From 0b4206421d1772a8693e36bc9d3d7dabd4c19acb Mon Sep 17 00:00:00 2001
From: Hang Zheng <54340561+HangZheng98@users.noreply.github.com>
Date: Tue, 10 Oct 2023 17:22:54 +0800
Subject: [PATCH] Update about.md

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 _pages/about.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_pages/about.md b/_pages/about.md
index 4e4993a365b..53a39a15bc4 100644
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 I am a fifth-year Ph.D. student in the College of Information Science & Electronic Engineering (ISEE) at [Zhejiang University](https://www.zju.edu.cn/english/), Hangzhou, China, where I am jointly advised by Prof. [Zhiguo Shi](https://person.zju.edu.cn/shizg) (Vice dean of the College of ISEE), Prof. Quan Yu (Academician of the Chinese Academy of Engineering), and Prof. Chengwei Zhou. From Oct. 2022 to Oct. 2023, I am a joint training Ph.D. student in the Department of Acoustics & Signal Processing at [Aalto University](https://www.aalto.fi/en), Espoo, Finland, where I am supervised by Prof. [Sergiy Vorobyov](https://users.aalto.fi/~vorobys1/) (IEEE Fellow). 
 
 
-My research interest includes *array signal processing*, *tensor signal processing*, and *AI for signal processing*. To effectively process large-scale high-dimensional array signals in future communication and sensing systems, I developed the theories of sub-Nyquist tensor processing, efficient tensor optimization, and resource-efficient tensorized neural networks. The proposed methods facilitate direction-of-arrival (DOA) estimation, adaptive beamforming, and channel estimation with low system costs and competitive performance, laying the foundations for wireless communication, target localization, and anti-interference.
+My research interest includes *array signal processing*, *tensor signal processing*, and *AI for signal processing*. To effectively process large-scale high-dimensional array signals in future communication and sensing systems, I developed the theories of <font color=blue>sub-Nyquist tensor processing<\font>, <font color=blue>efficient tensor optimization<\font>, and <font color=blue>resource-efficient tensorized neural networks<\font>. The proposed methods facilitate direction-of-arrival (DOA) estimation, adaptive beamforming, and channel estimation with low system costs and competitive performance, laying the foundations for wireless communication, target localization, and anti-interference.
 
 
 I have published 20+ papers at top journals and international conferences with google citations <a href='https://scholar.google.com/citations?user=V97Yn3MAAAAJ'><img src="https://img.shields.io/endpoint?url={{ url | url_encode }}&logo=Google%20Scholar&labelColor=f6f6f6&color=9cf&style=flat&label=citations"></a>.