Dr.-Ing. Tianyi Liu

Dr.-Ing. Tianyi Liu

Postdoctoral Research Associate

Communication Systems Group, TU Darmstadt

Professional Summary

Tianyi Liu obtained the M.Sc. degree in communications and computer networks engineering, with distinction, from the Politecnico di Torino, Italy, in 2018. He received the M.Sc. degree in electrical engineering and information technology from the Technical University of Darmstadt (TUD), Germany, in 2018, with the best student award from the Department of Electrical Engineering and Information Technology. He received the Dr.-Ing. degree in electrical engineering and information technology, with distinction, from TUD in 2024. Currently, he is working as a postdoctoral research associate at the Communication Systems Group, TUD.
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Education

Dr.-Ing. in Electrical Engineering and Information Technology

Technical University of Darmstadt

M.Sc. in Electrical Engineering and Information Technology

Technical University of Darmstadt

M.Sc. in Communications and Computer Networks Engineering

Politecnico di Torino

B.Sc. in Telecommunications Engineering

Politecnico di Torino

B.Eng. in Electronics and Information Engineering

Tongji University

Interests

Sparse Signal Processing Parallel Optimization Methods Sensor Array Signal Processing Graph Topology Inference Machine Learning: Graphical Models, Neural Networks Game Theory
Featured Publications
A parallel successive convex approximation framework with smoothing majorization for phase retrieval featured image

A parallel successive convex approximation framework with smoothing majorization for phase retrieval

This dissertation is concerned with the design and analysis of approximation-based methods for nonconvex nonsmooth optimization problems. The main idea behind those methods is to …

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Maximum a posteriori direction-of-arrival estimation via mixed-integer semidefinite programming

In this paper, we consider the maximum a posteriori (MAP) estimation for the multiple measurement vectors (MMV) problem with application to direction-of-arrival (DOA) estimation, …

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Gridless parameter estimation in partly calibrated rectangular arrays

Spatial frequency estimation from a mixture of noisy sinusoids finds applications in various fields. While subspace-based methods offer cost-effective super-resolution parameter …

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Extended successive convex approximation for phase retrieval with dictionary learning featured image

Extended successive convex approximation for phase retrieval with dictionary learning

Developing parallel algorithms for the phase retrieval with dictionary learning problem by extending the successive convex approximation framework using a smooth majorization.

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