Relevant Courses: Data Structures, Algorithms, Operating Systems, Number Theory, Prob & Stat I, Prob & Stat II, Real Analysis, Linear Algebra, Differential Equations

• Trained multi-billion parameter FORGE models (1.44B, 13B, 25.6B) based on the GPT-NeoX architecture on Frontier supercomputer using a 257B-token dataset of scientific papers and abstracts.
• Developed and integrated benchmark tests for OLCF-6 (Discovery), the upcoming Frontier successor, to evaluate performance of large-scale training runs; captured metrics including loss values, FLOPS/sec, iteration time, learning rate, and samples/sec.
• Built and configured a high-performance computing cluster from scratch, including network setup, OS installation, and optimization of inter-processor communication for parallel workloads.

• Completed intensive training on high-performance computing (HPC), focusing on scientific simulations and AI/ML workloads across leadership-class systems.
• Gained hands-on experience with core cyberinfrastructure components, including parallel computing, distributed data management, resource monitoring, and performance optimization for large-scale systems.

• Conducted data analysis on research data from community health centers using R and SAS.
• Managed surveys and data collection processes using Qualtrics and RedCap to ensure high-quality and reliable data.

• Developed a dynamic Android exam preparation app using React Native and Express.js, featuring secure payment integration, Firebase authentication, and Firestore for efficient data storage.
• Published the app on the Google Play Store, providing LLB, LLM, BA LLB, and Advocate License students with accessible, high-quality exam preparation resources.

• Implemented global meshless methods (Kansa and MAPS) with ghost points in MATLAB to approximate eigenvalues for PDEs on irregular domains, optimizing parameters like ghost point radius and node distribution.
• Conducted comparative analysis of Kansa and MAPS methods in MATLAB, evaluating the impact of shape parameters and ghost points on numerical stability and error reduction.

• Applied Latent Dirichlet Allocation (LDA) and one-class classification techniques (K-means, Gaussian Density Estimation, PCA) to analyze the CERT Insider Threat dataset and detect anomalous user behavior.
• Optimized data processing using Slurm Workload Manager on HPC clusters for scalable and accelerated analysis.

• Built an AI-powered web extension using LangChain and Microsoft Graph API to generate contextual email drafts and automate responses.
• Deployed backend on EC2 with a reverse proxy to handle server and AI model routes, enabling real-time draft generation.
• Added analytics to track user interactions and identify email trends for improved insights.

• Developed a freelancing platform for college students to offer small gigs (e.g., tutoring, hair cutting) using React for the frontend and Express.js on an EC2 server for the backend.
• Integrated GPT-4 API to generate tailored promotional content for student advertisements, enhancing user engagement.

• Built a mobile app with React Native (frontend) and Django (backend), deploying a pre-trained Kaggle model for plant disease detection to identify diseases in crops.
• Enhanced user experience with real-time weather alerts and push notifications for extreme weather conditions.

Presented research on meshless methods for approximating eigenvalues and eigenmodes of partial differential equations.

Participated in mathematical problem-solving contests and attended research talks in pure and applied mathematics.

Presented research on meshless methods for approximating eigenvalues and eigenmodes of partial differential equations.

Created a statistical model to forecast medal distributions at the 2028 Olympics; submitted findings in a paper.

Recieved funding from Cross Undergraduate Research Program to fund my research.

C++, Python, Node.js, JavaScript ES6+, Django, React, TensorFlow, PyTorch, Next.js, MATLAB, R

AWS, Google Cloud, PostgreSQL, Firebase, HPC, Slurm, RStudio