Difference between revisions of "User:Herschmi"
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| + | == Michael Hersche == | ||
| + | Michael Hersche received his M.Sc. degree from the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland, where he is currently pursuing a Ph.D. degree. Since 2019, he has been a research assistant at ETHZ in the group of Prof. Luca Benini at the Integrated Systems Laboratory. His research targets digital signal processing, artificial intelligence, and communication with a focus on hyperdimensional computing. | ||
| − | == | + | ==Interests== |
| + | * Hyperdimensional Computing | ||
| + | * Machine Learning | ||
| + | * Approximate Computing | ||
| + | * Communication | ||
| + | |||
| + | ==Contact Information== | ||
* '''Office''': ETZ J 76.2 | * '''Office''': ETZ J 76.2 | ||
* '''e-mail''': [mailto:herschmi@iis.ee.ethz.ch herschmi@iis.ee.ethz.ch] | * '''e-mail''': [mailto:herschmi@iis.ee.ethz.ch herschmi@iis.ee.ethz.ch] | ||
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[[Category:Digital]] | [[Category:Digital]] | ||
[[Category:Human_Intranet]] | [[Category:Human_Intranet]] | ||
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==Available Projects== | ==Available Projects== | ||
Revision as of 16:07, 29 October 2019
Contents
Michael Hersche
Michael Hersche received his M.Sc. degree from the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland, where he is currently pursuing a Ph.D. degree. Since 2019, he has been a research assistant at ETHZ in the group of Prof. Luca Benini at the Integrated Systems Laboratory. His research targets digital signal processing, artificial intelligence, and communication with a focus on hyperdimensional computing.
Interests
- Hyperdimensional Computing
- Machine Learning
- Approximate Computing
- Communication
Contact Information
- Office: ETZ J 76.2
- e-mail: herschmi@iis.ee.ethz.ch
- www: IIS Homepage
Available Projects
No pages meet these criteria.
Projects in Progress
No pages meet these criteria.
Completed Projects
- Memory Augmented Neural Networks in Brain-Computer Interfaces
- Hyper-Dimensional Computing Based Predictive Maintenance
- Low Latency Brain-Machine Interfaces
- Deep Convolutional Autoencoder for iEEG Signals
- Exploring features and algorithms for ultra-low-power closed-loop systems for epilepsy control
- TCNs vs. LSTMs for Embedded Platforms
- An Energy Efficient Brain-Computer Interface using Mr.Wolf
- Toward hyperdimensional active perception: learning compressed sensorimotor control by demonstration
- Exploring Algorithms for Early Seizure Detection
- Improving Resiliency of Hyperdimensional Computing
- Toward Superposition of Brain-Computer Interface Models
