{"id":13254,"date":"2025-11-17T10:57:19","date_gmt":"2025-11-17T15:57:19","guid":{"rendered":"https:\/\/blogs.mathworks.com\/student-lounge\/?p=13254"},"modified":"2026-03-09T08:10:32","modified_gmt":"2026-03-09T12:10:32","slug":"artificial-intelligence-challenge-meet-the-winners","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/student-lounge\/2025\/11\/17\/artificial-intelligence-challenge-meet-the-winners\/","title":{"rendered":"Artificial Intelligence Challenge 2025 – Meet the Winners"},"content":{"rendered":"
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For today\u2019s post, Roberto Valenti, who leads the MathWorks Challenge Projects program, will introduce the winners of the 2025 Artificial Intelligence Challenge. Over to you, Roberto..<\/span><\/div>\n
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Artificial intelligence is rapidly transforming how we approach complex problems across all kinds of fields. Inspired by this momentum, we launched the AI Challenge<\/a> as part of the MathWorks Challenge Projects Program<\/a>, inviting students to take on real-world problems from our curated AI list. Our challenge attracted many creative and technically rigorous submissions from students around the world, making the judging process both exciting and exceptionally difficult, but it was the three winning teams that stood out for their innovation and impact.<\/div>\n
I am excited to introduce the three winning teams and their projects, and to share a bit about their journeys in the challenge. Their stories are a great example of what can happen when talented students tackle real-world challenges with fresh ideas and teamwork.<\/div>\n

First Place Winner: Adit Shah \u2013 <\/span>Top Quark Detection Using Deep Learning and Big Data<\/span><\/a><\/h2>\n

Meet the Winner:<\/span><\/h3>\n
Adit Shah is a final-year undergraduate student in Electronics and Communication Engineering at Dwarkadas J. Sanghvi College of Engineering, Mumbai. He first heard about the AI Challenge 2025 from his professor on a WhatsApp group. What really piqued his interest was discovering that deep learning could be implemented in MATLAB\u2014something he had not realized before. After exploring the Deep Network Designer App<\/a> and finding it intuitive, Adit was drawn to the challenge statement on \u201cTop Quark Detection using Deep Learning and Big Data.\u201d As someone fascinated by particle physics and all the exciting research happening at CERN, the choice was obvious for him.<\/div>\n
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The Project:<\/h4>\n
Quarks are the fundamental building blocks of matter, and the top quark is the heaviest and most elusive of them all. Because it decays almost instantly, its presence must be inferred from the jets of particles it leaves behind in high-energy collisions\u2014a process that\u2019s both complex and error-prone. Adit\u2019s project tackled this challenge by using deep learning to distinguish top-quark events from the vast background of other jets, making identification faster and more accurate.<\/div>\n
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Innovative Approach:<\/span><\/h4>\n
Adit\u2019s method started by aligning the jet images in a consistent way. Since the dataset didn\u2019t have a standard orientation, he rotated each jet so that the second most energetic constituent pointed along the positive X-axis, helping the model focus on the distribution patterns rather than arbitrary directions. To further standardize the images, he ensured the area with the most energy was always at the bottom, flipping images when needed. This careful preprocessing meant the model learned the right features, not just orientation quirks. He also applied z-score normalization to make sure all features contributed fairly during training.<\/div>\n
To enrich the information available to the model, four additional global statistics were computed for each jet: Energy Skewness, Transverse Momentum Skewness, Energy Kurtosis, and Transverse Momentum Kurtosis. These parameters were calculated radially from the jet center and provided a summary of the jet\u2019s structure beyond what was visible in the images. This multimodal approach fused these statistics with the CNN\u2019s final layers, giving the network both local and global perspectives on each jet.<\/div>\n
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Neural Network Model and Channel Attention:<\/span><\/h4>\n
The core of the solution was a custom ResNeXt-based Convolutional Neural Network, which leverages aggregated residual transformations with grouped convolutions. In this architecture, the channels are divided into separate groups, and each group learns to extract distinct features from the data, which is more effective than processing all channels together. As the network deepens, the number of channels increases and their relative importance can vary significantly.<\/div>\n
To address this, Squeeze-and-Excitation (SE) blocks were integrated after major grouped convolution layers. These SE blocks apply channel-level attention by explicitly modeling the importance of each channel for the classification task. They assign higher weights to channels that carry more discriminative information and suppress less useful channels, ensuring that the network\u2019s focus remains on the most relevant features for distinguishing top-quark jets from background jets. This channel-wise attention mechanism was key to boosting the model\u2019s performance and interpretability. Training and experimentation were performed in MATLAB\u2019s Deep Learning Toolbox<\/a> and Deep Network Designer, enabling rapid iteration and testing of different architectures and preprocessing strategies.<\/div>\n
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The Neural Network architecture<\/div>\n

Results:<\/span><\/h4>\n
The final model achieved a testing accuracy of 90.87%, with an ROC of 0.968 and an AUC of 0.971, demonstrating strong discrimination between top-quark and background jets. The workflow also included converting the trained network into HDL code for FPGA deployment, highlighting the potential for real-time applications in experimental physics environments.<\/div>\n
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Experience & Reflections:<\/span><\/h4>\n
Adit spent about two weeks just getting to grips with the dataset and researching the key statistics needed to tell different types of jets apart. \u201cI was initially unaware that deep learning could be implemented in MATLAB, so I decided to explore the Deep Network Designer app and found it to be very intuitive<\/span>,\u201d he shared. The process was a deep dive into both particle physics and practical AI, combining his academic interests with hands-on learning.<\/div>\n
\u201cI learned so much while doing this project. It was full of head-scratching moments, but I loved it because I was naturally drawn to particle physics and Machine Learning. The ending days were particularly stressful because I had another competition submission just before the submission of this challenge and I was left with all the documentation, video recording and writing comments for this one. But this is the kind of stress that made the results worth it.<\/span><\/div>\n
What began as an experiment to combine my interests in particle physics and machine learning turned into one of the most rewarding moments of my academic journey. Winning this challenge inspired me to pursue research in AI and Electronics. I thank Dr. Roberto G. Valenti for continuous support and discussion on improving my project even further after the competition, and for his help with the related research paper.”<\/span><\/div>\n
Explore Adit\u2019s solution – <\/span>GitHub repository<\/span><\/a><\/div>\n
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Second Place Winners: <\/span>Nicola Gallucci, Matteo Malagrin\u00f2, Giacomo Aragnetti – Classify RF Signals Using AI<\/span><\/a><\/h2>\n
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Meet the winning team:<\/span><\/h4>\n
Nicola, Matteo, and Giacomo are undergraduate students in Computing Systems and Telecommunications Engineering at the Polytechnic of Milano. They discovered the AI Challenge through a LinkedIn post by MathWorks. Motivated by curiosity and a passion for wireless technologies, they decided to participate as an extracurricular project outside their regular coursework. For these students, this project was more than just a competition; it was their first major \u201cmission\u201d at university. It introduced them to the SmartBAN protocol\u2014an emerging standard for Body Area Networks (BANs)\u2014which was new to almost all of them. Their work not only deepened their technical expertise but also caught the attention of a professor connected to the European Telecommunications Standards Institute (ETSI) SmartBAN Technical Committee. As a result, they were invited to present their project at an ETSI meeting, giving them a unique opportunity to share their research with leading experts in the field.<\/div>\n
The team is now looking forward to research careers and has even written a paper about their project, which they hope will be accepted at a conference\u2014a testament to the impact this experience has had on their academic journey.<\/div>\n
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The team:<\/span> from left to right: Giacomo Aragnetti, Matteo Malagrin\u00f2, and Nicola Gallucci<\/div>\n

The Project:<\/h4>\n
In modern wireless environments, critical medical transmissions from Body Area Network (BAN) nodes can easily be masked by overlapping signals from technologies like WLAN, ZigBee, and Bluetooth. This interference poses a significant challenge: how can we reliably detect and classify BAN-related signals\u2014especially those using the emerging SmartBAN standard\u2014when the spectral environment is crowded and noisy?<\/div>\n

Their Approach:<\/span><\/h4>\n
The team focused on the SmartBAN standard, which is designed for ultra-low-power radio communication and features a simplified MAC layer with a central Smart Coordinator managing network nodes (such as physiological sensors). Since SmartBAN is still under development and not yet available in commercial devices, the team simulated what a SmartBAN transmission would look like in a spectrogram.<\/div>\n
To address the detection challenge, they developed a deep learning framework for Smart RF Signal Recognition. Their system is capable of detecting and classifying SmartBAN transmissions even in the presence of strong interference.<\/div>\n
Key Steps:<\/div>\n