Guanling Chen - Computer Science - UMass Lowell

Fall 2017 Schedule

Office Hours: Tu/Th 14:00-15:30pm
Courses: COMP 4630 Mobile App Programming I, COMP 3500/5270 Introduction to Human Computer Interaction.

Project: Understanding and Influencing Personal Health Behaviors

Mobile and wearable devices enable us to study people's health behaviors in great details. In this project we aim to develop practical, energy-efficient, and user-friendly systems to automatically recognizing and influencing health behaviors.

+ Respiratory Sounds Feature Learning with Deep Convolutional Neural Networks. IEEE CyberSciTech, Orlando, FL, November 2017.

+ Classifying Respiratory Sounds using Electronic Stethoscope. IEEE UIC, San Jose, CA. August 2017.

+ Early Detection of Diseases using Electronic Health Records Data and Covariance-Regularized Linear Discriminant Analysis. IEEE/EMB BHI, Orlando, FL, February 2017.

+ A New Deep Learning-based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure, IEEE Transactions on Service Computing (TSC), January 2017.

+ WalkMore: Promoting Walking with Just-in-Time Context-Aware Prompts. IEEE Wireless Health, Bethesda, MD, October 2016.

+ Sleep Quality and Mental Disorders Among College Nursing Students: The Mediating Role of Perceived Stress and Coping Styles. APHA Annual Meeting & Expo, Denver, CO, October 2016.

+ iHearFood: Eating Detection Using Commodity Bluetooth Headsets. IEEE CHASE, Washington DC, June 2016.

+ DeepFood: Deep Learning-based Food Image Recognition for Computer-aided Dietary Assessment. IEEE ICOST, Wuhan, China, May 2016.

+ Assisting Food Journaling with Automatic Eating Detection. ACM CHI, San Jose, CA, May 2016.

+ Automatic Eating Detection Using Head-Mount and Wrist-Worn Accelerometers, IEEE HealthCom, Boston, MA, October 2015.

+ Monitoring Sleep and Detecting Irregular Nights through Unconstrained Smartphone Sensing, IEEE UIC, Beijing, China, August 2015. DATA

+ Changing Health Behaviors through Social and Physical Context Awareness, IEEE ICNC, Anaheim, CA, February 2015.

+ MVPTrack: Energy-Efficient Places and Motion States Tracking. MobiQuitous, Tokyo, Japan, December 2013.

Project: Mobile Analytics and Usability Testing

2010--2016, funded by NSF
We developed toolkits and algorithms for preditive analytics of mobile networking and app usage. In particular, currently there are few automatic mobile app usability testing systems and industry practice still heavily relies on manual efforts. We designed the first fine-grained interaction event logging system and a model-based event analytical system that can automatically identify usability problems for mobile apps.

+ Unsupervised Detection of Abnormal Moments for Usability Testing of Mobile Apps. ACM CHI, May 2016.

+ Beyond Smartphone Overuse: Identifying Addictive Mobile Apps. ACM CHI, May 2016.

+ Implementation of a Computerized Screening Inventory: Improved Usability Through Iterative Testing and Modification, Journal of Medical Internet Research Usability Factors (JMIR UF). Vol 3, No 1: Jan-Jun, 2016.

+ Controlling Smart TVs using Touch Gestures on Mobile Devices. UFirst, Beijing, China, August 2015.

+ A Pilot Study of an Inspection Framework for Automated Usability Guideline Reviews of Mobile Health Applications. Wireless Health, NIH Bethesda, MD, October 2014.

+ A New Method for Automated GUI Modeling of Mobile Applications. MobiQuitous, Tokyo, Japan, December 2013.

+ Providing Diagnostic Network Feedback to End Users on Smartphones. IEEE IPCCC, San Diego, CA, December 2013.

+ Automatic Mobile Photo Tagging Using Context. IEEE TENCON, Xi'an, China, October 2013.

+ Design and Implementation of a Toolkit for Usability Testing of Mobile Apps. ACM/Springer Mobile Networks and Applications (MONET). 18(1):81-97. February 2013.

+ Nihao: A Predictive Smartphone Application Launcher. MobiCase, Seattle, WA, October 2012.

+ Predicting Mobile Application Usage Using Context Information. ACM UbiComp, Pittsburgh, PA. September 2012.

+ AppJoy: Personalized Mobile Application Discovery. ACM MobiSys, Washington, DC, June 2011. DATA

Project: Social Media Analysis and Crowdsourcing Modeling

2008--2015, funded partly by NSF
+ Near-Optimal Incentive Allocation for Piggyback Crowdsensing. IEEE Communications Magazine. June 2017.

+ iCrowd: Near-Optimal Task Allocation for Piggyback Crowdsensing. ACM Transaction of Mobile Computing (TMC). September 2015.

+ CrowdTasker: Maximizing Coverage Quality in Piggyback Crowdsensing under Budget Constraint. IEEE PerCom, St. Louis, Missouri, March 2015.

+ CrowdRecruiter: Selecting Participants for Piggyback Crowdsensing under Probabilistic Coverage Constraint. ACM UbiComp, Seattle, WA, September 2014.

+ Exploring Structural Analysis of Place Networks Using Check-In Signals. IEEE GLOBECOM, Atlanta, GA, December 2013.

+ Understanding Weight Change Behaviors Through Online Social Networks. International Journal of Computational Models and Algorithms in Medicine (IJCMAM), 2(3):46-69, July-September 2011.

+ Malware Propagation in Online Social Networks: Nature, Dynamics, and Defense Implications. ASIACCS, Hong Kong, China, March 2011.

+ Sharing Location in Online Social Networks. IEEE Networks Special Issue on Online Social Networks, September-October 2010.

+ Multi-Layer Friendship Modeling for Location-based Mobile Social Networks. MobiQuitous, Toronto, Canada, July 2009. Best Paper Award.

Project: Performance and Security Monitoring for Wireless LANs

2005--2009, funded by DHS, joint work with Dartmouth College
+ Sniffer Channel Selection for Monitoring Wireless LANs. Computer Communications. 35(16):1994-2003, September 2012.

+ Robust Detection of Unauthorized Wireless Access Points. ACM/Springer Mobile Networks and Applications (MONET), 14:4, August 2009.

+ Model-based Fault Diagnosis for IEEE 802.11 Wireless LANs. MobiQuitous, Toronto, Canada, July 2009.

+ MAP: A Scalable Monitoring System for Dependable 802.11 Wireless Networks. IEEE Wireless Communications, Special Issue on Dependability Issues with Ubiquitous Wireless Access. August 2008.

+ Detecting 802.11 MAC Layer Spoofing Using Received Signal Strength. IEEE InfoCom, Phoenix, AZ. April 2008.