RESUME

GRADUATE RESEARCH ASSISTANT, UNIVERSITY OF MEMPHIS

September 2016 - December 2017

Distributed Machine Learning for Biomarkers Detection from Wearable Sensor Big Data[Python] [January 2017 - April 2017]
- Developed Distributed Machine Learning (ML) module for training ML models on multiple clusters with Apache Spark for detecting bio-markers from multimodal wearable sensor data.
- Developed Grid & Random Grid Search CV module for training time and parameter search optimization.
- Detected bio-markers (psychological stress) from big stream data (accelerometer, ECG, respiration rate) from multi-modal wearable sensors with prediction accuracy (F-1 Score) of 87% with SVM radial kernel.


Survey on Machine Learning based Physical Activity Recognition Methods from Sensor Data [December 2016 – February 2017]
- Conducted research on machine learning based algorithms for physical activity recognition (e.g. walking, running, eating, and drinking) from multimodal wearable sensor data.


Big Data Application for Large Scale US Stock Market Data Analysis [Java, Apache Spark] [May 2017 - July 2017]
- Developed Big Data framework for processing and analysis of 7 years of historical US stock market data (50 TB) with nanosecond granularity from 13 US exchanges on multiple clusters with Apache Spark.
- Added support for information extraction from binary files based on field spec for multiple years, file formats.
- Conducted multi-market analysis (for market dominance detection), anomaly detection (for Flash crash day).


Analysis of Pediatric Asthma Data in City of Memphis [Excel, Python] [September 2017 - December 2017]
- Surveyed multiple pediatric asthma risk factors including environmental factors (air quality) & living conditions.
- Conducted data analysis on survey data collected from parents in multiple schools in Shelby county, Memphis, Tennessee, USA over few months on around 23 factors.

VOLUNTEER RESEARCH, UNITED NATIONS VOLUNTEERS

July 2020 - September 2020

Economic Model Development for Covid-19 Pandemic with Machine Learning    [Jul - Sep 2020]
-    Conducted analysis for detecting relationships among 24 economic features to develop map for economic crisis in COVID-19 and probable solution adopted by government.
-    Dataset consisted of 20 years of country level economic data with 24 features.
-    Applied machine learning for deriving relationship among the features and developed visualization.

VOLUNTEER RESEARCH, UNIVERSITY

May 2021 -  Current

Development of Semantic Knowledge and Category-based Reasoning [Python, R]
- Using natural language processing, data science and machine learning for analyzing development of semantic knowledge and category-based reasoning.