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BACSA Hacks

devpostHosted on Devpost

Fetched 3 months ago

Saturday, March 7, 2026

to Monday, March 9, 2026

•

2 days long

HealthtechMachine LearningArtificial IntelligenceBeginner FriendlySocial Impact
Student only
This hackathons is only open to students. Double check the event page for more information as this may mean only those from a particular university/country are eligible.

Event Type

in person

19

Participants

1

Est. Projects

About the challenge

BACSA Hacks is a 1-day, in-person hackathon hosted by the Biotech and Computer Science Association (BACSA) based at the University of Toronto.
Much like BACSA, the hackathon focuses on the intersection between technology and biology, challenging participants to work in teams while building innovative, data-driven solutions that address real-world health and forensic problems. Through workshops, structured challenges, and engaging activities, participants will gain both an educational and fun experience.

Challenge:

Open: An open format challenge, where participants can make any product that adheres to the themes of Disease Detection (DNA, image recognition, body scans) or Health Improvement (nutrition trackers, medication trackers etc.)


Closed: Participants will have to build a forensics support system that receives biological evidence from crime scenes and outputs a ranked list of suspects, confidence scores and an analysis explaining the reasoning. Success is measured not by finding one correct answer, but by how well teams model uncertainty, justify their assumptions, and reason their forensic interpretations

Judge Accessibility

Organizer email available25/25
Student-run event15/15
Actively looking for judges25/25
Small event (120 participants)10/10
No corporate sponsors10/10
New or emerging organizer10/10
Public registration available5/5
Online format (judge from anywhere)10/10

Top signals

Organizer email available
Student-run event
Actively looking for judges

Organizers

Alex Johnson

alex@example.org

Jamie Rivera

jamie@example.org

Sam Chen

sam@example.org

Estimated Audience

Mostly Students
ExperienceStudent
OccupationStudents
Beginner Friendly
Women in Tech

Technical Focus

AI95%
Web80%
Mobile25%

Industries

Healthcare
Education
Climate

Technologies

Python
React
OpenAI

Why this estimate

  • • Hosted by a university
  • • Open to students
  • • MLH member event

Estimate inferred from event metadata, not actual attendee data.

Quality Score

Quality Score

72/100
High confidence
Organiser16/20
Event Maturity14/20
Sponsors18/25
Participants12/20
Operations12/15

Why this score

Strong organiser track record
Returning event
Well-sponsored

Missing data

Prize details
Code of conduct