Arpeggio AWARE

Kubernetes Causal Signal Erasure

Unknown — Wed, 06 May 2026 00:00:00 +0000

This project investigates how cluster and region boundaries in Kubernetes systematically erase the causal signals (workload identity, trace context, and backpressure) that operators depend on to reason about distributed systems, and develops mechanisms to preserve those signals end-to-end.

Collaborator: Daniel Awodeyi

SwarmSim

Unknown — Wed, 06 May 2026 00:00:00 +0000

SwarmSim is a training gym for physical AI operating in swarms and flocks.
Collaborators: Anthony Russo, Julian Shniter

Warp & Weft

Unknown — Wed, 06 May 2026 00:00:00 +0000

An emerging exploration of the intersections between textiles and computer technology.
Collaborator: Tori Willis

garnr

Unknown — Thu, 15 Jan 2026 00:00:00 +0000

garnr is a declarative web scraping CLI built in Rust. Scraping jobs are defined in YAML configuration files rather than imperative scripts, supporting pagination, rate limiting, headless Chrome rendering, and failure recovery out of the box.
The goal is a tool that makes structured data collection from the web reliable and repeatable without requiring users to write and maintain brittle scraping code.
Collaborators: Dhyanna Patel, Santosh Iragvarapu

BounceBack

Unknown — Fri, 01 Aug 2025 00:00:00 +0000

BounceBack is a community-centric decentralized disaster recovery logistics services platform, conceived by Noah Guzinski after volunteering in tornado recovery efforts in the summer of 2025.
The core insight: in the immediate aftermath of a disaster, centralized coordination often lags behind the speed at which communities self-organize. BounceBack explores how local-first approaches — where data and decision-making live at the edge, close to the people doing the work — can support more effective grassroots disaster response.
The project sits at the intersection of several Arpeggio AWARE research areas: distributed systems engineering, cloud-native resilience, and the social dynamics of how communities coordinate under stress.
Collaborators: Noah Guzinski, Jack Crane, Samuel Kann, Mathew Shereni, Ngan Nguyen

Eye in the Sky

Unknown — Thu, 15 May 2025 00:00:00 +0000

Fall 2024–Spring 2025. Exploring next-generation approaches</a> to citizen science remote sensing using NDVI (Normalized Difference Vegetation Index). Investigating accessible, low-cost options for environmental observation and vegetation health monitoring.
Collaborator: Noah Guzinski

Optimizing Figure Skating Performance with ML

Unknown — Thu, 15 May 2025 00:00:00 +0000

Figure skating demands extreme precision, yet skaters often rely on trial and error to find their optimal training conditions due to limited competitive opportunities. This Spring 2025 Sigma Xi project explores how data-driven insights can identify a skater's ideal conditions for executing spins, jumps, and full programs. Using CatBoostRegressor and SHAP (SHapley Additive exPlanations), the study analyzes individual training factors to produce actionable, personalized recommendations for each skater.
Collaborator: Elizabeth Wangley

Legal Semiotics

Unknown — Sun, 15 Dec 2024 00:00:00 +0000

Legal Semiotics: A Focus on the Intersectionality between Technology and Intellectual Property. This project examined how semiotic frameworks can illuminate the evolving relationship between technology and intellectual property law.
Collaborator: Hermela Nebyu

PublicLab Kit Revitalization

Unknown — Sun, 15 Dec 2024 00:00:00 +0000

Spring 2023–Fall 2024. Revitalization of the PublicLab Kit</a>, an open-source toolkit for community-driven environmental monitoring. This project focused on modernizing the codebase and making it more accessible to citizen scientists.
Collaborators: Noah Guzinski, Samuel Kann

Personal Productivity & Knowledge Graph Tools UX Study

Unknown — Wed, 15 May 2024 00:00:00 +0000

A design review and UX study of personal productivity and knowledge graph tools, examining how users interact with and organize information across different tool paradigms.
Collaborator: Haneen AlSewari

systems.js

Unknown — Mon, 15 Jan 2024 00:00:00 +0000

Systems modelling techniques like causal loop diagramming are powerful for understanding feedback dynamics and emergent behavior, but the tooling available to practitioners ranges from expensive to awkward to nonexistent. systems.js aims to make these techniques accessible to a broader range of users — researchers, students, practitioners, and anyone trying to reason about complex systems.
The initial focus is causal loop diagrams, with an eye toward stock-and-flow models and other systems thinking visual formalisms.
Collaborators: Jessica Kerr, Dhyanna Patel, Santosh Iragvarapu, Ngan Nguyen, Siri Chandana Garimella, Vyshnavi Rao Ponnamaneni, Emra Meduseljac