SCyPSCenter for Smart Cyber-Physical Systems, UMass Lowell
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Research thrusts

Cyber-physical AI

How does a system that senses the physical world turn what it senses into decisions it can be trusted to act on?

This thrust owns the AI methods the other thrusts apply: sensing and fusion, state estimation, learning across sites without moving data, and computing that keeps its data honest under load. It builds machine awareness of what a physical system is doing, made to feed the people who decide, and it puts limits on what an AI agent may do to infrastructure on its own.

What the group builds

Digital twins, sensing, and state recovery

Live models that stay in step with a grid, a network, or a manufactured part, built from incomplete and sometimes corrupted measurements with physics in the model, not only data: SUMMIT's real-time simulators with hardware in the loop, NATIG's co-simulation of a grid and its wireless network, twins delivered from hybrid clouds, and physics-informed models that predict a part's reliability without destroying it.

Learning without moving the data

Federated learning across utilities, hospitals, and trial sites, so the model travels and the data does not, with the privacy guarantees written in from the start.

High-performance computing and data integrity

Compression and anomaly detection for scientific data, and computing systems that detect silent data corruption before it reaches a result.

Agentic control within limits

AI agents that plan and act on physical systems, from autonomous robotic planning for the Army to grid-aware data centers, with the guardrails that decide what they may do without a person.

Recent papers

The newest work from the faculty on this thrust. 96 papers since 2019 carry one of their names.

  1. C. A. Ng, S. Pagsuyoin, Y. Luo
    Novel microfluidic lab-on-circuit for QCM-P oscillators
    Sensors and Actuators A: Physical, vol. 410, art. no. 118210, Nov. 2026
  2. A. Rezaee, F. Arpanaei, R. McCann, H. Rabbani, J. A. Hernández, M. Brandt-Pearce, V. M. Vokkarane
    QoT-Aware Dynamic Resource Allocation and Grooming in Multi-Band Space-Division Multiplexing Networks
    IEEE/Optica Journal of Optical Communications and Networking, vol. 18, no. 10, Oct. 2026IF 5.1 (2025)
  3. C. Pozzi, C. Ng, S. Lyon, Y. Luo, C. Niezrecki, M. Inalpolat
    A Novel Distributed Sensor Network to Assess Wind Turbine Blade Acoustics for Structural Health Monitoring
    Wind Energy, vol. 29, no. 10, Sept. 2026IF 4.1 (2023)
  4. A. Rezaee, R. McCann, V. M. Vokkarane
    FUSION: A Unified Benchmarking Framework for Reproducible Optical Network Research
    IEEE/Optica Journal of Optical Communications and Networking, vol. 18, no. 9, pp. D90-D105, Sept. 2026 (Special Issue on Benchmarking in Optical Networks)IF 5.1 (2025)
  5. A. Rezaee, F. Arpanaei, R. McCann, L. Nadal, J. A. Hernández, V. M. Vokkarane
    QoT-Aware Spectral and Spatial Scaling Trade-offs in Multi-Band Space Division Multiplexing over EONs [Invited]
    IEEE/Optica Journal of Optical Communications and Networking, vol. 18, no. 8, pp. C160-C172, Aug. 2026IF 5.1 (2025)
  6. B. R. Jyoti Arka, M. Z. Islam, Y. Lin, V. M. Vokkarane, J. Zhao
    Communication Network-Aware Missing Data Recovery for Enhanced Distribution Grid Visibility
    IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5, July 2026
  7. T. Miskell, Y. Luo, P. Li, S. W. Lim
    LLM Based Static Malware Analysis for Windows Portable Executables
    35th International Conference on Computer Communications and Networks (ICCCN), pp. 1-6, July 2026
  8. H. Rabbani, A. Rezaee, H. Rabbani, V. M. Vokkarane, M. Brandt-Pearce
    Experimental Determination of Filter Bandwidth Requirements for Coherent Pluggable Transceivers in Optical Data Center Networks
    IEEE International Conference on High Performance Switching and Routing (HPSR), pp. 1-5, June 2026

All publications

Other thrusts

Most projects cut across two or three of them.

Resilient energy and infrastructureNext-generation networks and trusted devicesPeople in the loop