Transform Data into Real-World Impact

Showcase your skills. Solve challenging problems. Win big.

AND THE WINNERS ARE...

Nirlesh Thumbnail — 1st PRIZE: Customized, Emotion-Driven Music Selection Using Facial Expression Recognition

Nirlesh Saravanan, from Sri Sairam Engineering College, delivers a compelling first-prize project that seamlessly connects human emotion with artificial intelligence through music.

Built entirely in Altair AI Studio, his emotion-based music recommendation system combines facial-expression analysis and deep learning to create a personalized, mood-aware listening experience. Using DeepFace to detect emotions such as happiness, sadness, or neutrality, the system interprets real-time facial cues with high accuracy. A second deep learning model analyzes detailed audio features from thousands of songs to classify music by emotional tone and recommend a matching Spotify track.

By integrating computer vision, audio analytics, and intelligent workflow design, this project demonstrates how AI can translate human signals into adaptive, emotionally responsive digital experiences in real time.

Swietena Thumbnail — 2nd PRIZE: Improving Air Quality through Better, More Reliable Monitoring and Causation Discovery

Swietenia Naomi Medina Gasca from Anáhuac Cancún University takes a thoughtful, data-driven approach to air-quality monitoring by examining not pollution itself, but the strategy behind how monitoring networks are designed.

She chose to work in the 3rd contest category, in which she used the Altair-provided dataset but came up with her own problem statement. Using Altair AI Studio, she transformed the complex, unstructured data into a clean analytical workflow, built a predictive model, and visualized key patterns to reveal how station placement, pollutant focus, altitude, landform, and urban context shape monitoring coverage.

Her work demonstrates how AI can validate existing monitoring strategies and inform smarter, more effective network expansion—highlighting the power of data science to support environmental decision-making beyond simple measurement.

Annemarie Thumbnail — 3rd PRIZE: Designing Electric Aircraft Through AI Input with Simulation and ChatGPT-Generated Data

Annemarie Kautz from TU Munich, working with an international student team from Tongji University, demonstrates how AI can dramatically accelerate electric aircraft design.

Using simplified eVTOL models, simulation data, and AI-expanded datasets, the team explored how design decisions impact flight time, sustainability, and material use—revealing clear performance–sustainability trade-offs. With an A/B/C rating system and classification models built in Altair AI Studio, they created a faster, more transparent design workflow, including lifecycle tracking through a battery passport.

The project was inspired by Professor Juergen Grotepass, whose long-standing commitment to international, high-impact engineering education continues to motivate students to fuse speed, quality, and sustainable innovation in aerospace design.

Honorable Mentions

Predicting Coffee Flavor Using Measurable Quality Factors

Sarah Dambach, Honor Henry, Seth Green, Gavin Cooper, Fairfield University - USA

Coffee quality is often judged by tradition and taste, but this team brought data to the table. Using Altair AI Studio, they analyzed characteristics like aroma, acidity, and moisture to build models that predict overall quality and even forecast flavor.

Which measurable factors matter most?
Can flavor be predicted before the first roast? Their insights reveal the strongest predictors of flavor and give producers, traders, and certifiers a data-backed way to improve quality and forecast customer satisfaction.

A crisp, data-driven take on what makes great coffee.

Watch the video here.

Improving Bridge Safety Through Predictive Maintenance

Xijia Wang, Jilin University - China

Bridge maintenance is critical, yet traditional methods struggle to keep pace. This project uses Altair AI Studio to build a smarter, data-driven system that predicts maintenance priority, repair type, and cost using classification and regression models trained on historical data.

By analyzing how these targets relate, the solution uncovers patterns that help engineers act earlier and more accurately. With standardized data and Explainable AI, the insights remain transparent and reliable.

The results: improved safety, reduced failure risk, longer bridge life, lower costs, and more sustainable infrastructure planning. A proactive step toward resilient cities.

Watch the video here.

AI-Driven Data Analysis for Vehicle Energy Efficiency

José Diego González Fernández, Paulina Lilian Ruiz Servín, ITESM - México

In the Shell Eco Marathon, every bit of efficiency counts, and this team decided to let data lead the way.

