About

Ramesh Govindan holds the Northrop Grumman Chair in Electrical and Computer Engineering and is a Professor of Computer Science and Electrical and Computer Engineering at USC. The page lists him among the faculty members of the Thomas Lord Department of Computer Science at USC Viterbi School of Engineering. However, the provided text does not include any detailed information about his research focus, background, or key contributions.

Research topics

• Computer Science
• Artificial Intelligence
• Computer Security
• Distributed computing
• Computer network
• Algorithm
• Telecommunications
• Real-time computing
• Simulation
• Computer vision
• Engineering

Selected publications

• Near-optimal Online Traffic Engineering

  arXiv (Cornell University) · 2026-05-15

  preprintOpen accessSenior author

  Most deployed WAN Traffic Engineering (TE) systems use a logically centralized controller that periodically gathers traffic demands, runs a TE optimization or heuristic, and then programs the network. At scale, these solutions can be sub-optimal, and can take minutes to react to demand changes or failures. In this paper, we introduce OnlineTE, a system that reacts immediately to demand changes and failures, and delivers near-optimal solutions within seconds of a change. OnlineTE builds on the theory of optimization decomposition to devise scalable, near-optimal, distributed TE solvers for path-based MLU and Max-flow problems. In OnlineTE, each switch solves part of the optimization, and a central coordinator orchestrates the progress of the switches. As such, a switch can trigger a re-optimization as soon as it notices a demand change or failure, enabling high reactivity. OnlineTE scales to large WANs, and its compute requirements are well below the capabilities of modern WAN switches. It also enables a new opportunity, edge-based TE, which can utilize resources more efficiently than today's path-based approaches. On a testbed emulation of a 750-node WAN topology, OnlineTE can outperform the state-of-the-art by up to an order of magnitude.

  PublisherDOI

• Near-optimal Online Traffic Engineering

  ArXiv.org · 2026-05-15

  articleOpen accessSenior author

  Most deployed WAN Traffic Engineering (TE) systems use a logically centralized controller that periodically gathers traffic demands, runs a TE optimization or heuristic, and then programs the network. At scale, these solutions can be sub-optimal, and can take minutes to react to demand changes or failures. In this paper, we introduce OnlineTE, a system that reacts immediately to demand changes and failures, and delivers near-optimal solutions within seconds of a change. OnlineTE builds on the theory of optimization decomposition to devise scalable, near-optimal, distributed TE solvers for path-based MLU and Max-flow problems. In OnlineTE, each switch solves part of the optimization, and a central coordinator orchestrates the progress of the switches. As such, a switch can trigger a re-optimization as soon as it notices a demand change or failure, enabling high reactivity. OnlineTE scales to large WANs, and its compute requirements are well below the capabilities of modern WAN switches. It also enables a new opportunity, edge-based TE, which can utilize resources more efficiently than today's path-based approaches. On a testbed emulation of a 750-node WAN topology, OnlineTE can outperform the state-of-the-art by up to an order of magnitude.

  PublisherOA PDF

• DeGenTWeb: A First Look at LLM-dominant Websites

  arXiv (Cornell University) · 2026-04-30

  preprintOpen access

  Many recent news reports have claimed that content generated by large language models (LLMs) is taking over the web. However, these claims are typically not based on a representative sample of the web and the methodology underlying them is often opaque. Moreover, when aiming to minimize the chances of falsely attributing human-authored content to LLMs, we find that detectors of LLM-generated text perform much worse than advertised. Consequently, we lack an understanding of the true prevalence and characteristics of LLM content on the web. We describe DeGenTWeb which systematically identifies LLM-dominant websites: sites whose content has been generated using LLMs with little human input. We show how to adapt detectors of LLM-generated text for use on web pages, and how to aggregate detection results from multiple pages on a site for accurate site-level categorization. Using DeGenTWeb, we find that LLM-dominant sites are highly prevalent both in data from Common Crawl and in Bing's search results, and that this share is growing over time. We also show that continuing to accurately identify such sites appears challenging given the capabilities of the latest LLMs.

