About the Opportunity
Welcome to AlgOlympia 2026, a premier national ideation and algorithmic design hackathon hosted by the School of CSE,Nalanda Chandigarh University. This competition challenges the brightest minds to architect innovative, high-impact algorithmic solutions addressing critical global and societal issues.
Instead of traditional competitive programming, this event focuses on System Architecture, Algorithmic Logic, and Feasibility across three core tracks:
Algorithms in Healthcare (Resource optimization, predictive diagnostics, bio-informatics).
Algorithms in Women's Safety (Anomaly detection, safe navigation, decentralized emergency mesh networks).
Algorithms in Sustainable Development Goals (SDGs) (Clean energy allocation, smart waste management, carbon tracking).
Competition Tracks & Problem Domains
Track 1: Algorithms in Healthcare
Dynamic Resource & Patient Scheduling: Architectural models for matching emergency patients to specialized medical staff using advanced graph theory or network flow frameworks.
Genomic Sequence Mapping: Conceptualizing fast, low-overhead string alignment algorithms to identify pathogen mutations from massive bio-databases.
Track 2: Algorithms in Women's Safety
Multi-Criteria Safe Navigation: Designing routing algorithms that prioritize safety factors (lighting, crowds, police presence) over just the shortest physical distance.
Decentralized SOS Mesh Networks: Formulating topological and graph-based routing protocols for peer-to-peer distress signaling when cellular infrastructure is down.
Track 3: Algorithms in SDG Goals
Smart Grid Renewable Allocation: Developing optimization models (e.g., dynamic knapsack variants) to fairly distribute volatile solar/wind energy to high-priority sectors.
E-Waste Logistics Optimization: Architecting modified Traveling Salesperson (TSP) frameworks for autonomous recycling vehicles to minimize transit emissions.
Structure & Rounds
Round 1: Abstract & Algorithmic Architecture Submission (Online)
Teams will select one of the tracks and submit a comprehensive proposal detailing their chosen problem, proposed algorithmic logic, data structures to be utilized, computational complexity (Time & Space complexity analysis), and system workflow.
Round 2: The Marathon Presentation & Defense (Grand Finale)
Shortlisted teams will present their structural models, workflow designs, and algorithmic frameworks before a panel of industry experts. Teams will be evaluated on the mathematical rigor, optimization capabilities, and real-world viability of their models.
Eligibility & Team Format
Team Size: 3 members per team.
Open To: Undergraduate, postgraduate, and corporate tech enthusiasts who love solving structural problems with logical algorithms.
Evaluation Criteria
Submissions will be rigorously evaluated based on:
Algorithmic Innovation: Novelty and depth of the logical approach.
Technical Rigor: Proper selection of data structures, graph concepts, or optimization models.
Efficiency: Theoretical Time and Space complexity considerations.
Impact & Feasibility: How effectively the design solves the domain-specific challenge.
Prize Pool & Rewards
Compete for a total prize pool of ₹1,06,600:
Winner: ₹50,000
First Runner-Up: ₹30,000
Second Runner-Up: ₹20,000
️ Consolidation Awards: ₹6,600 pool
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.