Abstract: There has been a lot of research work that applies computational models to explore patterns and data from various sources and applications. While these advancements highlight the value of data science, they also demonstrate their power over people's lives and decision-making. However, there is not much discussion about their core values. Most of the existing discussion is about general principles that traditional data analytics approaches need to follow and, in particular, in areas of research that directly involve human subjects (e.g., biomedical domain, autonomous systems, or human-computer interaction). In this talk, we will explore factors that might introduce unfairness in a system we want to develop. The goal is to discuss what it means for a model to be fair and ethical, and present a number of practical guidelines that researchers need to consider during any phase of their work. We want to encourage a conversation about researchers' responsibilities, particularly when the data used are not collected from a well-defined research-oriented process.

Abstract: Fueled by the advances in IOT, autonomous driving and AR/VR, the data transmission and bandwidth requirements of next-gen systems are touted to be orders of magnitude higher than in the current day 5G systems. To achieve these data rates, the existing communication pipelines will need to be re-designed and machine learning is expected to play a big role. In this talk we will discuss the opportunities and challenges of applying ML in wireless communication. We will go over a few open problems in the field and then closely examine the problem of denoising and compression of channel state information.

Bio: Dr. Akshay Malhotra is a Machine Learning Researcher at the Emerging Technologies Lab in Interdigital. His research involves improving traditional wireless communication blocks and algorithms with the use of machine learning. Before joining Interdigital he was a Senior Researcher at Standard Cognition where he worked on ML and optimization methods for vision based perception and SLAM problems. He has a Ph.D and a M.Sc in Electrical Engineering from the University of Texas at Arlington.