I'm an undergraduate Computer Science student at SRM Institute of Science and Technology specializing in High Performance Computing, Distributed Systems, and AI Systems.
I enjoy building scalable systems that combine efficient software engineering with intelligent infrastructure. My primary interests include HPC schedulers, distributed computing, asynchronous systems, AI, and machine learning systems.
Currently exploring:
- ⚡ High Performance Computing
- 🖥️ Distributed Systems
- 🦀 Rust
- 🤖 AI & Machine Learning
- ☁️ Cloud Native Infrastructure
- 📊 ML Systems
- ⚙️ Systems Programming
An AI-Augmented workload intelligence platform for High Performance Computing clusters.
Features
- Asynchronous Rust scheduler
- PostgreSQL-backed workload pipeline
- Explainable workload analytics
- AI-assisted scheduling insights
- Wait-time prediction
- Interactive dashboard
- Workload visualization
Repository: https://github.com/Izpiz06/Aegis-Scheduler
Research project leveraging modern machine learning techniques for biological sequence analysis.
Technologies include:
- Transformers
- Graph Neural Networks
- Distributed Computing
- High Performance Computing
Research focused on workload characterization, scheduling optimization, queue prediction, and intelligent resource utilization for HPC environments.
- High Performance Computing
- Distributed Systems
- AI for Systems
- ML Systems
- Operating Systems
- Parallel Computing
- Cloud Computing
- Scheduling Systems
- Systems Engineering
- IEEE ICEAMST 2025
- Application of Quantum Technologies for Metro System Efficiency, Enhancement and Optimization
- AI-Driven Guardrails for Autonomy: Federated Learning with Quantum Search to Enhance Passenger Safety
- Q-SHIELD: A Quantum Safety-Aware Heuristic for Intelligent Evasive Lane Decision-Making
MPI • OpenMP • SLURM • PostgreSQL
Scikit-Learn • Pandas • NumPy
REST APIs • Async Rust • Linux Networking
⭐ Aegis Scheduler — AI-Augmented HPC Scheduler built in Async Rust
⭐ KinshipForge — High-performance graph-based relationship engine
⭐ Genome Intelligence — Machine Learning for biological sequence analysis
⭐ HPC Workload Intelligence — Research on scheduling, prediction, and workload optimization
Engineering intelligent systems for high-performance computing, distributed infrastructure, and scalable AI.