
Saikat Banerjee
Verified Expert in Engineering
Linear Regression Developer
Saikat is a postdoctoral scientist at the University of Chicago with a PhD in computational biophysics and a master's degree in chemistry. He is an expert in biostatistics, statistical genetics, Bayesian methods, and machine learning. As a graphic design and web development freelancer, Saikat co-founded a marketing management company. He enjoys solving problems, creating value, and learning new expert-level skills.
Portfolio
Experience
Availability
Preferred Environment
Ubuntu, Python, C++
The most amazing...
...method I've developed helped scientists to discover the network of human genome transcriptional regulation.
Work Experience
Postdoctoral Scientist
The University of Chicago
- Led multiple projects on Bayesian statistics with international collaborations and challenging deadlines.
- Developed machine learning algorithms for sparse multiple regression.
- Introduced gradient descent technique for variational inference.
Postdoctoral Scientist
Max Planck Society
- Developed statistical methods to understand disease mechanisms from large-scale biomedical data.
- Collaborated with medical doctors leading to two peer-reviewed publications.
- Presented our work at the 2019 International Society for Computational Biology conference and 2020 e:Med; invited to hold a visiting lecture at the University of Göttingen.
- Supervised a master's thesis and mentored three internship students.
Experience
Trans-eQTL Discovery from GTEx Data
https://doi.org/10.1186/s13059-021-02361-8Our goal was to develop a reliable method of identifying trans-eQTLs. We proposed a new model and created open-source software. Applying our method to the eQTL data from the Genotype-Tissue Expression Project (GTEx) proved its performance is significantly better than the state-of-the-art.
Bayesian Multiple Logistic Regression
https://doi.org/10.1371/journal.pgen.1007856We proposed a methodology using the point-normal prior for faster and more accurate Bayesian multiple logistic regression, developing open-source software for the project. Applying our method to human genetics data, we proved it outperforms state-of-the-art variable selection and prediction for sparse multiple logistic regression problems of high dimension (n >> p problems.)
Skills
Languages
Python, Bash, HTML, PHP, CSS, Fortran, C++, Hugo, CSS3
Libraries/APIs
NumPy, SciPy, Scikit-learn, Matplotlib, MPI, OpenMP
Tools
Jupyter, Shell, Adobe Illustrator, GitHub, Adobe Photoshop
Platforms
Ubuntu, Linux, Debian
Other
Bayesian Statistics, Statistical Methods, Linear Regression, Logistic Regression, Biostatistics, Predictive Modeling, Machine Learning, Research, Mechanics, Generalized Linear Model (GLM), Mixed-effects Models, Biophysics, Data Analysis, Computational Biological Physics
Paradigms
Data Science, Parallel Programming
Storage
MySQL, JSON
Education
PhD in Computational Biophysics
Indian Institute of Science - Bangalore, India
Master's Degree in Chemistry
Indian Institute of Science - Bangalore, India