Jialei Liu
M.S., The Ohio State University · B.S., Chongqing University
Research on resource-aware sequential decision-making, online provisioning and scheduling, experience sharing in reinforcement learning, and edge intelligence.
My educational goal is to help students connect rigorous models and proofs to decisions, networks, autonomous systems, and societal impact.
Mentoring combines ambitious theory, careful validation, independent ownership, and opportunities to communicate results through papers, talks, and outreach.
M.S., The Ohio State University · B.S., Chongqing University
Research on resource-aware sequential decision-making, online provisioning and scheduling, experience sharing in reinforcement learning, and edge intelligence.
M.S., Jilin University · B.S., Jilin University (ranked 2/90)
Research on Age of Intelligence, nonstationary learning, edge model synchronization, and multi-agent POMDPs with selective observation and information sharing.
Clarify what matters operationally, who is affected, and why a mathematical abstraction is needed.
Separate assumptions, information structure, constraints, and performance goals before adding complexity.
Use theory, lower bounds, numerical experiments, and systems evidence to understand when a method works and why it fails.
Develop technical papers, proposal narratives, research talks, visual explanations, and educational demonstrations.
Each course serves both undergraduate intuition and graduate-level depth through geometry, proofs, algorithms, examples, and open-ended projects.
Students learn to translate engineering and learning problems into optimization models, recognize convex structure, use duality, analyze algorithms, and interpret computational results.
Students connect protocols and architecture to performance, congestion, routing, wireless systems, security, and the decisions that increasingly make networks adaptive and intelligent.
Computer Communication Networks.
Introduction to Computer Communication Networks.
Use one-, two-, and three-dimensional examples, diagrams, and interactive demos to make abstract objects concrete.
Identify variables, information, objectives, constraints, assumptions, and the right performance metric.
Build proof intuition, derive guarantees, examine counterexamples, and understand fundamental limits.
Test algorithms numerically, compare baselines, inspect failure modes, and connect theory to systems.
Explain the result to classmates, researchers, engineers, teachers, and the public.
Outreach activities use smart grids, autonomous systems, robotics, XR, biology, health, and community-scale questions to help learners see mathematics as a tool for understanding and designing the world.
Student-led demonstrations connected mathematical modeling to smart grids and autonomous systems, showing how theory generates decisions, insights, and real-world impact.
Introduced engineering students to research questions, faculty mentoring, and pathways from coursework to open-ended investigation.
Supported undergraduate research experiences at The Ohio State University in AI, networking, and edge intelligence.
Engaged younger learners through robotics, teamwork, and problem-solving at The Ohio State University.
These activities are being developed to make feedback, uncertainty, resource allocation, and safe decisions visible through simulations and spatial computing.
Learners balance renewable generation, demand, storage, and reliability, then inspect how forecasts change decisions.
Learners control a robot or simulated vehicle while respecting collision, communication, and energy constraints.
Learners use reinforcement learning and optimization to explore growth, treatment, diagnosis, and resource tradeoffs in simplified biological systems.
Mixed-reality demonstrations show how sensing, communication, model updates, and control interact around a learner.
I welcome students interested in theory and mathematically grounded intelligent systems. A useful email briefly explains your background, the research questions that genuinely interest you, and how they connect to one or more current directions.
Describe your mathematical, algorithmic, or systems preparation.
Name a specific paper or research direction and explain what interested you.
Share a project, proof, implementation, or question that demonstrates initiative.
Attach a current CV and relevant transcript or writing sample.