Grants | Yao Zheng@UHM

DOE Genesis Mission: STRATOS: Security and Trust Runtime Architecture for Time-critical Operational Science

Mon, 17 Aug 2026 10:10:00 -1000

Executive Summary

STRATOS develops a cloud-native, model-agnostic security middleware for protecting AI models used in time-critical scientific and energy-system operations. AI is increasingly embedded in workflows such as grid forecasting, contingency analysis, and operational decision making, creating new risks from adversarial perturbations, inference-time evasion, and backdoor attacks that may remain physically plausible while corrupting model outputs. STRATOS addresses this problem through a physics-grounded DevSecOps architecture that continuously evaluates inference streams, computes certified trust scores, and distinguishes malicious manipulation from legitimate operational variability without requiring modification of the protected model.

The project combines physics-constrained adversarial emulation, imbalance-resilient certified detection, and a multi-tier runtime mitigation pipeline that includes targeted input purification, Control Barrier Function-based graceful degradation, and quarantine or rollback of compromised data and models. The Phase I system will be evaluated across the University of Hawaiʻi at Mānoa campus microgrid and Argonne National Laboratory’s Controller-Hardware-in-the-Loop platform, targeting at least 90% certified detection, no more than 5% false positives, at least 95% interception of synthesized adversarial payloads, and end-to-end latency of 20 ms or less. The longer-term goal is to transition STRATOS toward a federated security architecture for trustworthy AI across DOE scientific computing and critical-infrastructure environments.

CyberAI Innovation: Securing Artificial Intelligence Agents: A Scenario-Based Educational Platform for Future Cybersecurity Professionals

Sat, 15 Aug 2026 15:29:00 -1000

Executive Summary

AI agents that autonomously browse the web, manage email, execute code, and query databases are being deployed across federal agencies at accelerating pace. These agents create a qualitatively new attack surface: a poisoned email can cause an agent to exfiltrate files; a malicious code comment can redirect an execution pipeline; a hidden webpage instruction can hijack a browsing session. Yet cybersecurity education has not kept pace. Existing AI security tools (Lakera Gandalf, OWASP LLM labs, CTF competitions) teach only chatbot-level prompt injection, a single-turn, text-only model that does not prepare students for agent-mediated threats involving tool access, autonomous action, and multi-step reasoning chains.

This project develops AgentSec, a scenario-based educational platform that teaches undergraduate cybersecurity students to identify, exploit, and defend against threats unique to autonomous AI agents, organized into three thrusts. Thrust 1 creates the educational framework, where a trust boundary taxonomy organizes agent vulnerabilities into three progressive categories, i.e., data ingestion, tool-action, and reasoning chain boundaries. This gives students a transferable mental model. Based on the taxonomy, we design a three-module curriculum with six organization-contextualized scenarios, red-team/blue-team experiential learning cycles, and a configurable difficulty framework. Thrust 2 builds the educational technology, where we develop sandboxed environments to enable students’ interaction with real LLM-powered agents with controlled tool access, a layered assessment system with an AI tutoring agent, an instructor analytics dashboard, and a scenario authoring toolkit for community-contributed content. Thrust 3 evaluates impact through a quasi-experimental study, assessing platform quality, student learning, and workforce placement into government and industry CyberAI roles. All code will be open-source, self-hostable via Docker Compose with open-weight models, and released through CLARK for nationwide adoption.

NVIDIA Academic Grant Program: OmniPort: Real-Time Semantic Digital Twins via ISAC and AI-RAN for Smart Ports

Sat, 15 Aug 2026 15:29:00 -1000

Executive Summary

OmniPort develops a real-time intelligent wireless and digital-twin platform for safer and more efficient autonomous operations in container ports. Ports are particularly challenging environments for robots because stacked metal containers obstruct GPS and line-of-sight sensors while producing strong wireless multipath. Instead of treating these reflections solely as interference, OmniPort uses integrated sensing and communication (ISAC) to extract information from routine robot uplink signals, enabling around-corner hazard detection and GPS-free robot localization. An AI-RAN then combines these sensing results with robot mobility and network telemetry to predict wireless conditions, dynamically allocate network resources, and maintain low-latency communication for safety-critical operations.

