About

Priyanka Gohil is a highly motivated and skilled Master's in Computer Science student with a concentration in Data Science at the University of Massachusetts Amherst. She firmly believes that Data Science is going to revolutionize the way technology is utilized by studying different patterns, optimizing use cases, and implementing efficient models. Priyanka is passionate about making a change in the field of Data Science and is excited to utilize her skills and dive deeper into industry projects with the perspective of a Data Scientist. Currently, Priyanka works as a Graduate Student Researcher at Amazon where she has developed and deployed a new module of Big Data in Python for the NPTEL platform. She has optimized and automated error handling for Big Data issues such as network failures, schema errors, and latency issues. Priyanka has also automated the data analytics of students’ performance data based on real-time student analytics reports. In the past, Priyanka worked as a BigQuery Data Engineer at NPTEL where she designed, built, and deployed Google Cloud Platform infrastructure for the NPTEL platform that delivered NPTEL's data analytics requirements. She developed and implemented automation scripts for handling BigQuery issues such as network failures, schema errors, and latency issues. Priyanka also worked on the processing of unstructured data, including logs, backups & restore of BigQuery data in real-time, and automated the data analytics of students’ performance data based on real-time student analytics reports. Priyanka also worked as a Data Science Intern at Ashar IT where she performed detailed analysis on improving the CNN model used by Ashar with a focus on the effects of pruning a CNN model by experimenting with various sparsity ratios along with structured and unstructured pruning techniques. She analyzed the results with effective data visualizations to determine the top three combinations of pruning techniques, approaches, and sparsity ratios that will be best suitable for implementing the CNN model used by Ashar. The most efficient proposed way of pruning resulted in reducing model size up to 38% without affecting its performance. Priyanka's educational background includes a Master of Science - MS in Computer Science from the University of Massachusetts, which she is expected to complete in April 2023, and a Bachelor of Engineering in Computer Science from the University of Mumbai, which she completed in December 2019. Priyanka's tech stack includes Research Scientist, Data Scientist, Big Data, AWS, Data Engineering, Python, Web, CNN, GCP, and infra. With 2.