Genentech (AI scientist):
As an AI scientist I am responsible for developing machine learning tools for clinical pharmacology applications, that involves survival prediction, PK/PK analysis, and tumor dynamic modeling. In these applications I use various modeling techniques, including geometric deep learning, neural-odes, causal learning. The machine learning models are being used in different aspects of drug development process.
Bayer:
I was a data science (computer vision) intern in summer 2020 at Bayer Crop Sciences, where my research was focused on developing a deep learning based super resolution algorithm for increasing the resolution of low quality images. I integrated meta-learning, few-shot learning, transfer learning, unsupervised learning to perform the task. Bayer provides a very exciting and incentive environment for specifically data science intern to grow both personally and technically, as you have to interact with a team of data scientists and present your work in different groups for different managers. The model you develop as an intern will be deployed by other groups, as your product, so the data science intern learn model deployment, the ultimate task of machine learning engineering.
Genentech (intern):
I was a machine learning algorithm developer intern at the Department of Biomedical imaging. My task was developing a super resolution algorithm to convert low resolution 3D MRI of mice kidney into high resolution images. My second task was implementing a segmentation framework to automatically detect mice kidney and extract different features such as kidney volume, to speeding up the studies made on kidney at Roche and Genentech. The results of my work published in the SSIAI 2020 as a conference paper