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Classifying Non-Gaussian Transient Noise in LIGO
Using computer vision to analyse gravitational waves can provide valuable information about previously obscured information.
Maanya M.
| Summer 2023
Mentored by Anthony Cuturrufo
Comparison of Machine Learning Models to Best Predict Game Attendance in Major League Baseball
To forecast Major League Baseball game attendance, this study employs six different regression models commonly used for machine learning.
Seohyun P.
| Fall 2023
Mentored by Kasra Koushan
Individualizing Medicine: AI in Predicting the Effect of Yoga on Adrenomedullin Levels
I created and tuned four regression models that predicted a patient’s ADM levels after doing yoga: a Linear regressor, a Random Forest regressor, a Ridge regressor, and an Artificial Neural Network(ANN).
Smriti R.
| Summer 2023
Mentored by Varsha Sandadi
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.
| Fall 2022
Mentored by Odysseas Drosis
Drone Obstacle Detection Using YOLOv5
This project was built with the goal of performing real-time obstacle detection on a drone.
Vihaan B.
| Summer 2023
Mentored by
Is That Slang?
How can we better understand Gen-Z slang that is being used today? In order to tackle this challenge, I decided to make a NLP model that processes sentences and determines whether or not they contain Gen-Z slang.
Dhaniya V.
| Spring 2024
Mentored by Matan Gans
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.
| Summer 2023
Mentored by Ivan Villa-Renteria
Landfill Net: A Convolutional Neural Network (CNN) Architecture for the Detection of Landfills from Satellite Imagery in the Continental United States
We present a Convolutional Neural Network (CNN) model that uses image data in the United States to distinguish whether an image is a landfill or not.
Anika S.
| Fall 2023
Mentored by Ying Hang Seah
Telling Gunshots and Gunshot-like Sounds Appart
Sounds differ from each other. Having a strong ML model that can distinguish between the sounds and correctly classify them, may help with a variety of social problems, such as human and animal well being, (e.g. some sounds cause harm to autistic people and/or animals), safety drills (people with hearing disabilities may not be aware of the alarms), mass shootings, etc. Although reducing a false positive rate (e.g. a rate of other sounds falsely classified as gunshots) would help decrease the number of false alarms, it is more important (for the safety and security reason) to actually reduce the false negative rate (e.g. a rate of actual gunshots not being recognized as such).
Barbara T.
| Winter 2024
Mentored by Ivan Villa-Renteria
Relating Coffee Species to Brew Taste and Region with Machine Learning
If coffee flavor could be correlated to species and even country of origin, coffee purchasing would be much simpler, as consumers could use their own preferences to determine what to buy. This study aimed to find that correlation, using Data from the Coffee Quality Institute, taken from kaggle, and make predictions on the species based on the taste characteristics of the beans.
Sid S.
| Summer 2023
Mentored by Yin Kwong John Lee
A Novel ML Approach to Detect Possible Protein Allosteric Sites For Drug Discovery
In this study, we investigated different machine learning models and evaluated their performance, and found that the best models were the random forest classifier and a 5-layer neural network.
Sriram S.
| Fall 2023
Mentored by
Rare Coin Grading using Convolutional Neural Networks
The grading process, though, is time and resource-intensive, making it far too expensive for a casual coin collector to get a coin graded. To democratize this process, we developed a computer vision model that grades coins based on an image alone, which makes grading a coin essentially free of cost and provides a dramatic reduction in time.
Orhan H.
| Summer 2023
Mentored by
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.
| Summer 2023
Mentored by
Neural Radiance Fields (NeRF) for 3D Visualization Rendering Based on 2D Images
Reconstructing 3D scenes and being able to interact with them in virtual environments used to be something beyond our capabilities but now with the use of Neural Radiance Fields (NeRF) and Instant Neural Graphic Primitives (InstantNGP) we are able to create a world in a virtual space, opening the gateways to applications in object detection and generation, augmented reality, medical imaging, and more.
Joann C.
| Winter 2022
Mentored by Ivan Felipe Rodriguez
Computational Approaches to Enhance Idiopathic Intracranial Hypertension Diagnosis: A Neural Network-Based Framework for Improved Clinical Decision Support
A computer model, trained on eye images, can tell if optic nerves are healthy or not with 97% accuracy. This is important because it messes up less often than people do, making it a useful tool. The model looks at pictures of the back of the eye (fundus imaging) to make these predictions.
Rohan B.
| Fall 2023
Mentored by
Human-Robot Task Handoff: A Probabilistic Modeling Approach Explored through Cooperative Drawing
This paper investigates the task of handoff detection, crucial for the success of robot-assisted surgery, focusing on the creation of a synthetic dataset that can be used for training and benchmarking models for this task.
Lucas C.
| Fall 2022
Mentored by Ross Greer
Predicting Fake Job Listings from Real Ones using Machine Learning Models
The main way this problem is going to be solved is through the creation of models that can predict whether these job listings are real or fake.
Ronit M.
| Summer 2023
Mentored by Hassan Azmat
Classifying AI-Generated Music with AI Models
The importance of this research is twofold: addressing copyright issues and fostering the adoption of AI in the music industry. As AI-generated music blurs the line between human and AI creation, concerns regarding copyright ownership and artistic attribution become paramount [2]. To address these challenges, we created an AI model to differentiate AI-generated music from human-composed music, specifically focusing on music generated by the JukeBox model.
Siddharth M.
| Summer 2023
Mentored by Anthony Cuturrufo
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.
| Winter 2022
Mentored by Victoria Lloyd
Deep Learning Approach to Gated Coronary Artery Calcium Scan Segmentations
The work done in this paper highlights the use of Tversky loss in small segmentations and using an ensemble model to achieve higher accuracy.
Ryan C.
| Winter 2024
Mentored by
Relationship of Factors that Determine Tweet Virality
This paper addresses a growing dilemma: As Twitter becomes ever more influential in the modern landscape, predicting what will go viral can help in the discovery and pushing of new ideas and mitigation of risks associated with online networking.
Shawn Z.
| Fall 2023
Mentored by Shreyas Ramesh
Predicting the Performance of Code using Machine Learning
The goal of this research paper is to see the potential of machine learning to predict the performance of a given program.
Zachary R.
| Fall 2023
Mentored by Zachary Gittelman
Using Machine Learning Models to Analyze the Aerodynamic Properties of Airfoils
This research posits that leveraging artificial intelligence could significantly reduce financial and computational costs while identifying optimal airfoil geometries.
Aaron W.
| Summer 2022
Mentored by Ronil Synghal
Detecting Retinal Detachment using images with Machine Learning
This research is on how AI and machine learning can be used to detect retinal detachment using images.
Yi-Chen (.
| Summer 2023
Mentored by Samar Abu Hegly
Journal of Student Research
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Using Artificial Intelligence for Stock Price Prediction and Trading
This research paper aims to see how the emerging field of Artificial Intelligence (AI) can be used to predict a stock’s future movement and capitalize on it by anticipating the movement and automatically buying or selling the stock, in an attempt to safely generate profit while minimizing risk.
Logan B.
| Summer 2022
Mentored by Odysseas Drosis
Predictive Analysis of Aircraft Engine Lifespan: Leveraging Neural Networks on NASA's C-MAPSS Dataset
This research paper explores the application of neural networks for predictive maintenance of aircraft engines, focusing on the estimation of their Remaining Useful Life (RUL).
