USTC, Hefei, 2018
Previously: at West Lake, Hangzhou.

Xumiao Zhang
Ph.D. student at University of Michigan

4917 BBB Building
2260 Hayward Street
University of Michigan, Ann Arbor
Ann Arbor, MI 48109

Email: xumiao [at] umich.edu


I am a Ph.D. student in Computer Science and Engineering at the University of Michigan, Ann Arbor, advised by Prof. Z. Morley Mao. My research interests include: wireless/cellular network measurement and systems, mobile computing, and cutting-edge mobile applications.

Prior to UMich, I received my B.E. from the University of Science and Technology of China, under the supervision of Prof. Xiangyang Li. I was in School of the Gifted Young and was enrolled in the Talent Program in Computer and Information Science and Technology.

You can find my CV here.


What's New

May 2019
I served on the Shadow Program Committee for ACM IMC 2019.
February 2019
I was awarded ACM HotMobile 2019 Travel Grant.
August 2018
I moved to Ann Arbor and started my Ph.D. life at UMich!
July 2017
I started my internship supervised by Prof. Chunyi Peng, in Mobile System, Security and Networking (MSSN) Lab at the Ohio State University.
June 2017
My first paper (demo) was accepted to ACM MobiCom'17: The Sound of Silence: End-to-End Sign Language Recognition Using SmartWatch.
September 2015
I was enrolled in the Talent Program in Computer and Information Science & Technology, USTC.
August 2014
I started my college life at University of Science and Technology of China.

Research Projects

MobileInsight

MobileInsight [MobiCom '16] is a cross-platform software package for cellular network monitoring and analysis on end device. It is a tool which collects, analyzes and exploits runtime network information from operational cellular networks.

The Sound of
Silence
The Sound of Silence is a portable SmartWatch-based American sign language (ASL) recognition system. This system is based on the intuitive idea that each sign has its specific motion pattern which can be transformed into unique signals and then analyzed by neural networks.

Publications

3

MPBond: Efficient Network-level Collaboration Among Personal Mobile Devices MobiSys '20

Xiao Zhu, Jiachen Sun, Xumiao Zhang, Yihua Guo, Feng Qian, Z. Morley Mao
Proceedings of the 18th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys '20)

MPBond is an efficient system allowing multiple personal mobile devices to collaboratively fetch content from the Internet. For example, a smartwatch can assist its paired smartphone with downloading data. Inspired by the success of MPTCP, MPBond applies the concept of distributed multipath transport where multiple subflows can traverse different devices. We develop device/connection management schemes, a buffering strategy, a packet scheduling algorithm, and a policy framework tailored to MPBond’s architecture. We implement MPBond on commodity mobile devices such as Android smartphones and smartwatches. Our real-world evaluations using different workloads under various network conditions demonstrate the efficiency of MPBond. Compared to state-of-the-art collaboration frameworks, MPBond reduces file download time by 5% to 46%, and improves the video streaming bitrate by 2% to 118%. Meanwhile, it improves the energy efficiency by 10% to 57%.
2

Poster: Characterizing Performance and Power for mmWave 5G on Commodity Smartphones S3 '19

Xumiao Zhang, Xiao Zhu, Yihua Ethan Guo, Feng Qian, Z. Morley Mao
Proceedings of the 2019 on Wireless of the Students, by the Students, and for the Students Workshop (S3 '19)

