Practical guides I wrote for my students: venues, deadlines, tools, and the habits that make research easier. They are free to download, use in your own lab, and adapt — the LaTeX sources live in github.com/y3nk0/practical_guides. Corrections and additions are welcome; venues and tools move fast.

Starting a PhD in Machine Learning or NLP

A starter kit for new PhD students, in three formats with the same content at three levels of detail.

• The full guide (18 pages) — read it through in your first week.
• One-page cheat sheet (1 page) — print it and pin it above your desk.
• Slides (42 slides) — for a lab induction talk or a first-year seminar.

What they cover:

• The publication landscape — ML venues (NeurIPS, ICML, ICLR, AISTATS, AAAI, IJCAI, COLM) and NLP venues (ACL, EMNLP, NAACL/EACL/AACL, COLING/LREC, TACL, Findings), how the ACL Rolling Review submit-then-commit cycle works, and how to judge a venue.
• Deadlines — the countdown boards worth bookmarking, the AoE trap, and a calendar workflow that backward-plans from camera-ready.
• Writing and sharing — Overleaf, Prism, a local git + latexmk workflow, arxiv-latex-cleaner, anonymity periods, and releasing code and checkpoints.
• Zotero — the connector, Better BibTeX with pinned keys and auto-export into your paper repository, and the habits that keep a library trustworthy.
• A focused survey as the first project — a ten-step protocol that ends with three gaps stated as testable questions.
• Searching the literature — Google Scholar as an instrument, plus Semantic Scholar, OpenAlex, the ACL Anthology, OpenReview and DBLP.
• Reading the signals — citation velocity over volume, and what GitHub stars, commit history and issues really tell you about a baseline.
• Setting up an AI assistant — and five guardrails, starting with: never cite a paper you have not opened.
• Your personal site — GitHub Pages and the AcademicPages template, step by step.
• Infrastructure and habits — reproducible experiments, seeds and variance, experiment tracking, the weekly supervisor meeting, and checklists for week 1, month 1, months 2–3 and year 1.

Doing an Undergraduate Thesis in Machine Learning or NLP

• The guide (14 pages) — for final-year students starting a thesis.

Nobody expects an undergraduate thesis to beat the state of the art. The expectation is that you do something correctly, understand it completely, and explain it clearly. The guide covers:

• Finding a topic by three routes — a shared task or challenge (SemEval, workshop tasks at *ACL venues, WMT, CoNLL, BabyLM, CLEF labs and TREC tracks, Kaggle and the competition platforms), an application thesis (a method taken carefully to a new language, domain or deployment constraint), or an analysis/theoretical thesis (reproduction, ablation, robustness, probing, efficiency, or a critique of the metric itself).
• Six places where gaps are written down explicitly — limitations sections, future work, survey taxonomies, workshop calls for papers, task overview papers, and plateaued leaderboards.
• The one-page proposal to agree with your supervisor before writing any code, and a scope test that catches the most common way a thesis goes wrong.
• The toolkit — Zotero, LaTeX and Overleaf, git, free GPUs, experiment tracking, and data licensing.
• Using an AI assistant honestly — including the rule that matters at the defence: you must be able to explain every line you submit.
• Doing the work — baselines first, the mistakes that cost marks (leakage, one seed, unfair comparisons), and why negative results are results.
• Writing and defending — a chapter-by-chapter structure with sizes, writing advice, likely defence questions, a six-month timetable, and checklists.