Pooja Bhavsar
About Pooja Bhavsar
Pooja Bhavsar is a Technical Lead at STL - Sterlite Technologies Limited in Ahmedabad, India, with a background in computer engineering. She has experience in machine learning and network security, having interned at ISRO and Maxgen Technologies, where she achieved significant improvements in cyber threat detection and model accuracy.
Work at Sterlite Technologies
Pooja Bhavsar currently serves as a Technical Lead at Sterlite Technologies Limited (STL) in Ahmedabad, Gujarat, India. She has been in this role since 2023, contributing her expertise in technology and engineering. Her responsibilities include overseeing technical projects and leading teams to develop innovative solutions in the telecommunications sector.
Internship Experience at ISRO
In 2023, Pooja completed a five-month internship at the Space Applications Centre of ISRO in Ahmedabad, Gujarat. During her internship, she achieved a 92% average accuracy in developing and deploying machine learning models for network security. She processed over 50,000 real-time network data entries using Kafka and collaborated with the ITND department to integrate machine learning solutions, resulting in a 40% improvement in cyber threat detection.
Internship Experience at Maxgen Technologies
Pooja also interned at Maxgen Technologies Pvt. Ltd. in 2022 for one month. During this internship, she developed a Plant Disease Detection machine learning model that achieved an 85% accuracy rate. She optimized the model's performance through advanced image data preprocessing, which led to a 10% increase in accuracy.
Education and Expertise
Pooja Bhavsar earned her Bachelor of Engineering (BE) in Computer Engineering from Silver Oak University, completing her studies from 2019 to 2023. Her academic background provides her with a solid foundation in computer science and engineering principles, which she applies in her professional roles.
Achievements in Machine Learning
During her internship at ISRO, Pooja contributed to reducing potential cyber threats by 15% through predictive analysis. She created eight comprehensive reports and presentations that significantly boosted business development activities by 70%. Her work demonstrates her capability in applying machine learning techniques to real-world problems.