I am fortunate to work on projects involving Convolutional Neural Networks, Recurrent Neural networks, and LSTM.


Also, I worked on Text Analytics, Information Retrieval, Natural Language Processing, Scientific Spacy, Gensim, and Named Entity Recognition.


In healthcare areas, I have architected Gene Regulatory Network Architecture designing for identifying candidate driver genes for cancer for Multiple Myeloma from MMRF. 


At Present, I am developing a search engine to develop established AI products in Healthcare using PyTorch & Keras. I got an award of full free registration to attend the conference and workshop track in ICML 2021, attended the Google Developer’s program, serving as a reviewer of a journal on AI & Deep Learning, attended the RegML summer program, in Italy, and presented Attention is all you need paper at Portland State University, USA. I have contributed to real-world projects from multiple domain horizons & could transfer these skills to my work.

I am using hypothesis testing, modeling, training, and interpreting predictive and statistical models in Machine and Deep learning, and Bayesian statistical modeling. Overall, I have end-to-end data science work experience from corpus collection from heterogeneous sources and data management till deployment and model retraining using pre-trained and custom-built models.