AI Researcher | Data Science Specialist | Technical Content Creator
š§ saurab23@iisertvm.ac.in | š± +916201365207
š LinkedIn | š Portfolio | šŗ YouTube | āļø Medium
BS-MS student in Computational & Applied Mathematics at IISER TVM, working across AI research & engineering, spanning areas such as machine learning, large language models, knowledge-enhanced systems, agent-based architectures, and scalable model deployment. Iām contributing to research publications in NLP/IR and developing projects that bridge theory with practical impact. I share my learnings through technical writing and educational content, and Iām always open to collaborations.
Under Review
+30% accuracy improvement over single-agent baselines (p < 1e-38)
Under Review
+17.38% improvement in argument quality, +22% in judgment prediction accuracy
LLMs, Multimodal, RAG, RLHF, Multi-Agent Systems, Transformer Architectures
Python, R, SQL, TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain
AWS, Google Cloud, IBM Cloud, Advanced Algorithm Design
Medium Technical Blogger, YouTube Educational Content, Technical Documentation
Machine Learning ā¢ Large Language Models ā¢ Multi-Agent Systems ā¢ Retrieval-Augmented Generation ā¢ Reinforcement Learning with Human Feedback ā¢ Multi-Modals ā¢ Fine-tuning Methodologies ā¢ Natural Language Processing ā¢ Computer Vision ā¢ Human-AI Interaction
Advanced Multi-Agent Framework for distributed AI systems with collaborative decision-making capabilities
Complete implementation of Reinforcement Learning from Human Feedback for LLM alignment
Production-ready RAG pipeline with vector databases and semantic search optimization
Efficient fine-tuning strategies for Large Language Models with LoRA and QLoRA
Machine Learning | AI | Data Science | NLP | Technical Content Creator
BS-MS student in Computational & Applied Mathematics at IISER TVM, working across AI research & engineering, spanning areas such as machine learning, large language models, knowledge-enhanced systems, agent-based architectures, and scalable model deployment. Iām contributing to research publications in NLP/IR and developing projects that bridge theory with practical impact. I share my learnings through technical writing and educational content, and Iām always open to collaborations.
Machine Learning ā¢ Large Language Models ā¢ Multi-Agent Systems ā¢ Retrieval-Augmented Generation ā¢ Reinforcement Learning with Human Feedback ā¢ Multi-Modals ā¢ Fine-tuning Methodologies ā¢ Natural Language Processing ā¢ Computer Vision ā¢ Human-AI Interaction
An innovative learning solution leveraging Large Language Models (LLMs) to personalize learning pathways for competitive exam preparation. Integrated advanced natural language understanding, adaptive learning models, and intelligent scheduling systems to optimize learning outcomes.
Key Innovations:
LLM integration, adaptive algorithms, personalized curriculum design.
Research Impact:
Demonstrated enhanced user engagement and improved study efficiency.
Developed a predictive model to analyze the success of SpaceX Falcon 9 rocket landings using historical data. Implemented machine learning techniques to improve prediction accuracy, optimizing launch costs and enhancing space exploration efficiency.
Key Contributions:
Developed novel feature engineering pipelines and applied ensemble models for improved prediction accuracy.
Research Significance:
Boosted reliability and cost-effectiveness in aerospace systems.
Developed an ML-based solution to analyze car attributes and predict CO2 emissions, fostering sustainable transportation strategies.
Research Applications:
Environmental impact assessment, sustainable design innovation.
Tech Market Predictive Modeller:
Designed a regression-based pipeline for laptop price prediction, incorporating advanced feature engineering and model tuning.
Vehicular Valuation Predictor:
Created a comprehensive car valuation system using machine learning models for precise price estimation and market insights.
Designed an intelligent interface combining automatic natural language processing to generate precise, context-aware notes from YouTube videos. Advanced AI solutions were deployed to create concise summaries while enabling user customization.
Key Innovations:
LLM integration, ML algorithms, Text Summarization.
Applications:
Educational content curation, knowledge distillation, and e-learning.
Built a machine learning model to analyze and predict customer behavior in the telecommunications sector, enabling personalized service recommendations and marketing strategies.
Key Insights:
Behavioral analytics, clustering, and classification for targeted engagement.
Built a robust ML-driven system for precision medicine, utilizing patient data and decision tree algorithms to recommend drugs tailored to individual needs.
Key Methodologies:
Predictive analytics, healthcare data modeling, and classification algorithms.
Research Outcome:
Improved prescription accuracy and enhanced patient outcomes.
Revolutionized generative AI by integrating real-time knowledge retrieval, enabling contextually rich and accurate query responses.
Key Innovations:
Knowledge graph integration, model retrieval optimization, and hybrid generative systems.
Research Outcome:
Enhanced AI systems' ability to handle complex queries across domains like scientific research and legal advisory.
Advanced AI training pipelines by incorporating human feedback to align systems with ethical standards and values.
Key Innovations:
Reinforcement learning algorithms and human-in-the-loop systems.
Research Outcome:
Enabled the development of ethical AI systems that prioritize fairness, transparency, and inclusivity.
Leveraged multi-agent frameworks to refine language model performance through collaboration and contextual optimization.
Key Innovations:
Agent coordination, knowledge fusion, and dynamic contextual systems.
Research Outcome:
Improved multi-faceted decision-making and conversational AI accuracy.
saurab23@iisertvm.ac.in
+91 6201365207
Professional Network
Technical Articles
Educational Content
Code Repositories
I'm always excited to discuss AI innovations, collaborate on cutting-edge research, or explore new opportunities in machine learning and multi-agent systems. Whether you have questions about my publications, want to discuss potential collaborations, or are interested in my technical content, feel free to reach out!
Areas of Interest: LLMs, Multi-Agent Systems, RAG, RLHF, NLP, Computer Vision, Ethical AI
Available for: Research collaborations, technical consulting, speaking engagements, and mentoring opportunities.
Exploring the revolutionary combination of Retrieval-Augmented Generation and Multi-Agent Systems in transforming financial analysis and decision-making processes.
A comprehensive deep dive into Reinforcement Learning from Human Feedback - the critical technology behind aligning AI systems with human preferences and values.
Exploring how Multi-Agent Systems enable coordinated decision-making, distributed problem-solving, and the next evolution of artificial intelligence collaboration.
Stay updated with my latest research insights, technical deep-dives, and explorations in AI, Machine Learning, and Multi-Agent Systems. I regularly publish articles on cutting-edge topics in artificial intelligence.
