ESP Biography



SHOUVIK MANI, Computer Science PhD Student at Stanford




Major: Computer Science

College/Employer: Stanford

Year of Graduation: G

Picture of Shouvik Mani

Brief Biographical Sketch:

I'm a first-year PhD in Computer Science student and a Bay Area native from San Jose, CA! My research lies at the intersection of machine learning and cancer genomics. Specifically, I'm excited about developing new machine learning methods which can both learn from data and incorporate prior knowledge, and applying these methods to discover new biology and enable precision medicine.

In my free time, I enjoy running, biking, and playing soccer!



Past Classes

  (Clicking a class title will bring you to the course's section of the corresponding course catalog)

S7933: Computational Biology: Deciphering the Human Genome to Understand Biology and Disease in Splash Fall 2023 (Dec. 02 - 03, 2023)
In each one of your cells is a copy of your genome – a 3.2 billion-letter-long DNA biomolecule which serves as the blueprint of your body. These letters are the language of life and can offer valuable insights into the mechanisms underlying normal biology and disease. However, the sheer size and complexity of the genome makes it impossible for doctors and scientists to manually analyze and interpret genomic datasets. In recent decades, the field of computational biology has emerged to address this challenge. By developing methods and algorithms to analyze genomic sequencing data, computational biologists have made new biological discoveries and are advancing the dream of precision medicine. In this course, you will dive into this exciting field at the intersection of computer science and biology. You will learn about the central dogma of biology (DNA --> RNA --> protein) and modern sequencing technologies which can measure molecules in each step of this process. Then, you will learn about algorithms which can use this sequencing data to solve important tasks, from identifying disease-associated genes to predicting treatment response/resistance to designing new antibodies for next-generation therapies. You will leave the course with a foundation to build upon so that you can one day shape the future of medicine.