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Peersky Browser is an experimental personal gatekeeper to a new way of accessing web content. In a world where a handful of big companies control most of the internet, Peersky leverages distributed web technologies—IPFS, Hypercore, and Web3—to return control to the users.

Project Description Topics: Distributed systems Skills: Java, Go, Python, Bash scripting, Linux, Docker. Difficulty: Hard Size: Large (350 hours) Mentors: Fadhil I. Kurnia Replication is commonly employed to improve system availability and reduce latency.

Vector Embeddings Dataset Topics: Vector Embeddings LLMs Transformers Skills: software development, apis, scripting, python Difficulty: Moderate Size: Medium or Large (175 or 350 hours) Mentors: Jayjeet Chakraborty To benchmark vector search algorithms (aka ANN algorithms), there are several datasets available but none of them represent actual real world workloads.

Brahma is a lightweight framework for building collaborative and cross platform WebXR based experiences using Three.js for the front-end and a simple Node.js/WebSocket script on the backend. It was created at the Social Emotional Technology Lab to facilitate the development of novel collaborative interfaces and virtual environments capable of loading scientific datasets.

WildBerryEye leverages Raspberry Pi and YOLO object detection models to monitor pollinizers like bees and hummingbirds visiting flowers. This initiative aims to enhance environmental research by automating data collection and analysis of pollinator activities, which are crucial for ecological assessments and conservation efforts.

Garbage In Garbage Out (GIGO) is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI). As data is the fuel for AI, models trained on low-quality, biased data are often ineffective.

h5bench is a suite of parallel I/O benchmarks or kernels representing I/O patterns that are commonly used in HDF5 applications on high performance computing systems. h5bench measures I/O performance from various aspects, including the I/O overhead, and observed I/O rate.

HAgent is a platform to build AI hardware agent engine to support multiple components in chip design, such as code generation, verification, debugging, and tapeout. HAgent is build as a compiler for for Hardware Agents, it interfaces with typical EDA tools like compilers, synthesis, and verification.

Scenic is a probabilistic programming language for the design and verification of autonomous cyber-physical systems like self-driving cars. Scenic allows users to define scenarios for testing or training their system by putting a probability distribution on the system’s environment: the positions, orientations, and other properties of objects and agents, as well as their behaviors over time.

Data is generated at ever-increasing rates, yet it’s often processed more slowly than it’s collected. Scientific instruments frequently operate below their full capacity or discard valuable data due to network bottlenecks, security domain mismatches, and insufficient real-time processing capabilities.