Working on hyper-personalisation domain
Using Genetic Algorithm to dynamically action text and call-action-button on images
Improved user retention for stakeholders by 4.5% sending personalised notifications using reinforcement learning
Generation of unique notification image payload to increase user engagement with the help of genetic algorithms
Faster payload generation by 10% via improved database optimized queries
Exploration on user recency and RFM

• Extraction of high value analytics from Legal Corpus
• Building robust ML pipelines and providing concurrent API end points
• Improving speed of text-based captcha solving by using SOTA object detection
• Improvising fuzzy text matching to be 25% faster on large scale text matching on quoted text from two legal cases
• Building case summarization tool of relative chronology of case proceedings using SOTA transformers based models
• Building a first person reasoning model to identify the polarity of speaker
• Extraction of common provisions, clauses, and data points from contracts
• Building, scaling and deploying an ensemble model of transformer and supervised machine learning algorithms to extract information in form of one-class classification
• Improving in-house API to serve multiple requests and made it faster by 10%
• Built a sentence similarity model to find similar text wrt. context of the text using SOTA NLU techniques

Developing Deep Learning models using LSTM, GRU and xgboost to predict the rise and fall of NIFTY.
Working on high frequency time series data to automate the data gathering and prediction.
Developing barriers for uncertainty quantification to external noises.