Research Skill Bootcamp
Last updated
Last updated
To assist our new honours project students and new PhD students to get up to speed with basic research skills, we are planning to continue last year's wonderful AI Bootcamp delivered by Pascal to a new series of bootcamp workshops. This Research Skill Bootcamp, will cover content ranging from power-use of Overleaf for proposal and slides preparation, literature review dos and don'ts, effective communication through diagrams and mathematical equations, use of LLMs for smoothing up and improving your writing, to a quick run-through sequential modelling (MLP->1DCNN->RNN->LSTM->Transformers->Diffusion Models->Mamba).
These workshops will be more interactive and assistive in nature. Please bring your writing, presentation and research tasks to the bootcamp for feedback from your peers, more experienced PhD students, and academic staff. This is a rare occasion where we can all share our experience and pain points and work together to solve them.
Attendance is optional, but I strongly encourage all honours project students and new PhDs to attend. Staff feel free to attend if you have spare time and would like to offer a session or two that you see important for research skill building. We will set up Teams channels and Web-accessible tutorials to capture all the materials developed through these sessions for future re-use.
More details on the web pages and the content schedule will follow. Please also feel free to forward this if you see other students might benefit and for anyone that I might have missed. We currently plan to host it in a workshop room in Math G19, but will let you know if location has to change later.
The Research Skill Bootcamp will run according to the following schedule:
When: Starting from 2025.03.06, every Thursday afternoon from 2pm to 4pm
Duration: Throughout the entire semester
Format: Interactive workshops, progressing from basic to advanced skills
Location: UWA MATH G19, link:
Team: UWA NLP-TLP Group (Sirui Li, Pascal Sun, Daochang Liu, Jichunyang Li, Wei Liu)
Topics will vary from the toolsets, writing, etc. E.g.
How to use overleaf for writing and presentation? we also will provide latex templates.
How to do literate search and literature review? What questions to ask? Tools like connectedpaper.
How to design experiments and do research execrise?
How to draw diagrams? Tools like: draw.io, Canva, Figma, PPT, Miro, tikz
How to manage bib and literature papers? Endnotes or Mendley.
How to mathmatically formalize the research questions?
1. Research Pipeline Overview
Introduction to Research Process
• Research lifecycle from problem to publication • Different research paradigms • Understanding the academic ecosystem
2. Writing Tools
LaTeX, Overleaf and Reference Management
• Document templates and structure • Formula typesetting • Table and figure creation • Collaborative writing features • Bib tools and styles
3. Literature Review
Finding and Analyzing Existing Work
• Effective search strategies • Tools: Connected Papers, Semantic Scholar, Google Scholar • Critical reading and note-taking techniques • Identifying research gaps
4. Problem Formulation
Defining Your Research
• Formulating research questions • Mathematical formalization of problems • Hypothesis development • Scope definition
5. Experiment Design
Planning Your Investigation
• Selecting appropriate methodologies • Experimental design principles • Ethics considerations and approvals • Research validity and reliability
6. Data Management
Handling Research Data
• Data collection methods • Cleaning and preprocessing techniques • Storage and organization best practices • Data documentation and metadata
7. Visualization
Creating Effective Diagrams
• Tool selection (draw.io, Canva, Figma, TikZ) • Diagram design principles • Data visualization best practices • Creating publication-quality figures
8. Analysis Methods
Data Analysis Frameworks
• Statistical analysis techniques • Selecting appropriate methods
• Ablation Study • Tools and software • Results interpretation
9. Advanced Modeling
Deep Learning Models
• Neural network foundations (MLP) • Sequence models (CNN, RNN, LSTM) • Transformer architectures • Recent advances (Diffusion Models, Mamba)
10. Academic Writing
Creating Research Papers
• Paper structure and organization • Writing clear abstracts and introductions • Results presentation • Responding to reviewer feedback
11. Research Communication
Presenting Your Work
• Conference presentation skills • Poster design and delivery • Research "elevator pitch" • Slide design principles
Guest Talk
Previous. Student Sharing
Varun from Google Gemni Team
Schedule is subject to changes based on feedback.
Further reading list:
