PRESENTERS

Department of Computer Science & Engineering

Assessment of GAN-Based Models for Synthetic Pneumonia Chest X-Ray Generation and Quality Improvement

Medical imaging, such as chest X-rays, is crucial for diagnosing pneumonia. However, image noise and poor resolution often hide vital details, limiting the effectiveness of AI-based diagnostic tools. To address this, we used Generative Adversarial Networks (GANs) to synthesize and enhance chest X-rays. GANs use two competing neural networks to learn how to produce highly realistic medical scans. In this study, we trained several models including a baseline Simple GAN, a Deep Convolutional GAN (DCGAN), and a Wasserstein GAN (WGAN) on a dataset of pneumonia X-rays. We evaluated them using the Frechet Inception Distance (FID) score, where a lower score indicates a more realistic image. Our results show that the DCGAN architecture outperforms the alternatives, achieving the lowest FID scores and generating the highest-quality X-rays. This demonstrates that DCGANs are a powerful tool for improving medical image datasets and future diagnostic accuracy.

RESEARCHERS: Arya Addagarla, Amador Valley High School '27; Pranav Pulavarthi, Foothill High School School '27; Kayla Wijesekera, Holy Names Academy, '27; Krish Puthran, Monta Vista High School, '28

ADVISOR: Viktoriia Liu Lab, Chemistry & Computer Neurobiology & Explainable AI & Augmented Reality

KEYWORDS:  Medical Imaging | Generative Adversarial Networks | Machine Learning | Chest Radiography | Image Enhancement

Department of Computer Science & Engineering

Detection of Surface Level Cyanobacterial Algae in Freshwater Lakes using Cost Effective RGB Autonomous Unmanned Aerial Vehicles

Toxic cyanobacteria pose a significant health risk to humans through the consumption of poisoned fish or shellfish and hinder recreational activities. Harmful algae blooms (HABs), categorized by a deviation of regular algal biomass, develop when toxic or nontoxic cyanobacteria are exposed to a combination of nutrient runoffs and increased water temperature. Algae blooms deplete the lake of oxygen, causing hypoxia, killing or harming aquatic animals that require aquatic respiration, and killing underwater plants by obstructing sunlight. The rise of algal blooms in frequency is forcing lake managers and government bodies to constantly monitor algal levels and concentrations in their lakes. We aim to determine whether a sole RGB sensor can accurately quantify algae surface levels over a large period of time through autonomous waypoint flying and camera triggers. Images collected are plugged into a CNN (YOLO based model) for bounding boxes of algae areas. Polygons of algae-detected zones will be displayed on a public algal dashboard page. Ultimately, our research contributes to the growing body of autonomous UAVs for algal detection through the creation of a more practical and simpler solution, saving lake managers and governmental bodies time, costs, and personnel.

RESEARCHERS: Jeremiah Welch, Credo High '28; Matthew Chang, The King's Academy '27

ADVISOR: McMahan Lab, Quantum Computing & Computer Science

KEYWORDS:  Algae, Algae Detection, Mapping, Cyanobacteria, RGB, Unmanned Aerial Vehicles, Autonomous, Cost-Effective, Convolutional Neural Network

Department of Computer Science & Engineering

Applications of Quantum Annealing in Cybersecurity

Adversarial training stands as the most effective defense against adversarial attacks in machine learning, yet its efficacy is significantly constrained by the diversity and quality of the attacks used during training. Traditional classical methods, such as the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), are inherently local and restricted to gradient-following paths, which leave large regions of the adversarial perturbation space unexplored. Our project proposes a hybrid quantum-classical framework that leverages quantum annealing to overcome these local geometric limitations. By reformulating the adversarial perturbation search as a Quadratic Unconstrained Binary Optimization (QUBO) problem, we apply the combinatorial search capabilities of D-Wave’s quantum architecture to discover qualitatively distinct, non-local adversarial examples. The models will be benchmarked on standard datasets (MNIST/CIFAR-10) against rigorous robustness metrics, alongside an analysis of runtime and hardware efficiency. We aim to determine whether expanding the adversarial search space through quantum optimization yields classifiers with superior, more generalized adversarial robustness.

RESEARCHERS: Nitya Pisolkar, Archbishop Mitty High School '27

ADVISOR: McMahan Lab, Quantum Computing & Computer Science

KEYWORDS:  Machine Learning | Cybersecurity | Adversarial Attacks | Quantum Annealing

Department of Computer Science & Engineering

Autonomous UAV-Based Photogrammetry and Machine Learning for Predictive Coastal Erosion Analysis

Coastal erosion, the gradual wearing away of coastline land because of wave action, rising sea levels, and storms, poses a significant risk to coastal communities and habitats. As global sea levels continue to rise, the problem will only become more increasingly severe. This proposal aims to not only monitor coastal erosion but also use Artificial Intelligence and Machine Learning to predict its progression over time. By analyzing the data collected, this approach can highlight areas of concern, helping preserve infrastructure and natural habitats. This project will use an autonomous drone-based system built on a HolyBro X500 V2 Arf Kit which will be equipped with a M9N GPS, a CADDXFPV Farsight FPV Camera, and a Lidar Sensor to maintain altitude. Flight plans will be pre-determined using the GSHHG waypoint dataset and will be modified into a grid-shaped pattern. Images taken through this grid pattern will overlap 70-80% and will then be put through photogrammetry via open-source software like Meshroom to generate geotagged, timestamped DSMs. The training dataset will consist of historical coastline DSM data from sources such as USGS, customly labeled by subtracting the DSMs to get elevation change as a prediction metric. The live DSMs will be processed and analyzed using a Machine Learning model trained to detect shoreline changes, vegetation loss, and erosion patterns over time through the labeled dataset, outputting a final erosion score. A heatmap of areas at risk will then be generated, allowing coastline erosion mitigation efforts to be prioritized where they will have the greatest impact.

RESEARCHERS: Melody Dai, Basis Independent Silicon Valley High School '27; Srihari Anoop, American High School '28; Akshaj Seetharaman, Tilden Preparatory School '27; Sai Sanjay Devi, American High School '28; Hasini Enugu, Dougherty Valley High School '28; Akshara Gunturi, Dougherty Valley High School '29

ADVISOR: McMahan Lab, Quantum Computing & Computer Science

KEYWORDS:  Coastal Erosion | Machine Learning | Unmanned Aerial Vehicles | Remote Sensing | Mechanical Engineering