Payam Dibaeinia Email & Phone Number

United States

Chicago, Il, Us

United States

Studied and employed novel machine learning/deep learning models to understand genomics and RNA/DNA sequencing data. Developed a causal inference pipeline that identifies perturbations in causal graphs (such as a biological network) occurred due to an external intervention (e.g. disease). This pipeline relies on machine learning and attribution models to estimate causal quantities and can be generalized to studies of any causal graph. Developed a novel interpretable deep learning model to predict gene expression from DNA sequence. Interpretability was embedded in model architecture and it was shown that the trained model can be interpreted to elucidate the mechanisms of gene regulation learnt from the data.

United States

Studied biomolecular systems including proteins and DNA molecules through molecular dynamics simulations. Especially, studied nanopore sequencing and proposed a novel nanopore that improves DNA base detection by slowing down the translocation rate of DNA through the nanopore.

United States

Worked on the applications of causal inference in online advertising. Implemented an interpretable machine learning pipeline for making predictions in advertising. The pipeline is backed by a causal inference module, parallelized in PySpark, to assess the temporal impact of advertisement. Developed simulation tools for benchmarking purposes.

Designing a rotational viscometer for high viscosity fluids

Designing, manufacturing and assembling an impaction-based PM10 sampler head for an airquality monitoring system

Designing, manufacturing and assembling a perfusion stirred-vessel bioreactor for mesenchymal stem cell culture

Doctor Of Philosophy - Phd, Computer Science

Master'S Degree, Computer Science - Bioinformatics

Master'S Degree, Mechanical Engineering

Bachelor Of Science (B.S.), Mechanical Engineering