Digital twins for complex systems
I connect physical models, live sensing, machine learning, and human-facing visualization to make energy and infrastructure systems more observable and actionable.
Batteries · smart infrastructure · urban mobility
AI-enabled systems researcher
I build intelligent systems that help the physical world see, predict, and decide.
I'm Judith Nkechinyere Njoku-Vowels, PhD, a Distinguished Postdoctoral Fellow at the University of Wyoming. My work integrates digital twins, trustworthy AI, computer vision, and simulation for resilient transportation, energy, and infrastructure.
Research signature
From sensing and models to decisions people can trust.
1,350+Google Scholar citations
14h-index
$150Kresearch projects led
15+international interns supervised
Researcher · Engineer · Mentor
I learned to see intelligence as part of a system, not a model sitting alone.
I bring together artificial intelligence, simulation, sensing, and visualization to make complex systems more understandable, trustworthy, and useful. My research asks how intelligent systems can observe complex physical environments, explain what they infer, and support decisions under real operational constraints.
Meet Dr. Judith Njoku-VowelsResearch agenda
The questions I keep returning to.
I develop dependable AI-enabled systems that bridge computation and the physical world while keeping people, uncertainty, and real deployment constraints in the loop.
Trustworthy AI for autonomy
I develop explainable, uncertainty-aware, and deployment-conscious methods for safety-critical cyber-physical systems, not models that stop at benchmark accuracy.
XAI · uncertainty · resilient decision-making
Robust visual perception
I build efficient computer vision systems that restore and interpret degraded real-world scenes, especially for autonomous systems operating in adverse weather.
Image restoration · autonomous perception · edge AI
Selected systems
Ideas I have turned into working systems.
I work across the full research arc: framing a real problem, developing methods, building a system, evaluating it, and communicating what it enables.
Autonomous perceptionNTIRE image-restoration workshop at CVPR 2026
OmniRestore
A lightweight, parameter-efficient framework for universal adverse-weather image restoration, developed to strengthen visual perception in autonomous cyber-physical systems.
Explore this research system
Energy systemsThe crux of my PhD, with paper and repository in preparation
BatteryMetrix
My doctoral research: a user-centered digital twin for predictive, explainable, and secure battery management, spanning state estimation, prognosis, XAI, and immersive visualization.
Explore this research system
Smart communitiesActive research · University of Wyoming
PANDA
A lightweight predictive digital twin for multi-horizon occupancy and turnover forecasting in retail parking facilities.
Explore this research system
Infrastructure resilienceThree-bridge research validation
BridgeSync
A digital twin framework for intelligent bridge monitoring that brought multidisciplinary teams together around structural data from three bridges in South Korea.
Explore this research system
Transportation perceptionIEEE ICTC 2022, Jeju Island
SmartParking
A separate computer-vision project comparing six detector configurations for vehicles, pedestrians, cyclists, bicycles, buses, and traffic signs using the TraPedesVeh mini-dataset.
Explore this research systemScholarship
The scholarship behind the systems.
Explore my scholarship by research area or by the real-world problem addressed. Each record links to its paper or an exact-title scholarly record.
OmniRestore: A Parameter-Efficient Framework for Universal Adverse-Weather Image Restoration
IEEE/CVF CVPR Workshops
MetaWatch: Trends, Challenges, and Future of Network Intrusion Detection in the Metaverse
IEEE Internet of Things Journal
Explainable Data-Driven Digital Twins for Predicting Battery States in Electric Vehicles
IEEE Access
Recent news
What I am presenting, publishing, and building.
PANDA presented at i3CE 2026
I presented our predictive smart-parking digital twin virtually at the ASCE International Conference on Computing in Civil Engineering.
OmniRestore presented at CVPR Workshops
I presented our parameter-efficient adverse-weather image-restoration framework at the Colorado Convention Center in Denver.
Invited University of Wyoming REU colloquium talk
I delivered Can AI Drive in the Storm? Restoring Vision for Autonomous Vehicles to an undergraduate research cohort.
Remote practical training research accepted
Our paper on developing future workforce skills through remote practical training was accepted for publication in Computers and Education Open.
Build something consequential
Let's advance intelligent systems that earn trust.
I welcome conversations about research collaborations, invited talks, interdisciplinary projects, and student mentorship.
jnjoku@uwyo.edu