Stanford CVI’s Travel Awards

Attend a conference and exchange ideas!

The Stanford CVI Travel Awards Program provides CVI trainees with financial support to attend workshops and conferences, enabling them to enhance their training, share their research, network with peers, and gain new insights that can positively impact their work and that of others.


    

About the Program

Stanford CVI provides a $750 award to trainees who are giving presentations at scientific conferences. These stipends are awarded quarterly.

To be eligible for the award, applicants must be postdocs, instructors, graduate students, or nurses, and they must be a CVI member. The PI/mentor must also be a CVI member. The presentation must list Stanford CVI as an affiliation for the applicant.

Once the travel is complete, it is the responsibility of the trainee and their department finance administrator to submit the reimbursement request to the CVI finance department.


    

Details

1) You must be a CVI Member to apply -- to become a member click here

2) Your mentor must also be a CVI Member

3) An accepted abstract  to a national or international meeting related to cardiovascular research

4) The abstract must list  Stanford Cardiovascular Institute in the author affiliations when first submitted

5) Attendance and participation in the conference must occur after the CVI Travel Award has been conferred

6) For conferences occurring on or after June 13, 2025

Deadline: May 30, 2025, at 11:59 pm PST


    

Future Award Deadlines



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201

Awards since 2013

  

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Travel Awardees

February 2025

Gastruloids Enable Modeling of the Earliest Stages of Human Cardiac and Hepatic Vascularization

Multi-Cellular Engineered Living Systems (MCELS) Symposium 2025
 

Oscar Abilez, MD, PhD

Senior Scientist

 

Preeclamptic and Normotensive iPSC-Derived Endothelial Cells Have Distinct Responses to Maternal Circulating Factors

Society for Reproductive Investigation Annual Meeting

Xi Plummer

Maternal-Fetal Medicine Fellow

Analyzing Demographic Representation in Cardiothoracic Anesthesiology and Thoracic Surgery through Text-to-Image Generators

Society of Cardiovascular Anesthesiologists Annual Meeting

Megan Chung

MD Student

Automated Machine Learning Models of Baseline Electroanatomic Features Can Predict AF Catheter Ablation Outcomes

Heart Rhythm Society Conference
 

Muhammad Fazal

Cardiology Fellow