By capturing performance signals from their vehicle and analyzing them in Altair AI Studio, they cleaned the data, tested predictive models, and built their own to uncover which factors genuinely shape energy use during each run.
The result? Clear insights to guide the design of next season’s car and fresh ideas that could influence how energy-efficient vehicles are built in the real world.

From lowering consumption to reducing CO₂ emissions, their project shows how smart data analysis with Altair AI Studio can drive meaningful impact. A sharp, data-driven look at what makes a vehicle truly efficient.

Watch the video here.

Faculty Impact Acknowledgement

Dr. Vishnu Vinekar, Professor of Analytics, Dolan School of Business at Fairfield University - USA

We are pleased to recognize Dr. Vishnu Vinekar for his outstanding influence and dedication to student engagement in this year’s contest.

Through his mentorship and encouragement, more than 20 student teams submitted projects, representing the highest level of participation driven by a single faculty member. One of his student teams also received an honorable mention for its contribution.

Dr. Vinekar’s commitment to applied, project-based learning exemplifies the vital role faculty play in inspiring students to explore AI, data-driven problem solving, and real-world innovation beyond the classroom.

Quick Facts: Requirements and Prizes

Is this a hackathon?

While perhaps not the same as a typical hackathon, our contests share the same ethos - by challenging students to solve interesting and time-limited problems through the creative and ingenious use of Altair's software tools.

Who can enter?

Students of any age and geographic location.

When is the deadline?

You can submit any time from now until November 30th, 2025, 11:59 PM (EST).

What do I need to do?

Show off your most innovative data-driven project using Altair AI Studio by submitting a 5 minute video - think predictive models, AI solutions, visualizations, or optimization tools.

How do I get started?

Pick a category and get going! Available categories:

Work with your own dataset and use case.
Use the dataset and use case provided by us.
Use the dataset provided by us and define and solve your own use case.

What can I win?

The best 3 submissions earn substantial cash card prizes ($2000, $1000, $500).

GSC Workflow v1

The Process: How It Works

Download Altair AI Studio
Learn the tool using the learning resources on this page and the AI Studio Academic Hub.
Choose your challenge (see the 3 categories below).
Record a short 5 minute video explaining your project and your key results.
Upload your video to a public video hosting platform (YouTube, Vimeo, etc.).
Submit your video link through the submission form below for a chance to win a cash prize.

Submit Now

Choose Your Challenge

	Category 1: Your Data, Your Use Case	Category 2: Our Data, Our Use Case	Category 3: Our Data, Your Use Case

	Solve an interesting use case of your choice using any publicly available dataset.	Use the dataset and solve the use case provided by us.	Use the dataset provided by us, but define and solve your own use case.
Use Case	In this category, the objective is entirely up to you. It could be anything you are interested in or can think of - from predicting housing prices to detecting fraudulent transactions.	Your video must demonstrate how you addressed the following objective using the Air Quality Monitoring Dataset: Develop a prediction model to identify the primary pollutant in the air and classify air quality levels (Good, Moderate, or Unhealthy) across different locations. Generate insights into pollution patterns that support decisions for environmental management and public health initiatives.	Develop your own objective. Think outside the box and identify a new angle or hidden insights in the provided data. You can combine the dataset with other public datasets for richer analysis or define a completely new application of the same data.
Dataset	To find your own data set, you can use common sources like Kaggle, UCI Machine Learning Repository, or Data.gov. But you can also use any other open data portals.	You must use this data set for this category:	You must use this data set for this category:

How to Get Started with my Data Analysis?

Import your chosen data into AI Studio
Clean and prepare the data for your analysis
Analyze the data based on your selected use case
Derive insights from your data
Make a decision based on your derived insights

The example submission on the right gives you an idea of how your submission could look so that it follows the rules and fits the requirements

Submit and Win Big

Starting in September 2025 and ending in November 2025, we'll award prizes for the most exceptional student submissions.

Prizes:

🥇 First Prize - $2000
🥈 Second Prize - $1000
🥉 Third Prize - $500

https://47251.fs1.hubspotusercontent-na1.net/hubfs/47251/Students%20with%20cash.png

Rules Tool Requirements What to Submit How to stand out Judging Criteria Tips

Students must apply data science to improve a real-world application, demonstrating clear advancements in accuracy, efficiency, sustainability, usability, or other optimized outcomes.