During the first half of this year, three major operators in the US have announced their 5G deployment, which indicates the advent of next generation networks. To reduce the time to market, carriers utilize 5G NR for data plane operations while retaining the existing 4G infrastructure for control plane operations in what is called NSA (Non-Standalone) deployment mode defined as 5G system Phase I. EN-DC is a core technology of NSA 5G which supports the introduction of 5G services under 4G infrastructure. It enables a UE to connect to LTE and NR at the same time whereas the control plane connection is handled by LTE infrastructure. In this case, the UE will have only one single RRC state machine. Note that LTE RRC state machine contains RRC_CONNECTED and RRC_IDLE with different DRX settings while NR RRC has an additional state called RRC_INACTIVE. In this work, we aim to explore network and power characteristics for NSA 5G which introduces a surprisingly high data rate.
@inproceedings{Zhang:2019:PCP:3349621.3355729, author = {Zhang, Xumiao and Zhu, Xiao and Guo, Yihua Ethan and Qian, Feng and Mao, Z. Morley}, title = {Poster: Characterizing Performance and Power for mmWave 5G on Commodity Smartphones}, booktitle = {Proceedings of the 2019 on Wireless of the Students, by the Students, and for the Students Workshop}, series = {S3'19}, year = {2019}, isbn = {978-1-4503-6929-9}, location = {Los Cabos, Mexico}, pages = {14--14}, numpages = {1}, url = {http://doi.acm.org/10.1145/3349621.3355729}, doi = {10.1145/3349621.3355729}, acmid = {3355729}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {4g, 5g, lte, nr, power model, rrc state machine, smartphone}, }
1

Demo: The Sound of Silence: End-to-End Sign Language Recognition Using SmartWatch MobiCom '17

Qian Dai, Jiahui Hou, Panlong Yang, Xiangyang Li, Fei Wang, Xumiao Zhang
Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking (MobiCom '17)

Sign Language is a natural and fully-fledged communication method for deaf and hearing-impaired people. In this demo, we propose the first SmartWatch-based American sign language (ASL) recognition system, which is more comfortable, portable and user-friendly and offers accessibility anytime, anywhere. This system is based on the intuitive idea that each sign has its specific motion pattern which can be transformed into unique gyroscope and accelerometer signals and then analyzed and learned by using Long-Short term memory recurrent neural network (LSTM-RNN) trained with connectionist temporal classification (CTC). In this way, signs and context information can be correctly recognized based on an off-the-shelf device (e.g. SmartWatch, Smartphone). The experiments show that, in the Known user split task, our system reaches an average word error rate of 7.29% to recognize 73 sentences formed by 103 ASL signs and achieves detection ratio up to 93.7% for a single sign. The result also shows our system has a good adaptation, even including new users, it can achieve an average word error rate of 21.6% at the sentence level and reach an average detection ratio of 79.4%. Moreover, our system performs real time ASL translation, outputting the speech within 1.69 seconds for a sentence of 12 signs in average.
@inproceedings{Dai:2017:DSS:3117811.3119853, author = {Dai, Qian and Hou, Jiahui and Yang, Panlong and Li, Xiangyang and Wang, Fei and Zhang, Xumiao}, title = {Demo: The Sound of Silence: End-to-End Sign Language Recognition Using SmartWatch}, booktitle = {Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking}, series = {MobiCom '17}, year = {2017}, isbn = {978-1-4503-4916-1}, location = {Snowbird, Utah, USA}, pages = {462--464}, numpages = {3}, url = {http://doi.acm.org/10.1145/3117811.3119853}, doi = {10.1145/3117811.3119853}, acmid = {3119853}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {activity recognition, mobile sensing, wearable computing}, }

Teaching

Fall 2017
Fundamentals of Database Systems

Awards

February 2019
Student Travel Grant, ACM HotMobile 2019
April 2018
Outstanding Graduate, Provincial Department of Education of Anhui
2014 – 2017
Outstanding Student Scholarship, USTC (for 4 consecutive years)
January 2017
Honorable Mention of Mathematical Contest in Modeling 2017 (MCM)
June 2016
Second Prize of USTC Electronic Design Contest, Institute of Electronics, CAS
October 2015
Outstanding Student Leadership, USTC
May 2015
Social Responsibility Scholarship, USTC
September 2014
Outstanding Freshman Scholarship, USTC

Academic Activities


Contact

Please contact me via email: base64 -d <<< "eHVtaWFvQHVtaWNoLmVkdQ=="


You are the No. Web Counters th vistor of my homepage.