AI Careers and Research Panel

Join us for a panel of experts from academia and industry who will answer your questions about careers and research in the field of artificial intelligence (AI). This event is open to all undergraduate and graduate students.

This is a virtual panel. Please register on Zoom in advance.

With panelists:

Mercy Asiedu
Research Scientist
Google Research

Mercy works on using machine learning and generative AI for impact-driven research. Her work includes multimodal evaluation of generative AI models, and using machine learning models to extract digital biomarkers for personalized health. Before that, she was a Schmidt Science Postdoctoral Research Fellow at MIT working on interdisciplinary research projects using generative AI methods to improve mobile ultrasound imaging. She also worked on projects researching the use of language models to improve comprehension of health notes for breast oncology patients. She received her PhD in Biomedical Engineering and a certificate in Global Health from Duke University. Her dissertation focused on the research and development of a low-cost imaging device and machine learning algorithms to reduce barriers to cervical cancer screening. She has won several awards for her work including the Inaugural Patrick J. McGovern Tech for Humanity Changemaker Awards, the Lemelson-MIT Graduate Student Inventor Award, and Velji Emerging Leader in Global Health award. Additionally, she is a co-founder of GAPHealth Technologies. She received her bachelor’s degree in Biomedical Engineering from the University of Rochester, and high school degree from Holy Child Secondary School, Cape Coast, Ghana.

Kai Wang
Assistant Professor
School of Computational Science and Engineering at Georgia Institute of Technology

Kai Wang is an assistant professor in the school of Computational Science and Engineering at Georgia Institute of Technology. He received his Ph.D. in Computer Science at Harvard University. His research interests include AI for social impact, machine learning, optimization, and multi-agent systems, with a focus on applications in health and environmental sustainability. One of Kai’s key contributions is the first real-world deployment of decision-focused learning to provide intervention recommendations in maternal health in collaboration with an Indian non-profit, where his algorithm has been deployed and is currently used by more than 350,000 people with a 31% improvement in health information engagement. Kai’s work has been recognized with the Schmidt Science AI2050 Early Career Fellowship, Siebel Scholars, and the best paper runner-up award at AAAI.

Sindhu Kutty
Lecturer
Computer Science and Engineering at University of Michigan

Sindhu Kutty is a teaching-track faculty member in Computer Science and Engineering at the University of Michigan, Ann Arbor from where she received her Ph.D. in 2015. She is the Director of the Teaching Lab in which capacity she leads the teaching-focused initiatives of the division. She is also the Chair of Undergraduate Research Initiatives. In this latter role, she oversees the experience of undergraduate researchers in the division. She has introduced a new course in the department focused on introducing undergraduates to research in Machine Learning. Her research work with undergraduate students and other collaborators has been recognized by awards at various conferences and competitions. Her work as an educator has been recognized by the Jon R. and Beverly S. Holt Award for Excellence in Teaching. She has previously held faculty positions at the University of Detroit Mercy and at Swarthmore College.

This panel will be moderated by Alexis Stokes (Stokes Strategy & Consulting) and Elizabeth Bondi-Kelly (Assistant Professor of CSE at U-M).

Please submit your questions on careers and research in AI in advance. The speakers will address as many as possible during the panel. These questions will also be used as part of a study to understand perspectives and common questions about AI careers, as well as to develop tools and programming that support AI career development.

For any questions, please contact Sharon Jessica at [email protected].