ABOUT ME
Who Am I?
Hey! I am Aryan Rajguru, welcome to my website! I am an IT Assistant majoring in Computer Science & Engineering Technology at the University of Toledo with a passion for building intelligent, scalable software.
As an IT Assistant, I manage software and hardware-related issues, optimize workflows, and ensure secure, efficient technology operations at the IT Department. I also configure and image computers, set up institutional policies, and maintain system reliability across departments. My technical skills include Python, Java, JavaScript, HTML5, CSS and cloud technologies like AWS and Firebase, and I have built projects such as a cross-platform task management app, an AI-based Email Classifier, and an Autonomous Navigation Robot Car. I am passionate about prompt engineering, software development, and the field of artificial intelligence, with a strong focus on developing intelligent and scalable solutions.
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EDUCATION
The University of Toledo
Anticipated Grad: May 2027B.S. in Computer Science and Engineering Technology
Relevant Coursework: Object Oriented Programming, Client-Server Computing, Software Engineering & Human Interface, Database Driven Websites, Computer Networks & Data Communication, Financial Analysis.
Key Highlights
Gained a solid foundation in Software Engineering and IT principles and hands-on experience across areas like programming, and network management. The program's focus on practical learning allowed me to work directly with industry-standard tools and collaborate on real-world projects, preparing me to tackle complex IT challenges confidently. Through coursework in Software Design, Web Server Administration, Operating Systems, Client-Server Computing, I have built a strong technical skill set. In addition to technical expertise, I developed leadership and teamwork abilities through active involvement in professional organizations like AIML and ACM Club on-campus.
EXPERIENCE
As an IT Assistant in a hospital environment, I configure and image computers, manage software, hardware, and network-related incidents, and use Intune and Active Directory for device management and access control. I implement institutional policies to maintain secure and reliable IT operations, and support critical systems including Epic EMR, VMware, BitLocker, and Cisco Finesse. I leverage Imprivata for secure clinical authentication to reduce login-related escalations, and use Power BI to generate ticketing reports that improve visibility into recurring support trends.
Mathematics Instructor
May 2025 - July 2025I taught 10+ students aged 11–15 across arithmetic, algebra, geometry, and statistics through one-on-one and small group sessions via Zoom and Google Classroom. I built personalized lesson plans for each student, tracked progress through regular assessments, and adapted my teaching approach to close individual learning gaps — consistently improving grades and academic confidence.
Here are some of my favourite projects!
AquaTrack – Smart Hydration App
A cross-platform iOS/Android app that calculates your personalized daily water intake by pulling real-time weather data, temperature and humidity via the Open-Meteo API using your device's GPS. It tracks your hydration history and streaks with local storage, sends smart push notifications based on your schedule and activity, and uses gamification milestones to keep you consistent.
Amazon Product Recommender Chatbot
A context-aware chatbot that lets you search over 500,000 Amazon products using plain natural language. Built with LangChain and Groq LLM, it uses a RAG pipeline with HuggingFace sentence embeddings and Astra DB as the vector store. The system is containerized with Docker, deployed on GCP via Kubernetes, and monitored in real time using Prometheus and Grafana dashboards.
NetSimLab – Network Emulation Platform
An automated network lab that spins up multi-subnet Docker environments simulating ARP, ICMP, and broadcast traffic across 5+ sites with 200+ hosts, eliminating around 70% of manual IP planning. Includes custom packet generation and analysis tools built with Scapy and tcpdump. Full lab deployment takes under 3 minutes from scratch.
StreamLens – Churn Forecast System
A machine learning pipeline that predicts whether a user is likely to churn or stay, trained on a 7,000+ record telecom dataset. Applies feature engineering with one-hot encoding and binary mapping, uses Pandas and NumPy for preprocessing (improving training efficiency by 40%), and achieves 85% accuracy with a Logistic Regression classifier on held-out test data.
ACHIEVEMENTS
Certifications
Awards
- Dean's List
- Rocket Award Scholarship