Secure Backend & LLM Security Research Graduate

BHAGYASRI DOMMARAJU

+1-945-***-**** ******************@*****.*** linkedin.com/in/bhagyasri-dommaraju-a8640924a/ Summary

Graduate Computer Science student specializing in secure and reliable backend systems, with hands-on experience in Java

(Spring Boot), C/C++, Python, and SQL. Built and evaluated systems for encrypted storage, LLM-based vulnerability detection, and ML-driven analytics (healthcare and agriculture), with strong foundations in data structures, operating systems, networks, and cybersecurity.

Education

University of Texas at Dallas Richardson, TX

Master of Science in Computer Science Aug 2024 – May 2026 SRM University of Science and Technology Chennai, India Bachelor of Technology in Computer Science; GPA: 9.28/10 May 2024 Technical Skills

Programming and Core CS: Java, Python, C, Rust, C++, SQL, Javascript, Object-Oriented Programming, Data Structures Algorithms, System Design, SDLC, HTML, CSS, Linux, Git, NLP, Prompt Engineering, Generative AI, Machine Learning Frameworks: Spring Boot, Hibernate, FastAPI, Node.js, React, Next.js, REST APIs, Microservices, Kafka, Angular, Agile, Django Networking & Infrastructure: SDN, BGP, VXLAN, RDMA, Leaf-Spine Architecture, TCP/IP, HTTP/3, Wireshark, eBPF Cloud and DevOps: AWS Cloud(EC2, S3, DynamoDB, RDS, SageMaker, IAM), Docker, Kubernetes, ECS, EKS, CI/CD, Apache AI/ML: LLMs, Transformers, RAG, LangChain, OpenAI API, TensorFlow, Scikit-learn, Pandas, NumPy, OpenSearch, PyTorch Security and Testing: OAuth2, JWT, Secure APIs, Data Encryption, JUnit, Pytest, Logging(CloudWatch, Splunk) Projects & Experience

LLM for Software Security (Independent Study in CS- Research) UT Dallas Jan 2026 – Present

• Conduct LLM-assisted analysis of Linux kernel security patches, focusing on fine-grained classification of critical memory bugs

(e.g., UAF, OOB) inspired by DUALLM

• Built Python pipelines to parse patches and generate prompts, evaluating model predictions across multiple categories to achieve a 90%+ precision rate in identifying specific vulnerability types.

• Optimized the execution pipeline to handle large-scale vulnerability repositories (e.g., rAthena and OpenSSL), reducing the manual triage effort for security patches by approximately 30-40%. Graduate Teaching Assistant – Datacenter Networking, DSA in Java & Software Engineering Aug 2025 – Present University of Texas at Dallas Richardson, TX

• Facilitate instruction for Recent Trends in Datacenter Networking, taught Java Data Structures and Software Engineering, covering high-performance networking, SDN, and cloud infrastructure alongside core OOP principles to boost average quiz scores by 15%.

• Led weekly office hours on debugging, Collections, network protocol analysis and Streams, resolving complex logic snags and improving attendee lab performance by 10-15%.

• Evaluated 150 assignments per cycle, providing feedback on distributed systems and software design that identified error patterns and reduced repeat mistakes by 20-25% via custom practice sets. LLM Framework for Detecting Vulnerable Smart Contracts Apr 2025 – Present

• Developed an LLM-based framework in Python to detect vulnerabilities in Solidity smart contracts, leveraging pre-trained code models to improve accuracy

• Designed a modular pipeline (parsing, prompt generation, classification, reporting) and evaluated it on a benchmark set of contracts, improving vulnerability detection recall by approximately 28% relative to a baseline. Encrypted File System, Software Vulnerabilities & Web Security Apr 2025

• Implemented a user-space encrypted file system in C/C++ on Linux using AES-256 for transparent encryption/decryption, keeping throughput within 90% of unencrypted I/O for typical file sizes.

• Built and analyzed a PHP/JavaScript web application with deliberately vulnerable endpoints (XSS, SQLi, CSRF), applying secure coding practices

Autism Detection and Analysis (Publication: MIT Mumbai) Apr 2024

• Created a PHP + MySQL full-stack web application for early ASD screening, analyzing behavioral and genetic data for 100+ sample profiles

• Trained Random Forest and XGBoost models in Python to classify ASD risk, achieving approximately 96.4% accuracy and 95.8% F1 score and improving over a baseline model by 12%. Crop Yield Prediction Using Ensemble Methods (Publication - ISVE) Dec 2023 - Apr 2024

• Implemented a Random Forest regression model in Python to forecast crop yields using historical yield and weather data, reducing mean absolute error compared to linear regression

• Reduced mean absolute error by around 22% compared to a baseline linear regression model, and automated data preprocessing with Pandas to significantly speed up experimentation Relevant Coursework

Core CS: Data Structures, OOP, OS, Networks, DBMS, Software Engineering, Compiler Design, AI, AI- assisted debugging Distributed & Systems: Distributed OS, IoT, Cloud, Edge, Wireless Sensor Networks, data transformation Security: Database Security, Information Security, Network Security, Data & App Security, Advanced OS, Secure Software Development, System Security & Binary Code Analysis, Cloud Security, test data generation Certifications

• Graduate Certificate in Cyber Defense (NSA CAE), University of Texas at Dallas

• AWS Academy Graduate – AWS Academy Cloud Operations

• Google Cloud Fundamentals: Core Infrastructure

• SQL (Advanced) Certificate

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