Who: Erik Serrano is a CSUN alum who pivoted from molecular biology + minor in chemistry into computational biology, later joining a PhD program at the University of Colorado Anschutz. He has served on his program’s admissions committee and has reviewed applications and interviews.
Talk format: Discussion‑based, student‑question‑driven; no slides.

Key points:

• Real admissions experience reading 100+ essays per cycle.
  
  

• Program cohort sizes can be small (e.g., ~5 admits), so clarity and fit are critical.
  
  

• Choosing a program isn’t just brand: environment, stipend + cost of living, and personal fit can determine success.

1) Clear motivation: “Why a PhD?”

• Committees will probe consistency across essays and interviews.
  
  

• Be able to explain why a PhD (vs. MS) and the training you seek.
  
  

2) Contributions > credentials

• Evidence beats adjectives. Show what you built or advanced: methods, datasets, software, protocols, posters, preprints, publications.
  
  

• GPA thresholds exist (often ~3.0+), but impactful work can outweigh a perfect GPA.
  
  

3) Letters that actually say something

• Ask for “a strong letter” from mentors who know your work style, independence, and growth.
  
  

• Diverse recommenders (lab PI, collaborator, industry/clinic) can give a fuller picture.
  
  

4) Fit with program resources

• Demonstrate you’ve researched labs, techniques, training tracks, and the local living situation.

Think of the application as speed dating on paper. Be memorable, specific, and honest.

Personal Statement

• Arc: Brief origin → concrete research experience → skills & contributions → why PhD → why this school.
  
  

• Avoid detached childhood anecdotes. If used, tie them directly to your research path.
  
  

Statement of Purpose (SOP)

• Focused on academic fit: current skills, proposed research directions, labs of interest, and why this program’s training model fits your goals.
  
  

Style tips

• Active voice: “I analyzed… We implemented…” not “X was used.”
  
  

• Precision over praise: Replace “very good results” with the actual effect/metric you observed.
  
  

• Keep claims checkable (e.g., “shared with Lab X” vs. “used internationally”).
  
  

Use ‘Additional Materials’ wisely

• If allowed, add a 1–2 paragraph appendix with how you did a key project (methods, datasets, tools) or brief context for semester anomalies (e.g., health/family). Keep it short.

• Goal: Start a conversation; signal curiosity and initiative.
  
  

• If ignored once, follow up a week later—professors get heavy inbox traffic.
  
  

• Keep it short: 6–10 lines. State who you are, what you’ve done (link to CV/GitHub/Google Scholar), why their work, and a specific ask (15–20 min chat).
  
  

• Prepare paper‑driven questions that compare approaches (e.g., “Why logistic regression over elastic net for your classifier?”).