• i'm tushar, 22, a software engineer and a researcher.
• my research interests are nlp, llms, information retrieval, and dl.
• most of my learnings are from either hands-on projects that i build in hackathons or from interesting courses taught at my university.
• tags: problem solver, clear comms, good vibes only.
• programming languages: python, c, c++, swift, sql
• databases: supabase, mongodb, neo4j, chromadb, coredata, mysql
• expertise area: dsa, oops, ml, dl, nlp, llms, transformers, rag, cv, mlops, information retrieval
• tools & tech: tensorflow, pytorch, keras, mlc engine, streamlit, langchain, litellm, huggingface, faiss, aws, gcp, azure, scikit-learn, docker, redis, nats jetstream, inngest, opentelemetry, grafana, fastapi, flask, git, selenium, postman
spotgov | lisbon, remote
oct 2025 - present
audria | noida, india
jun 2025 - aug 2025
imagined studios | remote
dec 2024 - apr 2025
the translational biology lab, indraprastha institute of information technology, delhi
dec 2023 - may 2025 | guide: dr. jaspreet kaur dhanjal
etidm lab, indraprastha institute of information technology, delhi
aug 2023 - dec 2023 | guide: dr. kalpana shankwar
improving access to rare-disease knowledge: a retrieval-augmented question answering framework for wilson's disease
computers in biology and medicine, vol. 216, 111947 | 2026
tushar chandra, prateek paul, jaspreet kaur dhanjal
• introduces wilsonlitqa, a literature-grounded retrieval-augmented qa resource over the complete pubmed literature on wilson's disease. retrieval grounding substantially improves biological accuracy, and smaller ~7b-parameter models perform competitively when paired with a well-designed retrieval pipeline.
paper | pubmed | wilsonlitqa
objective: wilson's disease is a rare genetic disorder with a large but highly fragmented body of biomedical literature spanning more than a century of research. although thousands of articles are available through pubmed, this knowledge remains difficult to access in a consolidated, query-driven manner for researchers, clinicians, and students. existing large language models (llms) offer conversational access to information but often lack domain grounding, leading to hallucinations and unreliable responses in biomedical settings. in this work, we introduce wilsonlitqa, a literature-grounded retrieval-augmented question answering resource that enables the scientific community to query the complete pubmed literature on wilson's disease, capturing canonical biological, clinical, and pathophysiological knowledge related to the disease.
methods: our framework integrates hybrid information retrieval with generative language models, enabling users to query the wilson's disease literature as a unified knowledge resource rather than as isolated papers. we have systematically evaluated multiple open-source and proprietary llms under zero-shot and few-shot in-context learning settings, assessing their ability to deliver accurate, complete, and non-hallucinated responses when supported by retrieval.
results: our results demonstrate that retrieval grounding substantially improves biological accuracy and clinical relevance, and that smaller, deployable language models (on the order of 7b parameters) can perform competitively when paired with a well-designed retrieval pipeline.
conclusion: to support transparency, reproducibility, and community-driven development, the complete wilsonlitqa implementation is publicly available. together, this work positions retrieval-augmented question answering as a consolidated, continuously extensible knowledge interface for rare diseases, offering the scientific community a practical tool to navigate, interpret, and query the growing biomedical literature on wilson's disease.
indraprastha institute of information technology, delhi
aug, 2021 - july, 2025
loan.ly: ai lead generator for banks | demo
python, twilio, flask | vs code
• developed an automated cold-calling system that conducts intelligent loan and credit card application interviews, evaluates responses in real-time, and streamlines customer acquisition.
notegen: customized notes generator | demo
python, langchain, streamlit, conda, chromadb | vs code
• developed an ai-driven application allowing users to define topics and integrate custom knowledge bases for tailored notes generation. designed a flexible system architecture that supports markdown-formatted outputs, dynamic table of contents, and seamless integration of user-provided data pool.
prosearchai | demo
python, langchain, streamlit, conda, chromadb, gemini | vs code
• developed a semantic-based recommendation system that captures user intent by analyzing conversational context and product metadata, improving relevance beyond traditional keyword matches.
spambot: daily activity summarizer
python, streamlit, conda, gemini-pro | vs code
• spambot simplifies your life by summarizing daily activities from a transcript.txt file, providing both question-answering (qa) and summarization capabilities.
hindi translation - fre:ac (open source)
xml | vscode | guide: robert kausch
• added and tested hindi language support built from source.