Education
M.Tech — Computer Science and Engineering
CGPA: 8.29/10
B.Tech — Computer Science and Engineering
CGPA: 7.43/10
Education
- M.Tech — Computer Science and Engineering
- B.Tech — Computer Science and Engineering
Experiences
Research Intern - NIT-Trichy
(June 2025 – Aug 2025)
Apprentice Network Engineer - AT&T Communications India Pvt. Ltd.
(Nov 2022 – Nov 2023)
Experiences
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Research Intern — NIT-Trichy
- Conducted research on EEG signal classification for depression detection under the guidance of Dr. V. Sridevi, using real-world EEG datasets.
- Designed and trained deep learning models, experimenting with CNN and LSTM architectures to enhance classification accuracy and model robustness.
- Deployed deep learning models on HPC clusters using SLURM; submitted and monitored batch jobs via
sbatch,squeue,srun, andsinfo.
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Apprentice Network Engineer — AT&T Communications India Pvt. Ltd.
- Managed enterprise routers and switches over SSH, leveraging Unix commands for configuration and verification of network devices.
- Developed shell scripts for automating device logging and wrote Python scripts to streamline configuration management across network infrastructure.
- Collaborated on Layer 3/Layer 2 segmentation using BGP, OSPF, and VLANs.
Projects
Adaptive Handover Optimization in Open RAN
Seccomp-based System Call Filtering
CCTV Deployment Optimization
Projects
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Adaptive Handover Optimization in Open RAN
- Developed an ML-based adaptive handover model using LSTM on the Berlin V2X dataset for predictive handovers in 5G networks, achieving 86.67% accuracy.
- Compressed the model using structured pruning and quantization, reducing size by 87% for real-time edge deployment in Open RAN environments.
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Seccomp-based System Call Filtering
- Developed a system-level security module using Seccomp to filter Linux system calls in real time for containerized workloads.
- Enforced syscall whitelisting policies, reducing kernel attack surface and preventing unauthorized system access.
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CCTV Deployment Optimization
- Created a PyQt5 GUI tool for ILP-based camera placement, ensuring 100% area coverage with 28.6% cost savings in synthetic urban layouts.