The Fairness Analysis & Transfer Learning Hub is a newly funded project for the Cornell Future of Learning Lab housed in the Bowers College of Computing and Information Science on the Ithaca campus. The hub focuses on measuring and improving the fairness of ML/AI models in education with an emphasis on transfer learning between diverse educational contexts. This project is part of a new large-scale initiative aimed at improving math performance for low-income middle school students (“Learning Engineering Virtual Institute”). The Hub will bring together researchers examining algorithmic bias and fairness, transfer and federated learning, educational data mining, and AI in education. The Hub is led by Prof. Rene Kizilcec at Cornell University and his collaborators Prof. Chris Brooks at the University of Michigan and Prof. Renzhe Yu at Columbia University.
We seek a Postdoctoral Researcher who will help us examine the potential of using transfer learning to improve model fairness across diverse educational contexts by working closely with the PIs and several project teams of the Learning Engineering Virtual Institute. Our goal is to develop best practices for assessing and reducing algorithmic bias in education and testing the potential advantage of strategically transferring learned models across contexts to overcome data privacy challenges, the underrepresentation of groups in local datasets, and data infrastructure barriers.
The Postdoctoral Researcher will have the opportunity to help launch and shape this new Hub, conduct research, work closely with teams that are developing innovative solutions to improve math learning across the nation, and foster a truly interdisciplinary community. They will collaborate with the faculty supervisors associated with the Hub: Kizilcec, Brooks, and Yu. This is a one-year position with the possibility of a second year depending on performance and funding. The Postdoctoral Researcher will be located in Ithaca.
Applications are welcome from recent Ph.D. graduates with a range of disciplinary backgrounds (e.g., computer and information science, data science, education, the social sciences, public policy, or related fields). ABDs with strong backgrounds or work experience in related fields are also eligible to apply. An interest in and ability to work on applied problems with stakeholders is crucial. Diversity and inclusion are a part of Cornell University’s heritage. We encourage applications from candidates with identities that are minoritized and/or historically underrepresented in computing and information science.
Applications should be submitted to: https://academicjobsonline.org/ajo/jobs/24139 and should include a CV, up to two publications (or writing samples), the names and contact information of three references, and a cover letter summarizing the candidate’s relevant background, accomplishments, and fit with the position. A transcript of graduate work (unofficial is acceptable) may also be requested.
The position is available for a Spring, Summer or Fall 2023 start (sooner is preferred). Please indicate your desired start date in the cover letter. We will begin to review applications on January 15th.
#J-18808-Ljbffr
