Deep Learning

This project focuses on detecting defects in jet engines using ultrasound images, where the involved tasks included analyzing varied input data, preprocessing different data modalities, and implementing advanced techniques like anomaly detection, self-supervised learning, and transfer learning for machine vision applications in a military context.

The project aimed to investigate and implement state-of-the-art methods to enhance the explainability, interpretability, and robustness of deep learning networks in the context of 6G Sky applications.

The project aimed to assess the efficacy of data-driven machine learning methods, particularly deep learning models, for enhancing channel estimation in 5G communication systems using FunKI.

The project involves actively participating in a laparoscopic cholecystectomy operation to address and resolve the depth perception challenge associated with this surgical procedure.

The project aims to predict gene expression by analyzing millions of random promoter sequences, addressing challenges such as class imbalance and comparing the efficacy of convolution-based and attention-based networks in capturing local patterns.

The goal of this project is to predict prostate cancer by implementing the learning pipeline with self-supervised learning and transfer learning.