AI Challenge Winners

Artificial intelligence is reshaping how we tackle complex problems across disciplines. In this spirit, our AI challenge drew a remarkable range of creative, technically rigorous submissions from around the world, making judging especially difficult. After careful deliberation, we recognized three standout projects for their advanced uses of Artificial Intelligence in physics, signal processing, and fluid dynamics applications. Each offered a distinctive approach with practical implications and real-world validation. We are pleased to highlight these top entries and congratulate all participants for their creativity and dedication!

Winners at a Glance

🥇 1st Place

Adit Shah

SVKM's Dwarkadas J. Sanghvi College of Engineering, India

Challenge project completed: Top Quark Detection with Deep Learning and Big Data

Domain: High-Energy Physics
Method: ResNeXt + SE
Result: >90% test accuracy

In high-energy physics, particle collisions leave "sprays" of energy called jets. Adit’s solution turns raw jet data into multi-channel images and uses a custom CNN, designed with the Deep Network Designer app from the Deep Learning ToolboxTM, to distinguish top-quark events from background. The approach pairs thoughtful preprocessing with aggregated residual (ResNeXt-style) blocks and Squeeze-and-Excite attention so the model focuses on real physics patterns, not noise, achieving over 90% test accuracy. It also outlines a workflow for HDL code generation and FPGA deployment, bridging lab research to real-time systems.

This work highlights Adit’s innovative thinking and an end-to-end engineering mindset, reframing a physics problem as an image task, selecting modern model architectures, and planning for hardware deployment. Congratulations to Adit on an outstanding first-place achievement!

Tech stack: MATLAB®, Deep Learning ToolboxTM (Deep Network Designer app), HDL code generation & FPGA deployment workflow.

Project brief
Code repository

🥈 2nd Place

Nicola Gallucci, Matteo Malagrino, Giacomo Aragnetti

Polytechnic of Milano

Challenge project completed: Classify RF Signals Using AI

Domain: Signal Processing
Method: U-Net & DeepLabv3+
Validation: SDR

Nicola and Matteo tackle the crowded 2.4 GHz band by turning STFT spectrograms into pixel-level maps of Wi-Fi, Bluetooth, ZigBee, and SmartBAN activity. Their complete pipeline trains attention-gated U-Net and DeepLabv3+ (with ResNet-18/50 encoders) on a synthetic, channel-aware dataset with class-priority masks, then validates on real airwaves using two synchronized ADALM-Pluto SDRs to capture and stitch 80 MHz of spectrum. Built in MATLAB® with Computer Vision ToolboxTM and Parallel Computing ToolboxTM, and using Deep Learning ToolboxTM and Signal Processing ToolboxTM workflows, the solution is ready for practical spectrum monitoring and interference mitigation.

This work highlights Nicola, Matteo, and Giacomo’s innovative, end-to-end engineering, from careful dataset design and model choice to decisive real-world validation. Congratulations to Nicola, Matteo, and Giacomo on a fantastic achievement!

Tech stack: MATLAB®, Deep Learning ToolboxTM, Computer Vision ToolboxTM, Signal Processing ToolboxTM, Parallel Computing ToolboxTM, Two synchronized ADALM-Pluto SDRs for real-world capture.

Project brief
Code repository

🥉 3rd Place

Soham Gupta

Indian Institute of Technology, Ropar

Challenge project completed: Fluid Flow Simulation Using Physics-Informed Neural Networks

Domain: Fluid Dynamics
Method: PINNs
Outputs: u,v,p

Soham’s project builds a physics-informed neural network in MATLAB® to solve 2-D incompressible flow around a cylinder. Instead of learning from labeled examples, the network is trained to obey the governing equations and boundary conditions, so its predictions stay physically consistent. It produces the full flow field—velocity (u, v) and pressure (p)—and even estimates the fluid’s viscosity. A simple two-step training recipe and well-organized scripts make the work easy to reproduce, with clear visuals comparing predicted and true velocity, pressure, and flow rotation (vorticity).

This work highlights Soham’s blend of scientific rigor and practical engineering, turning core fluid-mechanics principles into a reliable learning system that others can run and extend. Congratulations to Soham on an elegant and insightful achievement!

Tech stack: MATLAB®, Custom PINN training and visualization scripts.

Project brief
Code repository