  PublisherDOI

• DeGenTWeb: A First Look at LLM-dominant Websites

  arXiv (Cornell University) · 2026-04-30

  articleOpen access

  Many recent news reports have claimed that content generated by large language models (LLMs) is taking over the web. However, these claims are typically not based on a representative sample of the web and the methodology underlying them is often opaque. Moreover, when aiming to minimize the chances of falsely attributing human-authored content to LLMs, we find that detectors of LLM-generated text perform much worse than advertised. Consequently, we lack an understanding of the true prevalence and characteristics of LLM content on the web. We describe DeGenTWeb which systematically identifies LLM-dominant websites: sites whose content has been generated using LLMs with little human input. We show how to adapt detectors of LLM-generated text for use on web pages, and how to aggregate detection results from multiple pages on a site for accurate site-level categorization. Using DeGenTWeb, we find that LLM-dominant sites are highly prevalent both in data from Common Crawl and in Bing's search results, and that this share is growing over time. We also show that continuing to accurately identify such sites appears challenging given the capabilities of the latest LLMs.

  PublisherOA PDF

• SplatPose: On-Device Outdoor AR Pose Estimation Using Gaussian Splatting

  2025-10-25

  articleOpen accessSenior author

  Outdoor AR applications on mobile devices need accurate estimates for the pose of the device. In this paper, we develop SplatPose, a novel pose estimation technique that uses a data-driven 3D modeling technique called Gaussian Splatting. SplatPose uses a trained Gaussian Splatting model to render an image at an estimated device location, then matches features with the camera image to estimate pose. % Because this matching can be fast, SplatPose can, in theory, estimate pose entirely on a mobile device, while existing approaches cannot. To this end, SplatPose trains Gaussian Splatting models to be robust to appearance changes, thereby improving accuracy. It also incorporates a novel fast renderer to improve rendering speed. Using an AR pose estimation benchmark dataset, we show that SplatPose outperforms the state-of-the-art in terms of accuracy, and is up to an order of magnitude faster on a mobile device.

  PublisherDOI

• Firefly: Scalable, Ultra-Accurate Clock Synchronization for Datacenters

  2025-08-27 · 1 citations

  articleOpen access

  Cloud-based financial exchanges require sub-10ns device-to-device clock synchronization accuracy while adhering to Coordinated Universal Time (UTC). Existing clock sync techniques struggle to meet this demand at scale and are vulnerable to clock drift, jitter, and path asymmetries. Firefly, a software-driven datacenter clock sync system, scalably, cost-effectively, and reliably achieves very high clock sync accuracy. It employs a distributed consensus algorithm on a random overlay graph to rapidly converge to a common time while applying gradual adjustments to device hardware clocks. To realize consistent sync-to-UTC (external sync) across devices while maintaining a stable device-to-device internal sync, Firefly uses a novel technique, layered synchronization, that decouples internal and external syncs. In a 248-machine Clos network, Firefly achieves sub-10ns device-to-device and ≤1μs device-to-UTC sync, and is resilient to time server failure and unstable clocks.

  PublisherOA PDFDOI

• Tiered Cloud Routing: Methodology, Latency, and Improvement

  ACM SIGMETRICS Performance Evaluation Review · 2025-06-16

  article

  Large cloud providers including AWS, Azure, and Google offer two tiers of network services to their customers: WAN-transit service and inet-transit service. Little is known about how each cloud provider offers different transit services, how well these services work, and whether the quality of those services can be further improved. In this work, we conduct a large-scale study to answer these questions. Using RIPE Atlas probes as vantage points, we explore how traffic enters and leaves the WAN of each of the three clouds. In addition, we measure the access latencies of these two network services of each cloud and compare them with emulated alternative routing strategies.

  PublisherDOI

• CATS: A framework for Cooperative Autonomy Trust & Security

  ArXiv.org · 2025-03-01

  preprintOpen accessSenior author

  With cooperative perception, autonomous vehicles can wirelessly share sensor data and representations to overcome sensor occlusions, improving situational awareness. Securing such data exchanges is crucial for connected autonomous vehicles. Existing, automated reputation-based approaches often suffer from a delay between detection and exclusion of misbehaving vehicles, while majority-based approaches have communication overheads that limits scalability. In this paper, we introduce CATS, a novel automated system that blends together the best traits of reputation-based and majority-based detection mechanisms to secure vehicle-to-everything (V2X) communications for cooperative perception, while preserving the privacy of cooperating vehicles. Our evaluation with city-scale simulations on realistic traffic data shows CATS's effectiveness in rapidly identifying and isolating misbehaving vehicles, with a low false negative rate and overheads, proving its suitability for real world deployments.

