Anish Dalal

Machine Learning Engineer

Anish Dalal is a Machine Learning Engineer at Manifold. In this capacity, he works on a range of projects—from prototyping new algorithms to delivering solutions to production—and collaborates with our data and software engineers to accelerate clients’ projects.

Prior to Manifold, Anish earned his graduate degree in data science. While doing so, he developed an end-to-end 3D image segmentation pipeline using deep learning and convolutional neural networks; this pipeline allowed physicians to assess patient safety by measuring the volume of brain ventricles. He also implemented a text classification pipeline to predict cancer patient survival from clinical, imaging, and pathology notes.

Before his graduate work, Anish was a Software Engineer at GenomOncology, a company that develops software-based support for oncologists. There, he worked on a variety of health-related projects, including a Python and Django-based REST API that aggregated clinical trials and therapy recommendations for cancer patients, based on the type of cancer and genetic mutations. He also gained product experience at GenomOncology, particularly around feature design.

Machine learning continues to progress rapidly—its key that practitioners are able to stay on top of new developments and think through complex solutions. Thanks to his startup experience, Anish is well-versed in fast-paced projects that require excellent prioritization skills. His graduate work and his work with health data exposed him to a variety of machine learning and product development tools; he’s excited to use these skills to delight clients.

“At Manifold, we’re encouraged to really think about the right things to build,” he says. “It’s very satisfying to combine software engineering and product development in order to solve our clients’ business challenges.”

When he’s not working on machine learning, Anish can be found playing tennis, learning how to play golf, and working through his to-read list.

Anish earned his BS in Biomedical Engineering from the University of Virginia, and his MS in Data Science from the University of San Francisco.