Laxman Singh Tomar

Assets Search

• Using Vision Language Models (GPT-4V and LLaVA) for Metadata Generation for assets (artworks, fonts) for Semantic Search.

Jiffy Copilot

• Building Jiffy Copilot - a shopping assistant for apparel purchase and recommendations using LLMs.

Conversational Search Engine

• Integrated generative capabilities of LLMs into products like Search, Text Generation, and multi-purpose Agents for SAP, P&G and AWS.

Knowledge Search Engine

• Built scalable search and knowledge retrieval systems serving over 1M documents for P&G, used vector databases and microservices framework.

Question-Answer Generation

• Built high-quality QA pair generation pipeline from text using Transformers, converted to a scalable async service, performed stress and unit testing.

Question-Answer Evaluation

• Annotated QA dataset using weak supervision, trained a classifier to filter out low-quality questions with 75% accuracy, evaluated low-confidence samples to improve performance.

Content Moderation Engine

• Built AI content moderation engine for Simpplr, trained a transformer model to detect inappropriate comments with 91% accuracy, optimized for deployment via ONNX and quantization; actively used by 500+ companies.

SafeNet

• Developed an AI-powered browser extension for UNICEF which detects NSFW textual and visual content present on the webpage in real-time using models like LSTMs, and MobileNetV2.

Network Anomaly Detection

• Worked on detecting different types of Network Attacks directly from the Network Interface; Obtained 94% accuracy using Stacked Model, improving upon the previous baseline of signature-based methods by 40%.

VoiceNet

• Worked on Voicenet - a comprehensive python library for performing speech/voice-based functions ranging from Speech to Text, Age and Emotion detection from Voice, and many more!

Akshat Gupta, Laxman Singh Tomar, Riddhima Garg

RecSys for Cart Abandonment and Recommendations. Used SIGIR Ecom2021 Challenge Dataset. Used Prefect as Orchestrator and Metaflow for ML DAGs. Built an LSTM Model with TF/Keras and deployed with Serverless.

Multi-Class Text Classification using Project Metadata. Used Dataset comprising Projects Title, Description, and associated categories/tags. Used Airbyte for Data Pipelines, Airflow as Orchestrator, and Feast for Feature Store. Built a Model using Tf-Idf Feature Vectorization and SGD Classifier to obtain a 0.85 f-1 score.

Replicating Semantic Podcast Search at Spotify. Used Listen Notes' Dataset comprising metadata of 100k Podcasts. Obtained Recall@30 of 0.57 compared to 0.29 via fine-tuned distilUSE when using the same model without fine-tuning.

Machine Learning, Artificial Intelligence, Applied Linear Algebra, Data Structures & Algorithms