- Consultant to the Data Science team at Dognosis

- Research and development of machine learning algorithms for breast cancer medical applications for surgical navigation, a major focus on CNN-based object detection with the object of interest being a biopsy marker in raw ultrasound data.- Performed end-to-end machine learning model development, overseeing data labeling, preprocessing, model training, evaluation, and deployment.- Implemented optimization techniques in training, including efficient data preprocessing through methods like tfrecord files and automated hyperparameter optimization.- Successfully minimized Inference model latency through various deployment optimizations.- Furthermore, designed and conducted various experiments for post-training analysis, delving into different features of the data. This deep analysis enabled me to fine-tune both the data and the model for enhanced performance.

Research and development of machine learning algorithms for breast cancer medical applications for surgical navigation, a major focus on CNN-based object detection with the object of interest being a biopsy marker in raw ultrasound data. (Contract transferred to Aura Health Technologies)

- Worked on Deep Learning based Segmentation algorithm as a part of master thesis.- Developed an MFC based C++ Software that facilitates the detection of a biopsy marker in a breast tumor with the help of an ultrasound device- Developed a python pipeline that reads the ultrasound data and converts it into a B-Mode Image-

Worked on Bachelor's Major Project: Smart Wireless Sensor Network for Monitoring an Agricultural Environment.

Worked on Bachelor's Minor Project: Automatic Street Light Control System