Curriculum Vitae

Jan 25, 2019

Qu Tang

Ph.D. student (Expected graduation date: 2019)

Department of Electrical and Computering Engineering, College of Engineering

Northeastern University, Boston, USA

Mailing Address


360 Huntington Avenue

Northeastern University

Boston, MA 02115, USA

Online Presence


Google Scholar

LinkedIn LinkedIn profile

Github Github Profile

Personal website and blog

ORCID orcid id: 0000-0001-5415-0205

mHealth Research Group

Knoury College of Computer and Information Sciences

Personal Health Informatics

Short Bio

Qu Tang, S.M., is a sixth year Ph.D. student in the Department of Electrical and Computer Engineering at Northeastern University, Boston, USA. His research focuses on the development of mobile health systems that integrate ideas from ubiquitous computing, machine learning, exercise science, and algorithms. Areas of special interest include algorithms for automated recognition of human behaviors using wearable sensors, and mobile technologies for measuring longitudinal human behaviors via biomarkers (e.g., motion, heart rate, etc).

Mr. Tang received his S.M. from Northeastern University in 2013 working on developing activity recognition algorithms at mHealth Research Group, and a B.S.E. degree in Electrical Engineering from the University of Electronic Science and Technology of China (UESTC) in 2010. He has published research on activity recognition, physical behavior measurement using accelerometers, and real-time system for measuring human behaviors. Mr. Tang has been serving as a reviewer for conferences and journals including IMWUT, Sensors, and JMIR.

Research Interests

Personal health informatics; computational sensing, applied machine learning for preventive medicine; interactive machine learning; interpretable machine learning; data visualization; big data engineering; sensor-enabled mobile health technologies.


Northeastern University, Boston, MA, US

mHealth Research Group

Ph.D. in Computer Engineering (In progress)

Area of specialization: interactive machine learning, interpretable machine learning, activity recognition, wearable sensing

Advisor: Dr Stephen Intille

Committee members: Dr Stephen Intille, Dr Misha Pavel, Dr Deniz Erdogmus

Northeastern University, Boston, MA, US

mHealth Research Group

M.S. in Eletrical Engineering

Master thesis: Automatic smoking detection with wrist accelerometers

Advisor: Dr Stephen Intille

Committee members: Dr Stephen Intille, Dr Waleed Meleis, Dr Deniz Erdogmus

Courses: Digital Signal Processing, Computer Vision, Machine Learning, Linear System Analysis, Computer Simulation and Evaluation, Mobile Application Development in Android, Adaptive Filtering, Time Series Analysis.

Area of specialization: digital signal processing, activity recognition, applied machine learning

University of Electronic Science and Technology of China (电子科技大学), Chengdu, China

Department of Opto-electronical Science and Technology

B.E. in Eletrical Science and Technology

Scholarships: National Scholarship of China, 2007-2009

Professional Appointments and Research Experience

Northeastern University, Boston, MA

Jan 2013-Now

mHealth Research Group

Research Assistant

Published research in applied machine learning for activity recognition using wearable sensors; built passive mobile sensing system and mobile ecological momentary assessment system to measure human behaviors; built software packages and visualization tools for data processing and machine learning.

Advisor: Prof. Stephen Intille

Schepens Eye Research Institute, Boston, US

Dec 2011-Aug 2012

Vision Rehabilitation Laboratory

Cooperative eduation (Coop) Research Assistant

Built software using professional eye tracking systems to measure and test vision rehabilitation technologies (i.e. prism glasses); built visualization tools to analyze vision rehabilitation measurements; IT support.

Advisor: Dr. Eli Peli

Publications in Refereed Journals

D. John, Q. Tang, F. Albinali, and S.S. Intille, A monitor-independent movement summary to harmonize accelerometer data processing. In: Journal of Measuring Physical Behavior (JMPB); 2018; Status: submitted.

Henwood, B.F., Redline, B., Dzubur, E., Madden, D.R., Rhoades, H., Dunton, G.F., Rice, E., Tang, Q. and Intille, S.S., 2018. Investigating health risk environments in housing programs for transition-aged youth using geographically explicit ecological momentary assessments. Contemporary Clinical Trials, preprint.

Houston, K.E., Bowers, A.R., Fu, X., Liu, R., Goldstein, R.B., Churchill, J., Wiegand, J.P., Soo, T., Tang, Q. and Peli, E., 2016. A pilot study of perceptual-motor training for peripheral prisms. Translational vision science & technology, 5(1), pp.9-9.

Paper Presentations at Refereed Conferences

Troiano, R., Intille, S., John, D., Chhetry, B.T. and Tang, Q., 2018, October. NHANES and NNYFS wrist accelerometer data: Processing 7TB of data for public access. In JOURNAL OF PHYSICAL ACTIVITY & HEALTH (Vol. 15, No. 10, pp. S19-S19). 1607 N MARKET ST, PO BOX 5076, CHAMPAIGN, IL 61820-2200 USA: HUMAN KINETICS PUBL INC.

