Senior Machine Learning Scientist / Genentech

Joshua Yao-Yu Lin is a Senior Machine Learning Scientist at Prescient Design, Genentech/Roche, where he develops machine learning approaches for drug discovery and protein engineering. His research sits at the intersection of machine learning and the natural sciences, with a particular interest in using deep learning to learn predictive and generative representations of complex scientific systems.

Joshua received his Ph.D. in Physics from the University of Illinois Urbana-Champaign, following an M.S. in Physics from National Taiwan University and a B.S. in Physics from National Tsing Hua University. During his Ph.D., he applied machine learning to problems in cosmology and astrophysics, including gravitational lensing, dark matter, and supermassive black holes, and contributed to research within the Event Horizon Telescope Collaboration.

Prior to his current work in biomedical research, Joshua conducted research at the Flatiron Institute and Google Research. His work has evolved from using machine learning to understand the natural world toward using it to design new biological molecules and therapeutics, with the broader goal of developing AI as a general-purpose tool for scientific discovery.

林曜宇（Joshua Yao-Yu Lin）現任 Genentech / Roche Prescient Design 的機器學習科學家，主要研究如何運用人工智慧與機器學習加速藥物發現與蛋白質工程。他的研究興趣橫跨機器學習、物理與生命科學，特別關注如何利用深度學習從複雜的科學資料中學習具有預測與設計能力的模型。

曜宇擁有美國伊利諾大學香檳分校（University of Illinois Urbana-Champaign）物理博士學位，並分別於國立臺灣大學與國立清華大學取得物理碩士與學士學位。博士期間，他將機器學習應用於宇宙學與天文物理研究，包括重力透鏡、暗物質與超大質量黑洞，並曾參與 Event Horizon Telescope Collaboration 的研究。

在投入生物醫學研究之前，他也曾於 Flatiron Institute 與 Google Research 從事研究工作。近年來，他的研究重心逐漸從以機器學習理解自然界，延伸至利用機器學習設計新的生物分子與藥物，致力於探索人工智慧如何成為跨越物理、生命科學與藥物研發的新型科學工具。