Application of Advanced Data Science and Machine Learning in modeling device usage models and tracking user behavior

The proliferation of smartphones opens a new horizon for behaviour change interventions. Equipped with cutting-edge sensing technology and high-end processors, smartphones can both unobtrusively sense human be- haviour and be an ideal platform for delivering feedback. In this article, we describe how modern-day smartphones are paving the way for future mobile-based behaviour change analytics, and discuss some of the smartphone-based behaviour sensing applications. Today, personal data is becoming a new economic asset. Personal data which generated from our smartphone can be used for many purposes such as identification, recommendation system, and etc. The purposes of this talk is to discover human behavior based on their smartphone life log data and to build behavior model which can be used for human identification. In this talk study, we have collected user personal data from 300 million smart phone users for 2 years which consist of 32 kinds of data sensors gyro, compass, speaker, proximity, pressure, accelerometers etc. There is still no ideal platform that can collects user personal data continuously and without data loss. The data which collected from user’s smartphone have various situations such as the data came from multiple sensors and multiple source information which sometimes one or more data does not available. I will demonstrate new approach to building human behavior usage model which can deal with those situations. Furthermore, I show new methods to apply advanced data science and machine learning to understand customer usage of smartphone from true big data containing billions of data points.

Experienced engineer, entrepreneur and seasoned Statistician / programmer with 14 years of combined experience working on product service/ channel /warranty management in electronics consumer products industry at Apple Inc. (4 years), yield engineering in semiconductor manufacturing at Qualcomm (6 years) and quality engineering in automotive industry at Ford Motor Company (OEM, Tier2 Suppliers) (3 years). MBA from Johnson Graduate School of Management at Cornell University, 3 Master of Science (MS) degrees in diverse engineering and mathematical sciences. Industrial Systems Engineering (Rochester '02), Applied Statistics (Cornell '04)..Developed skills in Big Data, Hadoop, NoSQL, Spark for Scalability and implementation of big data solution for the enterprises. Cloudera certified Hadoop developer, Cloudera certified Data Scientist and Amazon AWS Certified Solutions Architect- Associate. Industry renowned ASQ certifications: Certified Quality Engineer (CQE '06), Certified Reliability Engineer (CRE '07) and Six Sigma Master Black Belt (CSSBB '09). Skills across hardware and software platforms. Naturalized US Citizen.