Sambhav A.
| Summer 2023
Mentored by
Leveraging AI to Analyze Factors Relating to Social Anxiety
Our research aims to leverage ML to understand what factors play the most critical role in the presence of social anxiety disorder in a person.
Neha K.
| Summer 2023
Mentored by Udgam Goyal
A Logistic Regression Model for Intraoperative Hypotension Prediction
This paper presents a study on using a logistic regression model for the prediction of intraoperative hypotension, a common but critical situation occurring during surgeries where the patient's blood pressure drops significantly.
Maryam A.
| Summer 2023
Mentored by
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.
| Summer 2022
Mentored by Sriram Hathwar
Predictive Dynamics of Solar Cycles: An Analysis of Sunspot Patterns, Granger Causal Relationships, and Terrestrial and Space Weather Phenomena
This paper attempts to understand the complex nature of solar cycles, primarily through the lens of sunspot data analysis, to predict future solar activity and explore its causality with Earth and Space phenomena such as global temperatures and CO2 Emissions.
Mariah D.
| Summer 2023
Mentored by
Generative Adversarial Fusion for Supercell Thunderstorm Forecasting
This paper proposes a novel machine learning model that attempts to address this problem by taking advantage of two different types of crucial data.
Yash J.
| Fall 2022
Mentored by Sarthak Kanodia
T-RECSYS+: An Improved Music Recommendation System
In our research, we build a music recommendation system to make prediction of users' listening preference.
Zhou C.
| Summer 2022
Mentored by Ross Greer
Exposing Undercounts in the Census through Regression Modelling
Although many community leaders have proposed that language barriers pose significant obstacles to Census outreach, this paper explores the viability of using predictive models to quantify the extent the role language plays.
Tarun S.
| Fall 2022
Mentored by Katie O'Nell
Predicting International Commodity Prices, Production, and Land Usage Towards Reducing Agricultural Emissions
Our long-term goal for this research project was to analyze food insecurity and determine if we could predict the emissions and pricing of different food commodities.
Sahir G.
| Summer 2023
Mentored by Landon Butler
Predicting the Price of New York City Airbnbs
How can one predict the price of a New York City Airbnb? We are trying to create a machine learning model that can predict the price of a NYC Airbnb given some factors with high accuracy.
Bobby B.
| Summer 2022
Mentored by Tomer Arnon
Multi-Label Prediction of Protein Subcellular Localizations through Machine Learning and State-of-the-art Structural Embeddings
This research explores various machine learning models to predict protein subcellular localization using UniProt’s Swiss-Prot database and Meta’s high-performing ESM2 structural embeddings.
Alfred B.
| Summer 2022
Mentored by Ayush Pandit
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.
| Summer 2022
Mentored by Christopher Mauck
Analysis of Trending YouTube Videos: Finding Patterns in Viral Content
As the digital world continues to grow, content creators frequently have trouble building a community and producing videos that will interest their audience. Especially as these people look toward the internet for both recreational and monetary reasons, finding out techniques to build a community is important in today’s age. This paper analyzes the issues of video performance, revealing the patterns of what makes a video successful and viral. By training different models and testing different datasets, we were able to find the correlation between the potential chances of popularity and the video’s content. Using the most accurate model, the Random Forest model, content creators can see whether or not they are likely to do well based on patterns found in trending videos.
Vincent P.
| Summer 2022
Mentored by Amanda Wang
Using Linear Regression to Detect the Binding Efficiency of Ligands for Effective p53-MDM2 Inhibition
This research project targets the interaction between the MDM2 and p53 proteins to find out the most efficient ligands, or small molecules, that can bind to MDM2 and prevent the inhibition of p53 so as to stimulate the opportunity for p53 to signal for cell repair/death.
Hoshita U.
| Summer 2022
Mentored by Ayush Pandit
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.
| Summer 2022
Mentored by Katie O'Nell
Revolutionizing Football: Using Machine Learning to Predict Future Performances for Quarterbacks
It is important to have a reliable application that can aid users of betting and fantasy football in which players they should put bet on, or choose for their fantasy teams. My motivation behind this project was to create something that could further betting and fantasy football, and even increase traction.
Anya N.
| Winter 2023
Mentored by Eric Bradford
Can A Person’s MBTI Type Be Determined By A Sample Of Their Writing?
This project aims to lessen the reliance of self-report for personality typology through an artificial intelligence algorithm that can type people as one of the 16 MBTI types using an unedited writing sample by that person.
Parinita K.
| Summer 2022
Mentored by Philip Bell
Using EEG to Detect Eye Movement
In this paper, we show that it is possible to use EEG data to detect eye movement using machine learning. By recognizing eye movement through EEG results, our goal is to help individuals with disabilities better control object movement and perform daily activities independently.
Aishwaroopa N.
| Summer 2022
Mentored by Tomer Arnon
Predicting Populations: Modeling Demographic Predictions for Nations Around the World Using Population Pyramids and Demographic Transition Models
In this research project, I used multiple machine learning models(neural network and linear regression) in order to predict key demographic statistics over the next 5 years for each nation.
Michael Z.
| Summer 2023
Mentored by Kasra Koushan
Unmasking Fraud: Applying Machine Learning to Detect Bank Drops
This paper evaluates eight machine learning (ML) models that are capable of flagging bank accounts as fraudulent at the time of bank account registration.
James S.
| Fall 2023
Mentored by
Examination of Dragonflies (Pantala Flavescens): Characteristics, Identification and Migration Paths
This study explores the Pantala flavescens species' anatomy and mechanisms for long-distance migrations.
Alya K.
| Winter 2022
Mentored by West Foster
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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.
| Summer 2023
Mentored by Ivan Villa-Renteria
Improving User Retention in Video Game Industry
This research paper delves into the intersection of player data analytics and affective computing to predict and adapt game difficulty levels for the purpose of enhancing player retention in the gaming industry.
Ethan V.
| Summer 2023
Mentored by Anjali Singh
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 (.
| Winter 2022
Mentored by Shreya Parchure
Predicting State-Wide Cotton Yield using Geospatial Data
The focus of this study was to use Geospatial Linear Regression to predict cotton yield.
Gregory G.
| Summer 2023
Mentored by Ribhav Gupta
A Methodology for Learning Airplane Descent Patterns
In this study, we trained a plane to land without human assistance.
Michael T.
| Fall 2023
Mentored by Sean Konz
Using Neural Networks to Predict U.S. Corporate Profits on Electronic Goods
The goal of this project is to train two neural network AI models: a Multi-Layer Perceptron (MLP) neural network and a Long Short-Term Memory (LSTM) neural network, to predict U.S. corporate profits on electronic goods into the future.
Will K.
| Summer 2023
Mentored by Ana Sofia Muñoz Valadez
Utilizing Artificial Intelligence for the Identification of Students with Depression and Anxiety through Social Media Analysis
In response to the escalating concerns about the mental well-being of students, this research represents a significant stride in utilizing artificial intelligence (AI) to discern individuals experiencing depression and anxiety through their social media comments.
Taneesh S.
| Fall 2023
Mentored by
Generating Instagram Captions with ViT-GPT2 and GPT3
This paper presents a novel approach for generating Instagram captions based on visual features and language models. Our caption generator combines Vision Transformer-GPT2 and GPT3 to generate descriptive and engaging captions in the style of an Instagram post.
