• Leveraged state-of-the-art deep learning and computer vision architectures, including SAM (Segment Anything Model), UNI and UNET to enhance cell segmentation for analysing breast cancer tumour datasets from Stanford.• Gained hands-on experience using Stanford GPU servers to run extensive computer vision algorithms, utilizing PyTorch, Python, OpenCV, and NumPy.• Built a Biomarker Quantification UI with Python Flask, JavaScript, and CSS, integrating AI models for cell segmentation and irregular cell identification, and deployed on AWS.

• Conducted thorough manual testing and generated extensive test suites for PackageX Receive across its Web, iOS, and Android platforms, encompassing black-box, unit, integration, usability, and system testing techniques.

• Developed C code for serial communication on a PIC microcontroller and tested a custom-built PCB SILMCU for an Autopilot Simulation System, monitoring output with RealTerm software.