The project integrates these capabilities into an uncertainty-aware semantic digital twin that represents container geometry, robot movement, hazards, and wireless connectivity in real time. Rather than continuously transmitting camera video, operators can supervise robot fleets through a privacy-preserving VR environment generated from this semantic information, with warnings for collision risks, connectivity degradation, and emerging hazards. The platform uses four on-premises NVIDIA RTX PRO 6000 GPUs to support concurrent ISAC processing, AI-RAN control, NVIDIA Isaac Sim/Cosmos-based digital-twin reasoning, and Omniverse VR visualization, targeting an end-to-end control latency below 50 ms. The resulting technologies, software, and datasets are intended to provide a foundation for intelligent robotic operations in Hawaiʻi’s ports and other complex industrial environments.

Drone-Mounted mmWave Harmonic Radar for Invasive Insect Monitoring

Fri, 07 Nov 2025 00:00:00 -1000

Executive Summary

This project develops an innovative drone-mounted millimeter-wave (mmWave) harmonic radar system for tracking invasive pest insects in Hawai‘i. The technology targets destructive species such as the coconut rhinoceros beetle and melon fly, aiming to improve early detection, optimize control strategies, and protect the islands’ ecosystems and agriculture.

Traditional harmonic radar systems are limited to short ranges and heavy transponders. Our approach integrates 12 GHz/24 GHz phased-array beamforming, miniaturized Nitinol-based transponders, and multi-drone coordination for long-range, real-time tracking of small, fast-moving insects—achieving high precision with minimal behavioral impact.

Research Objectives

Goal: Build and validate a UAV-mounted harmonic radar network for aerial tracking of invasive insects across complex Hawaiian landscapes.

Task 1 – mmWave Beamsteering Harmonic Transceiver

Design a compact 12 GHz phased-array transmitter using COTS modules and a heterodyne architecture for coherent beamforming.

Operates at 12/24 GHz ISM bands
Achieves >10 m range with lightweight, steerable arrays
Enables UAV integration for agile tracking

Task 2 – Ultralight Harmonic Tag

Develop a miniaturized 24 GHz transponder using a hollow bowtie antenna made of shape-memory alloy (Nitinol) and a Schottky diode for harmonic generation.

Tag weight < 1 mg for compatibility with small insects
Structural resilience through Nitinol’s superelasticity
Broadband harmonic reflection optimized for 12→24 GHz doubling

Task 3 – Multi-Drone Localization Network

Implement a distributed multistatic radar system using one TX and multiple RX drones for real-time localization.

Uses synchronized bistatic ranging and multilateration
Integrates GPS time sync and low-latency communications
Coordinates drone formations for continuous insect tracking

Broader Impacts

This system aligns with HISC priorities for early detection and rapid response (Priority 1) and technological innovation for pest management (Priority 2). It promotes cross-disciplinary collaboration among engineers, biologists, and agricultural scientists to create deployable surveillance tools for invasive species control.
Potential benefits include:

Reduced pesticide dependence through precise targeting
Enhanced monitoring of remote or forested regions
Scalable open-source framework for ecological sensing

Team

PI: Dr. Yao Zheng – mmWave radar, phased array, RF sensing
Co-PI: Dr. Haofan Cai – RFID and low-power tag design
Co-PI: Dr. Daniel Jenkins – UAV systems, autonomous sensing, precision agriculture

Collaborators include USDA-PBARC and NIWC Pacific for field validation and data integration.

Timeline (Dec 2025 – Nov 2026)

Phase	Period	Milestone	Lead
Phase I	Months 1–3	Frequency translator upgrade and system specification	UHM ECE
Phase II	Months 4–6	Phased-array prototype and beamforming validation	UHM ECE
Phase III	Months 7–9	Harmonic tag fabrication and field characterization	UHM ECE
Phase IV	Months 10–12	Multi-drone localization demo and final reporting	UHM ECE

Expected Deliverables

Functional 12/24 GHz drone-mounted harmonic radar prototype
Miniaturized Nitinol-based insect tags (< 1 mg)
Validated multi-drone localization system (> 100 m effective range)
Technical documentation and open-source dataset for future HISC programs

📡 Project Lead:
🏛️ Institution: University of Hawai‘i at Mānoa – College of Engineering
🌺 Supported by: Hawai‘i Invasive Species Council (HISC) 2026–2027

Wearable Sweat Sensors for Cattle Heat Resistance and Metabolomics

Fri, 07 Nov 2025 00:00:00 -1000

Executive Summary

This CTAHR CARES pilot project develops non-invasive wearable sweat sensors to monitor heat stress in beef cattle at the molecular level. By coupling commercial sweat and internal temperature sensors with sweat metabolomics and gut microbiome profiling, the team aims to uncover biomarkers that distinguish heat-stressed from heat-resistant animals.

