The Efficient Intelligence Group develops energy-efficient intelligent systems, from algorithms and circuits to systems. The lab is headed by Qinyu Chen and is affiliated with the Leiden Institute of Advanced Computer Science (LIACS) at Leiden University.
Qinyu Chen has been an Assistant Professor at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, the Netherlands, since 2024. She received her PhD in Electronic Science and Technology from Nanjing University in 2021, supervised by Prof. Li Li, and her BEng in Communication Engineering from Shandong University in 2016, supervised by Prof. Haixia Zhang.
From 2019 to 2020, she was a visiting PhD student, and from 2022 to 2024, a postdoctoral researcher at the Sensors Group, Institute of Neuroinformatics, University of Zurich and ETH Zurich, working with Prof. Shih-Chii Liu and Prof. Tobi Delbruck.
Her research integrates neuroscience, computer science, and electronics to develop compact, energy-efficient, neuro-inspired intelligent systems for applications in healthcare, extended reality, and robotics. She has published in journals and conferences including TCAS-I, TCAS-II, TVLSI, TBIOCAS, TCAD, TCASAI, ICRA, CVPR, ISCAS, BioCAS, and AICAS.
Her contributions have been recognized with awards such as the 2022 BRIDGE Grant from the Swiss National Science Foundation (SNSF), the 2024 Dutch Research Council (NWO) Veni Talent Programme, and an Honorable Mention for the Best Paper Award from the IEEE Neural Systems and Applications Technical Committee (NSATC) in 2024.
She also actively contributes to the research community, serving as Chair-Elect of the IEEE CAS Neural Systems and Applications Technical Committee, Track Chair of ISCAS (2024–2026), an organizer of IEEE WiCAS-YP (2025) and FPL (2025, 2026), an Associate Editor for ICRA (2025) and IROS (2026), and a Guest Editor of IEEE JETCAS (2026).
The Efficient Intelligence Group engages in research activities on the following topics:
Compact models and hardware–software co-design for intelligence on resource-constrained devices.
We investigate efficient large language models, vision–language models, and neural networks that can operate with low latency and limited energy, memory, and compute. Our work connects model compression and adaptive inference with custom accelerators and embedded platforms.
Brain-inspired sensing, algorithms, architectures, and circuits built around sparse computation.
We draw inspiration from neural dynamics and event-based sensing to develop energy-efficient intelligent systems. The group works across event-based signal processing, spiking algorithms, digital architectures, processor design, and emerging hardware technologies.
Efficient learning from EEG, EMG, speech, and multimodal physiological signals.
We develop robust and lightweight models for complex physiological signals, together with neuromorphic and embedded implementations. The goal is real-time processing that can move closer to patients and everyday healthcare settings.
Office room BW.3.10
Gorlaeus BuildingEinsteinweg 552333 CC Leiden, the Netherlands
Efficient Intelligence Group