The look of the popular arXiv preprint style in one self-contained file, with no separate .sty to upload. Good for arXiv preprints, project proposals, research proposals, and course project reports. Compile with XeLaTeX; it uses the free TeX Gyre fonts and has an optional block for Chinese text. Your main.tex starts with \documentclass{article} and then inputs this file.

A 12pt single-column layout with a title rule, a running head with the date, and a page footer. Mostly for homework and problem sets, and also fine for lecture notes and short reports. Compile with pdfLaTeX and biber; it already sets the document class.

The two-column, 11pt version of the homework preamble, with narrow margins so more fits on a page. Mostly for homework and problem sets, and also for compact reports. Compile with pdfLaTeX and biber; it already sets the document class.

Load after any preamble above. Adds an answerpart environment that sets your answers apart from the questions in a homework, plus colored code listings for Python and JSON.

For manuscripts submitted to ACS journals: ACS-style numbered citations, Scheme and Chart floats, and chemical formulas with \ch{}. Compile with pdfLaTeX and biber; needs the biblatex-chem package and a references.bib file.

For manuscripts submitted to IOP Publishing journals, with chemistry, SI units, and code listings ready. Needs iopjournal.cls from IOP's author template in the same folder; the title and author block goes right after \begin{document}, as shown in the file.

Lecture notes for advanced topics in electrical engineering at MIT.

• Week 01 (2026-02-02): Week 01 lecture notes (PDF, 2026-02-02)
• Week 02 (2026-02-09): Week 02 lecture notes (PDF, 2026-02-09)
• Week 03 (2026-02-16): TBD

2nd Edition by Behzad Razavi. Published by McGraw-Hill Education in 2017. Comprehensive reference for analog CMOS circuit design and semiconductor device physics.

1st Edition by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press in 2020. Essential reference for linear algebra, vector calculus, optimization, probability, and statistics as applied to machine learning.