Master of Science in Computer Science
May 2026University of Arkansas · Fayetteville, ARGPA 3.75 / 4.0
Image ProcessingPrivacy Enhancing TechnologyFull Stack Deep LearningMachine Learning
About
I am a full stack engineer who builds AI integrated web applications. I work with React, Next.js, FastAPI, and Postgres with pgvector, and I have built features that use retrieval augmented generation, vector search, and agentic MCP tooling. My background also includes peer reviewed research in reinforcement learning and security.
I hold a Master of Science in Computer Science from the University of Arkansas. During my research years I worked on reinforcement learning for fuzzing and on anomaly detection in cyber-physical water systems, and that work led to two peer reviewed publications. Alongside the research I built the software around the models, including data pipelines, APIs, dashboards, and tooling.
This combination is what I bring to full stack and forward deployed engineering roles. I can take a customer problem, design the system, and ship it in React, Next.js, Node, or Python. When the problem calls for it, I can also build and evaluate the machine learning behind it.
02 peer-reviewed publicationsBest Student Paper nominee · VEHITS 2024MS Computer Science · GPA 3.75/4.0
Skills
Languages
- Python
- TypeScript
- JavaScript
- SQL
- C/C++
Web
- React
- Next.js
- Node.js
- FastAPI
- REST APIs
- HTML/CSS
AI & ML
- LLM integration
- RAG
- AI agents (MCP)
- PyTorch
- Reinforcement Learning
- Graph Neural Networks
Databases
- PostgreSQL
- MongoDB
- Supabase (pgvector)
Cloud & DevOps
- Docker
- AWS
- Git
- Linux
Education
Bachelor of Technology in Information Technology
May 2017College of Engineering Roorkee (COER) · Roorkee, India
Projects
AdvisorDesk: AI-Powered Advisory Content Platform
ReactNext.jsTypeScriptFastAPISupabase (Postgres + pgvector)OpenAIMCPDockerAWS
A content hub for financial advisory firms, with a CMS, a client app, a RAG assistant that cites its sources, and an agentic MCP content layer.
RAG answers grounded in published content, with source citations
Reinforcement Learning Guided Fuzzing
PythonReinforcement LearningFuzzingSecurity Tooling
An RL agent that learns multi-parametric input mutation strategies for fuzzing, improving vulnerability detection and coverage. Published at IEEE CSR 2025.
Published at IEEE CSR 2025
Anomaly Detection in Cyber-Physical Water Systems
PythonPyTorchGraph Neural NetworksTime-Series MLEnsembles
GNN based anomaly detection for water infrastructure, reaching an ensemble Oracle F1 of 0.854 on SWaT, with an operator dashboard and LLM explanations.
Ensemble Oracle F1 0.854 on SWaT, above the published GDN baseline
RL for Emergency Vehicle Traffic Optimization
PythonStable-Baselines3SUMOReinforcement LearningV2V
Lane-level reinforcement learning with V2V communication that speeds emergency vehicle traversal in simulated traffic. Best Student Paper nominee, VEHITS 2024.
Best Student Paper Award nominee, VEHITS 2024
City Complaint Management System
PHPWordPressJavaScriptHTMLCSSMySQL
A civic complaint reporting platform built at Codeventure Tech with PHP, WordPress, and JavaScript, shipped to real users to improve response times.
Generative Image Synthesis with Diffusion Models
PythonPyTorchDiffusion ModelsU-NetAutoencoders
DDPM texture synthesis on ALOT and latent diffusion models for text-to-image generation, evaluated on CelebA-HQ and LAION with FID and IS.
Deep Learning Image Steganography
PythonPyTorchComputer VisionEncoder-Decoder Networks
A deep learning framework that hides a full image inside another with high imperceptibility and robust recovery of the hidden data.
Real-time Pedestrian Detection and Safety Alert System
PythonYOLOMobileNet SSDTensorRTDeepStreamNVIDIA Jetson
Real-time pedestrian detection on the NVIDIA Jetson Nano with YOLO and MobileNet SSD, optimized with TensorRT, driving audio-visual safety alerts.
Experience
Research Assistant
Jan 2024 – Jun 2026Cybersecurity Lab, University of Arkansas · Fayetteville, AR · Advisor: Dr. Qinghua Li
- Developed reinforcement learning models that generate fuzzing test cases, significantly improving vulnerability detection and code coverage over traditional fuzzing. This work was published at IEEE CSR 2025.
- For my master's thesis, I built machine learning models for real-time anomaly detection in cyber-physical water testbeds (SWaT, WADI, and ACWA), improving the security and resilience of critical infrastructure.
- Built DDPM and latent diffusion models for high fidelity image synthesis and text-to-image generation with a cross-attention U-Net, evaluated on ALOT, CelebA-HQ, and LAION using FID and IS scores.
Case studies: Reinforcement Learning Guided Fuzzing · Anomaly Detection in Cyber-Physical Water Systems · Generative Image Synthesis with Diffusion Models
Research Intern, RL for Traffic Optimization
Jun 2022 – Dec 2023IIIT Hyderabad · Hyderabad, India · Advisor: Dr. Praveen Paruchuri
- Optimized lane-level dynamics and vehicle-to-vehicle communication by simulating multi-modal traffic environments in SUMO, improving overall traffic-control efficiency.
- Reduced traversal times significantly by evaluating RL strategies on grid-world and real-world road networks, surpassing human-level performance. This work was published at VEHITS 2024 and was nominated for the Best Student Paper Award.
Case study: RL for Emergency Vehicle Traffic Optimization
Software Engineer
Jun 2021 – Apr 2022Codeventure Tech LLP · Roorkee, India
- Designed and developed a city complaint management system using PHP, WordPress, HTML, CSS, and JavaScript, enhancing civic issue reporting and response times.
Case study: City Complaint Management System
Publications
Uwibambe, M.L., Tyagi, A. and Li, Q. (2025). A Reinforcement Learning Approach to Multi-Parametric Input Mutation for Fuzzing. 2025 IEEE International Conference on Cyber Security and Resilience (CSR), pp. 174–179.
Tyagi, A., Lowalekar, M. and Paruchuri, P. (2024). Improving Lane Level Dynamics for EV Traversal: A Reinforcement Learning Approach. International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS), pp. 134–143.
doi:10.5220/0012637200003702Best Student Paper Award Nominee
Contact
I am open to full stack and forward deployed engineering roles. The fastest way to reach me is by email.