nhyapu

nhyapu AI Lab

innovating the future with AI...

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nhyapu AI Lab brings students at every level together to spend a weekend doing real AI research, building, learning, training models, and pushing the boundaries of what's possible with artificial intelligence.

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Preorders close September 30.

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01 / About

What is
nhyapu?

nhyapu AI Lab is a student-run research initiative dedicated to exploring artificial intelligence and machine learning. We bring together students at every level—from first-time coders to people who have already trained models—to collaborate, explore, and push the boundaries of AI. nhyapu is designed for anyone and for everyone, experienced or beginner.

*Open-source AI Lab

nhyapu is built around open source. Researchers are encouraged to publish their work, use open-source tools, and share what they learn. Visit the lab portal.

3+researchers

∞hours of innovation

$0to attend

1weekend to start

Fun fact: Nobody is expected to arrive an expert. The people who teach the workshops are usually learning the material a few weeks ahead of you.

You don't need a perfect idea or years of experience. Just bring a laptop, some curiosity, and a willingness to explore the world of AI.

02 / Schedule

The research
agenda...

Every nhyapu project follows the same arc: source real data, clean it, explore it, train a model, test it honestly, then deploy it and watch it in production. Health and agriculture teams run those same steps on their own conditions, side by side.

nhyapu schedule with event durations
Time	Activities
Day oneSaturday October 10
9:00 AM	Check-in and lab access 9:00–10:00 AM
10:00 AM	Opening ceremony: how an nhyapu project works 10:00–11:00 AM
11:00 AM	Health Data sourcing: patient records and public health data 11:00 AM–1:00 PM	Agriculture Data sourcing: crop, soil and weather data 11:00 AM–1:00 PM	Data sourcing: open datasets and public APIs 11:00 AM–1:00 PM	Mentor office hours 11:00 AM–1:00 PM
11:30 AM				Mentor office hours 11:00 AM–1:00 PM
12:00 PM	Health Data cleaning and preprocessing 12:00–2:00 PM	Agriculture Field sampling and data collection 12:00–2:00 PM	Intro to Python for data 12:00–1:00 PM	Lunch 12:00–2:00 PM
1:00 PM	Health Data cleaning and preprocessing 12:00–2:00 PM		Intro to Python for data 12:00–1:00 PM	Lunch 12:00–2:00 PM
2:00 PM	Health Exploratory data analysis 2:00–4:00 PM	Agriculture Exploratory data analysis 2:00–4:00 PM	Feature engineering 2:00–4:00 PM	Labeling and annotation clinic 2:00–4:00 PM
3:00 PM	Health Exploratory data analysis 2:00–4:00 PM	Agriculture Exploratory data analysis 2:00–4:00 PM	Feature engineering 2:00–4:00 PM	Labeling and annotation clinic 2:00–4:00 PM
4:00 PM	Health Baseline models and evaluation setup 4:00–6:00 PM	Agriculture Baseline models and evaluation setup 4:00–6:00 PM	Intro to neural networks 4:00–5:00 PM	Intro to SQL and data APIs 4:00–5:00 PM
5:00 PM			Intro to neural networks 4:00–5:00 PM	Intro to SQL and data APIs 4:00–5:00 PM
5:30 PM	Health Model training begins 5:30–6:30 PM	Agriculture Model training begins 5:30–6:30 PM	Error analysis and metrics 5:30–7:30 PM	Mentor review: data and label quality 5:30–7:30 PM
6:00 PM	Health Model training continues 6:00–7:00 PM	Agriculture Model training continues 6:00–7:00 PM	Error analysis and metrics 5:30–7:30 PM	Mentor review: data and label quality 5:30–7:30 PM
6:30 PM	Health Model training continues 6:30–7:30 PM	Agriculture Model training continues 6:30–7:30 PM	Intro to machine learning frameworks 6:30–7:30 PM	Version control and reproducible notebooks 6:30–7:30 PM
7:00 PM	Dinner 7:00–8:00 PM
8:00 PM	Health Model training and tuning 8:00–10:00 PM	Agriculture Model training and tuning 8:00–10:00 PM	Data documentation and experiment logs 8:00–9:00 PM	Responsible AI and data ethics 8:00–9:00 PM
9:00 PM	Health Model training and tuning 8:00–10:00 PM	Agriculture Model training and tuning 8:00–10:00 PM	Hyperparameter tuning clinic 9:00–10:00 PM	Preparing the demo narrative 9:00–10:00 PM
10:00 PM	Track review: what our models get wrong 10:00–11:00 PM
11:00 PM	Health Overnight work: data and models continues overnight	Agriculture Overnight work: data and models continues overnight	Snack and stretch 11:00 PM–12:00 AM
Day twoSunday October 11
12:00 AM	Health Overnight work: data and models 12:00–2:00 AM	Agriculture Overnight work: data and models 12:00–2:00 AM	Rest and sleep 12:00–4:00 AM
1:00 AM	Health Overnight work: data and models 12:00–2:00 AM
2:00 AM	Health Model testing: cross-validation and metrics 2:00–4:00 AM	Agriculture Model testing: cross-validation and metrics 2:00–4:00 AM
3:00 AM
4:00 AM	Health Model testing: holdout scorecard and fairness checks 4:00–6:00 AM	Agriculture Model testing: holdout scorecard and fairness checks 4:00–6:00 AM	Deployment review with mentors 4:00–6:00 AM	Smoke tests and rollback plans 4:00–6:00 AM
5:00 AM			Deployment review with mentors 4:00–6:00 AM	Smoke tests and rollback plans 4:00–6:00 AM
6:00 AM	Breakfast 6:00–7:00 AM
7:00 AM	Health Deployment: packaging and serving the model 7:00–9:00 AM	Agriculture Deployment: packaging and serving the model 7:00–9:00 AM	Monitoring setup: drift and alerts 7:00–9:00 AM	Error analysis clinic 7:00–9:00 AM
8:00 AM	Health Monitoring: drift, alerts and dashboards 8:00–9:00 AM	Agriculture Monitoring: drift, alerts and dashboards 8:00–9:00 AM	Model card and documentation 8:00–9:00 AM	Publishing code and notebooks 8:00–9:00 AM
9:00 AM
10:00 AM	Model testing sign-off and final evaluation due 10:00 AM–12:00 PM
11:00 AM
12:00 PM	Closing ceremony and demo showcase

