As the head of a small Image Analysis Group in the Information Systems Department, my main area of expertise revolves around spearheading research and development computer vision models with a particular focus on biomedical tasks. My role encompasses tasks such as model and framework development and rigorous testing, team management to ensure optimal performance, mentoring and reviewing team members' design, algorithms, and code. I work with various research projects such as:-Prediction of endometrial receptivity on histology slide-Detection of cervical cancer cells in whole slide image-Segmentation of breast masses in mammogram images

As part of my professional undertakings, I developed machine learning models, including linear/logistic regression, random forest, gradient boosting, and fully connected networks, to accurately predict customer behavior, such as the probability of a successful call to a client. I successfully managed challenges like class imbalance, model overfitting, and inadequate training data, using robust strategies to ensure the accuracy and reliability of the models.

I had the privilege of working and conducting my graduate research in the esteemed laboratory of Statistical Analysis. During my tenure, my primary focus was centered on the creation of a predictive model aimed at accurately estimating the parameters of physical processes. To achieve this, I employed linear regression techniques that were fed with experimental data as input, enabling the development of a comprehensive and reliable model.