I am very interested to apply for the position of Volunteer at the Solve for Good platform.I am a second-year graduate student pursuing an MS in Environmental Science and Policy at the Harris School of Public Policy (University of Chicago) and I will be graduating in December 2020 only. I am currently involved in a climate science emulator research project and spatial data analysis for modeling air quality along the Chicago bus routes. I have gained skills in data analysis and worked on Big Data during my 2 year course here at the school. I am proficient with the use of statistical tools such as Python, R, STATA, SQL, NoSQL-MongoDB, Neo4j, Q-GIS, and MS Excel and have worked with big datasets. Along with quantitative skills, I have completed a Qualitative Analysis course at the University and I am proficient with MAXQDA software. I have built my skills in data visualization using R, Python, Tableau & Looker. I have completed projects in predictive analysis and have built a ETL pipeline to estimate the energy efficiency of the buildings in Chicago. I worked as a consultant to government projects in India for more than 5 years and was involved systems development and business functional consulting roles. I also had worked in techno-commercial consulting for off-shore projects and have project management experience of a thermal power plant and manufacturing industry. I am interested in data analysis and engaging insightful predictive analysis which creates value propositions and solutions for the organization.
|Project name||Organization name||Social impact area||Project summary||Task name||Task status|
|Cloud Enabled Social Media Analytics Framework for Crisis Management||Artificial Intelligence and Data Engineering (AIDE)Research Center||Education||This project aims to develop a cloud-based computing framework that will systematically monitor, collect and integrate disaster and crisis-related data streams from diverse social network channels, and turn them into actionable intelligence in a knowledge-base for intelligent inferencing, crisis handling, and decision making. The key components of the proposed framework are three: First, the collection of multilingual and multimodal data (text, audios, videos, images, and location information or other sensor-data) and their conversion into a unified machine-understandable dynamic knowledge-base. Second, the application of Natural Language Processing (NLP), Data Mining, Machine Learning, and Artificial Intelligence techniques for automatic identification, prediction, and detection of any type of crisis or disaster using the unified knowledge-base from the previous stage. 3) Third, the development of an interactive dashboard visualization to facilitate crisis monitoring and management.||Text Preprocessing||Started|