This week, our speaker is Dr. Hernisa Kacorri from University of Maryland. Dr. Kacorri is an Assistant Professor in the College of Information Studies and holds an affiliate appointment in the Computer Science and the Human-Computer Interaction Lab at University of Maryland. Her research focuses on data-driven technologies that address human challenges, faced due to health or disability, with an emphasis on rigorous, user-based experimental methodologies to assess impact. She will give a talk about “Accessibility and Assistive Technologies at the Intersection of Users and Data” in this week’s BBL.

Details:

Time: 04/19 (Thursday) from 12:30pm – 1:30pm
Place: HCIL, Room 2105, Hornbake building – south wing
Lunch: Bring your own lunch

Abstract:

Advances in artificial intelligence enable us to address key social issues. However, to see the benefit of this technology in many real-world applications, an integrative approach is necessary; effective solutions consist of a pipeline of processes or tasks involving both humans and machines. My research has integrated human computer interaction (HCI) techniques and data-driven methods applied to human data to steer technological innovations for people with visual impairments and for people who are deaf or hard-of-hearing. In this talk, I will provide an overview of my research program, and I will demonstrate the effectiveness of integrating machine learning and HCI methodologies with two concrete examples: i) teachable object recognizers trained by blind users, and ii) facial expression synthesis in sign language animations.

Bio:

Hernisa Kacorri is an Assistant Professor in the College of Information Studies and holds an affiliate appointment in the Computer Science and the Human-Computer Interaction Lab at University of Maryland, College Park. She received her Ph.D. in Computer Science in 2016 from The Graduate Center at City University of New York, and has conducted research at National and Kapodistrian University of Athens, IBM Research-Tokyo, Lawrence Berkeley National Lab, and Carnegie Mellon University. Her research focuses on data-driven technologies that address human challenges, faced due to health or disability, with an emphasis on rigorous, user-based experimental methodologies to assess impact. Hernisa is a recipient of a Mina Rees Dissertation Fellowship in the Sciences, an ACM ASSETS best paper finalist, and an CHI honorable mention award. She has been recognized by the Rising Stars in EECS program of CMU/MIT.