Northeastern Team Awarded the Vivli AMR Surveillance Data Challenge Student Innovation Prize
microBIS team members, L to R: Alexander Kwakye, Thomas Lim, Harry Akligoh, and Charlie Huh.
A team of Northeastern graduate students and alumni, led by Harry Akligoh, PhD ’28, bioengineering, participated in the 2024 Vivli Antimicrobial Resistance (AMR) Surveillance Data Challenge and were awarded the Student Innovation Prize for their Microbiological Information Management System (microBIS) project.
When Harry Akligoh, PhD ’28, bioengineering, arrived at Northeastern in 2023, he had conducted preliminary research to develop a system that would assist lab technicians in identifying bacteria and screening them against a range of antibiotics, while also creating a structured database to effectively track results.
Encouraged by the collaborative and entrepreneurial environment at Northeastern, he decided to continue research on his project, Microbiological Information Management System (microBIS), and reached out to peers to work with him. Within a year, that group was participating in the 2024 Vivli Antimicrobial Resistance (AMR) Surveillance Data Challenge and were awarded the Student Innovation Prize for their microBIS project.
Akligoh, who grew up in Ghana, began his research in response to his experiences working in a medical lab in his home country that relied on an error-prone, paper-based system to document and interpret lab results. In 2019, he began work on microBIS and with funding from the Microbiology Society in the United Kingdom, he built a prototype and completed initial testing.
At Northeastern, he shared his vision with peers and established a team that won the Vivli AMR Surveillance Data Challenge Student Prize. This team included Charlie Huh, PhD’28, bioengineering; Thomas Lim, MS’24, computer science; and Alexander Kwakye, who is pursuing a PhD in population genetics at Stony Brook University. Akligoh attributes their success to an interdisciplinary approach that resulted from their different areas of expertise, including AI/ML, data science, software engineering, and antimicrobial resistance research.
The team developed an advanced AI/ML model, deployed through the microBIS web application to improve bacterial identification and potential antibiotic resistance profiling. They believe the microBIS platform will significantly enhance clinical decision-making in infectious disease management, research, and surveillance. In resource-limited settings, microBIS can provide enhanced diagnostic capabilities without requiring costly equipment.
The team remains dedicated to advancing the platform and welcomes collaboration with AI/ML experts and research groups at Northeastern to refine and test microBIS further. The goal is to extend its use beyond Northeastern.
To learn more about this project, contact Akligoh at akligoh.h@northeastern.edu