Sohit Miglani

PhD Candidate, Quantitative Biology @ Princeton University

B.Sc. Computer Science, Minerva University


Bioinformatics | Functional Genomics | Proteomics | Therapeutics




I am a mathematician turned biologist with 3 years of experience using computational and experimental approaches in functional genomics.

I have a passion for building biologically-informed tools and protocols that can guide us towards hidden patterns. I also hold proficiency in experimental biology to prepare my own libraries and functionally validate my results.

My experience with DNA and RNA is summarized in this flowchart:




Recent Contributions

Machine learning for discovering alternative polyA-site switches

October 2021


-Link to publication
-Link to Repository

Statistical Learning to estimate stalling in Glycine Codons after tRNA knockout

March 2021


-Link to Nature Comms publication
-Link to Repository

Automated web-app for QC and Visualisations of RNA-seq data

May 2020


-Link to deployed application.
-Link to repository.

Research Experience

Graduate Student Researcher, Princeton University

Princeton, NJ  |   August 2022 - Present

Junior Specialist II, University of California San Francisco.

San Francisco, CA  |   September 2020 - June 2022
- Single cell RNA-sequencing, Lineage Tracing, Ribosome Profiling, CRISPR Screens.

Research Intern, Trex Bio.

San Francisco, CA  |   July 2020 - September 2020
- Single cell RNA-sequencing, Cell-Cell Interactions, RShiny App Development.

Research Intern, University of Chicago

Chicago, IL  |   September 2019 - September 2020
- Whole Exome Sequencing, Variant Callers, Parallel Computing.

Teaching Assistant, Minerva University

San Francisco, CA  |   January 2019 - May 2020
- Econometrics, Causal Inference, Biological Systems, Statistics in R and Stata.

Summer Scholar, Novartis Institutes for Biomedical Research

Boston, MA  |   June 2019 - August 2019
- RNA-Sequencing, Drug Sensitivity Testing, Machine Learning.

Awards and Honors:

  • 2022: Institute Scholar Award, Lewis-Sigler Institute for Integrative Genomics, Princeton University
  • 2020: Most Energy Efficient/Sustainable Energy Hack Award, TreeHacks 2020, Stanford University
  • 2019: Fellow of Impact Labs Coalition, a selective community of data scientists in the US.
  • 2017: Finalists at YouFab Global Creative Awards for the VIVITA Project in Tokyo.
  • 2016: Barbara Wiedner and Dorothy Vandercook Memorial Foundation Scholarship
  • 2016: Young Leaders for Active Citizenship, Summer Camp, Full Scholarship
  • 2015 KVPY Young Scientists Fellowship Recipient, Indian Institute of Science, Bangalore

Recent Articles from my blog:

Select Publications

Regulation of eIF4E Guides a Unique Translational Program to Control Erythroid Maturation.

Co-Author  |   San Francisco, CA  |   June 2022

A metastasis-suppressive pathway of mRNA processing governs translational control from the nucleus.

Co-Author  |   San Francisco, CA  |   October 2021
  • Modulating HNRNPC impacts the metastatic capacity of breast cancer cells in xenograft models.
  • Link to paper.

ERα is an RNA-binding protein sustaining tumor cell survival and drug resistance

Co-Author  |   San Francisco, CA  |   July 2021
  • ERα, a transcription factor critical for breast cancer, is an RNA-binding protein regulating post-transcriptional expression of stress response genes.
  • Link to paper.

Neuronal Nsun2 deficiency produces tRNA epitranscriptomic alterations and proteomic shifts impacting synaptic signaling and behavior

Co-Author  |   San Francisco / NYC  |   March 2021
  • Tracing tRNA functions in brain proteomic landscape, and its effect on cognition and behavior.
  • Link to paper.

Androgen Signaling Regulates SARS-CoV-2 Receptor Levels and Is Associated with Severe COVID-19 Symptoms in Men

Contributor  |   San Francisco, CA  |   October 2020
  • Elevated androgen increases COVID-19 susceptibility and severity in men.
  • Androgen signaling inhibition reduces SARS-CoV-2 infection in hESC lung organoids.
  • Link to paper in Cell Stem-Cell.

Other Experiences

Data Science Intern, Clean Energy Associates

San Francisco, CA (Remote)  |   May 2018 - March 2020
- Dashboard Design, Analytics, Database Management, Operational Efficiency.

Data Science Intern, Softbank Ventures

Seoul, South Korea  |   October 2017 - December 2017
- Investment Analysis, Research, Designing Rubrics for startup evaluation.

Summer Analyst, Mistletoe Ventures

Tokyo, Japan  |   June 2017 - August 2017
- Education Technology, Hands-On and Accessible Learning, Web Development.

Technical Projects

Computational Statistics and Machine Learning


Machine learning model for breast cancer detection

February 2019

  • Consolidated a public dataset of 700 breast cancer images and trained a Random Forest and SVM for a comparative analysis.
  • Devised an innovative probabalistic analysis model that extracts probabilities from sklearn's algorithms to redfine decision boundaries based on the bias in the initial algorithm.
  • Link to Repository

Machine learning model for predicting loan application decisions

January 2019

  • Used a public dataset of ~1 million loan applications and trained an ML model to automatically predict loan application decisions.
  • Used approximation models to fill missing data.
  • Completed a report on the importance of feature engineering and probabilistic analysis for best results.
  • Link to Repository

Machine learning model for image analysis of clothes

March 2019

  • Trained a machine learning model over the shirts/jerseys dataset.
  • Used cross validation techniques, feature reduction (PCA) and image processing techniques to achieve 97% accuracy on classification.
  • Conduced a comparative analysis on the accuracy of decision boundaries in PCA Vs. LDA for image classification.
  • Link to Repository

Automated Bayesian Inference Model for prediction of atmospheric CO2

December 2018

  • Trained a PyStan model over the CO2 levels for the last 60 years.
  • Utilized the model for prediction of CO2 levels in the next 40 years.
  • The model also uses gradually increasing confidence intervals to predict CO2 levels based on 3 different scenarios (decreasing, constant and increasing global temperatures)
  • Link to Repository

Analytical pricing model for grocery stores in Berlin

November 2018

  • Trained a PyStan model over price data from 60 stores.
  • Built visualizations in Python to understand various factors that make up the price of a commodity in Berlin.
  • Built a regression model to understand the relation of product prices with rent in different areas of Berlin.
  • Link to Repository

Modelling Financial Transactions using Kernel Density models

March 2019

  • Trained kernel density models on a dataset of financial transactions.
  • Modelled the number of transactions, the day of the transacton and month of a transaction to understand user behavior.
  • Extracted insights on using density models to track fraud transactions.
  • Link to Repository

Simulations, Networks and Object-Oriented Programming


A complex real-life simulation of a Roomba Vacuum Cleaner

April 2019

  • Built a complex simulation of a Roomba Vacuum Cleaner in Python
  • Built advanced rules for a realistic and intelligent vaccum cleaner.
  • Watch the simulation in action here.
  • Link to Repository

A simulation of interactions and change of opinions in humans

March 2019

  • Built a simulation to understand social dynamics and the trajectory of opinions in a society as humans interact
  • Coded intelligent rules for network nodes (humans) to exchange opinions and build/break relationships.
  • Link to Repository

A simple simulation of a Rocket using Object Oriented Programming

February 2019


Looking for a computational biologist or data scientist? Look at my CV here. My contact details are inside it.