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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

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Biography

Vyas Sekar is the Tan Family Professor of Electrical and Computer Engineering in the ECE Department at CMU with a courtesy appointment in the Computer Science Department. He is also affiliated with Cylab and co-direct the Future of Enterprise Security initiative at Cylab. He works broadly at the intersection of networks, systems, and security.

Vyas is also a cofounder of Rockfish Data and serves as Chief Scientist at Conviva.

Vyas 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

Risk of AI "going rogue is very real," but dire warnings are misplaced, CMU professor says

CBS News Pittsburgh  tv

2026-09-15

The leaders of multiple AI companies are calling for a global slowdown and government regulation and oversight of AI development because of fears that it could literally destroy humanity. Kristine Sorensen spoke with a Carnegie Mellon professor who specializes in AI security to get more understanding of the real risks.

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Can hackers pull your fingerprints from photos on social media? Experts explain.

CBS News  online

2026-06-01

"This sounds like the stuff out of spy novels or 'Mission Impossible,'" said Vyas Sekar, an electrical and computer engineering professor at Carnegie Mellon University. "In theory, it's possible, especially if people are posting high-resolution images."

A hacker also would need to be "fairly determined" and likely choose a "high-value target" that renders a fingerprint valuable, such as someone with access to a high-security facility, Sekar said.

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Power shift: How CMU is helping shape America’s energy evolution

Philadelphia Business Journal  online

2025-07-18

CMU researchers 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.

“Our work has shown that inconsistencies in threat assessments occur because of ad hoc simulation and modeling methodologies, as well as dataset errors,” Sekar said. “This shows the need for the creation of standardized public toolkits and datasets and for recommending ways to increase the accuracy of evaluations. This will enable us, as well as other researchers, to develop more rigorous foundations for securing tomorrow’s electric energy grid.”

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Spotlight

1 min

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 KolterGranger MorganLarry 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

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.

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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.

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Articles

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

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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.

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