Unleashing D'Amore-McKim potential with the DASH_Box
At Northeastern University, AI research thrives on cutting-edge computing power. With over 50,000 CPU cores and 500+ GPUs in the Discovery compute cluster, researchers have access to immense resources. But here's the thing: sometimes, you don't need all that firepower to get started.
That's where the DASH_Box comes in. This local computing solution is designed for usability and puts powerful AI capabilities at the fingertips of D'Amore-McKim researchers. Whether they're fine-tuning models, running experiments, or testing new ideas, the DASH_Box makes high-performance computing faster, more accessible, and seamlessly integrated into their workflow. It's not about replacing large-scale infrastructure; it's about making AI research more efficient, one breakthrough at a time.
AI for research
One of those researchers is Supply Chain and Information Management Professor Chris Riedl, whose work at the intersection of network science, AI, and the future of work is pushing boundaries—and the DASH_Box is helping him get there faster.
Riedl's research explores network dynamics and AI's role in shaping the future of work, and the DASH_Box has proven to be a powerful enabler, accelerating his research and unlocking new possibilities.
His research spans multiple areas, including:
- Graph Neural Networks (GNNs) to predict link formation in empirical network data.
- Large Language Model (LLM) inference to analyze textual data from human experiments while maintaining
strict data privacy. - AI-driven workforce studies examine how automation affects job satisfaction and professional identity.
These projects require substantial computational power, and before using the DASH_Box, Riedl faced several
challenges, including data privacy concerns, cloud-based GPU inefficiencies, and cumbersome data transfers.
Why “the box” is a game-changer
According to Riedl, the DASH_Box has significantly improved his workflow in three key ways:
- Speed and Efficiency — “The Box” allows Riedl to quickly iterate on different AI models and hypotheses. “It's so fast and so convenient! This is valuable in the early stages of a project when you do a lot of exploration,” he explains. “If everything is slow or requires extra steps to get going, it's really hard to be productive.”
- Privacy and Security — Riedl analyzes the sensitive data of many of his projects within Northeastern's
infrastructure. By running LLM inference locally, he protects data security and avoids high external service
costs. - Expanded Research Capabilities — Training graph neural networks on large datasets often requires
significant GPU and CPU resources. With the DASH_Box, Dr. Riedl can conduct large-scale synthetic data
generation and train deep learning models without the delays and constraints of cloud services.
Breakthroughs powered by DASH_Box
Riedl has made more progress in one month than in the previous six months combined. Some of his most
exciting applications include:
- Generating synthetic data for 1 million networks using Jupyter Notebook and PyTorch.
- AI-assisted essay writing to study worker identity and automation's impact on job satisfaction.
- Exploring collective intelligence in AI, analyzing how different prompts influence LLM-generated responses.