Ariel M.
| Summer 2022
Mentored by Roger Jin
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.
| Summer 2022
Mentored by Roger Jin
Vibration Analysis
This paper covers differences in accuracies of an artificial intelligence model that classifies different sets of sensor outputs (in this case, machinery shaft vibrations) collected by sensors into varying levels of weight on the shaft.
Akshay N.
| Fall 2022
Mentored by
Developing a novel 3D GNN and Random Forest Regression model for screening and predicting potential oxide electrocatalysts with greater accuracy and computational efficiency
Phase 1 of my research evaluated the Dimenet++ model and Graphormer 3D Transformer model on a subset of the OC20 (Open Catalyst 2020) IS2RE dataset to analyze the relationship between a 3D Transformer and a GNN, and establish a baseline model that can be used to compare to Phase 2.
Stanley C.
| Summer 2023
Mentored by
Predicting Mental Health Conditions Using Student Demographic Information
With this, we will train the data to extrapolate what symptoms the user has based on their demographics and academic/social life. This can help understand what symptoms a person has of certain characteristics.
Ashwith Y.
| Winter 2024
Mentored by
Curieaux 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.
| Fall 2022
Mentored by Peter Washington
Texas High School Dropout Rates
Dropout rates in high schools throughout Texas have been steadily increasing, especially following the Covid-19 pandemic. This project aims to solve this problem through an artificial intelligence algorithm that can predict the dropout rate of a Texas high school campus based on specific characteristics of the school.
Emily J.
| Summer 2022
Mentored by Philip Bell
Generation of Research Paper Titles
Can NLP accurately and effectively generate research paper titles? In this research paper, an effective and accurate artificial intelligence NLP model is tried to be determined by evaluating various models and methods for title generation.
Christopher G.
| Summer 2022
Mentored by Sean Konz
SoilingNet: Convolutional Neural Networks for Analysis of Soiling Defects in Photovoltaic Panels
In our study, we present a two-step, fully supervised deep learning approach for analyzing solar panel soiling on a per-panel level.
Thomas G.
| Fall 2022
Mentored by
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.
| Summer 2022
Mentored by Odysseas Drosis
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.
| Summer 2022
Mentored by Joseph Vincent
Is GPT-3 smarter than a sixth-grader?
Question answering (QA) and Large Language models (LLM) have been a major research focus in Artificial Intelligence for several years. In 2017, a task called Textbook Question Answering (TQA) was introduced. The task included lessons from a middle school science textbook consisting of texts, diagrams, and natural questions. Many people attempted to create question answer models but reported sub-par accuracies.
Anitej S.
| Summer 2022
Mentored by Eric Bradford
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.
| Fall 2022
Mentored by Jacklyn Luu
A new style of teaching: Exploring the benefits of visual language learning
This project was designed to investigate the efficiency and likelihood of people learning a new language through means of an AI that would help translate objects in day to day life to a language that the user would want to learn.
Yuna S.
| Summer 2022
Mentored by Shreya Parchure
DeepSolar Bangladesh: A Novel Convolutional Neural Network (CNN) Architecture for the Detection of Solar Panels from Low Resolution Satellite Imagery in Developing Countries
Due to its environmental benefits and decreasing costs, the supply of solar energy is growing at an accelerating pace globally. However, the decentralised nature of solar makes it difficult to keep track of the different photovoltaic (PV) systems deployed across a country. There is a critical need for highly accurate, comprehensive national databases of solar systems, which would allow policymakers, researchers, and the government to study socioeconomic trends in solar deployment. Manual surveys have shown to be inaccurate. The 2018 DeepSolar study by Yang et. al developed a deep-learning framework and national solar deployment database for the US using high-quality satellite imagery, which proved to be a much more efficient and accurate approach. However, satellite imagery in developing countries such as Bangladesh is of much lower resolution and quality, and performed poorly with the original DeepSolar model by Yang et. al. Our study highlights the implementation of a novel convolutional neural network (CNN) in detecting solar panels through low resolution Google Static Maps API satellite imagery data.
Khondoker F.
| Summer 2022
Mentored by Barbie Duckworth
Molecular Analysis of Stellar Clusters Final Paper
By testing a multitude of models from the python package scikit-learn upon the APOGEE dataset, we’ve managed to produce a resultant product that can predict a star’s iron concentration depending upon its gravitational pull (LOGG) value or effective temperature (TEFF) value.
James W.
| Summer 2022
Mentored by Aidan Donaghey
Building an Optimized algorithm that provides summaries of legal documents
The legal industry is built around documents as they provide evidence and reduce doubt in the court. Due to the large volume of documentation in the legal industry, the processing and summarization of these documents is important to a number of individuals. We were able to create a user interface that allows for the input of documents and makes use of the algorithm we created to output a summary of the document which can be copied by the user.
Aman B.
| Summer 2022
Mentored by Eric Bradford
Curieaux 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.
| Summer 2022
Mentored by
Prediction of Nitrogen Dioxide Level using Machine Learning Models
As we release more pollution into the atmosphere as factory and vehicle byproducts, acid rains are formed and are constantly damaging the environment and harming wildlives under and above the ocean. If the occurrence of acid rains can't be limited, the phenomenon of this man-made disaster will threaten the health and safety of billions. By predicting the potential development of acid rain with nitrogen dioxide (NO2) levels, one of the major components of acid rain, we could prevent it by limiting the number of specific gasses produced.
Audrey W.
| Summer 2022
Mentored by Matthew Radzihovsky
Dental Segmentation of The Mandible Using AI
Dental segmentation can help with decision-support issues in medical diagnosis, such as human identification and maxillofacial surgery, as well as orthodontic therapy, implant planning, and other issues. This leads to my research question; “How do I automate the task required to diagnose a dental problem during a dental visit?”
Khushi G.
| Fall 2022
Mentored by Joseph Vincent
Modeling The Impact of Electric Vehicle Adoption on NO2 Levels Using Machine Learning: A Predictive Analysis
Air pollution, notably nitrogen dioxide (NO2), poses severe health and environmental risks. The research question explores whether increasing EVs adoption can visibly reduce NO2 levels.
Arnav G.
| Summer 2023
Mentored by
Allez Go: AI Fencing Referee
Technology in fencing is generally an underdeveloped field and automated referees present potentially significant benefits to the sport. Automated referees will offer a more consistent call compared to a group of human referees with slightly different interpretations of the fencing rules.
Jason M.
| Spring 2022
Mentored by Anna Orosz
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.
| Summer 2022
Mentored by Ayush Pandit
Approaches to fraud detection on credit card transactions using artificial intelligence methods
In this paper, we study the problem of detecting fraudulent credit card transactions. We select the most relevant features using a heuristic approach, and fit three different model classes to a simulated dataset: Logistic Regression, Random Forests and Gradient Boosting Machines. We find that hyperparameter tuning has a big impact on the precision and recall of our classifiers. We also find that of the three classes, Gradient Boosting Machines were the best-performing model class, achieving 83% precision and 64% recall on unseen data.
Aryaman R.
| Summer 2022
Mentored by Yuan Lee
Sign Language Recognition In Deep Learning: A Comparative Study of Custom CNN Model and Pre-Trained Architectures
The goal of this research is to successfully train different Convolutional Neural Network (CNN) models to identify sign language images, compare the performance of each model, and figure out the best model for image recognition and classification.
