Beamer Presentation Guide
SkillDev toolsGuide to creating academic presentations with LaTeX Beamer
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Beamer Presentation Guide skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/43-wentorai-research-plugins/skills/writing/templates/beamer-presentation-guide/SKILL.md and read by ahel’s review.
Create professional academic presentations using LaTeX Beamer with themes, animations, and best practices for conference talks and research seminars.
Basic Beamer Document
\documentclass[aspectratio=169]{beamer} % 16:9 aspect ratio
% Other options: aspectratio=43 (4:3, default), aspectratio=1610
\usetheme{Madrid} % Visual theme
\usecolortheme{default} % Color scheme
\usefonttheme{professionalfonts}
\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\usepackage{booktabs} % Better tables
\usepackage{hyperref}
\title[Short Title]{Full Title of Your Presentation}
\subtitle{Conference Name 2025}
\author[A. Smith]{Alice Smith\inst{1} \and Bob Jones\inst{2}}
\institute[MIT, Stanford]{
\inst{1}MIT \and \inst{2}Stanford University
}
\date{March 15, 2025}
\begin{document}
\begin{frame}
\titlepage
\end{frame}
\begin{frame}{Outline}
\tableofcontents
\end{frame}
\section{Introduction}
\begin{frame}{Motivation}
\begin{itemize}
\item Research question and why it matters
\item Key challenge in the field
\item Our contribution in one sentence
\end{itemize}
\end{frame}
\end{document}
Popular Beamer Themes
| Theme | Style | Best For |
|---|---|---|
Madrid | Professional, structured headers | Conference talks |
Metropolis (mtheme) | Modern, minimal, flat design | CS/tech conferences |
CambridgeUS | Traditional academic | University seminars |
Singapore | Clean navigation sidebar | Long presentations |
Bergen | Compact, information-dense | Technical deep dives |
default | Plain, no decoration | Maximum content area |
Installing Metropolis (Recommended)
% Metropolis is a modern, clean theme widely used in CS/ML talks
\documentclass[aspectratio=169]{beamer}
\usetheme{metropolis}
% Customize colors
\definecolor{customPrimary}{RGB}{0, 83, 159} % University blue
\setbeamercolor{frametitle}{bg=customPrimary}
\setbeamercolor{progress bar}{fg=customPrimary}
% Optional: use Fira Sans font (matches Metropolis design)
% \usepackage[sfdefault]{FiraSans}
Slide Types and Templates
Title + Content Slide
\begin{frame}{Main Result}
\begin{theorem}[Our Main Theorem]
For any $\epsilon > 0$, Algorithm~\ref{alg:ours} achieves an
approximation ratio of $(1 - \epsilon)$ in time $O(n \log n / \epsilon)$.
\end{theorem}
\vspace{0.5em}
Key implications:
\begin{enumerate}
\item First polynomial-time approximation scheme for this problem
\item Improves over Smith et al. (2023) by a factor of $O(\log n)$
\item Extends to weighted variants
\end{enumerate}
\end{frame}
Two-Column Slide
\begin{frame}{Method Overview}
\begin{columns}[T]
\begin{column}{0.48\textwidth}
\textbf{Architecture}
\begin{itemize}
\item Encoder: 6-layer Transformer
\item Decoder: 6-layer Transformer
\item Hidden dim: 512
\item Attention heads: 8
\end{itemize}
\end{column}
\begin{column}{0.48\textwidth}
\textbf{Training}
\begin{itemize}
\item Optimizer: AdamW
\item Learning rate: $3 \times 10^{-4}$
\item Batch size: 256
\item Epochs: 100
\end{itemize}
\end{column}
\end{columns}
\end{frame}
Figure Slide
\begin{frame}{Experimental Results}
\begin{figure}
\centering
\includegraphics[width=0.85\textwidth]{figures/results-comparison.pdf}
\caption{Our method (blue) outperforms baselines across all benchmarks.}
\end{figure}
\end{frame}
Table Slide
\begin{frame}{Comparison with State of the Art}
\centering
\small
\begin{tabular}{lcccc}
\toprule
Method & Accuracy & F1 & Params & Speed \\
\midrule
Baseline A & 85.2 & 83.1 & 110M & 1.0x \\
Baseline B & 87.5 & 85.8 & 340M & 0.3x \\
\textbf{Ours} & \textbf{89.1} & \textbf{87.4} & 125M & 0.9x \\
\bottomrule
\end{tabular}
\end{frame}
Animations and Overlays
Progressive Reveal
\begin{frame}{Key Contributions}
\begin{itemize}
\item<1-> First contribution: novel problem formulation
\item<2-> Second contribution: efficient algorithm
\item<3-> Third contribution: theoretical guarantees
\item<4-> Fourth contribution: extensive experiments
\end{itemize}
\only<4>{
\vspace{1em}
\alert{All code and data are publicly available.}
}
\end{frame}
Highlighting
\begin{frame}{Pipeline}
Step 1: Data collection
\begin{itemize}
\item \alert<2>{Crawl 10M web pages}
\item \alert<3>{Filter and deduplicate}
\item \alert<4>{Annotate with human labels}
\end{itemize}
\uncover<5->{
\begin{block}{Result}
Final dataset: 2.3M high-quality labeled examples.
\end{block}
}
\end{frame}
Code Listings in Beamer
\usepackage{listings}
\lstset{
basicstyle=\ttfamily\scriptsize,
keywordstyle=\color{blue}\bfseries,
commentstyle=\color{gray},
stringstyle=\color{red},
breaklines=true,
frame=single,
backgroundcolor=\color{gray!10}
}
\begin{frame}[fragile]{Implementation} % [fragile] required for listings
\begin{lstlisting}[language=Python]
import torch
import torch.nn as nn
class TransformerBlock(nn.Module):
def __init__(self, d_model, n_heads):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_heads)
self.norm = nn.LayerNorm(d_model)
def forward(self, x):
return self.norm(x + self.attn(x, x, x)[0])
\end{lstlisting}
\end{frame}
Presentation Tips for Academic Talks
Content Planning
| Talk Length | Slides | Content |
|---|---|---|
| 5 min (lightning) | 5-7 | Problem, method, key result |
| 15 min (conference) | 12-18 | + motivation, related work brief, 2-3 results |
| 30 min (seminar) | 20-30 | + background, methods detail, analysis |
| 60 min (colloquium) | 35-50 | + extensive background, all results, future work |
Design Principles
- One idea per slide: If you need to explain two things, use two slides
- Minimize text: Use bullet points (max 5-6 per slide), not paragraphs
- Large fonts: Body text at least 20pt; never below 16pt
- High-contrast figures: Ensure readability on projectors (avoid light colors on white)
- Consistent style: Same fonts, colors, and layout throughout
- Backup slides: Put extra details, proofs, and additional results after
\appendixfor Q&A
% Backup slides (not counted in slide numbers)
\appendix
\begin{frame}{Proof of Theorem 1}
...
\end{frame}
Signals
- GitHub stars
- 4k
- Forks
- 531
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
beamer-presentation-guide- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
Related picks
Skill · wshobson
The pick for Pythonpython-pro
Skill · jeffallan
The pick for Pythonlatex-posters
Skill · k-dense-ai
The pick for LaTeXlatex-drawing-guide
Skill · brycewang-stanford
The pick for LaTeXgolden-pdf-ch
Skill · yusufkaraaslan
The pick for PDFpdf-co-automation
Skill · composio-community
The pick for PDF