2nd Place at San Diego BROADCOM Science Fair (Senior Division)
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A Machine Learning Approach to Understanding the Determining Factors of the Gender Wage Gap
By studying the affect of different attributes on the gender wage gap, we can better understand both the scale of this issue and its possible solutions. So, we explore the question, how does a worker’s marital status, along with other variables, impact the gap in hourly wage between male and female workers? We seek to create a model able to predict the gender wage gap given a set of variables—age, years of education, race, state, and marital status.
Sophia G.
Mentored by Ana Sofia Muñoz Valadez
Journal of Student Research
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A Hybrid CNN-LSTM Model For Predicting Solar Cycle 25
The goal of this study is to predict Solar Cycle 25 through the deep learning approach,and determine what parameters affect prediction accuracy and what the optimal number of historical solar cycles are used to reliably and accurately predict the upcoming solar cycle. The solar cycle predictions will help us prepare ahead of time for future solar activity.
Alice H.
Mentored by Tony Rodriguez
Massachusetts Science and Engineering Fair (MSEF)
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Applications of AI in Microfinance
I hope to explore how to use AI in the field of microfinance to help reduce income inequality. Microfinance has greatly helped decrease rural poverty rates in Bangladesh. The leader of this effort won the Nobel Peace Prize for his work. These micro-loans give opportunity to those who are not otherwise able to obtain financing for their entrepreneurial ideas. This can be applied in the U.S. too, in places where the population cannot otherwise obtain loans to start small businesses and climb their way out of poverty. It can be very difficult to start from rock bottom in the US, especially for those without access to resources. If people could obtain small loans and start small businesses, they could work their way out of poverty. The microloans have to be viable for banks as well. The problem I’d like to explore is whether AI/ML can be used to determine how to deploy microloans efficiently to address income inequality in the U.S.
Alex M.
Mentored by Odysseas Drosis
Journal of Student Research
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Stellar Classification based on Numerous Characteristics using Machine Learning
The task of stellar classification can be tedious and lengthy when done manually. One can expedite stellar classification by creating an artificial intelligence model to automate the process. The current stellar classification model serves to effectively categorize stars for research purposes regarding their distribution around the universe, so automating the development of this resource would allow professionals to allocate more time to explore the bounds of our current understanding of space and the universe. After finding and analyzing a dataset containing numerical and categorical features, a supervised learning approach was then used to train and test different models on their ability to classify the stars in the given test set. A Decision Tree Classifier, Random Forest Classifier, Ridge Classifier, and Support Vector Classifier were trained and tested using the data.
Roberto T.
Mentored by Sophia Barton
Journal of Student Research
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Predicting Running Injuries with Machine Learning Models
Is it possible to predict running injuries with only a dataset and machine learning models? This paper explores this question by using classification models, including the Logistic Regression model and the Random Forest Classifier model.
Elgin V.
Mentored by Joseph Vincent
Santa Clara ISEF Qualifier
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Combating Climate Fake News Using NLP
As fake news becomes more prevalent across the US, important issues become harder to solve. One such issue is climate change, where climate misinformation has worsened viewer’s abilities to distinguish between fake information and real information. This project’s objective is to tackle climate misinformation using an artificial intelligence model.
Rayyan M.
Mentored by Philip Bell
2nd Place at Orange County Science Fair, California Science and Engineering Fair (CSEF)
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The Differentiation of Viral and Bacterial Pneumonia using Deep Learning
This project aims to find out whether a Convolutional Neural Network can be used to classify x-ray scans as having either bacterial or viral Pneumonia.
Arnav D.
Mentored by Erick Siavichay
Curieux Academic Journal
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Impact of Class Weights and Feature Importance in Automated Stroke Detection
In this research paper, we do a parametric study of class weighting as a way to tackle imbalance during training. We then infer the most important features that should be taken into consideration for stroke prediction.
Avyukth H.
Mentored by
Journal of Student Research
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Evaluating Machine Learning Models on Predicting Change in Enzyme Thermostability
Our research problem is finding the best machine learning model to predict the change in enzyme thermostability after a single point mutation in the amino acid sequence.
Avnith V.
Mentored by Jacklyn Luu
medRxiv Medical Ethics preprint
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Differences in predicted rates of vaginal births after cesarean across racial groups in a ‘race-neutral’ model
A large body of work in machine learning has highlighted that supposedly de-biased systems often re-code sensitive variables like race in terms of proxy variables. In order to determine if this was the case in this calculator, we replicated their formula, then found base-rate statistics of all the input variables for three different racial groups: Black, White, and Asian.
Anjali S.
Mentored by Katie O'Nell
Journal of Emerging Investigators
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The Utilization of Artificial Intelligence in Enabling the Early Detection of Brain Tumors
This research aims to investigate the application of machine learning to enhance diagnosis.
Shanzeh H.
Mentored by Odysseas Drosis
2nd Place at Contra Costa Science and Engineering Fair
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Optimizing Prediction Accuracy Using Advanced Ensemble And Voting Classifier Methods
This project observes how various machine learning models, once tuned, can further be combined to create a complex model that uses NFL data from the past 18 years to predict the outcomes of matchups between any two competing teams.
Ashray P.
Mentored by Christopher Mauck
Journal of High School Research
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Diagnosing Brain Tumors from MRI Images Using Deep Transfer Learning
This study aims to utilize a transfer learning method in which the prior knowledge of a pretrained model is used to aid in a new classification problem.
Armita K.
