Systems I have designed, built, and shipped — at Nokia Bell Labs (2022–present) and during my PhD. Each one spans the full stack: algorithm design, infrastructure, and a working end-to-end deployment.

In progress

ElasticServe — SLO-aware LLM inference serving control plane

Personal project on NVIDIA DGX Spark · 2026

Building an adaptive, multi-tenant inference-serving control plane that routes mixed workloads across vLLM/SGLang model endpoints: admission control and backpressure, SLO-aware weighted-fair scheduling, adaptive concurrency, and model-residency policy, measured against static baselines on goodput (TTFT/TPOT SLOs), tail latency, fairness, and cache efficiency, with Prometheus/Grafana observability throughout.

Nokia Bell Labs

XRNet: network-aware XR delivery over 5G

NSF “Breaking Low” program (~$3.5M) · Oct 2025 – present

Multi-institution project with the University of Michigan (PI), USC, Columbia, Duke, and Nokia Bell Labs (Co-PI), targeting motion-to-photon latency requirements considered infeasible on today’s Internet. My contribution is one of the first open end-to-end implementations of the 3GPP Release-18 PDU Set Marking RTP header extension (TS 26.522) in a production WebRTC stack — so every video packet tells the network how important it is.

  • Implemented the extension across three codebases: Unity’s C# application API, its native C++ plugin, and a modified, rebuilt libwebrtc — every RTP packet carries standards-compliant frame-importance and sequencing metadata usable by 5G schedulers, negotiated via SDP.
  • Built a real-time H.264 NAL-parsing classifier (RFC 6184 STAP-A/FU-A aware) that maps keyframes, parameter sets, and reference frames to PDU Set Importance values during packetization of a 1080p/60 fps stream.
  • Delivered the full interactive cloud-XR loop: a Unity-rendered 3D scene streamed over DTLS-SRTP to a stock browser, user input returned through a WebRTC DataChannel, Node.js signaling, and STUN/TURN traversal for operation across a 5G core.
  • Validated at the wire level with Wireshark — byte-exact extension contents on every packet of the encrypted stream — and on a three-node Linux tc/netem testbed whose QoS classifier reads the importance marking directly from packet bytes: PDU-Set-aware handling kept the interactive stream smooth under loss and jitter that froze importance-blind delivery.
  • Demonstrated the complete end-to-end system live at the University of Michigan (June 2026), validating the program’s central approach in practice.

Edge–cloud scalable 3D Gaussian Splatting delivery

Patented · Jun 2025 – Mar 2026

  • Architected an edge–cloud 3DGS scene-delivery system that folds live network state into splat selection and prioritization, jointly optimizing network- and application-level performance.
  • Resolved 3DGS scalability limits for compute- and memory-constrained client devices and for simultaneous multi-user access under network contention.
  • Approved by the Nokia Patent Board; assessed by expert reviewers as a first-of-its-kind cross-layer XR streaming approach, relevant to Nokia’s MantaRay RIC and AI-RAN product lines.

ML-driven spectrum defragmentation for optical transport networks

Demonstrated at OFC 2025 · Mar 2024 – Mar 2025

  • Developed an automated, ML-driven spectrum-defragmentation method that improves energy efficiency in optical transport networks.
  • Built the automation pipeline end to end and demonstrated it live at the Optical Fiber Communications Conference (OFC 2025).

PSASlicing: SLA-aware reinforcement learning for O-RAN slice management

IEEE GLOBECOM 2024 · Dec 2023 – Aug 2024

  • Designed a perpetual, SLA-aware reinforcement-learning method for O-RAN network-slice management that sustains SLA compliance over long operating horizons — RL that has to keep working in production conditions, not just in a training run.

End-to-end 5G/O-RAN system and RIC applications

Jun 2022 – Dec 2022

  • Implemented a complete 5G end-to-end system with OpenAirInterface, Open5GS, and O-RAN components.
  • Developed xApps (near-RT RIC) and rApps (non-RT RIC) hosting ML-based radio resource management agents for RAN slicing.

PhD systems work (UNCC / NJIT)

FedVision — federated video analytics with edge computing

IEEE OJ-CS 2020, 45+ citations

  • Designed a federated video analytics system that jointly optimizes on-device inference and computation offloading across an edge network, trading off detection accuracy against end-to-end latency.
  • Built the testbed on NVIDIA Jetson TX2, nvidia-docker, OpenWRT, and desktop GPUs; used learned surrogates (neural processes) and black-box optimization to pick models, frame resolutions, and offloading rates at runtime.

FLEX — trading edge computing resources for federated learning

Demoed at IEEE INFOCOM 2021

  • Designed and implemented a blockchain marketplace (Ethereum smart contracts in Solidity) where edge nodes sell compute to federated learning tasks, including the TensorFlow-side workflow and interfaces between edge, global, and chain nodes.

Earlier systems

  • Decentralized video analytics — distributed testbed with heterogeneous edge hardware; consensus and incentive design accounting for both compute speed and network latency (2021).
  • SDN for power distribution systems — learning-based network management and DoS mitigation for distributed grid controllers, on ONOS/Ryu SDN controllers with NS-3, OPAL-RT, and OpenDSS co-simulation (2019–2020).
  • Underwater communication system — closed-loop underwater fault monitoring with custom RF/optical channel models in NS-3 and an OPAL-RT–GNU Radio bridge, prototyped in a lab seawater setup (2018).
  • Edge datastore for distributed vision analytics — key-frame/feature store written in Go with REST APIs and SDN-fed network awareness (ACM/IEEE SEC 2017 poster).