From 55303056cfc2dae16da558597f3411654b30db1c Mon Sep 17 00:00:00 2001 From: Jaedong Hwang Date: Wed, 14 Jan 2026 15:44:51 -0500 Subject: [PATCH] [update] brief bio --- src/content/people/jaedong.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/src/content/people/jaedong.md b/src/content/people/jaedong.md index e4d33fe..a5e1459 100644 --- a/src/content/people/jaedong.md +++ b/src/content/people/jaedong.md @@ -4,3 +4,20 @@ title: "Graduate student" avatar: "./images/jaedong.png" website: "https://jd730.github.io/" --- + + +Jaedong Hwang is a PhD student at MIT EECS advised by Ila Fiete and Paul Liang. + Prior to MIT, He received M.S. in ECE and B.S. in CSE from Seoul National University where he had a wonderful experience with Bohyung Han and Byoung-Tak Zhang. + Jaedong was a Research Intern at Adobe Research under the guidance of Joon-Young Lee and Seoung Wug Oh. + + +Jaedong focuses on reducing the need for extensive fine-grained data collection to train artificial intelligence (AI) models and bridging the gap between neuroscience and AI, particularly in learning from imperfect data, such as noisy, weakly labeled, or entirely unsupervised data. +He aims to develop more robust and realistic computer vision models and advance the field of embodied intelligence. +While artificial neural networks were originally inspired by neuroscience, many current models have diverged from their biological origins in pursuit of performance, often overlooking key insights from how the brain learns. +He is also reseaching on boosting both neuroscience and machine learning research to reconnect them each other again and make a more efficient AI system. +Jaedong's work addresses these challenges through three main research directions: + +* Learning from imperfect supervision: Developing methods to efficiently utilize imperfectly labeled and realistic data. +* Building Efficient AI via Neuroscience: Developing AI models that mimic human-like learning processes, aiming to significantly expand AI capabilities, drawing inspiration from efficiency of the human cognition and brain. +* Boosting Neuroscience Research via Machine Learning: Providing tools and models that offer deeper insights into neural data, helping to interpret and understand brain function more effectively, recognizing the extensive time and resources required for neuroscience experiments. +