Qiqi Hu bio photo

Master @ Tsinghua SIGS
AI for Energy Systems & Low-Carbon Innovation

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Quick Summary

  • 🎓 Master’s student at Tsinghua University (SIGS), focusing on AI for Energy & Sustainability
  • 🔬 Research: thermal safety modeling, AI-accelerated low-carbon energy systems, diffusion models, trustworthy machine learning
  • 🧰 Technical strengths: PyTorch, optimization, scientific ML, thermal modeling & simulation, ML systems
  • 🎯 Seeking PhD (Fall 2027) in Energy-AI, Battery Safety & Thermal Modeling, Scientific ML for Multiphysics Systems, and Physical Generative Models
  • 📧 Email: chelseyhu111@gmail.com


About Me

Here is Qiqi Hu (Chelsey, 胡齐齐).
I am currently a Master’s student at Tsinghua University, working in the Guangdong Provincial Key Laboratory of Thermal Management Engineering and Materials at the Shenzhen International Graduate School, supervised by Prof. Hongda Du.
Previously, I was a visiting research student at the Southern University of Science and Technology (SUSTech), advised by Prof. Feng Zheng(a recipient of the National Excellent Young Scientist Award), where I worked on trustworthy diffusion models and content security in AIGC. I received my Bachelor’s degree in Information Security from Qingdao University, where I conducted research under Prof. Hanlin Zhang — who completed his Ph.D. under Prof. Wei Yu, an NSF CAREER Awardee — focusing on privacy-preserving outsourcing computation and secure IoT systems. These experiences equipped me with solid research training in cybersecurity and trustworthy AI, providing a strong algorithmic foundation that now supports my interdisciplinary work in AI for Energy and Sustainability.

For a concise CV, please visit my Google Sites page.

🧭 I am seeking PhD opportunities（2027 fall） to advance research in energy system modeling and machine-learning optimization toward achieving carbon-neutral and sustainable energy systems.

I am always open to discussions and collaborations — feel free to email me.

🎯 Research Vision

My overarching goal is to harness artificial intelligence and optimization to accelerate innovation in low-carbon and sustainable energy systems.
I aim to bridge the gap between materials, systems, and decision-making through trustworthy, data-driven, and interpretable AI frameworks.

🔬 Research Interests

Thermal Management and Energy Safety

Previously, my research focused on thermal management of lithium-ion batteries, which naturally motivated my current interest in AI-driven energy systems.

  • Investigating mechanisms for suppressing thermal runaway in lithium-ion batteries and developing PCM-based hybrid cooling strategies
  • Thermal management of LIBs under high-power discharge conditions using finite element modeling and multiphysics simulation
  • Applications in energy storage and electronic thermal control

AI for Energy Systems & Battery Storage

  • Data-driven modeling and intelligent prediction of battery energy storage safety
  • Intelligent optimization for low-carbon, safe, and stable energy operation
  • ML-based modeling and optimization of energy systems and multi-energy networks
  • Scientific ML & generative models for physical processes in energy devices

Trustworthy AI & Secure Generative Models

  • Content safety for AIGC using diffusion models — PGD-based adversarial defense and encoder–decoder watermarking
  • Privacy-preserving in cloud computing and Internt of Things, including cloud-assisted and IoT security with matrix transformation methods for image denoising and segmentation
  • Extending trustworthy machine learning and robust optimization techniques toward digital low-carbon energy applications

🧩 Academic Background

  • M.Eng. in Environmental Science and New Energy Technology, Shenzhen International Graduate School, Tsinghua University
  • Visiting Research Student, Department of Computer Science and Engineering, SUSTech
  • B.Eng. in Information Security, School of Computer Science and Technology, Qingdao University

🗞 News and Updates

  • 2025.11 – Initiated research on AI-driven early warning of lithium-ion battery thermal runaway
  • 2025.09 – Studied thermal management of lithium-ion batteries under high-rate discharge using PCM and air-cooling coupling
  • 2025.05 – Investigated effects of PCM-assisted air cooling on thermal runaway propagation in LIB modules
  • 2024.03 – Explored copyright protection in diffusion-based image-to-image generation

🧠 Skills / Research Toolkit

  • Programming: Python, MATLAB, C, LaTeX
  • Frameworks: PyTorch, TensorFlow (for deep learning), scikit-learn (for machine learning)
  • Tools: Linux, VSCode, Anaconda, COMSOL, Microsoft Office
  • Languages: Chinese (Native), English (Fluent)