Hi, I'm Nihar Muniraju
GenAI Engineer & Researcher
I am a passionate and dedicated Machine Learning engineer with expertise in Data Science, Deep Learning, Computer Vision, NLP, and Generative AI. Throughout my journey, I have developed and deployed innovative machine learning models to solve complex problems.
Building the Future
with AI
Exploring the intersection of Machine Learning, Natural Language Processing, and Generative AI
I stand at the intersection of innovation and environmental responsibility, championing a revolutionary vision for sustainable AI development. Through groundbreaking research in decentralized AI architectures and transformative applications in therapeutic computing, I'm reshaping how we think about machine learning's role in society. My journey from developing high-precision food classification systems to architecting emotion-aware AI therapists reflects a singular mission: creating intelligent systems that not only push technological boundaries but also prioritize environmental stewardship and human well-being.
My current research centers on revolutionizing AI through the concept of "Decentralized AI," which aims to transform how AI agents operate in recommender systems of Large Language Models. This innovative approach, inspired by blockchain technology, proposes connecting different LLM models in a stacked architecture, leveraging collective knowledge while significantly reducing carbon emissions.
I am fascinated by AI's practical applications and have focused on developing intelligent systems that adapt seamlessly to real-world situations, including AI-powered automation and predictive analytics. I am deeply engaged in learning and applying the latest advancements in transformer architectures and generative AI, training and fine-tuning models such as Ollama, GPT, Mistral, Gemini, and others.
"Advancing the frontiers of AI through innovative research and practical applications in healthcare and financial analysis."
Current Research Focus
- αAdvanced Transformers and Generative AI
- αRAG Applications in Therapy
- αFinancial Analysis & AI Integration
Research Work
My work focuses on developing Retrieval-Augmented Generation (RAG) applications, particularly in the fields of therapy and stock market analysis, where AI-driven insights are revolutionizing decision-making processes and enhancing personalized experiences.
Education
Timeline
M.S. in Computer Science (ML & AI)
Specialized in Machine Learning and Artificial Intelligence, focusing on developing cutting-edge AI solutions and advanced neural network architectures.
Key Courses
B.E. in Electronics and Communication
Focused on electronics and communication systems, with emphasis on digital signal processing and wireless communications.
Key Courses
Experience
Timeline
Gen AI Engineer
- βCreated an AI-based audio learning platform to empower children with disabilities, transforming educational content into natural, high-quality audio and fostering an inclusive learning environment.
- βEnhanced interactivity through advanced AI and intuitive voice controls, enabling students to navigate lessons, ask questions, and engage with content seamlessly, with fluid, context-aware NLP-driven conversations.
- βIncorporated multilingual support, ensuring accessibility for diverse learners by catering to various languages and accents, making education inclusive and globally relevant.
Impact Grant Research Assistant
- βDeveloped and deployed conversational AI systems using OpenAI's GPT-4 and GPT-3.5 LLM models to handle real-time user interactions, implementing context-aware session management and database-driven response generation.
- βImplemented robust JWT-based authentication for secure user management in web applications. Utilized Flask and MongoDB for efficient credential storage and management, ensuring high-security standards.
- βEngineered scalable APIs for machine learning model inference and data processing. Utilized Flask to create RESTful services, optimizing performance and reliability.
- βLeveraged Azure and AWS cloud platforms for deploying and managing machine learning services, ensuring scalable and reliable microservices architecture through effective utilization of cloud resources.
Student Intern
- βDeveloped and deployed the Food Classification and Nutrient Identification (FCNI) tool, achieving 96.81% accuracy in food recognition and nutrient analysis by leveraging Convolutional Neural Networks (CNNs) and fine-tuning the model on the Food101 dataset.
- βEngineered a user-centric interface using PyQt5, integrating real-time image processing, HTTP request handling, and seamless communication with the Nanonets API to provide immediate audio and visual feedback.
- βConducted rigorous real-time testing and validation, ensuring accurate food classification and personalized dietary recommendations tailored to specific health conditions.
Digital
Library
Let's create something amazing together
Let's Connect
niharraju4@gmail.com
PHONE
+1 (312)-721-3634
LOCATION
Chicago, IL