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Visual Exploration of Smoking Cessation Data

February 7, 2018


Polo Chau, PhD
Assistant Professor
School of CS & Engineering
Georgia Institute of Technology


Moushumi Sharmin, PhD
Assistant Professor
Department of Computer Science
Western Washington University

About the Webinar:

Part 1: Discovery Dashboard
Drs. Chau and Sharmin will present Discovery Dashboard [1], a visual analytics system for exploring large volumes of time series data from mobile medical field studies, in the web-browser and in real time. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. They will demonstrate their system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking.

Part 2: MyQuitPal
The first step in designing effective smoking cessation systems is to objectively identify factors that contribute to lapse. To this end, we present MyQuitPal [2], a participant-centric cessation support system, which aims to assist individuals to better understand their smoking behavior. MyQuitPal combines an affective mobile application [2] and a web-based analytics tool [3] to support reflection. The design of MyQuitPal is informed by in-depth analysis of physiological data collected utilizing wearable sensors from a four day pre-quit, post-quit study (N=55). Visualizations presented in MyQuitPal are also grounded on theories of long term health-behavior change.

Citations & Relevant Links:

[1] mHealth Visual Discovery Dashboard. Dezhi Fang, Fred Hohman, Peter Polack, Hillol Sarker, Minsuk Kahng, Moushumi Sharmin, Mustafa al’Absi, Duen Horng (Polo) Chau. Demo, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UBICOMP) Sept 11-15, 2017. Maui, USA.

[2] Opportunities and Challenges in Designing Participant-Centric Smoking Cessation System. Moushumi Sharmin, Theodore Weber, Hillol Sarker, Nazir Saleheen, Santosh Kumar, Shameem Ahmed, Mustafa al’ Absi. IEEE Computer Software and Applications Conference (COMPSAC), 2017, 835-844, Turin, Italy.

About Polo Chau and Moushumi Sharmin:

Polo Chau Ph.D., is an Assistant Professor at Georgia Tech’s School of Computational Science and Engineering, and an Associate Director of the MS Analytics program. He holds a Ph.D. in Machine Learning and a Masters in human-computer interaction (HCI). His Ph.D. thesis won Carnegie Mellon’s Computer Science Dissertation Award, Honorable Mention. His research group bridges data mining and HCI — innovates at their intersection — to synthesize scalable, interactive tools that help people understand and interact with big data. His group has created scalable deep learning visualization tools (deployed by Facebook), interactive graph querying system (SIGMOD’17 Best Demo, honrable mention), novel detection technologies for malware (patented with Symantec, protects 120M+ people), auction fraud (WSJ, CNN, MSN), comment spam (patented & deployed with Yahoo), fake reviews (SDM’14 Best Student Paper), insider trading (SEC), unauthorized mobile device access (Wired, Engadget); and fire risk prediction (KDD’16 Best Student Paper, runner up). He received faculty awards from Google, Yahoo, and LexisNexis. Dr. Chau also received the Raytheon Faculty Fellowship, Edenfield Faculty Fellowship, Outstanding Junior Faculty Award. He is the only two-time Symantec fellow. He leads the popular annual IDEA workshop that catalyzes cross-pollination across HCI and data mining. He served as general chair for ACM IUI 2015, and is a steering committee member of the conference. More about Polo Chau.

Moushumi Sharmin, Ph.D., is an Assistant Professor of Computer Science department at the Western Washington University. At Western, Dr. Sharmin co-directs the NEAT (Novel, Effective, Affective Technology) Research Lab, which focuses on designing participant-centric affective technology. Her research focuses on human-computer interaction, affective computing, and technology design. Currently she is investigating novel visualization techniques that support sense-making, pattern identification, and decision making of large scale data for behavioral health problems including autism spectrum disorder, and addiction, and harassment prevention. Students at the NEAT Lab have presented their work on addiction (GHC2017 – ACM SRC 2017 Runner-up (Undergraduate Category), CompSAC 2017), and autism (SIGCHI 2018). Dr. Sharmin is serving as the program committee chair for the Human Computing and Social Computing (HCSC) Symposium for IEEE CompSAC 2016, 2017 and 2018. She is a member of Google’s Women TechMakers and a fellow of the American Association of University Women. More about Moushumi Sharmin.

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