Jiaming Li

Researcher · Educator · Engineer · Founder

Jiaming Li, Ph.D.

Assistant Teaching ProfessorSchool of Informatics, Computing, and Cyber SystemsNorthern Arizona University

My work connects interpretable acoustic modeling with AI-enabled engineering and embodied intelligence, from understanding complex physical systems to building practical intelligent applications.

Acoustic modelingAI-enabled engineeringEmbodied AIDesign optimizationData analyticsInformation systems

Research, teaching, and applied AI leadership

Physical insight becomes computational intelligence.

I am an Assistant Teaching Professor at Northern Arizona University. My research spans acoustic modeling, AI applications in engineering, including AI-enabled CAD and robotics, data analytics, and information systems.

I earned my Ph.D. in Mechanical and Aerospace Engineering from Rutgers University in 2022. Before joining NAU, I worked in seismic imaging at CGG and held teaching and academic-administration roles in the Rutgers School of Engineering and Rutgers Business School.

Since joining NAU in April 2024, I have served on faculty comprehensive review and doctoral admissions committees and as Assistant Director of Graduate Programs. I am also leading the development of a doctoral program centered on engineering applications of AI and closer integration between research and industry.

Across more than eight years of teaching in mechanical engineering and information systems, I have taught over 2,000 students and guided hundreds of graduate capstone projects. My current collaborations include researchers at Harvard University, the University of Oxford, and other international institutions.

Academic and professional path

Appointments

Assistant Teaching Professor

School of Informatics, Computing, and Cyber Systems · Northern Arizona University

Adjunct Professor, Business School & Assistant to the Dean, School of Engineering

Rutgers University

Seismic Imaging Analyst

North America Surface Imaging · CGG

Education

Ph.D. in Mechanical and Aerospace Engineering

Rutgers University

M.S. in Mechanical and Aerospace Engineering

Rutgers University

B.E. in Mechanical Design, Manufacturing & Automation

Hefei University of Technology

Analytical modeling and inverse design of complex acoustic structures

Classical Helmholtz models become less reliable as resonator geometries grow asymmetric, multi-necked, or otherwise complex. I developed geometry-aware analogy mass-spring models that translate standing-wave behavior into fast, interpretable prediction.

The resulting AMSS and ASPMSS frameworks connect target sound-reduction frequencies directly to feasible resonator geometry, replacing repeated trial-and-error simulation with an analytical design workflow.

View this work on Google Scholar
Research contribution map for analytical modeling and inverse design of acoustic resonators
Research contribution map: model limitation, analytical solution, and engineering value.
Acoustic-aware world-model framework for multimodal robot learning and execution
A proposed acoustic-aware world-model framework for multimodal learning and safer robot execution.

AI-enabled engineering design and acoustic-aware embodied intelligence

My current work extends interpretable physical modeling into AI-enabled engineering: physics-informed inverse design, natural-language-driven CAD, multimodal robot evaluation, and practical decision systems.

A central direction is acoustic-aware world modeling for embodied AI. By combining motion, audio, vibration, state, and action, these models can help robots anticipate contact outcomes, recognize failure, learn from difficult experience, and assess risk before acting.

Read the research statement

Publications

Journal articles, preprints, and conference work in acoustics, engineering design, applied AI, and information systems.

Complete Google Scholar profile

2026

2025

2024

2019

Applied AI for real engineering and business workflows.

Five working prototypes that translate AI capabilities into focused systems for procurement, international trade, robotics evaluation, commodity operations, and travel planning.

Humanoid Intelligence Benchmark interface
01

Embodied AI evaluation and certification

Humanoid Intelligence Benchmark

A structured evaluation environment for humanoid robots across mobility, manipulation, perception, autonomy, safety, and task success, with repeatable test suites and comparative scoring.

Mineral Trade Agent Console interface
02

AI decision support for commodity operations

Mineral Trade Agent Console

A multi-agent operating console that connects market analysis, supplier comparison, procurement, sales, and executive summaries for mineral and commodity trading decisions.

SmartTrade AI Operations interface
03

Multi-agent international trade assistant

SmartTrade AI Operations

A coordinated AI team for international B2B trade. Specialized agents interpret incoming email, review customers, prepare quotations, coordinate logistics, and support after-sales work.

Asterra Supply Intelligence interface
04

AI-assisted B2B food procurement

Asterra Supply Intelligence

A mobile-first procurement experience for independent food businesses, combining product discovery, repeat ordering, price-aware offers, delivery scheduling, and bilingual operation.

Go Travel Planner interface
05

AI itinerary generation

Go Travel Planner

A guided travel planner that converts destination, schedule, preference, and budget inputs into a structured day-by-day itinerary that can be saved or exported.

jiaming.li@nau.edu