Vanessa H.
| Summer 2023
Mentored by Matan Gans
SaShiMi: Adapted for Google Colab
In this project, I convert SaShiMi, a music generation software, into something more resource-friendly.
Leo R.
| Spring 2022
Mentored by Roger Jin
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.
| Fall 2022
Mentored by Odysseas Drosis
Facial Intoxication Detector with AI
The purpose of this study is to analyze whether Artificial Intelligence can be used to more effectively detect and prevent drunk driving and if a Machine Learning models like Logistic Regression and Decision Tree can be more accurate than a human police officer.
Rayyaan O.
| Summer 2022
Mentored by Linda Banh
Brightness helps CNN classify a subset of the images from Google Quick Draw
We made a CNN that learned to recognize and classify sketches from Google’s Quick Draw dataset and implemented a novel brightness feature to test if it increased the accuracy.
Serena F.
| Summer 2022
Mentored by Clayton Greenberg
Using Machine Learning to Classify Stars, Quasars, and Galaxies
In this project, we looked at data from stars, quasars, and galaxies from the sixteenth data release of the Sloan Digital Sky Survey Telescope. The aim of the project was to accurately and quickly classify these three types of objects using machine learning. We used three machine learning algorithms, namely logistic regression, multi-layer perceptron, and decision tree classifier.
Chinmay R.
| Summer 2022
Mentored by Amanda Wang
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.
| Summer 2022
Mentored by Philip Bell
AI Models Necessary to Reduce Racism in Criminal Justice In The United States of America
The goal was to ensure the AI model is accurate and free of skewed/biased data. Throughout this research project, three different regression models are looked at in order to narrow down the most accurate models.
Yudhiishbala V.
| Summer 2022
Mentored by Ana Sofia Muñoz Valadez
Predicting Dropouts Using Machine Learning Models
High dropout rates in high schools and universities have become a major complication in many countries following the upsurge of the Covid 19 pandemic. With the accuracy provided, this model should be held in high regard as it serves as an efficient method for predicting possible college dropouts in university.
Viksar D.
| Summer 2022
Mentored by Eric Bradford
Stroke Susceptibility Prediction Using Artificial Neural Networks
Our research question is centered around predicting stroke susceptibility and determining which demographic and medical factors significantly increase or decrease that risk. Our results indicate the potentiality of the use of AI in determining stroke risk based solely on collected medical and lifestyle data.
Vinati P.
| Summer 2022
Mentored by Samuel Kwong
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.
| Fall 2022
Mentored by
Optimizing Stroke Detection with Machine Learning Techniques
This paper addresses the critical question of how various medical factors influence vulnerability to stroke in patients, aiming to develop an effective machine-learning model for stroke detection.
Neil S.
| Summer 2023
Mentored by Ye Wang
Predicting Repeat Purchases in E-Commerce
The underlying problem here is identifying more efficient upselling strategies for retailer companies. These findings may potentially help a retail company determine the likelihood of a customer purchasing another item based on their first purchase, and then decide how to upsell based on that information.
Ayrton S.
| Summer 2022
Mentored by Bryce Johnson
Application of AI to Tennis Match Footage Transcription
One of the best ways for tennis players to improve their game is to record and watch their own match footage, find patterns in the points they win and lose, and practice based on these realizations. However, watching match footage and documenting each point shot by shot is a very time-consuming process. This paper investigates an AI approach to transcribing tennis match footage, combining a deep convolution neural network (YOLOv4), a pose estimation model (Movenet), and a long short- term memory (LSTM) deep neural network. Looking at a transcript of each point will be far more efficient than watching entire match footage for a player to understand how they are losing and winning and analyze patterns in their game. The LSTM model in this project achieved accura- cies of 73.33% and 79.31% when classifying shot type (forehand, forehand volley, forehand slice, backhand, backhand volley, backhand slice, over- head/smash, and serve) for players on the close side and opposite side of the net, respectively, and 55.17% and 60.00% when classifying the di- rection of a shot (cross-court, down the line, down the middle, inside in, inside out, out wide, down the t, and body) for players on the close side and opposite side of the net, respectively.
Marco Y.
| Summer 2022
Mentored by Eric Bradford
Height Prediction Using Basic Data
I used basic data sets to see if some learning models have a chance of predicting height based on country and age.
Daniel S.
| Summer 2023
Mentored by Jonathan Delgadillo Lorenzo
Comparing Machine Learning Models to Determine Which is Most Effective at Detecting Brain Tumors
By using machine learning techniques to analyze various brain tumor scans, the goal was to determine which techniques are the most efficient and accurate in determining the presence of brain tumors.
Samya C.
| Summer 2022
Mentored by Shreya Parchure
Predicting NH4 Levels for Corn Crop in Wisc
Ammonium (NH4), an organic matter that accumulates in the top portion of soil, can pose a serious risk to biodiversity. Using machine learning to construct regression models, NH4 levels can be predicted and therefore mitigated. In this paper we used linear, ridge, and lasso regressions. Through the evaluation of crop farming factors that contribute to the NH4 levels, it was concluded that NO3 and N2O have the most direct correlation to NH4. These factors yielded the best accuracy for regression models with the best performing model being a multiple feature linear regression which resulted in 60% accuracy. While certain measures did improve the model’s performance, outliers continuously worsened the results.
Julia S.
| Summer 2022
Mentored by Barbie Duckworth
The Impacts of Child-Mentor Relationships on Child Mental Health
In this paper, we use a dataset from the Substance Abuse and Mental Health Data Archive to analyze the impacts of child-mentor relationships on child mental health. Previous studies have come to similar conclusions: positive parent-student and teacher-student relationships often lead to signs of positive mental health in children. To expand upon these past findings, we used different measures of mental health and relationship qualities to create a predictive model. We conducted classification and coefficient weight analyses to see how strong of an impact different variables representing indicators of healthy relationships had on different aspects of child mental health. This information was used to create predictions of future cases. Like previous studies, we found that there was a general pattern that showed that good child-mentor relationships, defined specifically by the frequency of praise and fights, had an overall positive impact on child mental health, specifically when it came to symptoms of depression. Furthermore, parents seemed to have stronger impacts on child mental health than teachers did. Limitations include possibly biased respondents who may not be representative of the greater population, as well as the specificity of the variables that were chosen. Going forward, further steps to analyze different datasets and deepen the scope of the research will be helpful in finding patterns and developing more detailed conclusions.
Samantha K.
| Summer 2022
Mentored by Akshay Jagadeesh
One Class Classification for Overdose Death Detection
In the past year alone there were an estimated 107,622 drug overdose deaths in just the United States. With such an incredible amount of deaths from just this, I thought it would be beneficial to create a model to predict who is at risk of drug overdose deaths.
Ram N.
| Spring 2022
Mentored by Eric Bradford
Identifying EOL software
Throughout our project, we have been exploring ways to identify EOL and malicious websites using python. Creating a new variable with the keywords, we were able to correctly predict 99% of the EOL websites using the data given to us.
Neal D.
| Summer 2022
Mentored by Clayton Greenberg
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.
| Fall 2022
Mentored by
Using Machine Learning Algorithms to Predict Property Prices
I decided to investigate the connection between AI and real estate, and how machine learning algorithms can be effective in determining property prices, analyzing a dataset on AirBnb listings in New York City from 2016.
