- Built a computer vision model to help doctors quickly identify key stages of the heart's pumping cycle in ultrasound videos, as part of a team of data scientists.- Used FFT (Fast Fourier Transform) to improve accuracy of heart condition diagnosis, resulted in an improvement of 10 percent in accuracy.- Collaborated with medical experts to ensure the model's results were clinically useful and could be easily integrated into their workflow.- Tested and validated the model using real patient data to ensure it would perform well in a real-world hospital setting.

- Co-authored a major study published in a leading cancer research journal, data analysis of tumor progression patterns in over 400 brain cancer patients. The research revealed important insights into how a new electric field therapy affects cancer growth, potentially improving treatment strategies.- Automated extraction and analysis of data from cell culture experiments using Python, pandas, making data processing more efficient.

- Played a key role in analyzing data from a major clinical trial (EF-14), using simulations to optimize electrical field treatment delivery. This work contributed to the development of treatment planning software now used by doctors to improve patient care.- Used Python and MATLAB to identify and solve issues in the simulation process, improving the accuracy and efficiency of the models.- Detected and fixed bugs in the computational model of head segmentation data, enhancing the reliability of the simulations.