Nikhil M
- Research Program Mentor
PhD candidate at Stanford University
Expertise
Machine Learning, Statistical Learning, Statistical Genetics, Functional Genomics
Bio
I am interested in the field of statistical genetics, where I combine my passions of statistics, computer science, and genetics to better understand human traits and diseases. I love to understand and apply statistical and machine learning models. I have trained at the University of Cambridge as a Master's student, where I worked at the Wellcome Sanger Institute for a year to better understand the genetics underlying sepsis. Now, I have started a PhD at Stanford University in Genetics, where I also plan to pursue a Master's in Statistics! When I am not conducting research, I love to play musical instruments! I am a violinist by training, but have picked up the piano and guitar along the way. I also love to hike - the national parks in California provide beautiful places to do so!Project ideas
Machine learning approaches to better understand disease
The field of genomics is very good at sharing data publicly! Large studies conducted on humans have accrued millions of data points across many publicly-available data sets. These provide an amazing substrate to test the utility of machine learning approaches to better understand disease. A machine learning approach that is good at predicting disease from such data can be very helpful for medical professionals when making diagnoses. In this project, an interested student may start by downloading data from a few studies of interest and using machine learning or statistical learning approaches to identify genes that may contribute to disease. Students can compare various approaches and identify methods that work well in genomic datasets.
What can we learn from classical music?
Classical music spans multiple centuries, each with their own unique styles. This can make it quite fun to ask some questions using machine learning and statistical approaches: (1) Can we find out who composed a piece by looking at it? By hearing it? (2) What makes a composer famous? Is there anything similar about music across centuries? (3) Can computers make music that is as good as a trained composer?
