Welcome! 👋

I'm Chang Gao

Assistant Professor of Edge AI

Based in Delft, Netherlands

Work Email chang.gao@tudelft.nl
Office HB 19.280, Mekelweg 4, 2628 CD Delft, Netherlands
Research Group tudemi.com Bibliometric Google Scholar ORCID 0000-0002-3284-4078

About Me

Chang Gao is an Assistant Professor in the Department of Microelectronics at Delft University of Technology (TU Delft), a position he has held since 2022. His research focuses on hardware-software co-design for real-time edge intelligence, with a particular interest in embodied intelligence.

General Information

Nationality
Chinese
Location
Delft, Netherlands
Hobbies
Tennis, Hiking, Travel

Contact

Work Email

chang.gao@tudelft.nl

Office

HB 19.280, Mekelweg 4, 2628 CD Delft, Netherlands

Research Group

tudemi.com

Bibliometric

Google Scholar

ORCID

0000-0002-3284-4078

Research Grants & Strategic Projects

01

NWO Veni — Energy-Efficient Real-Time Edge Intelligence for Wearable Healthcare Devices

2024

Prestigious Dutch Young Scholar grant (€320k) for a 48-month program advancing energy-aware signal processing and inference for wearable healthcare platforms.

Edge AIHealthcareEnergy Efficiency
02

Top Consortium for Knowledge and Innovation (TKI) Partnership

2024

Co-funded PhD research (€495k cash · €186k in kind) with NXP Semiconductors on AI-assisted calibration for high-performance data converters.

Mixed-Signal ICAI-Assisted CalibrationIndustrial Partnership
03

Industry-Funded PhD — Ampleon Netherlands

2024

Joint research program on neural-network-driven digital pre-distortion of wideband RF power amplifiers for next-generation wireless communication.

Digital PredistortionWideband RFNeural Networks
04

GlobalFoundries GF12LP+ University Partnership

2024

Secured multi-year access to 12 nm FinFET technology and tape-out runs for AI-powered high-speed signal processing integrated circuits.

Advanced CMOSSignal Processing ICsTechnology Transfer
05

Marie Curie Postdoctoral Fellowship — AIRHAR

2023

European Commission fellowship (€203k) developing an energy-efficient portable radar system for human activity recognition.

Radar SensingEmbedded AIResearch Leadership

Honors & Recognition

MIT Technology Review Innovators Under 35 Europe

MIT Technology Review

2023

Recognized for innovations enabling wearable and portable devices to deliver 24/7 health monitoring.

Learn more

Mahowald Early Career Award for Neuromorphic Engineering

Misha Mahowald Prize Committee

2022

Honored for contributions to neuromorphic algorithms and hardware enabling real-time energy-efficient RNN inference.

Learn more

Distinction for Doctoral Thesis

Faculty of Science, University of Zürich

2022

Awarded to dissertations representing the top 5% in scientific quality within the faculty.

Best Paper Award — IEEE AICAS

IEEE Circuits and Systems Society

2020

Recognized for the EdgeDRNN recurrent neural network accelerator for edge inference.

MSc Outstanding Achievement Award

Imperial College London

2016

Acknowledged as the top-performing student in the Analog and Digital Integrated Circuit Design program.

Selected Publications & Patents

  1. Neural network-based inference method and apparatus (US Patent 12 299 576 B2)

    C. Gao, S.-C. Liu, T. Delbruck, X. Chen

    Issued May 13, 2025

  2. DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion

    Y. Wu, Y. Zhu, K. Qian, Q. Chen, A. Zhu, J. Gajadharsing, L. de Vreede, C. Gao

    IEEE MTT-S International Microwave Symposium (IMS), 2025 — Top 50 Paper

  3. Spartus: A 9.4 TOp/s FPGA-Based LSTM Accelerator Exploiting Spatio-Temporal Sparsity

    C. Gao, T. Delbruck, S.-C. Liu

    IEEE Transactions on Neural Networks and Learning Systems, 2024

  4. MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Pre-distortion of Wideband Power Amplifiers

    Y. Wu, A. Li, M. Beikmirza, G. D. Singh, Q. Chen, L. de Vreede, M. Alavi, C. Gao

    IEEE Microwave and Wireless Technology Letters, 2024 — IMS Top 50 Paper

  5. EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference

    C. Gao, A. Rios-Navarro, X. Chen, S.-C. Liu, T. Delbruck

    IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2020

  6. DeltaRNN: A Power-efficient Recurrent Neural Network Accelerator

    C. Gao, D. Neil, E. Ceolini, S.-C. Liu, T. Delbruck

    ACM/SIGDA FPGA, 2018

Academic Appointments

Assistant Professor, Department of Microelectronics

Delft University of Technology

Aug 2022 – Present
  • Lead the Neuromorphic Edge Intelligence research thrust with a focus on energy-efficient HW-SW co-design for AIoT.
  • Co-lead the MSc Digital Systems Profile and coordinate courses including ET4351, EE4C13, EEX05, and EE2C2.
  • Supervise 3 PhD and 5 MSc candidates; graduated 10 MSc students since 2022.
  • Secured competitive grants including NWO Veni, TKI Partnership, and GlobalFoundries GF12LP+ program.

Postdoctoral Researcher

Institute of Neuroinformatics, University of Zürich & ETH Zürich

Jan 2022 – Jul 2022
  • Advanced energy-efficient deep-learning hardware in collaboration with Prof. Shih-Chii Liu and Prof. Tobi Delbruck.
  • Managed a transitional research program while preparing the new research agenda at TU Delft.
  • Mentored postgraduate researchers on neuromorphic accelerators and embedded AI systems.

Education

Ph.D. in Neuroscience (Distinction)

Institute of Neuroinformatics, University of Zürich & ETH Zürich

Jan 2017 – Dec 2021
  • Dissertation: Energy-Efficient Recurrent Neural Network Accelerator for Real-Time Inference.
  • Funded by Samsung Advanced Institute of Technology and the Swiss National Science Foundation.
  • 200+ hours of teaching assistance across instrumentation, measurement, and digital electronics.

M.Sc. Analog and Digital Integrated Circuit Design (Distinction, Rank 1)

Imperial College London

Sep 2015 – Dec 2016
  • Thesis: Full-custom design of a mixed-signal physical unclonable function in 350 nm technology.
  • Awarded the MSc Outstanding Achievement Award for top performance.

B.Eng. in Electronics (First-Class Honours, 2+2 Joint Program)

University of Liverpool & Xi’an Jiaotong-Liverpool University

Sep 2011 – Aug 2015
  • Thesis: Simulation & Measurement of Zinc Oxide Thin-film Transistors.
  • Received scholarships for academic excellence, including 50% tuition support.

Professional Service & Community

Inaugural Committee Member

IEEE Circuits and Systems Society Technical Committee on Machine Learning Circuits and Systems (MLCAS)

2024 – Present

Lead Organizer

Efficient Event-based Eye-Tracking (3ET) Challenge, CVPR 2025 Workshop on Event-based Vision

2024 – 2025

Review Committee Member

IEEE ISCAS 2024 & 2025

2024 – 2025

Reviewer

40+ journal reviews across IEEE JSSC, TPAMI, TNNLS, TMTT, TCAS-I, TBioCAS, JETCAS, MWTL, TVLSI, Nature Computational Science, and more

2017 – Present