SKILLS - FRAMEWORKS - IDE
I’m a computer science master’s student at Washington State University, specializing in machine learning and AI. I’ve dedicated my academic and professional journey to developing and optimizing high-impact AI models that solve real-world challenges. I am passionate about tech innovation, I stay engaged through hackathons and community events, continuously exploring ways to advance AI's impact on complex problem-solving.
I’m an active researcher with multiple publications under review. My projects focus on AI and large language models. From LLM-driven code generation and augmented reality applications to advancements in social and environmental data analysis, aiming to create impactful and scalable solutions.
I’ve honed my skills in AI development, working on innovative projects and building AI-enabled applications. My technical expertise includes proficiency in Python, TensorFlow, PyTorch, and cloud platforms like Azure and AWS, complemented by a strong foundation in data science and model optimization.
Jan. 2025
Relevant Coursework: Neural Network Design, Big Data Analysis, Reinforcement Learning, Advanced Algorithms
May. 2022
Relevant Coursework: Neural Network Design, Big Data Analysis, Reinforcement Learning, Advanced Algorithms
June 2025 - September 2025
One of less than 2.5% accepted into Meta and MLH’s Internship. Working with Meta Production Engineers to design reliable, scalable, production-ready systems.
Directed the full project lifecycle for the development and deployment of open-source software tools.
Designed and maintained robust CI/CD pipelines, automating the building, testing, and Developed monitoring and alerting systems to improve operational efficiency
Implemented and managed scalable cloud infrastructure to support high-volume data processing and real-time model serving for large-scale
Collaborated with cross-functional engineering teams to establish best practices for code management, version control, and infrastructure as code, ensuring seamless collaboration and operational efficiency.
Championed the integration of security-first principles, implementing automated security scanning and monitoring tools to proactively address vulnerabilities in production environments.
Present
Conducting advanced research focused on machine learning and Bayesian optimization, with multiple research papers currently under review. Working under the guidance of Dr. Janardhan Rao Doppa, contributing to innovations in test-driven code generation and program synthesis using LLMs.
May 2023 – August 2023
Developed deep/machine learning models to estimate the prevalence of chronic wasting disease (CWD) in wild cervid populations.
Conducted real-time location analysis using National Satellite Imagery data, achieving 96.4% accuracy in disease detection.
Enhanced model efficiency by 67% on the US Government's SCI-Net High-Performance Computing System (HPC).
September 2021 – January 2023
Selected as the only undergraduate to work on AI-based human identification in low-accuracy conditions.
Created a comprehensive dataset of human skeletal features, utilized prescriptive analysis with an ML model, and presented 3D visualizations of the outputs.
September 2021 – February 2022
Gained in-depth understanding of Azure, Machine Learning, AI, and Computer Vision tools through group projects led by Microsoft engineers.
Developed an AI-enabled Windows application to detect improper body postures during exercise.
Here are some of the selected works done lately. Feel free to check them out.
Developed a novel prompt design algorithm using Bayesian optimization to enhance LLM-driven program synthesis, incorporating a surrogate model to generate more informative and error-resistant prompts. This approach significantly improved the accuracy and efficiency of program synthesis.
Designed a machine learning model for human identification based on walking and body posture by analyzing 32 human-joint skeletal data points mapped to a 3D point space, enabling accurate recognition based solely on posture and movement patterns.
Conducted analysis of FBI's annual hate crime dataset to reveal racial hate crime trends by bias criteria, geography, and frequency. Pre-processed over 21,000 entries, reducing processing time by 34%, and visualized trends with Pandas and Matplotlib to enhance interpretation and insights.
Developed an AI-powered Physical Therapy and Yoga Assistant Windows Application using machine learning and deep learning with computer vision to analyze form and posture in real-time. This tool offers personalized guidance and posture correction to help users safely practice exercises at home, reducing the risk of injury.
Developed a multi-object trajectory detection system capable of identifying and tracking object paths within video sequences, even for objects as small as a few pixels. This project leverages advanced computer vision techniques to detect and follow multiple trajectories simultaneously, enabling precise monitoring and analysis of object movement patterns in dynamic scenes.
Built a multifactor authentication system using facial recognition and additional verification methods to enhance campus security. This system combines face recognition with secondary authentication factors, providing a secure, user-friendly solution for identity verification across university systems and facilities.
Developed a steganography software tool that enables secure data concealment within digital images. This tool hides sensitive information in image files through advanced encoding techniques, allowing data to remain hidden yet accessible only with authorized retrieval methods, enhancing confidentiality and data protection in digital communications.
Will be happy to connect, listen and help. Let's work together and build something awesome. Let's turn your idea to an even greater product. Email Me.
