I'm a systems researcher who spent 4.5 years trying to understand how programmable networks break : and how to catch them before they do. I've published work on P4 data plane validation, eBPF bytecode analysis, and adversarial attacks on probabilistic network monitoring.
Now I'm channeling that same obsession with "how do systems actually work" into AI infrastructure engineering : building the DevOps and cloud skills to work on the systems that run AI at scale.
I believe the best infrastructure engineers are the ones who understand systems deeply, not just operationally. That's what I'm building toward.
| Paper | Venue | What it does |
|---|---|---|
| In-Network Probabilistic Monitoring under Adversarial Inputs | APNet 2023 | Demonstrates how tens of malicious flows corrupt network telemetry with 99% accuracy drop |
| Yaksha-Prashna: eBPF Bytecode NF Behavior | Arxiv 2026 | Query language + Data flow analysis engine for understanding third-party eBPF bytecodes |
| DBVal: P4 Data Plane Runtime Validator | SOSR 2021 | Compiler + Framework for validating P4 runtime behavior using embedded assertions |
I'm currently going deep on the engineering side: Linux internals, containers, Kubernetes, and cloud infrastructure. The goal is to bridge research depth with hands-on systems engineering.
Systems: C++ · Python · P4 · eBPF/XDP · Prolog
Compilers: Flex · Bison · LLVM-Clang
Tools: Libbpf · bpftool · Wireshark
DevOps: Linux · Git · Docker (learning)
Cloud: AWS (learning)
Programmable networks · eBPF · AI infrastructure · Distributed systems · Network telemetry · Systems security
If you're working on hard infrastructure problems, especially at the intersection of networking and AI, I'd love to connect.
- 🎓 Google Scholar
- 🌐 Personal Website
- 📍 Bhilai, Chhattisgarh, India