Goodwin, M.S., Haghighi, M., Tang, Q., Akcakaya, M., Erdogmus, D. and Intille, S., 2014, September. Moving towards a real-time system for automatically recognizing stereotypical motor movements in individuals on the autism spectrum using wireless accelerometry. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 861-872). ACM.

Tang, Q., Vidrine, D.J., Crowder, E. and Intille, S.S., 2014, May. Automated detection of puffing and smoking with wrist accelerometers. In Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare (pp. 80-87). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

Industrial Training Programs

Wayfair LLC

Jan 8 2018 - Jan 11 2018

PhD Data science immersion program

Invited PhD student

Learned structure, operation, supply chain and workflow for data-driven e-commerce company; understood industrial thinking (minimal viable product, business-oriented, KPIs in marketing and pricing); completed and presented a 2-day hackathon project with two teammates; used Random Forest and Gradient Boost algorithms to predict customer conversion rate based on TV advertising.

Mentor: Nathan Vierling-Claassen

Teaching Experience

Northeastern University, Boston, US

Jun 2018 - Aug 2018

Research experiences for undergraduates program REU-D3

Student Mentor

Guided an assigned undergraduate student for an applied machine learning project for activity recognition using wearable accelerometers.

Mentee: Ryan Cleary

Program Director: Prof. David Kaeli

Northeastern University, Boston, US

Spring 2016

CS4300: Computer Graphics (Undergraduate)

Teaching Assistant

Held weekly Office hours; graded homeworks

Lecturer: Amit Prakash Shesh

Other Experience

Northeastern University, Boston, US

Sep 2014-Now

mHealth Research Group

IT Admin

Managed linux server and virtual machines; wrote IT admin documents; managed group and PHI program websites.

Northeastern University, Boston, US

Sep 2014-Dec 2014

Ph.D. program of personal health informatics

Seminar coordinator

Organized weekly Ph.D seminar (7-8 times per semester, 10-20 people); selected, invited and hosted speakers (internal or external faculties and researchers); advertisement.

Participated Research Projects

CamSPADES physical activity measurement study To collect passive motion and location data in a multi-day scale with multiple types of annotations (i.e., first-person camera) and develop activity recognition algorithms.

May 2018-Now

Developed softwares for data collection, cleaning and screening; Developed sensor configuration invariant activity recognition algorithms.

NHANES accelerometer data processing project

Sep 2017-Sep 2018

Developed data cleaning and screening procedure and algorithms; Developed a novel accelerometer data summarization algorithm called MIMS; Helped designing distributed data processing system to process big data (~1.66TB).

Log My Life study To explore the connection between supporting house and high risk behaviors for teenagers using mobile momentary ecological assessment surveys and geological data.

Sep 2016-Now

Developed a mobile survey and location sensing system on Android.

SPADES physical activity measurement study To collect passive motion and location data in a multi-day scale with experience sampling surveys and develop activity recognition algorithms.

Jan 2015-Now

Developed softwares for data collection, cleaning and screening; Developed a mobile passive sensing system on Android phone and smartwatch to ensure collecting data at high sampling frequency (50Hz, 100Hz) sensory data and run 24/7 for three months; Developed activity recognition algorithms with multiple (up to seven) accelerometers.

Autism stereotypical movement disorder (ASD) study To develop ASD detection algorithm using wearable accelerometers.

Sep 2013-Jan 2014

Developed the ASD detection algorithm using decision tree; Developed statistical methods to compare results from different studies.

Smoking cessation study to collect passive sensing data from smokers and develop puffing and smoking detection algorithms using wearable accelerometers.

Sep 2012-Dec 2013

Developed and validated a novel hierarchical machine learning model to detect puffing and smoking using Random Forest and Moving Average Filtering.

Pilot study for perceptual-Motor training for peripheral prisms to explore methods to train vision-impaired people to develop perceptual motor skills when using peripheral prisms.

Dec 2011-Aug 2012

Developed the percpetual-motor training software in VB and C#; Developed 3D virtual mall system with professional eye tracking system to measure scotoma field and field expansion when using peripheral prisms.

Participated Projects in Open Source Community

Hexo theme: cutie

Jun 2017-Now

Developed a blog theme for Hexo static website engine.

“MIMSunit” R package

Nov 2016-Oct 2018

Developed MIMSunit algorithm (for summarizing motion using accelerometer data) as an R package.

Tower Airdrop: an Android exercise game

Sep 2013-Dec 2013

Designed the game concept and developed the physical engine system using Box2D, and tilting and shaking motion detection using inertial sensors on Android.

Collaborator: Zessie Zhang


Dr Stephen Intille

Ph.D. Advisor

Associate Professor

College of Computer and Information Science

Northeastern University

360 Huntington Ave

Boston, MA 02115 US

Dr Eli Peli

Coop Advisor

Professor and Senior Scientist

Schepens Eye Research Institute

20 Staniford St Boston, MA 02114 US