rugantio /
MachineLearningCourse
A collection of notebooks of my Machine Learning class written in python 3
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aaryansamanta / repository
Collection of my peer-reviewed high-school research: IEEE AIAM 2025 (quantum-inspired GA + GNN for multimodal classification) + IJHSR/UCSB SRA (C. elegans mitochondrial resilience genetics). Code, papers, certs, and pipelines from a 10th grader with dual USACO 2025 perfect scores.
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| Publication / Project | Venue / Institution | Focus | Status | Repo |
|---|---|---|---|---|
| Quantum-Inspired Hybrid Genetic Algorithm and Graph Neural Network Ensemble for Multimodal Classification | 2025 7th Intl. Conf. on AI & Advanced Manufacturing (AIAM) • IEEE Press | Quantum-inspired GA + GNN for high-dim multimodal data fusion; interpretable SHAP + surrogate fitness | Accepted & Published (AIAM-6203) | ieee-aiam-2025-ga-gnn → |
| Natural Genetic Variation in Mitochondrial Health-Regulating Genes: Resilience in C. elegans Strains CX11314 & EG4725 | International Journal of High School Research (IJHSR) • UCSB Summer Research Academy Capstone | Computational biology: variant analysis, mitophagy pathways, mitochondrial resilience in resilient C. elegans mutants | Peer-Reviewed & Published • Youngest SRA Scholar | ijhsr-uscb-sra → |
| Pelvic Floor Biomechanics & Ultrasound Analysis | Stanford University School of Medicine | Research Scholar (Mentor: Christos E. Constantinou, Associate Professor of Urology, Emeritus, Stanford University School of Medicine), Jan’26–Present: Analyzed anonymized pelvic floor ultrasound data across cohorts & stimulation states; developed 3D models from records/publication data for kinematic & pathophysiologic insights. | Ongoing | stanford-urology-2026 → |
ieee-aiam-2025-ga-gnn/ → Quantum-GNN hybrid paper: code (PyTorch), paper PDF, acceptance certificate, figures, data previewijhsr-uscb-sra/ → C. elegans mitochondrial resilience paper: bioinformatics pipeline, manuscript, UCSB/IJHSR docs, figuresstanford-urology-2026/ → Stanford Urology research (ongoing): reference papers (PDFs), ultrasound video demos (.mov), 3D modeling materialsdocs/ → (Optional) Shared certificates, resumes, or supplementary files@INPROCEEDINGS{samanta2025qihga,
author = {Samanta, Aaryan},
booktitle = {2025 7th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)},
title = {Quantum-Inspired Hybrid Genetic Algorithm and Graph Neural Network Ensemble for Multimodal Classification},
year = {2025},
publisher = {IEEE}
}
@ARTICLE{samanta2025ijhsr,
author = {Samanta, Aaryan and Mandal, Mansi and Meeran, Mohammed Yasar},
title = {Natural Genetic Variation in Mitochondrial Health-Regulating Genes: Resilience in C. elegans Strains CX11314 and EG4725},
journal = {International Journal of High School Research (IJHSR)},
year = {2025},
note = {UCSB Summer Research Academy Capstone}
}
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rugantio /
A collection of notebooks of my Machine Learning class written in python 3
71/100 healthSaremS /
Loose collection of Jupyter notebooks, mostly for my blog
29/100 healthDuring my undergrad, I implemented a music recommendation system based on music digital track analysis. However, it's time for me to use text mining technology on lyrics to upgrade that project. Goals: (1)build a music mood(happy or sad) classifier based on lyrics analysis (2)what words and their distributions are in different mood categories? (3)How are the key words change in songs for the recent years? Project evaluation: (1)data collection: the training data and validation data will be collected from the largest lyric database on Lyricwiki.org (2)feature selection: the most common feature type to consider are BOW(bag of word) and POS(part of speech) combined with stemming using word-net (3)Training model : SVM, Naive Bayes using grid search method. (4)data visualization for goal two and three This project will be done using python on jupyter notebook. reference: Hu, X. (2010). Improving music mood classification using lyrics, audio and social tags (Doctoral dissertation, University of Arizona).
edmundhong /
This repository contains a collection of Jupyter notebooks that I have created to analyze Formula 1 data. The notebooks cover a wide range of topics, including race results, driver performance, and car performance. On top of that, I have created a number of visualizations to help illustrate my findings.
67/100 healthdrericstrong /
A collection of resources and Jupyter notebooks from my blog.
44/100 healthjolynch /
My collection of various Jupyter notebooks and useful command line scripts for analyzing performance of services and code.
71/100 health