Beamer Presentation Guide

SkillDev tools

Guide to creating academic presentations with LaTeX Beamer

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

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

ThemeStyleBest For
MadridProfessional, structured headersConference talks
Metropolis (mtheme)Modern, minimal, flat designCS/tech conferences
CambridgeUSTraditional academicUniversity seminars
SingaporeClean navigation sidebarLong presentations
BergenCompact, information-denseTechnical deep dives
defaultPlain, no decorationMaximum 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 LengthSlidesContent
5 min (lightning)5-7Problem, 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 \appendix for Q&A
% Backup slides (not counted in slide numbers)
\appendix
\begin{frame}{Proof of Theorem 1}
  ...
\end{frame}

Signals

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Last commit
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Item type
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Key
beamer-presentation-guide
Source
github.com/brycewang-stanford/auto-empirical-research-skills