IEEE Computational Intelligence Society Student Chapter, IIT

Build the system that sees what people can't.

Expertise does not scale. So most people decide half‑blind.

Someone can always read the signals: what a price rise will do to a household, what a swelling river means for the village below it, why an illness is spreading where it is. There have never been enough of them.

For most people, most days, that expert is unavailable, unaffordable, or unreachable. They decide anyway, on whatever they can see.

ModelX 2.0 / The Expertise Gap

Turn real data into insight a person could not have reached alone.

Full details of the competition are announced when it opens on 1 October. You will find your own problem to solve, and choose the user you solve it for. A real problem, affecting real people.

You choose the user. You find the data. Machine learning, deep learning, agentic systems, retrieval, anything. The method is yours.

Your system must tell its user something they did not already know: a pattern, a risk, a trend, an anomaly. Retrieval is not insight. The intelligence has to come from the data.

The hard part is producing an output worth trusting: knowing what the data cannot support, and failing safely when a decision depends on you.

Two submissions. Two cuts. One final.

Dates and deadlines.

7 to 27 Sep

Registration

28 to 30 Sep

Teams locked, no changes after this

Thu 1 Oct

ModelX 2.0 opens

1 to 10 Oct

Phase one: Research

Sat 10 Oct

Project proposal due

Submission 1

Tue 13 Oct

Results announced

Gate 1

Date to be announced

Workshop 1

11 to 23 Oct

Phase two: Implementation

Date to be announced

Workshop 2

Fri 23 Oct

Implementation due

Submission 2

Wed 28 Oct

Fifteen finalists announced

Gate 2

Sat 31 Oct

The Final: live pitches, winners and awards

Two submissions.

Project proposal

Due Sat 10 Oct

The kind of proposal you would hand a client. Every section must be present and substantively answered.

The problem you have chosen, who it affects, and the user you are building for. A review of what already exists and why it does not solve this for them. Your proposed solution and methodology. Your data: where it comes from, how you will extract it, whether it is historical, recent or live, and what right you have to use it. How you will judge whether it worked, and what you will do if it does not.

Implementation

Due Fri 23 Oct

What you actually built, and the code behind it.

A technical report covering the architecture, the stack you chose and why, and an honest evaluation against your proposal. An account of the data you finally used, its temporality and its gaps. A git repository anyone can open, with a README that gets it running. A two-page summary, and a three-minute video of the system working on real data.

Detailed instructions and document templates will be provided at the start of the event.

How you are judged.

Implementation: cuts to 15

Data work30%

Sourcing, extraction, and above all temporality. Plus cleaning, honesty about gaps, ethics and licensing.

Insight delivered25%

Does it tell the user something they could not have reached alone?

Engineering quality25%

Structure, dependency handling, a git history showing sustained work, and a report that matches the code.

Functionality demonstrated20%

What your demo video actually shows working, on real data, end to end.

The Final: decides the winners

The system, working35%

Running live, on real data, in front of the panel. Demonstrated beats described. Includes how it handles what it doesn't know.

Insight and its value25%

Would your named user actually act on what it tells them?

Problem significance20%

How big a problem you chose to take on, and how many people it affects.

Depth under questioning20%

Whether you can defend your choices when pressed, and explain the work clearly and honestly.

Rules and eligibility.

Team size

One to four members, from any university, solo entry included. Each person may be on one team only.

Rosters lock 27 September

No additions, substitutions or swaps once registration closes. A team that loses a member continues with whoever remains.

Original work

Everything you submit must be your own, produced during the competition. Bringing a finished system, or plagiarism in code, documents or data, is disqualifying.

Open source is expected

Libraries, pretrained models and public datasets are freely usable. Credit them.

AI assistance is permitted

Use the tools. Disclose which ones and what for, and own what you submit. "The model wrote it" is not a defence.

Your data must be yours to use

Licence, terms of service, or documented permission. Respect robots.txt directives. Minimise and anonymise personal data. Data obtained through deception is disqualifying.

Deadlines are firm

Late submissions are not accepted, and a missed gate is an elimination. A private repository nobody can open counts as no submission.

Conduct

Harassment, discrimination or abuse means immediate removal. So does sabotage, interference with another team, misrepresenting your results, or any attempt to influence how your work is assessed.

Appeals

Within 48 hours of a result, on grounds of procedural error only, not disagreement with a score. The committee's decision is final.

What you're competing for.

Overall placings

Winner

Rs 75,000

First runner-up

Rs 50,000

Second runner-up

Rs 25,000

Special award

Best Data Vision

The clearest thinking about data: where you found it, how you got it, and above all whether it was current enough to be worth acting on. Judged from your implementation rather than your pitch, so every team that submitted one is in contention.

Contact us.

Dinugi Nimnara

Volunteer Management Lead

dinugi.20231363@iit.ac.lk 071 995 1662

Chanuka Wijeratna

Event Co‑chairperson

chanuka.20232021@iit.ac.lk 076 654 8877

Adrian Malcolm

Event Co‑chairperson

mellitus.20250440@iit.ac.lk 074 037 5521

Registration opens 7 September.

Teams of one to four. Any university.