कर्मण्येवाधिकारस्ते मा फलेषु कदाचन |
(Karmaṇy-evādhikāras te mā phaleṣu kadācana)
"You have a right to perform your prescribed duties,
but you are not entitled to the fruits of your actions."
— Bhagavad Gita, Chapter 2, Verse 47
I am a scholar at IISc Bangalore pursuing M.Tech in Computer Science Engineering (2024-2026), in the Computer Science and Automation (CSA) department. I am part of Database Systems Lab, SERC, IISc Bangalore (DSL) doing research in database systems. I am B.Tech CSE graduate from RGPV Bhopal. I secured AIR 26 in GATE CSE 2024.
I have a work experience of about 2.5 years having worked as a 'Full Stack Java Developer' at TCS. I have a keen interest in advanced studies and research. I love theoretical and applied Computer Science.
Operating Systems | Database Management Systems | Computer Networks | Computer Organization | Digital Logic Design | Discrete Mathematics | Compiler Design | Theory of Computation | C/C++ System Programming | Linux/UNIX | Object Oriented Programming
Algorithms | Probability and Statistics | Databases | Computer Architecture | Optimizations | Machine Learning | Systems for Machine Learning | Cryptography | Distributed Systems | Graphics and Visualization | Computer Systems Security
Computer Systems Security Course Project | GitHub | Docs | Releases
2025 | Bangalore, IN
A powerful tool for analyzing and comparing VM disk images. Built with Flask and a C++ core (pybind11) that uses libguestfs for robust disk access. Features file browsing, side‑by‑side file diffs, directory and block comparisons, image conversion, VM launch utilities, and exportable JSON/PDF reports. Supports web-based and CLI (vmt) interfaces.
Technologies Used: Python (Flask) • C++ (pybind11) • libguestfs • Docker • SQLite
Interactive 3D Volume Renderer | GitHub | Releases | Docker image
2025 | Bangalore, IN
Lightweight OpenGL-based 3D volume renderer with a compact PyQt6 UI. Loads medical and scientific volumes (NIfTI: .nii/.nii.gz, DICOM: .dcm, VTK: .vtk) and supports GPU-accelerated volume rendering, slice/slicer view, isosurface rendering, custom & interactive transfer functions, overlays/annotations, and saving/exporting images. Distributed as a standalone Linux binary and shared library. Docker image available for easy deployment.
Technologies Used: C++ • OpenGL • Python • PyQt6 • pybind11 • VTK • DCMTK • Docker • NIfTI
Distributed Systems Course Project | GitHub
2024 | Bangalore, IN
Implemented three microservices (account-service, marketplace-service, wallet-service) using a dockerized Akka cluster. These services handle concurrent RESTful requests through a CLI interface while maintaining consistency and correctness.
Technologies Used: Java 21 • Spring Boot 3 • Akka Library • Docker • Kubernetes • IntelliJ • Git
Database Systems Course Project | GitHub
2024 | Bangalore, IN
Implemented two operators, 'Join' and 'GroupJoin' in the open-source DuckDB source-code, with 'Join' being simple nested loop join, and 'GroupJoin' being used when we have any 'join' followed by 'group by' in the query.
Technologies Used: C++ • CMake • Git • SQL • VSCode
Computer Architecture Course Project | GitHub
2024 | Bangalore, IN
ChampSim-IISc is an adapted version of ChampSim (open-source simulator) with various kinds of branch predictors implemented for calculating performance metrics like prediction accuracy, MPKI, IPC etc. from program traces.
Technologies Used: C|C++ • Shell Scripting • Git • VSCode
Computer Architecture Course Project | GitHub
2024 | Bangalore, IN
Used 'perf mem' tool to obtain a sampled report of TLB misses at different logical addresses, and identified top N TLB-miss regions and allocated large pages to improve performance.
Technologies Used: C|C++ • Python • Git • Make • VSCode
Systems for Machine Learning Project | GitHub
2025 | Bangalore, IN
Built from scratch in Python without using deep-learning frameworks. Implements core Transformer components including positional encoding, scaled dot-product self-attention, multi-head attention, layer normalization, residual connections, and final output prediction following the original Transformer architecture.
Technologies Used: Python3 • NumPy • Git • VSCode • Shell Scripting
Machine Learning Project | GitHub | Report
2025 | Bangalore, IN
Profiled and optimized CNN inference across RTX 3060, GTX 1050, and Tesla T4 GPUs. Techniques include FP16 inference, mixed-precision (AMP+AMC), and tiled inference to reduce peak memory and improve throughput. Experiments were performed on ResNet-20/32/44/56 models trained on CIFAR-10 and Mini-ImageNet, with detailed analysis of memory, latency and accuracy tradeoffs.
Technologies Used: Python3 • PyTorch • CUDA • NVIDIA GPUs • Git • VSCode
Graduate Aptitude Test in Engineering (GATE)
| 2024 | GATE CS | AIR 026 |
| 2023 | GATE CS | AIR 608 |
| 2024 | GATE DA | AIR 648 |
Scan to Chat!
