Virtual Dean's Lunch and Learn featuring the Department of Computer Science and Engineering
Dean Kaveh and Department of Computer Science & Engineering (CS&E) head Mats Heimdahl hope that you are able to join them for a virtual “Lunch and Learn." Hear news from the college, updates from CS&E as it celebrates its 50th anniversary, and overviews of innovative research underway in the areas of robotics and machine learning.
While this event was previously scheduled to take place on campus in March, we are pleased to now have the opportunity extend our invitation to alumni and friends near and far!
Featured speaker: Volkan Isler, Professor
Professor Isler will give an overview of his group's efforts to build robotic systems that can collect semantic data for agricultural and environmental monitoring applications. In particular, he will present examples of systems built for mapping yield in apple orchards and for tracking radio-tagged land animals and fish. Isler will also share results from his group's recent efforts to go beyond data collection toward actively changing the environment via applications such as picking fruit and mowing pastures.
Featured speaker: Steven Wu, Assistant Professor
The vast collection of detailed personal data has enabled machine learning to have a tremendous impact on society. Concerns have been raised that our heavy reliance on personal data and machine learning might compromise people’s privacy, produce new forms of discrimination, and violate other kinds of social norms. Professor Wu’s research seeks to address this emerging tension between machine learning and society by focusing on two interconnected questions: 1) how to make machine learning better aligned with societal values, especially privacy and fairness, and 2) how to make machine learning methods more reliable and robust in social and economic dynamics. In this talk, Professor Wu will provide an overview of his research and highlight some of his recent work on fairness in machine learning and differentially private synthetic data generation.