https://pages.htgaa.org/2026a/aline-orrantia/index.htmlAbout me: Hi! I’m Aline Orrantia. I recently graduated from Imperial College London’s I-X MSc in AI Applications and Innovation, where my thesis explored using Deep Q-Network (DQN) agents to mitigate data bias in federated learning systems, with a focus on fairness-aware decision-making in healthcare. I’m currently involved with Imperial’s AI Superconnector community, working at the intersection of founders, investors, and emerging AI ventures, and I’m part of investor-facing communities focused on translating technical innovation into real-world impact. I’m taking How to Grow (Almost) Anything because I want to bridge AI with hands-on biological engineering and build a deeper understanding of living systems, biomaterials, and biofabrication. I’m especially interested in women’s health—particularly endometriosis, aromatase and estrogen-driven conditions, IVF, and ovarian longevity—and I’m always open to connecting with others working in these areas, whether for research conversations, collaborations, or venture building ideas.Hugoenhttps://pages.htgaa.org/2026a/aline-orrantia/homework/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://pages.htgaa.org/2026a/aline-orrantia/homework/index.htmlWeekly homework submissions: Week 1 HW: Principles and Practiceshttps://pages.htgaa.org/2026a/aline-orrantia/labs/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://pages.htgaa.org/2026a/aline-orrantia/labs/index.htmlLab writeups: Week 1 Lab: Pipettinghttps://pages.htgaa.org/2026a/aline-orrantia/projects/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://pages.htgaa.org/2026a/aline-orrantia/projects/index.htmlFinal projects: Individual Final Project Group Final Project