Students must use Altair AI Studio to create their submission.
Students may enter as individuals or as part of a team. There is no limit to team size, but the prizes are only awarded to the person who submits the entry, not to each person on the team.
Students may enter several entries, but can only submit one submission per category. If more than one entry per category is submitted, only the first one will be reviewed by the judges. Each submission must be unique.
All judging is at the discretion of Altair, and all decisions made by Altair are final.
Download and read the terms and conditions of the Altair Global Student Contest before you submit.

Note for Minors: Students under the age of 18 who want to submit to the contest must have a parent or legal guardian read and accept the consent form for minors.

REQUIRED: All data workflows must be created using Altair AI Studio.

Optional tools you may use in addition:

Python/R integration (e.g., Python Learner, Python Model, R Model operators).

Extensions available in AI Studio (e.g., Generative AI, text analytics, deep learning, etc.).

A short video explaining your project and accomplishing the following:

5 minutes maximum length
Clear presentation of your results and gained insights.
Verbal narration of results by at least one student.
Explanation of how your analysis helped you make effective decisions or solve the problem.
Show your face as you tell your story! We need proof that it's really you. 😉

Make sure to follow the rules and meet the requirements.

Tell a clear story

Define your use case
Explain/ show your process (e.g., planning --> learning --> solving --> testing --> results)
End with impact (e.g., How does your project make a difference, or what’s next?)
Use clear, natural narration (i.e., avoid mumbling or rushing)
Record in a quiet space (avoid background noise)
If you're using background music, keep it low so your voice is always easy to hear.

Be creative

Show yourself and your personality
Use clear visuals
Show, don’t just tell—include footage of your prototype, tests, simulations, or data visualizations.

Watch and imitate the Sample Submission Video

The video will be judged by:

Relevance (real-world use case) and originality of the problem.
Workflow Design - Data preparation and modeling quality.
Clarity of insights and results (i.e., how clearly the results are presented and whether meaningful insights were drawn from the analysis).
Decision Support (i.e., how effectively the submission connects results to practical decisions or real-world applications).
Presentation Quality (i.e., video and audio quality, creativity of presentation, etc.)

Extra credit will be awarded for:

using multiple or advanced features of Altair software
posting or promoting your video on your preferred social media platform, tagging Altair in the post, and adding the link to the post in their video description.

For Category 1

You can submit a project you’ve already completed using Altair AI Studio (e.g., from a school project or another contest or hackathon), as long as it follows the contest requirements.
Creativity counts and will get you more points!
You can choose the type of analysis (e.g., predictive modeling, clustering, sentiment analysis, visualization, or a combination).
This category is great for those who are creative, innovative, and have a strong interest in specific topics or types of data.

For Category 2

Experiment with different modeling approaches and visualize your results for better interpretability.
This option is great if you want a guided challenge with clear evaluation criteria to guide your work.

For Category 3

Be creative and think outside the box
You can adjust your objective to your own interests around the provided data.
This option rewards originality, innovation, and creative use of the provided dataset.

The more creativity and own input you provide in your project, the more points you will get from the judges.

E.g., a great category 1 project is going to get more points than a great category 2 or 3 project.
But a great category 3 project and video can get more points than a poorly executed or explained category 1 project.

Learning Platform

Why Participate?

Learn new or showcase existing data science skills and stand out to potential employers.
Get hands-on experience using AI Studio, an industrial-strength software trusted by professionals worldwide.
Practice practical problem-solving while working on real applications of AI, ML, and data science, not just theory.
Get a chance to win one of the substantial cash awards

Students sitting at a table in a library while learning and working on a laptop

Don’t Know How to Use AI Studio Yet?

No worries! You can quickly get up to speed with our free learning resources:

RapidMiner Academy – Step-by-step courses to master the tool.

Altair RapidMiner YouTube Channel – Video tutorials, tips, and project demos.

Got stuck?

Post your question in the comment section of our community post here to get a quicker answer from our team. Or post your question in the academic forum with the #Altair Student Contest tag to get the whole community to help.

For answers to common questions, explore the FAQ page.

Previous Contest Highlights

Optimization Rockstars 2024-25

See the winners of our previous Global Student Contest who optimized robotics applications with Altair tools.

See Previous Winners

https://47251.fs1.hubspotusercontent-na1.net/hubfs/47251/Untitled%20design%20(1).gif