Charles Yang

About

I’m a third-year undergraduate student at UC Berkeley double majoring in Electrical Engineering and Computer Science (EECS) and Material Science and Engineering (MSE). I'm currently interning at Lawrence Berkeley Lab and conducting research under Grace Gu in the Mechanical Engineering Department at Berkeley. My research focuses on applying machine learning and deep learning to various scientific applications.

So, whether you eat or drink, or whatever you do, do all to the glory of God.
1 Corinthians 10:31

News

Recent Publications

Figure Abstract
Using CNNs to predict composite properties beyond the elastic limit
Material Research Society Communications (MRS Comm.), 2019
Charles Yang, Youngsoo Kim, Seunghwa Ryu, and Grace X. Gu

Power grid voltages after PV installation
Sequential Optimal Placement of Distributed Photovoltaics using Downstream Power Index  
North American Power Symposium 2017 (NAPS 2017)
Mir Hadi Athari, Charles Yang, Zhifang Wang

Journal cover image
Facile synthesis of ZnO@ZIF core–shell nanofibers: crystal growth and gas adsorption  
Crystal Engineering Communication (Cryst. Eng. Comm.), 2017
Xiang He, Charles Yang, Dawei Wang, Stanley E. Gilliland III, Da-Ren Chena, and Wei-Ning Wang

Blog[Medium][Substack]

Interviewing the 1.5B GPT-2 model by OpenAI
Distributed by Data Science and Machine Learning curators at Medium!
Published in Towards Data Science!

Using the 1558MB version of OpenAI’s GPT-2 model and Max Woolf’s gpt2-simple package on github powered by Google colaboratory, I gave GPT-2 a series of data-science focused prompts and analyzed their responses. I also analyze the generated text and discuss implications of such powerful language models.


Personal Asset Growth in the US

A quick data exploration of historical data for the S&P 500, median personal income, and median house sales prices in the US since 1975. Learned how to use bokeh in python for embedding html plots.


ML4Sci: #1: Discovering new materials from abstracts; Designing diffractive metagratings with GAN's; How ML can help fight climate change; +Industry Highlight

Check out the inaugral issue of ML4Sci, my newsletter on applications of machine learning in various scientific fields.

Deep Learning in Science
Distributed by Machine Learning, Science, and Data Science curators at Medium!
Published in Towards Data Science!

Deep Learning has traditionally been motivated by the concerns of tech companies such as Facebook and Google. However, it also has many applications in scientific fields, ranging from molecular chemistry to astrophysics. While there are subtle differences in implementation and different data structures, Deep Learning has the potential to fundamentally alter the way we do science. In this post, I introduce Deep Learning in the context of scientific and engineering applications, consider the opportunities for using Deep Learning, and discuss different patterns of Deep Learning for scientific problems.


Achieving Mathematical Maturity
Distributed by Math curators at Medium!
Published in Towards Data Science!

"Mathematical maturity" is an often-used catch phrase in higher education that it is rarely defined, and even more rarely is one told how to achieve it. In this post, I share some insights I've gained on how one should approach learning math, from my experience in both math classes and various technical classes that use math.


Metal Organic Frameworks - A Brief Introduction
Distributed by Science curators at Medium!

Metal Organic Frameworks (MOF) are one of the most exciting classes of materials discovered in the 21st century. MOF's are versatile not only in potential applications, ranging from catalysis, to gas storage, to thereapeutic drug delivery, but also in terms of functionality and synthesis. Composed of metallic nodes and organic linkers, a wide variety of combinations of transition metals and simple organic compounds can be used to synthesize MOF's in a variety of environments. In this post, I discuss various synthesis methods, properties of MOF's, and some example applications of MOF's. All included articles are open-source!