Hulbert, Lindsey


Associate Professor/Animal Behavior
127 Call Hall

Area(s) of Specialization

Comparative stress physiology, behavior, and immunology

Education and Training

  • Postdoctoral Scholar at University of California, Davis (UCD). Davis, CA. January 2011-2013
  • Doctor of Philosophy in Animal Science at Texas Tech University (TTU) August2008-December 2010
  • Biological Technician: USDA-ARS-Livestock Issues Research Unit, Lubbock, TX. May 2007- August 2008
  • Master of Science in Animal Science at TTU. Lubbock, TX. August 2005- December 2006
  • Bachelor of Science in Animal Science at TTU. Undergraduate Research Scholar, Howard Hughes Medical Institute (HHMI)-TTU. Lubbock, TX. 2001-2005


Brief Bio

Dr. Lindsey Hulbert grew up in the southwest (AZ, NM) then began her career in animal physiology and behavior in Lubbock, TX through an undergraduate research program at Texas Tech University. Her first research projects involved understanding how housing and management conditions affect the behavior and stress responses in swine. Her research evolved into how stress affects the health and immune systems in other species, including laboratory rodents, beef and dairy calves, and poultry. She also worked for the USDA-Agriculture Research Services, Livestock Issues Research Unit in Lubbock, TX. Dr. Hulbert was a post-doctoral at the University of California, Davis before moving to KSU in January of 2013. Dr. Hulbert has a passion for animals, science, and training students. In addition, she enjoys spending time with her family and her hobbies include Zumba and Salsa.

Dr. Hulbert’s research team studies:

  • Development and validation of automated technologies to monitor health and welfare of domestic animals
  • Understanding the effects of early-life stressors on nutritive and non-nutritive oral behaviors and immunity in calves
  • Improving resilience to stressors and immunocompetence through housing, management, and feeding strategies in calves and pigs
  • Determining biomarkers of stress and inflammation for predicting and identifying disease