Research

The Center’s underlying technologies integrate advances in wireless optical communications, edge-cloud computing, situational awareness, and privacy and security, while balancing public sphere data collection requirements with community-defined benefits.

With its extensive network of partners, CS3 delivers innovations across five engineering and scientific areas:

THRUST 1

Connectivity & Wi-Edge

Current Research Projects:

  1. Foundations of mmWave Wireless
  2. Cognitive Wireless and Full Duplex Communication
  3. Edge-Cloud Integration and Management
  4. Joint Communication and Sensing
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THRUST 2

Situational Awareness

Current Research Projects:

  1. Data and Systems Support for Scene and Activity Understanding
  2. Scene and Activity Understanding
  3. Trajectory Analysis and Prediction
  4. Multi-Modal Integration
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THRUST 3

Privacy, Security & Fairness

Current Research Projects:

  1. Analysis of Emerging Smart Streetscape Threats
  2. Mitigation of Emerging Smart Streetscape Threats
  3. Community Legibility of Threats and Guarantees
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THRUST 4

Public Interest Technology

Current Research Projects:

  1. Needs Assessment and Priority Mapping through Participatory Research
  2. Analysis of Community Tradeoffs
  3. Community Co-Production and Co-Design
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THRUST 5

Streetscape Applications

Current Research Projects:

  1. Human-Computer Interaction Design for Smart Streetscapes
  2. Streetscape Application Stack and Runtime Design
  3. Smart Streetscape Operating System or Hypervisor Design
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Recent Publications

Connectivity & Wi-Edge

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Ghasemi, Mahshid, et al. “Real-Time Video Analytics for Urban Safety: Deployment over Edge and End Devices.” Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing [the Hilton Arlington National Landing Arlington VA USA], 2025, pp. 1–17, https://doi.org/10.1145/3769102.3770618.
Dzaferagic, Merim, et al. “Decentralized AI-Control Framework for Multi-Party Multi-Network 6G Deployments.” 2025 IEEE International Conference on Communications Workshops (ICC Workshops) [Montreal, QC, Canada], 2025, pp. 1227–32, https://doi.org/10.1109/ICCWorkshops67674.2025.11162408.
Chizhik, Dmitry, et al. “Backscatter Measurements and Statistical Models for RF Sensing in Indoor Cluttered Environments.” IEEE Transactions on Antennas and Propagation, vol. 73, no. 10, 2025, pp. 8063–75, https://doi.org/10.1109/TAP.2025.3596807.
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Situational Awareness

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Ghasemi, Mahshid, et al. “Real-Time Video Analytics for Urban Safety: Deployment over Edge and End Devices.” Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing [the Hilton Arlington National Landing Arlington VA USA], 2025, pp. 1–17, https://doi.org/10.1145/3769102.3770618.
Dave, Ishan Rajendrakumar, et al. “FinePseudo: Improving Pseudo-Labelling Through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition.” Computer Vision – ECCV 2024, edited by Aleš Leonardis et al., vol. 15066, Springer Nature Switzerland, 2025, pp. 389–408, https://doi.org/10.1007/978-3-031-73242-3_22.
Khalili, Boshra, and Andrew W. Smyth. “AutoDrive-QA: A Multiple-Choice Benchmark for Vision-Language Evaluation in Urban Autonomous Driving.” arXiv, 2025, https://doi.org/10.48550/ARXIV.2503.15778.
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Triedman, Harold, et al. “‘Having Confidence in My Confidence Intervals’: How Data Users Engage with Privacy-Protected Wikipedia Data.” arXiv:2512.06534, arXiv, 6 Dec. 2025, https://doi.org/10.48550/arXiv.2512.06534.
Cummings, Rachel, et al. “Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model.” arXiv, 2025, https://doi.org/10.48550/ARXIV.2505.23682.
Hao, Luoyao, et al. “Advancing IoT System Dependability: A Deep Dive into Management and Operation Plane Separation.” arXiv, 2025, https://doi.org/10.48550/ARXIV.2511.11204.
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Public Interest Technology

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Walter, Hedaya, et al. “Enhancing Urban Data Analysis through Large Language Models: A Case Study with NYC 311 Service Requests.” 2024, https://human-llm-interaction.github.io/workshop/hri24/papers/hllmi24_paper_11.pdf.
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Streetscape Applications

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Fan, Yuang, et al. “Poster: Split-and-Combine Rectification of Ultra-Wide Fisheye Images into Cubemaps.” Proceedings of the 31st Annual International Conference on Mobile Computing and Networking [Kerry Hotel, Hong Kong Hong Kong China], 2025, pp. 1359–61, https://doi.org/10.1145/3680207.3765686.
Chen, Anjiang, et al. “LiDAR Vehicle Trajectory Reconstruction with Arterial Shockwave Detection and Space–Time Analysis.” Transportation Research Record, Sept. 2025, p. 03611981251357924, https://doi.org/10.1177/03611981251357924.
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