03 / Lab Challenges

Showcase your
AI skills!

Three open challenges, three specialty tracks, and recognition for everyone who takes part.

First place

Data Visualization Challenge

Lab membership one year, whole team

Full GPU cluster access, pre-trained models, and research datasets

One for each team member

Second place

Data Cleaning Challenge

Mentor time six sessions per member

A research mentor works with your team on your own pipeline, from sourcing through to evaluation.

One for each team member

Third place

Best Algorithm Challenge

Published write-up featured on the lab site

We publish your model write-up on the lab site, with your code and notebooks released as open source.

One for each team member

On top of first place

First-place extras

A data sourcing starter pack — API and scraping credits, plus a curated list of open health and agriculture datasets
Data preparation tooling for cleaning, deduplication, and unifying mismatched schemas at scale
A provenance and documentation template so every source, license, and transformation stays traceable
A data quality review with a mentor on coverage, missing values, and bias in the sources you picked
Compute for exploration — GPU time to run EDA and build the feature pipelines that follow

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First, second & third

For every winning
researcher

A curated reading list of the papers behind the methods your track is using
Paper-reading groups with mentors to work through those papers together
A research writing workshop on turning your results into a clear write-up
Guidance on publishing your work — posters, abstracts, and papers
Help releasing your research as open source, with your code and notebooks public

Specialty tracks

Track prizes

Best AI Project: Featured write-upPublished on the lab site, with your code as open source
Best Project related to AI Ethics: Ethics reviewTwo sessions with our responsible AI mentor
Best Project using nhyapu Lab Tools: Lab fellowshipA term of full lab access and mentor time

For everyone

Open to every participant

A certificate of participation for everyone who finishes a project
A place in our public project gallery, listed under your name
Mentor office hours for the whole cohort, after the event
The lab reading list and every past project, open to you
An invite to the nhyapu alumni channel for ongoing collaboration

There is no cash prize pool here. Recognition goes to every team that takes part, and winning work is featured on the lab site. For how each challenge is judged, visit our lab portal.

04 / FAQs

Before you
get started!

Everything first-time and returning researchers usually want to know. Still wondering about something? Email [email protected]

01 Who can attend nhyapu AI Lab?

nhyapu AI Lab 2026 is open to students at every level—middle school, high school, university, or self-taught. You do not need any prior AI experience, just curiosity and a laptop. If you're past the student stage and would like to help instead, we always need mentors: email [email protected] and tell us how you'd like to be involved.

02 How do I apply?

To request a spot, email us with your name, your school or university, and a short note about what you'd like to build—health, agriculture, or something in between. We read every message and reply with next steps.

03 How do I become a mentor or volunteer?

Email [email protected] with how you would like to help, your availability, and any relevant experience—you don't need a machine learning background. Reviewing code, running a workshop, or just being around to answer questions are all genuinely useful.

04 What will be provided during the event?

Free food and drinks will be provided, including options for common dietary restrictions. We will also have designated quiet sleeping areas and electrical outlets throughout the research lab for charging devices. GPU access and cloud compute will be available for all participants.

05 What should I bring?

Bring a laptop, charger, any project hardware, a sleeping bag, a change of clothes, toiletries, and anything else you need to stay comfortable overnight.

06 How do I form a team?

Teams of up to 4 people are encouraged, though not mandatory—plenty of projects start solo. If you'd rather not work alone, join one of the tracks: the health team and the agriculture team each have a mentor for the first afternoon, and the shared workshops, meals, and overnight sessions are the easiest places to meet the people you'll end up building with.

07 Is there a Code of Conduct?

Yes. Every researcher at nhyapu is expected to treat everyone else with respect, to share compute and ideas fairly, and to help keep the lab a safe and welcoming place to work. Participants agree to follow our Code of Conduct and the rules of any venue we use. If something doesn't feel right, tell a mentor or organizer—we will take it seriously and act on it.

08 What can I win?

nhyapu hopes to emphasize learning and collaboration more so than competition—nonetheless, there will be awesome prizes to be won for some of the best AI projects built during the event! Check out the lab challenges, including AI tools, research scholarships, and subscriptions. There are also perks for every participant!

09 What can I learn?

You'll work through the whole arc of a real project: sourcing data, cleaning it, exploring it, engineering features, training a baseline model, tuning it, testing it honestly, deploying it, and monitoring it once it's live. Alongside the track work we run a series of short introductory workshops (about an hour each) for people who are new to AI—Python for data, SQL and data APIs, neural networks, machine learning frameworks, error analysis and metrics, and the responsible side of working with data. Check our schedule for the full list!

10 Where can I find nhyapu on social media?

Follow @nhyapu on Instagram for announcements and behind-the-scenes updates.

11 My question is not answered here.

Send it to [email protected] and the team will help.

05 / Technologies