Trustworthy & Applied AI
Reliable decision support, explainable multimodal learning, and human-centered AI for public-good applications.
Truman State University · Computer & Data Sciences
Intelligence that
meets the world.
MI² Lab studies practical, trustworthy, and resource-aware AI—where machine intelligence interacts with people, devices, and high-stakes environments.
Lab DirectorDirector
Chen-Yeou Yu, Ph.D.
Assistant Professor · Truman State University
I build intelligent systems that remain useful beyond the lab: on constrained devices, amid uncertainty, and in collaboration with people. My work spans applied AI, cybersecurity, edge and IoT systems, multimodal learning, and intelligent decision support.
Before joining Truman, I completed my Ph.D. in Computer Science at Iowa State University and my M.S. in Computer Science at New York University. I have also worked at Intel, Pacific Northwest National Laboratory, and Academia Sinica.
Research
Systems designed for the messy middle.
Between a model and its real-world consequences lies the work we care about most.
Edge, IoT & Cyber-Physical Systems
Resource-aware learning and intelligent services that operate close to vehicles, drones, sensors, and mobile users.
AI Security & Digital Trust
Lightweight defenses against scam messages, fraudulent services, and evolving online threats.
Intelligent Software Systems
AI-assisted software engineering, Agile DevOps, and deployable systems that connect research with student learning.
Selected projects
From signals to decisions.
Smart Cities · 2022
Situ-aware traffic intelligence
Multi-agent reinforcement learning and edge servers coordinate vehicle rerouting while accounting for efficiency, incentives, and changing road conditions.
Read the paper →EE
Emergency AI · 2025
TruRescue & TruCrisisAware
Federated, generative, and human-centered AI for drone-assisted road emergencies and disaster response.
A path through systems
From NYU to Iowa State to Truman
Computer science, mobile systems, edge intelligence, and the human decisions that connect them.
Publications
Published work.
Peer-reviewed works listed on Google Scholar. Accepted, in-press, and submitted manuscripts are intentionally excluded.
- 2025
TruScamBuster: Federated Latent-Thought Models for Zero-Shot Scam SMS Detection on Mobile Devices with Lightweight LLMs
Chen-Yeou Yu, Jiaxuan He, Wensheng Zhang · ICARTI 2025, ACM ICPS
- 2025
Explainable and Efficient Hateful Meme Detection using Multimodal Vision-Language Fusion
Jiaxuan He, Chen-Yeou Yu, Wensheng Zhang · ICARTI 2025, ACM ICPS
- 2025
TruRescue: A Federated Learning-Based Drone-Driven Road Emergency Assistant with Generative AI
Chen-Yeou Yu, Jiaxuan He, Olivia Lewis, Wensheng Zhang · CSCE 2025
- 2025
TruCrisisAware: Integrating Naturalistic Decision Making into AI for Enhanced Disaster Response
Chen-Yeou Yu, Jiaxuan He, K. Amaeshi, Wensheng Zhang · CSCE 2025
- 2024
TruDevOps: Towards Pairing Agile Software Development Curriculum with AI-Agile DevOps
Chen-Yeou Yu, Paige Su, Wensheng Zhang · CSCI 2024
- 2022
Edge-based Situ-aware Reinforcement Learning for Traffic Congestion Mitigation
Chen-Yeou Yu, Wensheng Zhang, Carl K. Chang · IEEE ISC2 2022, pp. 1–7 · PDF
- 2020
An Edge Computing Based Situation Enabled Crowdsourcing Blacklisting System for Efficient Identification of Scammer Phone Numbers
Chen-Yeou Yu, Carl K. Chang, Wensheng Zhang · CSCI 2020
- 2020
A Situation Enabled Framework for Energy-Efficient Workload Offloading in 5G Vehicular Edge Computing
Chen-Yeou Yu, Carl K. Chang, Wensheng Zhang · IEEE SERVICES 2020, pp. 61–68
- 2019
PocketAID: The Pocket Atlas of Infectious Diseases Mobile Application
Bonnie Gale, Lauren E. Charles, Hamid Mansoor, Chen-Yeou Yu · Online Journal of Public Health Informatics, 2019
- 2018
Situ-Centric Reinforcement Learning for Recommendation of Tasks in Activities of Daily Living in Smart Homes
Richie O. Oyeleke, Chen-Yeou Yu, Carl K. Chang · IEEE COMPSAC 2018, pp. 317–322
- 2018
Situation Based Energy Management Framework on Mobile Devices
Chen-Yeou Yu, Richie O. Oyeleke, Carl K. Chang · IEEE COMPSAC 2018, pp. 284–289
People · Fall 2026
Course collaborators.
Graders and course assistants supporting project-based learning. No student photographs are published.
Rupak Ashik Dey
Artificial Intelligence
Grader & Course Assistant
Huy B. Huynh
Internet Programming
Grader & Course Assistant
Shreeya Gurung
Foundations of Computer Science I
Grader & Course Assistant
Yuchen Ye
Foundations of Computer Science I
Grader & Course Assistant
Funded projects
Project Research Assistants.
Student researchers supporting faculty-led applied AI projects.
Huy B. Huynh
Research Assistant
lu15568@truman.eduAnshu Dahal
Research Assistant
vc78886@truman.eduTeaching · Fall 2026
Learn by building.
Courses connect concepts to working systems, clear technical communication, and responsible use of modern tools.
Artificial Intelligence
Search, machine learning, and reinforcement learning.
Internet Programming
From browser fundamentals to REST APIs and full-stack team projects.
Foundations of Computer Science I
Programming foundations, problem solving, and disciplined software construction.
Big Data Management
Big-data processing, distributed computing, and scalable data-management workflows.