Armon S.
| Summer 2022
Mentored by Bryce Johnson
AI in Recycling
By using a machine learning algorithm, we have been able to create a tool that can detect what type of material an item is and determine whether it is recyclable or not.
Nicholas K.
| Summer 2022
Mentored by Shreyas Muralidharan
Fake News Detection with BERT
In this paper, we propose a fake news detection model using a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model.
David S.
| Summer 2022
Mentored by Roger Jin
Brain Cancer Detection
Current methods for determining the presence and type of brain tumor in a given patient’s MRI scan can oftentimes be inefficient and are prone for error. By using a machine learning algorithm, the error in these classifications is reduced significantly, and the process is made much more efficient.
Rohan T.
| Fall 2022
Mentored by
Sketch Recognition using Artificial Intelligence
This paper is about an AI project that helps people learn about animals.
Joseph N.
| Fall 2022
Mentored by
Journal of Emerging Investigators
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Comparison of Different Approaches for Stock Price Prediction
To prove the hypothesis, stock prices of Tesla, Apple, and Papa Johns during the past five years were used to train LR and NN models to predict a given stock price.
David A.
| Fall 2022
Mentored by Odysseas Drosis
Predicting Precipitation and Other Weather Conditions With Logistic Regression and Random Forest Classifiers
An issue we have with our current algorithms is the inability to predict weather accurately for any time range further than ten days. Our intentions with this project were to create a model that could get an accuracy within 5% of our current weather prediction algorithms.
Vadim Y.
| Fall 2022
Mentored by Darnell Granberry
Standardized Testing is it Really Effective?
How effective is standardized testing in assessing students' knowledge? With data from previous students on sat scores and GPAs from college and high school, I can use this data and plug it into different models to determine how well these sat scores determine GPA.
Rohan S.
| Summer 2022
Mentored by Christina Cheng
Predicting Future Phonological Changes Of Mandarin Chinese
We created a system of translating IPA representation to vectors that captures characteristics of phonemes such as their articulatory location, and experimented with several machine learning models to capture existing trends from Old, Middle to Modern Chinese (Mandarin).
Peijie G.
| Summer 2022
Mentored by Kush Khosla
Fake news detection, methods and data processing
In this project we studied several models to discern fake news articles from real news articles to find the best method.
Soham P.
| Summer 2023
Mentored by Erick Ruiz
Classification of Exon and Intron Boundaries
The goal was to accurately classify exon and intron boundaries based on DNA sequences. Scientists can learn more about proteins if divisions between exons and introns are clear. We used multiple different machine learning approaches including a logistic regression model, a multilayer perceptron, a LSTM, and a model that included a LSTM, autoencoder and a multilayer perceptron. LSTMs performed well, pointing to the idea that order of nucleotides is important when classifying DNA sequences.
Milo B.
| Summer 2022
Mentored by Samuel Kwong
Predicting Drug-Drug Interaction Severity Using Network Characteristics
Drug-drug interactions (DDIs), which can add to or diminish the effect of one drug or impact the metabolism of one drug, have harmful effects on health in patients that take multiple drugs. Testing for DDIs is slow and costly, so computational models have recently been used to predict them. Network information is useful in describing a drug’s known interactions and mechanisms to determine whether two drugs could be interacting. This research explores various machine learning models to predict the severity of unknown interactions of existing drugs (major, minor, or moderate), using the DDInter database.
Deetya N.
| Summer 2022
Mentored by Linda Banh
What Factors Correlate with the Relationship Between Gender and Race and Pursuing STEM?
In this paper, I sought to answer the question, “How do races and genders differ in the way they pursue STEM, and what factors correlate with these differences?” Identifying which factors cause this lack of representation is the most important step in fixing the diversity problem.
Saket R.
| Summer 2022
Mentored by Bradley Yam
Insect Identification Project for Agricultural Advancement
Through the use of image data, we developed an artificial intelligence system which is able to predict an insect’s species based on a photo of a given insect.
Archith S.
| Summer 2022
Mentored by Barbie Duckworth
What Data is Needed to Accurately Determine Someone's Mental Health?
If we determine which data is actually necessary, we can build better user trust while maintaining the efficacy of a chatbot.
Claire L.
| Summer 2022
Mentored by Katie O'Nell
Predicting Solar Array Output Using Weather Sensor Data
With the recent push for renewable energy sources, solar energy is one that is readily available. This paper will explore how to predict the power output of a solar array based on weather data, collected from sensors throughout each day.
Maximilian P.
| Summer 2022
Mentored by
Plant Toxicity Classification by Image
Since differentiating between dangerous and safe plants is a complex task for a human brain, this study approaches the issue through machine learning models starting with a convolutional neural network (CNN) and discovering that a logistic regression model—trained on a dataset with manually designed features—has the best performance with the particular dataset used.
Eera B.
| Summer 2022
Mentored by Clayton Greenberg
Application of Machine Learning to Distinguish Premature Leukemia Cells from Healthy Blood Cells
Convolutional Neural Networks (CNNS) to solve this problem have been created with classification validation accuracy rates as high as 96.15%.
Noor E.
| Summer 2022
Mentored by Yiran Li
World Academy of Science Engineering and Technology (WASAT)
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Stock Prediction Project
As of 2022, 60%-73% of the trading volume done in the U.S stock market is done by algorithms, illustrating the dependency and importance of these types of algorithms in stock predictions. Additionally, a recent census by Gallup shows that 145 million Americans (roughly 56% of the population) invest and own stocks, showing the population’s keen interest in the stock market. I endeavored to create a model that takes into account opening, closing, high, and low prices from the last 3 days to predict the opening price on the fourth day. Of course, the model is not just limited to only being able to approximate the opening price the following day; for example, it will also be able to predict the opening price 5 days later.
Sofia S.
| Summer 2022
Mentored by Odysseas Drosis
Predicting emotion ratings from color statistics of images
We utilized this data to see how visual elements affect our everyday lives by engineering features of images and running that through a variety of neural networks.
Claire M.
| Summer 2022
Mentored by Clayton Greenberg
Is AI Necessary In Deciding Whether an Offender Is Likely To Recidivate, With & Without the Effect of Protected Characteristics
Our research topic addresses the issue of whether or not an AI algorithm can be developed to forecast whether or not a criminal would recidivate by removing protected characteristics from models in terms of both accuracy, precision and recall and their equality across different groups, as well as if such a tool is necessary
Thalia S.
| Summer 2022
Mentored by
Brain Tumor Classification
Early classification and diagnosis of Brain Tumors are essential for providing the right treatment to a patient. It is crucial to get treatment as soon as possible because the survival rate for someone with an untreated brain tumor can range from as low as 3 months to as high as 5 years. In this project, we classified brain tumor images into 4 categories: glioma, meningioma, pituitary, and no tumor. With the use of baseline and deep learning models, the deep learning models demonstrated a significantly higher performance due to their ability to analyze images. The model with the highest accuracy was the MobileNet, a pre-trained transfer learning model trained on 5,608 images. This model yielded a validation accuracy of 98.24%. Using metrics including Kappa cohen score, precision, and recall, we validated the machine learning model's performance. We deployed the MobileNet model to a web app using Streamlit, where users submit MRI images and receive diagnoses of tumor class. We found that the model performed very well while utilizing the web app, indicating that it is safe to be used. However, since we only have 4 classes and there are over 150 total types of brain tumors, it could easily get a diagnosis wrong if it is not in one of these 4 classes.
