Development and test related to AI-Web, Browser, Chrome extensions, Chrome APIs, Natural Language Processing (NLP)

Fine-tuned machine translation models hosted by Azure and GCP for European and Asian languages

Developed multi-lingual datasets

Evaluated semantic similarity models (LASER, LABSE, SONAR) to use for evaluating quality of translation when a human reference is not available.

Identified unsolved problems in translations and future directions.

Evaluated performance of open source AI models (inference time, accuracy, Blue Score)

Used LLMs to clean training datasets

Build semantic search / cross-lingual semantic search

Implemented AI Models and ML algorithms for different domains and use cases (Econ, Bio)

The size/scale of datasets/models was restricted to academic research.

Made a pipeline that scraped 25k urls based on user search criteria in 2 days on a node

with 40 cores. Encoded the text. Fed the data into ML classifiers. Fitted the classifiers based

on user-designed labels. Predicted labels on new text. Provided metrics on accuracy of

model. Provided graphs that help grasp distribution of accurate predictions.

Coded and demo’d prototype dashboard to take action based on leading indicators and refute hypotheses not supported by data. Leading indicators were identified using correlation and lag in internal/external time-series data. Automatic daily updates via cron job for continued tracking of real world data. Delivered code to be integrated into a more comprehensive dashboard.