  PublisherOA PDFDOI

• Tiered Cloud Routing: Methodology, Latency, and Improvement

  2025-06-04

  article

  Large cloud providers including AWS, Azure, and Google offer two tiers of network services to their customers: WAN-transit service and inet-transit service. Little is known about how each cloud provider offers different transit services, how well these services work, and whether the quality of those services can be further improved. In this work, we conduct a large-scale study to answer these questions. Using RIPE Atlas probes as vantage points, we explore how traffic enters and leaves the WAN of each of the three clouds. In addition, we measure the access latencies of these two network services of each cloud and compare them with emulated alternative routing strategies.

  PublisherDOI

• Preprint: Poster: Did I Just Browse A Website Written by LLMs?

  arXiv (Cornell University) · 2025-07-18

  preprintOpen access

  Increasingly, web content is automatically generated by large language models (LLMs) with little human input. We call this "LLM-dominant" content. Since LLMs plagiarize and hallucinate, LLM-dominant content can be unreliable and unethical. Yet, websites rarely disclose such content, and human readers struggle to distinguish it. Thus, we must develop reliable detectors for LLM-dominant content. However, state-of-the-art LLM detectors are inaccurate on web content, because web content has low positive rates, complex markup, and diverse genres, instead of clean, prose-like benchmark data SoTA detectors are optimized for. We propose a highly reliable, scalable pipeline that classifies entire websites. Instead of naively classifying text extracted from each page, we classify each site based on an LLM text detector's outputs of multiple prose-like pages to boost accuracies. We train and evaluate our detector by collecting 2 distinct ground truth datasets totaling 120 sites, and obtain 100% accuracies testing across them. In the wild, we detect a sizable portion of sites as LLM-dominant among 10k sites in search engine results and 10k in Common Crawl archives. We find LLM-dominant sites are growing in prevalence and rank highly in search results, raising questions about their impact on end users and the overall Web ecosystem.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: CNS Core: Medium: Network-Enabled Cooperative Perception for Future Autonomous Vehicles

  NSF · $900k · 2020–2026

• NetSE: Medium: Collaborative Research: Green Edge Networks

  NSF · $600k · 2009–2013

• CPS: Synergy: Collaborative Research: Harnessing the Automotive Infoverse

  NSF · $467k · 2013–2019

• Collaborative Research: CNS Core: Medium: Panoptes: Next Generation Multi-Perspective Video Delivery at Internet Scale

  NSF · $600k · 2020–2025

• SENSORS: Robust and Efficient Data Dissemination for Data-Centric Storage

  NSF · $500k · 2003–2007

Frequent coauthors

• Deborah Estrin

  Cornell University

  95 shared

• Hang Qiu

  University of California, Riverside

  46 shared

• Jeongyeup Paek

  Chung-Ang University

  45 shared

• Gaurav S. Sukhatme

  45 shared

• Scott Shenker

  University of California, Berkeley

  44 shared

• Marcos A. M. Vieira

  Universidade Federal de Minas Gerais

  38 shared

• Krishna Chintalapudi

  Microsoft (United States)

  36 shared

• John Heidemann

  33 shared

Labs

• Artificial Intelligence, Machine Learning, Privacy/FATEPI

Education

• Ph.D., Computer Science

  University of Southern California

  1995

• M.S., Computer Science

  University of Southern California

  1991

• B.S., Electrical and Electronics Engineering

  University of Madras

  1988

Awards & honors

• 2014 Indian Institute of Technology Distinguished Alumnus Aw…
• 2014 Institution of Electrical and Electronics Engineers Fel…
• 2014 Internet Research Task Force (IRTF) Applied Networking…
• 2011 Association of Computing Machinery Fellow of the ACM
• 2008 IEEE Symposium on Information Processing in Sensor Netw…