Our Faculty, Staff & Students

Contact Us

Professor and Department Head

Dr. Monty Escabi
Phone: (860) 486-0063
Email: monty.escabi@uconn.edu


Lisa Ephraim
Undergraduate Academic Advisor
Phone: (860) 486-0163
E-mail: lisae@engr.uconn.edu

Jennifer Seyford
Financial Assistant II
Phone: (860) 486-0116
E-mail: jennifer.seyford@uconn.edu

Birgit Sawstrom
Admin Service Assistant III
Phone: (860) 486-1267 
E-mail: birgit.sawstrom@uconn.edu

Sowmya Ramesh
Financial Assistant I
Phone: (860) 486-7139
E-mail: sowmya.ramesh@uconn.edu

Sarah Dunnack
Administrative Program Support I
Phone: (860) 486-5838
E-mail: sarah.dunnack@uconn.edu



Main Office Address

Biomedical Engineering Department
A.B. Bronwell Building, Room 217
260 Glenbrook Road, Unit 3247
University of Connecticut
Storrs, CT 06269-3247
Phone: (860) 486-5838
Fax: (860) 486-2500

Dr. Monty Escabi and Collaborators Receive NIH Research Grant

Dr. Monty Escabi and Dr. Heather Read

Prof. Monty Escabi, a BME core faculty member, and his collaborators at UConn have received an official notice of a new research grant entitled “The role of sound statistics for discrimination and coding of sounds” from the NIH. It is a 5-year grant in the amount of $1.87M.  Prof. Escabi (core BME faculty) is the PI and Profs. Heather Read and Ian Stevenson (both are affiliated BME faculty members) from Psychology department are Co-PIs.  

The brain’s ability to accurately recognize and categorize sounds despite highly variable acoustic structure and in the presence of environmental noises is a remarkable trait of the normal functioning auditory system.  This study examines how the normal auditory system of mammals encodes high-level statistical regularities in sounds that are essential for sound recognition and will develop computational approaches based on physiological principles for sound recognition. Such knowledge is essential for the development of auditory prosthetics that mimic normal hearing physiology, treatment strategies that overcome detrimental effects of hearing loss, and sound recognition systems that can deal with everyday acoustic variability.