Projects
Built a Transformer from scratch, inspired by Attention Is All You Need, showcasing expertise in neural networks. Trained with PyTorch on a custom dataset, optimizing hyperparameters for a 15% BLEU boost. Analyzed performance using attention heatmaps, loss curves, and evaluation metrics.
Fine-tuned Llama on English-to-SQL queries using LoRA and efficient fine-tuning, cutting inference errors by 25%. Built a preprocessing pipeline to clean, tokenize, and balance data, boosting training efficiency and SQL accuracy.
Built a Retrieval-Augmented Generation (RAG) pipeline using LangChain, ChromaDB, and an open-source LLM. The key achievements include creating a context-aware text generation system, improving query latency and response relevance by 10% through ChromaDB indexing, and ensuring production readiness by utilizing comprehensive evaluation frameworks.
Publications
Dr. Gerard Deepak, Anubrat Bora, MS Roopa, KR VenugopalOSSS is a Web 3.0 framework for automatic ontology generation in environmental journalism. It integrates Bi-LSTM and AdaBoost classification, Pearson correlation–based feature federation, Google Knowledge Graph API and YAGO enrichment, Morista’s Overlap Index for semantic relevance, and Imperialist Competitive Algorithm optimization.
Anubrat Bora, Dr. Gerard Deepak and Mohammed Salman SAThis Web 3.0 medical annotation framework targets genito-urinary and urological pathology documents. It uses knowledge encompassment with TF-IDF, generative AI via YaLM-100B for text and caption extraction, and high-density metadata generation through OpenCalais and structural-content feedback integration.
Anubrat Bora and Dr. Gerard DeepakThis Web 3.0 video recommendation framework integrates hybrid machine intelligence, generative AI, and semantic AI. It generates ontologies, selects linked-similarity features, classifies via logistic regression, applies NPMI thresholds, and optimizes using the Jian-Konrad Index and Elephant Optimization algorithm for relevant, diverse recommendations.
Anubrat Bora and Dr. Gerard DeepakThis paper proposes a Web 3.0 microblog annotation framework integrating semantic intelligence with deep learning models. It leverages domain-specific knowledge stacks to enrich metadata, applies RNN for inter-class classification, and refines top categories using XGBoost.
Anubrat Bora and Dr. Gerard DeepakThis research presents a strategic framework for classifying criminology and news datasets, enhancing their relevance for expert systems. It integrates LLaMA with XGBoost for efficient categorization and enrichment and uses standard news APIs and the Cricket Algorithm with a diversity index for further optimizations.
Experiences
AI Engineer InternDeveloped an agent-first, multi-turn FastAPI backend for cocktail intelligence supporting web and mobile. Implemented structured agents for intent classification, recipe generation, device control, vision-based ingredient recognition, and action cards using typed Pydantic schemas and JSON APIs. Designed persona-aware routing (emotion, occasion, readiness) and session-based context handling. Optimized latency, quality, and cost through per-agent model selection and fallback strategies. Contributed to structured responses, debugging tools, and system-level enhancements, with roadmap features including persistent memory, tool support, vector databases, and production-ready deployment improvements.
AI Engineer InternBuilt a simple chatbot for greetings and conversation tracking. Also developed a college admission chatbot using PDFs for queries on requirements, deadlines, and processes with Streamlit, Groq, LangChain, and LangChain-Groq, enabling multi-turn interactions and contextual memory.