Rohan S.
| Summer 2022
Mentored by Sriram Hathwar
Biased News Detection Using Artificial Intelligence
Biased news has become especially problematic in modern times. In this paper, we investigate the abilities of various machine learning models to correctly identify biased news. Our results indicate that artificial intelligence is very capable of doing this detection. In particular, both logistic regression and K-nearest neighbor classifier performed rather well in cross validation.
Arianna H.
| Summer 2022
Mentored by Matteo Santamaria
The Motions of Forest Fires
Can I make an AI model that can predict how forest fires will move? I am trying to take data about the environment and a current fire and predict where the fire will go / if it will grow or shrink.
Giuliano T.
| Summer 2022
Mentored by Sophia Barton
Skin Cancer Detection
The goal of this research project is to predict whether or not a patient has skin cancer through a machine learning model that is developed from an image dataset. Skin cancer is extremely dangerous, as over 9500 people in the US are diagnosed with it daily. If detected early, patients will have a more likely chance of survival. I tested an MLP Classifier, Decision Tree Regressor, a Logistic Regression Model, and a KNN Model to compare various results and ultimately determine the best accuracy. The MLP Classifier had a 74.5% accuracy, the Decision Tree Regressor had a 74.1% accuracy, the Logistic Regression Model had a 68.8% accuracy, and the KNN Model had a 74.6% accuracy (all testing). We can see that the MLP Classifier, Decision Tree Regressor, and the KNN Model had around the same accuracy while outperforming the Logistic Regression Model. However, when comparing training data, there seems to be a large overfitting problem with most of the models.
Jaida G.
| Summer 2022
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.
| Fall 2022
Mentored by Sophia Barton
Machine Learning Algorithms for Consumer Plastics Identification and Sorting
Seeking a solution to maintain plastic recycling costs while increasing the output of reusable material, this paper poses the question “what type of machine learning algorithm is most suitable for a plastic identification system in consumers’ homes?” It investigates five total machine learning algorithms to determine which one best balances accuracy, management of resources, and time efficiency, ultimately arriving at the conclusion that a Support Vector Machine that uses a Polynomial Kernel is the best algorithm and serves to demonstrate that algorithms such as the ones analyzed have become advanced enough for more efficient, AI driven systems to replace those of the status quo.
Alexander K.
| Summer 2022
Mentored by Eric Bradford
A Comprehensive Study of Machine Learning Models for the Prediction of pH in Fairfax County
Water quality is drastically declining as bodies of water become more and more acidic due to global warming. Because of this, it is crucial to understand what factors determine pH, and how to predict pH values. In this study, we determine which features are most important in determining pH, and also what combination of features and machine learning models give us the best accuracy in the prediction of pH values. We demonstrate that pH values from the previous year and the percentage of rehabilitated sanitary sewer lines are the most important features in determining pH. We also demonstrate that Decision Tree Models and Random Forest Models perform the best across all features and feature subsets.
Matthew N.
| Spring 2023
Mentored by Sarthak Kanodia
Predicting the ask price in options using different machine learning methods
The goal of this project is to determine how to predict important aspects of options, including ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and dataset.
Krishang S.
| Summer 2022
Mentored by Matan Gans
Most Important Soil Properties to Consider for High Crop Yield
With the current population of Earth massively growing, there is a real risk coming up in the near future of not having enough food to feed the planet. One of the best ways to solve this upcoming problem is to improve the quality of the soil because that will help increase Earth’s overall crop yield in a sustainable way.
Arpan A.
| Summer 2022
Mentored by Mirna Kheir Gouda
An Investigation Into Applications of Machine Learning Algorithms on Solar Flare Data and Distance Prediction
The current method that NASA uses for flare prediction involves studying various solar cycles that range from 11 days to 80 years. However, there are far too many factors to consider using this method of prediction and the forecasts are often wrong.
Isaac A.
| Summer 2022
Mentored by Aidan Donaghey
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.
| Summer 2022
Mentored by Odysseas Drosis
Does Physiology Really Help a Volleyball Player Succeed?
As a child, I always went to my sister’s volleyball games. From club games to school games, I went to them all. Because I saw so many volleyball games by watching my sister’s games, I always saw that there was always a trend that I kept seeing from each team.
Justin H.
| Summer 2022
Mentored by Christina Cheng
The Impact of the Covid-19 Pandemic on the Test Scores of Various Demographics
The research problem that this research paper attempts to address is, How did Covid impact and affect the test scores of different demographics of students in various schools in New York? The overall approach that was used to answer this question involved using statistical analysis to examine the correlation between how certain factors such as being economically disadvantaged or being hispanic affected student proficiency scores on New York State regents exam tests in 2018 and 2021 in different schools.
Deniz G.
| Summer 2022
Mentored by Kush Khosla
Training a Machine Learning Model to Recognize Signs of Depression
Many people have not had a professional diagnosis, and could have depression without being aware of it. To attempt to answer this question, I used a person’s daily habits and how they change, and attempt to predict if they will experience a depressive episode in the future.
Bwohan W.
| Spring 2022
Mentored by Barbie Duckworth
Predicting Recidivism in the United States Criminal Justice System
There are real-life effects of machine bias in courtrooms. American lives are completely changed based on criminal sentencing, which can mean the difference between rehabilitation and recidivism. This issue is urgent and finding a solution to ensure equality is imperative.
Alexander F.
| Summer 2022
Mentored by
Gamma Ray Classification
This research paper is in this domain of physics, specifically using Artificial intelligence (AI) to classify high-energy Gamma particles as background or signal. I was able to create an AI model to classify these gamma particles with high accuracy. I was able to accomplish this by using a wide range of data, from the MAGIC Gamma Telescope Dataset, which consists of key features, to construct a model.
Eesha S.
| Summer 2022
Mentored by Victoria Lloyd
Stock Price Prediction Project
Can an Algorithm be created to accurately predict the stock price of any company on a given day? Within my project, I'm trying to create a code that can accurately predict any stock's price on a given day.
Jackson D.
| Summer 2022
Mentored by Odysseas Drosis
Predicting Skin Cancer using Machine Learning
Skin cancer affects an increasing number of people which is the main motivation for my research. The goal is to develop a model that is accurate enough for real world application and that could be distributed to more rural areas which have decreased access to medical technology.
Keithan P.
| Spring 2022
Mentored by Eric Bradford
Using Logistic Regression in the Early Detection of Tsunamis caused by Earthquakes
Many tsunamis have enough energy to cause a large amount of human death, as well as trillions of dollars in damage repair. The problem arises on how to better prepare for these natural disasters and limit its damage, especially in countries such as Japan that are frequented by tsunamis.
Ian C.
| Summer 2022
Mentored by
Optimizing Skin Cancer Classifiers By Applying Multiplicative Weight Update Into A Mobile Application
Skin cancer is slowly becoming the most common and frequent type of cancer in the world. In the United States alone, research suggests that skin cancer is the most common cancer in and that one in every five people will acquire skin cancer at some point throughout their lifetime.
