We are thrilled to announce the results of the first call for NeurIPS High School Projects. With a theme of machine learning for social impact, this track was launched to get the next generation excited and thinking about how ML can benefit society, to encourage those already pursuing research on this topic, and to amplify that work through interaction with the NeurIPS community.
In total, we received 330 project submissions from high schoolers around the globe. Among those, 21 projects were chosen to be spotlighted and 4 were chosen as award winners. We congratulate all of the students and encourage community members to attend the joint poster session on Tuesday December 10, where representatives of the four award-winning projects will present their work.
ALLocate: A Low-Cost Automatic Artificial Intelligence System for the Real-Time Localization and Classification of Acute Myeloid Leukemia in Bone Marrow Smears
Ethan Yan, Groton School, MA, USA
Abstract: Accurate leukemia detection in current clinical practice remains challenging due to limitations in cost, time, and medical experience. To address this issue, this research develops the first integrated low-cost automatic artificial intelligence system for the real-time localization and classification of acute myeloid leukemia in bone marrow smears named ALLocate. This system consists of an automatic microscope scanner, an image sampling system, and a deep learning-based localization and classification system. A region classifier using a convolutional neural network (CNN) model was developed to select usable regions from unusable blood and clot regions. For real-time detection, the YOLOv8 model was developed and optimized. These models show high performance with a region classifier accuracy of 96% and YOLOv8 mAP of 91%. In addition, a low-cost automatic microscope scanner system was developed using 3D-printed pieces controlled by stepper motors and a programmed Arduino-based RAMPS control board. When ALLocate was applied to marrow smears, its leukemia detection results were similar to results from a doctor but were produced much faster. This is the first report to integrate a deep learning system with a low-cost microscope scanner system for leukemia detection with high performance, which can benefit small community practices and clinics in underserved areas, making healthcare more accessible and affordable to all.
Image Classification on Satellite Imagery For Sustainable Rainwater Harvesting Placement in Indigenous Communities of Northern Tanzania
Roshan Taneja and Yuvraj Taneja, Sacred Heart Preparatory, CA, USA
Abstract: In the remote regions of Northern Tanzania, women and children of the Maasai Tribe walk nine hours a day to collect water. Over four years, collaborative efforts with the Maasai communities have led to the installation of water harvesting units, enhancing the local socio-economic conditions by facilitating educational opportunities and economic pursuits for over 4,000 individuals. This project is a novel approach to integrating satellite data and image classification to identify densely populated areas marked by uniquely shaped Maasai homes. It will also use density maps to plan the best placement of rainwater harvesting units to help 30,000 Maasai. The backbone of this project was developing an image classification model trained on 10,000 hand-selected satellite image samples of Bomas (Maasai living units). This model generated a density heat map, enabling strategically placing rainwater harvesting units in the most critical locations to maximize impact. The project underscores the use of satellite technology and machine learning to address humanitarian needs such as water, particularly in harder-to-reach areas with no infrastructure.
Multimodal Representation Learning using Adaptive Graph Construction
Weichen Huang, St. Andrew’s College, Ireland
Abstract: Multimodal contrastive learning trains neural networks by leveraging data from heterogeneous sources such as images and text. Yet, many current multimodal learning architectures cannot generalize to an arbitrary number of modalities and need to be hand-constructed. We propose AutoBIND, a novel contrastive learning framework that can learn representations from an arbitrary number of modalities through graph optimization. We evaluate AutoBIND on Alzhiemer’s disease detection because it has real-world medical applicability and it contains a broad range of data modalities. We show that AutoBIND outperforms previous methods on this task, highlighting the generalizablility of the approach
PumaGuard: AI-enabled targeted puma mitigation
Aditya Viswanathan, Adis Bock, Zoe Bent, Tate Plohr, Suchir Jha, Celia Pesiri, Sebastian Koglin, and Phoebe Reid, Los Alamos High School, NM, USA
Abstract: We have trained a machine learning classification algorithm to detect mountain lions from trail cam images. This algorithm will be part of a targeted mitigation tool to deter mountain lions from attacking livestock at the local stables. Our algorithm that uses the Xception algorithm is 99% accurate in training, 91% accurate on validation and successful in identifying mountain lions at the stables.
Diagnosing Tuberculosis Through Digital Biomarkers Derived From Recorded Coughs
Sherry Dong, Skyline High School, MI, USA
GeoAgent: Precise Worldwide Multimedia Geolocation with Large Multimodal Models
Tianrui Chen, Shanghai Starriver Bilingual School, China
INAVI: Indoor Navigation Assistance for the Visually Impaired
Krishna Jaganathan, Waubonsie Valley High School, IL, USA
Implementing AI-driven Techniques for Monitoring Bee activities in Hives
Tahmine Dehghanmnashadi, Shahed Afshar High School for Girls, Iran
AquaSent-TMMAE: A Self-Supervised Learning Method for Water Quality Monitoring
Cara Lee, Woodside Priory School, CA, USA; Andrew Kan, Weston High School, MA, USA; and Christopher Kan, Noble and Greenough School, MA, USA
AAVENUE: Detecting LLM Biases on NLU Tasks in AAVE via a Novel Benchmark
Abhay Gupta, John Jay Senior High School, NY, USA; Philip Meng, Phillips Academy, MA, USA; and Ece Yurtseven, Robert College, Turkey
FireBrake: Optimal Firebreak Placements for Active Fires using Deep Reinforcement Learning
Aadi Kenchammana, Saint Francis High School, CA, USA
Vision-Braille: An End-to-End Tool for Chinese Braille Image-to-Text Translation
Alan Wu, The High School Affiliated to Renmin University of China, China
Advancing Diabetic Retinopathy Diagnosis: A Deep Learning Approach using Vision Transformer Models
Rhea Shah, Illinos Mathematics & Science Academy, IL, USA
LocalClimaX: Increasing Regional Accuracy in Transformer-Based Mid-Range Weather Forecasts
Roi Mahns and Ayla Mahns, Antilles High School, Puerto Rico, USA
HypeFL: A Novel Blockchain-Based Architecture for a Fully-Connected Autonomous Vehicle System using Federated Learning and Cooperative Perception
Mihika A. Dusad and Aryaman Khanna, Thomas Jefferson High School for Science and Technology, VA, USA
Robustness Evaluation for Optical Diffraction Tomography
Warren M. Xie, Singapore American School, Singapore
Translating What You See To What You Do: Multimodal Behavioral Analysis for Individuals with ASD
Emily Yu, Pittsford Mendon High School, NY, USA
SignSpeak: Open-Source Time Series Classification for ASL Translation
Aditya Makkar, Divya Makkar, and Aarav Patel, Turner Fenton Secondary School, ON, Canada
SeeSay: An Assistive Device for the Visually Impaired Using Retrieval Augmented Generation
Melody Yu, Sage Hill School, CA, USA
Predicting Neurodevelopmental Disorders in rs-fMRI via Graph-in-Graph Neural Networks
Yuhuan Fan, The Experimental High School Attached to Beijing Normal University, China
Realistic B-mode Ultrasound Image Generation from Color Flow Doppler using Deep Learning Image-to-Image Translation
Sarthak Jain Silver Creek High School, CA, USA
