Interactive Visualization for Algorithms Permalink
Published:
Interactive Streamlit apps for visualizing how stochastic optimization and machine learning algorithms behave — built to make the mechanics tangible instead of abstract.
Projects
Enterprise Customer Support AI Platform Permalink
Published:
Routing a support ticket correctly, finding the right answer, and executing account actions safely are three problems that most systems solve in isolation. This project treats them as one pipeline. A LangGraph agent classifies intent using one of 15 trained scikit-learn models, retrieves context through a hybrid BM25 and pgvector search, applies deterministic policy rules, and routes high-risk operations to human approvers. The data foundation is 1,000 support tickets across four channels and 90 knowledge documents chunked into 185 passages. Full architecture and benchmarks in project_overview.md.
Scalable LLM Systems Permalink
Published:
- Developed scalable infrastructure for high-throughput LLM inference and deployment.
- Optimized model serving pipelines using efficient batching, token scheduling, and inference optimization techniques.
- Designed architecture capable of supporting multiple concurrent users and high request volumes.
- Integrated vector databases, retrieval pipelines, and LLM inference components into end-to-end AI applications.
- Built monitoring and evaluation pipelines to ensure performance, scalability, and reliability of production LLM systems.
RAG Evaluation Framework Permalink
Published:
- Designed a comprehensive evaluation framework for Retrieval-Augmented Generation (RAG) systems to assess retrieval relevance, answer faithfulness, and hallucination rates.
- Built automated pipelines to evaluate retrieval quality using metrics such as context precision, recall, and ranking performance.
- Implemented LLM-based evaluation techniques for answer grounding, factual consistency, and response completeness.
- Developed benchmarking workflows to compare different retrievers, chunking strategies, and embedding models.
- Enabled systematic experimentation and monitoring to improve reliability of production RAG applications.
LLM-based Scientific Question Answering Permalink
Published:
- Built a knowledge-grounded question-answering system using Wikipedia corpus and transformer-based language models.
- Implemented a RAG pipeline combining document retrieval, re-ranking, and LLM answer generation.
- Applied hybrid retrieval and contextual re-ranking techniques to improve answer accuracy and reduce hallucinations.
- Designed preprocessing pipelines for document chunking, indexing, and metadata management.
- Evaluated system performance using curated datasets and automated evaluation metrics for scientific knowledge queries.
Hybrid Retrieval Architecture Permalink
Published:
- Designed a hybrid retrieval pipeline combining sparse retrieval (BM25) and dense embedding-based search to improve document recall and ranking accuracy.
- Implemented vector search infrastructure using FAISS for efficient similarity search over large document collections.
- Integrated cross-encoder re-ranking models to improve final document ranking quality for downstream LLM tasks.
- Optimized chunking strategies and embedding generation for better semantic retrieval performance.
- Demonstrated improvements in retrieval accuracy and contextual relevance for knowledge-intensive LLM applications.
Toxic Comment Classification Challenge Permalink
Published:
Built a multi-label model which is capable of accurately detecting different types of toxicity like threats, obscenity, insults, and identity-based hate from comments made on social media posts. Used Data Transformation strategies such as Binary relevance, Label PowerSet, Classifier Chains and RaKel to solve multi-label problem. Used EDA, Text Data Preprocessing, Word Embedding, and Machine Learning models such as XGBoost and Logistic regression. Deep Learning models such as LSTM, and BERT.
StumbleUpon Evergreen Classification Challenge Permalink
Published:
Built a classifier which will evaluate a large set of URLs and label them as either evergreen or ephemeral. Used EDA, Text Data Preprocessing, Word Embedding, Feature engineering, Machine Learning models such as CatBoost and Logistic regression, Deep Learning models such as LSTM and BERT
Classification of atomic clusters using Machine learning
Published:
Collaboration with CSIR-National Chemical Laboratory, Pune: Machine Learning-based classification of atomic clusters of Gallium considering shape, atomic distances, and geometrical properties. Utilizing both supervised and unsupervised Machine Learning methods.
Forecast Indian summer monsoon rainfall using ensemble techniques
Published:
Collaboration with Indian Institute of Tropical Meteorology (IITM), Pune. Using ensemble techniques of Machine Learning for prediction of Indian summer monsoon rainfall.
WNS Analytics Wizard 2018
Published:
Predict whether a potential promotee at checkpoint will be promoted or not after the evaluation process. Used ML models with EDA, feature selection, Data Imbalance techniques (Imbalance ratio: 92:8), Grid search, etc.
Ayurveda Prakriti Classification using Machine Learning
Published:
Collaboration with C-DAC, Pune University Campus. Applying Machine Learning for Ayurveda Prakriti (Dosha) Classification. Addressing it as a Multi-class and Multi-label problem.
Loan Prediction Competition by Analytics Vidhya
Published:
Given imbalanced dataset for prediction of whether a person will get a loan or not for which they have applied. Used ML models with EDA, feature selection & engineering, Data Imbalance techniques, Grid search, etc. Rank: 15/81895 Accuracy : 82.638%
Detection of specific antibodies/allergens using Machine Learning
Published:
Collaboration with Bioinformatics Centre, Pune University. From AllerBase, a comprehensive knowledge base of allergens. Classification of allergen-related features such as IgG, IgM, IgA, and IgE using Machine Learning. Research paper contribution
Feature Selection for Multi-label dataset Permalink
Published:
Reducing multi-label dataset dimensionality through feature selection. Utilizing filter methods like Multilabel Informed Feature Selection, Robust Feature Selection, and Mutual Information