Liu, Ming

• Huang X.M., M. Liu, and E. Ehimen-Ebitibituwa. (2025). Gig Work at the Margins: Algorithmic Control and Human Dignity. AMCIS TREO 2025.
• Liu M., Q. Sun, D.E. Brewer, T.M. Gehring, and J. Eickholt. (2022). An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio. Remote Sensing 14 (15), 3816. DOI: http://doi.org/10.3390/rs14153816.
• M Liu, PG Kinnicutt, RG Amirkhiz, DL Swanson (2022) Arthropod prey and diets of woodland migrants are similar between natural riparian woodlands and anthropogenic woodlots in the northern prairie region. Avian Conservation and Ecology 17 (2), 2317. DOI: https://doi.org/10.5751/ACE-2317-170245.

• B.S., Anhui Science Technology University 
• M.S., Central Michigan University
• Ph.D., University of South Dakota
• Graduate Certificate in Cybersecurity, Central Michigan University

My research lies at the intersection of artificial intelligence (AI), data analytics, and cybersecurity, with a strong emphasis on ethical, human-centered, and sustainable applications. Drawing from my interdisciplinary background—spanning biology, computer science, and cybersecurity—I develop AI-driven solutions that address complex challenges in business, healthcare, environmental conservation, and cybersecurity resilience. My work is guided by three core themes:

1. Ethical AI & Workforce Well-being:
   Investigating the impact of algorithmic control on gig workers, particularly marginalized groups, to promote fairness, dignity, and psychological well-being.
   
   Leveraging predictive analytics to improve workforce conditions in high-stress sectors (e.g., healthcare), informing policy changes to reduce burnout and enhance retention.
   
2. Cybersecurity & Adversarial Machine Learning:
   Enhancing the robustness of intrusion detection systems against adversarial attacks, ensuring resilience in critical infrastructure.
   
   Exploring zero-knowledge analysis and forensic techniques for data recovery in compromised or damaged systems.
   
3. Big Data-Driven Decision-Making:
   Optimizing resource allocation through predictive modeling (e.g., donor prediction for university fundraising, crime pattern analysis for public safety).

Applying machine learning to ecological conservation (e.g., automated avian species detection in bioacoustics) and business intelligence (e.g., data-driven strategic planning).

Interdisciplinary Approach
My PhD in biology and MS in computer science allow me to approach AI and analytics with a systems-thinking perspective, balancing technical rigor with societal impact. For instance:

• My work on machine learning for avian vocalization detection (Remote Sensing, 2022) reduced manual review time by 66%, demonstrating AI’s potential in ecological research.
• As a cybersecurity educator and practitioner, I integrate risk management, governance, and adversarial ML into both research and curriculum development.

Future Directions
I aim to expand my work in:

• Explainable AI (XAI) for transparent decision-making in high-stakes domains (e.g., healthcare, criminal justice).
• AI-driven business continuity planning, merging cybersecurity resilience with organizational risk management.
• Cross-disciplinary collaborations that bridge computational methods with social science and environmental sustainability.

By advancing technologically innovative yet ethically grounded solutions, I strive to ensure AI and data science serve as tools for equitable progress.