Neural Networks and Deep Learning, Big Data Analysis and Machine Learning Mobile Sensing and Cloud Computing Environmental MonitoringPhD Student @ Information Processing on Sensor Networks, Statistical Pattern Recognition and Machine Learning From August 2011 to Present (4 years 3 months) Research Intern @ End-2-end Deep Learning platform for mobile and smart-watch applications with backend server. Scene labeling....
Neural Networks and Deep Learning, Big Data Analysis and Machine Learning Mobile Sensing and Cloud Computing Environmental MonitoringPhD Student @ Information Processing on Sensor Networks, Statistical Pattern Recognition and Machine Learning From August 2011 to Present (4 years 3 months) Research Intern @ End-2-end Deep Learning platform for mobile and smart-watch applications with backend server. Scene labeling. From May 2015 to August 2015 (4 months) Mountain view, caResearch Intern @ Developed the first real-time system of its kind that that addresses the heterogeneity problem in today’s smart-home systems. This system, aka SPOT (smartphone-based platform to tackle heterogeneity in smart-home systems) is buuilt on open standards and public proprietary APIs. and acts as a gateway to the smart-home and based on user preferences, provides a vendor agnostic seamless interaction to multiple appliances and devices located in the home. Although SPOT is designed as a platform for whole-home smartphone based application, but also as an app, won was awarded the silver prize in the 21st International Conference on Mobile Computing and Networking (MobiCom 2015) held in Paris from September 7 – 10, 2015. A short abstract of the work is outlined as follows. The recent advances in smart-home technologies, including broad penetration of Internet-connected smart appliances and IoT devices such as remotely controllable LED lights, thermostats, cameras, motion sensors, and door locks, has changed the way we interact with home appliances and perform our daily activities. However, the significant heterogeneity in the emerging smart appliances and IoT devices has led to fragmented smart-home systems in which each single appliance vendor provides proprietary solution for specific connectivity and user experience. To address this problem, together with my collaborators at Fujitsu Laboratories of America, we developed the first smartphone-based platform for multi-vendor smart-home appliances that provides a unified solution and a user interface tuned to the features of each appliance using open source software. From May 2014 to April 2015 (1 year) Sunnyvale, CaliforniaStudent @ From 2002 to 2007 (5 years) Master's degree, Computer Science, 3.88/4.0 @ Michigan State University From 2009 to 2011 Bachelor, Computer Engineering, 3.5/4.0 @ Sharif University of Technology From 2002 to 2007 Diploma, Physics & Math @ Sampad From 1995 to 2002 Mohammad Moazzami is skilled in: Data Analysis and..., Machine Learning, Probability Theory, Stochastic Processes, Information Theory, Statistical Inference, Sensor Fusion, Algorithms, Pattern Recognition, Matlab, Linux, C++, Data Mining, Programming, Java, C, R, Python, Software Engineering, Data Structures, Statistics, Optimization, Signal Processing, LaTeX, Mathematical Modeling
Michigan State University
August 2011 to Present
Samsung Research America Inc.
May 2015 to August 2015
Mountain view, ca
Fujitsu Laboratories of America
May 2014 to April 2015
Sharif University of Technology
2002 to 2007
What company does Mohammad Moazzami work for?
Mohammad Moazzami works for Michigan State University
What is Mohammad Moazzami's role at Michigan State University?
Mohammad Moazzami is PhD Student
What industry does Mohammad Moazzami work in?
Mohammad Moazzami works in the Computer Software industry.
Issued by MobiCom · September 2015
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