UC OSPO

Mentors: Student: Avantika Pandey1

The Network Simulation Bridge (NSB) is a powerful co-simulation framework, but its current onboarding experience creates a barrier for new users due to fragmented documentation, absence of a clear “first success” path, and setup complexity. This project aims to design and develop a user-centric website that transforms onboarding into a structured and intuitive flow. The core focus is enabling users to run a working NSB example quickly through a quickstart-first approach, followed by progressive introduction of concepts, configuration, and simulator integration. The solution includes building a complete website with a guided Get Started system, reorganized documentation, and clearly defined learning paths, along with a progressive simulator integration strategy that begins with a simplified environment before moving to full simulator workflows. Key deliverables include a deployable NSB website with structured navigation, a guided onboarding flow centered on first success, unified documentation migrated from the repository, and tutorials with progressive learning pathways, validated through user testing and iteration. This project will reduce onboarding friction, improve usability, and make NSB more accessible to students, researchers, and developers.

Mentors: Student: Sohan Shingade

The VINE project at Iron Horse Vineyards collects continuous agricultural data through LoRaWAN sensors (soil moisture, temperature, CO2, weather) and multispectral drone imagery, but lacks the ML models to turn this data into actionable predictions. This project builds three interconnected model tracks for the VINE precision agriculture platform, all trained on NRP's GPU clusters. First, predictive irrigation scheduling: time-series forecasting models (ARIMA, Prophet, LSTM) that predict soil moisture at multiple horizons and recommend when to irrigate before crop stress occurs. Second, plant health computer vision: CNN-based models (fine-tuned ResNet/EfficientNet) that analyze multispectral drone imagery to classify plant stress, detect pest damage, and estimate yield, producing spatial health maps per vineyard block. Third, harvest timing prediction: forecasting models (XGBoost, LSTM) that predict optimal harvest windows from sensor data, weather, and historical records. Deliverables: (D1) data ingestion and feature pipeline for sensors, imagery, and historical records, (D2) irrigation scheduling models with decision layer, (D3) plant health CV models with spatial health maps, (D4) harvest timing models, (D5) systematic model evaluation report with ablation studies, (D6) Dockerized inference services deployed on NRP Kubernetes with REST API, (D7) documentation and bi-weekly blog posts.

Mentors: Student: Harshal Atre

Mobile users currently lack native access to P2P protocols like hyper://, limiting the reach of decentralized web technologies. Although desktop browsers like PeerSky support these protocols, mobile platforms remain largely inaccessible without centralized gateways. To address this, this project proposes building PeerSky Mobile Browser, a React Native application for Android using Holepunch's bare-expo template, which provides the Bare runtime necessary for running Hypercore libraries that standard React Native cannot support. The project will implement native hyper:// protocol support with DHT-based peer discovery, basic HTTPS browsing via WebView, Holesail P2P tunneling through QR codes, and mDNS for offline discovery. So the core features will include secure key management using Android KeyStore, mobile-optimized UI with tab management, and comprehensive testing across Android versions. The deliverable will be a functional Android application that brings P2P browsing capabilities to mobile and extends the PeerSky ecosystem.

Mentors: Student: wmadsmit

CXL is an interconnect which uses memory disaggregation to overcome the memory bandwidth bottleneck from most workloads. OCEAN, one such CXL emulator built on-top of QEMU, has support for modern CXL3.0 features and supports multi-host communication is one emulator which has the potential to enable CXL-based studies. The infrastructure and studies performed by OCEAN are relatively new and limited. This project seeks to mature OCEAN's infrastructure by improving documentation, improving the accessibility and ease of setting up OCEAN, and improving the capturing of HW counter metrics which will later be used to asses how CXL can be integrated and used within the memory hierarchy to enable performance gains on HPC-class workloads.

Mentors: Student: Dhanush_

This project extends AIDRIN’s data ingestion layer beyond CSV and basic tabular formats to better match real scientific workflows. I will (1) audit and harden the existing HDF5 path, (2) add new readers for Zarr and ROOT (via uproot), and (3) introduce a simple custom ingestion interface that normalizes user‑supplied data sources into a pandas DataFrame and metadata dictionary before AIDRIN’s metrics run. The work includes dataset/TTree selection UX, chunk‑aware reading for large N‑dimensional arrays, metadata extraction for FAIR-related metrics, and a full set of tests and documentation. By the end of the project, researchers will be able to use AIDRIN directly on common scientific formats and plug in their own loaders without modifying the core codebase.