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.

Accelerating research with high-performance computing

As an early pilot user of the DASH_Box, Supply Chain and Information Management Associate Professor Guohou Shan has experienced firsthand how this tool enhances research efficiency, expands possibilities, and fosters skill development. His research focuses on understanding the impact of AI, both theoretically and practically, and it contributes to industry through algorithm development and informs academic discourse by testing theories such as social learning, cognitive load, and source
credibility theories. Additionally, he plays a key role in equipping students with hands-on AI applications that enrich their learning experience.

One of his recent studies, MT-GPD: A Multimodal Deep Transfer Learning Model Enhanced by Auxiliary Mechanisms for Cross-domain Online Fake News Detection (Zhang et al., 2025), exemplifies the intersection of AI and real-world applications. Similarly, his previous work, Poligraph: Intrusion-Tolerant and Distributed Fake News Detection System (Shan et al., 2021), showcases the critical role of AI in information security.

Before using the DASH_Box, Shan relied on local computing resources, which limited his ability to run large-scale AI models efficiently. The DASH_Box provided a significant boost in three key areas:

  • Speed & Efficiency — Running complex machine learning models requires extensive computational power. The DASH_Box accelerated one of his research projects, significantly reducing processing time. According to Shan, “The 50 hours on the DASH_Box was easier to access than running a similar task on the Discovery Cluster because DASH_Box is easier to use and doesn't need to wait for resources to become available.”
  • Expanded Research Scope — With the ability to run large language models (LLMs) beyond the capabilities of standard computers, he could explore new research questions previously constrained by hardware limitations.
  • Skill Development — The DASH_Box not only facilitated research but also allowed him to enhance his
    expertise in scaling AI models for larger datasets and applications.

A strategic AI research resource

For junior faculty members like Shan, access to high-performance computing can be a game-changer. The DASH initiative provides critical support by aligning AI research with strategic institutional goals. He believes the DASH_Box helps strengthen his research and foster innovation within the academic community.

“As a junior faculty member, I am still developing my research portfolio,” Shan shares. “DASH and the DASH_Box provide the resources and confidence to continue pursuing AI-related research with theoretical and practical implications. The DASH_Box definitely sped up some of my data analysis in one of my working papers!”