B.Tech CSE @ PES University · Founder-track builder @ PESU Venture Labs · building traceable, auditable AI systems for regulated industries.
I build AI systems that can explain themselves. Currently founding EdgeDaemon — an AI decision-reconstruction and provenance platform — while shipping agentic pipelines, ML research systems, and safety-stack architectures across 10+ hackathons and research programs.
I'm a Computer Science Engineering student at PES University, Bengaluru, working at the intersection of AI safety, agentic systems, and full-stack engineering. Most of my work centers on one question: can an AI system explain, after the fact, exactly why it did what it did?
That question became EdgeDaemon, an AI decision-reconstruction and provenance platform for regulated industries, which I'm building as a Technology Associate through PESU Venture Labs' competitive founder-track ABC program.
Outside EdgeDaemon, I've shipped five-agent trading safety stacks, RL-based ML pipeline agents, legal-defense multi-agent systems, and a ResNet18-based crop disease classifier with full inference provenance — across GitLab, Meta × PyTorch, IEEE, and FantomCode hackathons. I'm also a national-level Ice & Inline Hockey athlete representing Karnataka, which is where I actually learned how to perform under pressure as part of a team.
The centerpiece of my current work — an AI decision-reconstruction and provenance platform tackling one of the hardest unsolved problems in enterprise AI: making autonomous decisions auditable after the fact.
An AI decision-reconstruction and provenance platform for regulated industries — healthcare, legal, and finance — where every model decision needs to be explainable, traceable, and defensible after the fact. Selected for PESU Venture Labs' Accelerating Bold Concepts program, a competitive founder-track incubation cohort, and pivoted the technical thesis from on-device continual AI personalization to decision-provenance after structured customer discovery surfaced systemic traceability failures across enterprise AI pipelines.
Every project here is built around the same throughline — making agentic AI systems observable, constrained, and auditable, not black boxes.
Actively researching how language models internally generate responses — moving below the behavioral surface to understand the exact mechanisms: how specific attention heads, MLP layers, and activation circuits produce particular outputs, and what structures correspond to reasoning, memory, and decision-making.
Motivated by observed alignment failures — autonomous agents developing emergent covert communication between sub-agents, taking untracked actions, and exhibiting goal-directed behavior that bypasses intended constraints. Behavioral-interface safety alone isn't enough.
Goal: develop formal traceback methods for AI decisions at the weight/activation level — extracting a precise, mechanistic account of why a model produced a specific output, enabling proactive safety guarantees instead of post-hoc behavioral observation.
Building toward a graduate research thesis on low-level neural safety infrastructure — interpretability tooling, activation auditing, and formal provenance systems operating at the model-internals layer, not just the output layer.
Looking for AI engineering / AI safety / full-stack internships at teams building serious things. If you're working on something that needs to be provably correct — I'd love to talk.
yxshas565@gmail.com