Yin Li
Research Assistant
München
Summary
Working field: wireless communication, joint communication and sensing, neuromorphic computing, spiking neural network.
Overview
3
3
years of professional experience
7
7
years of post-secondary education
2
2
Languages
Work History
Research Assistant
Technical University of Munich
Munich
10.2023 - Current
- Neuromorphic Computing for Communication and Sensing: Dealing with harmonic retrieval problem using spiking neural network
- Implementing the trained neural network on Spinnaker2 hardware
- Tools: PyTorch Lightning, snnTorch, Spinnaker2
Master Thesis Student
Infineon Technologies
Munich
03.2023 - 08.2023
- Gesture classification for FMCW Radar data Using Liquid State Machines: Implemented a liquid state machine for gesture classification
- Both time-domain and frequency-domain data were processed by models with different parameters
- Tools: Python, NEST simulator, snnTorch
Internship
Infineon Technologies
12.2022 - 02.2023
- Machine Learning for FMCW Radar data Using Spiking Neural Network: Explored basics of FMCW radar and spiking neural network
- Learned about the processing of FMCW data for gesture classification using FFT and convolutional neural network
- Tools: Python, NEST simulator, snnTorch
Research internship
Technical University of Munich
04.2022 - 10.2022
- Quantitative Evaluation of Deep Generative Models for CSI Generation: Implemented a generative model for Channel State Information(CSI) based on Gaussian Mixture Model(GMM)
- Researched on how the number of GMM components and channels influence the estimation
- Evaluated the generated samples by different methods, e.g
- KL divergence and MSE
- Tools: Python, Scikit-learn
Education
Master of Science - Electrical and Computer Engineering
Technical University of Munich
Germany 10.2020 - 08.2023
Bachelor of Science - Electrical Engineering
Zhejiang University
China 09.2016 - 06.2020
Skills
Python(PyTorch, Lightning, snnTorch, NEST simulator, hydra, git),
MSOffice
C, Matlab
Projects
The role of familiarization with speech in neural tracking and speech reconstruction
Processed EEG data
Practical Project at Technical University of Munich
- Set up a prediction model for both neural activity (forward model) and speech stimuli (backward model)
- Evaluated the accuracy of prediction and proved the accuracy improves with familiarization/repetition of stimulus.
- Tools: EEGLAB(Matlab)
Timeline
Research Assistant
Technical University of Munich
10.2023 - Current
Master Thesis Student
Infineon Technologies
03.2023 - 08.2023
Internship
Infineon Technologies
12.2022 - 02.2023
Research internship
Technical University of Munich
04.2022 - 10.2022
Master of Science - Electrical and Computer Engineering
Technical University of Munich
10.2020 - 08.2023
Bachelor of Science - Electrical Engineering
Zhejiang University
09.2016 - 06.2020
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