Dr. Xinze Li
From AI-aided to AI-native power electronics life cycle management

From AI-aided to AI-native power electronics life cycle management

Postdoctoral Fellow & Lecturer @ University of Arkansas (Fayetteville).

Incoming Assistant Professor @ Florida State University (Tallahassee).

Email
GitHub XinzeLee
Location Fayetteville, AR, USA
Google Scholar Profile
Video
AI-generated page note
Video
How the site was generated
810
Citations (as of Feb-13-2026)
16
h-index
19
i10-index
Research focus
  • AI for power electronics — simulation & modeling; maintenance & reliability; process automation.
  • AI for power semiconductor manufacturing — digital twins for fabrication; fabrication process optimization.
  • Cross-cutting AI themes — physical AI, explainable AI, and agentic AI.

I will join the Florida State University, FL, USA, as an Assistant Professor starting August 2026, and I am recruiting motivated PhD students and postdoctoral researchers to join my research group.

Experience

Post Doctoral Fellow, Lecturer — University of Arkansas

Jul 2024 – Jul 2026 (Expected)
  • Supervisor: Alan Mantooth, IEEE Fellow, IEEE Division II Director, NAI Fellow
  • Project: Machine Learning Optimized Power Electronics (DoD-funded; collaboration with Eaton, UWM, MetaMorph)
  • Scope: Explainable AI-based PHM of power semiconductors
  • Teaching: “Fundamentals of AI for Power Electronics Design” (3 credits, 40 hours)

Research Fellow — Nanyang Technological University (Singapore)

Oct 2023 – Jun 2024
  • Project: AI for high-frequency & 3D wireless power transfer
  • Scope: Physics-informed AI for power electronics modeling & design

AI Engineer — Singtel (Singapore)

May 2021 – Sep 2021
  • Computer-vision patient anomaly behavior monitoring (polygon object detection + pose detection)
  • Open-sourced project: PolygonObjectDetection

Education

Ph.D., Electrical & Electronic Engineering — NTU Singapore

Jul 2018 – Mar 2023 • GPA: 4.75/5 (top 1%)

  • Thesis topic: AI applications in circuit & modulation design of DC-DC converters
  • Supervisor: Prof. Kezhi Mao
  • Also: text style transfer under low-data regime (NLP)

B.Eng., Electrical & Electronic Engineering — Shandong University

Sep 2014 – Jun 2018 • GPA: 94/100 (top 1%)

Industry & Project Highlights

Eaton — ML optimized power electronics

  • Led a 3-engineer team; coordinated collaborators; built multi-physics multi-objective design tools
  • Coordinated DPT and 300-kW high-power test for hardware prototype

PHM for power semiconductors

  • Light AI solutions to predict remaining useful life of SiC power modules / traction inverters
  • Collaborations include Toyota America

Wireless power transfer optimization

  • Multi-physics AI to improve magnetic field uniformity for 3D spatial & PCB coils
  • AI-based adaptive controller improving transient dynamics

Rolls-Royce @ NTU Corporate Lab

  • Probabilistic AI optimization for fleet fuel consumption (Germany)
  • AI-optimized DAB-inverter cascaded system; controllability analysis

Teaching & Mentoring

Lecturer — Fundamentals of AI for Power Electronics Design

University of Arkansas • 3 credits • 40 hours

  • Meta-heuristics, ML, generative AI engineering, RL for control
  • Project-based learning; hands-on coding/debugging mindset

Student supervision

  • Mentoring PhD + undergrads; REU supervision; publication outcomes
  • Topics: failure mechanism analysis, RUL prediction, converter control/modulation
Outstanding Mentor Award, University of Arkansas, 2026
Outstanding Mentor Award, University of Arkansas (2026)
Teaching / mentoring graduate students
Mentoring graduate students
Teaching / mentoring REU students
Mentoring REU students
Outcome of Graduate Course "Fundamentals of AI for Power Electronics Design"

Fundamentals of AI for power electronics (open education)

I maintain an open GitHub-based resource for Fundamentals of AI for Power Electronics: a structured path of Jupyter notebooks, data, and documentation aimed at practitioners and students who want hands-on skill at the intersection of machine learning and power electronics—not only slides, but runnable code you can adapt.

The repo accompanies the invited tutorial article Fundamentals of Artificial Intelligences for Power Electronics (IEEE Transactions on Industrial Electronics, 2026). Module folders are mapped to paper sections so you can read a topic, then open the matching notebooks. There is also a companion education piece (pilot course at the University of Arkansas) in the repo’s docs/ folder on reforming power-electronics education in the AI era.

What you’ll find: a progressive curriculum—from environment setup and metaheuristic optimization, through classical ML, ensembles, neural networks (including 3D thermal-field regression and sequence models), physics-informed modeling (PINN), reinforcement learning on converter examples, simulation automation (e.g. LTspice / PLECS workflows), to applied case studies on buck, DAB, IGBT health, and magnetic modeling. Most notebooks include Open in Colab flows; one automation-heavy module expects local simulators.

Companion tools: an interactive AI-for-PE algorithm selector to narrow methods to your task, plus a documented ChatGPT-based tutor path in the repository README for deeper Q&A aligned with the material. Content is actively refined; code is Apache-2.0 and educational text is CC BY-NC 4.0 per the repo license files.

Fundamentals_of_AI_for_PE on GitHub

Academic Service

Invited talks / tutorials / chairs

  • IEEE DMC 2025: Organizer — AI Challenge in Power Electronics Design
  • IEEE WiPDA 2025: Tutorial — AI in Power Electronics Design
  • IEEE APEC 2025: 3-hour education seminar (S0.5 AI in Power Electronics Design - Present and Future) + session chair
  • IEEE ECCE 2025 tutorial: Reimagine Power Electronics Design with AI
  • IEEE ECCE 2024: Special session on next-gen AI for power electronics

Editorial roles & reviewing

  • Guest editor: Frontiers in Electronics; MDPI Mathematics
  • 90+ reviews across IEEE journals and others

Awards

  • Outstanding Mentor Award, University of Arkansas (2026)
  • Geneva Invention Salon: Silver (Plug-and-Play device fingerprint detector + stability analysis with TinyML)
  • ESI Highly Cited Paper (Triple Phase Shift modulation for DAB converter)
  • IEEE IAS Prize Paper Award: Second Prize
  • NTU Graduate College Collaborative Research Award (2023–2024)
  • APEC 2023 Outstanding presentation award
AI transparency note (what “AI-generated” means here)

This page was generated from the official PDF CV and then formatted into a web layout. The PDF is the source of truth. If anything looks off, update the text in assets/script.js or replace assets/CV.pdf.

Tip: keep the PDF link so reviewers can cite/verify details quickly.

Skills

Artificial Intelligence

PythonPyTorchDeep Learning Computer VisionNLPGANs MetaheuristicsLightweight / deployable AI

Power Electronics

Hardware designFPGADSP HIL (Opal-RT)dSPACEMatlab PLECSLTspiceAnsys

Contact

Email
LinkedIn
GitHub
Address
University of Arkansas, Fayetteville, USA
Download CV

About this AI-generated page

This site is intentionally labeled AI-generated: it was produced from the official PDF CV and formatted into an interactive web CV with search/filter tools.

  • Source of truth: the PDF (assets/CV.pdf)
  • Editable content: publication list in assets/script.js
  • Goal: fast scanning for humans + verifiable details