Heat stress in cattle is typically assessed using a combination of visual observation, physiological measurements, and environmental indices such as the Temperature–Humidity Index (THI). These methods are indirect, labor intensive, and often fail to capture individual animal differences in tolerance. This project will generate continuous, individual-level data on sweating, body temperature, movement, and molecular signatures to enable climate-resilient herd management and breeding strategies in Hawaiʻi and beyond.

Problem Statement

Beef cattle in tropical and subtropical environments face increasingly frequent heat-stress events driven by climate change. Current evaluation methods:

Rely on THI and farm-level weather data
Use intermittent measurements of body temperature and respiration
Depend on subjective observation of panting, drooling, and behavior

However:

Individual and breed variation in heat tolerance is large
Early, sub-clinical heat stress is often missed
Invasive or sporadic measurements are not scalable for large herds

There is a critical need for fast, accurate, and non-invasive tools to determine when a specific cow is experiencing heat stress, and to identify animals that remain resilient under extreme conditions.

Research Objectives

Overall Goal: Develop an integrated framework that links wearable sweat and temperature sensing with metabolomics and microbiome data to quantify heat stress and heat resistance in cattle.

Objective 1 – Characterize Heat Stress Physiology

Relate sweating rate, internal body temperature, and movement patterns to environmental conditions (THI, day/night cycles, seasonal variation)
Capture how apocrine sweat glands in cattle respond under different levels of heat load

Objective 2 – Identify Molecular Biomarkers

Perform sweat metabolomics to identify compounds associated with heat stress vs. heat resistance
Analyze gut microbiota (from fecal samples) and blood-based markers
Measure thyroid hormone levels, heat shock proteins (HSPs), and mitochondrial ATP-related gene expression as potential indicators of chronic heat load

Objective 3 – Enable Climate-Resilient Cattle Selection

Integrate physiological and molecular data to identify candidate biomarkers and genetic markers of heat stress and heat resistance
Provide a foundation for future selection and breeding programs targeting climate-resilient cattle in Hawaiʻi’s diverse environments

CyberTraining: Pilot: O-RAN-Based Cyberinfrastructure Training for Future-Generation Wireless Communication and Sensing

Sat, 07 Dec 2024 00:00:00 -1000

Executive Summary

The national spectrum strategy emphasizes spectrum infrastructure and workforce development in the full range of operational, technical, and policy roles to establish U.S. leadership in future-generation (FutureG) wireless techniques. However, achieving the strategic goal of spectrum workforce development involves non-trivial challenges, including limited capacity and availability of advanced wireless cyberinfrastructure (CI) and specialized tools, skills, and knowledge sets to develop, manage, and utilize wireless CI. This project responds to the national call for spectrum workforce development and trains the FutureG workforce by extending their research abilities through a novel ?immersed? approach to promote project-based hands-on learning. An open radio access network (O-RAN) wireless testbed will be utilized to allow trainees to practice the operation and programming of FutureG wireless instruments and develop wireless applications. Cloud-based access to the O-RAN testbed and a suite of template projects will be offered to address the technical barriers and complexities of wireless CI access. New course modules, vertical-integration projects, and summer courses will be offered to both student trainees and existing students at PIs? institutions. The training materials will be disseminated through public platforms including the ACCESS Knowledge Base to train a broader and diverse group of wireless professionals.

Specifically, this pilot project includes three tasks: Task 1 is to extend the abilities of wireless professionals with a publicly and remotely accessible wireless CI based on the O-RAN architecture. The CI integrates advanced RF and computing instruments including NI USRP X410 and 2974 supporting sub-6G Hz to mmWave bands, TMYTEK mmWave BBox at 28GHz and 39GHz, phrased-array beamformer and reconfigurable intelligent surface (RIS), and a GPU server with 8x NVIDIA RTX A5000. Task 2 aims at training wireless professionals with the development of AI/ML tools and services to allow automatic wireless data collection and intelligent analytics. Based on this unique CI, Task 3 develops a suite of hands-on projects to train and educate wireless professionals under different scenarios ranging from basic wireless instrument operation to advanced wireless research.

