Graduate Orientation - Blackmon Lab

Learning Objectives

This course is designed to provide a foundation for success in graduate school and beyond. By the end of the course, successful students will be able to:

Build productive mentor/mentee relationships.
Meet and exceed all requirements for a Ph.D. in a timely manner.
Design and structure experiences during your Ph.D. to make yourself competitive in the job market.
Take control of your professional image and curate it as appropriate for your career.
Embrace the Ph.D. process as a challenging but fulfilling experience.
Apply responsible AI practices to enhance research productivity and academic integrity.

Course Information: Class meets Thursdays 5:30-7:00 PM in Butler 309

Over the course of the semester I will share with you what I know, what I have experienced, and what I believe to be the best approaches to graduate school. Many of these are opinions and I expect you to share your own experiences and opinions. You should discuss these topics with your cohort, friends, and mentors. Ultimately it will be your Ph.D.-you must take ownership and responsibility for your science and your experience.

For more information email Dr. Blackmon

Weekly Schedule

Week	Topic	Materials
1	Welcome and Calibrating Expectations	Huey and Stearns Reading
2	Starting Off Right in Graduate School Discuss Huey and Stearns	First Semester Compact Mentor/Mentee Compact
3	Equity in Science Guest lecture by Dr. Dulin
4	The Publication Process	Presentation Slides
5	Funding as a Graduate Student	Grant and Fellowship List
6	Life Sciences Job Market	Presentation Slides
7	Branding Yourself	GitHub Pages Tutorial Example: Michelle Jonika
8	Scientific Writing	Strunk and White Guide Elements of Style - Demas
9	Philosophy and Ethics of AI in Science Responsible use, attribution, limitations, and academic integrity	This week explores the philosophical foundations and ethical considerations of integrating artificial intelligence into scientific research. Topics include: Understanding AI capabilities and limitations in research contexts Attribution and disclosure: when and how to acknowledge AI use Academic integrity in the age of AI: ensuring proper citation and methodology Bias recognition and mitigation in AI-generated outputs Responsible use of AI for literature review and data analysis Institutional policies and journal guidelines on AI
10	Vibe Coding Using AI to generate and iterate code effectively	Learn practical strategies for leveraging AI coding assistants to enhance productivity and skill development. This week covers: Using Claude, ChatGPT, and other AI tools for R and Python code generation Providing effective context to AI assistants for accurate code output Debugging and iterating on AI-generated code Understanding what the code does: reading, testing, and validating outputs Building your skills while using AI as a tool, not a replacement Real-world examples: data wrangling, statistical analyses, visualization Common pitfalls and how to avoid them
11	Writing Serious Prompts Prompt engineering for impactful research applications	Master the art of communicating clearly and effectively with AI systems to achieve research goals. This week emphasizes: Prompt structure and clarity: getting AI to understand your research needs Systematic prompt iteration and refinement Using AI for literature reviews: synthesis, summarization, and analysis AI as a writing assistant: drafting, editing, and improving manuscripts Generating and refining research hypotheses and methodologies Creating custom prompts for your specific research domain Advanced techniques: chain-of-thought, role-playing, and multi-step reasoning
12	CV Construction	Year 4 CV - Michelle Jonika Graduation CV - Dr. Blackmon Current CV - Dr. Blackmon Resume - Michelle Jonika
13	Emails and Other Forms of Communication
14	Internships Guest discussion led by Michelle Jonika
15	Open Discussion

About the AI Weeks (9-11)

The integration of three dedicated weeks on AI represents a recognition that artificial intelligence tools are now fundamental to modern scientific practice. These weeks are not meant to replace traditional research skills, but rather to extend them. Students will develop proficiency with AI as a powerful tool while maintaining critical thinking, academic integrity, and deep understanding of their research domains.

These topics build on foundational graduate skills and prepare you to use AI responsibly and effectively throughout your research career.