext, and ACM Multimedia.

Areas of Expertise

Network Monitoring and Measurement
Cybersecurity
Information Networking
Security and Systems
Networking
Network Security
Distributed Systems
Computer Security
Content/Video Delivery Systems
Middleboxes
Energy Grid Security

Media Appearances

Power Shift: How CMU Is Leading America’s Energy Evolution

CMU News  online

2025-07-11

From reimagining AI data centers to modernizing and securing the electric grid, CMU researchers are working on practical solutions to pressing challenges in how the U.S. produces, moves and secures energy.

Lujo Bauer, Larry Pileggi and Vyas Sekar are calling on the research and policy communities to develop more comprehensive and accurate grid evaluation frameworks and datasets, and for updating threat models and grid resiliency requirements to match cyber attackers realistic capabilities.

View More

This is AI's brain on AI

Axios  online

2024-07-27

The bottom line: AI-generated data is "an amazingly useful technology, but if you use it indiscriminately, it's going to run into problems," Vyas Sekar, a professor of electrical and computer engineering at Carnegie Mellon University, told Axios.

View More

7 ways to protect your iPhone from being hacked

Business Insider  online

2022-08-19

Vyas Sekar, a professor of electrical and computer engineering at Carnegie Mellon University, says staying safe is all about "good digital hygiene."

View More

Show All +

Spotlight

1 min

Power Shift: How CMU Is Leading America’s Energy Evolution

Carnegie Mellon University, long known for its prowess in computer science and engineering, is now emerging as a key innovator within America’s energy landscape. As AI models grow more powerful, so too does their appetite for energy, straining an aging and outdated grid and prompting urgent questions about infrastructure, security and access. From reimagining AI data centers to modernizing and securing the electric grid, CMU researchers are working on practical solutions to pressing challenges in how the U.S. produces, moves and secures energy. Learn what CMU experts have to say about their Work That Matters.

Vyas SekarZico KolterDimitrios SkarlatosGranger MorganAudrey Kurth CroninLarry Pileggi

Media

Social

Industry Expertise

Computer/Network Security
Education/Learning

Accomplishments

SIGCOMM Rising Star Award

2016

NSA Science of Security Award

2016

President of India Gold Medal

2003

Education

Carnegie Mellon University

Ph.D.

Computer Science

2010

Indian Institute of Technology, Madras

BTech

Computer Science

2003

Patents

Timeline framework for time-state analytics

US11943123

Determining a time-state metric includes receiving a stream of raw data values of an attribute. Each received raw data value of the attribute is associated with a timestamp. It further includes converting the received stream of raw data values into a timeline representation of the attribute over time. The timeline representation comprises a sequence of spans. A span comprises a span start time, a span end time, and a span value. The span value comprises an encoding of one or more values of the attribute over a time interval determined by the span start time and the span end time. It further includes determining a time-state metric according to a timeline request configuration. The timeline request configuration comprises one or more timeline operations. The time-state metric is computed at least in part by performing a timeline operation on the timeline representation of the attribute.

View more

Reconfigurable wireless data center network using free-space optics

US10924183

A reconfigurable free-space optical inter-rack network includes a plurality of server racks, each including at least one switch mounted on a top thereof, where each top-mounted switch includes a plurality of free-space-optic link connector, each with a free-space optical connection to a free-space-optic link connector on another top-mounted switch, a single ceiling mirror above the plurality of server racks that substantially covers the plurality of server racks, wherein the single ceiling mirror redirects optical connections between pairs of free-space-optic link connectors to provide a clear lines-of-sight between each pair of connected free-space-optic link connectors, and a controller that preconfigures a free-space optical network connecting the plurality of server racks by establishing connections between pairs of free-space-optic link connectors, and that reconfigures connections between pairs of free-space-optic link connectors in response to network traffic demands and events.

View more

Articles

CANdid: A Stealthy Stepping-stone Attack to Bypass Authentication on ECUs

Journal on Autonomous Transportation Systems

2024

A high-entropy source of randomness is an essential component in any secure protocol, required to ensure that protocol elements, such as cryptographic keys, nonces, or salts, are unpredictable for the attackers. Resource-constrained embedded devices, such as Electronic Control Units (ECUs) in modern vehicles, often utilize weak sources of randomness due to the unavailability of true sources of randomness. In this article, we illustrate the ability of a relatively simple adversary to exploit this weakness within ECUs of vehicles produced by major manufacturers.

View more

Why spectral normalization stabilizes gans: Analysis and improvements

Advances in Neural Information Processing Systems

2021

Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs). However, current understanding of SN's efficacy is limited. In this work, we show that SN controls two important failure modes of GAN training: exploding and vanishing gradients. Our proofs illustrate a (perhaps unintentional) connection with the successful LeCun initialization. This connection helps to explain why the most popular implementation of SN for GANs requires no hyper-parameter tuning, whereas stricter implementations of SN have poor empirical performance out-of-the-box. Unlike LeCun initialization which only controls gradient vanishing at the beginning of training, SN preserves this property throughout training. Building on this theoretical understanding, we propose a new spectral normalization technique: Bidirectional Scaled Spectral Normalization (BSSN), which incorporates insights from later improvements to LeCun initialization: Xavier initialization and Kaiming initialization

View more

Powered by