Shuqi Wang

Shuqi Wang is a Doctoral Assistant at the Computational Neuroscience Laboratory (LCN) within the Brain Mind Institute (BMI) of the School of Life Sciences at Swiss Federal Institute of Technology in Lausanne (EPFL), holding dual staff appointments across the School of Life Sciences (SV) and School of Computer and Communication Sciences (IC). Wang simultaneously pursues doctoral studies in the Doctoral Program in Computer and Communication Sciences (EDIC), reflecting deep interdisciplinary engagement between neuroscience and computational disciplines.

Research focuses on adapting machine learning for neural data analysis, specifically characterizing neural activity structure/properties in response to task inputs and reconstructing neural circuits from extracellular recordings. This work bridges theoretical neuroscience with advanced computational methods to decode neural coding principles and circuit organization, emphasizing high-dimensional neural representations and experience-dependent plasticity mechanisms.

Recent publications (2021-2025) reveal consistent innovation in applying machine learning to neuroscience challenges, including neural coding hierarchies, inhibitory circuit reorganization, manifold learning in recurrent networks, and differentiable neural simulation. These contributions demonstrate expertise in spiking network modeling, statistical inference of neural data, and developing computational frameworks that integrate theoretical neuroscience with cutting-edge machine learning.

Wang operates within EPFL's Computational Neuroscience Laboratory (LCN), a cross-school initiative between Life Sciences and Computer Science that develops mathematical models of neural computation under Professor Wulfram Gerstner's leadership. The lab's collaborative environment fosters work on neural dynamics, learning algorithms, and brain-inspired computing through strong ties to BMI and IINFCOM.