portfolio
shipped systems: the deployment record behind the research
Arjun Joshi builds perception systems that run on-device, in real time, in uncontrolled environments, and takes them from research through optimization, edge deployment, and live operation. The research side of the work grew out of that record: the failure modes he kept meeting in production vision pipelines and vision-language models are the questions REVEAL exists to answer.
Q-DRIVE — driver-safety platform
A driver-safety training and risk-scoring perception platform, built and deployed end to end: a hardware-in-the-loop driving simulator with a custom secondary physics layer, and a Jetson NX in-vehicle device running the real-time perception stack (DeepStream/TensorRT) for live driver coaching. In beta with a regional transit agency; demonstrated live at three industry conferences.
Several fragmented CV models became one multi-camera pipeline with sensor fusion (camera, IMU, GPS), producing distance, collision, lane, and driver-behavior signals on constrained hardware. Real-time perception latency dropped from 300ms to under 40ms p99. The scoring layer extends NHTSA and insurance-industry risk models with a predictive tier in beta and a Safe-RL (CMDP) policy layer in development.
Facial-recognition edge platform
A standalone recognition device (Jetson Orin Nano) serving 210+ enrolled users at a live client site, integrated with the client's workforce-management platform and running as a monitored production service at 99.9% logged uptime. No identifying data is held on the edge device.
Multi-subject and real-time: 30ms median detection, up to 14 simultaneous faces. When layered temporal validation cannot confirm an identity, the system withholds the match. An 18-day production evaluation recorded zero false identifications, including under adversarial enrollment tests.
Dispatch optimization — current
Currently leading the ground-up design of an MCTS/CMDP framework for paratransit dispatch optimization, a full-scale overhaul of a transit technology company's dispatch system. Reports directly to the CEO, with hiring authority for the framework's research team.
Cloud & IoT integration
The GPU cloud layer behind the edge systems: FastAPI/Docker services on AWS for embedding and VLM analysis, remote enrollment, and IoT event delivery into client dashboards. An LLM call-summarization pipeline processes 400+ calls per day for client performance analytics.
Stack: NVIDIA Jetson Orin Nano / NX, DeepStream (C/Python), TensorRT, ONNX, GStreamer, vLLM, FastAPI, AWS, Docker, Linux, systemd.
Independent research & IP
Sole inventor of REVEAL (provisional patent, USPTO 2026), a conditioning framework for visual attention in reasoning vision-language models. The independent research program (biomechanics, corpus generation, training infrastructure) is described across /projects and /research, and is unaffiliated with his employer.