Animish J.
| Summer 2022
Mentored by Odysseas Drosis
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.
| Summer 2022
Mentored by Tony Rodriguez
Stock Price Prediction
The most advanced stock prediction models take into account fundamental and technical analysis. Fundamental analysis involves analyzing the stock’s intrinsic value, financial statements, tangible assets, management effectiveness, consumer behavior, and overall company outlook.
Harrison S.
| Summer 2022
Mentored by Odysseas Drosis
Detecting Facial Expressions
This paper shows that machine learning algorithms can be used in order to classify emotions into different categories. The most promising results were from the K-nearest neighbor model, with 61.5% accuracy in the training set, and 32.1% accuracy in the testing set.
Bertran M.
| Summer 2022
Mentored by Odysseas Drosis
Using Machine Learning Models to Determine Medical Features Most Indicative of Gestational Diabetes
With the recent overruling of Roe v. Wade, pregnancies have become especially dangerous since many females who previously abort fetuses no longer can. By doing this research project, I can help figure out what medical factors may lead to gestational diabetes, and hopefully from there, the results can be used to counteract those features.
Fiona B.
| Summer 2022
Mentored by
Identifying Cancer Types in Microscope Images of Lung and Colon Cells
Lung adenocarcinoma cells make up large proportions of lung cancer cases and colon cancer is one of the most prevalent cancers in the United States. Although many different medicines have been recently developed to attack these cancers, the most effective way to stop it is early detection.
Adam S.
| Summer 2022
Mentored by Odysseas Drosis
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.
| Summer 2022
Mentored by
Developing an Accurate AI Algorithm for Histopathologic Cancer Detection
In this specific research project, we will be focusing on the lymph node scans of women with breast cancer, which is the most common cancer for women residing in the US, other than skin cancer. Research statistics show that about 1 in 8 women in the United States will develop invasive breast cancer throughout her life.
Leah N.
| Summer 2022
Mentored by Odysseas Drosis
Identifying Parameters in Water Potability Analysis Through Machine Learning
Predicting whether the water is potable or not can be helpful for people who are reliant on bodies of water and redirect them to safer options. It will also be beneficial to apply the algorithm to other places where it is expensive and inefficient to send people out and collect water samples. Over the past couple of decades, researchers have often commented on the lack of funding as a source of error when it comes to data analysis and the accuracy of the research.
Molly H.
| Summer 2022
Mentored by Sharon Chen
AI Detection of Emotions through EEG
We proceeded to develop a recurrent neural network (RNN) model structure that could analyze an individual’s brain waves and predict their emotional condition.
Srinivas S.
| Summer 2022
Mentored by Nima Leclerc
Cardiac Auscultation: Metrics of Smartphones and Digital Stethoscopes
The objective of our research is to figure out how feasible and accurate a mobile device solution to cardiac ascultation is, compared to a digital stethoscope.
Nicholas T.
| Summer 2022
Mentored by Sophia Barton
Findings Provided by a Machine Learning Clustering Model of Stellar Kinematics in Star Clusters Can be Used to Identify Runaway Stars
The need for developing a machine learning model that can detect these runaway stars is established in this research as it attempts to expand the domain of astrophysics to help astrophysicists understand tidal tail formation and the theoretical dark matter subhalos’ effects on celestial bodies.
Roneet D.
| Summer 2022
Mentored by Tony Rodriguez
Using Machine Learning to identify the Habitability of Exoplanets from TESS Transit Data
With so many new tools and methods to detect exoplanets, it is time to start determining which are deserving of more research in the hunt for other habitable planets.
Eve B.
| Summer 2022
Mentored by Tomer Arnon
Attention LSTMs in Multimodal Models: A holistic approach to predicting COVID infection trends
In our search for a way to simultaneously predict all state-level COVID infection rates in the United States with COVID heat maps and domestic flight graphs, we propose novel methods of processing graph and image sequences with attention-based LSTM layers as well as evaluate the effectiveness of different multimodal fusion techniques.
Nuo W.
| Spring 2022
Mentored by Eric Bradford
The Effect of the News on the Stock Market
I remember opening my investing account one day and looking at my stocks, noticing that the majority of them were declining pretty severely. Ever since the Russia and Ukraine war, this had been the case. I saw many news headlines talking about the market, and how it was affected from this war. It was then I realized, why isn’t there a way for us to predict these changes in the market given sufficient news headlines?
Pranav B.
| Summer 2022
Mentored by Eric Bradford
Using Machine Learning Architecture to Detect Brain Tumors
Not all tumors in the brain are cancerous, so irregular cell growths can often be determined as benign and are ignored completely. However, many of these low-danger cell growths can develop into higher-danger cancerous tumors. Identifying these low-danger tumors early is key to preventing cancer in the future.
Mike S.
| Summer 2022
Mentored by Matthew Radzihovsky
Stock Price Prediction
In my project, I used two different methods, the linear model and the neural network. These two were a big part of my project as they involved a pattern for my values while predicting future stocks. The linear model basically uses the relationship between the data points to draw a straight line through all of them. This line can be used to predict future values.
Sai P.
| Summer 2022
Mentored by Odysseas Drosis
Fake News Classification
As fake news becomes more of a problem across the US, its consequences are becoming more and more damaging. This project aims to combat this disinformation through an artificial intelligence algorithm that can classify real articles versus fake ones.
Roshni K.
| Summer 2022
Mentored by Philip Bell
How Does One’s Background Determine Their Mental State?
Mental health issues have become very prevalent in recent times, and although significant progress has been made in terms of treatment in the form of counseling, medication, and other methods, in order to truly find out the root cause of many mental health issues, a correlation has to be drawn between one’s mental state and another factor, such as family history, age, work environment, etc. It is also beneficial to correlate one’s mental state with a multitude of factors to see the compilation.
Sohum T.
| Summer 2022
Mentored by Akshay Jagadeesh
Predicting the Severity of Alzheimer’s/Dementia from Magnetic Resonance Imaging Scans Using a Deep Learning Approach
The overall approach used in this research was utilizing a deep learning model, specifically a convolutional neural network. A convolutional neural network is a type of neural network architecture that specializes in identifying and making sense of patterns in image data. For this research, this type of neural network was mostly used for classifying images of dementia MRI scans based on their severities.
Akshaj S.
| Winter 2024
Mentored by Kasra Koushan
Predicting Dementia using AI
This project involved researching and utilizing AI models to predict whether an individual is demented or non-demented based on factors like age, gender, dominant hand, brain size, and education.
Satvi M.
| Fall 2023
Mentored by James Thomson
Predicting Shoe Prices Using Machine Learning Algorithms
This paper investigates the use of machine learning algorithms, particularly Support Vector Regression, to accurately predict shoe prices based on factors like material, color, brand, type, gender, and size, using a dataset of 5000 entries.
Akshar S.
| Summer 2024
Mentored by John Basbagill
Melanoma detection through AI models
This project explores the use of AI models to detect skin cancer from images of skin lesions, achieving 80-90% accuracy in predicting malignancy and revealing key physical attributes that could aid early detection.
Aditya J.
| Spring 2024
Mentored by Alaisha Alexander
The Impact of Computer Vision on Assisting Visually Impaired People
This research aims to develop computer vision algorithms that can provide accurate positioning data from video input, with the goal of creating a program to assist visually impaired individuals in navigating their environment.