Mentored by
Journal of Student Research
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Diversified AI Techniques for Augmenting Brain Tumor Diagnosis
This research explores the application of AI technology to expedite the diagnosis of brain tumors.
Dhruv M.
Mentored by Odysseas Drosis
Journal of Emerging Investigators
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Diagnosing Hypertrophic Cardiomyopathy Using Machine Learning Models on CMRs and EKGs of the Heart
In this project, we presented a pair of models, one CNN model and one Long Short Term Memory (LSTM) model, that are capable of classifying cardiac magnetic resonance (CMR) and heart electrocardiogram (EKG) scans, respectively.
Surya K.
Mentored by Sriram Hathwar
Synopsys Science Fair
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AI-Based Image Classification Used to Accurately Distinguish Recyclable Material Versus Non-Recyclable Material
One cause of this improper disposal of materials is that it can be difficult to tell if a material is able to be recycled. In response, I created a machine learning model that can distinguish recyclable materials from trash through image classification.
Katarina A.
Mentored by Ayush Pandit
Curieux Academic Journal
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Investigating Data Augmentation Strategies for Computer Vision Facial Expression Recognition
I aim to help people with autism better recognize emotions by developing improved artificial intelligence (AI) models to recognize facial expressions.
Jack L.
Mentored by Peter Washington
3rd Place in the Alameda County Science Fair
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A Novel Approach to Promote Equity in Skin Disease Diagnosis by AI Models
The goal is to increase the accuracy of existing models in diagnosing skin disease across various skin tones within 10% of that obtained in diagnosing fairer skin tones, which is about 95.8%.
Varsha N.
Mentored by Roger Jin
Journal of Student Research
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Machine Learning Approaches to Detect Brain Tumors from Magnetic Resonance Imaging Scans
Our study utilized a comprehensive dataset of brain magnetic resonance imaging (MRI) scans to compare and assess the performance of different baseline AI models.
Cherry (.
Mentored by Shreya Parchure
2nd place at Massachusetts Region V Science Fair
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Using Machine Learning to Detect Alzheimer’s Disease in MRI Scans
We aimed to answer the question about if Magnetic Resonance Imaging (MRI) scans, which are often used in the diagnosing of other neurological disorders, can be used to diagnose AD in patients.
Sam L.
Mentored by Ivan Villa-Renteria
Accepted for Exhibition at TNJSF
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Exploring Asteroid Orbits: Insights from Neural Network Modeling and Data-driven Analysis
In this study, orbital data from the NASA Jet Propulsion Lab was used in the classification of asteroid orbits through a machine-learning approach.
Aarav S.
Mentored by
The Stanford Journal of Science, Technology, and Society
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Artificial Intelligence in the Stock Market: Predicting Prices
This research project focuses on stock price prediction through A.I. models as well as machine learning algorithms to maximize profit potential, improve investments, and eliminate risk.
Emin C.
Mentored by Odysseas Drosis
Frontiers in Environmental Science
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Predicting Climate Change Using an Autoregressive Long Short-Term Memory Model
This study aims to create a baseline machine learning model that utilizes an Autoregressive Recurrent Neural network with a Long Short term memory implementation for the purpose of predicting climate.
Seokhyun C.
Mentored by Victoria Lloyd
Journal of Student Research
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Evaluating the Efficacy of the 3D U-Net Architecture For Glioblastoma Multiforme Tumor Segmentation
This research evaluates the performance of the 3D U-Net model for automated glioblastoma tumor segmentation from MRIs, achieving 98.6% accuracy and significantly faster processing times than human oncologists, crucial for effective radiation therapy.
Arnav J.
Mentored by Erick Siavichay
3rd Place in the Texas Science and Engineering Fair
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The Use of Artificial Intelligence in Gravitational Microlensing Detection for Dark Matter Discoveries
This research utilizes Artificial Intelligence to enhance the speed and accuracy of microlensing detection using data from 600,000 stars observed by The Zwicky Transient Facility. Through comparative analysis and hyperparameter tuning, the KNN model was identified as the most accurate, achieving 97% accuracy in detecting microlensing events, which may include dark matter, within the Milky Way.
Suhaan K.
Mentored by Tony Rodriguez
1st Place in the Alameda County Science Fair
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VisionAssist: Enhancing Accessibility for Individuals with Visual Impairment Through AI
This project explores how AI can support individuals with visual impairments by developing a system that converts images containing text or math into audio and Braille in near real-time. Using a fine-tuned OCR model, the system achieves high accuracy and low latency, demonstrating that AI can be a powerful tool for improving accessibility to educational content.
Azaan R.
Mentored by Joe Xiao
Tackling Social Issues with AI | Inspirit AI Research Symposium | Category Winner
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Landfill Net: A Convolutional Neural Network (CNN) Architecture for the Detection of Landfills from Satellite Imagery in the Continental United States
This project presents a Convolutional Neural Network (CNN) model that identifies landfills in the U.S. using image data, achieving a 97.1% test accuracy. By supporting the creation of a national landfill database, the model can help detect illegal sites and guide the expansion of methane capture systems to reduce greenhouse gas emissions.
Anika S.
Mentored by Ying Hang Seah
The National High School Journal of Science
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Towards Monarch Butterfly Preservation: Applications of AI in Species Identification
This study developed a deep-learning model to distinguish monarch butterflies from visually similar species, achieving 83.5% accuracy on the testing set. The model could help novice conservationists identify monarchs more efficiently and support monitoring and conservation efforts.
Ajay G.
Mentored by Henry Cerbone
