Résumé

Graduate Research Assistant

University of Maryland, Baltimore County

Skills: PyTorch, Python, Hugging Face, Scikit‐Learn, LLM, SQL, Spacy, NLTK, Pandas, Numpy, Matplotlib, Seaborn, Weight & Biases, Hydra

• Spearheaded a novel, hierarchical, semi-supervised event modeling framework, achieving up to 8.5% improvement over prior state-of-the-art approaches in 2 datasets and across 4 evaluation metrics. (Published on *SEM 23, ACL)
• Pioneering first-of-its-kind multimodal counterfactual generation dataset (8k+ real-life events), merging text and images for nuanced alternate timeline, a novel contribution to counterfactual reasoning and multimodal real-life event understanding.
• Collaboratively developing a Graph Convolutional Network for event understanding and reasoning on 2 complex datasets (250k+ data-driven event graphs), advancing research in the field of graph-based machine learning.
• Mentoring and providing research guidance to an undergraduate who is a member of an underrepresented group in CS.

Full Stack Software Engineer

Sapien.Network

Tech Stack: Python, JavaScript, Elixir, Node.js, React, PostgreSQL, GraphQL, Redis

• Multi-factor Authentication
• Backend posts caching using Redis
• Implemented Google ReCaptcha to prevent bots and frauds
• Worked with elixir to implement a scalable chat messenger
• Integrated kickofflabs for referral contest
• Integrated sendgrid for 3rd party mailing service
• Integrated ActiveCampaign for campaign management and marketing
• Integrated OneSignal for push notification in iOS, Android & Web
• Fixed a good number of Backend and Frontend issues

Founder & Chief Technology Officer

UniShopr

Tech Stack: Python, Node.js, React, PostgreSQL, GraphQL, AWS Lambda, AWS Lightsail

• Single-handedly grew e-commerce to 1000+ active users and $40000/month orders.
• Achieved a successful startup exit, securing a $50,000 sale, demonstrating strategic acumen.

Machine Learning Engineer

Backpack

Skills: PyTorch, Keras, Hugging Face, Large Language Model, NLTK, Python, MySQL, Elasticsearch, AWS EC2, Node.js, React

• Boosted the sale by ≈23% by developing a product recommendation system using Product2Vec embedding.
• Engineered a Chatbot combining AI algorithms with logic-based if-else, decreasing response time by ≈1 hour.
• Decreased server cost by ≈10% by implementing an AWS Lambda-based ML pipeline for online learning.
• Build a model to predict the dimension of a product from the known datasets which helped the traveller team to allocate the luggage by ≈20% more efficiently.
• Implemented a microservice to refresh the inventory every 12 hours which is later fetched by Facebook Ads, decreasing marketing labour by ≈10%.
• Implemented super menu on the e-commerce site to categorise products which helped ~60% users to find the right product more quickly.
• Give a ≈110% better user experience on search using word embeddings.
• Handled whole Facebook ads system, regarding this implemented many scripts and microservices
• Implemented curation scripts to curate data from Amazon, Ebay or other US based e-commerce sites.

MSc

University of Maryland, Baltimore County

Computer Science

Grade: 4.00/4.00

PhD

University of Maryland, Baltimore County

Computer Science

Grade: 4.00/4.00