Quinn Arnold
Machine Learning & Applied Mathematics
Researcher · Bryant University · Advanced Analytics Intern
I work on reinforcement learning, deep learning, responsible ML, optimization, and algorithm engineering. Current work spans recurrent PPO for partially observed combinatorial auctions, prompt-injection defense for LLM systems, ML model governance and LLM workflows, and route-optimization research.
Research
-
FastCombo — Deep RL for Combinatorial Auctions
A recurrent policy maps public CATS auction histories directly to item prices. It reached 92.4% deterministic exact stage-1 clearing on Paths, above the published 88% Bayesian result under a different protocol.
-
Ask Tupper — Hardened Campus Chatbot
QLoRA fine-tune with a six-layer prompt-injection defense pipeline, reducing attack success from 20% to 0% across 15 tested vectors. RoBERTa classifier achieves 95.3% injection recall.
-
RAPTOR: Route-Audited Patrol Territory Optimization
Routing-aware graph partitioning with integer staffing and audit gates, producing route-audited district geometries and officer allocation recommendations across Providence, Boston, and Seattle street-network instances.
Supporting Systems
-
MAPFRE Insurance
-
GuidePost Technologies LLC
-
Rhode Island Novelty
-
SafeWalk
Interests
- Reinforcement learning
- Deep learning
- LLM safety
- Responsible ML evaluation
- Decision-focused learning
- Optimization
- Graph algorithms
- Game theory
- Autodiff & backpropagation
- Applied ML systems