Aaryan W.
| Summer 2023
Mentored by Anjali Singh
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.
| Summer 2024
Mentored by Erick Siavichay
Machine Learning Models for Cardiovascular Disease: A Holistic Review on Early Diagnosis Performance
This paper evaluates the effectiveness of lightweight machine learning models for predicting cardiovascular disease severity, finding high accuracy and potential for practical use, while emphasizing the need for better explainability and integration with wearable technology.
Anirudh C.
| Fall 2024
Mentored by Matthew Radzihovsky
A Late Fusion Approach with Multimodal Image-Text Data
This project explores late fusion methods in multimodal machine learning to improve emotion detection by combining image and text predictions, and evaluates their effectiveness compared to other fusion approaches using the MVSA-Single dataset.
Shreyes B.
| Summer 2024
Mentored by Varsha Sandadi
Optimizing Student Success Predictions using Artificial Intelligence
This work investigates using AI and neural networks to enhance predictions of student success and optimize resource allocation in education, finding that advanced models can significantly improve support for students and address resource imbalances.
Sameeksha V.
| Summer 2024
Mentored by Jose Reyes
Predictive Modeling of Diabetes Using Python Neural Networks
This research develops a neural network model using machine learning to predict diabetes occurrence, achieving strong accuracy and performance metrics, and highlights the need for ongoing model improvements to enhance early detection and healthcare for at-risk individuals.
Advik V.
| Summer 2024
Mentored by Ronil Synghal
Understanding Object Detection’s Role in Decision-Making for Self-Driving Vehicles
This research focuses on computer vision for self-driving vehicles by evaluating various AI models for object detection, finding that VGG16 outperformed others with precision of 0.7209 and recall of 0.7298, highlighting the importance of model selection and optimization in ensuring safe navigation in autonomous driving systems.
Shishir B.
| Summer 2024
Mentored by Hassan Azmat
From Text to Visuals: Leveraging AI for Immersive Storytelling
This research investigates the use of AI to generate visual representations of Edgar Allan Poe's literary works, aiming to enhance reader engagement by bridging traditional literature with modern digital media through AI-generated imagery, while identifying challenges related to visual consistency and narrative complexity.
Ian M.
| Summer 2024
Mentored by Alaisha Alexander
Using Machine Learning to Detect Parkinson’s Disease Through Drawing Data
This research explores the use of machine learning models, particularly logistic regression, on patients' spiral and wave drawings as an accessible and cost-effective method to improve Parkinson's disease diagnosis, achieving an accuracy of 91%, precision of 92%, and recall of 89%.
Katie Y.
| Spring 2024
Mentored by Sriram Hathwar
Utilizing AI for Alzheimer's Diagnosis
This research assesses the potential of AI to improve early Alzheimer’s diagnosis by evaluating various classifier models, with results showing that while MLP classifier achieved only 32% accuracy, the Random Forest classifier excelled at 91.5%, highlighting the importance of factors like the mini mental state evaluation over others, as revealed in the Logistic Regression Feature Importance analysis.
Trinity M.
| Summer 2024
Mentored by Kevin Phan
Applying Machine Learning to Historical Cyber Attack Data to Predict and Prevent Future Attacks
This research demonstrates that machine learning algorithms can leverage historical data to predict the nature and severity of future cyber attacks with reasonable accuracy, revealing that despite the sophistication of state-sponsored actors, their attack patterns often exhibit digital fingerprints that offer valuable clues for anticipating future threats amidst global geopolitical tensions.
Isabella F.
| Fall 2023
Mentored by Simeon Sayer
Fast and Accurate Gamma-Ray/Hadronic Particle Shower Classification Using Machine Learning
This research applies dimensionality reduction techniques, such as Pearson Correlation and Principal Component Analysis, to machine learning models like Random Forests and Support Vector Classifiers to simplify the prediction of atmospheric gamma-ray particle showers, achieving a modest accuracy increase while reducing the number of features used.
Leonardo V.
| Fall 2024
Mentored by Pablo Bonilla
Predicting Stock Market Returns Through Twitter Sentiment Analysis
This study aims to explore the impact of tweets on stock returns using time-series analysis and Random Forest, highlighting how investor sentiment, as reflected on platforms like Twitter, influences market behavior alongside traditional financial data.
Vidur A.
| Fall 2024
Mentored by Abdulla Kerimov
Predicting the Presence of Autism Spectrum Disorder Based on Eye-Tracking Scan Path Images
This research explores using eye-tracking data and machine learning to predict Autism Spectrum Disorder (ASD), achieving 74.6% accuracy with logistic regression, highlighting eye-tracking's potential for early, non-invasive ASD diagnosis and its applicability to other neurological conditions.
Sara C.
| Fall 2024
Mentored by Emily Broadhurst
Using Artificial Intelligence to Predict the Return of Mutual Funds
This research focuses on developing a profitable AI for investment by analyzing a US mutual funds dataset from Yahoo Finance, utilizing models like Random Forest and XGBoost with hyperparameter tuning to predict mutual fund returns over three years using past financial ratios and cash flow statistics.
Ethan C.
| Summer 2024
Mentored by Abdulla Kerimov
Using Deep Learning to Predict the Half-Lives of Isotopes Given Proton and Neutron Count
This study explores using deep learning regression models to predict superheavy isotopes' half-lives based on proton and neutron counts, finding that additional complexity in input variables and model architecture may be necessary to improve prediction accuracy.
Akilan P.
| Summer 2024
Mentored by Erick Ruiz
Predicting Dementia Risk with Machine Learning
This research addresses the urgent need for early dementia detection by using machine learning to analyze patient health factors, such as heart rate and diabetes, for dementia prediction, achieving a 52% accuracy with a random forest model and paving the way for future model improvements and deeper insights into influential risk factors.
Min W.
| Fall 2024
Mentored by John Basbagill
A Comprehensive Review on Deep Learning Architectures for Image Segmentation
This paper provides a comprehensive review of recent advancements in deep learning-based models for image segmentation, including U-Net, Mask R-CNN, and transformer-based models, analyzing their performance, challenges like data shortage, and potential future directions for improved real-time deployment and interpretability.
Madhurima M.
| Fall 2024
Mentored by Victoria Lloyd
Using Machine Learning Models to Predict Asthma Hospitalizations Based on Air Pollutants
This study uses a Decision Tree regression model to analyze the relationship between air pollution (PM2.5, NO2, O3) and asthma hospitalizations in New York City, revealing significant prediction challenges and highlighting the potential for targeted healthcare interventions based on regional health vulnerabilities.
Nikhil T.
| Fall 2024
Mentored by Kyra Kraft
Smart Agriculture: Optimizing Soil pH and Crop Recommendations Using Machine Learning
This study explores how hyperparameter tuning enhances the performance of machine learning models, achieving a 99.5% accuracy in crop classification and highlighting the potential of AI-driven optimization for sustainable precision agriculture.
Pracheth G.
| Fall 2024
Mentored by Henry Cerbone
Using Machine Learning for Calculus
This research aims to develop a machine learning program that verifies the correctness of integral equalities by leveraging a language processing approach, achieving 40-60% accuracy across different classifiers and datasets, unlike traditional mathematical software that only evaluates one-sided equations.
Paul N.
| Fall 2024
Mentored by Erick Ruiz