5G/B5G Intelligent Reflecting Surfaces for Assured Aircraft Mission Readiness

Wed, 25 May 2022 00:00:00 -1000

Executive Summary

To improve Mission Capable (MC) and Aircraft Availability (AA) rates, military aircraft require regular maintenance to ensure flight readiness. When aircraft are on the ground and/or in hangars, the signal between onboard radio systems and ground base stations can be subpar due to undesigned antenna angles as well as blockages by aircrafts’ hulls and/or hangar walls, therefore, causing difficulties for in-situ communication and maintenance. To support 5G/B5G aircraft maintenance and mission readiness use cases, the Joint Base Pearl Harbor-Hickam (JBPHH) 5G initiative is upgrading hangars to support 5G Ultra-Wideband (UW) communication. Typical base stations at these bands may leverage massive antenna arrays to form directional signal beams pointing line-of-sight (LoS) toward receivers, hence amplifying the receive signal strength (RSS) and improving the signal-to-noise ratio (SNR). However, such LoS communication links can have coverage gaps due to the aforementioned challenges. For example, aircraft can create 5G dead zones (e.g. shadowing) for LoS small cells in hangars. Thus, there is a need for beyond LoS signal propagation to increase 5G signal coverage in aircraft maintenance environments.

Naval Information Warfare Center Pacific (NIWC Pacific) will provide project oversight and support to the University of Hawaiʻi at Mānoa (UHM) to demonstrate improved 5G signal coverage using beam-steering Intelligent Reflecting Surfaces (IRS). Our approach to improve signal coverage uses the electrical actuation of liquid metal (LM) to modify signal reflection and thus reconfigure the gain and phase of an IRS to steer a 5G signal between a transmitter and receiver. In Year 1, we will demonstrate increased 5G band 78 (3.5 GHz) small-cell signal coverage with the proposed IRS and accompanying design control software and algorithms. We will also design an IRS for 5G band 260 (39 GHz). In an optional Year 2, we will demonstrate increased 5G band 260 small-cell coverage by creating an IRS optimized for mm-wave signals. We will also show that the IRS’s link-selection properties can enable multi-user multiple-input multiple-output (MU-MIMO) communication for concurrent data transmissions.

CRII: NeTS: Power Efficient Millimeter Wave Data Delivery for Remote Invasive Species Monitoring

Fri, 01 May 2020 00:00:00 -1000

Executive Summary

The growing data resolution in remote sensing spurs the adoption of millimeter-wave (mmWave) communication modules on energy-harvesting devices to increase the data delivery bandwidth. The energy conditions on these devices are not always satisfiable to initiate or maintain the mmWave links and require systems to be capable of anticipating communication failures and take preemptive actions to minimize energy expenditures. Fortunately, the environmental information provided by remote sensors contains sufficient knowledge to enable the design of such systems. The goal of this project is to develop algorithms and tools to exploit this information and augment the solar-harvesting remote invasive species monitoring system in the State of Hawaii with mmWave data delivery capability. The work in this project will enable researchers, industry, and students to realize high bandwidth real-time remote sensing with power-constrained devices in real-world applications. The results of this research will impact fields across scientific, industrial, and military interests, including agriculture, ecology, meteorology, infrastructure, and public utility monitoring, etc., where timely communication of high-resolution sensory data is essential.

The fundamental intuition of the proposed approach is that environmental factors, such as weather conditions, signal blockages, can be recognized via the inherent capability or interactions between the remote sensors. Knowledge of these factors can be utilized to optimize device awakening, beam scanning, and signal amplification, etc., at the physical layer. Three complimentary research thrusts are pursued: 1) extracting the correlation between solar harvesting conditions and mmWave signal attenuations; developing models and circuits to estimate the mmWave signal attenuations at specific solar conditions; 2) designing a distributed sensing architecture to detect mmWave beam blockage and accelerate beam alignment, by exploiting the low-power decimeter band communication implemented by the existing system; 3) formulating and solving a constrained route placement problem for an autonomous aerial data collector to optimize its mmWave signal reception as it maneuvers between sensor clusters and flight restricted regions. All products of this work will be made freely available to the research community, along with documentation and tutorials. The in-lab testbed to be established during the project will be made available online for remote testing. The hardware schematic of the sensor platform, deployment profiles, data traces, and important meta-data will be posted online to spur further use, test, and research to advance the field.