Sagar Setru

About Sagar

I am a scientist with broad and growing interests. Physics, biology, machine learning, artificial intelligence, drug development, marketing, and crypto are areas where I have done research, worked, and built. Sometimes this is professionally or academically. Other times it is as a hobbyist. I enjoy working with data, designing and conducting experiments, and using machine learning and artificial intellligence to help people and solve business and scientific problems. Always be learning.

You can see my published academic work at Google Scholar, code repositories for various projects at GitHub, and my professional and academic history at LinkedIn.

Products

I made a Chrome extension, Debiaser, that recommends news articles related to the one you are reading, but from news sources across the political spectrum. It’s for curious consumers of news who value diverse perspectives. At the moment, you have to download it locally to use it, but I plan to list it at the Chrome web store.

Selected academic works

• Acentrosomal spindles assemble from branching microtubule nucleation near chromosomes
• A hydrodynamic instability drives protein droplet formation on microtubules to nucleate branches (Press coverage)
• The role of hydrodynamics in branching microtubule nucleation and the role of branching microtubule nucleation in acentrosomal spindle assembly (PhD thesis)
• Silicon Detector (SiD) Tracking Geometry Optimization Studies for the ILC
• Active drift stabilization in three dimensions via image cross-correlation

Learning

The best way to learn is to do, build, or teach. That said, I plan to update this section of my webpage with a longer list of the various resources I’ve used over the years that I found great for learning.

I’m a fan of Coursera. Here are the courses I’ve completed:

• Essential Causal Inference Techniques for Data Science
• Deep Neural Networks with PyTorch
• Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
• AI for Medical Prognosis
• Drug Development
• Neural Networks and Deep Learning